Recent Advances in Cytometry, Part B, Volume 103, Fifth Edition: Advances in Applications (Methods in Cell Biology)

Methods in Cell Biology VOLUME 103 Recent Advances in Cytometry, Part B: Advances in Applications

Series Editors Leslie Wilson Department of Molecular, Cellular and Developmental Biology University of California Santa Barbara, California

Paul Matsudaira Department of Biological Sciences National University of Singapore Singapore

Methods in Cell Biology VOLUME 103 Recent Advances in Cytometry, Part B: Advances in Applications Edited by

Zbigniew Darzynkiewicz Brander Cancer Research Institute, Department of Pathology, New York Medical College, Valhalla, NY, USA

Elena Holden CompuCyte Corporation, Westwood, MA, USA

Alberto Orfao Cancer Research Center (CSIC/USAL) University of Salamanca Salamanca (Spain)

William Telford National Cancer Institute, Bethesda, MD, USA

Donald Wlodkowic The BioMEMS Research Group Department of Chemistry University of Auckland Auckland, New Zealand

AMSTERDAM  BOSTON  HEIDELBERG  LONDON NEW YORK  OXFORD  PARIS  SAN DIEGO SAN FRANCISCO  SINGAPORE  SYDNEY  TOKYO Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 225 Wyman Street, Waltham, MA 02451, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 32, Jamestown Road, London NW1 7BY, UK Linacre House, Jordan Hill, Oxford OX2 8DP, UK Fifth edition 2011 Copyright # 2011 Elsevier Inc. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made ISBN: 978-0-12-385493-3 ISSN: 0091-679X

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CONTRIBUTORS

Numbers in parentheses indicate the pages on which the authors’ contributions begin.

Markus J. Barten (267), Department of Cardiac Surgery, Heart Center, University of Leipzig, Leipzig, Germany Hartmuth B. Bittner (267), Department of Cardiac Surgery, Heart Center, University of Leipzig, Leipzig, Germany Wojtek Blogowski (31), Department of Gastroenterology, Pomeranian Medical University, Szczecin, Poland Robert A. Bray (285), Department of Pathology, Emory University, Atlanta, Georgia, USA Maurizio Carbonari (189), Clinical Medicine Department, University Sapienza, viale dell’Universita, Roma, Italy Angela Catizone (189), Histology and Medical Embryology Department, University Sapienza, via Scarpa, Roma, Italy Alden Chesney (311), Department of Laboratory Medicine and Pathobiology, University of Toronto, Department of Clinical Pathology, Sunnybrook Health Sciences Centre, Toronto, Canada Sohee Cho (149), Department of Life Science, University of Seoul, Seoul, Republic of Korea Sue Chow (205), Ontario Cancer Institute/Princess Margaret Hospital, Toronto, Ontario Canada Marina Cibati (189), Clinical Medicine Department, University Sapienza, viale dell’Universita, Roma, Italy William Cronin (221), Genzyme Genetics (New York Laboratory), New York, New York, USA Zbigniew Darzynkiewicz (55, 115), Brander Cancer Research Institute and Department of Pathology, New York Medical College, Valhalla, New York, USA Maja-Theresa Dieterlen (267), Department of Cardiac Surgery, Heart Center, University of Leipzig, Leipzig, Germany Katja Eberhardt (267), Department of Cardiac Surgery, Heart Center, University of Leipzig, Leipzig, Germany Luis Escribano (333), Instituto de Estudios de Mastocitosis de Castilla La Mancha, Hospital Virgen del Valle, Toledo, Spain Massimo Fiorilli (189), Clinical Medicine Department, University Sapienza, viale dell’Universita, Roma, Italy Howard M. Gebel (285), Department of Pathology, Emory University, Atlanta, Georgia, USA

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Contributors

David Good (311), Department of Laboratory Medicine and Pathobiology, University of Toronto; Department of Clinical Pathology, Sunnybrook Health Sciences Centre, Toronto, Canada Magaret A. Goodell (21), Stem Cell and Regenerative Medicine Center; Center for Cell and Gene Therapy; Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA Wojciech Gorczyca (221), Genzyme Genetics (New York Laboratory), New York, New York, USA H. Dorota Halicka (115), Brander Cancer Research Institute and Department of Pathology, New York Medical College, Valhalla, New York, USA David W. Hedley (205), Ontario Cancer Institute/Princess Margaret Hospital, Toronto, Ontario Canada Eun Seong Hwang (149), Department of Life Science, University of Seoul, Seoul, Republic of Korea Jar-How Lee (285), Research Department, One Lambda, Inc., Canoga Park, California, USA Soo Fern Lee (99), Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Xiaoyu Li (221), Genzyme Genetics (New York Laboratory), New York, New York, USA Kuanyin K. Lin (21), Stem Cell and Regenerative Medicine Center; Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, Texas, USA Rui Liu (31), Stem Cell Biology Institute, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA Wojtek Marlicz (31), Department of Gastroenterology, Pomeranian Medical University, Szczecin, Poland Sophal Mau (221), Genzyme Genetics (New York Laboratory), New York, New York, USA Anja Mittag (1), Department of Pediatric Cardiology, Heart Centre; Translational Centre for Regenerative Medicine (TRM), University of Leipzig, Leipzig, Germany Jos e M. Morgado (333), Instituto de Estudios de Mastocitosis de Castilla La Mancha, Hospital Virgen del Valle, Toledo, Spain Shazib Pervaiz (99), Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore; Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, Singapore; Singapore-MIT Alliance, Singapore Mariusz Z. Ratajczak (31), Stem Cell Biology Institute, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA Marciano Reis (311), Department of Laboratory Medicine and Pathobiology, University of Toronto; Department of Clinical Pathology, Sunnybrook Health Sciences Centre, Toronto, Canada Laura S anchez-Muñoz (333), Instituto de Estudios de Mastocitosis de Castilla La Mancha, Hospital Virgen del Valle, Toledo, Spain

Contributors

xiii Nicla Sette (189), Clinical Medicine Department, University Sapienza, viale dell’Universita, Roma, Italy T. Vincent Shankey (205), Systems Research/Cellular Analysis Business Group, Beckman Coulter, Inc., Miami, Florida, USA Joanna Skommer (55, 115), School of Biological Sciences, University of Auckland, Auckland, New Zealand Teresa Starzynska (31), Department of Gastroenterology, Pomeranian Medical University, Szczecin, Poland Zhong-Yi Sun (221), Genzyme Genetics (New York Laboratory), New York, New York, USA Attila Tarnok (1, 267), Department of Pediatric Cardiology, Heart Centre; Translational Centre for Regenerative Medicine (TRM), University of Leipzig, Leipzig, Germany Christine Tarsitani (285), Research Department, One Lambda, Inc., Canoga Park, California, USA William Telford (55), Experimental Transplantation and Immunology Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland, USA Cristina Teodósio (333), Servicio General de Citometrıa, Instituto de Biologıa Molecular y Celular del Cancer, Centro de Investigación del Cancer/IBMCC (CSIC-USAL) and Departamento de Medicina, Universidad de Salamanca, Salamanca, Spain Frank Traganos (115), Brander Cancer Research Institute and Department of Pathology, New York Medical College, Valhalla, New York, USA Sorina Tugulea (221), Genzyme Genetics (New York Laboratory), New York, New York, USA Donald Wlodkowic (55, 115), The BioMEMS Research Group, Department of Chemistry, University of Auckland, Auckland, New Zealand Wojtek Wojakowski (31), Third Division of Cardiology, Medical University of Silesia, Katowice, Poland Hong Zhao (115), Brander Cancer Research Institute and Department of Pathology, New York Medical College, Valhalla, New York, USA Ewa Zuba-Surma (31), Department of Medical Biotechnology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland

PREFACE TO FIFTH EDITION

Two hundred sixteen chapters presenting different cytometric methodologies and instrumentation consisting of six volumes (33, 41 & 42, 63 & 64, and 75) were published in the four editions (1990, 1994, 2001, and 2004) of the series of Methods in Cell Biology (MCB) dedicated to cytometry. The chapters presented the most widely used methods of flow- and quantitative image-cytometry, outlining their principles, applications, advantages, alternative approaches, and potential pitfalls in their use. These volumes received wide readership, high citation rates, and were valuable in promoting cytometric techniques across different fields of cell biology. Thirty-nine chapters from these volumes, selected based on high frequency of citations and relevance of methodology, were updated and recently published by Elsevier within the framework of the new series defined ‘‘Reliable Lab Solutions’’ as a special edition of the ‘‘Essential Cytometry Methods.’’ Collectively, these volumes contain the most inclusive assortment of articles on different cytometric methods and the associated instrumentation. The development in instrumentation and new methods as well as novel applications of cytometry continued at an accelerating pace since the last edition. This progress and the success of the earlier CYTOMETRY MCB editions, which become the proverbial ‘‘bible’’ for researchers utilizing these methods in a variety of fields of biology and medicine, prompted us to prepare the fifth edition. The topics of all chapters in the present edition (Volumes A and B) are novel, covering the instrumentation, methods, and applications that were not included in the earlier editions. The present volumes thus complement and not update the earlier editions. There is an abundance of the methodology books presenting particular methods in a form of technical protocols such as ‘‘Current Protocols’’ by Wiley-Liss, ‘‘Practical Approach’’ series by Oxford Press, ‘‘Methods of Molecular Biology’’ series by Humana Press, and Springer or Nature Protocols. The commercially available reagent kits also provide protocols describing the use of these reagents. Because of the proprietary nature of some reagents the latter are often cryptic and do not inform about chemistry of the components or mechanistic principles of the kit. While the protocols provide the guidance to reproduce a particular assay their standard ‘‘cook-book’’ format is restrictive and does not allow one to explain in detail the principles of the methodology, discuss its limitations and possible pitfalls. Likewise the discussion on optimal choice of the assay for a particular task or cell system, or review of the method applications, is limited. Yet such knowledge is of importance for rational use of the methodology and for extraction of maximal relevant information from the experiment. Compared to the protocol-format series the chapters in CYTOMETRY MCB volumes provide more comprehensive and often

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complementary to protocols description of particular methods. The authors were invited to review and discuss the aspects of the methodology that cannot be included in the typical protocols, explain theoretical foundations of the methods, their applicability in experimental laboratory and clinical setting, outline common traps and pitfalls, discuss problems with data interpretation, and compare with alternative assays. While authors of some chapters did include specific protocols, a large number of chapters can be defined as critical reviews of methodology and applications. The 35 chapters presented in CYTOMETRY Fifth Edition cover a wide range of diverse topics. Several chapters describe different approaches to downsizing cytometry instrumentation to the microfluidic and lab-on-a-chip dimension. Application of these miniaturized cytometric platforms in high-throughput analysis, as reported in these chapters, opens new possibilities in drug discovery studies. It also offers the means for real time, dynamic clinical assays that may be customized to individual patients, which could be a significant asset in targeted therapy. The microfluidic cytometry platforms are expected to play a major role in the era of the introduction of micro- and nanodimensional tools to modern biology and medicine, which we currently witness. Imaging cytometry, by providing morphometric analytical capabilities, makes it possible to measure cellular attributes that cannot be assessed by flow cytometry. Different approaches and applications of imaging cytometry are addressed in several other chapters of this edition. Capturing intercellular interactions during the immune response in situ, quantifying, and imaging the blood-circulating tumor cells as well as measuring apoptosis in fine-needle biopsy aspirates are the chapters describing highly relevant applications of imaging cytometry with a potential for use in the clinical setting. Also of interest and of importance is the chapter addressing the assessment of mutagenicity by buccal micronucleus cytome assay. The use of imaging cytometry was also instrumental for dissecting consecutive mitotic stages and states, revealed by highly choreographed molecular and morphological changes, as presented in yet another chapter. Further chapters describe advances in development of flow cytometry instrumentation, new probes, and methods. Among them are reviews on new lasers that are applicable to flow cytometry, applications of quantum dots, progress in development of red fluorescent proteins and biosensors, application of lanthanide elements to eliminate the autofluorescence background, surface-enhanced Raman scattering cytometry (SERC), and recent advances in cell sorting. The novel use of cytometry in analysis of bacteriological samples maintained on hollow fibers is also presented. Reviews of new applications of cytometry in cell biology are presented in several other chapters. Two chapters of this genre are focused on the use of cytometry for identification and isolation of stem cells. Other chapters present the advances in use of cytometry in studies of cell necrobiology, in assessment of oxidative DNA damage, in DNA damage response, and in analysis of cell senescence.

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Still another group of chapters present reviews on preclinical and clinical applications of cytometry. Of particular interest is the chapter addressing the use of cytometry in monitoring the intracellular signaling, which outlines the possibilities of assessing the effectiveness of the protein kinases-targeted therapies. The chapter describing advances in immunophenotyping of myeloid cell populations is very comprehensive, being illustrated by as many as 33 figures. Other chapters of interest for pathologists and clinicians describe the cytometry advances in monitoring transplantation patients, progress in HLA antibody detection, in erythropoiesis and nonclonal red cell disorders, as well as in mast cells disorders. The latter received recognition of the World Health Organization (WHO) as an example of the clinical utility of flow cytometry immunophenotyping in the diagnosis of mastocytosis. Both volumes contain the introductory chapters from the laboratory of Dr. Attila Tarnok, the Editor-in-Chief of the Cytometry A, outlining in more general terms the advances in development in cytometry instrumentation, probes, and methods (Part A), as well as in applications of flow and image-assisted cytometry in different fields of biology and medicine (Part B). In tradition with the earlier CYTOMETRY MCB editions, the chapters were prepared by the colleagues who either developed the described methods, contributed to their modification, or found new applications and have extensive experience in their use. The list of authors, thus, is a continuation of ‘‘Who’s Who’’directory in the field of cytometry. We are thankful to all contributing authors for the time they devoted to share their knowledge and experience. Applications of cytometric methods have had a tremendous impact on research in various fields of cell and molecular biology, immunology, microbiology, and medicine. We hope that these volumes of MCB will be of help to many researchers who need these methods in their investigation, stimulate application of the methodology in new areas, and promote further progress in science. Zbigniew Darzynkiewicz, Elena Holden, Alberto Orfao, William G. Telford and Donald Wlodkowic

Note to the readers: For interpretation of the references to color in the figure legends, please refer to the web version of this book. Also, note that all the color figures will appear in color in online version.

SECTION I

New applications in cell biology

CHAPTER 1

Recent Advances in Cytometry Applications: Preclinical, Clinical, and Cell Biology Anja Mittag*,y and Attila Tarnok*,y *

Department of Pediatric Cardiology, Heart Centre, University of Leipzig, Leipzig, Germany y Translational Centre for Regenerative Medicine (TRM), University of Leipzig, Leipzig, Germany

Abstract I. Preclinical and Clinical Applications II. Cell Biology and Cell Transplantation Therapy Acknowledgment References

Abstract The acceptance of flow cytometry (FCM) in clinical laboratory medicine is a major stepping stone towards development new cell analyses, improvement of accuracy, and finally a new range of diagnostic tests. Applications range from differential blood count determination to the identification of fluorescence-labeled subpopulations of disease-specific cell types in cell suspensions. Even new disease patterns can be identified by FCM. However, FCM is not only applicable for making a diagnosis but also for disease monitoring and routine check-ups. It is often used in oncology-related analyses, such as for leukemia and lymphoma patients. Here, not only cell numbers are relevant but also the degree of antigen expression which can be determined in a standardized way. Next to FCM also image cytometry has entered clinical applications although manual review by pathologists is still standard. In general, the multicolor approach and hence the ability for multiparametric analyses has led FCM to a central cornerstone in cell biology research. This review is intended METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

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to present an overview of cytometric applications which have entered clinical practice and led to deeper understanding in biological processes.

I. Preclinical and Clinical Applications The Coulter principle, founded by Wallace H. Coulter, is the reference method for counting and sizing microscopic particles in suspension. It led to automation in blood cell analysis and became a cornerstone of modern laboratory hematology and diagnostic industry. Considering the labor-intensive process of counting and testing blood cells in the past, the acceptance of flow cytometry (FCM) in clinical laboratory medicine was a major stepping stone in the development of analytical capabilities. By entering the field of differential blood count determination (Miller, 1981), automated flow analysis drastically reduced the analysis time in hematology. It is probably the main application of clinical flow analysis to date. However, even today, in 10–50% of the instances, the results of automated cell counters need to be confirmed by a visual screening of blood smears [‘‘manual white blood count (WBC) differential’’] (Roussel et al., 2010). Criteria for deciding whether the result of an automated analyzer needs visual verification or yields sufficient and reliable information are not defined as general rules. There is little uniformity among different laboratories (Barnes et al., 2005). As a reference WBC differential, a cell count of 400 is recommended by The Clinical and Laboratory Standards Institute (CSLI, 2007). The analyzed cell count of automated flow analyzers is far beyond this number but the analytical capabilities of nonfluorescent blood cell analyses based on measurement of scattered and absorbed light are restricted. This leads to confines in the identification of abnormal or diagnostic relevant cells such as activated lymphocytes or nucleated red blood cells. In such cases, the manual analysis of blood smears allowing for a detailed morphological cell assessment is necessary. However, a good portion of microscopic reviews in routine blood analysis is due to misclassification, that is, false positively flagged events in hematology instruments (Barnes et al., 2005). Fluorescence labeling of cells allows for identification of cell types usually determined in the blood count and also subpopulations of lymphocytes, blasts, etc. in a standardized way. Although several working groups have proposed convenient antibody combinations (Bj€ ornsson et al., 2008; Faucher et al., 2007), thus far there is no consensus protocol for a WBC differential using fluorescence-labeled antibodies. An initial investigation on the use of FCM as part of the diagnostic process for an automated validation of samples with suspect cells resulted in the conclusion that it is a robust tool in supplementing the automated hematology analyzer but cannot fully replace the manual blood differential analysis (Roussel et al., 2010). It is a highthroughput tool in which all processes (from sample preparation to gating and data management software) can be automated. Although multicolor FCM is not a substitution for a manual review, it reduces the need for time-consuming blood smear analyses to detect morphological abnormalities of blood cells. Most protocols used

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for blood assays are not suitable for a full differential blood count as they focus on single-cell type identification, such as immature granulocytes (Fujimoto et al., 2000), lymphocytes (Finn et al., 2004), monocytes (H€ ubl et al., 1996), or dendritic cells (Della Bella et al., 2008; Giannelli et al., 2008). Such protocols can be used to confirm specific diagnoses or disease monitoring. For specific clinical questions, FCM is frequently used for diagnosis, subsequent to histopathological tissue analysis. Friese et al. (2010) came up with the provocative finding that chronic lymphocytic leukemia (CLL) patients who had FCM performed early in their diagnosis experience an overall survival benefit. There may be several reasons for that finding, but nevertheless, FCM is a valuable tool for clinical cell analyses. Even new disease patterns were identified by FCM such as monoclonal B-cell lymphocytosis (MBL). MBL is rather ‘‘accidentally’’detected and diagnosed in the blood by an elevated lymphocyte count of asymptomatic individuals, but its significance is still unknown. It may be associated with an autoimmune abnormality or may be related to aging (immunosenescence) (Nieto et al., 2010). MBL is considered to be the precursor of CLL (Landgren et al., 2009; Marti et al., 2007; Rawstron et al., 2008) or to be associated with non-Hodgkin’s lymphoma (NHL) (Marti et al., 2005). At present, MBL is defined by its distinction from CLL and NHL by FCM characteristics (Marti et al., 2005; Nieto et al., 2010; Shanafelt et al., 2010). Original protocols were based on two or three colors. However, evolution in FCM instrumentation led to more complex and precise identification of MBL but global consensus guidelines have not been yet generally accepted. The diversity in reagents, instruments, and methods of analysis needs to be aligned (Nieto et al., 2010). One important example of successful unbiased leukemia diagnosis approach comes from EuroFlow Consortium. The approach based on eight-color protocols seems to unify the analytical principles for all laboratories involved in leukemia diagnostics according to EuroFlow standard and there is a hope that even more diseases will be diagnosed based on similar approach (Pedreira et al., 2008a). Severe combined immunodeficiency (SCID) is a combination of different congenital immunodeficiency syndromes that have in common a malfunction or reduced number of T-lymphocytes. However, also B- and NK-cells may be affected. The occurrence of SCID is estimated for around 1:100,000 of life births. The most successful treatment method for SCID is bone marrow transplantation (BMT) or hematopoietic stem cell transplantation (HSCT). Transplantation can be performed within the first 3 months of life and offers a 95% survival rate (Buckley et al., 1999). Children with untreated SCID rarely pass the age of two. The earlier the diagnosis of SCID is done, including prenatally, the better, as immune reconstitution must be monitored closely following transplantation. Delayed or incomplete reconstitution can put patients at risk for lethal infections and complications from autoimmunity (Gennery et al., 2010; Puck and SCID Newborn Screening Working Group, 2007). FCM assays performing T- and B-cell subset enumeration are a powerful tool to assess immune reconstitution immediately following transplantation and for longterm follow-up postsurgery to ensure that lymphocyte counts reach and remain at normal levels (Curtis et al., 2010).

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Essential for the use of FCM in clinical analysis is also the standardization of measurements and results. Next to ‘‘simple’’ immunophenotyping and cell counting, the determination of the degree of antigen expression is of practical utility in diagnosis of, for example, leukemia and lymphomas (Atra et al., 1997; D’Arena et al., 2000; Jasper et al., 2010; Ginaldi et al., 1998). It should be clear that terms such as ‘‘dim’’ or ‘‘bright’’ are not useful in this context. Also, the measured mean fluorescence intensity (MFI) value of a cell population provides no reliable information for making a profound diagnosis. In order to avoid changes due to variations in particular antibody batches or instrument performance, data must be standardized by quality control measures such as a comparison with standard beads fluorescence intensity. If the mean fluorescence is set off against the value of standard particles concurrently measured, standardized results are obtained, which allows monitoring over time and can be compared between specimen and laboratories (McLaughlin et al., 2008). One example for such quantitative FCM is the analysis of the CD22 antigen. An abnormally bright CD22 expression is a characteristic of hairy cell leukemia. In contrast, typical for CLL is an abnormally dim expression of CD22 (Jasper et al., 2010). The quantitative analysis of CD22 is considered as very precise if a sufficient number of cells are analyzed, making it an appropriate test for longitudinal studies (Jasper et al., 2010). Also, the absolute quantification of other antigens is clinically relevant; for example, CD38 is useful in the prognosis of CLL patients (Hsi et al., 2003). Likewise, the determination of CD52 expression can be applied to patients with T/NK-cell malignancies being considered for Alemtuzumab therapy (Jiang et al., 2009). FCM enters more and more classical fields of pathology. For example, traditionally, the diagnosis of classical Hodgkin’s lymphoma (CHL) is based on morphological evaluation of hematoxylin and eosin (H&E) stained tissue sections. Fromm et al. demonstrated recently that Hodgkin and Reed–Sternberg cells can be identified and hence CHL diagnosed by an appropriate antibody panel by FCM. With 89% sensitivity and a specificity of 100%, immunophenotyping by FCM could be used for routine diagnosis or supplementing the immunohistochemical analysis (Fromm et al., 2009). Also, the assessment of CD123 may be useful in supporting the diagnosis of CHL (Fromm, 2010). Furthermore, an unequivocal discrimination between low-grade NHL and reactive lymphoid hyperplasias is problematic, but a combined approach of fine needle aspiration cytology and FCM has a high diagnostic value. With this combination, it is possible to distinguish between benign and malignant lymphoid infiltrates (Bangerter et al., 2007). Moreover, it enables discrimination between reactive hyperplasias and neoplastic proliferations and the classification of low-grade B-cell NHL on cytological samples by FCM with high reliability (Schmid et al., 2010). The subclassification of NHL by FCM is avoiding the need for invasive surgical biopsies in many cases (Demurtas et al., 2010). Another focus of FCM in oncology is on the detection of circulating cells in the peripheral blood with the characteristics of tumor cells, known as circulating tumor cells (CTC). They are found not only in patients with metastatic disease but also in

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patients with apparently localized tumors (Ring et al., 2004). Hu et al. (2010) presented a multiparameter FCM analysis for effectively detecting CTC in breast cancer as a valuable tool for prognosis assessment among breast cancer patients. Their results demonstrated that the overall survival was closely correlated both with CTC levels and with metastasis and age, but was independent of clinical pathology and tumor size. Hence, monitoring of CTC values in the clinical course of metastatic breast cancer patients is believed to be of great importance and becomes a valuable tool in clinic (Li, 2010). Moreover, this approach is proposed as a possibly better prognostic tool than functional imaging (De Giorgi et al., 2009; Hayes et al., 2006; Hu et al., 2010) provided that a standardized approach for CTC level assessment is developed (De Mattos-Arruda et al., 2010). The determination of the level of circulating endothelial lineage cells (ELC) might also be of clinical relevance. Their presence in blood could serve as a biomarker of tumor neovascularization in patients with pancreatic ductal adenocarcinoma (PDCA) as increasing ELC levels after PDAC resection seem to be associated with cancer recurrence (Sabbaghian et al., 2010). Another prognostic tool and potential therapeutic target may be expression of CD157 as it plays a pivotal role in the control of ovarian cancer cell migration (Ortolan et al., 2010). The quantification of vascular circulating endothelial cells (CEC) in the peripheral blood can be used for assessing endothelial damage (Lampka et al., 2010). CECs share several surface antigens with other stem and progenitor cells but have a unique pattern of them (Adams et al., 2009). CEC counts correlate with disease activity across a broad variety of diseases (Erdbruegger et al., 2006). Lampka et al. (2010) evaluated the flow cytometric assessment of CEC in peripheral blood by CD31, CD146, and CD45 in patients with coronary artery disease. They found a significant higher CEC count in patients with acute myocardial infarction compared to healthy controls, whereas CEC numbers in stable angina patients remained inconspicuous. Estes et al. (2010) further refined the assay and developed a polychromatic panel for the detection and discrimination of circulating angiogenic and nonangiogenic stem and progenitor cells. These methods may in future be used to improve diagnosis of an acute coronary syndrome. In majority of flow cytometric tests, analysis of cell populations is based on identification of markers or marker combinations in or on the surface of cells. In contrast to that, diagnosis of paroxysmal nocturnal hemoglobinuria (PNH) is based on the demonstration of antigen absence. Common markers are CD55 and CD59. However, several studies discuss that PNH testing by FCM has significant problems with regard to false-positive and false-negative results (Rachidi et al., 2010; Richards et al., 2008). As for almost all flow cytometric assays for diagnostic purposes, also in this case there is an urgent need for standardized protocols (Richards et al., 2008). While certain authors state that the use of CD59 and/or CD55 combined with careful gating analysis is reliable and reproducible for PNH diagnosis (Kim et al., 2010; Parker et al., 2005; Tembhare et al., 2010) and may even be useful in differentiating PNH from patients with aplastic anemia (Kim et al., 2010), other markers have also been proposed for a reliable diagnosis. Furthermore,

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the assessment of the percentage of abnormal cells based on erythrocyte analysis is affected by blood transfusion as the latter leads to an increase in proportion of cells with normal CD55 and CD59 expression. For an accurate diagnosis, at least two different monoclonal antibodies, directed against two different glycosylphosphatidylinositol (GPI)-anchored proteins, on at least two different cell lineages should be used to diagnose a patient with PNH (Rachidi et al., 2010). Hern
andez-Campo et al. (2008) demonstrated that the best combination of markers for the diagnostic screening of PNH included evaluation of CD14 on monocytes and CD16 on neutrophils. Further analysis of CD55 and CD59 expression, however, may contain additional clinically useful information. The findings of Richards et al. (2008) suggest that the use of CD16 and CD66b provides more accurate results compared to CD55 and CD59, when testing granulocytes for GPI deficiency. Another FCM technique for PNH diagnosis uses a fluorescein-labeled proaerolysin variant (FLAER) as a ligand. As it directly binds GPI-anchor, it gives a more accurate assessment of this molecule’s deficit than does CD55 or CD59 on most cell lineages (Brodsky et al., 2000), with the exception of red cells and platelets (Brodsky, 2009). In conclusion, the multiparametric capabilities of FCM should be exploited in using several antibodies for a simultaneous analysis of different cell lines and a reliable PNH diagnosis. Since low volumes of blood are adequate for FCM analysis, this instrumentation is of practical utility for neonatal diagnosis (Michelson et al., 2000). In fact, FCM can be applied even for prenatal diagnosis as well. The sensitivity of detecting fetal cells within the maternal circulation has been improved. However, there are still several challenges that need to be overcome before application in prenatal diagnosis, mainly the lack of specific fetal cell markers and the paucity of fetal cell numbers, that range from 1 in 104 to 1 in 106 in the maternal blood (Curtis et al., 2010). However, when the fetal cells are enriched, which can be accomplished by magnetic or fluorescence activated cell sorting, FCM offers a promising alternative to the current methods of amniocentesis or chorionic villus sampling. Compared to FCM, the latter approaches for prenatal diagnosis are invasive, carry a risk of fetal injury, may result in pregnancy loss, and are expensive (Curtis et al., 2010). Curtis et al. (2010) provide an excellent review of FCM methods for prenatal and neonatal diagnoses of common immunological and hematological abnormalities. A clinical application where FCM has always been playing a crucial role is HIV diagnosis, monitoring, and research. It can be said that the history of these two fields is intimately linked (Chattopadhyay and Roederer, 2010). Determination of CD4+ T-cell count by FCM has been designated as the gold standard by the WHO (World Health Organization, 2007). The CD4+ T-cell counting technology has gradually evolved from a pure cellular research assay technique into a routine clinical diagnostic test (Arewa, 2010). This test has undergone several improvements and its evolution is still going on. Paradoxically, the last to benefit from the impact of the technology (or its technological improvements) are the ones who need it most but cannot afford it. That is why there are two major branches of developments in HIV diagnosis/monitoring by FCM. On the one side, there is the cellular research in order to understand mechanisms of infection and its influence

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on the immune system and the sophisticated (truly multiparametric) analysis in the Western World (Chattopadhyay and Roederer, 2010; Perfetto et al., 2004). But there is another side, namely, resource-poor countries with their inability to provide adequate HIV diagnosis and treatment. There are many attempts to simplify the technology to absolute necessary, reduce the costs with it, and make it easier to operate. Improvements in this directions are, for example, acoustic micromanipulating techniques to focus particles in a flow stream instead of the commonly used hydrodynamic focusing (Zaragosa, 2006), flow rate calibration (Nantakomol et al., 2010), or cost reduction in preparation with a no-lyse, no-wash technique (Cassens et al., 2004; Greve et al., 2003) or instrumentation in general (Pattanapanyasat et al., 2005; Zijenah et al., 2006). However, many approaches focus on image analysis for HIV diagnosis (Bae et al., 2009; Li et al., 2007a, 2007b, 2010; Moon et al., 2009; Rodriguez et al., 2005; Ymeti et al., 2007). Regardless whether CD4+ T-cell counting is based on imaging or flow analysis, there is still a great need for affordable and reliable HIV diagnostics. Malaria and tuberculosis have to be kept in mind, too. Also, for these patients essential healthcare is urgently needed (visit www.partec.com to see the great work Dr. Wolfgang G€ ohde is doing in this direction). It is not that only whole cells are relevant for diagnostic purposes in the peripheral blood. There are plenty of microparticles, that is, membranous vesicles, some of them apoptotic bodies, in the plasma of normal individuals, which express the surface markers from the parental cells and are therefore interesting subject for FCM analyses. By virtue of multiparametric and quantitative analyses, FCM offers a suitable method for clinical investigations of the microparticles. Orozco and Lewis (2010) review FCM analysis and applications of microparticles with the focus on fetal-derived microparticles found in maternal plasma. Elevated levels of microparticles, mainly derived from circulating blood cells, are associated with various diseases such as thrombosis, congestive heart failure, or breast cancer (Orozco and Lewis, 2010). The authors try to draw a roadmap for the assessment of microparticles as biomarkers for a variety of conditions, including an appeal for standardization of preanalytical and analytical procedures. Secretion of microparticles is one of the many ways cells can transfer information between one another. A related alternative method is the relatively recently observed trogocytosis where membrane fragments are transferred from one cell to another in direct cell–cell interaction. This may happen, for example, during antigen presentation (Daubeuf et al., 2006) and is considered responsible for immune modulation. Trogocytosis may be clinically relevant and was found to be related to HIV spread (Aucher et al., 2010) or transfer of antitumor activity from T- to NK-cells (Domaica et al., 2010), among others. Several FCM-based methods have been developed to detect specific trogocytosis (Daubeuf et al., 2009) so that these assays may become diagnostically relevant in future. Besides the hematology laboratories, diagnostic microbiology can also benefit from flow cytometric analyses. Pieretti et al. (2010) demonstrated that the determination of bacteria and leukocyte counts by a urine cytometer is acceptable for routine use and can considerably reduce the number of bacterial cultures needed.

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Still, there are some limitations to using FCM as a diagnostic tool. FCM assays often require expensive reagents that may not be routinely available in a clinical setting and some analyses require highly skilled personnel to perform (Notarangelo and Sorensen, 2008). The ongoing development of new easier to operate instrument platforms capable of measuring multiple parameters, along with the advances in reagents and dye conjugations, will help this technology continue to make transition from the research setting to a key diagnostic tool in clinical medicine (Curtis et al., 2010). To make a long story short, FCM analyses have entered the field of clinical diagnosis in many different disease patterns. For example, FCM has proven to be useful in celiac disease by assessing diagnostic, prognostic, and disease activity biomarkers (Leon, 2010). Furthermore, it is known that basophils contribute to anaphylaxis and allergies. Hence, they are a suitable target for disease monitoring. Gernez et al. (2010) demonstrated the usefulness of determining the CD203c level on basophils for baseline diagnosis and therapeutic monitoring in subjects with nut allergy. Detection of inflammation is usually not problematic. However, in case of concomitant diseases such as rheumatoid arthritis (RA), it may be difficult to diagnose local musculoskeletal infections. Specific biomarkers would be of great use in that case. Nishino et al. (2010) reported that CD64 can provide a useful marker to discriminate local infection from RA-related inflammation. In general, identification of biomarkers specific to one disease pattern would improve diagnosis of many diseases. Due to its easy operability, FCM would be applicable for assessing diagnostically relevant levels of biomarkers, for example, activation markers on certain blood cell types. It would allow both diagnosis and monitoring disease progression and therapeutic effects. Likewise, specific biomarkers in tissue samples, for example, biopsy material, can also be identified by image cytometry. Image analysis is a well-known technique in clinical analysis. Optical inspection of blood smears or chromatically (e.g., H&E) stained tissue sections by a pathologist is the simplest form of image analysis and has been carried out for ages. Routine histomorphometric analysis and subjective scoring methods have traditionally been used to define, either in a quantitative or in a qualitative manner, morphologic endpoints in chromogenic immunohistochemistry (IHC) and routine histochemically stained sections. However, survey by pathologists of cellular samples of different origin, tissue sections, biopsies, swabs, etc., is not cytometry by definition. In most cases, they cannot be described as objective and quantitative analyses. There is a substantial interobserver variance in assessment of those samples that apparently cannot be diminished by training (Furness et al., 2003; LiVolsi, 2003). An objective quantitation would be preferable. This can be realized by image cytometry. Image cytometry is capable of extracting a multitude of parameters from cells. Cytometric slide-based assays are widely used in clinical and preclinical research but only rarely for clinical diagnosis (reviewed by Gerstner et al. (2009). Cytometric imaging techniques are available for analysis of solid tissue for many decades, but manual and hence subjective scoring of samples is still a common practice. However, automated high-content quantitative methods such as laser scanning cytometry

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(LSC) offer more efficient unbiased data collection (Peterson et al., 2008). Application of fine-needle biopsies and swabs (Gerstner et al., 2003, 2004, 2005, 2006; Schwock et al., 2007) and of a variety of tissue specimens (Haider et al., 2003; Kayser et al., 2006; Mosch et al., 2007; Persohn et al., 2007) has been reported. Image cytometry provides insight into biological systems and their complex interactions such as the immune system (Harnett, 2007). Multiplexed analyses can thereby be performed with even very small samples such as FNAB or swabs. Concurrent multicolor analyses are possible or data can be obtained by sequential analyses of the same cells. Sequential analyses may be useful in drug screening assays. Holme et al. (2007) demonstrated that several classical parameters associated with apoptosis can be measured on the same cells in sequential analysis such as plasma membrane permeability changes, DNA integrity and hypodiploidy, chromatin condensation, loss of mitochondrial membrane potential, and cell cycle profile. Moreover, morphological features that cannot be assessed by FCM can be determined by image cytometry. For example, the efficacy of a drug to separate cell clusters into single cells might be an indication of inhibition of tumor colony formation and can be measured by LSC. In combination with the cell cycle profile of the treated cells, for example, the determination of G1 cell cycle arrest, one can conclude about the drug’s potential effectiveness (Holme et al., 2007). Main applications of image cytometry are DNA content and cell cycle analyses. DNA ploidy can be determined by quantitative fluorescence analysis of cells stained with DNA dyes. This proved to be useful in distinguishing malignant from benign lesions in breast cancer (Zhang et al., 2006). In addition, ploidy analysis was combined with determination of DNA copy number aberrations that was closely related to DNA aneuploidy in lung adenocarcinomas (Hayashi et al., 2005) or colorectal carcinoma (Liu et al., 2004). An increased hyperploidy in neurons in patients with Alzheimer’s disease compared to normal brain was found by LSC. Furthermore, the majority of those tetraploid neurons were found to express cyclin B1 indicating a reactivation of their cell cycle (Mosch et al., 2007).

II. Cell Biology and Cell Transplantation Therapy Both cell senescence and cell death are critical endpoint measurements for diagnosis, therapy monitoring, or cell-based drug discovery. Regarding cell senescence, it was shown that successful aging or longevity is correlated with reduced senescence of circulating NK-cells (Kaszubowska et al., 2008) and could be predictive for life expectancy. Furthermore, it can be useful in screening for tumor cells (Gancarc
ıkov
a et al., 2010). Various FCM methods exist to determine cellular senescence. Senescence-associated beta-galactosidase (SA-beta-gal) activity is a widely used marker. SA-beta-gal activity is routinely detected cytochemically, manually discriminating negative from positive cells. This method is time consuming, subjective, and therefore prone to operator error. The FCM assays for SA-beta-gal show that under nonstressed conditions, fibroblasts from very old

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subjects show higher SA-beta-gal activity than fibroblasts from young subjects as found by FCM. Under stress-induced conditions, a significantly higher SA-beta-gal activity in fibroblasts from very old compared to young subjects was reported (Noppe et al., 2009). An alternative assay is the determination of telomere length and telomerase activity by FCM (Carbonari et al., 2010) by in situ hybridization. Telomere length in NK-cells seems to be predictive for live expectancy in the elderly (<80 years of age). Moreover, physical exercise also leads to a shortening in telomere length of circulating cytotoxic T-cells but not of NK-cells (Simpson et al., 2010). Cell death quantitation by the measurement of apoptotic or necrotic processes is of central value in drug discovery and clinical screening. So far, various assays have been developed to measure cell death. Annexin-V staining is a common method to determine apoptosis (Wlodkowic et al., 2010). However, hypotonic lysis of erythrocytes may lead to the release of erythrocyte-derived microvesicles that may transfer to the surface of nucleated cells. Due to a phosphatidylserine transfer by microvesicles, cells may then be misleadingly positive for Annexin-V indicating apoptosis (Liu et al., 2009). Cell transplantation therapy for replacement and repair of diseased or not properly working organs or organ systems is a new, very promising approach and bears the hope for many. Natural T regulatory cells (nTregs) play a key role in inducing and maintaining immunological tolerance. Cell-based therapy using purified nTregs is under consideration for several conditions, but procedures employed to date have resulted in cell populations that are contaminated with cytokine secreting effector cells. Boolean gating analysis of cytokine-expressing cells by FCM for 32 possible profile endpoints revealed that 96% of expanded nTregs did not express any cytokine. From a single buffy coat, approximately 80 million pure nTregs can be harvested after expansion under cGMP conditions; these cell numbers are adequate for infusion of approximately one million cells kg
1 for cell therapy in clinical trials (Pahwa et al., 2010). Moreover, there are still many open questions regarding identification, isolation, and in vitro propagations of Tregs (Law et al., 2009; Trzonkowski et al., 2009). The same stands true for various stem cells for cell transplantation therapy such as organ function repair or replacement. Optimal as starting points could be pluripotent stem cells such as embryonic-like stem cells. Identification and isolation of embryonic-like stem cells may be influenced by their very small size. Common isolation, that is, sorting protocols exclude events smaller than erythrocytes by gating. Next to specific markers for identification of embryonic-like stem cells, bead particles as size markers may be appropriate in sorting these cells (Zuba-Surma et al., 2010). Very small embryonic-like stem cells have been identified as CD34+CD133 +/CXCR4+/Lin/CD45 cells that express PSC markers including Oct-4, Nanog, and SSEA-4 (Kucia et al., 2007). Several other steps such as Ficoll–Paque preparation or volume depletion may be responsible for loss of small embryonic-like stem cells. However, optimized three-step isolation procedures for these cells have already been reported by Zuba-Surma et al. (2010).

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Taken together, unequivocal diagnosis and identification of hematological malignancies, CTCs, and cell senescence, among others, require cell-based analysis of a multitude of phenotypic and functional markers and it is best done by polychromatic (seven colors or more) flow or image cytometry. Several laboratories have started to develop such polychromatic panels for flow and the first standardized protocols became available for a broad audience (Chattopadhyay et al., 2010; Mahnke and Roederer, 2010). It has to be kept in mind, however, that each type of disease requires its own specific polychromatic panel. In many cases, these panels are not available but have to be established by each laboratory. This is a tedious and time-consuming process and may take up to a year. Furthermore, panel development is very costly as several clones and dye combinations have to be tested (Roederer and T
arnok, 2010) and expensive instruments are needed that have multiple light sources and light detectors (McLaughlin et al., 2008). An alternative and potentially simpler approach was recently introduced by Pedreira et al. (2008b). The authors developed a mathematical approach that combines data acquired by several four-color panels into a metafile. This metafile then has virtually infinite numbers of colors. The advantage of this method is that low-cost instruments and reagents can be used and four-color panels are already on the market. A bottleneck of multiplexed cell measurement is the data analysis. These massive and dramatic advances in measuring and staining technology are still followed by the (archaic) way of manually analyzing different cell states and phenotypes, at least traditionally. But when one tries to identify and characterize cells that have been carefully measured in a 10 or even higher dimensional space (with respect to colors and scatter properties), still the stone-age tools of expert knowledge (which is important) and a cascade of (sometimes erratic) combination of one- and two-dimensional plots is used. On the one hand, this approach that is in use for nearly three decades makes sense, as it is ‘‘known’’ which cell populations exist and which have to be quantified. On the other hand, it is biased by the investigators prejudice of preset expectations and knowledge about cell phenotypes one is looking for. Thus, the manual way to analyze complex date bears two major problems. From the aspect of research and discovery, the investigator is chained to his expectations and not free to discover the unexpected. Regarding standardized and unbiased analysis, as it is required for clinical diagnosis and quality control for GLP and GMP, high standards are required that can be only warranted for complex data by highly experienced experts. In the recent past, several tools have been developed for automation and data reduction that enable to perform an interpretation, unbiased identification, and analysis of complex data beyond the 2D flat world of dot-plots. Over the last decades, many more or less successful approaches have been made to overcome this bias and tools have been developed for automated data analysis and reduction of complex data to its essential components. Aghaeepour et al. (2011) present a novel approach named flowMeans. They compare their approach for the analysis of complex FCM data not only with manual but also with alternative automated analysis tools. As also commented by Luta (2011), this approach proves favorable

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over manual scoring and alternative automated approaches. This indicates that we may be entering an era of reliable automated flow data analysis (Lugli et al., 2010). Such automated approaches will apply both for multiplexed analysis of stem cells (Adams et al., 2009; T
arnok et al., 2010; Zuba-Surma et al., 2009) and for the unbiased analysis of complex imaging data. Image cytometry analysis comprises a multitude (thus multidimensionality) of phenotypic and morphometric data, including nuclear size, elevated nuclear content, chromatin texture, among others. Acknowledgment The authors would like to thank Dr. Arkadiusz Pierzchalski, Translational Centre for Regenerative Medicine (TRM), University of Leipzig, Germany, for his help with this manuscript.

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Hayes, D. F., Cristofanilli, M., Budd, G. T., Ellis, M. J., Stopeck, A., Miller, M. C., Matera, J., Allard, W. J., Doyle, G. V., Terstappen, L. W. W. M. (2006). Circulating tumor cells at each follow-up time point during therapy of metastatic breast cancer patients predict progression-free and overall survival. Clin. Cancer Res. 12, 4218–4224. Hern
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CHAPTER 2

Detection of Hematopoietic Stem Cells by Flow Cytometry Kuanyin K. Lin*,y and Magaret A. Goodell*,y,z *

Stem Cell and Regenerative Medicine Center, Baylor College of Medicine, Houston, Texas, USA y Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, Texas, USA z

Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA

Abstract I. Introduction II. The side population is highly enriched in hematopoietic stem cells III. Surface markers for LT-HSC purification References

Abstract Various studies have been conducted to identify hematopoietic stem cells (HSCs) using flow cytometry. The technique is primarily based on fluorochrome-conjugated antibodies against cell surface markers of HSCs and the physiological properties of HSCs such as high-efflux activity of certain fluorescent dyes. The surface marker schemes are based on using c-Kit, Sca-1, and Lineage markers, resulting in ‘‘KSL’’ population. Markers in KLS scheme can be used to further subfractionate this KLS population to distinguish HSCs from differentiating progenitors. The ‘‘signaling lymphocyte activation molecule’’ (SLAM) family of proteins can also be used to enhance the KLS enrichment scheme. The other strategy is to identify HSCs based on their high efflux ability of fluorescent dyes. This chapter describes the method used for identifying the side population (SP) in combination with surface markers to METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

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0091-679X/10 $35.00 DOI 10.1016/B978-0-12-385493-3.00002-4

Kuanyin K. Lin et al.

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isolate HSCs from murine bone marrow and to discuss the advantages and pitfalls of this method.

I. Introduction Hematopoietic stem cells (HSCs) normally reside in the bone marrow and represent about
0.01% of nucleated cells marrow cells. A long history of attempts to enrich for stem cells through various strategies ultimately led to the application of flow cytometry by several groups, including Weissman and colleagues, who enriched HSCs based on the lack of expression of a group of lineage differentiation markers (the so-called lineage-negative cocktail) and positive expression of known mouse stem cell markers, Sca-1 and Thy1.1 (Spangrude et al., 1988). Since then, many additional studies from a number of groups have contributed to refining the enrichment strategy, adding new markers that both increase purity and simplify the procedure, as more features of HSCs have been identified. Sophisticated FACS-based strategies account for HSCs being the best studied somatic stem cells. Two main strategies are employed to identify HSCs using flow cytometry, both based on the characteristic features of HSCs: (1) fluorochrome-conjugated antibodies against cell surface markers of HSCs, and (2) the physiological properties of HSCs such as high-efflux activity of certain fluorescent dyes. The surface marker schemes are primarily based on using c-Kit, Sca-1, and Lineage markers, resulting in the so-called ‘‘KSL’’ population (c-Kit+, Sca-1+, and Lineage) (Morrison and Weissman, 1994; Okada et al., 1992). KSL cells represent about 0.1% of whole bone marrow cells, and are enriched for progenitors, but bona fide stem cells comprise at best
10% of the KSL population. Additional markers included in the KLS scheme, such as CD34 (Osawa et al., 1996), Tie2 (Arai et al., 2004), and EPCR (Balazs et al., 2006), can be used to further subfractionate this KLS population to distinguish HSCs from differentiating progenitors. More recently, the ‘‘signaling lymphocyte activation molecule’’ (SLAM) family of proteins, including CD150, CD48, and CD244, were identified and can be used to enhance the KLS enrichment scheme. CD48 and CD244 are used to identify and eliminate differentiating progenitor lineage cells, while CD150 is used to positively identify HSCs with potent stem cell activity (Kiel et al., 2005), although there is continuing discussion on whether all HSCs express CD150 (Beerman et al., 2010; Challen et al., 2010; Morita et al., 2010). The other strategy is to identify HSCs based on their high efflux ability of fluorescent dyes such as a DNA-binding dye, Hoechst 33342 (Goodell et al., 1996) or a mitochondrial-binding dye, Rhodamine123 (Li and Johnson, 1992). The high efflux ability of HSCs is attributed to their high expression level of multidrug-resistant transporters such as ATP-binding cassette (ABC) transporters that pump out these fluorescent dyes and result in low fluorescence retention in HSCs (Zhou et al., 2001, 2002). When Hoechst 33342-stained bone marrow cells are

2. Detection of Hematopoietic Stem Cells by Flow Cytometry

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[(Fig._1)TD$IG]

Fig. 1

Mouse whole bone marrow stained with Hoechst 33342, excited with a UV laser and emission collected at blue and red wavelengths. The side population (arrow) represents around 0.01–0.03% of bone marrow and is highly enriched with hematopoietic stem cells (HSCs). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

subjected to a flow cytometer with UV laser excitation, and emission is detected through both blue and red wavelengths, the low Hoechst 33342 retention results in a ‘‘side population’’ (SP) that is highly enriched in HSCs and is distinctive from the rest of bone marrow differentiated cells (Fig. 1). The expression of HSC surface markers as well as the ability of HSCs to efflux fluorescent dyes varies during mouse development. Thus, the selection of markers to purify HSCs becomes crucial in each context. In midgestation mouse fetal liver, where definitive HSCs (adult-like HSCs) predominantly reside, the high efflux cell population of Rhodamine123 and Hoechst 33342 cannot be found (Uchida et al., 2004). In addition, the side population (SP) is minimal in mice that are younger than about 6 weeks old (unpublished), indicating that the expression of multidrug-resistant transporters is acquired later with age. Efforts have been made by several groups to identify surface markers to purify embryonic HSCs: at E14.5, fetal liver HSCs can be purified with AA4.1 (Petrenko et al., 1999) and a combination of markers including Tie-2 (Hsu et al., 2000), CD150, CD48 (Kim et al., 2006), EPCR (Iwasaki et al., 2010), c-Kit, Sca-1, and CD45 (Zhang and Lodish, 2004) while at earlier stages of embryonic development, such as the Yolk sac at E9.5 and the aorta-gonads-mesonephros (AGM) at E10.5, the definitive HSCs can be enriched with CD34 and CD41 (McKinney-Freeman et al., 2009). The goal of this chapter is to describe the method used for identifying the side population in combination with surface markers to isolate HSCs from murine bone marrow and to discuss the advantages and pitfalls of this method.

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II. The side population is highly enriched in hematopoietic stem cells Hoechst 33342 is a bis-benzimide derivative that binds to AT-rich sequences in the minor grove of double-stranded DNA. The emission is very bright, and for the purposes of the SP strategy, is detected at two emission wavelengths after UV excitation: Hoechst Red (630–650 nM) and Hoechst Blue (405–450 nM). Hoechst 33342 can also be utilized as a DNA binding dye to determine cell cycle status and apoptosis assays, as well as a nucleus indicator in microscopic studies (Ramos et al., 2003); however, in HSCs, the dye is so efficiently effluxed that the Hoechst fluorescence does not correspond to DNA content. When the emission signals of both Hoechst Red and Hoechst Blue are simultaneously displayed, the side population is seen as a streak of cells stretched toward the lower-left corner, reflecting the lower dye-content and higher efflux ability of HSCs relative to the rest of bone marrow cells (Fig. 1). The side population pattern is a result of both the nonlinear emission of Hoechst 33342 dye under UV excitation (Petersen et al., 2004) and the distinct ability of bone marrow cells to retain Hoechst dyes, and also likely the chromatin conformation within cell populations, which results in differential access of the dye to the DNA. The advantage of Hoechst 33342 staining is the low cost of reagent and high degree of HSC enrichment, making the side population an appealing method to isolate HSC; even without additional purification, about 75% of SP cells express the cell surface markers of mouse HSC as defined by most other methods (i.e., KSL, CD34low, CD48neg). However, the parameters such as chromatic shift from blue to red of Hoechst 33342 in cell fluorescence and the expression level of multidrug-resistant pumps in bone marrow cells attributed to the side population pattern may also contribute to inaccuracy in Hoechst 33342 staining and thereafter affect the purity of result population. It is found that the fluorescence emission of Hoechst 33342 varies in low and high concentrations of dye, and the fluorescence of the cells shifts from blue to red with an increased dye intake, indicating a critical dye/cell ratio for accurate Hoechst 33342 staining (Petersen et al., 2004). In addition, the side population pattern is found to form early in the bone marrow incubation with Hoechst 33342 before the enrichment of HSCs is achieved. The cells constantly move along from the side population to the main population during the incubation and reach an appropriate distribution such that the side population is most enriched in HSCs at a specific time point and dye concentration (Ibrahim et al., 2007). Therefore, the degree of enrichment for HSCs in the side population is highly dependent on accuracy in performing of Hoechst staining, which in turn relies on the concentration of Hoechst 33342, the concentration of the cells, and the duration and temperature of incubation. The meticulousness of Hoechst staining often varies from laboratory to laboratory, as evident by the reported discrepancies of marker expressions and function of

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2. Detection of Hematopoietic Stem Cells by Flow Cytometry

SP and non-SP cells found among different groups, leading to the side population sometimes being considered a ‘‘controversial’’ marker. For instance, the side population in one report was shown to fail to included all HSCs as there were functional long-term HSCs in the non-SP cells (Morita et al., 2006); and in another report, only a subset of SP cells were considered to be bona fide HSC (Matsuzaki et al., 2004). In our laboratory, the non-SP cells have not successfully functioned as long-term repopulating HSC (Camargo et al., 2006; Challen et al., manuscript in preparation; Goodell et al., 1996), and SP cells throughout the population are highly enriched for stem cell activity, albeit with somewhat different properties when observed long term (Challen et al., 2010; Goodell et al., 1997). To ensure the purity and the accuracy of SP staining and identification, it is highly recommended to include HSC markers such as Sca-1, c-Kit in the analysis, along with a negative selection of lineage markers (Lin), allowing confirmation of the level of purity and further enrichment, rendering SPKLS cells (which can be affectionately called ‘‘sparkles’’).

III. Surface markers for LT-HSC purification HSCs are pluripotent, that is, able to give rise to all lineages of blood cells. HSC are functionally defined by their ability to generate hematopoietic progeny and subtypes can be distinguished by the kinetics of hematopoietic regeneration. HSCs that are able to give rise to lifelong engraftment in transplantation assays, which test for the power of HSCs to self-renew and regenerate, are called long-term HSCs (LT-HSC). In mouse models, a variety of purification schemes have been utilized to purify LT-HSCs (Table I). Although the schemes of marker combination seem overwhelmingly inconsistent, it is thought that the resulting HSC populations are highly overlapping and the best populations are relatively homogeneous in terms Table I Common utilized schemes to purify HSCs Marker phenotype

Cell type

Reference

SPKLS Flk2CD34KLS CD150+CD48CD244 CD150+CD48CD34KLS CD45midLinRhodamineloSP CD150+Thy1.1loFlk2KLS Lower SPKLS Upper SPKLS CD150hiCD34KLS CD150CD34KLS

Long-term HSCs Long-term HSCs Long-term HSCs Long-term HSCs Long-term HSCs Long-term HSCs Myeloid skewed long-term HSCs Lymphoid skewed long-term HSCs Myeloid skewed long-term HSCs Lymphoid skewed long-term HSCs

Goodell et al. (1996) Adolfsson et al. (2001) Kiel et al. (2005) Wilson et al. (2007) Dykstra et al. (2007) Papathanasiou et al. (2009) Challen et al. (2010) Challen et al. (2010) Morita et al. (2010) Morita et al. (2010)

Kuanyin K. Lin et al.

26

of their LT-HSC activity. In general, the key markers on cell populations that possess HSC activities are CD34/low (Okada et al., 1992), Flk2 (Christensen and Weissman, 2001), CD48, CD244 (Kiel et al., 2005), Lin, cKit+, Sca-1+ (Okada et al., 1992), EPCR+ (Balazs et al., 2006), Tie2+ (Arai et al., 2004), Rhodamine123 (Li and Johnson, 1992), and localization in the SP when Hoechst 33342 staining applies. In addition, the relationship between side population and surface markers has been investigated. SPKLS are positive in the expression of EPCR (CD201) (Challen et al., 2009), negative in the expression of CD48 and CD34 (Fig. 2), the markers that distinguish differentiating progenitor cells (Kiel et al., 2005; Osawa et al., 1996), and negative in the expression of Flk2, a short-term HSC marker (Adolfsson et al., 2001; Christensen and Weissman, 2001).

[(Fig._2)TD$IG]

SPKLS (side population, c-Kit+, Lin, Sca-1+) cells are negative for CD48 expression. When the Hoechst 33342 staining is carried out, the majority of the SP cells should be Lin, Sca-1+, and c-Kit+. Of these SPKLS cells, most will not express CD48.

Fig. 2

2. Detection of Hematopoietic Stem Cells by Flow Cytometry

27

[(Fig._3)TD$IG]

Fig. 3

The differentiation hierarchy of hematopoietic system. The hematopoietic differentiation hierarchy starts from HSCs toward the multipotent progenitors (MPPs), lymphoid and myeloid progenitor cells (CLPs and CMPs), and each terminally differentiated blood cells.

Recent studies of the properties of single HSCs highlight the heterogeneity within the LT-HSC of the kinetics and capability of repopulating hematopoietic system. Although it has been customarily considered that an HSC needs to possess multilineage potential, that is, the ability to give rise to all hematopoietic lineages (both myeloid and lymphoid branches) (Fig. 3), it has recently been found that self-renewing HSCs present distinct lineage preferences (Challen et al., 2010; Dykstra et al., 2007; Morita et al., 2010; Papathanasiou et al., 2009). In addition, the preference for HSC to differentiate into either myeloid or lymphoid lineages is not strictly biased, as the myeloid-skewed HSCs are able to give rise to lymphoid lineages when the lymphoid-skewed HSCs are lacking (Challen et al., 2010; Dykstra et al., 2007). The lineage preference of HSCs can be distinguished with their marker expression and Hoechst 33342 efflux ability. In single-cell transplantation assays, the CD150+ HSCs shows myeloid preferences while the CD150 HSCs present lymphoid preferences (Morita et al., 2010). The lineage preference has also been found to associate with the Hoechst 33342 efflux ability of HSCs, such that the lower-SP cells (localizing at the tip of side population) preferentially give rise to myeloid lineage while the upper-SP cells preferentially give rise to lymphoid lineages (Challen et al., 2010). The expression of CD150 also fractionates SP such that approximately half of the SP expresses CD150 and half does not (Challen et al., 2010; Weksberg et al., 2007). In addition, CD150+ preferentially marks lower-SPKLS

28

Kuanyin K. Lin et al.

cells that are myeloid skewed and possess more robust engraftment in transplantation assays, consistent with the findings from other groups that CD150 marks a subset of LT-HSCs with myeloid preference and more robust engraftment kinetics (Kent et al., 2009; Morita et al., 2010). HSCs have served as a paradigm for studying stem cell biology, owing to the clearly defined differentiation of the hierarchy and cell surface markers for HSC isolation. However, the necessity to utilize multiple markers to identify HSCs impedes the application of in situ imaging to identify HSCs in bone tissues, which has become a driving force to continue to identify unique markers for HSCs. With constantly evolving purification schemes, one has to validate the schemes carefully and use the scheme that is optimal in the context of the equipment and style of the laboratory. More importantly, although the immunophenotype of HSCs is a good measurement that identifies HSCs during hematopoietic regeneration, it is essential to test the purified stem cells for their function and activity by means of in vitro and in vivo assays.

References Adolfsson, J., Borge, O. J., Bryder, D., Theilgaard-Monch, K., Astrand-Grundstrom, I., Sitnicka, E., Sasaki, Y., Jacobsen, S. E. (2001). Upregulation of Flt3 expression within the bone marrow Lin() Sca1(+)c-kit(+) stem cell compartment is accompanied by loss of self-renewal capacity. Immunity 15, 659–669. Arai, F., Hirao, A., Ohmura, M., Sato, H., Matsuoka, S., Takubo, K., Ito, K., Koh, G. Y., Suda, T. (2004). Tie2/angiopoietin-1 signaling regulates hematopoietic stem cell quiescence in the bone marrow niche. Cell 118, 149–161. Balazs, A. B., Fabian, A. J., Esmon, C. T., and Mulligan, R. C. (2006). Endothelial protein C receptor (CD201) explicitly identifies hematopoietic stem cells in murine bone marrow. Blood 107, 2317–2321. Beerman, I., Bhattacharya, D., Zandi, S., Sigvardsson, M., Weissman, I. L., Bryder, D., Rossi, D. J. (2010). Functionally distinct hematopoietic stem cells modulate hematopoietic lineage potential during aging by a mechanism of clonal expansion. Proc. Natl. Acad. Sci. USA 107, 5465–5470. Camargo, F. D., Chambers, S. M., Drew, E., McNagny, K. M., and Goodell, M. A. (2006). Hematopoietic stem cells do not engraft with absolute efficiencies. Blood 107, 501–507. Challen, G. A., Boles, N. C., Chambers, S. M., and Goodell, M. A. (2010). Distinct hematopoietic stem cell subtypes are differentially regulated by TGF-beta1. Cell Stem Cell 6, 265–278. Challen, G. A., Boles, N., Lin, K. K., and Goodell, M. A. (2009). Mouse hematopoietic stem cell identification and analysis. Cytometry A 75, 14–24. Christensen, J. L., and Weissman, I. L. (2001). Flk-2 is a marker in hematopoietic stem cell differentiation: a simple method to isolate long-term stem cells. Proc. Natl. Acad. Sci. USA 98, 14541–14546. Dykstra, B., Kent, D., Bowie, M., McCaffrey, L., Hamilton, M., Lyons, K., Lee, S. J., Brinkman, R., Eaves, C. (2007). Long-term propagation of distinct hematopoietic differentiation programs in vivo. Cell Stem Cell 1, 218–229. Goodell, M. A., Brose, K., Paradis, G., Conner, A. S., and Mulligan, R. C. (1996). Isolation and functional properties of murine hematopoietic stem cells that are replicating in vivo. J. Exp. Med. 183, 1797–1806. Goodell, M. A., Rosenzweig, M., Kim, H., Marks, D. F., DeMaria, M., Paradis, G., Grupp, S. A., Sieff, C. A., Mulligan, R. C., Johnson, R. P. (1997). Dye efflux studies suggest that hematopoietic stem cells expressing low or undetectable levels of CD34 antigen exist in multiple species. Nat. Med. 3, 1337–1345.

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Hsu, H. C., Ema, H., Osawa, M., Nakamura, Y., Suda, T., Nakauchi, H. (2000). Hematopoietic stem cells express Tie-2 receptor in the murine fetal liver. Blood 96, 3757–3762. Ibrahim, S. F., Diercks, A. H., Petersen, T. W., and van den Engh, G. (2007). Kinetic analyses as a critical parameter in defining the side population (SP) phenotype. Exp. Cell Res. 313, 1921–1926. Iwasaki, H., Arai, F., Kubota, Y., Dahl, M., and Suda, T. (2010). Endothelial protein C receptor-expressing hematopoietic stem cells reside in the perisinusoidal niche in fetal liver. Blood 116, 544–553. Kent, D. G., Copley, M. R., Benz, C., Wohrer, S., Dykstra, B. J., Ma, E., Cheyne, J., Zhao, Y., Bowie, M. B., Gasparetto, M., et al. (2009). Prospective isolation and molecular characterization of hematopoietic stem cells with durable self-renewal potential. Blood 113, 6342–6350. Kiel, M. J., Yilmaz, O. H., Iwashita, T., Terhorst, C., and Morrison, S. J. (2005). SLAM family receptors distinguish hematopoietic stem and progenitor cells and reveal endothelial niches for stem cells. Cell 121, 1109–11021. Kim, I., He, S., Yilmaz, O. H., Kiel, M. J., and Morrison, S. J. (2006). Enhanced purification of fetal liver hematopoietic stem cells using SLAM family receptors. Blood 108, 737–744. Li, C. L., and Johnson, G. R. (1992). Rhodamine123 reveals heterogeneity within murine Lin, Sca-1+ hemopoietic stem cells. J. Exp. Med. 175, 1443–1447. Matsuzaki, Y., Kinjo, K., Mulligan, R. C., and Okano, H. (2004). Unexpectedly efficient homing capacity of purified murine hematopoietic stem cells. Immunity 20, 87–93. McKinney-Freeman, S. L., Naveiras, O., Yates, F., Loewer, S., Philitas, M., Curran, M., Park, P. J., Daley, G. Q. (2009). Surface antigen phenotypes of hematopoietic stem cells from embryos and murine embryonic stem cells. Blood 114, 268–278. Morita, Y., Ema, H., and Nakauchi, H. (2010). Heterogeneity and hierarchy within the most primitive hematopoietic stem cell compartment. J. Exp. Med. 207, 1173–11782. Morita, Y., Ema, H., Yamazaki, S., and Nakauchi, H. (2006). Non-side-population hematopoietic stem cells in mouse bone marrow. Blood 108, 2850–2856. Morrison, S. J., and Weissman, I. L. (1994). The long-term repopulating subset of hematopoietic stem cells is deterministic and isolatable by phenotype. Immunity 1, 661–673. Okada, S., Nakauchi, H., Nagayoshi, K., Nishikawa, S., Miura, Y., Suda, T. (1992). In vivo and in vitro stem cell function of c-kit- and Sca-1-positive murine hematopoietic cells. Blood 80, 3044–3050. Osawa, M., Hanada, K., Hamada, H., and Nakauchi, H. (1996). Long-term lymphohematopoietic reconstitution by a single CD34-low/negative hematopoietic stem cell. Science 273, 242–245. Papathanasiou, P., Attema, J. L., Karsunky, H., Xu, J., Smale, S. T., Weissman, I. L. (2009). Evaluation of the long-term reconstituting subset of hematopoietic stem cells with CD150. Stem Cells 27, 2498–2508. Petersen, T. W., Ibrahim, S. F., Diercks, A. H., and van den Engh, G. (2004). Chromatic shifts in the fluorescence emitted by murine thymocytes stained with Hoechst 33342. Cytometry A 60, 173–181. Petrenko, O., Beavis, A., Klaine, M., Kittappa, R., Godin, I., Lemischka, I. R. (1999). The molecular characterization of the fetal stem cell marker AA4. Immunity 10, 691–700. Ramos, C. A., Venezia, T. A., Camargo, F. A., and Goodell, M. A. (2003). Techniques for the study of adult stem cells: be fruitful and multiply. Biotechniques 34, 572–591. Spangrude, G. J., Heimfeld, S., and Weissman, I. L. (1988). Purification and characterization of mouse hematopoietic stem cells. Science 241, 58–62. Uchida, N., Dykstra, B., Lyons, K., Leung, F., Kristiansen, M., Eaves, C. (2004). ABC transporter activities of murine hematopoietic stem cells vary according to their developmental and activation status. Blood 103, 4487–4495. Weksberg, D. C., Chambers, S. M., Boles, N. C., and Goodell, M. A. (2007). CD150 negative side population cells represent a functionally distinct population of long-term hematopoietic stem cells. Blood. Wilson, A., Oser, G. M., Jaworski, M., Blanco-Bose, W. E., Laurenti, E., Adolphe, C., Essers, M. A., Macdonald, H. R., Trumpp, A. (2007). Dormant and self-renewing hematopoietic stem cells and their niches. Ann. N.Y. Acad. Sci. 1106, 64–75. Zhang, C. C., and Lodish, H. F. (2004). Insulin-like growth factor 2 expressed in a novel fetal liver cell population is a growth factor for hematopoietic stem cells. Blood 103, 2513–2521.

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Kuanyin K. Lin et al. Zhou, S., Morris, J. J., Barnes, Y., Lan, L., Schuetz, J. D., Sorrentino, B. P. (2002). Bcrp1 gene expression is required for normal numbers of side population stem cells in mice, and confers relative protection to mitoxantrone in hematopoietic cells in vivo. Proc. Natl. Acad. Sci. USA 99, 12339–12344. Zhou, S., Schuetz, J. D., Bunting, K. D., Colapietro, A. M., Sampath, J., Morris, J. J., Lagutina, I., Grosveld, G. C., Osawa, M., Nakauchi, H., et al. (2001). The ABC transporter Bcrp1/ABCG2 is expressed in a wide variety of stem cells and is a molecular determinant of the side-population phenotype. Nat. Med. 7, 1028–1034.

CHAPTER 3

Identification of Very Small Embryonic/ Epiblast-Like Stem Cells (VSELs) Circulating in Peripheral Blood During Organ/Tissue Injuries Mariusz Z. Ratajczak,* Rui Liu,* Wojciech Marlicz,y Wojciech Blogowski,y Teresa Starzynska,y Wojciech Wojakowskiz and Ewa Zuba-Surmax *

Stem Cell Biology Institute, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA y Department of Gastroenterology and Department of Physiology, Pomeranian Medical University, Szczecin, Poland z

Third Division of Cardiology, Medical University of Silesia, Katowice, Poland x Department of Medical Biotechnology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland

Abstract I. Introduction II. Background A. Very Small Embryonic-Like Stem Cells (VSELs) B. Mesenchymal Stem Cells (MSCs) C. Endothelial Progenitor Cells (EPCs) III. Materials A. Preparation of Peripheral Blood (PB) for Analysis B. Staining of Total PB-Derived Nucleated Cells (TNCs) for Analysis IV. Methods A. Isolation of Total PB-Derived Nucleated Cells (TNCs) by Lysing Red Blood Cells (RBCs) METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

31

0091-679X/10 $35.00 DOI 10.1016/B978-0-12-385493-3.00003-6

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32

V. VI. VII. VIII.

B. Staining of PB-Derived TNCs for Flow Cytometric Analysis C. Setting Up Instrument for FACS Analysis D. Identification of VSELs in Human PB and UCB E. Calculation of Absolute Numbers of Target Cells in 1mL of PB F. Sorting of Cells Results Critical Aspects of the Methodology Applications Future Directions Acknowledgments References

Abstract We have identified in adult tissues a population of pluripotent very small embryonic/epiblast-like stem cells (VSELs) that we hypothesize are deposited at onset of gastrulation in developing tissues and play an important role as backup population of tissue-specific/committed stem cells. We envision that during steady-state conditions these cells may be involved in tissue rejuvenation and in processes of regeneration/repair after organ injuries. VSELs similarly as epiblast-derived migrating primordial germ cells change the epigenetic signature of some of the imprinted genes and therefore remain quiescent in adult tissues. These epigenetic changes in methylation status of imprinted genes prevent them also from teratoma formation. Mounting evidence indicates that VSELs are mobilized into peripheral blood during tissue/organ injuries and enumeration of these cells may be of prognostic value (e.g., in stroke or heart infarct). In this chapter, we will present FACS-based strategies to detect and enumerate these cells in human peripheral blood and umbilical cord blood.

I. Introduction The rapidly developing regenerative medicine is searching for safe and therapeutically efficient sources of stem cells (SCs) that should give rise to cells from all three germ layers and could be employed to regenerate damaged organs/tissues. SCs endowed with such broad spectrum of differentiation are described during embryogenesis and are called pluripotent stem cells (PSCs) (Ratajczak et al., 2007a). Our group identified and isolated a population of pluripotent Sca1+Lin
CD45
very small embryonic-like stem cells (VSELs) from adult murine bone marrow (BM), murine fetal livers (FLs), and several adult murine organs including brain, liver, kidney, lungs, skeletal muscles, and retina (Kucia et al., 2006a; Liu et al., 2009; Zuba-Surma et al., 2008d, 2009b). These cells express several morphological (e.g., relatively large nuclei containing euchromatin) and molecular (e.g., expression of SSEA-1, Oct4, Nanog, Rex1) markers characteristic

3. Identification of Very Small Embryonic/Epiblast-Like Stem Cells (VSELs)/Tissue Injuries

33

for embryonic SCs (ESCs) or epiblast SCs (EpiSCs). We hypothesize that VSELs are deposited during early gastrulation in developing tissues/organs, survive into adulthood, and play an important role as a backup population of PSCs in the turnover of tissue committed stem cells (TCSCs) (Kucia et al., 2007b; Ratajczak et al., 2007a). However, the existence of PSCs in adult tissues had been postulated by several investigators; such cells were never purified and identified at the single-cell level. Thus, the presence of pluripotent VSELs in adult tissues may reconcile previously published data stating that adult tissues may contain a population of PSCs (Ratajczak et al., 2007a, 2007b). The presence of such cells with multilineage differentiation capabilities was postulated mainly based on experiments showing that some populations of cells isolated from adult tissues enriched among adherent cells may contain primitive cells that differentiate into various tissues. Such cells were defined as either (i) mesenchymal stem cells (MSCs); (ii) multipotent adult progenitor cells (MAPCs); (iii) marrow-isolated adult multilineage inducible (MIAMI) cells; (iv) multipotent adult stem cells (MASCs); and (v) OmniCytes (Beltrami et al., 2007; D’Ippolito et al., 2004; Jiang et al., 2002; Radcliff and Jaroszeski, 1998). It is conceivable that all these cells are closely related, overlapping populations of SCs described by different investigators and given various names according to circumstance. The potential relationship between these cells and VSELs is not clear at this moment. However, since all these cells are largely derived from the adherent fraction of BM- or adult organ-derived cells, they could as we envision potentially contain from beginning some VSELs. Similar population of CD133+Lin
CD45
cells was identified in human umbilical cord blood (UCB), mobilized peripheral blood (PB), and adult BM (Kucia et al., 2007a; Zuba-Surma et al., 2010). Table I summarizes the most important features of murine and human VSELs. We noticed that the number of these circulating cells increases both in mice and in humans during stress situations related to tissue organ injuries (e.g., heart infarct, stroke, acute colitis) as well as after administration of certain drugs employed in hematology to mobilize hematopoietic stem progenitor cells (HSPCs) into PB (Abdel-Latif et al., 2010; Kucia et al., 2008; Paczkowska et al., 2009; Wojakowski et al., 2006, 2009; Zuba-Surma et al., 2008b). We noticed that when purified murine VSELs are plated over a C2C12 myoblast feeder layer, they form spheres that resemble embryoid bodies (EBs) (Kucia et al., 2006a). The VSEL-derived spheres (VSEL-DSs) contain primitive stem cells that, after replating into tissue differentiation-specific media, differentiate into cells from all three germ layers. Furthermore, while we observed that freshly isolated VSELs do not exhibit in vitro and in vivo hematopoietic potential, after coculture over OP9 stromal cells they can differentiate along the hematopoietic lineage in a similar way as ESCs or induced pluripotent stem cells (iPSCs). Cells derived from these OP9primed VSELs acquire expression of several hemato/lymphopoiesis-specific genes and markers, give rise to hematopoietic colonies in vitro, and protect lethally irradiated mice in both primary and secondary transplant models upon transplantation (Zuba-Surma et al., 2008c, 2009a). We also observed that, compared to hematopoietic stem/progenitor cells (HSCs), VSELs are highly resistant to irradiation. Based

Mariusz Z. Ratajczak et al.

34 Table I Morphological and phenotypic comparison of murine and human VSELs Source of cells

Murine BM-derived VSELs

Human UCB-derived VSELs

Size (diameter calculated by employing ISS) Nucleus

3–5 mm

4–7 mm

Large – contains euchromatin. Diploid number of chromosomes Tiny rim of cytoplasm enriched in mitochondria Sca-1+, CXCR4+, CD45
, Lin
, MHC-I
HLA-DR
, CD90
CD105
CD29
SSEA-1; Oct4, Nanog, Rex1; high telomerase activity +

Large – contains euchromatin. Diploid number of chromosomes Tiny rim of cytoplasm enriched in mitochondria CD133+, CXCR4+, CD45
, Lin
, MHC-I
HLA-DR
, CD90
CD105
CD29
SSEA-4; Oct4, Nanog, Rex1; high telomerase activity ?

No

Not applicable

Cytoplasm Surface markers

ESC markers Differentiation in vitro into cells from all three germ layers Complementation of blastocyst development

on these observations, we postulate that VSELs are the most primitive murine BMresiding population of stem cells that have the potential to become specified into the hematopoietic lineage and thus may share some of the characteristics of long-term repopulating HSCs (Zuba-Surma et al., 2008c, 2009a). Thus, BM-residing VSELs nicely support a concept that these cells are precursors of TCSCs – in this particular case they are precursors of HSPCs. We also reported that VSELs are mobilized into PB during organ injuries (e.g., heart infarct, stroke), which suggests that these cells could participate in the regeneration of damaged tissues (Abdel-Latif et al., 2010; Kucia et al., 2006b; Paczkowska et al., 2009; Wojakowski et al., 2006, 2009; Zuba-Surma et al., 2008b). In this chapter, we will discuss cytometry-based methods to detect and to enumerate VSELs circulating in PB. We already noticed that enumeration of these cells may be also of clinical/prognostic value in patients after heart infarct or stroke (Abdel-Latif et al., 2010; Paczkowska et al., 2009; Wojakowski et al., 2006, 2009). More studies, however, are needed to support these observations. Identification of circulating VSELs requires unique gating strategies to focus on small events that are slightly smaller than red blood cells (RBCs) (Ratajczak et al., 2009; Zuba-Surma and Ratajczak, 2010). Furthermore, since both small size and density VSELs are lost (up to 50%) during Ficoll-Paque centrifugation (ZubaSurma et al., 2010), the recommended way to preserve these cells is lysis of PB samples to preserve VSELs for staining and subsequent analysis.

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35

II. Background A small percentage of HSPCs are continuously released from BM niches into PB (Levesque et al., 2007; Quesenberry et al., 2007). Thus, PB may be envisioned as a highway by which HSPCs can relocate between distant stem cell niches to keep the pool of BM stem cells in balance. In addition to HSPCs, some other rare stem cells [e.g., MSCs, endothelial progenitor cells (EPCs), and VSELs] may also appear in the PB during various stress situations (Bittira et al., 2003; Leone et al., 2005; Shintani et al., 2001). In adult organisms, circulating stem cells (i) show a circadian rhythm in the circulation with the peak occurring early in the morning and the nadir at night, (ii) are mobilized during strenuous exercise, inflammation, and tissue organ injury (e.g., heart infarct or stroke), and (iii) may increase in number up to 100-fold after administration of certain drugs (Bittira et al., 2003; Dimmeler, 2010; Hess and Borlongan, 2008; Leone et al., 2005; Shintani et al., 2001; Takahashi et al., 1999; Urbich and Dimmeler, 2004). This enforced translocation of HSPCs from BM into PB induced by pharmacological agents (e.g., G-CSF or AMD3100) is called ‘‘mobilization’’ (Gordon et al., 2006; Petit et al., 2002) and mobilized PB (mPB) as discussed in other chapters in this book is an easily accessible source of HSPCs for hematopoietic transplantation. A. Very Small Embryonic-Like Stem Cells (VSELs) These small cells, identified by our research team, express several morphological (e.g., relatively large nuclei containing euchromatin) and molecular (e.g., expression of SSEA-1, Oct4, Nanog, Rex1) markers characteristic for embryonic stem cells (ESCs) (Kucia et al., 2006a, 2007a; Ratajczak et al., 2008d; Zuba-Surma et al., 2008d). The true expression of Oct4 and Nanog in BM-derived VSELs (BM-VSELs) was recently confirmed by demonstrating transcriptionally active chromatin structures of Oct4 and Nanog promoters (Shin et al., 2009, 2010a). In ImageStream system (ISS) analysis, which is flow cytometry combined with fluorescence image analysis (Ratajczak et al., of this Volume), we found that VSELs exhibit a significantly higher nuclear/cytoplasm (N/C) ratio and a lower cytoplasmic area as compared with HSCs (Zuba-Surma et al., 2008a, 2010). We also described a mechanism based on parent-of-origin-specific reprogramming of genomic imprinting that keeps VSELs quiescent in a dormant state in tissues. The expression of germ line markers (Oct4 and SSEA-1) and modulation of somatic imprints suggest a potential developmental similarity between VSELs and germ line-derived primordial germ cells (PGCs) (Shin et al., 2009, 2010a, 2010b). Mobilization of VSELs into PB was reported thus far both in murine models and in human patients after heart infarct, stroke, retina damage, and acute colitis (Abdel-Latif et al., 2010; Kucia et al., 2006b; Paczkowska et al., 2009; Wojakowski et al., 2006, 2009; Zuba-Surma et al., 2008b). We have calculated that the number of VSELs in murine BM gradually declines with age, ranging from 0.052  0.018 to 0.003  0.002% between ages of 2 months

Mariusz Z. Ratajczak et al.

36

and 3 years, respectively (Ratajczak et al., 2008b; Zuba-Surma and Ratajczak, 2008). More importantly, the ability of sphere formation by these cells decreases with age; thus, no VSEL-DSs were observed in cells isolated from older mice (>2 years). The aging-dependent decrease of the pool and function of VSELs in BM may explain the decline of the regeneration potential during aging. This hypothesis has been further confirmed by looking for differences in the content of these cells among BM mononuclear cells (BMMNCs) in long- and short-lived mouse strains. The concentration of VSELs was much higher in the BM of long-lived (e.g., C57B6) as compared to short-lived (DBA/2J) mice (Kucia et al., 2006a).

1. Developmental Origin of VSELs An adult organism develops from the most primitive SC called a zygote, which is an oocyte fertilized by a sperm cell. This totipotent zygote, the ‘‘mother of all stem cells’’ in the developing body, first gives rise to morula that consists of PSCs and, subsequently, at blastocyst level a population of PSCs that is maintained in inner cell mass; the blastocyst will give rise to the epiblast, a part of the developing embryo, which is the origin of SCs committed to all the three germ layers (meso-, ecto-, and endoderm) (Tam and Loebel, 2007). Thus, the PSCs that form epiblast could be considered the origin for the TCSCs for all the organs and tissues in the developing embryo proper. PSCs in the epiblast undergo a sequel of specification events, first into multipotent and subsequently into versatile TCSCs, which play a role in the formation and rejuvenation of various organs (Tam and Loebel, 2007; Tam et al., 2007). The most important questions emerge of whether some of these primitive epiblast-forming PSCs can ‘‘escape’’ specification into more differentiated populations of SCs and retain their pluripotential character, thus surviving among differentiated daughter TCSCs. Conversely, would all of them undergo tissue-/organ-specific differentiation and then ‘‘disappear’’ after embryogenesis, not be found in the adult body. We envision that VSELs are epiblast-derived PSCs deposited early during embryonic development in developing organs as a potential reserve pool of precursors for TCSCs and thus this population has an important role in tissue rejuvenation and regeneration. We also hypothesize that VSELs originate or are closely related to a population of proximal epiblast migratory EpiSCs that approximately at embryonic day (E)7.25 in mice, become specified to PGCs, and egress from the epiblast into extra-embryonic tissues (extra-embryonic mesoderm) (Hayashi et al., 2007). These cells subsequently make a turn and through the primitive streak return to the embryo proper and migrate to genital ridges, where they ultimately give rise to precursors of sperm or oocytes. Accumulating evidence also indicates that PGCs could somehow be related to HSCs, another population of highly migratory SCs (Kritzenberger and Wrobel, 2004; Ohtaka et al., 1999; Rich, 1995). To support this notion, the first primitive HSCs appear in the extra-embryonic tissues in yolk sac blood islands at a time when proximal epiblast-specified PGCs enter the extra-embryonic mesoderm (Mikkola

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37

and Orkin, 2006). Furthermore, the appearance of definitive HSCs in the aortagonad-mesonephros (AGM) region in the embryo proper corresponds in time with migration of PGCs to the genital ridges through the AGM. To support this hypothesis further PGCs isolated from murine embryos were described as being able to grow HSC colonies and robust hematopoietic differentiation was observed in some classical germ tumors (Ohtaka et al., 1999; Rich, 1995; Saito et al., 1998). Thus, all this collective evidence suggests developmental overlap between PGCs and HSCs. Our data indicate that VSELs share several characteristics with both PGCs and HSCs. To support this notion our recent molecular analysis data indicate that in fact VSELs share several markers characteristic for epiblast/germ line (Shin et al., 2010b). Furthermore, VSELs follow developmental route of HSCs colonizing together with HSCs first FL and subsequently BM (Zuba-Surma et al., 2009b). Furthermore, in appropriate culture conditions they could also be differentiated toward hematopoietic lineage (Zuba-Surma et al., 2009a). In the future, it will be important to evaluate the potential presence of VSELs in yolk sac blood islands and to determine whether VSELs are detectable in Ncx1
/
embryos (Zuba-Surma et al., 2009c) that do not initiate a heart beat and thus lack definitive HSCs in embryonic tissues (Lux et al., 2008). Thus PGCs, HSCs, and VSELs form all together a unique highly migratory population of interrelated SCs that could be envisioned to be a kind of ‘‘fourth highly migratory germ layer.’’ Due to this unique developmental origin, VSELs not only show characteristic epigenetic reprogramming and gene expression in stemness-, germ line-, and imprinted-genes that maintain their pluripotency but also prevent their unleashed proliferation and teratoma formation (Shin et al., 2010a, 2010b).

2. VSELs and Their Unique Molecular Characteristics We employed several molecular strategies to evaluate molecular signature of VSELs. Highly purified Sca-1+Lin
CD45
VSELs from murine BM or FL were evaluated for expression of (i) ESCs, (ii) epiblast/germ line markers, and (iii) expression of developmentally crucial imprinted genes. We found that at mRNA and protein level VSELs express transcription factor Oct4 that is characteristic for ESCs (Shin et al., 2009, 2010b). However, recently some doubts were raised if cells isolated from adult tissues may express these embryonic genes and it has been postulated that positive PCR data showing Oct4 expression may be due to amplification of Oct4 pseudogenes (Lengner et al., 2008; Liedtke et al., 2007). Thus, to prove true expression of the Oct4 gene in VSELs we investigated the epigenetic status of Oct4 promoter in these cells. The Sca-1+Lin
CD45
VSELs were double purified and we examined the DNA methylation status of the Oct4 promoter in these cells by employing bisulfite sequencing. We noticed that the Oct4 promoter in VSELs, similar to cells isolated from ESCs-derived EBs, is hypomethylated (28 and 13.2%, respectively) (Shin et al., 2009). Next, to provide additional direct evidence that the Oct4 promoter in VSELs is in an active/open state, we

38

Mariusz Z. Ratajczak et al.

performed the chromatin-immunoprecipitation (ChIP) assay to evaluate its association with acetylated-histone3 (H3Ac) and dimethylated-lysine-9 of histone-3 (H3K9me2), the molecular features for open- and closed-type chromatin, respectively. By employing Carrier-ChIP assay using human hematopoietic cell-line THP-1 as carrier, we found that Oct4 promoter chromatin is associated with H3Ac and its association with H3K9me2 is relatively very low (Shin et al., 2009). Since VSELs also express Nanog, we evaluated the epigenetic status of the Nanog promoter in these cells as well. We found that the Nanog promoter was methylated (50%); however, quantitative ChIP data confirmed that the H3Ac/H3K9me2 ratio supports the active status of the Nanog promoter in these cells (Shin et al., 2009). Based on these results, VSELs truly express Oct4 and Nanog. Of note we also reported that VSELs also express several other markers of PSCs such as SSEA-1 antigen as well as Sox2 and Klf4 transcription factors. While the expression levels of transcripts of Oct4 and Nanog in VSELs was around 50 and 20%, respectively, compared to ESC-D3 cells, VSELs express a similar level of Sox2 transcript and 3.5 times more Klf4 as compared to ESC-D3 cells (Shin et al., 2010a, 2010b). Next, since we hypothesized that VSELs could be epiblast-derived precursors of TCSCs, we focused on expression, in adult BM-derived VSELs, of genes that are characteristic for EpiSCs (Gbx2, Fgf5, and Nodal) and ESCs from inner cell mass of blastocyst (Rex1/Zfp42). It is known that Gbx2, Fgf5, and Nodal are upregulated in EpiSCs, but expressed at lower levels in ESCs isolated from the inner cell mass of blastocysts (Hayashi et al., 2008). In contrast, the level of Rex1/Zfp42 transcripts is highly expressed in inner cell mass cells. We found that VSELs highly express Gbx2, Fgf5, and Nodal, but express less Rex1/Zfp42 transcript as compared to ESC-D3s what suggests that VSELs are more differentiated than ICM-derived ESCs and share several markers with more differentiated EpiSCs (Shin et al., 2010a, 2010b). Next since we hypothesize that VSELs could be developmentally related to epiblast-derived PGCs we evaluated the expression of genes involved in the germ line specification of the epiblast (e.g., Stella, Prdm14, Fragilis, Blimp1, Nanos3, and Dnd1) (Hayashi et al., 2007). By employing RQ-PCR, we noticed that VSELs highly expressed all the genes involved in germ line specification from the epiblast. Subsequently, we confirmed the expression of Stella, Blimp1, and Mvh in purified VSELs at the protein level by immunostaining (Shin et al., 2010a, 2010b). Furthermore, our ChIP results show that the Stella promoter in VSELs displays transcriptionally active histone modifications [H3Ac and trimethylated-lysine-4 of histone3 (H3K4me3)] and was less enriched for transcriptionally repressive histone markers [H3K9me2 and trimethylated-lysine-27 of histone3 (H3K27me3)] (Shin et al., 2010a, 2010b). Thus, collectively, our results demonstrate that VSELs express specific genes and display a Stella promoter chromatin structure that is characteristic for germ line specification. VSELs also highly express Dppa2, Dppa4, and Mvh, which characterize late migratory PGCs. However, they do not express Sycp3, Dazl, and LINE1 genes that are highly expressed in postmigratory PGCs (Shin et al., 2010a, 2010b). In totality, thus, our results support a concept that VSELs deposited

3. Identification of Very Small Embryonic/Epiblast-Like Stem Cells (VSELs)/Tissue Injuries

39

into murine BM show some similarities in gene expression and epigenetic signatures to epiblast-derived migratory PGCs (E10.5–E11.5).

3. Epigenetic Changes of Imprinted Genes that Regulate VSELs Pluripotency The rapidly developing field of regenerative medicine is searching for safe and therapeutically efficient sources of PSCs. By definition, PSCs should (i) give rise to cells from all three germ layers; (ii) complete blastocyst development; and (iii) form teratomas after inoculation into experimental animals (Ratajczak et al., 2007a). Unfortunately, in contrast to immortalized embryonic ESC lines or inducible PSCs (iPSCs), these last two criteria have not been obtained thus far with any potential PSC candidates isolated from adult tissues. There are two potential explanations for this discrepancy. The first is that PSCs isolated from adult tissues are not fully pluripotent; the second is that there are some physiological mechanisms involved in keeping these cells quiescent in adult tissues to preclude their unleashed proliferation and risk of teratoma formation. We postulated that VSELs similarly as PGCs may modify methylation of imprinted genes that prevents them from unleashed proliferation and may explain their quiescent status in adult tissues. We noticed that Oct4+ VSELs do not proliferate in vitro if cultured alone and that the quiescence of these cells is epigenetically regulated by DNA methylation of genomic imprinting, which is an epigenetic program that ensures the parent-of-specific monoallelic transcription of imprinted genes (Shin et al., 2009). It is well known that the imprinted genes play a crucial role in embryogenesis, fetal growth, totipotential status of the zygote, and pluripotency of developmentally early stem cells (Reik and Walter, 2001). The expression of imprinted genes is regulated by DNA methylation on differential methylated regions (DMRs), which are CpG-rich cis-elements in their loci. We noticed that VSELs freshly isolated from murine BM erase the paternally methylated imprints (e.g., Igf2H19, Rasgrf1 loci); however, at the same time they hypermethylate the maternally methylated ones [e.g., Igf2 receptor (Igf2R), Kcnq1-p57KIP2, Peg1 loci]. Because paternally expressed imprinted genes (Igf2, Rasgrf1) enhance the embryo growth and maternally expressed genes (H19, p57KIP2, Igf2R) inhibit cell proliferation (Reik and Walter, 2001), the unique genomic imprinting pattern observed on VSELs demonstrates growth-repressive imprints in these cells. VSELs highly express growth-repressive genes (H19, p57KIP2, Igf2R) and downregulate growth-promoting genes (Igf2, Rasgrf1), which explains the quiescent status of VSELs (Shin et al., 2009). Importantly, the quiescent pattern of genomic imprinting was progressively recovered during the formation of VSEL-DSs, in which stem cells proliferate and differentiate. These results suggest that epigenetic reprogramming of genomic imprinting should maintain the quiescence of the most primitive pluripotent adult stem cells (e.g., Oct4+ VSELs) deposited in the adult body and protect them from premature aging and tumor formation. Therefore, it will be important to investigate whether this genomic imprinting pattern differs between VSELs isolated from young versus old mice and whether these potential epigenetic

Mariusz Z. Ratajczak et al.

40

changes could contribute to the previously mentioned decrease in the pool and function of VSELs during aging.

B. Mesenchymal Stem Cells (MSCs) Mesenchymal stem cells are a population of BM-derived adherent bone-/cartilageforming progenitor cells. It is known that BM-adherent cells grow colonies of fibroblastic-like cells, which have a high replating potential (colony-forming units of fibroblasts; CFU-F) (Pochampally et al., 2004; Prockop, 1997). It is now widely accepted that MSC cells contribute to the regeneration of mesenchymal tissues (e.g., bone, cartilage, muscle, ligament, tendon, adipose, and stroma). Because various inconsistencies have come to light in the field of MSC research, in particular if they truly represent a population of stem cells, the International Society for Cellular Therapy recently recommended avoiding the name of MSCs and changing it to multipotent mesenchymal stromal cells instead (Dominici et al., 2006). Of note recently it has been demonstrated that VSELs may give rise to population of MSCs (Taichman et al., 2010).

C. Endothelial Progenitor Cells (EPCs) It is postulated that the BM is endowed with neoangiogenetic activity and EPC, which is a rare and very primitive founder population of endothelial cells, may be released during stressed situations and circulate in PB (Asahara et al., 1999; Massa et al., 2005; Rafii and Lyden, 2003; Shintani et al., 2001). Furthermore, BM was also identified as a source of more differentiated circulating endothelial cells (CEC). BM-derived EPC and CEC subsequently circulate in PB at very low levels (0.0001 and 0.01%, respectively) and may play a role in the repair of damaged endothelium and contribute to postnatal neoangiogenesis (Asahara et al., 1999). While EPC are probably progeny of PSC or perhaps direct descendants of hemangioblasts, the more differentiated CEC originate in the myeloid compartment from a common myeloid progenitor (CMP). The level of contribution of BM-derived cells to organ/tissue vascularization, however, still requires further study.

III. Materials In protocols described below in this chapter, we will focus on identification and enumeration of VSELs. Human PB-derived samples are collected from the patients into tubes with anticoagulant. To avoid the loss of small cells (e.g., VSELs) during separation of cells on Ficoll-Paque gradient we remove red blood cells by employing lysing buffer. Total nucleated cells are subsequently stained by using antibodies listed in Table II. For isolation of VSELs, we may use size-predefined beads, to define a sorting region containing small objects (2–10 mm), as indicated on the dotplot presenting FSC and SSC parameters of analyzed objects (Fig. 1, region P1). This

41

3. Identification of Very Small Embryonic/Epiblast-Like Stem Cells (VSELs)/Tissue Injuries

Table II Antibodies employed in staining for identification and sorting of human PB-derived HSCs and VSELs by flow cytometry Antibody

Clone

Fluorochrome

Vendor

Anti-CD2 Anti-CD3 Anti-CD14 Anti-CD16 Anti-CD19 Anti-CD24 Anti-CD56 Anti-CD66b Anti-CD235a Anti-CD45 Anti-AC133 Anti-CD34 ANTI-CD184

RPA-2.10 UCHT1 M5E2 3G8 HIB19 ML5 NCAM16.2 G10F5 GA-R2 HI30 AC133 581/CD34 12G5

FITC FITC FITC FITC FITC FITC FITC FITC FITC PE-Cy7 APC APC APC

BD Biosciences BD Biosciences BD Biosciences BD Biosciences BD Biosciences BD Biosciences BD Biosciences BD Biosciences BD Biosciences BD Biosciences Miltenyi Biotec BD Biosciences BD Biosciences

FITC FITC FITC PE-Cy7 APC APC APC

BD Biosciences BD Biosciences BD Biosciences BD Biosciences Miltenyi Biotec BD Biosciences BD Biosciences

Mouse IgG1, k Mouse IgG2a, k Mouse IgG2b, k Mouse IgG1, k Mouse IgG1 Mouse IgG1, k Mouse IgG2a, k

Isotype controls MOPC-21 G155-178 27–35 MOPC-21 IS5-21F5 MOPC-21 G155-178

region not only contains mostly cellular debris but also includes rare nuclear cellular objects (Zuba-Surma et al., 2008a, 2010).

A. Preparation of Peripheral Blood (PB) for Analysis 1. Laboratory tubes containing anticoagulant or medium supplemented with anticoagulant. 2. Lysing buffer (BD PharmaLyse; BD Biosciences, cat. No. 555899). 3. RPMI 1640 medium with 2% fetal bovine serum (FBS; Invitrogen). 4. Fifty milliliters plastic tissue culture-grade tubes (BD Biosciences). 5. Centrifuge with 50 mL tube holders. 6. Hemocytometer to enumerate nucleated blood cells.

B. Staining of Total PB-Derived Nucleated Cells (TNCs) for Analysis 1. Medium used for staining RPMI 1640 with 2% fetal bovine serum (FBS; Invitrogen). 2. Flow Cytometry Size Calibration Kit microspheres (Invitrogen; Molecular Probes).

Mariusz Z. Ratajczak et al.

42

[(Fig._1)TD$IG]

Fig. 1 Gating strategy for analyzing/sorting human PB-VSELs by FACS. Panel A: PB-derived TNCs are visualized by dot-plot based on FSC versus SSC signals (region P3). Panels B–F: Cells from lymphocyte gate extended to the left region P1 are further analyzed for hematopoietic lineages marker expression and all the Lin
events are included in region P2. Panel C: The Lin
population from region P2 is subsequently analyzed based on CD133 and CD45 antigen expression and two populations of CD133+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CD133+ (VSELs: region Q2) and Lin
/CD45+/CD133+ (HSPCs: region Q4). Panel E: The Lin
population from region P2 is analyzed based on CD34 and CD45 antigen expression and two populations of CD34+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CD34+ (VSELs: region P5) and Lin
/CD45+/CD34+ (HSPCs: region P4). Panel G: The Lin
population from region P2 is subsequently analyzed based on CXCR4 and CD45 antigen expression and two populations of CXCR4+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CXCR4+ (VSELs: region P5) and Lin
/CD45+/CXCR4+ (HSPCs: region P4).

3. Fifty-milliliter plastic tissue-grade culture tubes (BD Biosciences) and 5 mL round-bottom tubes (BD Biosciences). 4. Seventy and 40 mm strainer/mesh filters (BD Biosciences). 5. Centrifuge with 50 and 5 mL tube holders. 6. Monoclonal antibodies used for staining human HSCs, VSELs, MSCs, and EPCs include mouse monoclonal antibodies against human epitopes, predominantly directly conjugated with fluorochromes – are listed in Table II. 7. Flow cytometer (LSR II; Becton Dickinson).

3. Identification of Very Small Embryonic/Epiblast-Like Stem Cells (VSELs)/Tissue Injuries

43

IV. Methods A. Isolation of Total PB-Derived Nucleated Cells (TNCs) by Lysing Red Blood Cells (RBCs) 1. Collect patient PB into tubes containing anticoagulant (EDTA). 2. Distribute PB sample into 50 mL plastic tubes in amount of 10 mL of PB per tube, fill the tubes by adding 40 mL 1 PBS, and centrifuge samples for 10 min at 500g at room temperature (RT). 3. Discard supernatant and remove the RBCs by employing BD Pharm Lyse – lysing buffer. Add 40 mL of 1 lysing solution to each tube. Immediately after adding the lysing solution gently vortex each tube. Incubate at RT for 10 min and protect from light. 4. Centrifuge cells for 10 min at 500g at RT and remove supernatant carefully. 5. Repeat lysis of RBCs by resuspending the pellet of cells with 20 mL BD Pharm Lyse buffer. Incubate cells for 10 min in RT then spin the samples for 10 min at 500g at 4 ˚C. 6. Discard supernatant, resuspend remaining pellet in 50 mL of RPMI 1640 media with 2% FBS, and transfer cell suspension to a new 50 mL tube passing through a 70 mm strainer/mesh filter to remove cellular clumps, then spin down for 10 min at 500g at 4  C. 7. Resuspend cells in 1 mL of RPMI 1640 media with 2% FBS; count TNC cells with hemocytometer. 8. Separate the cell suspension into tubes (5 mL round-bottom tubes). The number of cells in each tube should be kept around 3–5 million/tube. Stain cells as described in the next section.

B. Staining of PB-Derived TNCs for Flow Cytometric Analysis 1. Stain PB-derived TNCs in RPMI 1640 media supplemented with 2% FBS with antibodies (for list of required antibodies, see Table II). Staining should be performed according to recommendations provided by vendor in 5 mL roundbottom tubes kept for 30 min on ice. 2. Wash all samples by adding 3 mL of RPMI 1640 medium with 2% FBS and centrifuge tubes for 10 min at 500g at 4 ˚C. 3. Resuspend cells for analysis in 0.5 mL RPMI 1640 medium with 2% FBS. Keep samples on ice until analysis by FACS. 4. Cells to be sorted have to be resuspended in RPMI 1640 medium supplemented with 2% FBS and transferred to new tubes (5 mL round-bottom tubes) after filtration through 40 mm strainer/mesh filter to remove cell clumps. Adjust volume of cell suspension to 4/tube and keep cells on ice until sorting.

Mariusz Z. Ratajczak et al.

44 C. Setting Up Instrument for FACS Analysis

1. Set up the forward- and side-scatter parameters (FSC and SSC, respectively) in logarithmic or linear scale and adjust the threshold on FSC parameter. 2. Run the mixture of the size-predefined beads (size calibration beads with standard diameters of 1, 2, 4, 6, 10, and 15 mm) and adjust the threshold of cytometer to be able to include for sort all objects that are bigger/equal 2 mm. 3. Set up minimal threshold to be able to see 2 mm beads on FSC versus SSC dotplot. 4. Set up the gate that will include all objects larger than 2 mm on dot-plot showing objects according to their FSC and SSC parameters. 5. Run the stained samples and adjust the gate to include agranular objects larger in size than 2 mm. 6. Perform compensation calculations and prepare the logical gating strategy resulting in identification. Analyze human VSELs by FACS (as shown in Fig. 1). 7. The phenotypes of VSELs are described below.

D. Identification of VSELs in Human PB and UCB 1. Human VSELs in PB (Fig. 1) or UCB (Fig. 2) are identified and enumerated as (i) Lin
/CD45
/CD133+, (ii) Lin
/CD45
/CD34+, and (iii) Lin
/CD45
/ CXCR4+. Lineage markers include CD2, CD3, CD14, CD16, CD19, CD24, CD56, CD66b, and CD235. 2. HSPCs in human PB (Fig. 1) or UCB (Fig. 2) are identified according to cell surface markers as (i) Lin/CD45+/CD133+, (ii) Lin
/CD45+/CD34+ and Lin
/ CD45+/CXCR4+ cells. Lineage markers include CD2, CD3, CD14, CD16, CD19, CD24, CD56, CD66b, and CD235.

E. Calculation of Absolute Numbers of Target Cells in 1 mL of PB 1. Calculate TNCs number in 1 mL of PB according to formula – TNCs number in 1 mL PB = amount of TNCs (by counting)/volume of PB (mL). 2. Calculate absolute numbers of target cells in 1 mL of PB by employing formula – absolute numbers of target cells in 1 mL of PB = (TNCs number/mL PB  identified target cells number)/amount TNC cells recorded in each sample by FACs.

F. Sorting of Cells By employing the above-described gating strategies, VSELs can be sorted from BM, UCB, or PB.

3. Identification of Very Small Embryonic/Epiblast-Like Stem Cells (VSELs)/Tissue Injuries

45

[(Fig._2)TD$IG]

Fig. 2 Gating strategy for analyzing/sorting human UCB-VSELs by FACS. Panel A: UCB-derived TNCs are visualized by dot-plot based on FSC versus SSC signals. The TNCs events are shown in region P3. Panels B–F: Cells from region P1 are further analyzed for hematopoietic lineage marker expression and all the Lin
events are included in region P2. Panel C: The Lin
population from region P2 is subsequently analyzed based on CD133 and CD45 antigen expression and two populations of CD133+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CD133+ (VSELs: region Q2) and Lin
/CD45+/CD133+ (HSPCs: region Q4). Panel E: The Lin
population from region P2 is subsequently analyzed based on CD34 and CD45 antigen expression and two populations of CD34+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CD34+ (VSELs: region P5) and Lin
/CD45+/ CD34+ (HSPCs: region P4). Panel G: The Lin
population from region P2 is subsequently analyzed based on CXCR4 and CD45 antigen expression and two populations of CXCR4+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CXCR4+(VSELs: region P5) and Lin
/CD45+/CXCR4+ (HSPCs: region P4).

V. Results Our data indicated that VSELs are much smaller than their hematopoietic counterpart as well as mature erythrocytes (Ratajczak et al., 2009; Zuba-Surma and Ratajczak, 2010; Zuba-Surma et al., 2008a, 2010). Thus, the very small size of these stem cells may be considered as marker for their identification and isolation by FACS. We employed this novel size-based approach, controlled by the size-bead markers, for isolating rare and small VSELs from murine BM by FACS including the gating strategy with regions containing small size (2–10 mm) events. Such region

46

Mariusz Z. Ratajczak et al.

established on FSC versus SSC dot-plot contains predominantly cellular debris, but also rare nuclear cell events – VSELs, which may be further characterized based on specific phenotypic markers expression (Zuba-Surma et al., 2008a, 2010). In our opinion the fact that most of the sorting protocols exclude events smaller than erythrocytes (less than 6 mm in diameter) as debris or platelets may explain why exceptionally small VSELs were overlooked before among other sorted stem/progenitor cell populations. Fig. 1 shows the example of analysis of VSEL content in human PB following the lysis of RBCs and staining with antibodies for specific VSEL markers, as described above. The small events enclosed in region P1 (Panel A), including predominantly fraction of lymphocytes and primitive/stem cells, were further analyzed for the expression of hematopoietic lineage markers (Lin), and Lin
events are included in region P2 on histogram (Panels B–F). Panel C: The Lin
population from region P2 is subsequently analyzed based on CD133 and CD45 antigen expression, and two populations of CD133+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CD133+ (VSELs: region Q2) and Lin
/CD45+/CD133+ (HSPCs: region Q4). Panel E: The Lin
population from region P2 is analyzed based on CD34 and CD45 antigen expression and two populations of CD34+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CD34+ (VSELs: region P5) and Lin
/CD45+/CD34+ (HSPCs: region P4). Panel G: The Lin
population from region P2 is subsequently analyzed based on CXCR4 and CD45 antigen expression and two populations of CXCR4+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CXCR4+ (VSELs: region P5) and Lin
/CD45+/CXCR4+ (HSPCs: region P4). Such gating strategy occurred to be very successful for identification and isolation of PB-derived VSELs, as indicated by our further genetic analysis of expression of pluripotent/embryonic markers in the sorted cells (Ratajczak et al., 2008a, 2008c). Analogous analytical strategy has been employed for identification and isolation of human UCB-derived VSELs (Fig. 2) (Kucia et al., 2007a; Zuba-Surma and Ratajczak, 2010; Zuba-Surma et al., 2010). UCB-derived TNCs are visualized by dot-plot based on FSC versus SSC signals. The TNCs events are shown in region P3. Panels B–F: Cells from region P1 are further analyzed for hematopoietic lineage maker expression and all the Lin
events are included in region P2. Panel C: The Lin
population from region P2 is subsequently analyzed based on CD133 and CD45 antigen expression and two populations of CD133+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CD133+ (VSELs: region Q2) and Lin
/CD45+/CD133+ (HSPCs: region Q4). Panel E: The Lin
population from region P2 is subsequently analyzed based on CD34 and CD45 antigen expression and two populations of CD34+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/CD34+ (VSELs: region P5) and Lin
/CD45+/CD34+ (HSPCs: region P4). Panel G: The Lin
population from region P2 is subsequently analyzed based on CXCR4 and CD45 antigen expression and two populations of CXCR4+ cells are distinguished based on CD45 expression, that is, Lin
/CD45
/ CXCR4+(VSELs: region P5) and Lin
/CD45+/CXCR4+ (HSPCs: region P4).

3. Identification of Very Small Embryonic/Epiblast-Like Stem Cells (VSELs)/Tissue Injuries

47

Such gating strategy occurred to be very successful for identification and isolation of UCB-derived VSELs, as indicated by our further genetic analysis of expression of pluripotent/embryonic markers in the sorted cells (Ratajczak et al., 2008a, 2008c). Such optimized analytical strategy was successfully employed not only for identification of VSELs in human UCB (Kucia et al., 2007a; Zuba-Surma and Ratajczak, 2010; Zuba-Surma et al., 2010) but also for their detection in other human specimens including the investigation of VSELs circulating in PB of patients with several severe organ injuries such as myocardial infarction as well as inflammatory diseases (colitis) and in patients suffering with gastrointestinal tumors (Abdel-Latif et al., 2010; Paczkowska et al., 2009; Wojakowski et al., 2006, 2009). In such cases the very rare VSEL’s presence in PB was confirmed by several imaging technologies including imaging cytometry (ISS) (Abdel-Latif et al., 2010; Wojakowski et al., 2009; Zuba-Surma et al., 2008b). Such approach allowed for distinguishing VSELs from cellular debris and artifacts, thereby providing strong evidence for their existence and mobilization into blood due to tissue injury (Figs. 3 and 4). Based on the ability of the imaging cytometry, we confirmed that similarly to murine BM-derived VSELs, human VSELs are also smaller than mature erythrocytes and leukocytes (Fig. 3). The examples of ISS technology provided multicolor images of mobilized very small VSELs circulating in blood of patients are shown in figure 4. Both classical and imaging cytometry have become the major technology for VSEL identification, detailed characterization, and purification from multitude murine and human specimens, and the well-established flow cytometric protocols (Zuba-Surma and Ratajczak, 2010) provide vast and reliable material for further molecular and genetic analysis of these unique cells.

VI. Critical Aspects of the Methodology 1. Since VSEL, MSCs, and EPCs are exceptionally rare in PB, minimum 10 mL of PB is required to enumerate these cells – in particular in steady-state (nonpathological) conditions. 2. Similar protocol may be employed for enumeration of UCB-derived or human BM-derived cells. Since both UCB and BM contain more stem cells, the volume of the harvested samples may be decreased to 5 mL. 3. To remove efficient RBC, the 1 lysing solution should be warmed to RT. Lysing buffer prewarmed to RT works much better than the cold one. After adding the lysing solution, the cells should be very well resuspended. 4. Remember to use single-color stained samples to prepare proper compensation profile for flow cytometric analysis/sorting as well as samples stained with isotype controls only (for isotype control antibodies, see Table II). 5. In order to obtain valuable data record at least 1 million TNCs events from each sample.

Mariusz Z. Ratajczak et al.

48

[(Fig._3)TD$IG]

Fig. 3 Comparison of cellular size and morphology between human blood-derived VSELs and mature hematopoietic cells by imaging cytometry. Cells were isolated from peripheral blood of patients with acute myocardial infarction and were stained for specific markers to distinguish mature monocytes (CD14), granulocytes (CD66b), and erythrocytes (CD235a; glycophorin A). VSEL stem cell was identified as nucleated small cell negative for CD45 and hematopoietic lineages markers (FITC; green), which exhibits expression of Oct4 – pluripotent marker (PE; orange) and CXCR4 – receptor for SDF-1 (PE-Cy5; magenta). Nuclei were stained with 7-aminoactinomycin D (7-AAD; red). The specific size of each cell (shown in red) was calculated by ImageStream system software as a length of minor cellular axis and is expressed in micrometers. The scale bars indicate 10 mm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

6. The preferable concentration of cells resuspended for analysis/sort should be between 10 and 15  106 mL
1. 7. While analyzing VSELs and HSCs, the cells are gated in extended to the left lymphocytic cells area. 8. During sorting, do not exceed the speed 20,000 of events/s to keep the recovery and purity of sorted cells high. Use typical high-purity sorting mode (e.g., purify 1 drop for MoFlo cell sorter). 9. ImageStream technology is a useful tool to verify if all the sorted events are truly cells (Fig. 4).

3. Identification of Very Small Embryonic/Epiblast-Like Stem Cells (VSELs)/Tissue Injuries

49

[(Fig._4)TD$IG]

Fig. 4 Representative images of VSEL and hematopoietic stem/progenitor cell (HSPC) circulating in blood of patients with acute myocardial infarction by imaging cytometry (ImageStreamX system). Human blood cells were stained for markers distinguishing VSELs such as (i) CD45 panleukocytic antigen (APCCy7, cyan), (ii) hematopoietic lineages markers (FITC, green), and (iii) stem cell antigens CD133 (PE, yellow) and CD34 (APC, violet). Nuclei were stained with Hoechst 33342 dye (red). The lower panel shows magnified combined images related to the expression of indicated antigens by the VSEL stem cell. The scale bars indicate 10 mm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

VII. Applications VSELs are detectable at extremely low level in steady-state conditions PB (150–300 mL
1) (Zuba-Surma et al., 2010). However, there are several pathological situations (e.g., heart infarct or stroke) in which VSELs are mobilized into PB and circulate at a much higher level. They could be mobilized into PB in order to participate in tissue/organ repair. However, it is likely that if VSELs are released from the BM, even if they are able to home to the areas of tissue/organ injury, they may function only in the regeneration of minor tissue injuries. Heart infarct or stroke, on the other hand, may involve severe tissue damage beyond the effective repair capacity of these rare cells. We are also identifying crucial factors involved in mobilization of VSELs into PB, and our data indicate a crucial role of stromal-derived factor-1 (SDF-1), complement cascade cleavage fragments, and sphingosine-1-phosphate (S1P) in this process (Ratajczak et al., 2010). Further, the allocation of these cells to the damaged areas depends on homing signals that may be inefficient in the presence of proteolytic enzymes released from leukocytes and macrophages associated with damaged tissue. Thus, it may happen that VSELs may potentially circulate as a homeless population of SCs in PB and return to the BM or home to other organs. We envision that a level of these cells in PB not only could be of scientific value but also could be elaborated in the future as an important diagnostic tool. For example, since it is a link between these cells and tissue organ rejuvenation, changes in the level

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of these cells circulating in PB could reflect overall ‘‘regeneration potential’’ of an adult organism. Next, we already noticed that the number of VSELs circulating in PB correlates with extend of heart infarct (Abdel-Latif et al., 2010; Wojakowski et al., 2006, 2009) and stroke (Paczkowska et al., 2009), and what is more important it possesses some prognostic value. An intriguing observation is also an increase in the number of circulating VSELs in PB of cancer patients. It is possible that VSELs are mobilized into growing tumor to participate in its better vascularization and provide precursors for cancer-associated fibroblasts. Thus, we encourage other investigators to study biological consequences of VSELs mobilization in all these above-mentioned situations. Finally, gating strategies described in this chapter could be employed in the future to gate these cells not only to enumerate their number in PB, UCB, and BM but also to sort these cells for research and potential clinical applications.

VIII. Future Directions VSELs isolated from adult tissues can be considered as an alternative, source of SCs for regenerative medicine, that is not ethically controversial, However, before VSELs can find their potential application in regenerative medicine there are missing answers to this timely issue, especially in view of the current and widely performed clinical trials with BM-derived SCs in cardiology and neurology. First, there is the obvious problem of isolating a sufficient number of VSELs from the BM, UCB, or mPB. The number of these cells among BM MNCs is very low. For example, VSELs represent 1 cell in 105 of BM MNCs (Kucia et al., 2006a; ZubaSurma et al., 2008a). Furthermore, our data show that these cells are enriched in the BM of young mammals and their number decreases with age (Ratajczak et al., 2008b; Zuba-Surma and Ratajczak, 2008). Our data also indicate that VSELs could potentially provide a therapeutic alternative to the controversial use of human ESCs and strategies based on therapeutic cloning. Hence, while the ethical debate on the application of ESCs in therapy continues, the potential of VSELs is ripe for exploration. The current work in our laboratory indicates that VSELs could be efficiently employed in the realm of regenerative medicine, and that they are physiologically more important than merely being potential developmental remnants. Finally, we believe that the controlled modulation of somatic imprint status in VSELs such as we hypothesized, a proper de novo methylation of somatic imprinted genes on maternal and paternal chromosomes, could increase a regenerative power of these cells. The coming years will bring important answers to these questions. Acknowledgments This work was supported by NIH R01 CA106281-01, NIH R01 DK074720, EU structural funds, Innovative Economy Operational Program POIG.01.01.01-00-109/09-01, KBN grant (No. N401 024536), and the Henry M. and Stella M. Hoenig Endowment to MZR.

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CHAPTER 4

Apoptosis and Beyond: Cytometry in Studies of Programmed Cell Death Donald Wlodkowic,* William Telford,y Joanna Skommerz and Zbigniew Darzynkiewiczx * The BioMEMS Research Group, Department of Chemistry, University of Auckland, Auckland, New Zealand y

Experimental Transplantation and Immunology Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland, USA z School of Biological Sciences, University of Auckland, Auckland, New Zealand x

Brander Cancer Research Institute, New York Medical College, Valhalla, New York, USA

Abstract Introduction The Biology of Apoptosis Cytometry in Cell Necrobiology Cytometric Methods to Detect Apoptosis A. Light Scattering Changes in Apoptotic Cells B. Dissipation of Mitochondrial Transmembrane Potential (Dcm) C. Activation of Caspases D. Changes in the Plasma Membrane During Apoptosis E. Nuclear Hallmarks of Apoptosis F. SYTO-Based Detection of Apoptosis V. Time-Window in Measuring Incidence of Apoptosis VI. Multiparameter Detection of Apoptosis: Choosing the Right Method VII. Beyond Apoptosis – Analysis of Alternative Cell Death Modes A. Autophagy B. Necrosis C. Cell Senescence I. II. III. IV.

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0091-679X/10 $35.00 DOI 10.1016/B978-0-12-385493-3.00004-8

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Abstract A cell undergoing apoptosis demonstrates multitude of characteristic morphological and biochemical features, which vary depending on the inducer of apoptosis, cell type and the ‘‘time window’’ at which the process of apoptosis is observed. Because the gross majority of apoptotic hallmarks can be revealed by flow and image cytometry, the cytometric methods become a technology of choice in diverse studies of cellular demise. Variety of cytometric methods designed to identify apoptotic cells, detect particular events of apoptosis and probe mechanisms associated with this mode of cell death have been developed during the past two decades. In the present review, we outline commonly used methods that are based on the assessment of mitochondrial transmembrane potential, activation of caspases, DNA fragmentation, and plasma membrane alterations. We also present novel developments in the field such as the use of cyanine SYTO and TO-PRO family of probes. Strategies of selecting the optimal multiparameter approaches, as well as potential difficulties in the experimental procedures, are thoroughly summarized.

I. Introduction During the past decade mechanisms underlying cell death have entered into a focus of interest of many researchers in diverse fields of biomedicine. These mechanisms include a wide range of signaling cascades that regulate initiation, execution, and postmortem cell disposal mechanisms (Darzynkiewicz et al., 1997, 2001b, 2004). The term cell necrobiology (biology of cell death) was introduced to collectively define all these cellular activities (Darzynkiewicz et al., 1997; see Cell Necrobiology in Wikipedia). Particular interest in cell necrobiology comes from the appreciation of the multitude of complex regulatory circuits that control the cellular demise. Considerable progress is currently being made in our understanding of a diversity of existing modes of programmed cell death (Blagosklonny, 2000; Leist and Jaattela, 2001; Zhivotovsky, 2004). Burgeoning data show that although the elimination of many cells relies heavily on classical apoptotic pathways, the alternative, quasiapoptotic, and nonapoptotic mechanisms, may also be involved in a plethora of biological processes (Kroemer and Martin, 2005; Leist and Jaattela, 2001). Undoubtedly, the cell propensity to undergo classical apoptosis still remains a key mechanism in the pathogenesis of many human diseases (Brown and Attardi, 2005; Danial and Korsmeyer, 2004). Genetic alterations that affect circuitry of the apoptotic machinery are reportedly linked to many disorders that are characterized by either diminished (cancer) or excessive (neurodegeneration) proclivity of cells to

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suicide. Thus, the in-depth understanding of different regulators of apoptosis at molecular level offers vast opportunities for innovative pharmacological intervention (Brown and Attardi, 2005; Green and Kroemer, 2005). In this context, there is an ever-increasing demand for convenient analytical tools to rapidly quantify and characterize diverse cell demise modes. Since cell death is a stochastic process, high-throughput single-cell analysis platforms are often of essence to deliver meaningful insights into intrinsically heterogeneous cell populations (Darzynkiewicz et al., 1997, 2004). Here, a gross majority of classical attributes of apoptosis can be quantitatively examined by flow and image cytometry, platforms that allow assessment of multiple cellular attributes on a single cell level (Darzynkiewicz et al., 1997, 2001a, 2001b, 2004; Telford et al., 2004). To date, diverse methods have been introduced that allow implementation of apoptotic assays on both live and/or fixed specimens (Darzynkiewicz et al., 2001a, 2001b, 2004). Some of them have evolved toward commercially available kits supplied by countless vendors. Although kits offer an advantage of simplicity and easy step-by-step protocols, the information accompanying is generally enigmatic. Adequate information about chemistry of the components or even mechanistic principles of the kit is often lacking because of the proprietary nature of patented reagents (Darzynkiewicz et al., 2004). Therefore, interpretation of the results and potential pitfalls may be particularly cumbersome for researchers unfamiliar with the biology of apoptosis. This chapter has been designed to complement the protocol-format literature by providing additional background information, methods’ comparison, and discussion about advantages and limitations of commonly used assays. Some steps of individual methods are discussed to emphasize their critical role and avoid the likelihood of artifacts. We update also some earlier reviews on the application of cytometry in analysis of cell death (Darzynkiewicz et al., 1992, 1994, 1997, 2001a, 2001b, 2004; Telford et al., 2004).

II. The Biology of Apoptosis Archetypically cells can disassemble in two morphologically and biochemically distinct processes: apoptosis and necrosis (Darzynkiewicz et al., 1997; Kerr et al., 1972; Lockshin and Zakeri, 2001). Both were initially identified based on characteristic changes in cell morphology (Kerr et al., 1972). Despite subsequent development of numerous molecular markers, the morphological changes still remain the ‘‘gold standard’’ to define the mode of cell death (Darzynkiewicz et al., 1997; Majno and Joris, 1995). Fig. 1 outlines major morphological and molecular changes occurring during apoptosis versus accidental cell death (herein termed necrosis). These were thoroughly discussed in some of our earlier reviews (Darzynkiewicz et al., 1997, 2001b, 2004). Alterations in cellular parameters, as presented in Fig. 1, become a basis to development of specific markers for microscopy, cytometry, and molecular techniques (Darzynkiewicz et al., 1997, 2001b, 2004). Importantly, however, constellation of apoptotic markers can vary depending on the stimuli and

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[(Fig._1)TD$IG]

Fig. 1

Simplified diagram of molecular pathways that regulate caspase-dependent apoptotic cell

death.

stress level, cell type, and unique cellular microenvironment that modulate cellular stress responses. In this context, some markers (such as oligonucleosomal DNA fragmentation) may not be detected in specimens challenged with divergent stimuli or microenvironmental conditions (e.g., cytokines, growth factor deprivation, heterotypic cell culture, etc.). It is, thus, always advisable to study several parameters at a time, which provide a multidimensional view of the advancing apoptotic cascade (Darzynkiewicz et al., 1997, 2001b, 2004). Noteworthy, recent reports have also provided closer insights into the mechanisms of cell death sentence and led to the characterization of several alternative demise modes (caspase-independent apoptosis-like PCD, autophagy, necrosis-like PCD, mitotic catastrophe) with serious connotations to disease pathogenesis and treatment (Edinger and Thompson, 2004; Leist and Jaattela, 2001; Lockshin and Zakeri, 2002; Okada and Mak, 2004). These important discoveries also initiated an ongoing debate aiming at the definition and classification of different modes of cell death that is of particular importance for the development of novel cytometric assays (Blagosklonny, 2000; Zhivotovsky, 2004). The general term apoptosis, exploited commonly in many research articles, tends sometimes to misinterpret the actual mechanisms underlying cell suicide program (Leist and Jaattela, 2001; Zhivotovsky, 2004). Therefore, it has been postulated to restrict the term apoptosis to only the traditional cell demise program featuring all ‘‘hallmarks of apoptotic cell death,’’ namely (i) activation of caspases as an absolute biomarker of cell death; (ii) condensation of chromatin; (iii) activation of endonucleases(s) causing internucleosomal DNA cleavage leading to extensive DNA fragmentation; (iv) appearance of

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[(Fig._2)TD$IG]

Fig. 2

Mitochondrial pathway of caspase-dependent apoptosis.

distinctive cellular morphology with preservation of organelles; (v) cell dehydration leading to its shrinkage; (vi) plasma membrane blebbing; and (vii) nuclear fragmentation and formation of apoptotic bodies (Figs. 1 and 2; Blagosklonny, 2000; Leist and Jaattela, 2001; Zhivotovsky, 2004; Ziegler and Groscurth, 2004). The use of the general term apoptosis should be always accompanied by listing the particular morphological and/or biochemical apoptosis-associated feature(s) that was (were) detected. It is also advisable to exploit a plethora of different assays to cross-analyze action of, for example, novel anticancer compounds and bear in mind that the characteristic changes in cell morphology revealed by cell imaging (light or electron microscopy) still remain the gold standard in the ultimate classification of the cell demise mode (Darzynkiewicz et al., 1997; King et al., 2000; Smolewski et al., 2003). Proper experimental approaches will help to avoid any potential misclassifications as the evidence accumulates that the roads to cellular disintegration represent a much more diverse and interconnected course than previously anticipated (Ferri and Kroemer, 2001; Leist and Jaattela, 2001). Not surprisingly the development of novel functional probes for cell death and thorough understanding of the mechanisms underlying properties of existing ones

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are of utmost importance for the future progress in cell necrobiology (Darzynkiewicz et al., 1997, 2001a, 2004). This is particularly relevant in view of the growing appreciation of the multitude of cell demise modes, and the need for sensitive and high-throughput cytometric assays capable to discriminate them.

III. Cytometry in Cell Necrobiology The major advantages of flow cytometry include the possibility of multiparameter measurements (correlation of different cellular events at a time), single cell analysis (avoidance of bulk analysis), and rapid analysis of cell populations (thousand of cells per second) (Bonetta, 2005; Melamed, 2001). Flow cytometry overcomes, thus, a frequent problem of traditional bulk techniques such as fluorimetry, spectrophotometry, or gel techniques (e.g., Western blot, WB). These are based on analysis of a total cell population that averages the results from every given cell (Darzynkiewicz et al., 1997, 2001a; Melamed, 2001). Moreover, by virtue of multiparameter analysis, cytometry allows correlative studies between many cell attributes based on both light scatter and fluorescence measurements (Darzynkiewicz et al., 1997; Melamed, 2001; Robinson, 2006). For example, when cellular DNA content, the parameter that reports the cell cycle position, is one of the measured attributes, an expression of other measured attribute(s) can be then directly related to the cell cycle position without a need for cell synchronization (Darzynkiewicz et al., 1997, 2004; Halicka et al., 1997). Furthermore, the change in expression of particular cell constituents, or coexpression of different events, if correlated within the same cell, may yield clues regarding a possible cause–effect relationship between the detected events (Darzynkiewicz et al., 1997, 2001b, 2004). It is why during the past two decades cytometric methodology has been applied in a gross majority of cell demise studies (Darzynkiewicz et al., 1997, 2004; Halicka et al., 1997; Huang et al., 2005). Novel technologies such as cell imaging in flow and laser scanning cytometry (LSC) deliver even more sophisticated features that combine superior statistical power of cytometric analysis coupled with low-resolution imaging capabilities (Darzynkiewicz et al., 1999; Deptala et al., 2001; George et al., 2004; Smolewski et al., 2001). Finally, high-speed sorting capabilities of newly designed bench-top equipment expand further cytometric applications by allowing detailed studies on the purified cell subpopulations of interest (Eisenstein, 2006; Melamed, 2001). Expectedly, the current pace in the development of novel cytometric technologies will open up new horizons for future research on cell demise (Bernas et al., 2006; Darzynkiewicz et al., 2004; Robinson, 2004). Applications of cytometry in cell necrobiology studies have archetypically two goals (thoroughly reviewed in Darzynkiewicz et al., 2001a, 2004). One aim is to elucidate molecular mechanisms associated with cell death. Here cytometric assays have been applied to quantify the expression of cell constituents involved in apoptotic circuitry [such as members of the Bcl-2 protein family (caspases), inhibitors of caspases, etc.]. Cytometric methods have been also developed to study many

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changes in metabolic attributes, such as mitochondrial metabolism, redox status, intracellular pH or calcium fluxes. The second goal of cytometry in cell necrobiology is to estimate the viability of individual cells in a given population. This includes identification and quantification of dead cells and discrimination between apoptotic versus necrotic mode of death. Such discrimination is generally based on the change in cell morphology and/or on the presence of characteristic biochemical or molecular markers (Fig. 1). Most of these changes serve as markers to identify and quantify apoptotic cells by cytometry. To stress it again, morphological criteria (examined by the light, fluorescent, and electron microscopy) are still the ‘‘gold standard’’ to define the mode of cell death and confirm flow cytometric results (Darzynkiewicz et al., 1997, 2004; Majno and Joris, 1995; Ziegler and Groscurth, 2004). Therefore, lack of microscopic examination may potentially lead to the misclassification and false-positive or -negative artifacts, and is a common drawback of the experimental design (Darzynkiewicz et al., 1997, 2001a, 2004). The striking example of such misclassification is identification by flow cytometry of phagocytes that engulfed apoptotic bodies as individual apoptotic cells (Bedner et al., 1999).

IV. Cytometric Methods to Detect Apoptosis A. Light Scattering Changes in Apoptotic Cells Flow cytometry allows quantitative measurements of laser light scatter characteristics that reflect morphological features of cells. Cell shrinkage due to the dehydration can be detected at early stages of apoptosis as a decrease in intensity of forward light scatter (FSC) signal (Ormerod et al., 1995; Swat et al., 1981). Either unchanged, or often increased side scatter signal (SSC, measured at 90
angle) is concomitantly observed as cell shrinkage; the condensation of nucleus and cytoplasm driven by cell dehydration leads to enhancement of light refraction and reflection (Fig. 3). When apoptotic cascade advances the cells become progressively smaller, and intensity of side scatter also decreases. Late apoptotic/secondary necrotic cells, therefore, are characterized by markedly diminished ability to scatter light in both, forward and right angle directions (Fig. 3). Necrosis, on the contrary, often proceeds through the simultaneous and rather drastic reduction in intensity of both light scatter parameters, which is believed to reflect rapid loss of the cell membrane integrity and leakage of cytoplasmic constituents. Primary necrotic cells fall, thus, into subpopulation similar to secondary necrotic cells and cannot be properly distinguished by light scattering measurements (Darzynkiewicz et al., 1997, 2004; Majno and Joris, 1995). It should be noted, however, that observable changes in light scattering are not a reliable marker of apoptosis or necrosis by themselves. Mechanically broken cells, isolated nuclei, cell debris, and individual apoptotic bodies all display reduced light scatter properties and may be mistakenly accounted for as apoptotic cells. Furthermore, activation of tissue transglutaminase 2 (TGase 2) has recently been

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[(Fig._3)TD$IG]

Fig. 3 Changes in light scattering properties during apoptosis. Human B-cell lymphoma cells were untreated (left panel) or treated with small molecule Bcl-2 inhibitor HA14-1 (right panel) as described (Skommer et al., 2006). Note that viable cell population (V) from the treated culture has similar light scattering properties as control cells. Apoptotic cells (A) have diminished forward scatter while their side scatter is enhanced. The late apoptotic/secondary necrotic cells (LA/N) have diminished both scatter parameters. Apoptotic bodies and cell debris exhibit extremely low light scatter values (D).

reported to influence light scattering properties detected by flow cytometry in some models of apoptosis (Darzynkiewicz et al., 2004; Grabarek et al., 2002). TGase 2 activity results here in protein crosslinking and enhancement of nuclear/cytoplasmic condensation. This is reflected by transient increase in intensity of the side scatter signal and moderate decrease in forward scatter signal. Conversely, apoptosis proceeding in absence of TGase 2 activation is reflected by the decrease in both forward and side scatter signals (Darzynkiewicz et al., 2004; Grabarek et al., 2002). It should be stressed that morphological features revealed by laser light scattering in flow cytometry should be considered as auxiliary parameters and be used only in conjunction with more specific markers of cell death. However, novel platforms such as LSC and multispectral imaging cytometry (cell imaging inflow), by providing low-resolution imaging of individual cells and expanding analytical capabilities to morphometric analysis deliver substantial improvements over classical flow cytometry in cell necrobiology studies (Bedner et al., 1999; Darzynkiewicz et al., 1999; George et al., 2004; Kamentsky, 2001; Pozarowski et al., 2006). B. Dissipation of Mitochondrial Transmembrane Potential (Dcm) The mitochondrion stands at the nexus of sensing and integrating diverse incoming stress signals, and mitochondrial disturbances often occur long before any marked morphological symptoms of apoptosis (Green, 2005; Skommer et al., 2007). In recent years multiple mechanisms have been revealed that explain

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mitochondrial function in apoptosis, including release of apoptogenic proteins into the cytosol upon mitochondrial outer membrane permeabilization (MOMP), loss of mitochondrial physiological processes indispensable for cell survival and generation of reactive oxygen species (ROS). The MOMP is a fundamental event leading to a release of holocytochrome c (cyt c) and an array of cell death modulating small proteins such as AIF, EndoG, Omi/HtrA2, Smac/DIABLO, Smac b, normally enclosed in the intermembrane space of the organelle (Saelens et al., 2004; van Gurp et al., 2003). Dissipation of mitochondrial inner transmembrane potential (Dcm) is frequently associated with MOMP (Kroemer, 1998; Zamzani et al., 1996, 1998). There are, however, examples of divergence where loss of Dcm can precede, coincide, or follow MOMP (Li et al., 2000; Skommer et al., 2007). Interestingly, as described by us and others, dissipation of mitochondrial inner transmembrane potential may not be an ultimate point of no return for cell commitment to die (Milella et al., 2002; Wlodkowic et al., 2006). The cytometric detection of Dcm loss is a sensitive marker of early apoptotic events. Procedures are based on lipofilic cationic probes that are readily taken up by live cells and accumulate in mitochondria according to the Nernst equation (Castedo et al., 2002). The extent of their uptake, as measured by intensity of cellular fluorescence, is proportional to Dcm status (Fig. 4). Majority of Dcm-sensitive probes are easily applicable for multiparameter detection with other apoptotic markers including caspase activation by fluorescently labelled inhibitors of caspases (FLICA), phosphatidylserine (PS) exposure by Annexin V and plasma membrane permeabilization by propidium iodide (PI) or YO-PRO 1 (Fig. 5; Castedo et al., 2002; Pozarowski et al., 2003; Wlodkowic et al., 2006, 2007a). In this context, lipophilic cationic fluorochromes rhodamine 123 (Rh123) or carboxycyanine dyes such as 3,30 -dihexiloxa-dicarbocyanine [DiOC6(3)] can serve as markers of Dcm loss (Darzynkiewicz et al., 1981, 1982; Johnson et al., 1980). Historically, a combination of Rh123 and PI was introduced as a viability assay that discriminates between live cells that stain with Rh123 but exclude PI versus early apoptotic cells that lost ability to accumulate Rh123 versus late apoptotic/necrotic cells that stain with PI only (Darzynkiewicz et al., 1982, 1994). The specificity of Rh123 and DiOC6(3) as selective Dcm-sensitive probes has been questioned (Salvioli et al., 1997). The apparent controversy may be due to the fact that to be a specific marker of Dcm Rh123 or DiOC6(3) has to be used at low concentration (1 mM), which was not the case in many studies. The alternative probes such as chloromethyltetramethylrosamine analogues or tetramethylrhodamine esters have became now more widely used to detect mitochondrial depolarization during apoptosis. MitoTrackerTM dyes (chloromethyltetramethylrosamine analogues) were introduced by Molecular Probes Inc. as new mitochondrial potential markers (Haughland, 2003). One of them is MitoTracker Red CMXRos, a probe considered to be highly sensitive and specific to Dcm and (Castedo et al., 2002; Pendergrass et al., 2004; Poot and Pierce, 1999). The previously reported retainability of CMXRos after fixation with formaldehyde was recently challenged by some authors (Ferlini et al., 1998; Macho et al., 1996; Poot and Pierce, 1999). It has been shown

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[(Fig._4)TD$IG]

Fig. 4 Dissipation of mitochondrial transmembrane potential (Dcm). (A) Analysis by staining with tetramethylrhodamine methyl ester (TMRM). Human B-cell lymphoma cells were either untreated (Ctrl) or treated with cycloheximide (CHX) to induce apoptosis and supravitally loaded with TMRM as described (Castedo et al., 2002; Wlodkowic et al., 2006). Cells with collapsed mitochondrial transmembrane potential (mito loss) have decreased intensity of orange TMRM fluorescence. (B) Analysis by staining with the Jaggregate dye JC-9. Human B-cell lymphoma cells were either untreated (Ctrl) or treated with crosslinking anti-CD95 antibody (anti-CD95) to induce apoptosis and loaded with JC-9 as described (Pritchard et al., 2001; Skommer et al., 2006). Cells with collapsed mitochondrial transmembrane potential (mito loss) have decreased intensity of red fluorescence (mitochondrial J-aggregates) and increased intensity of green fluorescence (cytoplasmic J-monomers). Note that by only employing the Dcm-sensitive probe there is no distinction between early, late apoptotic and necrotic cells. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

that although uptake of CMXRos by live cells is a function of mitochondrial gradient, its retention following fixation depends rather on the availability of intramitochondrial thiols (Poot and Pierce, 1999). Thus, it is not advisable to apply CMXRos with measurements of another cell attributes that require subsequent cell

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fixation such as DNA fragmentation detected by the TDT-mediated dUTP-biotin nick-end labeling (TUNEL) assay or immunocytochemical detection of intracellular protein (see below). Other useful probes sensitive to Dcm changes are tetramethylrhodamine methyl ester perchlorate (TMRM) and tetramethylrhodamine ethyl ester perchlorate (TMRE). The application of TMRM combined with the marker of caspase activation (FLICA) and small cyanine cation YO-PRO 1 is illustrated in Fig. 5 (Pozarowski et al., 2003; Wlodkowic et al., 2006). Due to convenient spectral characteristics these probes are especially useful for multiparameter assays combining diverse apoptotic markers (Pozarowski et al., 2003; Wlodkowic et al., 2006, 2007a, 2007b). Major improvements have recently been made by implementing ratiometric J-aggregate forming cationic fluorochromes: JC-1 (5,50 ,6,60 -tetrachloro-1,10 , 3,30 -tetraethylbenzimidazolcarbocyanine iodide) and JC-9 (3,30 -dimethyl-aanaphthoxacarbocyanine iodide) (Cossarizza and Salvioli, 2001; Pritchard et al., 2001). Their uptake by energized mitochondria leads to formation of aggregates in the mitochondrial matrix and emission of orange/red fluorescence. Loss of Dcm leads to dissociation of the J-aggregates and transition to monomeric, cytoplasmic form that exhibits green fluorescence (Fig. 4). The major disadvantage of J-aggregate probes lies in occupation of crucial fluorescent channels that makes it difficult to multiplex on single 488 nm laser instrumentation. Furthermore, poor solubility of these probes in aqueous media may occasionally lead to staining artifacts. Nevertheless, when used concurrently with violet or red excitable fluorochromes they offer substantial improvements over traditional mitochondrial probes. Finally, probes such as 10-nonyl acridine orange (NAO), MitoFluor Green, and MitoTracker Green were previously advertised as markers of mitochondrial mass that are insensitive to Dcm changes (Pendergrass et al., 1997; Ratinaud et al., 1988). For increased sensitivity it was, thus, proposed to simultaneously measure both Dcm and mitochondrial mass with a combination of Dcm-sensitive and Dcm-insensitive probes (e.g., Petit et al., 1995). Disappointingly, further observations revealed that all three probes are dependent on changes in Dcm and cannot be used as mere markers of mitochondrial mass (Keij et al., 2000). NAO can be, however, conveniently applied to track peroxidation of mitochondrial cardiolipin by flow cytometry. This simple assay measures an early event that is a prerequisite for cytochrome c release during apoptosis (Castedo et al., 2002; Garcia Fernandez et al., 2004). Measurement of Dcm is particularly sensitive to changes in cellular environment. Therefore, samples assigned for comparison should be incubated and measured under identical temperature, pH, time elapsed between the onset of incubation and fluorescence measurement. Moreover, according to Nernst equation, the intracellular distribution of any cationic mitochondrial probe reflects the differences in the transmembrane potential across both the plasma membrane (i.e., between exterior vs. interior of the cell) and the outer mitochondrial membrane (Castedo et al., 2002; Shapiro, 2003). Thus, apart from mitochondria the probes can also accumulate in the cytosol. This is facilitated by both active and passive transport across the

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[(Fig._5)TD$IG]

Fig. 5

Multiparameter analysis employing mitochondrial potential sensitive probes. (A) Concurrent analysis of collapse of

Dcm and early plasma membrane permeability during apoptosis. Cells were treated as in Fig. 4A and supravitally stained with

both YO-PRO 1 and TMRM probes (Wlodkowic et al., 2007a). Their green and orange fluorescence was measured by flow cytometry. Live cells (V) are both TMRMhigh and exclude YO-PRO 1. Early apoptotic cells (A) exhibit loss of Dcm (TMRMlow) and moderate uptake of YO-PRO 1. Late apoptotic/secondary necrotic cells (LA/N) are highly permeant to YO-PRO 1 probe. (B) Concurrent analysis of collapse of Dcm and caspase activation during apoptosis. Apoptosis of Jurkat cells was induced by oxidative stress (growth in the presence of 30 or 60 mM H2O2; Ctrl, untreated cells). The cells were then supravitally exposed to FAM-VAD-FMK and MitoTracker Red CMXRos, rinsed and their green and red fluorescence measured by flow cytometry (Pozarowski et al., 2003). Two subpopulations of apoptotic cells can be detected. Subpopulation B represents the cells that lost mitochondrial potential but did not activated caspases, while cells in subpopulation C are characterized by both, collapsed Dcm and caspase activation (FLICA binding). At 60 mM H2O2 caspase activation was accelerated as reflected by the increased proportion of cells in C compared to B. Note that multiparameter analysis of Dcm-sensitive probe with YO-PRO 1 or FLICA allows for excellent distinction between live, early, late apoptotic and necrotic cells. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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plasma membrane. To decrease the passive transport, mitochondrial probes should be used at their lowest possible concentration, that is, the minimal concentration that is still adequate to detect mitochondrial changes. This may, however, necessitate relatively high settings of the photomultiplier (photomultiplier voltage) and higher laser power outputs (Darzynkiewicz et al., 2004). Caution should be also taken, as cationic probes may be targeted to other organelles like endoplasmic reticulum (ER) or lysosomes. Moreover, accumulation of some probes may be influenced by the activity of multidrug efflux pumps (MDR). In each experiment it is advisable to assess probes’specificity by preincubation of cells for 20–30 min with 50–100 mM protonophores CCCP or FCCP. Both agents cause a collapse of the mitochondrial transmembrane potential and are used as positive controls (Castedo et al., 2002; Darzynkiewicz et al., 2004).

C. Activation of Caspases One of the hallmarks of classical apoptosis is the activation of unique cysteine aspartyl-specific proteases having a conserved QACXG consensus site containing active cysteine, called caspases (from cysteinyl aspartate-specific proteases; Fig. 2) (Alnemri et al., 1996; Kaufmann et al., 1993; Thornberry and Lazebnik, 1998). In mammals there are probably at least 14 members of the caspase family proteins that form a closely related family of proteases (Boyce et al., 2004; Zhivotovsky, 2003). Although individual caspases have specific functions, some degree of overlapping specificity and redundancy among them is apparent (Earnshaw et al., 1999). At present only eight caspases are known to participate in execution of apoptotic cell dismantling (caspase-2, -3, -6, -7, -8, -9, -10, -12). Remaining members of the caspase family participates in cytokine processing and inflammatory responses (Boyce et al., 2004; Lavrik et al., 2005; Zhivotovsky, 2003). Under normal physiological conditions caspases are constitutively expressed in the cytoplasm as zymogens with very low intrinsic activity. They become activated upon transcatalytic cleavage followed by dimerization. Specifically, cleaved molecules assemble to form a single heterotetramer with two active enzymatic sites in head-to-tail configuration (Earnshaw et al., 1999; Zhivotovsky, 2003). Once activated, caspases function in an orchestrated proteolytic cascade leading to self-amplification, cleavage of vital cell substrates, and ultimate cell disassembly (Fig. 2; Earnshaw et al., 1999; Zhivotovsky, 2003). Several methods were developed to detect activation of caspases by flow and laser scanning cytometry (thoroughly reviewed in Darzynkiewicz et al., 2001b, 2004; Telford et al., 2004). Here, we outline two commonly used techniques based on the affinity labeling of the caspase active centers and cleavage of the poly (ADP-ribose) polymerase (PARP).

1. Fluorochrome-Labeled Inhibitors of Caspases (FLICA) Use of fluorochrome-labeled inhibitors of caspases (FLICA, recognized also under commercial names: CaspaTag, CaspACE, CaspGLOW, FLIVO) allows for a

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[(Fig._6)TD$IG]

Fig. 6

Activation of caspases. (A) Detection of caspases activation by fluorescently labeled inhibitors of caspases (FLICA) combined with plasma membrane permeability assessment (propidium iodide; PI).Human Bcell lymphoma cells were either untreated (Ctrl) or treated with Brefeldin A (BFA) to induce apoptosis as described (Wlodkowic et al., 2007a). Cells were subsequently supravitally stained with FAM-VAD-FMK (pan caspase marker; FLICA) and PI. Their logarithmically amplified green and red fluorescence signals were measured by flow cytometry. Live cells (V) are both FAM-VAD-FMK and PI negative. Early apoptotic cells (A) bind FAM-VAD-FMK but exclude PI. Late apoptotic/secondary necrotic cells (LA) are both FAM-VADFMK and PI positive. Primary necrotic and some very late apoptotic cells (N) stain with PI only. (B) Detection of PARP cleavage combined with DNA content (cell cycle) analysis. To induce apoptosis, HL-60 cells were treated with TNF-a in the presence of CHX for 30–360 min (Li and Darzynkiewicz, 2000). Upper panel shows immunoblots of the treated cells, stained with PARP plus PARP p89 (upper gel) or PARP p89 only (lower gel) Abs.Lower panelsshowbivariatedistributionsofPARPp89versusDNAcontent(stainedwithPI)oftheuntreated (Ctrl) and treated for 30 and 60 min cells. Note the appearance of the first PARP p89 positive cells already after 30 min of treatment, coinciding in time with the detection of PARP cleavage on gels. There is no evidence of cell cycle phase specificity of apoptosis induced by TNF-a. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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convenient estimation of apoptosis by both cytometry and fluorescence microscopy (Figs. 5B and 6A; Bedner et al., 2000; Smolewski et al., 2001). FLICAs were designed as affinity ligands to active centers of individual caspases (Bedner et al., 2000; Pozarowski et al., 2003; Smolewski et al., 2001). Each molecule has three functional domains: (i) the fluorochrome (carboxyfluorescein, FAM; fluorescein, FITC; or sulforhodamine, SR), (ii) the caspase recognition element comprising of a four amino-acid peptide, (iii) the chloro- or fluoromethyl ketone (CMK or FMK) binding moiety (Bedner et al., 2000; Pozarowski et al., 2003). The specificity toward individual caspases is provided by the recognition element. Currently, several FLICA kits are commercially available. The most common contains the valylalanyl-aspartic acid residue sequence (VAD). The VAD sequence allows binding to activated caspase-1, -3 -4, -5, -7, -8, and -9 providing a pan-caspase marker. Other inhibitors were subsequently developed and contain DVAD, DEVD, VEID, YVAD, LETD, LEHD, or AEVD peptide residues. They preferentially bind to activated caspase-2, -3, -6, -1, -8, -9, or -10, respectively. After docking of the FLICA molecule to the caspase active center, the FMK reacts with the active cysteine and forms a thiomethyl ketone (Thornberry et al., 1997; van Noorden, 2001). This irreversible, covalent reaction is deemed to inactivate the target enzyme. Presence of the fluorescent tag (FITC or SR) allows detection of FLICA–caspase complexes inside the cells. FLICAs are highly permeant to plasma membrane and relatively nontoxic. This provides a unique opportunity to detect caspase activation in living cells where uptake of these reagents is followed by covalent binding to activated caspases. To date, no interference resulting by MDR efflux pump activity has been reported for FLICA uptake. Unbound FLICAs are readily removed from the cells that lack caspase activity by rinsing with PBS buffer. When FLICAs are applied together with the plasma membrane permeability marker PI, several consecutive stages of apoptosis can be distinguished (Fig. 6A) (Pozarowski et al., 2003; Smolewski et al., 2001). Green fluorescent FLICAs (FAM, FITC) can also be used together with Dcm sensitive probes, such as MitoTracker Red CMXRos and TMRM as shown in Fig. 5B (Pozarowski et al., 2003; Wlodkowic et al., 2006). Moreover, other multiplexing combinations are compatible with both single and multilaser instrumentation. Because intracellular binding is covalent, FLICAs withstand cell fixation (with formaldehyde) and subsequent cell permeabilization with ethanol and methanol. As a result, this assay can be combined with the analysis of cell attributes that can require prior cell permeabilization such as DNA content measurement, DNA fragmentation (TUNEL assay), and so on. Recent reports shed, however, new light onto the FLICA binding mechanistic during apoptosis and cast doubts onto their absolute specificity toward caspase active centers (Kuzelova et al., 2007; Pozarowski et al., 2003). Namely, in apoptotic cells only a minor proportion of total FLICA binding was attributed to their FLICA–caspase interactions (Pozarowski et al., 2003). Likewise there is also no significant competition for the binding sites between FLICAs and unlabelled

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caspase inhibitors (e.g., z-VAD-fmk, z-DEVD-fmk) that are based on the same principle (Pozarowski et al., 2003). In the recent report, Kuzelova et al. (2007) confirmed that the overall fluorescence intensity of apoptotic cells labeled with FLICA does not reflect unique binding to caspase active centers. Moreover, FLICA appears to be incapable to arrest apoptosis a feature that initially formed the basis of ‘‘stathmo-apoptosis’’ assay (Pozarowski et al., 2003; Smolewski et al., 2002). These inconsistencies may be due to contamination of the early batches of FLICAs with the unlabelled caspase inhibitors. It remains evident, however, that other cellular constituents apart from caspase active centers contribute to FLICA staining. As FLICA reagents withstand fixation, this strongly suggests covalent interactions with the intracellular targets becoming accessible in the course of apoptosis (Darzynkiewicz and Pozarowski, 2007; Kuzelova et al., 2007; Pozarowski et al., 2003). The reactivity of FMK moiety with intracellular thiols may provide some explanation of these interactions. In this context, opening of the disulfide cysteine bridges (inter- and/or intramolecular) may provide as yet unidentified affinity sites (Darzynkiewicz and Pozarowski, 2007). Noncovalent hydrophobic interactions between fluorochrome domain and intracellular targets have also been postulated to play a role in FLICA retention (Pozarowski et al., 2003). It should be stressed that the covalent labeling of apoptotic cells with FLICA make these probes, called FLIVO, the markers of choice for detection of apoptosis in vivo, both in real time and after fixation of the tissue (Griffin et al., 2007). In any case FLICA reagents have proven to be reliable and sensitive markers of apoptotic cell death. Necrotic cells do not exhibit FLICA staining and caspase-3 activation assay correlates well with results obtained by FLICA. Recently published data suggest also their superior applicability in a plethora of multiparametric applications. Nevertheless, in light of recent reports one should be aware that staining with FLICA apparently does not represent affinity labeling of individual caspase active centers (Darzynkiewicz and Pozarowski, 2007; Pozarowski et al., 2003). The alternative approach for assessment of caspases activation involves the use of caspase substrates that upon cleavage generate fluorescent products (Lee et al., 2003; Telford et al., 2002). Another assay is based on the use of substrates consisting of two variants of fluorescent protein that differ in emission spectrum connected with a peptide linker whose cleavage by caspase leads to a loss of fluorescence resonance energy transfer (FRET) between the respective fluorescent proteins (He et al., 2004; Lee and Segal, 2004). It should be underscored that the use of labeled or unlabeled caspase inhibitors as well as caspase substrates poses uncertainty with respect to their specificity. The tetrapeptide moiety of these reagents is designed to confer their specificity. However, in studies of live cells they are used at four orders of magnitude higher concentration (20–50 mM) than their binding constants (0.2–2.2 nM) estimated on isolated caspases (Thornberry et al., 1997). Since their intracellular concentration and in situ accessibility to active caspase centers are unknown the published data

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on specificity of individual caspases detection should be in treated with a reservation. Immunocytochemical detection of activated (cleaved) caspases essentially has no problems with specificity provided that the antibody does not cross-react with other proteins. Antibodies to different activated caspases are available from variety of vendors. Flow cytometric analysis of immunocytochemically detected caspase-3 activation concurrently with DNA content (cell cycle analysis) has been reported most frequently (e.g., Pozarowski et al., 2003; Tanaka et al., 2007).

2. Detection of PARP Cleavage Another approach to study caspase activation is based on the analysis of the cleavage of specific caspase substrates. In this context, PARP is known as one of the characteristic endogenous ‘‘death substrates.’’ PARP is a nuclear enzyme involved in DNA repair that is activated in response to DNA damage (de Murcia and Menissier-de Murcia, 1994). Following initiation of proteolytic cascade, PARP is cleaved by executioner caspase-3 and -7, which is considered as hallmark of classical apoptosis (Alnemri et al., 1996; Kaufmann et al., 1993; Lazebnik et al., 1994). The specific cleavage results in generation of 89- and 24-kDa fragments that can be easily detected on Western blots. An antibody that recognizes the 89-kDa product of PARP cleavage has been adapted to label apoptotic cells for detection by both flow and laser scanning cytometry (Li and Darzynkiewicz, 2000). Since measurement of DNA content provides valuable information about the cell cycle position and DNA ploidy, attempts have been made to combine PARP cleavage assay with DNA labeling. Multiparameter analysis of the cells differentially stained for PARP p89 and DNA and correlating apoptosis with the cell cycle phase is shown in Fig. 6B (Li and Darzynkiewicz, 2000; Li et al., 2000). Because of the immunocytochemical detection principle, the assay requires prior cell fixation (with formaldehyde) and subsequent permeabilization (usually with ethanol). It should be stressed that the methanol-free formaldehyde obtained by hydrolysis of paraformaldehyde is often incorrectly named ‘‘paraformaldehyde.’’ Paraformaldehyde is the condensed, polymerized, solid state of formaldehyde. Since alcohol preserved samples may be stored for extended periods of time, this assay is particularly suitable for analysis of archive sample collections. Extensive kinetic studies are also straightforward as cells may be collected, fixed at the respective time intervals, and subsequently mass analyzed. Bias related to differential labeling conditions and/or progression of apoptotic cascade during the period of cell preparation is thus avoided. It should be noted that to enhance permeability of plasma membrane and to increase accessibility of the detected epitope (e.g., the cleaved 89kDa form of PARP) to the primary Ab (and also to secondary Ab, if needed) a nonionic detergent (e.g., Triton X-100) at final concentration 0.1% into the solution containing Ab is often included, together with the blocking reagent (1% w/v bovine serum albumin).

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To preserve physiological functions each cell strives to maintain an intact plasma membrane. The preservation plasma membrane integrity until late stages of cellular disintegration is also a distinctive feature of apoptosis that differentiate this process from accidental cell death, necrosis (Darzynkiewicz et al., 1997, 2004; Majno and Joris, 1995). Plasma membrane represents, thus, an active and dynamic organelle that plays an important part in the cascade of signaling events leading to a final removal of dying cell. However, alterations in both lipid composition and permeability to small cationic probes have been reported as relatively early signs of apoptotic cascade (Idziorek et al., 1995; Koopman et al., 1994; van Engeland et al., 1998). These usually follow Dcm collapse, caspase activation and chromatin condensation but precede nuclear disassembly and DNA laddering (van Engeland et al., 1998). Here, we describe common markers for both hallmarks that allow convenient analysis of live cells by flow cytometry.

1. Externalization of Phosphatidylserine A characteristic feature of healthy cell is the asymmetric distribution of plasma membrane phospholipids between inner and outer leaflets. Under physiological conditions, choline phospholipids (phosphatidylcholine, sphingomyelin) are exposed on the external leaflet while aminophospholipids (phosphatidylserine, phosphatidylethanolamine) are exclusively located on the cytoplasmic surface of the lipid bilayer. This asymmetry is scrambled during apoptosis when PS, constituting less than 10% of the total membrane phospholipids, becomes exposed on the outside leaflet of the membrane (Fadok et al., 1992; Koopman et al., 1994; van Engeland et al., 1998). Exposition of PS on cell surface provides signaling to macrophages, which then become attracted and initiate to phagocytize apoptotic cells and apoptotic bodies. The detection of exposed PS allows for a precise estimation of apoptotic incidence. The assay usually employs fluorochrome-tagged 36kDa anticoagulant protein Annexin V (van Engeland et al., 1998). This probe reversibly binds to PS residues in the presence of millimolar concentration of divalent calcium ions. Annexin V conjugated to fluorochromes of different absorption and emission wavelength has found many applications as a marker of apoptotic cells, in particular for their detection by flow cytometry and fluorescence microscopy (van Engeland et al., 1998; Van Genderen et al., 2006). Noteworthy, a C2A domain of Synaptotagmin I exhibits similar properties to Annexin V and was successfully used in cytometric applications (Jung et al., 2004). The cells become reactive with Annexin V prior to the loss of the plasma membrane’s ability to exclude cationic dyes such as PI or 7-aminoactinomycin D (7-AAD). Thus, when using Annexin V in conjunction with plasma membrane permeability marker a distinction can be made between live, apoptotic, and late apoptotic/secondary necrotic cells. Live cells stained with fluorochrome-tagged Annexin V and PI, have minimal Annexin V fluorescence and minimal PI fluorescence. At the early

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stages of apoptosis, cells stain brightly with Annexin V but still exclude PI. Finally, when apoptotic cascade advances to later stages, the secondary necrotic cells stain intensely with both probes. Of note the primary necrotic cells will also fall into the last group as Annexin V will penetrate cells with ruptured membrane and stain PS residues displayed on the inner leaflet of the plasma membrane. Moreover, cells with severely damaged membranes and very late apoptotic cells stain rapidly and strongly with PI and may not exhibit Annexin V staining. It should also be mentioned that even intact and live cells may become permeable to PI upon prolonged incubation times. Therefore, cytometric analysis should be performed shortly after addition of this dye. Our recent studies revealed also that the time-window of apoptosis detected by FLICA binding is much wider than by the Annexin V binding (Pozarowski et al., 2003). These data also suggest that activation of caspases is a prerequisite for externalization of PS since essentially no FLICA-negative cells that bind Annexin V are apparent (Pozarowski et al., 2003). Although commonly applied, the interpretation of results from Annexin V assay may be difficult after mechanical disaggregation of tissues to isolate individual cells, enzymatic (e.g., by trypsinization) or mechanic detachment (e.g., by ‘‘rubber policeman’’) of adherent cells from culture flasks, cell electroporation, chemical cell transfection, or high-titer retroviral infections. All these conditions have been reported to influence PS flipping and introduce substantial experimental bias. Interestingly, a high surface expression of PS has also been detected on some healthy cells such as differentiating monocytes, activated T cells, positively selected B lymphocytes, activated neutrophils, or myoblasts fusing into myotubes (Callahan et al., 2003; Elliott et al., 2005; van den Eijnde et al., 2001; Van Genderen et al., 2006). Furthermore, as PS serves as ‘‘eat me’’ signal for professional phagocytes, healthy macrophages/monocytes, become Annexin V positive upon ingestion of apoptotic bodies. In all these instances Annexin V binding may be mistakenly identified as a marker of apoptotic cells leading to false-positive identification of nonapoptotic cells (Marguet et al., 1999). Noteworthy, there are increasing examples of programmed cell death proceeding without exposure of PS, which may bring in false-negative bias when relying solely on Annexin V assay (King et al., 2000). Currently, a range of Annexin V conjugates with organic fluorescent probes is commercially available with the predominant popularity of FITC, PE, and APC conjugates. There is also a considerable interest in inorganic, semiconductor nanocrystals (Quantum Dots; QDs) conjugates (Dicker et al., 2005; Le Gac et al., 2006). Their significant advantages over currently available organic fluorochromes are rapidly attracting attention in both cytometric and imaging applications (Chattopadhyay et al., 2006; Jaiswal and Simon, 2004; Jaiswal et al., 2003). Moreover, a recent development from Alexis-Axxora introduced fluorescently labeled monoclonal antibodies against PS residues that can be used instead of Annexin V. This new class of reagents reportedly alleviates dependence on

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calcium-supplemented buffers without compromising sensitivity of detection. Progress is also being made in the field of inorganic zinc coordination complexes (fluorescent Zn2+–dipicolylamines, DPA) that under Ca2+-free conditions selectively bind to membranes enriched in anionic phospholipids (Hanshaw et al., 2005; Koulov et al., 2003). Finally, a small cationic molecule merocyanine 540 (MC540) can reportedly be used to detect apoptotic cells based on the altered phospholipids composition (Laakko et al., 2002).

2. Changes in Plasma Membrane Permeability Externalization of anionic phospholipids is not a sole hallmark occurring early during apoptosis at the cell surface. Structural integrity and most of the plasma membrane transport function are preserved during the early phase of apoptosis. However, the permeability to certain fluorochromes, such as 7-AAD, Hoechst 33342, or Hoechst 33258 is increased (Ormerod et al., 1993; Schmid et al., 1992, 2007). Recent work by Idziorek et al. (1995) also revealed that following initiation of apoptotic cascade plasma membrane becomes selectively permeable to small, cationic molecules such as cyanine dyes. At the same time it remains impermeable to larger cations such as PI or 7-AAD. Live, noninduced to apoptosis cells, exclude both classes of probes. As a result, a new assay has been developed based on green florescent YO-PRO 1 and more recently violet fluorescent PO-PRO 1 cyanine probes (Idziorek et al., 1995). Of note, violet excitable PO-PRO 1 probe features similar properties to YO-PRO 1 and can provide increased multiplexing capabilities on highend analyzers. The assay is rapid and only short incubation (20 min, at RT) is required to supravitally discriminate viable cells (YO-PRO 1neg/PIneg events) from early apoptotic cells characterized by initial cell membrane permeabilization (YOPRO 1+/PIneg events). Cells in late stages of apoptosis and primary necrotic cells are characterized by pronounced loss in cell membrane integrity, and are thus permeable to both YO-PRO 1 and PI probes (YO-PRO 1+/PI+ events) (Idziorek et al., 1995; Wlodkowic et al., 2007a). Some reports have recently postulated that entry of YO-PRO 1 cation (629 Da) into early apoptotic cell depends on the activation of P2X7 ion-gated channel, event concurrent with scramblase activation and PS externalization (Holme et al., 2007). Early changes in lipid composition, structural relaxation, and/or impaired active dye efflux cannot, however, be also excluded as similar hypotheses have previously been raised for bisbenzimide dye, Hoechst 33342 (Idziorek et al., 1995; Ormerod et al., 1993; Schmid et al., 2007). Interestingly, our recent studies revealed that the time-window of apoptosis detected by YO-PRO 1 when analyzed by multiparameter flow cytometry is substantially wider than assessed by Annexin V binding (Wlodkowic et al., 2007a; unpublished data). Comparable results are also often achieved when Dcm selective probe TMRM is used in conjunction with YO-PRO 1 (Wlodkowic et al., 2007a). These observations reinforce the notion that YO-PRO 1 is a convenient and sensitive marker of

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early apoptotic events. Caution should be, however, exercised as dye uptake and/ or efflux may vary between different cell types and some cells may not exhibit differential staining with YO-PRO 1 and PI. This holds to be especially true in cells of murine origin such as FL5.12, BaF3, and primary fetal-liver progenitors (Wlodkowic et al., unpublished data). Result achieved by means of YO-PRO 1 or PO-PRO 1 may, thus, introduce false-negative bias if not confirmed by other methods. Finally, similar to the Annexin V binding assay, the interpretation of results from YO-PRO 1 assay may be difficult after enzymatic (e.g., by trypsinization) or mechanic detachment (e.g., by ‘‘rubber policeman’’) of adherent cells from culture flasks, cell electroporation, chemical cell transfection, or high-titer retroviral infections. Furthermore, some drugs or culture conditions may distort lipid bilayer structure leading to enhanced permeability in the absence of apoptosis. It is always advisable to test selective uptake of cyanine dyes by apoptotic cells in every new experimental system. E. Nuclear Hallmarks of Apoptosis Upon initiation of executioner caspase-3 and -7, caspase-activated DNase (CAD/DFF40) becomes activated by the cleavage of its putative inhibitor (ICAD/DFF45) (Enari et al., 1996). CAD translocates then to the nucleus where its activity leads to characteristic DNA fragmentation (Arends et al., 1990; Kerr et al., 1972; Nagata, 2000). Although CAD is the best-characterized enzyme, DNase-I, DNase-II, DNase-X, and AIF are also postulated in the execution of DNA degradation (Barry and Eastman, 1993; Los et al., 2000; Peitsch et al., 1993; Sussin et al., 1999). Apoptotic DNA fragmentation proceeds in three consecutive steps: (i) type-I DNA fragmentation (high molecular weight fragmentation to 0.05–1 Mb sections); (ii) type-II DNA fragmentation (intermediate fragmentation to 300 kb sections); and (iii) type-III DNA fragmentation (internucleosomal fragmentation to mono- and oligonucleosomal sections). The latter is often detected by a characteristic pattern during agarose DNA electrophoresis (DNA-ladder) and considered as a hallmark of apoptosis (Nagata, 2000; Nagata et al., 2003). Of note, DNA fragmentation during classical apoptosis may be terminated at 50–300 kb fragments. As a result characteristic ‘‘DNA-ladder’’ is absent due to a lack of internucleosomal-sized fragments (Darzynkiewicz et al., 1997; Oberhammer et al., 1993). Not surprisingly DNA fragmentation provided basis for two commonly used cytometric assays that allow identification of apoptotic cells: (i) estimation of fractional DNA content (sub-G1 fraction; Gong et al., 1994; Nicoletti et al., 1991; Umansky et al., 1981) and (ii) labeling of DNA strand breaks (DSBs) with fluorochrome-tagged deoxynucleotides by exogenous terminal deoxynucleotidyltransferase, TdT (TUNEL; Gorczyca et al., 1992, 1993; Li and Darzynkiewicz, 1995; Li et al., 1996) (Fig. 7).

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[(Fig._7)TD$IG]

Fig. 7 DNA fragmentation analysis. (A) Detection of fractional DNA content (‘‘sub-G1’’ peak). Apoptosis of human follicular lymphoma cells was induced with dexamethasone (Dex). Ethanol-fixed and propidium iodide (PI)-stained cells were analyzed on a flow cytometer. Red fluorescence of PI was collected using linear amplification scale. Debris were gated out electronically. Note distinctive sub-G1 peak. For further details refer to text. (B) Detection of DNA strand breaks (‘‘TUNEL’’ assay) using different deoxynucleotides. Apoptosis of HL-60 cells was induced by camptothecin, which selectively targets Sphase cells (Li and Darzynkiewicz, 1995). In the reaction catalyzed by terminal deoxynucleotidyl transferase, the DNA strand breaks were labeled (from right to left): directly with FITC and BODIPY-conjugated dUTP, or indirectly with biotinylated (biot) dUTP detected by FITC-avidin, digoxygenin-conjugated dUTP detected by digoxygenin-FITC Ab, and with BrdUTP detected by FITC-BrdU Ab. The highest resolution provides labeling with BrdUTP. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

1. Assessment of Fractional DNA Content (Sub-G1 Fraction) The fragmented, low molecular weight DNA can be extracted from cells during the process of cell staining in aqueous solutions. Such extraction takes place when the cells are treated with detergent and/or hypotonic solution instead of fixation, to make them permeable to fluorochrome. Alternatively fixation in precipitating fixatives such as ethanol can be used for the same purpose. Fixation with crosslinking fixatives such as formaldehyde, on the other hand, results in the retention of low molecular weight DNA in the cell as they become crosslinked to intercellular proteins. Therefore, a formaldehyde fixation is incompatible with the ‘‘sub-G1’’

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assay. As a result of DNA extraction, apoptotic cells exhibit a deficit in DNA content. Following staining with a DNA-specific fluorochrome they can be recognized by cytometry as cells having fractional DNA content. On the DNA content frequency histograms it is often characterized by a distinctive ‘‘sub-G1’’ peak (Fig. 7A, Gong et al., 1994; Nicoletti et al., 1991; Umansky et al., 1981). Interestingly, apoptotic DNA fragmentation detected by several distinctive sub-G1 peaks has recently been reported as a discontinuous process, relaying on sequential activation of different deoxynucleases and also modulated by chromatin structure (Kajstura et al., 2007). Optimally, the ‘‘sub-G1’’ peak representing apoptotic cells should be separated with little or no overlapping from the G1 peak of the nonapoptotic cell population. However, the degree of low molecular weight DNA extraction varies markedly depending on the extent of DNA degradation (duration of apoptosis), the number of cell washings, pH, and molarity of the washing/staining buffers. Shedding of apoptotic bodies containing fragments of nuclear chromatin may also contribute to the loss of DNA from apoptotic cells. As a result, the separation of ‘‘sub-G1’’ is not always satisfactory. On the other hand, when DNA degradation does not proceed to internucleosomal regions but stops after generating 50–300 kb fragments (Oberhammer et al., 1993), little DNA can be extracted. This method fails, thus, to detect such atypical apoptotic cells. Furthermore, the loss of DNA from G2/M and late S-phase cells undergoing apoptosis, may be inadequate to generate clear ‘‘sub-G1’’ peak. In such situations cells often end up with DNA content equivalent to that of G1 or early S phase and are indistinguishable during cytometric analysis. Noteworthy, a reduced stainability with DNA fluorochromes, that resembles fractional DNA content, may be present during cell differentiation or even necrosis (Darzynkiewicz et al., 1984; Oberhammer et al., 1993). Unfortunately numerous investigators still apply sub-G1 analysis as the sole method for enumeration of apoptotic cells. Because without additional assays fractional DNA content cannot be used as decisive marker of cell death caution should be exercised interpreting such data. It is a common practice to use detergents or hypotonic solutions instead of fixation in DNA staining protocols (Nicoletti et al., 1991). This simple approach causes lysis of plasma membrane and nuclear isolation and yields excellent resolution for DNA content analysis. When used to quantify apoptotic cells, however, this method is poised to generate a significant bias. Namely, nuclei of apoptotic cells are often fragmented and upon cell lysis a multiplicity of chromatin fragments/nuclear bodies are released from a single cell. Lysis of mitotic cells additionally releases individual chromosomes and/or chromosome aggregates. Furthermore, after cell irradiation or treatment with clastogens the generated micronuclei are often released during hypotonic procedures. As a result, each nuclear fragment, chromosome or micronucleus is recorded by flow cytometer as an individual object with sub-G1 DNA content. Such objects are then erroneously classified as individual apoptotic cells. This bias is particularly pronounced when logarithmic scale is used to display DNA content on the histograms, which allows one to detect objects with minute DNA content such as 0.1% of that of G1 cells. These events certainly cannot be classified as individual apoptotic nuclei, and their percentage overestimates the actual percentage of apoptotic cells in the sample.

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2. Assessment of DNA Strand Breaks (TUNEL Assay) DNA fragmentation during apoptosis generates a multitude of DSBs in the nucleus (Arends et al., 1990; Oberhammer et al., 1993). The 30 -OH termini of the breaks may be marked by attaching a fluorochrome to them. This is generally done directly or indirectly (e.g., via biotin or digoxygenin) by using fluorochrome-labeled triphosphodeoxynucleotides in a reaction catalyzed preferably by exogenous terminal deoxynucleotidyltransferase (Gorczyca et al., 1992, 1993; Li and Darzynkiewicz, 1995; Li et al., 1996). The reaction is commonly known as TUNEL from ‘‘TDT-mediated dUTP-biotin nick-end labeling’’ (Gavrieli et al., 1992). This acronym is a misnomer since the double strand breaks are labeled rather than the single strand nicks. Furthermore, other than dUTP deoxynucleotides are often used in this assay. Of all the deoxynucleotides BrdUTP appears to be the most advantageous to label DSBs, in terms of high sensitivity, low cost and simplicity of the assay (Li and Darzynkiewicz, 1995; see Fig. 7B). BrdU attached to DSBs (as poly-BrdU) is detected with an FITC-conjugated anti-BrdU Ab; the very same Ab that is used to detect BrdU incorporated during DNA replication (Fig. 7B). PolyBrdU at the DSBs, however, is accessible to the Ab without acid- or heat-induced DNA denaturation, which otherwise is needed to detect the precursor incorporated during DNA replication. The detection of DSBs by this assay requires cell prefixation with a crosslinking reagent such as formaldehyde, which unlike ethanol, prevents the extraction of small DNA fragments. Labeling DSBs in this procedure, which utilizes fluorescein-conjugated anti-BrdU Ab, can be combined with staining of DNA with the fluorochrome of another color (PI, red fluorescence). Cytometry of cells that are differentially stained for DSBs and for DNA allows one to distinguish apoptotic from nonapoptotic cell subpopulations and reveal the cell cycle distribution in each of these subpopulations (Fig. 7B; Gorczyca et al., 1992, 1993). Since late apoptotic cells may have diminished DNA content because of prior shedding of apoptotic bodies or due to such extensive DNA fragmentation that small DNA fragments cannot be retained in the cell after fixation with formaldehyde such cells may have sub-G1 DNA content and be TUNEL-positive. Several types of kits are commercially available, which utilize either directly fluorochrome-tagged triphospho deoxynucleotides or BrdUTP and BrdU Ab. The extensive DNA fragmentation during apoptosis, similar to radiation-induced DNA breakage, leads to an early attempts by the cell to repair the damage that manifests by activation of Ataxia Telangiectasia mutated protein kinase (ATM) and phosphorylation of histone H2AX on Ser-139. Both ATM activation as well as H2AX phosphorylation can be detected immunocytochemically by phosphospecific antibodies (Huang et al., 2005; Kurose et al., 2005; Tanaka et al., 2007). The extent of H2AX phosphorylation in early apoptotic cells is extremely high, by an order of magnitude higher than the maximal level that can be induced by the DNA damaging drugs or radiation (Kurose et al., 2005). Multiparameter cytometric analysis of H2AX phosphorylated on Ser-139 concurrently with DNA content makes

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it thus possible to identify subpopulations of cells with primary DSBs induced by the drug or radiation versus the cells with secondary, apoptosis-associated DSBs, and characterize cells in each subpopulation with respect to cell cycle phase (Kurose et al., 2005). F. SYTO-Based Detection of Apoptosis Progress in the modern field of cell necrobiology necessitates exploitation of novel methods that support high-throughput and multivariate analysis of critical cellular parameters at a single cell level (Darzynkiewicz et al., 1997, 2001b, 2004). To this aim diverse cytometric assays have been introduced, as described in previous sections of this chapter. Some of them include cell permeant DNA selective stains, such as Hoechst 33342, DRAQ5, and, more recently, probes from Vybrant DyeCycle family (Haughland, 2005; Schmid et al., 2007; Smith et al., 2000). All allow staining of unfixed cells and restrict cumbersome procedures to a simple step. To date, however, live-cell assays based on cell permeant DNA selective probes suffered mostly from their unfavorable spectral characteristics that necessitate UV excitation source and dedicated optics. Excessive toxicity/phototoxicity precludes also long-term studies such as cell sorting with subsequent cell cultivation (Durand and Olive, 1982; Fried et al., 1982; Martin et al., 2005). Nevertheless, progress has recently been made by the development of cell permeant, cyanine SYTO stains. This novel class of probes spans a broad range of visible excitation and emission spectra: (1) SYTO blue (Ex/Em 419–452/445–484 nm); (2) SYTO green (Ex/Em 483–521/ 500–556 nm); (3) SYTO orange (Ex/Em 528–567/544–583 nm); and (4) SYTO red (Ex/Em 598–654/620–680 nm) (Frey, 1995; Haughland, 2005). Exploitation of SYTO probes to cytometric detection of apoptosis started in 1990s (Frey, 1995; Poot et al., 1997) and is slowly gaining appreciation as an easy to perform, live-cell assay (Poot et al., 1997; Schuurhuis et al., 2001). Although, the fundamental mechanism underlying differential staining of SYTO-labeled apoptotic versus viable cells still remains uncertain, several hypotheses have been raised in the recent years (reviewed in Wlodkowic and Skommer, 2007). Following initiation of caspase-dependent apoptosis cells loaded with selected SYTO stains exhibit gradual reduction in fluorescence signal intensity to dim values. This phenomenon substantially precedes plasma membrane permeability changes (Fig. 9) (Frey, 1995; Poot et al., 1997; Wlodkowic et al., 2007b). Evidence from recently published data indicate an overall higher sensitivity of SYTO probes in detection of early apoptotic events as compared to Annexin V-based assays (Eray et al., 2001; Schuurhuis et al., 2001; Sparrow and Tippett, 2005). When progression toward the terminal stages of cellular demise advances, loss of SYTO fluorescence intensifies, and this usually coincides with the increased plasma membrane permeability to PI and 7-AAD (Poot et al., 1997; Schuurhuis et al., 2001; Wlodkowic et al., 2007b). Fig. 8 illustrates results obtained from a green fluorescent SYTO 11 probe used in conjunction with plasma membrane permeability marker PI. Both probes are exited by 488 nm line permitting their concomitant application on

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Fig. 8 Detection of apoptosis by SYTO 11 probe. Human B-cell lymphoma cells were untreated (left panel) or treated with dexamethasone (right panel), as described (Wlodkowic et al., 2007b). Cells were subsequently supravitally stained with SYTO 11 and PI probes. Their logarithmically amplified green and red fluorescence signals were measured by flow cytometry. Live cells (V) are SYTO 11bright and PI negative. Early apoptotic cells (A) are SYTO 11dim but still exclude PI. Late apoptotic/secondary necrotic cells (N) are both SYTO 11low and PI positive. Primary necrotic cells do not exhibit SYTOdim staining pattern and rapidly take up propidium iodide while loosing SYTO to low values (not shown; Wlodkowic et al., 2007b). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

single laser analyzers. The assay requires only a short incubation (20 min, at RT) to supravitally discriminate viable cells (SYTOhigh/PIneg events; V) and early apoptotic cells. The latter population is characterized by initial loss of SYTO 11 fluorescence and preservation of plasma membrane integrity (SYTOdim/PIneg events; A). Cells in later stages of apoptosis feature progressive loss of SYTO fluorescence and gain bright PI staining (SYTOneg/PI+ events; N) (Wlodkowic and Skommer, 2007; Wlodkowic et al., 2007b). Of note, the primary necrotic cells will also fall into the last group with minimal SYTO 11 and bright PI fluorescence. We have recently shown that yet another green fluorescent probe, SYTO 16 allows discrimination between primary and secondary necrotic cells (Wlodkowic et al., 2007b). Therefore, SYTO 16 provides substantial enhancement over the standard PI exclusion assay in discerning cell demise mode by flow cytometry (Wlodkowic et al., 2007b). Importantly, SYTO probes prove in many instances inert and safe for tracking cells over extended periods of time. This may open up new opportunities for single cell real-time analysis protocols by both fluorescent activated cell sorting (FACS) and Lab-on-a-Chip platforms. Recent noteworthy reports provided strong evidence that at least some SYTO probes can be substrates for MDR efflux pumps (e.g., P-glycoprotein; P-gp) (Schuurhuis et al., 2001; van der Pol et al., 2003). Caution should be, thus, exercised when using SYTO probes in cells with active ABC-class transporters. It is always advisable to confirm MDR status of studied cell population. In cells

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with active P-gp its inhibition (e.g., by verapamil hydrochloride, PSC833, cyclosporin A) is required to avoid masking of apoptotic SYTOdim subpopulation by SYTOdim subpopulation engendered by an active dye efflux (Schuurhuis et al., 2001; van der Pol et al., 2003). Truly apoptotic reduction of SYTO fluorescence to dim values is not affected by the presence of P-gp inhibitors (Schuurhuis et al., 2001). Moreover, one should always bear in mind that results obtained using SYTO-based assays may vary when compared to assays detecting different cellular processes. Results acquired with SYTO probes should, therefore, never be considered conclusive without verification by independent methods (Wlodkowic and Skommer, 2007; Wlodkowic et al., 2007b).

V. Time-Window in Measuring Incidence of Apoptosis Apoptosis is a stochastic event of a variable induction and execution kinetics. There is a short time-window when apoptotic cells display their characteristic features. Moreover, the induction and the onset of apoptosis vary strongly depending on the cell type. For instance HL-60 (human promyelocytic leukemia) and MCF-7 (human breast cancer) cells treated with the same DNA damaging agent can succumb to apoptosis between 2 and more than 24 h, respectively (Del Bino et al., 1999). In general, the induction time in cells of hematopoietic lineage is much shorter compared to other cell types, such as fibroblasts of cells of solid tumors lineage. This induction-to-execution interval profoundly varies depending on the stimulus applied (Li and Darzynkiewicz, 2000). Furthermore, the length of apoptosis (i.e., from the initiation to complete cell disintegration) is cell type dependent parameter. In vivo, under homeostatic conditions when cell death rate balances proliferation rate mitotic index (MI) is often seen to exceed apoptotic index (AI). This is an indication that duration of apoptosis is actually shorter than that of mitosis (the latter is about of 1 h duration) (Darzynkiewicz et al., 2004). In cell culture, however, apoptotic cells remain detectable for extended periods of time before complete disintegration. This reflects lack of phagocytic clearance that characterizes homotypic cell culture conditions. Identification of apoptotic cells generally relies on a specific marker that is detectable in variable time intervals. Knowledge of time-windows when specific markers are being detected is, thus, essential for the rational use of the methodology. In this context, loss of the mitochondrial transmembrane potential appears to be initially a transient event, followed by permanent collapse later during apoptotic cascade (Li et al., 2000). Depolarization of mitochondrial membrane is followed by activation of caspases while binding of fluorescently labeled inhibitors of caspases (FLICA) substantially precedes externalization of PS (Pozarowski et al., 2003; Wlodkowic et al., 2006). In HL-60 cells challenged with DNA damaging agents, for example, DNA fragmentation follows caspase activation indirectly detected by cleavage of PARP, by approximately 20 min (Li and Darzynkiewicz, 2000). Furthermore, at early stages of apoptosis cells negative for the fractional DNA

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content (sub-G1 fraction) may be positive in TUNEL assay and expose PS residues (Darzynkiewicz et al., 2001a). Common application of cytometry is a comparison made between incidences of apoptosis (AI) in different samples (Darzynkiewicz et al., 1997, 2001a, 2004). This task is particularly problematic in view of the above-discussed variability and the snapshot measurement of AI may fail to target comparable time-windows of apoptosis. Observed AI indices may, thus, not reflect the actual differences in apoptosis incidence between samples (Darzynkiewicz et al., 2004; Smolewski et al., 2002). Attempts have recently been made to obtain the cumulative AI, by measuring the rate (kinetics) of cell entrance into apoptosis and preventing disintegration of apoptotic cells (Smolewski et al., 2002). The alternative solution is to count the absolute number of cells in culture and account for cell loss while estimating the AI based on specific markers (Darzynkiewicz et al., 2004; Pozarowski et al., 2003).

VI. Multiparameter Detection of Apoptosis: Choosing the Right Method As discussed before, in view of recent seminal discoveries, the universal term ‘‘apoptosis,’’ has a propensity to misinterpret the actual phenotype of cell suicide program (Leist and Jaattela, 2001; Zhivotovsky, 2004). Nevertheless, single cytometric assays such as the estimation of sub-G1 fraction or Annexin V binding are still being exploited in many research articles. Moreover, data from such single assays are persistently referred to as ‘‘apoptotic cells.’’ One should remember, however, that positive identification of apoptotic cells is far from straightforward. Furthermore, the reliance on single cytometric readout without proper understanding of the underlying assay mechanistic may lead to profound artifacts. It was only recently proposed to define apoptosis as a ‘‘caspase-mediated cell death’’ (Blagosklonny, 2000). Logically, caspase activation would be the most specific marker of apoptosis (Shi, 2002). There are, however, many examples of cell death that resembles classical apoptosis yet there is no evidence of caspase activation (Joza et al., 2001; Leist and Jaattela, 2001; Lockshin and Zakeri, 2002). Extensive DNA fragmentation is also considered as a specific marker of apoptosis. The number of DSBs in apoptotic cells is usually so large that intensity of their labeling in the TUNEL assay ensures their discrimination from the cells that underwent primary necrosis (Gorczyca et al., 1992). As mentioned, the high degree of phosphorylation of histone H2AX on Ser139 in apoptotic cells makes it also possible to positively identify them (Kurose et al., 2005). There are, however, mushrooming examples where apoptotic or apoptotic-like cell death proceeds without extensive internucleosomal DNA degradation (Catchpoole and Stewart, 1993; Cohen et al., 1992; Collins et al., 1992; Knapp et al., 1999; Ormerod et al., 1994). In these instances, the intensity of cell labeling in TUNEL assay will be inadequate to positively identify apoptotic cells. Furthermore, estimation of the sub-G1 fraction fails when DNA degradation does not proceed to internucleosomal regions but stops after generating 50–300 kb

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fragments (Oberhammer et al., 1993). Little DNA can be extracted then from the cells and rigid reliance on this method provides false-negative results (Darzynkiewicz et al., 2001a, 2004). Noteworthy, if G2/M or even late S-phase cells undergo apoptosis, the loss of DNA from these cells may not produce the sub-G1 peak. These apoptotic cells often end up with DNA content equivalent to G1/early S phase and are, thus, indistinguishable (Darzynkiewicz et al., 2001a, 2004). Finally, while nuclear fragmentation is commonly observed during apoptosis of hematopoietic lineage cells, it may not occur in cells of epithelial- or fibroblast-lineage. Likewise, cell shrinkage, at least early during apoptosis, is not a universal marker of the apoptosis or necrosis, which has been discussed earlier in this chapter. There are many other difficulties and potential pitfalls in analysis of classical apoptosis by flow cytometry (thoroughly reviewed in Darzynkiewicz et al., 2001a, 2001b, 2004). Cell harvesting by trypsinization, mechanical or enzymatic cell disaggregation from tissues, extensive centrifugation steps, may all lead to preferential loss of apoptotic cells. On the other hand, some cell harvesting procedures interfere with apoptotic assays as discussed earlier in this chapter. Because of cell shrinkage the density of apoptotic cells is markedly increased while volume is diminished. This change should be taken under consideration, for example, when isolating cells by density (ficoll-hypaque, percoll) gradient centrifugations or elutriation. The most common problem, however, is the inability to distinguish late apoptotic cells (called also ‘‘necrotic phase of apoptosis’’ or ‘‘secondary necrosis’’) from the primary necrosis (accidental cell death). In both cases, the integrity of plasma membrane is lost and the cells cannot exclude cationic dyes such as propidium iodide or Trypan blue. The loss of cell surface antigens during apoptosis creates another problem in the studies aimed to identify the lineage of apoptotic cells by their immunophenotype (Philippe et al., 1997; Potter et al., 1999; Schmid et al., 1992). Antigen loss often occurs at early stages of apoptosis and selectively depends on the antigen and the inducer of apoptosis. Therefore, regardless of the apoptotic marker used, the attempts to identify lineage of apoptotic cells by immunophenotyping are prone to significant errors. All these potential pitfalls together with means to avoid them are discussed in extent elsewhere (Darzynkiewicz et al., 2001a, 2001b). Perhaps the most important feature of flow cytometry is the capability of multiparameter gating analysis, which allows one to quantitatively correlate, within the same cell, expression of several measured attributes. Divergent cellular processes can be, therefore, simultaneously assessed, which has profound practical implications in cell necrobiology studies. For instance, since DNA content is the most frequently measured attribute, the expression of other parameters can be then directly related to the position in the cell cycle phase and/or to DNA ploidy of the tumor cell population. Thus, flow cytometry overcomes a limitation of traditional bulk techniques based on analysis of total cell population (such as fluorimetry, spectrophotometry, Western blots, etc.) that average the results from heterogeneous samples. As discussed above, the preference of an optimal multiparametric method depends on the cell type, stimuli, desired information, and technical restrictions. For

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example, the need for sample transportation or prolonged storage prior to analysis requires cell fixation. This excludes the use of ‘‘supravital’’ methods such as the assays of plasma membrane integrity (exclusion of YO-PRO 1, PI, 7-AAD), PS exposure (Annexin V binding) or dissipation of mitochondrial membrane potential (JC-1, TMRM). At the same time, however, the cell fixation allows to obtain information on the cell cycle phase specificity of apoptosis by concurrent analysis of cellular DNA content. In this context considerable progress has recently been made by the introduction of amine reactive viability dyes (ViD) (InvitrogenMolecular Probes; Prefetto et al., 2006). These allow for a convenient discrimination of cells with intact and damaged plasma membrane in fixed specimens. Reportedly, ViD probes span a broad range of visible excitation and emission spectra. Their uptake by cells with permeabilized membranes is followed by covalent binding to cytoplasmic amine residues that withstand formaldehyde fixation and alcohol permeabilization procedures (Prefetto et al., 2006). Technical restrictions of the cytometer, such as a single laser excitation source, few fluorescence detectors may further limit number of multiplexing possibilities. Importantly, restricted number of organic fluorochromes that have nonoverlapping spectra hampers broader introduction of multiparameter approaches. This impasse has recently been superseded by the development of semiconductor nanocrystals (QDs). Their unrivalled specifications such as prolonged stability, reduced photobleaching, broad excitation with narrow emission spectra are poised to profoundly transform multicolor cell analysis (Jaiswal and Simon, 2004; Jaiswal et al., 2003). Successful attempts have already been made to implement semiconductor nanocrystals in multiparameter flow cytometry (Chattopadhyay et al., 2006). Undoubtedly, future applications of QDs in multiplexed cytometric detection of apoptosis are of substantial commercial interest (Dicker et al., 2005; Le Gac et al., 2006).

VII. Beyond Apoptosis – Analysis of Alternative Cell Death Modes Although detection of classical, caspase-dependent apoptosis is still the major ground for the advancement of cytometric techniques there is an increasing demand for novel analytical tools that can rapidly quantify noncanonical modes of cell death. Although still a matter of debate, these noncanonical pathways appear to have wide reaching connotations in pathogenesis and treatment of human diseases (Edinger and Thompson, 2004; Lockshin and Zakeri, 2001; Okada and Mak, 2004). Moreover, they present an increasingly complex network of molecular cross-talks reflecting in a diversity of phenotypes. A. Autophagy Autophagy is an intracellular bulk degradation system for long-lived proteins and whole organelles (Meijer and Codogno, 2009). Emerging evidence suggests that

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while autophagy may enhance survival of cancer cells exposed to nutrient deprivation, hypoxia or certain chemotherapeutics, it may also contribute to cell death when induced above an acceptable for cellular homeostasis threshold (EisenbergLerner et al., 2009). Accurate estimation of autophagosome formation and/or functional catabolic autophagy is, therefore, important for preclinical drug screening (Corcelle et al., 2009; Vousden and Ryan, 2009). To date only a handful of methods have been introduced to quantify autophagy, including electron and fluorescent microscopy to follow steady-state accumulation of autophagosomes, and long-lived protein degradation assay to access the catabolic autophagic activity (Gurusamy and Das, 2009; Swanlund et al., 2010). Fluorescent microscopy is generally used to follow autophagosome accumulation using markers such as LC3 protein tagged with fluorescent protein GFP. In this assay, after induction of autophagy, cytoplasmatically localized LC3-I is cleaved and lipidated to form LC3-II. The latter is associated with the formation of an isolation membrane (Gurusamy and Das, 2009). Using, for example, adenoviral delivery of LC3-GFP it is possible to follow the changes in LC3-GFP distribution from diffuse cytoplasmic into punctuate, the latter indicative of autophagosome accumulation with reasonable precision. Current methods designed to quantify autophagic activity using LC3 are, however, time consuming, labor intensive, and require substantial expertise in accurate data interpretation (Shvets and Elazar, 2009; Shvets et al., 2008). Several attempts have recently been made to quantify autophagy in cells stably expressing GFP-LC3 reporters using flow cytometry (Shvets and Elazar, 2009; Shvets et al., 2008). Flow cytometry collects, however, only integrated fluorescence over each cell. This in turn is generally not sensitive enough to detect subtle redistribution at a subcellular level. More recently, a successful attempt has been made to employ the multispectral imaging flow cytometry to quantify autophagosome formation (Lee et al., 2007). Authors utilized the ‘‘virtual sort’’ capability to enumerate cells exhibiting the bright, punctuate spots of GFP-LC3. The inflow imaging is the first example of an automated and unbiased detection of autophagy in rare subpopulations of cells (Lee et al., 2007). Surprisingly there have been no attempts to adapt Laser Scanning Cytometry (LSCTM , CompuCyte Corp, Cambridge, MA, USA) for multivariate quantification of autophagosome formation. LSC has many attributes of both flow cytometry and low-resolution image analysis that proved to be optimal for multiparameter studies of apoptotic cell death. We postulate that adaptation of LSC to detection of autophagy based on maximal pixel analysis of vesicular LC3-GFP protein might prove beneficial for high-throughput screening routines. By combining bivariate analysis of the DNA content and LC3-GFP redistribution one can potentially examine the cell cycle specificity of autophagosome formation, for example, in different tumor cell lines. Recently a new elegant solution has been proposed by Farkas et al. (2009) to measure the dynamics of autophagic flux. The design of a luciferase-based reporter assay (RLuc-LC3) allows to measure an autophagic flux in real time. Particular advantage of the RLuc-LC3 assay lies in a broad dynamic range and applicability to a dynamic analysis on cell population. This system has already been validated by

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screening a small-molecule kinase inhibitor library and results demonstrated its applicability for tracking of dose- and stimulus-dependent differences in autophagy kinetics (Farkas et al., 2009). B. Necrosis The recent discovery of alternative cell death modes such as necrosis-like PCD, necroptosis, and paraptosis calls for the development of new and robust markers to distinguish between molecularly divergent cell death processes (Br€ oker et al., 2005; Galluzzi and Kroemer, 2008; Hetz et al., 2005; Krysko et al., 2008). As discussed previously in this article, the cell impermeant DNA binding dyes such as PI, YOPRO 1, or SYTOX are very convenient markers for the detection of accidental cell death (primary necrosis) and late stages of apoptosis. They all fail, however, to distinguish whether the labeled population is of late apoptotic, primary necrotic, or necrosis-like PCD origin. Even in conjunction with other probes it is often a matter of speculation whether, for example, Annexin Vneg/PI+ or FLICAneg/PI+ population represents programmed necrotic phenomenon. This cannot be resolved by mere flow cytometric analysis. Recently, however, an innovative assay based on a high-mobility group B1 protein (HMGB1) has been proposed that can reportedly differentiate primary necrotic cells (Ito et al., 2006; Scaffidi et al., 2002). HMGB1 protein is an architectural chromatin-binding factor that bends DNA and promotes protein assembly on specific DNA targets (Scaffidi et al., 2002). It normally resides in the nucleus and is passively released when cells die during necrotic cell death. HMGB1 remains, however, tightly sequestered in cells undergoing caspase-dependent apoptosis or autophagic cell death (Fig. 9) (Ito et al., 2006; Scaffidi et al., 2002). Interestingly, even during secondary necrosis that follows caspase-dependent apoptosis cells do not release HMGB1 (Scaffidi et al., 2002). This unique process has been associated with the prevention of chromatin deacetylation during necrosis (Scaffidi et al., 2002). As such immunohistochemical detection of HMGB1 can be readily applied in both flow cytometry and imaging cytometry to detect and quantify cells undergoing primary and necrosis, necroptosis, and/or necrosis-like PCD (Fig. 9) (Ito et al., 2006; Krysko et al., 2008). C. Cell Senescence In many solid tumors the anticancer treatment instead of apoptosis induces irreversible impairment of cell reproductive capacity, which is defined either as ‘‘reproductive cell death,’’ ‘‘senescence-like growth arrest,’’ ‘‘accelerated senescence,’’ ‘‘premature senescence,’’ or ‘‘drug- or radiation-induced senescence’’ (Gerwitz et al., 2008; Ohtani et al., 2009). Overexpression of certain oncogenes and excessive mitogenic signaling can also lead to cell proliferation arrest characterized by senescence-like features. Both the induction of apoptosis as well as senescence play important role as the barriers to tumor development (Campisi, 2001). Normal cells become senescent in the course of organismal aging and also

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[(Fig._9)TD$IG]

Fig. 9

Selective detection of necrosis using monoclonal antibody against a high-mobility group B1 protein (HMGB1). (A) Human osteosarcoma U2OS cells cultured on glass coverslips were induced to undergo necrosis (freeze/thawing cycle) or apoptosis (Tet-On p53). Cells were then fixed and stained with DAPI (blue) and anti-HMGB1 antibody (red). Note that HMGB1 normally resides in the nucleus and is passively released when cells die during primary necrosis. It remains, however, tightly sequestered in cells undergoing caspase-dependent apoptosis. Immunohistochemical detection of HMGB1 can be readily applied in both flow cytometry and imaging cytometry to detect and quantify cells undergoing primary and necrosis, necroptosis, and/or necrosis-like PCD. (B) Specific release of HMGB1 from necrotic cells can be detected using Western blot (WB) analysis. Necrotic cell death was induced in U2OS cells by repeated freeze/thawing cycle. Both cells and medium were then harvested and protein extracts analyzed using WB. Note that HMGB1 band is detected in the medium. (See plate no. 1 in the color plate section.)

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after completion of certain number of cell divisions in cultures as a result of telomere shortening (Hayflick, 1985). Several markers characterize senescent cells. The most characteristic are morphological alterations (Cristofalo and Pignolo, 1993). Senescent cells show low saturation density at the plateau phase of growth, ‘‘flattened’’ appearance, enlarged, often irregular nuclei and cytoplasmic granularities. Their increased overall size is paralleled by an increase in nuclear and nucleolar size. They have numerous vacuoles in the cytoplasm, increased number of cytoplasmic microfilaments, the presence of large lysosomal bodies, and prominent Golgi apparatus (Cristofalo and Pignolo, 1993; Funayama and Ishikawa, 2007). The prominent abnormality of nuclear chromatin of senescent cells is the presence of senescence-associated heterochromatic foci (SAHF) that are abundant in histone H3 modified at lysine 9 (K9M H3) and its binding partner heterochromatin protein 1 (HP1) (Li et al., 2007). Senescent cells are also characterized by expression of CDKs inhibitors p21WAF1, p16, and p27KIP1; the feature common but not specific to these cells (Shen and Maki, 2010). Among all biomarkers of cell senescence the most specific are the characteristic changes in cell morphology and the induction of senescence-associated b-galactosidase activity, the latter considered to be the hallmark of cell senescence (Dimri et al., 1995). An excellent review of the cytometric methods to identify senescent cells is provided by Hwang and Cho (Chapter 7). Most recently, the imaging analytical capabilities of LSC have been used to assess morphological features considered to be typical of the senescent phenotype (Zhao et al., 2010). The characteristic ‘‘flattening’’ of senescing cells was represented by the decrease in the density of staining (intensity of maximal pixel) of DNA-associated fluorescence (DAPI). This change was paralleled by an increase in nuclear size (area). The decline in ratio of maximal pixel to nuclear area was even more sensitive senescence biomarker than the change in maximal pixel or nuclear area, each alone (Fig. 10). Also, the saturation cell density at plateau phase of growth recorded by LSC was found to be dramatically decreased in cultures of senescent cells, thereby additionally serving as a convenient marker (Zhao et al., 2010). This morphometric approach utilizing LSC complements other cytometric methods to identify senescent cells reviewed by Hwang and Cho (Chapter 7).

VIII. Future Outlook Development of novel bioassays was the driving force for the immense progress in research in cell necrobiology field during the past two decades (Darzynkiewicz et al., 1997, 2001b, 2004). Paradoxically, despite all the advances in flow cytometry the morphological changes defined by light and electron microscopy back in 1972 are still being considered to be the ‘‘gold standard’’ for the identification of cellular demise mode. Although detection of classical, caspase-dependent apoptosis is still the major ground for the advancement of cytometric techniques there is an increasing demand for novel analytical tools to rapidly quantify noncanonical modes of cell death.

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[(Fig._0)TD$IG]

Fig. 10 LSC-assisted morphometric analysis of nuclear changes of A549 cells in cultures treated with mitoxantrone (Mxt). The cells were untreated (Ctrl) or to induce cell senescence treated with 2 nM Mxt for 24, 48, or 72 h, their DNA was stained with DAPI. Intensity of maximal (max) pixel of DNA/DAPI reveals degree of chromatin condensation and in untreated cells has the highest value in mitotic (M) and immediately postmitotic (pM) cells (shown by the arrows). In the cells undergoing senescence while nuclear area increased, the intensity of maximal pixel decreased likely due to the ‘‘flattening’’ of the cell. The insets in the top left panels show DNA frequency histograms of cells from the respective cultures. The bar plots at the bottom panels show mean values (SD) of nuclear DNA/DAPI area, DNA/DAPI maximal pixel, and the ratio of maximal pixel to nuclear area. The ratio of maximal pixel/nuclear area of the Mxt-treated cells is expressed as a fraction of such ratio of the untreated cells (Ctrl = 1.0) (Zhao et al., 2010).

It can be expected that novel technologies and instrumentation like LSC and cell imaging in flow (multispectral imaging cytometry) are just a prelude to a major transformation that cytometric field will experience in the coming years (Darzynkiewicz et al., 1999; Deptala et al., 2001; George et al., 2004; Smolewski et al., 2001). Here especially the LSC by having many attributes of both flow cytometry and low-resolution image analysis appears to be an optimal instrumentation for multiparameter studies on cell demise (Bedner et al., 1999; Darzynkiewicz et al., 1999; Kamentsky, 2001; Zhao et al., 2010). Application of nanocrystal quantum dots (Qdots) as convenient multispectral markers (Alivisatos et al., 2005) will also contribute toward expansion of cytometric methods in necrobiology. Furthermore, we expect to witness soon the massive rise of microand nanotechnologies that form a cornerstone for Lab-on-a-Chip platforms. Although still in their infancy the latter technologies warrant a major ‘‘quantum leap’’ in studies of cell death at a single cell level (Chan et al., 2003; El-Ali et al., 2006; Huh et al., 2005; Qin et al., 2005).

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90 Acknowledgements

The views and opinions described in this chapter were not influenced by any conflicting commercial interests. The study is supported in part by NCI RO1 28 704.

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CHAPTER 5

Assessment of Oxidative Stress-Induced DNA Damage by Immunoflourescent Analysis of 8-OxodG Soo Fern Lee* and Shazib Pervaiz*,y,z,x *

Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore y NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore z Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, Singapore x Singapore-MIT Alliance, Singapore

Abstract I. Redox Regulation of Cell Fate Signaling II. 8-OxodG as a Marker of Oxidative DNA Damage A. 8-OxodG is a Highly Mutagenic DNA Lesion B. 8-OxodG as a Biomarker of Cellular Oxidative Damage and its Clinical Relevance III. Detection of Oxidative DNA Damage Involving 8-OxodG A. Chromatography-Based Direct Assessment of 8-OxodG B. The Indirect Assessment of OxodG Modification of DNA IV. Concluding Remarks References

Abstract Oxidative stress refers to the imbalance between the generation of reactive oxygen species (ROS) and their scavenging by the inherent antioxidant defenses of the cell. The abnormal accumulation of ROS is the underlying pathology in a variety of human diseases such as neurodegenerative phenomena, inflammatory diseases, METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

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0091-679X/10 $35.00 DOI 10.1016/B978-0-12-385493-3.00005-X

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Soo Fern Lee and Shazib Pervaiz

metabolic disorders, and cancer. The mechanism by which abnormal accumulation of ROS contributes to pathological conditions involves damage or oxidative modification of biomolecules, such as nucleotides, lipids, and proteins. One of the most common targets of ROS is DNA, modifications of which have been associated with cellular transformation and genome instability. There are a number of experimental strategies to assess oxidative modification of DNA bases, such as chromatographybased assays and indirect immunofluorescence. While the former provide quantitative assessment of oxidative modification, the latter is a much simpler assay for qualitative determination of DNA base modification in very small sample sizes. Here, we present a brief background of the various methodologies for the assessment of a specific oxidative DNA modification, 8-oxodG, and present a more detailed account of the indirect immunofluorescence assay.

I. Redox Regulation of Cell Fate Signaling Growth homeostasis is a function of a tight balance between the rates of cell proliferation and cell loss, regulated by diverse processes and/or signaling networks, such as kinases and phosphatase, cyclins and cell cycle inhibitors, oncogenes and tumor suppressors, proteases and lysosomal factors, transcription enhancers and repressors, ion transporters, and DNA damage and repair sensing machinery. Although, these pathways are responsive to a myriad of extracellular and intracellular signals, evidence is accumulating to implicate altered cellular redox status as an important effector mechanism underlying cellular response to stress signals. The cellular redox status is a product of the rates at which intracellular reactive oxygen species (ROS) are generated and the efficiency with which they are scavenged or decomposed. There is now evidence that depending on their intracellular levels and the nature of the ROS, the effects could be as diverse as activation of gene transcription and proliferation, DNA damage, and cell death induction (Droge, 2002). Along these lines, a mild increase in intracellular ROS, in particular O
2 , promotes cell survival by inhibiting apoptotic execution and by activating prosurvival signaling (Ahmad et al., 2003; Clement et al., 2003; Clement and Stamenkovic, 1996; Pervaiz et al., 2001). Contrarily, an increase in intracellular hydrogen peroxide facilitated death execution by creating a permissive intracellular milieu for protease activation (Ahmad et al., 2004; Clement and Pervaiz, 2001; Clement et al., 1998; Hirpara et al., 2001). The link between a prooxidant state and cell survival was further corroborated by findings from oncogene-induced models of cell transformation, such as activated Rac, Bcl-2, and Akt/PKB (Clement et al., 2003; Lim and Clement, 2007; Pervaiz et al., 2001). As a result, an abnormal ROS accumulation has been implicated in the pathogenesis of various diseases, including cancer, atherosclerosis, diabetes mellitus, and neurodegenerative disorders, and in oxidative protein damage associated with age-related sarcopenia (muscle wasting, reviewed in Droge, 2002).

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Although, the intracellular targets of ROS are varied and diverse, one of the most frequent modifications associated with oxidative stress is damage to DNA. Not only is DNA damage an invariable finding in cancer but also is strongly linked to genome instability, an acquired hallmark of cancer. One of the most common targets of oxidation-induced DNA modification is the guanine nucleotide resulting in the formation of 8-oxo-7,8-dihydroguanine (8-oxoG) or its nucleoside form 8-oxo7,8-dihydro-20 -deoxyguanosine (8-oxodG) (Steenken and Jovanovic, 1997; Kanvah et al., 2010; Steenken, 1989). Here, we will focus on this oxidative modification of DNA, its biology, and methods for its detection as an index of DNA damage.

II. 8-OxodG as a Marker of Oxidative DNA Damage Guanine has been shown to be the preferential target during oxidative DNA damage owing to its lowest oxidation potential at 1.29 V among the four bases (dA 1.42 V, dC 1.6 V, dT 1.7 V). Aside from this unique property conferred by the electron-rich purine structure, guanine also acts as a ‘‘hot spot’’ for electron migration. The oxidation of guanine as a result of exposure to OH radical, one-electron oxidants, and singlet oxygen (1O2) generally gives rise to the formation of 8-oxoG or its nucleoside form 8-oxodG (Steenken and Jovanovic, 1997; Kanvah et al., 2010; Steenken, 1989). As a matter of fact, mutational research of this modified nucleobase has led to the realization that approximately 0.5–5 lesions per 100,000 guanine residues in human cellular DNA are oxidized at C-8 (ESCODD, 2003; Loft et al., 2008; Ravanat et al., 2002). Furthermore, the presence of 8-oxodG within DNA has been shown to induce other base damages. Hence, the 8-oxoG lesions have to be removed by 8-oxoguanine glycosylase 1 (OGG1) rapidly in order to protect the integrity of neighboring bases (Radicella et al., 1997). Meanwhile, there is evidence showing that 8-oxodG is highly susceptible to further oxidation under physiological conditions and therefore serves as an excellent ‘‘electron sink’’ during oxidative damage. This is explained by the fact that upon oxidation to 8-oxodG, the oxidation potential is further lowered to 0.74 V, thereby making this species highly reactive toward various radical species compared to other unmodified bases and highly prone to oxidation (Cadet et al., 2008; Steenken et al., 2000). This further oxidation and other chemical transformations of 8-oxodG can lead to the formation of a variety of secondary DNA adducts. For instance, through 1 O2 or peroxynitrite (OONO
)-mediated oxidation, 8-oxodG is further oxidized to yield lesions such as cyanuric acid, oxaluric acid, and oxazolone. In addition, 5-guanidinohydantoin and 2-imino-5,50 -spirohydantoin could also be generated from the one-electron oxidation of 8-oxoG or through direct oxidation and photooxidation of guanine. As these secondary lesions are significantly more mutagenic than the primary lesion, 8-oxodG (Duarte et al., 2001; Gasparutto et al., 1999; Henderson et al., 2002, 2003; Luo et al., 2000; Tretyakova et al., 1999), the chain reaction triggered upon oxidative stress-induced nucleotide modification could either facilitate transformation or severely impact the cell fate.

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A. 8-OxodG is a Highly Mutagenic DNA Lesion Normal homeostatic growth is maintained by efficient and elaborate DNA repair machinery that ensures error-free DNA delivery through the cell cycle. A defect and/ or deficiency in the repair pathways promotes DNA mutations such as base substitution, deletions, or strand fragmentation resulting in severe genomic instability, an acquired hallmark of most cancer cells. Therefore, the fact that 8-oxodG and its oxidative byproducts are highly mutagenic compared to the other oxidatively modified DNA adducts, 8-oxodG has been receiving heightened attention as a bonafide marker of oxidative stress-induced DNA damage. The mutagenic potential of 8-oxodG stems from its miscoding properties. By default, most of the DNA damage lesions stall or block the progression of DNA replication, yet such high-fidelity mechanism is not always fail-proof, especially in the case of 8-oxodG modification. Studies carried out using the primer extension method demonstrated that dAMP and dCMP could be incorporated opposite 8-oxodG in vitro (Cheng et al., 1992; Shibutani et al., 1991). Furthermore, a study based on the crystal structure revealed that 8-oxodG induced an inversion of the mismatch recognition mechanism that should normally proofread DNA; the 8-oxodG:dA base pairing resembles a Watson–Crick base pair and is thereby exempted from removal by the 30 –50 exonuclease activity during the DNA polymerase error detection process (Hsu et al., 2004). Therefore in living cells, if the 8-oxodG lesion remains unrepaired before replication, the lesion itself is very likely to result in G:C ! T:A transversion, a common somatic mutation observed in human cancers (Kamiya et al., 1995). In addition to the G:C ! T:A transversion promoted by 8-oxodG, there is also the potential risk of 8-oxodG causing A:T ! C:G transversion in the genome. More importantly, as mammalian DNA is constantly exposed to ROS, the dGTP of the entire dNTP pool could also be a potential target as the spontaneous oxidation of dGTP results in the formation of 8-oxodGTP. The presence of 8-oxodGTP in the dNTP pool is potentially disastrous as it can be incorporated by the DNA polymerases opposite the dC or dA residues of the template DNA with almost equal efficiency, which eventually results in the A:T ! C:G transversion during the subsequent round of replication (Cheng et al., 1992; Maki and Sekiguchi, 1992).

B. 8-OxodG as a Biomarker of Cellular Oxidative Damage and its Clinical Relevance Ever since the first discovery by Kasai and Nishimura in 1984 that deoxyguanosine could be readily oxidized to 8-oxodG, this pivotal finding has spurred research in the area of oxidation-induced DNA damage (Kasai et al., 1984). These efforts have yielded remarkable results with tremendous progress being made not only in unraveling the mechanism of 8-oxodG formation but also in the development of more sensitive and accurate assays to analyze modified nucleobases in biological samples. Of note, due to the high potential risk of promoting the G:C ! T:A

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transversion in cellular DNA, the 8-oxodG lesion is believed to contribute significantly to the spontaneous mutations in human cancers. Therefore, 8-oxodG has emerged as an important biomarker both for evaluating cellular oxidative damage and for assessing cancer risk associated with exposure to carcinogens or environmental pollutants. For instance, carcinogens such as aflatoxin B1, ethylbenzene, pentachlorophenol, and benzo(a)pyrene have been shown to induce the formation of 8-oxodG both in animal organs and in cell lines (Briede et al., 2004; Lin et al., 2002; Midorikawa et al., 2004; Shen et al., 1995; Umemura et al., 1999). In similar in vivo studies, cigarette smoking was shown to induce oxidative DNA damage to a variety of tissues, primarily the lungs (Izzotti et al., 1999; Park et al., 1998); cigarette smoking is known to induce excessive amount of ROS production. In agreement with that, higher levels of 8-oxodG have been reported in the lungs, as well as in leukocyte DNA and urine, of cigarette smokers (Asami et al., 1997) compared to non-smokers. Of note, there appears to be a positive correlation between the number of cigarettes smoked per day and the 8-oxodG content, which strongly implicates ROS as one of the major contributing factors in cigarette smoking-induced lung carcinogenesis (Lodovici et al., 2000; Malayappan et al., 2007; Vulimiri et al., 2000). Further support for the critical role of oxidative DNA damage in cellular transformation and carcinogenesis is provided by studies on workers exposed to the genotoxic carcinogen, asbestos (Kamp, 2009). Experimental studies in cell cultures had identified that asbestos-induced oxidative DNA injury by producing hydroxyl radicals and other ROS (Kamp et al., 1992; Shukla et al., 2003). This finding was supported by data from epidemiological studies showing that high levels of 8-oxodG lesions were detected both in the leukocyte DNA and in the urine of workers chronically exposed to asbestos (Takahashi et al., 1997; Yoshida et al., 2001). Collectively, these studies provide evidence that the presence of oxidative DNA damage, in particular 8-oxodG lesions, is strongly correlated with exposure to genotoxic and/or mutagenic agents. The aforementioned studies provide ample evidence that the presence of 8-oxodG is an indicator of oxidative stress-induced DNA damage upon exposure to carcinogenic agents. However, due to the close association between 8-oxodG DNA lesions and carcinogenesis, 8-oxodG has emerged as a potentially reliable biomarker for predicting an individual’s cancer risk associated with oxidative stress. This is supported by clinical data from patients with various malignancies that indicate significantly higher 8-oxodG levels than the healthy control counterparts. Similarly, biopsy specimens from patients with cervical intraepithelial neoplasia (CIN) with human papilloma virus (HPV) displayed higher levels of 8-oxodG, and more importantly, 8-oxodG immunoreactivity correlated significantly with the CIN grade. Interestingly, upregulation of inducible nitric oxide synthase (iNOS), the enzyme responsible for nitric oxide (NO) production, has also been reported in CIN specimens. These data indicate that oxidative DNA injury as a result of chronic inflammation induced by the high-risk HPV viral infection is one of the factors contributing to cervical dysplasia and carcinogenesis (Hiraku et al., 2007).

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Chronic inflammation is also associated with an increase in incidence of cancer development and invasion. For example, intrahepatic cholangiocarcinoma (ICC) is an adenocarcinoma originating from intrahepatic bile duct epithelium, primarily caused by Opisthorchis Viverrini (OV) infection. Interestingly, a significantly higher level of 8-oxodG was detected in the cancerous tissue compared to the noncancerous surrounding tissue in biopsy specimens from affected patients (Pinlaor et al., 2005). Similarly, oxidative DNA damage has also been associated with Helicobater pylori infection and in the hepatocytes of patients with chronic hepatitis C. Of note, H. pylori infection is strongly associated with gastric carcinoma, while chronic hepatitis predisposes to the development of hepatocellular carcinoma (Horiike et al., 2005; Ma et al., 2004). Oral lichen planus (OLP) is a chronic inflammatory mucosal disease. Of note, significantly higher accumulation of 8-oxodG was identified in the oral epithelium of OLP- and oral squamous cell carcinoma (OSCC)-affected individuals. In turn, this finding relates the pathogenesis of OLP to the initiation of oral cancer development, which strongly suggests that the chronic inflammatory state in OLP plays a key role in disease progression (Chaiyarit et al., 2005). A recent report demonstrated elevated levels of 8-oxodG in leukocytes of patients carrying BRCA1 mutation that are at high risk of breast and ovarian cancer. In the report, it was suggested that the elevated 8-oxodG level in the BRCA1 carrier resulted from the deficiency in DNA repair. Thereby, one of the key mechanisms responsible for the pathogenesis of cancer development in women with BRCA1 mutation could be likely attributed to the DNA repair deficiency-mediated 8-oxodG accumulation in the cellular genome, which in turn promotes tumor progression via a subsequent series of mutations (Dziaman et al., 2009). Similarly, analysis of leukocyte DNA isolated from patients suffering from upper digestive tract cancer and colon cancer, and tissues from breast cancer patients show significantly higher level of 8-oxodG compared to the healthy control groups. These data confirm the strong correlation between the extent of oxidative DNA damage and the onset of tumorigenesis (Breton et al., 2005; Li et al., 2001; Obtulowicz et al., 2010). As such, 8-oxodG could serve as an important biomarker for cancer risk assessment and in the early diagnosis of the disease.

III. Detection of Oxidative DNA Damage Involving 8-OxodG The methodology for analysis of oxidative DNA damage in the form of 8-oxodG is generally divided into two categories: the first type of assay is the more direct approach that uses both physical and chemical methods to single out the DNA lesion from cells and tissues followed by quantitative analysis performed by one of the three chromatographic methods, that is, gas chromatography-mass spectrometry (GC-MS) (Dizdaroglu, 1994), HPLC coupled with electrochemical detection (HPLC-EC) (Shigenaga et al., 1994), or HPLC electrospray tandem mass spectrometry (HPLC-MS/MS) (Ravanat et al., 1998a). It is worth mentioning that the GC-MS

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assay was first introduced more than 20 years ago to measure the level of oxidized bases. Since then, notable efforts have been made in the development of more sensitive and accurate methods for detection of oxidative DNA lesions. Subsequently, HPLC-EC was used to replace the skeptical GC-MS method, which was followed by the development of the more precise HPLC-MS/MS technique. The second type of assay uses mainly an indirect approach whereby the whole DNA structure is usually preserved during the process and DNA lesions are detected in situ. The measurement of DNA lesions is normally carried out by enzymatic assays, which require the use of DNA repair enzymes to reveal the formation of oxidized bases in the DNA. A. Chromatography-Based Direct Assessment of 8-OxodG Initial reports highlighted discrepancies in the results reported from direct analysis of 8-oxodG modification of DNA using the various chromatographic techniques. This was mainly attributed to variations in the experimental protocols and different isolation methodologies used among laboratories (Dizdaroglu, 1998; England et al., 1998; Jenner et al., 1998; Senturker and Dizdaroglu, 1999). Such inconsistency unavoidably resulted in the exaggeration of the background DNA oxidation level during measurements. Therefore, the European Standards Committee on Oxidative Damage was set up with the aim of establishing a standard guideline for the optimized conditions for DNA extraction and enzymatic hydrolysis (ESCODD, 2002; Lunec, 1998). Despite the formation of the committee, the GC-MS assay is becoming less favorable nowadays as this method requires a sample derivatization step, which has been shown to generate spurious oxidation on nucleobases resulting in an artifactual increase (almost two to three orders of magnitude) in the background oxidation levels (Cadet et al., 1997; Dizdaroglu, 1994; Ravanat et al., 1995). HPLC-EC is considered a sensitive and accurate assay, which can detect oxidized nucleobase with a sensitivity of 1 in 106 normal bases. However, this method requires a complicated setup and multiple runs per sample. In addition, this assay requires relatively large quantities of starting material for the analysis (Bogdanov et al., 1999; Germadnik et al., 1997; Ravanat et al., 1998b; Shigenaga et al., 1994), which limits the use of this method in clinical settings where the specimen size is usually a ratelimiting factor. The HPLC-MS/MS assay has been described as the best HPLC procedure among the few existing chromatographic approaches because of its high sensitivity and precision (Malayappan et al., 2007; Podmore et al., 2000; Ravanat et al., 1998a; Weimann et al., 2001). This is due to the fact that this procedure does not require the derivatization step or the extensive sample preparation that carry the risk of introducing artifactual oxidation. Although the equipment is much more expensive, compared to GC-MS and HPLC-EC, the HPLC-MS/MS is fast and easily automated. Besides, the greatest advantage of this technology is its ability to measure two and potentially more oxidative DNA products and at the same time provides

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unambiguous information on the identity of analytes, which makes it suitable for large-scale epidemiological studies. B. The Indirect Assessment of OxodG Modification of DNA The indirect approach for the measurement of oxidized nucleobases in cellular DNA requires the use of base excision DNA repair enzymes, such as the formamidopyrimidine DNA glycosylase, which converts the 8-oxodG lesions into strand breaks. The quantitative measurement of the strand breaks is subsequently determined by performing the comet assay, the alkaline elution technique, or the unwinding elution method (Collins et al., 1993, 1996; ESCODD, 2003; Hartwig et al., 1996; Pflaum et al., 1997). Compared to the chromatographic approaches, which generally involves lengthy DNA isolation and hydrolysis procedures that tend to give rise to oxidation artifact, this enzymatic in situ detection method gives an estimation of endogenous oxidative base damage at several fold lower than the estimation value from the HPLC techniques. Therefore, this assay is more suitable for detecting low level of oxidative base damage, which could be close to a few lesions per 107 bases in a sample.

1. Indirect Assessment of Oxidative DNA Damage by Immunofluorescence Compared to the various approaches discussed above, the simplest and easiest methodology for the qualitative assessment of 8-oxodG modification of DNA is based on an immunofluorescence detection method. This assay relies on the use of a monoclonal antibody raised against 8-oxodG lesions, and similar to the enzymatic assays, the immunofluorescence detection method does not give rise to artifactual background oxidation during the sample preparation step. Of note, it provides in situ assessment of oxidative stress-induced DNA damage, which alleviates the problem of sample destruction associated with chromatography-based approaches. Furthermore, the analysis could be performed on a very small sample material (unlike some other techniques requiring relatively large quantity of DNA) with an average confocal microscope, thereby obviating the need for highly sophisticated and expensive equipments. Therefore, the immunofluorescence-based assay is very suitable for clinical research where the quantity of the starting material for DNA extraction and analysis is usually a rate-limiting factor. Also, the relative simplicity of the assay has conferred on this approach broader applications in different fields of studies such as oxidant and antioxidant research. For example, this technique can be used to study genes associated with antioxidant defenses at the cellular level and to identify potential agents that can cause extensive oxidative DNA damage. Furthermore, since oxidative DNA damage is generally perceived as the leading cause in the initiation and promotion of tumorigenesis, this relatively simple and easy-to-use assay could be employed for screening individuals at high risk of oxidative stress-induced DNA insults, such as those exposed to industrial pollutants, cigarette smoke, and other potential mutagens.

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Materials 1. HCT116 human colorectal carcinoma cell line. 2. Culture medium: Mc Coy’s 5A (Invitrogen) supplemented with 10% FBS (Hyclone) and 5 mM glutamine solution. 3. Fixative: cold acetone stored at
20  C. 4. RNase stock solution: 10 mg/mL in 10 mM Tris-HCl (pH 7.5), 15 mM NaCl. 5. HCl solution: 2 N HCl in distilled water. 6. Washing solution: phosphate buffered saline (PBS). 7. Blocking buffer: 2% bovine serum albumin (BSA) + 10% FBS in 1 PBS. 8. Primary antibody: anti-8-oxodG monoclonal antibody (clone 2E2, Trevigen). 9. Secondary antibody: goat anti-mouse IgG Alexa 568 (Molecular Probes). 10. Nuclear staining solution: DAPI (Molecular Probes) diluted in antifade mounting media at 1:1000. 11. Antifade mounting media: Vectashield (Vector Lab).

Methodology 1. HCT116 cells were seeded on glass slides in a six-well plate until 80–90% confluency. 2. The culture medium was removed and replaced with fresh growth medium. 3. Cells were then treated with the ROS-producing agent, such as hydrogen peroxide (H2O2) for 2–4 h at 37  C. 4. Following incubation, the medium was removed and cells were washed once with 1 PBS. 5. Cells were then fixed with cold acetone at
20  C for 15 min. 6. The slides were transferred to new container and washed two times (5 min each with shaking) with 1 PBS at room temperature. 7. Cells were treated with the RNase solution (final concentration at 100 mg/ml) for 1 h at 37  C to get rid of the RNA. 8. Slides were washed three times (3 min each with shaking) with 1 PBS at room temperature. 9. To denature DNA, the slides were treated with 2 N HCl solution for 10 min at room temperature. 10. The slides were washed three times (5 min each with shaking) with 1 PBS at room temperature. 11. The slides were kept in the blocking buffer for at least 1 h with gentle shaking. (Slides can be kept in blocking buffer for overnight at 4  C with gentle shaking.) 12. Slides were then washed with 1 PBS for 5 min at room temperature with moderate shaking. 13. A working dilution (1:300) of the primary antibody (mouse monoclonal anti8-oxodG) was prepared in the blocking buffer. 14. The slides were incubated with the primary antibody for 2 h at room temperature with gentle shaking.

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15. Following incubation with the primary antibody, the slides were washed three times with 1 PBS (5 min each), at room temperature with moderate shaking. 16. A working dilution of the secondary antibody (1:500) was prepared in the blocking buffer. 17. The slides were incubated with the secondary antibody for 1 h at room temperature (in dark) with gentle shaking. 18. Following incubation with the secondary antibody, the slides were washed four times (5 min each) with 1 PBS, at room temperature with moderate shaking. 19. A working dilution of the DNA binding dye DAPI (1:1000) was prepared in the antifade mounting media. 20. The slides were mounted with the antifade mounting media (from step 19). 21. The edges of the slides were sealed with nail polish after the mounting media had completely dried. 22. The slides were stored in a humidified chamber at 4  C until analysis by confocal microscopy.

Results HCT116 cells were treated with 2 mM H2O2 for 2 h and the cells were prepared and analyzed as described above. Results show that exposure to H2O2 resulted in the increased reactivity with anti-8-oxodG (Fig. 1B), shown as the increase in red

[(Fig._1)TD$IG]

Fig. 1

ROS-induced 8-oxodG lesion. HCT116 cells were incubated in the absence (A) or presence (B) of 2 mM H2O2 for 2 h. Modified DNA bases were detected by the use of antibody that specifically recognizes 8-oxodG. Goat anti-mouse IgG(H+L) Alexa Fluor 568 was used as the secondary antibody for detection by immunofluorescence.

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fluorescence (Alexa Fluor 568) compared to the untreated cells (Fig. 1A). These results highlighted the effect of oxidative stress on 8-oxodG modification of DNA and could be very useful as a screening assay.

Shortcomings of the Immunofluorescence Assay Although an easy and simple assay with potential for use as a screening tool, the immunofluorescence detection method does have its pitfalls. Unlike the chromatographic measurements or enzymatic assays whereby the amount of 8-oxodG lesions in the DNA is quantifiable at high sensitivity and accuracy, the immunofluorescence assay for 8-oxodG detection provides mainly a qualitative assessment of the oxidized bases. Despite this limitation, this method is a reliable and simple to use with cultured cell lines and/or clinical tissues for determining the presence of oxidative DNA injury.

IV. Concluding Remarks In conclusion, the chromatographic approaches, which comprise of HPLC-EC, HPLC-MS/MS, and GC-MS, are frequently used in experimental studies to detect any abnormal changes in nucleobases due to their extremely high sensitivity and specificity. This preference is especially reflected by the increasing popularity with the use of HPLC-MS/MS method, which is showing an additional advantage by giving unambiguous information on the identity of the analytes. However, the chromatographic approaches do have imperfections and therefore find limited application under certain circumstances. In general, these approaches are sample-destructive and issues associated with high background oxidation that always give rise to overestimations can be quite difficult to tackle. On the other hand, the enzymatic approaches do not have significant issues with the high background oxidation problem. Although these approaches are more suitable for the assessment of very low amount of inflicted base damage, there is still concern regarding the way the calibration is performed for the extent of DNA nicks, with the hope of developing a more feasible protocol in near future (Cadet et al., 2003). References Ahmad, K. A., Clement, M. V., and Pervaiz, S. (2003). Pro-oxidant activity of low doses of resveratrol inhibits hydrogen peroxide-induced apoptosis. Ann. N. Y. Acad. Sci. 1010, 365–373. Ahmad, K. A., Iskandar, K. B., Hirpara, J. L., Clement, M. V., and Pervaiz, S. (2004). Hydrogen peroxidemediated cytosolic acidification is a signal for mitochondrial translocation of Bax during drug-induced apoptosis of tumor cells. Cancer Res. 64, 7867–7878. Asami, S., Manabe, H., Miyake, J., Tsurudome, Y., Hirano, T., Yamaguchi, R., Itoh, H., Kasai, H. (1997). Cigarette smoking induces an increase in oxidative DNA damage, 8-hydroxydeoxyguanosine, in a central site of the human lung. Carcinogenesis 18, 1763–1766.

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CHAPTER 6

Analysis of Individual Molecular Events of DNA Damage Response by Flowand Image-Assisted Cytometry Zbigniew Darzynkiewicz*, Frank Traganos*, Hong Zhao*, H. Dorota Halicka*, Joanna Skommery and Donald Wlodkowicz * Brander Cancer Research Institute and Department of Pathology, New York Medical College, Valhalla, New York, USA y

School of Biological Sciences, University of Auckland, Auckland, New Zealand

z

The BioMEMS Research Group, Department of Chemistry, University of Auckland, Auckland, New Zealand

Abstract I. Introduction II. Events of the DDR A. Chromatin Decondensation (Relaxation) B. Activation of Phosphatidyl Inositol 30 -Kinase-Related Kinases (PIKKs) C. Activation of Checkpoint Kinases D. Histone H2AX Phosphorylation III. Detection of DDR Events by Cytometry A. Chromatin Relaxation (Decondensation) B. Recruitment of Mre11 C. Immunocytochemical Detection of DDR-Associated ATM, DNA-PKcs and Chk2 Activation, Phosphorylation of p53 and Histone H2AX IV. Application of Cytometry to Detect DDR Induced by Different Genotoxic Agents A. Assessment of DDR Induced by DNA Topoisomerase Inhibitors B. Induction of DDR by Ionizing Radiation and UV Light C. Induction of DDR by Cigarette Smoke (CS) and Other Environmental Mutagens D. Oxidative DNA Damage

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0091-679X/10 $35.00 DOI 10.1016/B978-0-12-385493-3.00006-1

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V. Interpretation of Cytometric Data: Role of Image-Assisted Cytometry VI. Role of Microfluidic Lab-on-a-Chip Platforms for DDR Analysis References

Abstract This chapter describes molecular mechanisms of DNA damage response (DDR) and presents flow- and image-assisted cytometric approaches to assess these mechanisms and measure the extent of DDR in individual cells. DNA damage was induced by cell treatment with oxidizing agents, UV light, DNA topoisomerase I or II inhibitors, cisplatin, tobacco smoke, and by exogenous and endogenous oxidants. Chromatin relaxation (decondensation) is an early event of DDR chromatin that involves modification of high mobility group proteins (HMGs) and histone H1 and was detected by cytometry by analysis of the susceptibility of DNA in situ to denaturation using the metachromatic fluorochrome acridine orange. Translocation of the MRN complex consisting of Meiotic Recombination 11 Homolog A (Mre11), Rad50 homolog, and Nijmegen Breakage Syndrome 1 (NMR1) into DNA damage sites was assessed by laser scanning cytometry as the increase in the intensity of maximal pixel as well as integral value of Mre11 immunofluorescence. Examples of cytometric detection of activation of Ataxia telangiectasia mutated (ATM), and Check 2 (Chk2) protein kinases using phospho-specific Abs targeting Ser1981 and Thr68 of these proteins, respectively are also presented. We also discuss approaches to correlate activation of ATM and Chk2 with phosphorylation of p53 on Ser15 and histone H2AX on Ser139 as well as with cell cycle position and DNA replication. The capability of laser scanning cytometry to quantify individual foci of phosphorylated H2AX and/or ATM that provides more dependable assessment of the presence of DNA double-strand breaks is outlined. The new microfluidic Labon-a-Chip platforms for interrogation of individual cells offer a novel approach for DDR cytometric analysis.

I. Introduction Intricate and highly choreographed series of molecular events broadly defined as the DNA damage response (DDR) take place in the live cell upon induction of DNA damage. The events of DDR involve a multitude of posttranslational modifications of proteins that trigger interactions between intracellular molecules activating several signaling pathways. Activation of these pathways has four critical aims: (i) stopping cell cycle progression and division and thereby preventing transfer of damaged DNA to progeny cells; (ii) enhancing accessibility of the damage site to the DNA repair machinery; (iii) activating and engaging repair machinery; and (iv) triggering apoptosis or inducing cellular senescence (reproductive cell death) to eliminate cells whose damaged DNA cannot successfully be repaired (reviews,

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Bakkenist and Kastan, 2003, 2004; Bonner et al., 2008; Helt et al., 2005; Kastan, 2008; Lee and Paull, 2005; Nakamura et al., 2010). This review briefly describes the molecular mechanisms of DDR and outlines applications of cytometry in analysis of particular events and stages of DDR.

II. Events of the DDR A. Chromatin Decondensation (Relaxation) One of the early events of the DDR is remodeling of chromatin structure that involves its decondensation (Murga et al., 2007; Pandita and Richardson, 2009; Rouleau et al., 2004; Ziv et al., 2006). Chromatin decondensation appears to be triggered by decline of torsional strain of the DNA double helix occurring upon DNA damage, particularly when the damage involves formation of DNA double-strand breaks (DSBs) (Fig. 1). DNA torsional strain (topological stress) is otherwise maintained by its winding onto histone octamers of the nucleosome core and supercoiling to form the supra-nucleosomal chromatin structure (Marko, 2010). High mobility group proteins (HMGs) play a key role in providing a rapid dynamic response by local decondensation of chromatin triggered by DNA damage (Gerlitz and Bustin, 2009; Kim et al., 2009; Sinha and Peterson, 2009). HMGs and histone H1 are persistently moving along the chromatin fiber and interacting with each other and with internucleosomal DNA. Compared with other nuclear proteins, HMGs are the most extensively modified, being rapidly phosphorylated, acetylated, methylated, ribosylated, and/or sumoylated in response to changes in the physiological state of the cell, induction of stress, or cell cycle phase (Zhang and Wang, 2008). This network provides a continuous highly dynamic interplay between a variety of nuclear structural proteins, modulating their binding to each other and to nucleosomes (Lim et al., 2004; Misteli and Soutoglou, 2009). Of particular importance is the binding of the HMGN1 protein to the nucleosome, which alters the architecture of chromatin and affects the levels of posttranscriptional modifications of the tails of nucleosomal histones. Specifically, upon HMGN1 binding to nucleosomes, phosphorylation of histone H3 on Ser10 is reduced (Lim et al., 2004). Since histone H3 phosphorylation on Ser10 is required to maintain chromatin in a condensed state such as during mitosis (Juan et al., 1998) or premature chromosome condensation (Huang et al., 2006a) the reduction of its level of phosphorylation facilitates chromatin decondensation (relaxation). Thus, the DNA damage-induced activation of HMGN1 and its binding to nucleosomes preventing histone H3 phosphorylation may directly mediate chromatin decondensation. Histone acetyltransferase TIP60 also plays a role in modulation of chromatin dynamics. After damage to DNA, in addition to acetylation of histone H2AX, which is a prerequisite for its phosphorylation on Ser139, TIP60 regulates the ubiquitination of H2AX via the ubiquitin-conjugating enzyme UBC (Ikura et al., 2007; Kruhlak et al., 2006). Sequential acetylation and ubiquitination of H2AX by TIP60-UBC promotes enhanced histone dynamics, which in turn stimulates the

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[(Fig._1)TD$IG]

The ATM signaling pathway triggered by induction of DSBs [(Kitagawa et al., 2004, updated (Darzynkiewicz et al., 2009)]. Induction of DSB leads to lessening of torsional strain and unwinding of DNA superhelical structure that triggers local decondensation of chromatin and recruits the Mre11, Rad50, and NBS1 proteins (MRN complex), as well as BRCA1 to the DSB site (A, dashed arrows). These events activate ATM, which occurs by autophosphorylation of Ser1981 and leads to dissociation of the ATM dimer onto two monomers that are enzymatically active. Activated ATM is then recruited to the site of the DSB (B, dashed arrow) where it phosphorylates several substrates including NBS1, BRCA1, and SMC1 (C). NBS1 phosphorylation is required for targeting ATM to phosphorylate Chk1 and Chk2. Phosphorylation of SMC1 activates S-phase checkpoints whereas BRCA1 phosphorylation engages this protein in the DSB repair pathway. ATM also phosphorylates E2F-1, Chk1, p53, Mdm2, Chk2, and H2AX and several other substrates. Activated p53 (phosphorylated on Ser15) induces transcription of p21WAF1 and/or Bax genes whose protein products arrest cells in G1 or promote apoptosis, respectively.

Fig. 1

DDR. It should be noted that the presence of wt p53 appears to be critical for the induction of chromatin relaxation upon DNA damage (Murga et al., 2007; Rubi and Milner, 2003) perhaps through its effect on the tumor suppressor p33ING2 (Wang et al., 2006). Chromatin relaxation augments accessibility of the repair machinery to DNA damage sites and appears to also provide the signal for activation of Ataxia telangiectasia mutated (ATM) protein kinase. The MRN complex consisting of Meiotic Recombination 11 Homolog A (Mre11), Rad50 homolog and Nijmegen Breakage Syndrome 1 (NMR1) proteins undergoes translocation into the site of DNA damage at the time of chromatin decondensation

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and activation of the ATM protein kinase (Abraham and Tibbetts, 2005; Downs and Cote, 2005; Kitagawa and Kastan, 2005; Paull and Lee, 2005). It should be noted that ATM activation takes place at some distance from the DNA break site and the activated kinase moves then to the site. B. Activation of Phosphatidyl Inositol 30 -Kinase-Related Kinases (PIKKs) The DDR is regulated by three PIKKs: ATM, ATM and Rad3-related (ATR), and DNA-dependent protein kinase (DNA-PKcs) (Cuadrado et al., 2006; Helt et al., 2005; Hill and Lee, 2010; Lovejoy and Cortez, 2009). These kinases are primarily responsible for signaling the presence of DNA damage and phosphorylate hundreds of proteins whose function is to maintain the integrity of the genome. The substrates phosphorylated by these PIKKs are implicated in regulation of DNA damage repair, cell cycle progression, apoptosis, and cell senescence. In many instances these PIKKs can have redundant activities and backup each other in terms of phosphorylation of the same proteins. Among the PIKKs that are activated in response to DNA damage the most extensively studied was ATM, which is the key component of the signal transduction pathways mobilized by the induction of DSBs (Li and Zou, 2005; Shiloh, 2003). Activation of ATM occurs through its autophosphorylation on Ser1981 and requires its prior acetylation that is mediated by the Tip60 histone acetyltransferase (Sun et al., 2005). ATM phosphorylation leads to dissociation of the inactive ATM dimers onto monomers that have kinase catalytic activity (Bakkenist and Kastan, 2003, 2004) (Fig. 1). The MRN protein complex plays a critical role in the process of ATM activation as it detects DNA damage, recruits ATM to the damage site, and targets ATM to the respective substrates to initiate their phosphorylation (Lee and Paull, 2005). Whereas ATM phosphorylation on Ser1981 is a prerequisite for dissociation of the dimer, the catalytic domain of ATM is outside of the Ser1981 site and becomes accessible to the kinase substrates only when ATM is in its monomeric conformation (Bakkenist and Kastan, 2004). As schematically presented in Fig. 1, ATM phosphorylates several substrates at the site of the DSB, including NBS1, structural maintenance of chromosomes 1 (SMC1), and breast cancer 1 (BRCA1) proteins. Phosphorylated NBS1 targets ATM toward Chk1, phosphorylated SMC1 engages the S-phase checkpoints halting DNA replication (Kitagawa et al., 2004; Wakeman et al., 2004) and BRCA1 phosphorylation is required to activate this protein along the DNA repair pathway. The BRCA1 (E3-ubiquitin ligase) is involved in several biochemical processes related to DNA repair (Kastan, 2008; Kitagawa and Kastan, 2005). BRCA2 is essential for locating Rad51 to the sites of DNA damage and both BRCA proteins are involved in DNA repair by homologous recombination (HR) (Yuan et al., 1999). The mediator of DNA damage checkpoint 1 (MDC1) is also recruited to the DSB site (Stucki and Jackson, 2004). This nuclear protein activates the S-phase and G2/M-phase cell cycle checkpoints and interacts with phosphorylated histone H2AX near sites of DSB facilitating recruitment of the ATM and other repair factors to the damage foci.

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Other than ATM, PIKKs activated in response to DNA damage are ATR and DNAPKcs (Cuadrado et al., 2006; Helt et al., 2005; Hill and Lee, 2010; Lovejoy and Cortez, 2009). Activation of ATR occurs in response to replication stress Kurose et al., 2006a, 2006b; Ward et al., 2004) rather than to direct induction of DSBs such as caused by ionizing radiation, which triggers activation of ATM. However, activation of DNA-PKcs takes place during repair of DSBs where it is an essential factor for the nonhomologous end-joining (NHEJ) mechanism of DNA repair (Hill and Lee, 2010; Smith and Jackson, 1999). Because the NHEJ mechanism also operates during V(D)J recombination and is responsible for antibody diversity (Smith, 2004) DNA-PKcs is a critical element for normal immune development. DNA-PKcs is also strongly implicated in telomere maintenance (Samper et al., 2000). The process of activation and inactivation of DNA-PKcs is mediated by its extensive posttranslational modification (Hill and Lee, 2010). Among several sites of its phosphorylation Thr2609, which becomes autophosphorylated in response to DNA damage by ionizing radiation, has been the most studied (Chan et al., 2002). C. Activation of Checkpoint Kinases The most important downstream target substrates phosphorylated by PIKKs include p53 (TP53), checkpoint kinase 2 (Chk2), and histone H2AX (Bakkenist and Kastan, 2004; Wakeman et al., 2004). The main purpose of checkpoint pathways activation is to halt progression through the cell cycle until integrity of DNA is restored by the repair mechanisms (Ahn et al., 2002; Matsuoka et al., 2000; Zhou and Elledge, 2000). Chk2 plays a key role in response of the cell cycle progression machinery to DNA damage. Upon induction of DSBs ATM activates Chk2 by phosphorylating Thr68 of this protein (Fig. 2). This leads to dimerization of Chk2 and acquirement of the kinase catalytic activity (Ahn et al., 2002, 2004). It should be noted that phosphorylation of Chk2 on Thr68 may also be mediated by ATR; this occurs however in response to replication stress (Matsuoka et al., 2000). Intermolecular phosphorylation on Thr383, Thr387, and Ser516 takes place within the Chk dimers, which leads to dissociation of the dimers. Both the monomers and the multiphosphorylated dimers are enzymatically active (Fig. 2). The DNA damage-activated Chk2 undergoes dissociation from chromatin that facilitates further signal amplification and translocation to soluble substrates (Li and Stern, 2005). The enzymatically active monomers as well as dimers of Chk2 phosphorylate numerous downstream substrates including Cdc25A and Cdc25C phosphatases, which upon activation induce cell arrest at the G1 or at the transition from G2 to M, respectively (Fig. 2). In addition to cell cycle arrest Chk2 plays a role in mediating the response to DNA damage by promoting apoptosis. For example, after DNA damage induced by topo2 inhibitor etoposide, Chk2 phosphorylates and activates the E2F-1 transcription factor that activates apoptotic pathways (Stevens et al., 2003). Likewise, phosphorylation of p53 by Chk2 may lead to upregulation of Bax, an event promoting apoptosis. However, phosphorylation of p53 may also lead to upregulation of p21Waf1 providing an additional means to halt cell progression

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[(Fig._2)TD$IG]

Fig. 2 Activation of Chk2 and Chk2’s major substrates. DNA damage (induction of DSBs) triggers activation of ATM (Fig. 1), which in turn phosphorylates Chk2 on Thr68 causing its dimerization. Phosphorylation of Chk2 can also be mediated by ATR but this occurs in response to replication stress rather than DSB. Within the dimer of Chk2 phosphorylation at Thr383, Thr387, and Ser516 takes place that leads to dissociation of the dimer onto monomers. Both multiphosphorylated dimers and monomers of Chk2 are enzymatically active and able to phosphorylate the downstream substrates. Among these substrates are the Cdc25C and Cdc25A phosphatases whose phosphorylation by Chk2 promotes binding to a 14-3-3 protein (Rudolph, 2007) thereby preventing translocation into the nucleus and dephosphorylation of inhibitory phosphorylations at Thr14 and Tyr15 on cyclin/CDK complexes. This halts cell cycle transitions from G2 to M (Cdc25C) and G1 to S (Cdc25A), respectively. Phosphorylation of Cdc25 phosphatases also accelerates their proteasomal degradation (Boutros et al., 2006). A redundant mechanism of cell arrest in G1 involves phosphorylation of p53 by Chk2 that may lead to upregulation of the cdk2 inhibitor p21CIP1/WAF1. Phosphorylation of p53 may also result in upregulation of the proapoptotic protein Bax. Apoptosis may additionally be promoted by phosphorylation of PML and E2F-1. Phosphorylation of BRCA1 engages it in the DNA repair pathway.

through G1 (Lin et al., 1996). BRCA1 and promyelocytic leukemia (PML) proteins may be phosphorylated by Chk2 as well (Lee et al., 2000; Yang et al., 2002). Phosphorylation of BRCA1 engages this protein in the DNA repair pathway whereas phosphorylation of PML increases cells proclivity to undergo apoptosis (Ahn et al., 2004). Activated Chk2 also stabilizes the FoxM1 transcription factor thereby enhancing expression of DNA repair genes (Tan et al., 2007). There is strong redundancy between Chk1 and Chk2 as well as among all three isoforms of Cdc25 (Cdc25A, Cdc25B, and Cdc25C) (Boutros et al., 2006, 2007; Rudolph,

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2007) in their enzymatic activities of phosphorylation (Chk1, Chk2) or dephosphorylation (Cdc25A, Cdc25B, Cdc25C), respectively. D. Histone H2AX Phosphorylation Histone H2AX, one of the variants of histone H2A (Thatcher and Gorovsky, 1994), is one of the critical proteins responsible for surveillance of genome integrity (Bassing et al., 2003; Celeste et al., 2003). In response to DNA damage, particularly when the damage involves induction of DSBs, H2AX becomes phosphorylated on Ser139 (Rogakou et al., 1998; Sedelnikova et al., 2002). The phosphorylation can be mediated by ATM (Anderson et al., 2001; Burma et al., 2001), ATR (Furuta et al., 2003), and/or DNA-PKcs (Park et al., 2003) and takes place on nucleosomes on both sides flanking DSBs along a megabase domain of DNA (Rogakou et al., 1999). The Ser139-phosphorylated H2AX is defined as g H2AX. Of notice, H2AX is also phosphorylated during induction of DSBs in physiological processes such as DNA recombination in V(D)J class-switch during the process of immune system development and in meiosis (Modesti and Kanaar, 2001; Smith, 2004). DNA fragmentation in cells undergoing apoptosis also induces extensive H2AX phosphorylation (Huang et al., 2003, 2004, 2006a).

III. Detection of DDR Events by Cytometry A. Chromatin Relaxation (Decondensation) We have recently reported that the DNA damage-induced chromatin decondensation can be detected and measured by flow cytometry (Halicka et al., 2009b). The method is based on the use of the metachromatic fluorochrome acridine orange (AO) that differentially stains double-stranded (ds) versus single-stranded (ss) nucleic acids (Darzynkiewicz et al., 1975). Specifically, AO intercalates between the base pairs of the dsDNA and as a monomer fluoresces green (530 nm). However, when AO binds to ss nucleic acid sections it causes their condensation (transition of the AO–ssDNA complex to solid state) that manifests as red luminescence (>640 nm) that occurs as a result of intersystem crossing (triplet excitation) (Kapuscinski and Darzynkiewicz, 1984a, 1984b). The lifetime of the green fluorescence is 3 ns while the lifetime of the red luminescence is about 9 ns. The susceptibility of DNA to denaturation when stressed by heat or acid varies with the degree of chromatin condensation and the most susceptible is DNA in highly condensed chromatin of mitotic and apoptotic cells (Dobrucki and Darzynkiewicz, 2001). This propensity of AO to differentially stain DNA in condensed versus decondensed chromatin, assessed by cytometry, has been described by us in different cell systems including spermatogenesis, differentiation, G0 to G1 or G2 to M transition, and during apoptosis. In fact, this application of AO to detect

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abnormal chromatin condensation during spermatogenesis (Evenson et al., 1980) that resembles apoptotic chromatin condensation (Gorczyca et al., 1993) has become a widely recognized male fertility assay, defined as the ‘‘sperm chromatin structure assay’’ (SCSA). As it is evident in Fig. 3, the treatment of cells with UV led to an increase in intensity of green and a decrease of red emission indicating that DNA in the UVtreated cells was more resistant to acid-driven denaturation. Thus, this simple approach, based on the use of AO, detects chromatin decondensation induced by DNA damage. A similar response was observed in other cell types including human lymphocytes, as well as following oxidative DNA damage by H2O2 (Halicka et al., 2009a, 2009b). The degree of DNA denaturation is presented as the at index, which represents the ratio of the mean value of red luminescence intensity of the cell subpopulations (reporting AO interactions with the denatured, ssDNA) to the mean total (red plus green) intensity of the emission. Of interest is the observation that while chromatin decondensation induced by DNA damage caused by UV was global, occurring more or less equally in all phases of the cell cycle, the subsequent events of DDR induced by UV (activation of ATM and induction of g H2AX) were limited to S-phase cells only (Zhao et al., 2010).

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Fig. 3 Relaxation of chromatin of TK6 cells treated with UV light detected as susceptibility of DNA to denaturation after staining with acridine orange (AO). The left panel shows schematically the principle of differential staining of double-stranded (ds) versus single-stranded (ss, denatured) DNA sections with the metachromatic fluorochrome AO. AO binding to ssDNA results in red luminescence (>640 nm) whereas its binding to dsDNA results in green fluorescence (530 nm) (Darzynkiewicz, 1990; Darzynkiewicz and Kapuscinski, 1990; Kapuscinski and Darzynkiewicz, 1984a, 1984b). The center panels show bivariate distributions of human leukemic TK6 cells untreated (Ctrl) or exposed to 100 J/m2 UV, then cultured for 30 min, fixed, treated with RNase A, subsequently with 0.1 M HCl to induce partial DNA denaturation, and then stained with AO at pH 2.6 (Halicka et al., 2009). The extent of DNA denaturation is assessed by flow cytometry as the intensity of red luminescence (ssDNA) and green fluorescence (dsDNA). Note a decrease of red luminescence and an increase of green AO fluorescence of the UV-treated cells compared to control, reporting decondensation of chromatin. DNA in mitotic cells (M) is much more susceptible to denaturation than in interphase cells and this is reflected by their high red and low green intensity of emission (Darzynkiewicz et al., 1977). The G1, S, G2, and M cell subpopulations thus can be identified and gated as shown by the dashed-line borders. The mean intensity of red luminescence (ssDNA) to total (red + green) intensity of emission (reporting ssDNA + dsDNA) was calculated for cells in each of these subpopulations. This DNA denaturation index (reporting approximate fraction of denatured DNA, at) is plotted (as at  100) in the right panel. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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As mentioned earlier in this chapter, the recruitment of MRN complex of proteins consisting of Mre11–Rad50–NBS1 to the DNA damage site is one of the earliest events of the DDR. This event is essential for activation of ATM. The MRN complex then targets ATM to initiate phosphorylation of the respective substrates (Fig. 1). We attempted to measure this event by cytometry expecting that the recruitment of these proteins to the damage site will be reflected by the increase in maximal pixel of Mre11 immunofluorescence (IF). This would be analogous to the recruitment of Bax to the mitochondrial membrane when this protein, normally diffusely distributed throughout the cell, becomes translocated and locally concentrated in mitochondria upon induction of apoptosis (Bedner et al., 2000). Our data presented in Fig. 4 indicate that the recruitment of MRN complex induced by DNA oxidative damage in A549 cells can be detected by cytometry as the increase in intensity of Mre11 IF (Zhao et al., 2008a). However, rather unexpectedly we observed that not only the intensity of maximal pixel increased but Mre11 IF integrated over the whole nucleus also increased. The latter could indicate that either (i) Mre11 was synthesized after induction of the damage; (ii) Mre11 was translocated from cytoplasm to the nucleus; or (iii) the accessibility of the Mre11 epitope to the Ab was increased when this protein was recruited to the site of DNA damage. The rapidity of the response (<10 min) makes the possibility of synthesis of new Mre11 rather unlikely. Furthermore, Mre11 is generally localized in the nucleus although in exceptional instances (viral infection) can be located in the cytoplasm (Araujo et al., 2005). Most likely, therefore, the observed increase in expression of Mre11 (integral) may reflect a change in its conformation that makes the epitope more accessible to the Ab. The epitope of Mre11 detected by this Ab (31H4, rabbit)

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Fig. 4 Detection of Mre11 in A549 cells treated with H2O2. Exponentially growing cells untreated (Ctrl) or treated with 200 mM H2O2 for 10 min or 2 h were fixed and the expression of Mre11 in cell nuclei, detected immunocytochemically, was measured by LSC. Cellular DNA was counterstained with DAPI. Bivariate distributions show expression of Mre11 with respect to the cell cycle phase measured either as maximal pixel or as integrated value of Mre11 immunofluorescence (IF). The dashed skewed lines show the upper threshold level of Mre11 IF for 95% of cells in Ctrl. The maximal increase in Mre11 IF was seen during the initial 10 min followed by a decline (see Zhao et al., 2008a, 2008b, 2008c for further details and kinetics data).

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is a small peptide domain corresponding to a stretch of amino acids in the vicinity of Lys496 of human Mre11A (Zhao et al., 2008a). The peak of the Mre11 increase occurred within 10 min of exposure of cells to H2O2 and it preceded by 20 min the peak of activation of ATM and Chk2 as measured by their phosphorylations on Ser1981 and Thr68, respectively, and by 60 min the peak of phosphorylation of histone H2AX on Ser139, all measured by laser scanning cytometer (LSC) (Zhao et al., 2008a). As it is evident in Fig. 4, in analogy to chromatin relaxation (Fig. 3), the level of Mre11 recruitment was similar in all phases of the cell cycle. However, the DDR events downstream of Mre11 recruitment (ATM and Chk2 activation and H2AX phosphorylation) were distinctly cell cycle phase specific being maximal in S-phase cells (Zhao et al., 2010). This observation suggests that the cell cycle phase associated factors appear to modulate activation of the events subsequent to Mre11 recruitment.

C. Immunocytochemical Detection of DDR-Associated ATM, DNA-PKcs and Chk2 Activation, Phosphorylation of p53 and Histone H2AX The development of phospho-specific Abs that detect proteins responding to DNA damage by phosphorylation at specific sites (as shown in Figs. 1 and 2) and their application in flow- and image-assisted cytometry opened a vast area of experimentation in several directions. Using these Abs it is possible to detect activation of ATM (ATM-S1981P), Chk2 (Chk2-Thr68P), and DNA-PKcs (DNA-PKcsThr2609P), and phoshorylation of H2AX (g H2AX) as well as p53-Ser15P. All these proteins are players in the process of DDR. One direction of their use was in the basic research designed to investigate the mechanisms of DDR, especially the relationship of particular events of DDR vis-
a-vis cell cycle progression, induction of apoptosis, or cell senescence. Another direction was to reveal mechanisms of action of antitumor agents targeting DNA such as DNA topoisomerase I (topo1) and II (topo2) inhibitors, alkylating agents, ionizing and UV radiation. Still another direction was to use this approach to detect and characterize the genotoxicity of a variety of endogenous and exogenous agents. Because the detection of DNA damage by this approach is much more sensitive than by the prior methods (e.g., comet assays) it was possible to detect minute effects including the level of constitutive DNA damage induced by endogenous oxidants in untreated cells, healthy cells. Also, the cytometric assessment of DDR has been proposed and tested as a biomarker of severity of DNA damage and as potentially prognostic reporter of the in vitro or in vivo effectiveness of cytotoxic drugs. Since the appearance of the first publications on the detection of DDR events by cytometry (Banath and Olive, 2003; Huang et al., 2003), significant progress has been made in all these directions (reviews, Olive, 2005; Tanaka et al., 2006a, 2006b, 2006c, 2006d, 2006e, 2007a, 2007b, 2007c, 2007d; Zhao et al., 2007). The examples of application of cytometry along these lines are described below and listed in Figs. 5–10.

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IV. Application of Cytometry to Detect DDR Induced by Different Genotoxic Agents A. Assessment of DDR Induced by DNA Topoisomerase Inhibitors In one set of experiments we explored the mechanisms of the DDR in A549 cells treated with topo1 (topotecan) and topo2 (mitoxantrone and etoposide) inhibitors in relation to the cell cycle phase and induction of apoptosis (Huang et al., 2003, 2004, 2006b; Kurose et al., 2005; Zhao et al., 2008b, 2008c). Topotecan (Tpt), the analog of camptothecin and irinotecan, binds in live cells to DNA–topo1 complexes stabilizing these otherwise cleavable complexes. Collisions between moving replication forks or RNA polymerase molecules and these complexes transform the latter into DSBs resulting in potentially lethal lesions. Topo2 inhibitors were thought to kill cells by a similar mechanism (D’Arpa et al., 1990; Hsiang et al., 1989). However, by studying the DDR induced by these inhibitors, we observed significant differences between the two types of inhibitors. The data indicate that the mechanism of induction of DNA damage and subsequent apoptosis is very much different for Tpt, versus mitoxantrone (Mxt) versus etoposide. Fig. 5 exemplifies the analysis of kinetics of DDR induced in A549 cells by Tpt, revealed as an activation of ATM (through its phosphorylation on Ser1981) and of Chk2 (through phosphorylation on Thr68) and phosphorylation of p53 on Ser15 (Zhao et al., 2008b). It is quite apparent from these data that the S-phase cells were much more affected by Tpt than G1 or G2M cells. At the peak of response, nearly all S-phase cells had IF above the maximal level of the constitutive expression of ATMSer1981P, Chk2-Thr68P, or p53-Ser15P found in untreated cells (0 time; marked by the respective skewed dashed lines on these distributions). It is also apparent that the kinetics of the induction of ATM-Ser1981P was different than that of Chk2-Thr68P and p53-S15P. Whereas the peak of the expression of activated ATM was seen after 1 h of treatment with Tpt, the maximal induction of Chk2-Thr68P and p53-Ser15P was after 4–6 h of treatment. The cell cycle arrest induced by TPTwas revealed as an accumulation of cells in early S phase after 6 h of treatment (see the arrow on the DNA histogram). In parallel to the experiment with Tpt shown in Fig. 5, we studied the DDR kinetics of A549 cells treated with Mxt. The data revealed an entirely different pattern of DDR compared to that induced by Tpt. Specifically, unlike in the case of Tpt, phosphorylation of ATM, Chk2, and p53 triggered by Mxt was more pronounced in G1 rather than in S-phase cells, and the kinetics of phosphorylation of these proteins was also different (Zhao et al., 2008b). Fig. 6 illustrates an approach to correlate the Tpt-induced activation of ATM with phoshorylation of histone H2AX (Tanaka et al., 2006d, 2007b). This was achieved by labeling g H2AX and ATM-S1981P with different color fluorochromes followed by a multivariate ‘‘paint-a-gate’’ analysis. The data clearly indicate that the cells in which H2AX become phosphorylated upon treatment with Tpt have also activated ATM. Furthermore, a distinct correlation in the degree of H2AX phosphorylation (intensity of g H2AX IF) and the degree of ATM activation (ATM-S1981P IF) is

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Fig. 5 Kinetics of induction of phosphorylation of ATM on Ser1981, Chk2 on Thr68, and p53 on Ser15 in A549 cells treated with the DNA topoisomerase I inhibitor topotecan (Tpt). The bivariate distributions of DNA content versus ATM-S1981P (top), Chk2-Thr68P (mid), and p53-S15P (bottom panels) of A549 cells treated with 150 nM Tpt for up to 6 h. Cells in G1, S, and G2M can be identified based on differences in DNA content as marked in the control (time 0) culture. The dashed skewed lines represent the upper threshold level of IF for 97% of interphase (G1 and S) cells in the respective control cultures. The insets in the DNA versus ATM-S1981P distributions show DNA content frequency histograms of cells from time 0 (left) or 6 h Tpt treated (right) cultures. Note the accumulation of cells in early S phase (arrow) as a result of cell arrest in S by Tpt after 6 h.

apparent. Thus, the data strongly suggest that phosphorylation of H2AX was mediated by ATM (Tanaka et al., 2006d, 2007b). In several studies on the mechanism of induction of DDR and subsequent cell death (apoptosis) induced by topo1 and topo2 inhibitors, it was possible to reveal further differences between these inhibitors (Huang et al., 2003, 2004, 2006b; Kurose et al., 2005; Zhao et al., 2008b, 2008c). In the case of the topo1 inhibitor Tpt, the mechanism of cell death induction was rather straightforward and can be explained solely by the mechanism proposed by D’Arpa et al. (1990) and Hsiang et al., (1989) in which stabilization of the topo1–Tpt complex is followed by collision (stalling) of DNA replication forks upon encountering these complexes, leading to DSBs formation. The Tpt-induced apoptosis was highly selective to DNA replicating cells and no evidence of Tpt-induced DDR among the cells in G1

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Fig. 6

Correlation between ATM activation and H2AX phosphorylation in A549 cells treated with Tpt. Untreated (control; A, B, C) and Tpt-treated (150 nM, 1 h; D, E, F) A549 cells were subjected to immunostaining using phospho-specific Abs to differentially label g H2AX and ATM-S1981P with Alexa Fluor 4881 and Alexa Fluor 670 Abs, respectively. Cellular DNA was counterstained with DAPI; the emitted blue, green, and far-red fluorescence was measured using a three-laser LSC. Using ‘‘paint-a-gate’’ analysis, the cells expressing g H2AX were colored red (A, D). Note that nearly all cells with elevated expression of ATM-1981P (B, E) are red colored that indicates that H2AX is phosphorylated in the same cells that have activated ATM. On the bivariate distribution of g H2AX versus ATM-S1981P, a good correlation between intensity of expression of there phosphoproteins is seen in the Tpt-treated cells (F) (Tanaka et al., 2007b). Among the untreated cells (control) only premitotic and mitotic cells constitutively express g H2AX and ATM-S1981P (Zhao et al., 2007, 2008c). (See plate no. 2 in the color plate section.)

or G2M phase of the cell cycle was apparent. Also there was no involvement of reactive oxidative species (ROS) in this mode of cell death induced by Tpt (Huang et al., 2003, 2004). In the case of topo2 inhibitor Mxt, all events of the DDR, namely strong activation of ATM and Chk2, and subsequent phosphorylation of p53 and histone H2AX were seen in all phases of the cell cycle. In fact, the intensity of the DDR was much more pronounced in G1 than in S phase of the cycle. Furthermore, a strong involvement of ROS was evident since most of these events were strongly attenuated when the treatment with Mxt was combined with exposure of cells to the ROS scavenger N-acetyl-L-cysteine (NAC; Huang et al., 2006b). Interestingly, despite the fact that G1 cells were most affected by Mxt in terms of induction of DDR, the apoptosis occurring subsequent to treatment was selective to S-phase cells. This observation implied that regardless of the degree of DNA damage, only the damage in the cells replicating DNA but not in G1- or G2M-phase cells was effective in triggering apoptosis (Huang et al., 2006b). In this case the collision of replication forks with the primary lesions, whether representing stabilized Mxt–topo2 complexes or ROSinduced oxidative damage, led to their stalling and formation of DSBs, which was the lethal signal triggering apoptosis. Interestingly, etoposide (VP-16), which like Mxt is also a topo2 inhibitor, induced maximal expression of DDR in G1 cells (Smart et al., 2008). Also, as in the case of Mxt, the DDR induced by etoposide was to a large extent attenuated by scavenging ROS with NAC. However, unlike Mxt, etoposide-induced apoptosis not exclusively in S but in other phases of the cycle as well, particularly affecting G1 cells (Tanaka et al., 2007a). It should be noted that Mxt belongs to the anthraquinone family of topo2 inhibitors and binds directly to DNA by intercalation (Kapuscinski and Darzynkiewicz, 1986), while etoposide is a member of the podophyllotoxin

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family, does not bind to DNA but binds stoichiometrically to topo2 (Kingma et al., 1999). These results implied that in addition to the generally accepted mechanism involving collision of replication forks with the ‘‘cleavable complexes’’ (Hsiang et al., 1989) other mechanisms, different for etoposide as compared to Mxt, contribute to the formation of DSBs and to the triggering of apoptosis. This information is of particular importance when a combination of drugs, including Mxt and etoposide, is being considered for cancer treatment. Since topo2 inhibitors are widely used in oncology, particularly to treat leukemias, we explored the feasibility of assessing the DDR as a potential biomarker predicting clinical outcome during treatment of human leukemias (Halicka et al., 2009a). Toward this end, activation of ATM and phosphorylation of H2AX in leukemic blast cells from the blood of 20 patients diagnosed with acute leukemias and treated with topo2 inhibitors doxorubicin, daunomycin, Mxt, or idarubicin was measured. The blood was collected 1 h after completion of the drug infusion and the level of phosphorylation of these proteins was compared by flow cytometry to the pretreatment level of the same patient. The population of blast cells was identified as CD45-dim and by subsequent gating the analysis of expression of ATM-S1981P and g H2AX was restricted to this population. The postinfusion increase in the extent of ATM activation and H2AX phosphorylation was observed in all 20 patients and a modest correlation between the induction of ATM activation and H2AX phosphorylation in blasts of individual patients was observed. While the number of the observed patients studied (20) and the number of those not responding to treatment (2) was inadequate to conclude whether the assessment of DDR can be clinically prognostic, the findings demonstrated the feasibility of assessment of DDR during the treatment of leukemias with drugs damaging DNA (Halicka et al., 2009a). B. Induction of DDR by Ionizing Radiation and UV Light Olive and her collaborators pioneered the use of flow cytometry to detect and measure histone H2AX phosphorylation in response to DNA damage caused by ionizing radiation and also by some anticancer drugs including etoposide and cisplatin (Banath and Olive, 2003; Banath et al., 2004; MacPhail et al., 2003a, 2003b; Olive, 2005; Olive and Banath, 2004, 2009; Olive et al., 2004). In the early studies, the authors used flow cytometry to correlate the induction of g H2AX by ionizing radiation with cell cycle phase. In several studies they observed a strong correlation between the dose of radiation and intensity of the induced g H2AX IF. The induction of g H2AX 60 min after irradiation was detected with as low as a 20 cGy dose of X-rays (MacPhail et al., 2003b). The half-times of the radiation-induced g H2AX IF ranging from 1.6 to 7.2 h, were correlated with a rate of decrease in the number of IF foci and also correlated with clonogenic survival for 10 cell lines (Banath et al., 2004). Also strongly correlated with cell death, assessed by clonogenicity, was expression of g H2AX IF measured 60 min after treatment of Chinese hamster V79 cells with several radiomimetic drugs examined in a range of over two decades

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of cell kill (Banath and Olive, 2003). This strong relationship between the induction of g H2AX expression and loss of clonogenicity led the authors to postulate that the assessment of H2AX phosphorylation by flow cytometry can be used as a surrogate of the estimate of cell kill (Banath and Olive, 2003). In another study of several human cervical cancer lines, these authors observed that the cell line-dependent differences in the rate of disappearance of X-irradiation-induced g H2AX IF were associated with the status of p53 (wt vs. p53 deficient) and also related in part to intrinsic radiosensitivity of the lines (Banath et al., 2004). In the case of treatment of human and rodent DNA-repair proficient and deficient cell lines with cisplatin, the authors observed that while the initial intensity of H2AX phosphorylation was not of much relevance, the level of the retention of g H2AX foci 24 h after treatment was highly correlated with the fraction of cells that lost their clonogenic potential (Olive and Banath, 2009). Phosphorylation of H2AX induced by UV is primarily mediated by ATR (Hanasoge and Ljungman, 2007). However, activation of ATM and DNA PKcs in UV-treated cells can be redundant to activation of ATR, occur concurrently and may contribute to phosphorylation of H2AX and other downstream protein substrates (Yajima et al., 2009). As many as 570 sites phosphorylated on 464 proteins were detected in M059K glioblastoma cells in response to UV-irradiation (Stokes et al., 2007). Using multiparameter cytometry, the induction of DDR by UV was seen exclusively in DNA replicating cells (Zhao et al., 2010). Inhibition of DNA replication by the DNA polymerase inhibitor aphidicolin was shown to prevent the induction of H2AX phosphorylation in UV-irradiated cells (Halicka et al., 2005). Fig. 7 illustrates strikingly similar pattern of incorporation of the DNA precursor 50 -ethynyl-2-deoxyuridine (EdU) and the

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Fig. 7

Correlation between DNA replication and phosphorylation of H2AX after exposure of cells to UV light. Exponentially growing A549 cells, untreated (panels A and D) or exposed to 50 J/m2 of UV-B light (B and C) were incubated for 60 min with 50 ethynyl-2-deoxyuridine (EdU) then fixed. Incorporation of EdU was detected using the click chemistry approach (Salic and Mitchison, 2008) with Alexa Fluor 488 tagged azide (‘‘click-iTTM imaging kit,’’ Invitrogen/Molecular Probes, Carlsbad, CA). Expression of g H2AX was detected using Alexa Fluor 633 secondary Ab (far-red fluorescence), DNA was counterstained with DAPI (Zhao et al., 2009). Subpopulations of cells entering S (eS) and G2 (eG2) during the 60 min pulse with EdU are outlined with the dashed oval lines.

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induction of g H2AX by UV-B, vis-
a-vis the cell cycle phase, in exponentially growing A549 cells. During 60 min of exposure to EdU one can identify cells with variable degrees of EdU labeling having DNA content close to that of G1- and G2M-phase cells (panel A). These are the cells entering S phase (eS; initiating DNA replication) as well as the cells entering G2 (eG2; terminating DNA replication) being exposed to the precursor while replicating DNA for variable time intervals, between 0 and 60 min. As is evident in panel B, the incorporation of EdU was dramatically suppressed after exposure to UV. It is also evident that the pattern of g H2AX expression in UV-treated cells (panel C) resembles very much that of the EdU incorporation into UV-untreated cells. These data and other findings led us to postulate that the mechanism of induction of DDR by UV involves stalling of DNA replication forks upon encountering the UV-induced primary DNA lesions [known to be cyclobutane-pyrimidine dimers and 6-4 (T-C) photoproducts; Sinha and H€ ader, 2002], which likely leads to the formation of DSBs (Zhao et al., 2010). The observed suppression of EdU incorporation (Fig. 7) would be consistent with the stalling of DNA replication forks. Since we were unable to observe DDR in the cells that do not replicate DNA, it is rather unlikely that it is induced during the nucleotide excision repair process, which is the primary mechanism of repair of DNA damage induced by UV (Marti et al., 2006).

C. Induction of DDR by Cigarette Smoke (CS) and Other Environmental Mutagens Cigarette smoke (CS) is the primary cause of lung cancer and it also contributes to the development of other malignancies. We have recently developed a rapid and sensitive assay to test the genotoxicity of CS utilizing LSC. The assay is based on the detection of DDR revealed as activation of ATM and phosphorylation of H2AX in normal human bronchial epithelial cells and in pulmonary carcinoma A549 cells shortly following their exposure to CS (Albino et al., 2004, 2006, 2009; Jorgensen et al., 2010; Tanaka et al., 2007c; Zhao et al., 2009a). Fig. 8 demonstrates the use of this assay to compare the genotoxic properties of tobacco- and nicotine-free cigarettes (T&N free) with that of CS from 2R4F cigarette, a standard cigarette developed by the University of Kentucky (Jorgensen et al., 2010). It is quite evident that 20 min exposure of A549 cells to CS from the 2R4F cigarettes induced both, ATM activation as well as H2AX phosphorylation, and that these effects were the most pronounced in S-phase cells. It is also apparent that exposure of cells to whole smoke from T&N-free CS induced both ATM activation and H2AX phosphorylation. The effect, however, was less S-phase specific since G1 and G2M cells showed elevated levels of expression of ATM-S1981P and g H2AX as well. However, when the smoke from T&N-free cigarettes was diluted with air by creating vents (pores) in the cigarette filters, the effect become more S-phase specific, similar to that of whole smoke from 2R4F cigarettes. These data as well as additional tests revealed that the smoke from T&N-free cigarettes, which are

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Fig. 8 Induction of g H2AX and ATM-S1981P in A549 cells after their exposure to either standard (2R4F) cigarette smoke (CS) or to smoke from the tobacco- and nicotine-free cigarettes (T&N free). Cells were either mock treated (exposed to the ambient air) or exposed to whole smoke from 2R4F (considered a standard ‘‘light cigarette’’) or to a T&N-free cigarette for 20 min and then incubated in culture for 1 h. The right panels (‘‘vents’’) show the cells that were exposed to smoke from T&Nfree cigarettes with open vents whose function is to dilute the smoke with air (Jorgensen et al., 2010). The bivariate DNA content (DNA index; DI) versus g H2AX (top panels) or DNA content versus ATM-S1981P (bottom panels) distributions show the expression of g H2AX and ATM-S1981P with respect to the cell cycle phase; cells in G1, S, and G2M phases of the cell cycle were identified based on differences in DNA content as shown. The dashed skewed lines indicate the upper threshold for g H2AX or ATM-S1981P IF for 95% of the mock-treated cells.

commercially available as a substitute for tobacco cigarettes (with the aim to curtail smoking), are even more genotoxic than the tobacco-containing cigarettes (Jorgensen et al., 2010). As exemplified by analysis of genotoxicity of CS, cytometric analysis of DDR provides high sensitivity, convenience and rapidity in detecting the genotoxic potential of different agents. Clearly, the sensitivity of detection of the most deleterious DNA lesions such as DSBs is many-fold higher than that provided by the alternative method, the comet assay. One would expect, therefore, wide application of the cytometric-DDR approach in testing environmental genotoxins as well as agents promoted as neutralizers of such toxins. In one such application we have observed that DNA damage in live cells caused by the acridine mutagen ICR 191 was greatly attenuated by concurrent exposure of cells to a mutagen interceptor, chlorophyllin (Pietrzak et al., 2008). In another application, exposure of cells to supravital DNA probes such as Hoechst 33342 or DRAQ5 induced DDR that manifested as activation of ATM and Chk2 as well as phosphorylation of H2AX

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and p53 on Ser15, all or which is evidence of significant DNA damage (Zhao et al., 2009b). Still another example of detection of genotoxic effects by this approach was the finding that the nitrogen oxide-releasing aspirin induces H2AX phosphorylation, ATM activation, and leads to apoptosis of TK6 cells (Tanaka et al., 2006b). This is an important observation since so modified aspirin, which as it appears from these data to have genotoxic properties, is being promoted as a novel anti-inflammatory agent.

D. Oxidative DNA Damage DNA in live cells is continuously exposed to intracellular oxidants, the byproducts of metabolic activity, as well as to exogenous oxidants or oxidant-inducers. The effect of such exposure is progressive oxidative DNA damage. It has been estimated that, during a single cell cycle of 24 h duration, the oxidants generate approximately 5000 DNA single-strand lesions (SSLs) in the average cell in the human body (Vilenchik and Knudson, 2003). These lesions are singlestrand breaks, apurinic/apyrimidinic sites, oxidation products such as 8-oxoguanine and thymine glycol, and some alkylation products (Beckman and Ames, 1997). While about 99% of SSLs are repaired by essentially error-free mechanisms, 1% of them (50) become converted to DSBs, predominantly during DNA replication. Recombinatorial repair (homologous recombination repair) and NHEJ are the major pathways for repair of DSBs. The NHEJ pathway is error-prone, often resulting in deletion of a few base pairs (Pastwa and Blasiak, 2003). This leads to an accumulation of DNA damage with each cell division. Such permanent damage is considered to be the primary cause of cell aging and senescence and promotes development of preneoplastic changes (Gorbunova and Seluanov, 2005). Strategies designed to slow down aging or prevent cancer often rely on protection of DNA from oxidative damage. The presence of oxidants (Pham et al., 2000) and certain DNA primary lesions resulting from oxidative damage detected immunocytochemically such as 8-oxoguanine (Cheng et al., 2003) can be assessed by cytometry. However, because of the scarcity of DSBs generated by endogenous oxidants, the possibilities for detecting them are limited. The comet methodology (Olive et al., 2001) lacks the desired sensitivity. It also cannot provide information on the relationship between the presence of DSBs and the cell cycle phase or DNA ploidy of the cell being examined. We observed, however, that the background level of H2AX phosphorylation and activation of ATM seen in normal cells and cell lines during unperturbed growth, in the absence of any added exogenous genotoxic agents, is a reporter of the DNA damage induced by endogenous oxidants generated during aerobic respiration (Tanaka et al., 2006a, 2006c, 2006e, 2007a; Zhao et al., 2007). We defined this background level of H2AX phosphorylation and ATM activation as constitutive DDR. The level of constitutive DDR varies depending on the cell type (line) and on the phase of the cell cycle, being the highest for cells in S and G2M phase. Given the same level of

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Fig. 9 Attenuation of constitutive expression of g H2AX in TK6 cells exposed to N-acetyl-L-cysteine (NAC). The bivariate (DNA content vs. g H2AX IF) distributions show a decrease in the level of constitutive expression of g H2AX in cells growing in the presence of 10 or 50 mM NAC, added into cultures for 1 h prior to cell harvesting compared to the untreated cells (Ctrl). The percent declines in mean values of g H2AX IF of G1-, S-, and G2M-phase cell subpopulations in cultures treated with NAC in relation to the respective subpopulations of the untreated (Ctrl) cells, are marked. The inset in the left panel shows the DNA content frequency histogram representative of the cells in these cultures. The right panel shows the plot of the mean values of g H2AX IF for G1, S, and G2M cells, estimated by gating analysis, in relation to NAC concentration (Tanaka et al., 2006).

reactive oxidants detectable in a cell, the level of constitutive DDR differs depending on the status (wt, mutated, or null) of TP53 (Tanaka et al., 2006e). Numerous experiments were designed to ensure that the constitutive DDR, revealed as the ‘‘background’’ H2AX phosphorylation and ATM activation in untreated cells, indeed is reporting DNA damage caused by endogenous oxidants. Initial experiments provided evidence that the level of H2AX phosphorylation and ATM activation can be markedly attenuated by exposure of cells to scavengers of reactive oxygen species such as NAC (Tanaka et al., 2006a). The reduction in constitutive DDR was NAC-concentration dependent; the expression of g H2AX was reduced by 37–48% in cells exposed to NAC for only 1 h (Fig. 9). In subsequent experiments we observed that a variety of factors that decreased the cell’s metabolic rate such as growth in the presence of 2-deoxy-D-glucose or 3-bromopyruvate, hypoxic (3% O2) conditions, or growth at low serum concentrations, all markedly reduced the constitutive level of expression of g H2AX and of activated ATM. Such a reduction was also seen after cell treatment with the antioxidant (vitamin C) (Tanaka et al., 2006a, 2006c; Zhao et al., 2007). In contrast, the increased metabolic activity such as induced by mitogenic stimulation or treatment with dichloroacetate, an agent known to shift metabolism from anaerobic to oxidative glycolysis through its effect on pyruvate dehydrogenase kinase, enhanced the level of constitutive expression of g H2AX and activated ATM (Zhao et al., 2007). Thus, all these data indicate that

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cytometric analysis of constitutive levels of H2AX phosphorylation and ATM activation offers the possibility of measuring the effectiveness of factors such as antioxidants, ROS scavengers, or caloric restriction mimetics on protection of DNA from damage caused by endogenous or exogenous oxidants.

V. Interpretation of Cytometric Data: Role of Image-Assisted Cytometry The majority of applications in which cytometry is used to detect and measure DDR are directed toward evaluation of the extent of actual DNA damage caused by different genotoxins that would be predictive of potential mutagenic and cytotoxic consequences. Because DSBs represent the most deleterious DNA lesions, both in terms of their mutagenic potential as well as their role in providing the signal for activation cell death pathways, it is of importance to correlate the particular events of the DDR measured by cytometry with formation of DSBs. Initially, phosphorylation of H2AX on Ser139 was considered to be the specific marker of induction of DSBs. Indeed in instances of ionizing radiation and radiomimetic agents that directly generate DSBs, the intensity of expression of g H2AX, measured as the integrated fluorescence per nucleus (g H2AX IF) correlated well with the extent of DSBs. However, the expression of g H2AX after induction of DSBs is a kinetic event of relatively short duration. The expression of g H2AX, on the other hand, is not measured dynamically, in real time, but at an end-point at the time of cell harvest/fixation. Although in many instances g H2AX peaks at 1–2 h after induction of the DSB, the rate of its dephosphorylation varies markedly depending on the cell cycle phase of the cell, the nature of the inducer of DSBs, and the rate of DNA repair (Chowdhury et al., 2005). As a result, there is uncertainty as to whether g H2AX IF is being measured at the ‘‘peak’’ of the response and thus whether the intensity of IF correlates well with the number of DSBs. Furthermore, there are instances such as following replication stress, when H2AX is phosphorylated (Kurose et al., 2006a, 2006b) in the absence of DSBs (Ichijima et al., 2010). Thus, the expression of g H2AX per se is not conclusive proof of the presence of DNA DSBs. As mentioned earlier there is strong evidence that H2AX phosphorylation, triggered by formation of DSBs, is mediated by ATM (Burma et al., 2001; Sedelnikova et al., 2002). Therefore, when induction of g H2AX is accompanied by ATM activation, one is more assured that H2AX phosphorylation reports formation of DSBs. However, there are instances such as during condensation of chromatin in mitosis (Ichijima et al., 2005) or premature chromosome condensation (Huang et al., 2006a) when both the expression of g H2AX and activation of ATM are seen and yet there is no straightforward evidence of formation of DSBs. Also, because of the redundancy of PIKKs in phosphorylation of different substrates, even when one of them is initially activated, activation of other PIKKs may soon follow. We observed, for example, that while after induction of DNA damage by UV activation of ATR is the

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Fig. 10 Analysis of g H2AX foci by laser scanning cytometry (LSC). A549 cells were either ‘‘mock treated’’ (A, B) or exposed to smoke from 2R4F tobacco-containing cigarettes for 8 min (C, D) and then incubated in culture for 1 h, as described in the legend to Fig. 8. After fixation, the presence of g H2AX was detected immunocytochemically (Albino et al., 2009; Jorgensen et al., 2010). The expression of g H2AX was confined to the characteristic IF foci that were more abundant in the smoke-exposed cells. The LSC software, initially developed to quantify fluorescence in situ hybridization (FISH) foci (Kamentsky et al., 1997) has been used to contour and count images of the g H2AX foci (B, D). The multiparameter analysis of LSC was used to plot the distribution of cells from the smoke-exposed cultures with respect to number of foci per cells (E) and the relationship between expression of g H2AX per nucleus and DNA content (F). The gating analysis was performed to select cells with greater than three foci (F, ‘‘red gate’’) and through ‘‘paint-a-gate’’ analysis to visualize these cells as colored red on the g H2AX versus DNA content bivariate distribution (F), and their cell cycle position on the DNA content histogram (G). (See plate no. 3 in the color plate section.)

first to be seen, activation of ATM shortly follows. One should be careful therefore with data interpretation because even when ATM activation and H2AX phosphorylation are observed to occur concurrently this may not necessarily be an assurance of the presence of DSBs. The presence of distinct characteristic g H2AX or ATM-S1981P IF foci, each focus reported to represent a single DSB (Rogakou et al., 1998, 1999; Sedelnikova et al., 2002), appears to be the most reliable marker of DSBs. The number of individual IF foci per nucleus, especially if double labeled with ATMS1981P and g H2AX (Tanaka et al., 2006d) may provide a quantitative analysis of the number of DSBs induced in individual cells. This can be accomplished by imageassisted cytometric analysis utilizing, for example, an LSC (Darzynkiewicz et al., 1999; Pozarowski et al., 2005) or similar instruments (Fig. 10). Image analysis was shown to be able to detect very low levels of DNA damage, for example, as occurs in vivo in lymphocytes of patients subjected to computed tomography examinations (Lobrich et al., 2005). In practical terms, the count of individual foci per nucleus is limited to relatively small (<20) foci numbers. With greater numbers, foci proximity to each other and the possibility of their overlap create problems that may prevent accurate contouring. As is shown in Fig. 10, the gating of cells with more than three foci per nucleus made it possible to identify them on DNA versus g H2AX distributions as having higher

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than the minimum level of g H2AX expression. However, many cells with the highest level of g H2AX were not selected by the gate (black dots, most in S phase of the cell cycle). Identification of the cells by visual inspection using the LSC revealed that they had high numbers of foci, which led to overlapping of their contours that precluded foci quantification. Thus, quantification of foci by LSC works ideally when relatively few foci are present per nucleus such as during analysis of constitutive DDR triggered by endogenous oxidants (Tanaka et al., 2006a). There are other issues that should also be considered when using image-assisted cytometry for foci quantification. Namely, the rates of phosphorylation and dephosphorylation of H2AX and/or ATM in individual foci in the same cell may vary depending on the rate of DNA repair at each particular DSB. Furthermore, when the induction of DSBs are asynchronous within the cell (e.g., as is the case of progressive DNA damage following treatment with low doses of genotoxins), the ‘‘age’’ and thus the IF intensity of individual foci may also vary. This variability in intensity of fluorescence of individual foci may complicate the setting of optimal thresholds for contouring all foci.

VI. Role of Microfluidic Lab-on-a-Chip Platforms for DDR Analysis Advances in conventional flow- and image-assisted cytometry provide the instrumentation of choice for studies requiring quantitative analysis of DDR. Surprisingly, however, commonly used high-content approaches are still based on the static principle, yielding information on the cell status at a particular time point. Capabilities of high-speed, multiparameter, and real-time analysis of small numbers of patient-derived cells are still very limited (Wlodkowic et al., 2010). The improvements in such capabilities are of particular importance for the development of personalized therapeutic approaches (point-of-care diagnostics) and the increasing role of cost and time savings in drug screening pipelines. Not surprisingly, enabling strategies that can reduce expenditures while at the same time increase throughput and content of information are attracting growing interest (Wlodkowic and Cooper, 2010a, 2010b; Wlodkowic et al., 2010). The last decade, in particular, has brought some spectacular innovations in the field of miniaturized cytometric technologies that can open up new avenues for highthroughput DDR analysis. Namely, the up-and-coming microfluidic Lab-on-a-Chip (LOC) technology and the micrototal analysis systems (mTAS) are two of the most promising avenues for massively parallelized studies with single-cell resolution (ElAli et al., 2006; Sims and Allbritton, 2007; Whitesides, 2006). The transfer of traditional bioanalytical methods to a microfabricated format provides a means to reduce drug screening expenditures while vastly increasing throughput and content of information from a given sample (Manz and Dittrich, 2006). On the other hand, mTAS increase both the resolution of analysis while reducing the assay running costs

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(Hong et al., 2009; Kang et al., 2008). Most importantly, however, only small cell numbers and operational reagent volumes are required for microfabricated technologies as compared to the conventional counterparts such as flow cytometry (Sims and Allbritton, 2007; Wlodkowic and Cooper, 2010a; Wlodkowic et al., 2009a, 2009b). By providing an alternative to expensive instrumentation such as flow or laser scanning cytometers and sorters, user-friendly LOC technologies can prospectively enable routine DDR analysis on patient-derived specimens. A number of emerging, microfluidic LOC technologies for cell-based assays have recently been reported such as microflow cytometry (mFCM), microfluorescently activated cell sorting (mFACS), and inflow magnetically activated cell sorting (mMACS) that are all up-and-coming examples of miniaturized on-chip flow cytometric technologies with substantial potential in DDR analysis and personalized diagnostics (Adams et al., 2008; Chan et al., 2003; Fu et al., 2002; Wolff et al., 2003). Microfluidic chip-based cytometry is slowly entering a commercial phase with increasing numbers of user-friendly devices capable of multiparameter fluorescent analysis of cells and particles (Wlodkowic and Cooper, 2010a; Wlodkowic et al., 2009b). The most notable examples involve the CellLab Chip (Agilent Technologies, Santa Clara, CA, USA), Fishman-R (On-Chip Biotechnologies Co, Tokyo, Japan), and the GigasortTM System (CytonomeST LLC, Boston, MA, USA), which all employ enclosed and disposable chip-based cartridges (Chan et al., 2003; Takao et al., 2009; Takeda and Jimma, 2009; Wlodkowic et al., 2009b). These approaches are particularly attractive for the clinical and diagnostic laboratories as they allow rapid analysis of only small amounts of patient-derived cells. Technological foundations initially developed for DNA microarrays have recently provided the starting point for development of chemical, protein microarrays, carbohydrate, and tissue microarrays (Camp et al., 2008; Gomase et al., 2008; Ma and Horiuchi, 2006; Uttamchandani and Yao, 2008). They all offer miniaturization, low reagent consumption, automation as well as qualitative and quantitative approaches to analyze gene and protein expression on a population level (Sobek et al., 2006). They do, however, suffer from a lack of capabilities to monitor single living cells in real time and as such represent a binary system that averages the results from every given cell while capturing a snap-shot of the intermittent cellular reaction (Wlodkowic and Cooper, 2010b; Wlodkowic et al., 2010). These drawbacks have recently fueled the development of new technologies: the living cell microarrays and microfluidic cell arrays that advance the spatiotemporal control of biomolecules and cells (Wlodkowic and Cooper, 2010b; Yarmush and King, 2009). Cell microarrays in general allow creating positioned arrays composed of single living cells (Di Carlo et al., 2006; Tokimitsu et al., 2007; Yamamura et al., 2005). Unlike flow cytometry, however, measurements are made at multiple time points, and in contrast to conventional time-lapse microscopy, image analysis is greatly simplified by arranging the cells in a spatially defined pattern and by their physical separation (Di Carlo et al., 2006; Wlodkowic and Cooper, 2010a; Wlodkowic et al., 2009a). As such they are ideal for drug screening routines and scalable for constructing high-throughput screening platforms (Wang et al., 2007). They also have

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the ability to perform kinetic and multivariate analysis of signaling events on a single-cell level (Faley et al., 2009; Wlodkowic et al., 2009a). Thus, cell microarray technology seems to be particularly suitable to uncover intricacies in cell-to-cell variability and its relevance to cancer therapy including the DDR analysis at a singlecell resolution (Wlodkowic and Cooper, 2010b). In this context, our recent studies have validated the application of live-cell microarrays for the kinetic analysis of drug-induced programmed cell death in hematopoietic cancer cells and hematopoietic cancer stem cells (Faley et al., 2009; Wlodkowic et al., 2009a). DDR analysis on living cell microarrays is a next logical example that can provide innovative diagnostic and screening applications. We have recently postulated that the combination of microfluidic cell arrays with integrated on-chip gene delivery technology (genomics), functional and dynamic live-cell analysis (cytomics), and intracellular antibody staining of selected proteins (proteomics) can provide innovative, multivariate assays for high-content data mining, and enhanced elucidation of cell signaling pathways (Wlodkowic and Cooper, 2010b; Wlodkowic et al., 2010). It is largely anticipated that advances in many innovative microfluidic technologies will provide innovative analytical tools for studies requiring quantitative analysis of the DDR. References Abraham, R. T., and Tibbetts, R. S. (2005). Guiding ATM to broken DNA. Science 308, 510–511. Adams, J. D., Kim, U., and Soh, H. T. (2008). Multitarget magnetic activated cell sorter. Proc. Natl. Acad. Sci. U. S. A. 105, 18165–18170. Ahn, J. Y., Li, X., Davis, H. L., and Canman, C. E. (2002). Phosphorylation of threonine 68 promotes oligomerization and autophosphorylation of Chk2 protein kinase via the forkhead-associated domain. J. Biol. Chem. 277, 19389–19395. Ahn, J., Urist, M., and Prives, C. (2004). The Chk2 protein kinase. DNA Repair 3, 1039–1047. Albino, A. P., Huang, X., Jorgensen, E., Gietl, D., Traganos, F., Darzynkiewicz, Z. (2006). Induction of DNA double-strand breaks in A549 and normal human pulmonary epithelial cells by cigarette smoke is mediated by free radicals. Int. J. Oncol. 28, 1491–1505. Albino, A. P., Huang, X., Yang, J., Gietl, D., Jorgensen, E., Traganos, F., Darzynkiewicz, Z. (2004). Induction of histone H2AX phosphorylation in A549 human pulmonary epithelial cells by tobacco smoke and in human bronchial epithelial cells by smoke condensate: a new assay to detect the presence of potential carcinogens in tobacco. Cell Cycle 3, 1062–1068. Albino, A. P., Jorgensen, E., Rainey, P., Gillman, G., Clark, T. J., Gietl, D., Zhao, H., Traganos, F., Darzynkiewicz, Z. (2009). g H2AX: a potential DNA damage response biomarker for assessing toxicological risk of tobacco products. Mutat. Res. 678, 43–52. Anderson, L., Henderson, C., and Adachi, Y. (2001). Phosphorylation and rapid relocalization of 53BP1 to nuclear foci upon DNA damage. Mol. Cell Biol. 21, 1719–1729. Araujo, F. D., Stracker, T. H., Carson, C. T., Lee, D. V., and Weitzman, M. D. (2005). Adenovirus type 5 E4orf3 protein targets the Mre11 complex to cytoplasmic aggresomes. J. Virol. 79, 11382–11391. Bakkenist, C. J., and Kastan, M. B. (2003). DNA damage activates ATM through intermolecular autophosphorylation and dimer dissociation. Nature 421, 499–506. Bakkenist, C. J., and Kastan, M. B. (2004). Initiating cellular stress responses. Cell 118, 9–17. Banath, J. P., Macphail, S. H., and Olive, P. L. (2004). Radiation sensitivity, H2AX phosphorylation, and kinetics of repair of DNA strand breaks in irradiated cervical cancer cells. Cancer Res. 64, 7144–7148.

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Zhao, H., Traganos, F., and Darzynkiewicz, Z. (2008b). Phosphorylation of p53 on Ser15 during cell cycle caused by Topo I and Topo II inhibitors in relation to ATM and Chk2 activation. Cell Cycle 7, 3048–3055. Zhao, H., Traganos, F., and Darzynkiewicz, Z. (2008c). Kinetics of histone H2AX phosphorylation and Chk2 activation in A549 cells treated with topotecan and mitoxantrone in relation to the cell cycle phase. Cytometry A 73A, 480–489. Zhao, H., Traganos, F., and Darzynkiewicz, Z. (2010). Kinetics of the UV-induced DNA damage response in relation to cell cycle phase. Correlation with DNA replication. Cytometry A 77A, 285–293. Zhao, H., Traganos, F., Dobrucki, J., Wlodkowic, D., and Darzynkiewicz, Z. (2009b). Induction of DNA damage response by the supravital probes of nucleic acids. Cytometry A 75A, 510–519. Zhou, B. B., and Elledge, S. J. (2000). The DNA response: putting checkpoints in perspective. Nature 408, 433–439. Ziv, Y., Bielopolski, D., Galanty, Y., Lukas, C., Taya, Y., Schultz, D. C., Lukas, J., Bekker-Jensen, S., Bartek, J., Shiloh, Y. (2006). Chromatin relaxation in response to DNA double-strand breaks is modulated by a novel ATM- and KAP-1 dependent pathway. Nat. Cell Biol. 8, 870–876.

CHAPTER 7

Fluorescence-Based Detection and Quantification of Features of Cellular Senescence Sohee Cho and Eun Seong Hwang Department of Life Science, University of Seoul, Seoul, Republic of Korea

Abstract I. Introduction II. Features Associated with Loss of Reproductive Cell Capability A. Shortened Telomeres in Replicative Senescence B. Telomere Dysfunction-Induced Foci (TIF) in Replicative Senescence C. Loss of the Doubling Capacity of Cells III. Cellular Hypertrophy IV. Changes Associated with Lysosomes A. Lipofuscin Accumulation B. Increased Lysosome Content C. Senescence-Associated b-Galactosidase (SA b-Gal) Activity V. Changes Associated with Mitochondria A. Increased Mitochondrial Mass B. Altered Structural Dynamics of Mitochondria C. Decreased Membrane Potential D. Decreased Autophagy VI. Changes in the Level of Reactive Oxygen Species (ROS) A. Changes in Level of Superoxide B. Changes in Level of Hydroxyl Radicals VII. Changes Associated with Nucleus and Chromosomes VIII. Use of Flow Cytometry for Analysis of Cellular Senescence IX. Conclusion Acknowledgments References

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0091-679X/10 $35.00 DOI 10.1016/B978-0-12-385493-3.00007-3

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Abstract Cellular senescence is a spontaneous organismal defense mechanism against tumor progression which is raised upon the activation of oncoproteins or other cellular environmental stresses that must be circumvented for tumorigenesis to occur. It involves growth-arrest state of normal cells after a number of active divisions. There are multiple experimental routes that can drive cells into a state of senescence. Normal somatic cells and cancer cells enter a state of senescence upon overexpression of oncogenic Ras or Raf protein or by imposing certain kinds of stress such as cellular tumor suppressor function. Both flow cytometry and confocal imaging analysis techniques are very useful in quantitative analysis of cellular senescence phenomenon. They allow quantitative estimates of multiple different phenotypes expressed in multiple cell populations simultaneously. Here we review the various types of fluorescence methodologies including confocal imaging and flow cytometry that are frequently utilized to study a variety of senescence. First, we discuss key cell biological changes occurring during senescence and review the current understanding on the mechanisms of these changes with the goal of improving existing protocols and further developing new ones. Next, we list specific senescence phenotypes associated with each cellular trait along with the principles of their assay methods and the significance of the assay outcomes. We conclude by selecting appropriate references that demonstrate a typical example of each method.

I. Introduction It is a known fact that normal cells have a finite capacity for proliferation while cancer cells have unlimited capacity for growth. The growth-arrest state of normal cells after a number of active divisions is termed cellular senescence or replicative senescence (Hayflick, 1965). Normal human somatic cells that are frequently cultured in vitro, such as endothelial cells, keratinocytes, chondrocytes, lymphocytes, and certain stem cells, all have low telomerase activity, and their serial division is accompanied with progressive shortening and failure in protection of telomeres at the ends of chromosomes, thus triggering DNA damage response and eventually a state of irreversible growth arrest (Campisi et al., 2001). Replicative senescence is only one type of cellular senescence. In fact, there are multiple experimental routes that can drive cells into a state of senescence. Normal somatic cells enter a state of senescence upon overexpression of oncogenic Ras or Raf protein (Serrano et al., 1997; Zhu et al., 1998). This phenomenon, termed premature senescence or oncogene-induced senescence, may be a manifestation of a cellular strategy to cope with oncogenesis (Campisi, 2007). A state of senescence is also induced in both normal and cancer cells by imposing certain kinds of stress, referred to as stress-induced senescence. DNA damage (e.g., by adriamycin, bleomycin, or actinomycin D) (Elmore et al., 2002; Linge et al., 2007; Robles and Adami, 1998; Toussaint et al., 2000), oxidative stress (e.g., by

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[(Fig._1)TD$IG]

Fig. 1 Induction of senescence in normal and cancer cells. (Left) Normal human fibroblasts at an early passage (P4) were either cultivated continuously until the cell number stopped increasing (P34) or treated with 0.5 mM adriamycin for 4 h and then chased for 9 days (P4 + Adr). Most of the cells at P34 and those treated with adriamycin appeared to be positive for SA b-Gal activity (panel A). (Right) HeLa cells were either mock-treated (mock) or treated to express the BPV1 E2 gene and undergo induced senescence (Pava1) (HeLa cells, a human cervical carcinoma line, express the E6 and E7 oncoproteins of human papillomavirus, which inactivate the p53 and Rb proteins, respectively). BPV1 E2 protein blocks the expression of the E6 and E7 genes and thereby relieves p53 and Rb to activate the growth arrest pathway (Goodwin et al., 2000). Regardless of the method of induction and cell type, the volume of cells and the number of cells positive for SA b-Gal activity evidently increased. In addition, an increase in lysosome content is another cellular trait associated with senescence, as demonstrated here through fluorescence imaging of lysosomes (panel B: by using LysoTrackerRed (left) and LysoTrackerGreen (right); panel C: by using an antibody specific to Lamp2a). The increase in lysosome content is also shown by histograms from flow cytometry of LysoTrackerRedstained cells (panel D). (Reprinted by permission from Park et al., 2007). (See plate no. 4 in the color plate section.)

H2O2 and t-butylhydroperoxide (t-BH)), or certain other chemicals such as bromodeoxyuridine (BrdU) (Suzuki et al., 2001) commonly activate tumor suppressor proteins, p53 or p16INK4a (Campisi and d’Adda di Fagagna, 2007, and see also Fig. 1, for examples). Activation of p53 also appears to be the major mechanism responsible for enforcing oncogene-induced senescence (Campisi, 2007). Therefore, both types of induced senescence are executed by cellular tumor suppressor function. Importantly, cells expressing certain senescence phenotypes are often detected in the tumor masses of animals (Mooi and Peeper, 2006). Considering this, oncogeneinduced senescence in normal cells and stress-induced senescence in cancer cells are regarded as evidence that cellular senescence is a spontaneous organismal defense mechanism against tumor progression that is raised upon the activation of oncoproteins or environmental stresses that must be circumvented for tumorigenesis to occur (Braig et al., 2005; Chen et al., 2005; Courtois-Cox et al., 2006; Michaloglou et al., 2005; Ohtani et al., 2009).

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Early on, Hayflick (1980) published a paper providing an extensive description of a variety of cellular changes associated with replicative senescence. Since then, a number of cytosolic and nuclear senescence-associated cellular traits have been newly recognized with details at the molecular or subcellular level, and their underlying mechanisms have been investigated (Hwang et al., 2009). Major cellular features associated with cellular senescence are listed along with their detection methods in Table I. Certain prominent features such as the presence of b-galactosidase activity Table I Summary of cellular traits associated with cellular senescence and major methods for their detection Senescence-associated cellular traits

Practical plausibilitya

Features associated with cell mortality Shortened telomeres in replicative senescence Decrease in the number of proliferating cells or in the doubling capacity Features associated with cell stasis Increase in cell volume Increase in Vimentin intermediate filaments Increase in focal adhesion Changes associated with lysosome Lipofuscin accumulation Increased lysosome content SA b-Gal activity Changes associated with mitochondria Altered structural dynamics of mitochondria Increased mitochondria content Decreased autophagy activity Changes associated with nucleus and chromosome Appearance of g -H2AX foci Appearance of TIF Appearance of heterochromatin foci (SAHF) Appearance of Hutchinson–Gilford progeria syndrome (HGPS)-like nuclear dysmorphism Increased ROS and oxidative damage adducts Increased mitochondrial superoxide level Increased mitochondrial hydroxyl radical level Increased cytosolic superoxide level Increased cytosolic hydroxyl radical level Increased protein oxidation Other features of cellular senescence Increased granule content (SSC) Increased glycogen granule

p p

p p

Major detection methodsb

GE (Harley et al., 1990), FC (Halaschek-Wiener, 2008), FISH FC with BrdU (Linge et al., 2007), [3H]-thymidine incorporation (Goodwin et al., 2000) CI (Chen et al., 2000) CI (Nishio et al., 2001) CI (Chen et al., 2000) FC (Sitte et al., 2001), FL, FM FC & CI (Park et al., 2007) IS (Dimri et al., 1995), FC (Kurz et al., 2000) CI (Yoon et al., 2006) FC (Lee et al., 2002), CI (Moiseeva et al., 2009) GE & CI (Kang and Hwang, 2009)

p p

p p p p p

CI (Sedelnikova et al., 2004) CI (Herbig et al., 2004) CI (Narita et al., 2003) CI (Scaffidi and Misteli, 2006) FC (Moiseeva et al., 2009; Invitrogen, in press) FC (G€ orlach and Kietzmann, 2007; Invitrogen, 2010) FC (Moiseeva et al., 2009; Invitrogen, in press) FC (Moiseeva et al., 2009; Invitrogen, in press) GE (Conrad et al., 2000) FC IS (Seo et al., 2008)

GE, gel electrophoresis of nucleic acids or proteins; FC, flow cytometry; FISH, fluorescence in situ hybridization; CI, confocal imaging; FL, fluorometry; FM, fluorescence microscopy; IS, in situ staining. a b

Practical plausibility is based on both how frequently the phenotype is referred to and how reliably and easily it is assayed. A major method is referenced by a representative paper.

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at pH 6.0 (SA b-Gal), increase in autofluorescence and ROS, and senescence-associated heterochromatin foci (SAHF) have been well characterized and are being extensively utilized as markers for senescence and in the study of aging. One of the characteristics of cellular senescence regarding the phenotypes is the wide range of their expression levels among cells in a culture population. Due to such heterogeneity, analysis of the changes in certain cellular properties (e.g., change in the quantity of nucleic acids and proteins) in a senescent cell population could not only suffer from decreased sensitivity and specificity but also provide misleading results. A senescing cell population can be better analyzed by studying the individual cells or subpopulations sorted according to the levels of a certain phenotype. With the simple and easy nature of the procedure, single cell analysis by confocal microscopy and population analysis by flow cytometry, together or separately, are quite well suited for this purpose, and have become the dominant strategies in studies on cellular senescence as summarized in Table I. In this chapter, fluorescence methodologies including confocal imaging and flow cytometry that are frequently utilized to study a variety of senescence phenotypes are reviewed (any missed phenotypes are those that have not been assayed by the fluorescence methods). Key cell biological changes occurring during senescence are listed, and the current understanding on the underlying mechanisms of these changes is reviewed with the goal of improving current protocols and further developing new ones. And, specific senescence phenotypes associated with each cellular trait are listed with the principles of their assay methods and the significance of the assay outcomes are discussed along with pitfalls and limitations. Finally, references are selected that demonstrate a typical example of each method.

II. Features Associated with Loss of Reproductive Cell Capability In actively proliferating early-passage cells, the ends of telomeres form ‘‘t-loop’’ structures that are bound by multiple proteins. However, short telomeres in cells approaching the end of their replicative life span lack such protective structures (de Lange, 2002). The short and unprotected telomeres appear to be recognized as double-strand breaks and trigger the DNA damage response, which eventually activates DNA damage checkpoints executed by p53 and Rb (Deng et al., 2008). Since the shortened telomeres are kept unprotected, the DNA damage response is not extinguished and leads to a state of irreversible growth arrest, constituting the most basic phenotype of replicative senescence. Ongoing DNA damage response at these short telomeres has been demonstrated by the continuous presence of DNA damage response factors such as 53BP1, g -H2AX (phosphorylated histone H2AX), Rad17, ATM, and Mre11 in an ATM-dependent manner at the ends of chromosomes (Takai et al., 2003). Visualized structures are frequently called telomere dysfunction-induced foci (TIF, see below).

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Meanwhile, acute senescence induced in normal and cancer cells is not accompanied by telomere shortening. In these cells, growth arrest is dominantly enforced by p53/Rb or p16INK4a, but the reason for their activation is not completely understood. DNA damage induced by high-level reactive oxygen species (ROS) is likely involved (Campisi, 2007).

A. Shortened Telomeres in Replicative Senescence Telomere shortening is frequently demonstrated through Southern blotting analysis of telomere-restriction fragments (TRFs) using radio-labeled (CCCTAA)4 oligonucleotide, which hybridizes the ‘‘TTAGGG’’ repeat sequence of human telomeres (Harley et al., 1990). In this process, isolated genomic DNA is restrictiondigested (by Hinf1+RsaI, for example), and the lengths of DNA fragments that contain the entire telomeres are visualized by X-ray film exposed to the Southern blot. TRFs from a given population of cells form not a single band but instead a mass of multiple bands of different sizes, reflecting the various lengths of telomeres within and between cells in a population. Still, a gradual decrease in TRF size occurs as the number of population doublings increases (Harley et al., 1990). To numerically demonstrate telomere shortening, the lengths of telomeres in a cell population are determined and plotted. To determine the representative TRF length in a mass of multiple telomeric DNA bands, the lane is serially sectioned into same-sized squares, and the signal intensity of each square is determined by densitometric scanning. Since the signal intensity of a probed DNA band in a Southern blot is the combined outcome of the number of fragments and their lengths, the signal in each section should be normalized based upon the estimated MW of the fragment, and the MWof the section that gives the highest number (this represents the length of the most abundant TRF) is used as the representative TRF length. To assist, a program named Telorun was developed by Harley et al. (1995). A recent version of Telorun is posted at http://www4.utsouthwestern.edu/cellbio/shay-wright/ research/sw_lab_methods.htm. Recently, telomere length has been determined more easily and quickly using fluorochrome-labeled peptide nucleic acid probes (PNAs), which specifically and efficiently bind to telomere repeat sequences (Hacia et al., 1999). PNAs are oligonucleotide analogues in which the natural phosphodiester backbone is replaced by neutral amide linkages (of N-(2-aminoethyl)glycine), increasing their permeation through lipid bilayers (Corey, 1997; Nielsen, 1999). PNAs form a stable duplex with DNA at low salt concentrations without prior fixation with formamide, which causes reduced accessibility of target DNA sequences. Cy3conjugated (CCCTAACCCTAACCCTAA) PNA has been proven to bind to (TTAGGG) repeat sequences with high affinity and efficiently stain telomeres (Hamilton et al., 1997). This specific and quantitative binding allows easy estimation of the length of individual telomeres by flow cytometry (flowFISH (fluorescent in situ hybridization)) (Martens et al., 1998) or digital

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[(Fig._2)TD$IG]

Fig. 2 Fluorescence-based telomere length quantification by flow cytometry (flow-FISH). Cord blood T cells expressing either CD4+ (CB CD4+) or CD8+ (CB CD8+) or adult T cell subsets, naı¨ve, memory, or effector, were hybridized with FITCconjugated PNA-(C3TA2)5 probe (or such PNA probe as specific to sequences at the X chromosome as a control, X-probe) and applied to flow cytometry to obtain fluorescence histograms (A and B). In C and D, the mean telomere fluorescence levels from the different T cell subsets of 10 adult donors and from cord blood T cells were compared with each other. Telomere fluorescence of cord blood T cells is higher than that of adult naı¨ve cells (CD4 + RA+, in A and C or CD8 + RA + 27+, in B and D), which, in turn, is higher than that in the memory cells (CD4 + RO+, in A and C or CD8 + RA
27+, in B and D) or the effector cells (CD8 + RA
27+ in D). The numbers in C and D are the means of telomere fluorescence from the respective T-cell groups. The difference between the subsets of the adult T-cell population from an individual (linked by a line) likely reflects the difference in their replicative history in vivo. (Reprinted by permission from Rufer et al., 1998. Copyright 1998 Macmillan Publishers Ltd.)

fluorescence microscopy (Telomere/centromere-FISH (T/C-FISH)) (Perner et al., 2003; Poon et al., 1999). Flow-FISH can generate data with great ease and speed and also provide information on cell subpopulations. In addition, this method can produce data from quite a small number of cells (Fig. 2). Incorporation of an internal standard (cells of known telomere length) allows reasonable estimation of the mean length of telomeres in a test cell population. Meanwhile, T/C-FISH supported by software modules that determine the integrated fluorescence intensities and reference signal of telomeres also allows measurement of individual telomeres at a single chromosome arm (Perner et al., 2003). Baerlocher et al. (2002) described in detail the important parameters and possible pitfalls of the flow-FISH protocol. A modification of the flow-FISH protocol is presented in Chapter 8 of this Volume.

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B. Telomere Dysfunction-Induced Foci (TIF) in Replicative Senescence

g -H2AX, a DNA damage response factor, localizes en masse at the sites of DNA double-strand breaks (Rogakou et al., 1999) and is visualized as foci (called g -foci) in cells after staining with specific fluorescence-tagged antibody. Likewise, an ongoing DNA damage response can be demonstrated in situ by visualizing the presence of other DNA damage response factors such as 53BP1, Rad17, ATM, and Mre11 at chromosome lesions (Takai et al., 2003). At the same time, the telomeres of individual chromosomes can be visualized by FISH using either Cy3-conjugated PNA probes as mentioned above or immunofluorescence for TRF1, a telomere-associated protein. The telomere ends in association with the DNA damage response complex TIF, has been visualized by dual immunofluorescence for TRF1 and 53BP1 (or g -H2AX or others) or by an immuno-FISH technique (Takai et al., 2003) (Fig. 3), which combines immunofluorescence for g -H2AX (or 53BP1 or others) with telomere FISH (Herbig et al., 2004). The number of TIF has been found to be associated with cellular senescence. In one study, 20% of g -H2AX foci in a replicatively senescent fibroblast coincided with telomere signals, whereas such colocalization is found only occasionally in early-passage cultures (Sedelnikova et al., 2004). Therefore, TIF may be used as a marker for senescence induced by telomere shortening (i.e., replicative senescence). However, the same result also suggests a nontelomeric origin of the majority of the DNA damage foci in senescent cells. Importantly, a higher incidence of TIF in tissues isolated from old baboons (Jeyapalan et al., 2007) suggests its potential as a marker for in vivo aging. [(Fig._3)TD$IG]

Fig. 3

Telomere dysfunction-induced foci. (Left) Cells of a human fibroblast line whose telomeres were uncapped by knockdown of endogenous TRF2 activity, which is essential for telomere protection, were fixed and processed for immunofluorescence against 53BP1 (green) and TRF1 (a telomere-binding protein) (red). The left two images (1.0) are a single nucleus showing the fluorescence image of TRF1 alone and that of TRF1 and 53BP1 merged. The right enlarged images (1.5 and 2.0) show 53BP1 foci localized to telomeres, demonstrating that DNA damage-response machinery containing 53BP1 forms foci at unprotected telomeres. (Reprinted by permission from Takai et al., 2003. Copyright 2003 Elsevier.) (Right) Human fibroblasts at an early passage (p9), at a late passage (p37), in senescence (Sen) or immortalized by telomerase transduction (TERT) were processed by immunofluorescence to visualize g -H2AX foci (green) and by FISH using (C3TA2)3-Cy3-labeled PNA to visualize telomeres (red). Nucleus was counterstained with DAPI (blue). Arrows point to sites of colocalization of green and red fluorescence. The nuclei of senescent cells have more telomeres colocalized with the g -H2AX foci. (Reprinted from Herbig et al., 2004. Copyright 2004 Elsevier.) (See plate no. 5 in the color plate section.)

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C. Loss of the Doubling Capacity of Cells Senescence, by definition, is a state of irreversible cell cycle arrest. However, cell cycle arrest is hardly seen in all constituent cells in a culture that is replicatively senescent. This is in large part due to the heterogeneity of cells regarding doubling capacity. Such heterogeneity is, in turn, likely due to the difference in the timing of telomere-associated DNA damage signaling between individual cells (Martin-Ruiz et al., 2004; Von Zglinicki et al., 2003). The rate of telomere shortening differs from cell to cell (possibly due to different levels of ROS-induced telomere damage), which leads to different times at which DNA damage signaling is triggered (Martin-Ruiz et al., 2004). However, this heterogeneity is not observed with induced senescence, in which growth arrest occurs simultaneously in all cells of a culture. In many cases of induced senescence, cells appear to be arrested within the first 12 h (Elmore et al., 2002, and Sohee Cho, unpublished data). A growth curve is one way to convincingly demonstrate senescence. To construct a long-term growth curve of replicative senescence, cultures are continuously split (at an 1:4 ratio, for example). At each passage, cells are counted, and the number of population doubling (PD) (n) is calculated using the equation, n = log2 F/I, where I and F are the numbers of cells seeded at the beginning and obtained at the end of one passage, respectively. For induced senescence, a short-term growth curve should be sufficient to demonstrate a lack of population growth. In this case, it is important to verify that the absence of population growth is not due to cell death. A senescence-associated decrease in population doubling or an increase in the number of cells under growth arrest can be demonstrated by a significant decrease in DNA synthesis or in the number (or percentage) of cells synthesizing DNA in a given period of time. Quantitative DNA synthesis assay measuring the incorporation of [3H]-thymidine (Goodwin et al., 2000) and immunofluorescence or flow cytometry-mediated counting of the cells positive for nuclear incorporation of BrdU (Linge et al., 2007; Ota et al., 2006) have been the methods of choice in many studies. An increase in the number of cells in G1 phase (or G2/M in certain senescence models, such as those induced by adriamycin or bleomycin) and a concomitant decrease in the cells in S phase can also be used as a supportive measure of senescence induction. This ploidy analysis, although requiring only propidium iodide (PI) staining of the nucleus, does not always give rise to a dramatic result. In replicative senescence, the fraction of cells in S phase does decrease but rarely approaches zero (Linge et al., 2007; Ota et al., 2006).

III. Cellular Hypertrophy Increases in cell surface area and volume are features so prominent in cellular senescence that an experienced eye can easily recognize using only light microscopy. In the case of fibroblasts, cell volume increases at least several folds (Cristofalo and Kritchevsky, 1969; Greenberg et al., 1977). Furthermore,

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senescent cells are flat, which makes the increase in surface area appear even higher (Wang and Gundersen, 1984). The molecular mechanism underlying the increase in cell volume in senescence is under speculation. Doubling of all normal cells requires a growth factor (mitogen) for cell cycle progression and macromolecule synthesis. A major regulator of cellular protein synthesis is the mammalian target of rapamycin (mTOR), which, upon activation, stimulates translation initiation factor 4E binding protein 1 (4EBP1) to form the translation initiation complex at the 50 cap site of mRNAs (Mamane et al., 2006). During cellular senescence, cell cycle arrest occurs in the presence of mitogen, which maintains mTOR activity and overall protein synthesis (Blagosklonny, 2008). As a consequence, both cellular protein content and volume would increase. This is a plausible speculation supported by previous studies reporting upregulated activity of phosphatidylinositol 3-kinase (PI3K), an upstream activator of mTOR in fibroblasts undergoing premature senescence (Tu et al., 2002; Wang et al., 2004). However, there also is accumulated evidence indicating that the rate of protein synthesis is lower in senescent cells (Hayflick, 1980). It is possible that, until a certain point in the growth-arrest state in senescence, protein synthesis may be actively maintained and then turned down later. An easy and simple way to assess a change in cell size is through measurement of intensity of forward light scatter (FSC) by flow cytometry (Fig. 4). Comparison of the mean or peak values in a FSC histogram provides information on the fold change in cell surface area. The surface area and volume of individual cells can also be directly measured by mounting cells on a microslide field finder. The diameter of the cells can be determined by the grids of a field finder, and cell volume may be calculated based on the equation for a sphere: V = (4/3)pr3. Of note, there is a large heterogeneity in FSC levels among cells at or close to senescence. While an FSC histogram of early-passage human fibroblasts gives rise to a single sharp peak at low

[(Fig._4)TD$IG]

Fig. 4

Increased FSC and SSC of senescent human fibroblasts. Cells at an early passage (P4), those undergoing replicative senescence (P34), or those undergoing induced senescence by the treatment with adriamycin, as shown in Fig. 1 (left), were applied to flow cytometry without any treatment. The cells undergoing either type of senescence show an increase in both FSC and SSC. Meanwhile, the population approaching replicative senescence shows high heterogeneity in both FSC and SSC (the population of small FSC and SSC in the dot plot of P34 is likely of the cells fractured during trypsinization). (E. S. Hwang, unpublished data.)

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value, that of the cells in replicative senescence displays a broad curve with a small peak at high values. Such heterogeneity is less prominent in cells undergoing induced senescence in which growth arrest occurs rather synchronously in all cells of a culture, indicating again that the heterogeneity may be in large part related to the difference in the timing of the growth arrest pathway (Martin-Ruiz et al., 2004; Von Zglinicki et al., 2003) (Fig. 4). The morphological changes occurring during cell senescence have been recently analyzed by laser scanning cytometry and were shown to provide a sensitive biomarker of the ‘‘depth’’ of senescence of individual cells (Zhao et al., 2010).

IV. Changes Associated with Lysosomes As cells progress toward replicative senescence, the number and size of lysosomes increase (Comings and Okada, 1970; Park et al., 2007; and Fig. 1). Such a change in lysosome content appears to occur only in senescent cells cultured in vitro, since the lysosome content is not high in fibroblasts freshly isolated from aged individuals but increases after a number of passages in vitro (Robbins et al., 1970). The increase in lysosome content is probably due in large part to an increase in the number of secondary lysosomes that contain indigestible materials such as lipofuscins (see below). If true, it is likely that, in senescent cells, lysosomal enzymes may be wasted in nonfunctional lysosomes, as postulated by Terman et al. (2003). This downshift in lysosome activity would result in reduced turnover of cellular waste materials such as damaged mitochondria. This may be the major reason for the accumulation of dysfunctional mitochondria, which, as will be mentioned later on, produce ROS and thereby initiate a vicious pro-senescence cycle between dysfunctional mitochondria and ROS (Kurz et al., 2008). Meanwhile, the presence of unaltered primary lysosomes and an increase in the activity of certain lysosomal enzymes have also been observed in late-passage cells and senescence-induced cells (Johung et al., 2007; Knook and Sleyster, 1976; Robbins et al., 1970; Sanchez-Martin and Cabezas, 1997). However, the possibility of increased lysosome biosynthesis in senescence has not yet been systematically investigated. Meanwhile, the size of lysosomes also increases in postmitotic cells from aged animals and human subjects (De Priester et al., 1984; Porta et al., 1982; Schmucker and Sachs, 2002). A. Lipofuscin Accumulation Lipofuscins are produced mainly by peroxidation of unsaturated fatty acids in complex with proteins and are deposited as yellow brown pigments in aged tissues. Metals (mercury, aluminum, iron, copper, and/or zinc) are present in lipofuscins, and iron is especially known to be actively involved in lipofuscin genesis by initiating the peroxidation reaction (Brunk and Terman, 2002a). Lipofuscins are believed to be formed within lysosomes, where both transition metals and oxidized lipid peroxides

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are present in high concentrations and undergo Fenton-type reactions to produce hydroxyl radicals, which in turn cause peroxidation of lysosomal contents (Brunk et al., 1992). Lysosomes loaded with these indigestible materials may not turn over and thus remain in the cytosol to become residual bodies (Brunk and Terman, 2002b). As early as 1912, progressive accumulation of lipofuscins concurently with aging was recognized in animal tissues, and recently in various types of cells undergoing senescence (Sitte et al., 2001), and therefore, lipofuscins are considered a genuine marker for senescence and aging. However, the level of lipofuscins increases in cells during temporal growth arrest, whereas, in vivo, they mainly deposit in postmitotic cells such as neurons and cardiac myocytes (Collins and Brunk, 1976). These reports suggest the possibility that cells may continuously produce lipofuscins but then dilute them through cell division, which implies that lipofuscin accumulation may not be an exclusive indicator of cellular senescence.

1. Lipofuscin Autofluorescence in Fluorescence Microscopy Lipofuscins are autofluorescent possibly due to Schiff bases formed by reactions between carbonyls and amino compounds. Their detection in cells and fixed tissues by fluorescence microscopy is rather straightforward; under any excitation wavelength ranging from 360 to 647 nm, lipofuscins appear as irregular granules that emit yellow-orange fluorescence between 500 and 640 nm (Eldred et al., 1982; Eldred and Katz, 1988; Sohal and Brunk, 1989; Strehler, 1964). To obtain quantitative data, micrographic fluorescence images are digitized and quantified using appropriate image analysis software (Sheehy, 1996). Various conditions for fluorescence quantification of lipofuscin have been studied in detail by Thaw (1987). Despite the simplicity of the detection method, some cautionary notes should be mentioned when quantitative detection is attempted. First, fluorescence may also be emitted from normal cellular components, perturbing the detection of lipofuscin fluorescence. Such components include tryptophan (emission of 340 nm), pyridoxine (390 nm), flavins and riboflavins (550 nm), NADH and NADPH (470 nm), porphyrins (630/690 nm), and proteins containing these molecules (RichardsKortum and Sevick-Muraca, 1996). Ceroid pigments, which are similar to lipofuscins in chemistry, are produced under experimental or in vivo pathological conditions unrelated to aging and are known to accumulate in lysosomes emitting fluorescence between 360 and 430 nm (Porta et al., 1988). The background fluorescence contributed by these materials should be subtracted for proper quantification of lipofuscin. Usage of filters for excitation at 390–490 nm/emission at 515 nm can result in mostly yellowish lipofuscin autofluorescence at high intensities (Dowson and Harris, 1981). Second, the broad emission spectra of lipofuscin may cause inaccuracy in the fluorescence-mediated quantification of cellular proteins or ROS in senescent cells. However, no information is available thus far regarding the significance of the errors made by overlapping emission profiles of lipofuscin autofluorescence and commercial fluorophores. Third, exogenous fluorophores (such as phenol red present in most commercial media as a pH indicator) and

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fluorescent probes (such as LysoTrackers or MitoTrackers; see below) can perturb proper data acquisition for lipofuscin fluorescence by causing photobleaching that leads to shifts in the emission signal (Woodburn, 2001). Fourth, formaldehyde, which is frequently used to fix cells for fluorescence microscopy, cross-links various cellular components. Cross-linking of amine residues leads to formation of Schiff bases, which are the chemical base responsible for complex formation between lipid peroxides and proteins in lipofuscin (Ploem, 1971). Therefore, formaldehyde fixation can produce lipofuscin-like artifacts and should be avoided.

2. Flow Cytometric Analysis of Cellular Lipofuscin Levels A change in the level of lipofuscin can be easily determined through flow cytometric analysis. Cells, without fixation, are simply applied to a flow cytometer at an excitation wavelength of 488 nm, and fluorescence is collected by a 530/30 nm bandpass filter. Either the peak or mean fluorescence of a histogram is recorded and compared between different samples. The reported increase in lipofuscin in replicative senescence can be as big as one order of magnitude (Sitte et al., 2001). The basal levels and rates of lipofuscin accumulation in replicative senescence appear to differ between various cell lines. For example, both the level of lipofuscin in early passage cells and the rates of accumulation during progression to senescence are much higher in MRC-5 than BJ cells, which are both primary human fibroblast lines (Sitte et al., 2001). In induced senescence, the level of lipofuscin increases linearly with the duration of incubation (Goodwin et al., 2000; Fig. 5). B. Increased Lysosome Content To detect change in lysosome content, cells are stained live with commercially available acidotropic probes such as LysoTrackerTM (Molecular Probes, Eugene, OR, USA), which basically consists of fluorophores attached to amines. Amines, as weak bases, accumulate in acidic compartments such as late endosomes, secretory vesicles, and lysosomes by a process called pH partitioning (Kaufmann and Krise, 2007). In neutral pH, amines exist as free bases and permeate membranes, but they become protonated and trapped in acidic organelles. In addition, significant binding of amines to acidic polysaccharides and glycolipids, which are abundant in the lysosomal membrane, also localizes these probes to lysosomes (Bulychev et al., 1978; Kaufmann and Krise, 2007). These LysoTracker probes preferentially localize to acidic organelles more so than classical basic dyes such as neutral red and acridine orange (Allison and Young, 1969). However, the probes are still present in the cytosol at quantities high enough to cause background problems. Therefore, quantification of lysosome content based on acidotropic signals in fluorometry and flow cytometry could be unreliable. Meanwhile, immunofluorescence-based lysosome imaging or tagging does not suffer significantly from this background problem. For example, staining lysosomes with a fluorescently tagged antibody for Lamp2a, a lysosome membrane protein, gives rise to a lysosome-specific fluorescence pattern

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Fig. 5 Increase in autofluorescence during senescence induced in HeLa. HeLa cells were induced to undergo senescence by expression of the BPV1 E2 gene as in Fig. 1 (right), further cultivated for the number of days indicated in B, and then applied to flow cytometry at an excitation of 488 nm. The peak fluorescence values of each time point were normalized to the peak value of the mock-control cells to yield a fold increase in autofluorescence (B). In (A), cells photomicrographed at 17 days post BPV1 E2 transduction (left) are highly fluorescent while those mock-treated are so only faintly (right). (Reprinted with permission from Goodwin et al., 2000. Copyright 2000 National Academy of Sciences, USA.)

similar to but more distinct than that of LysoTracker (Fig. 1A and B left). Imaging lysosomes with Lamp2a antibody also helps to eliminate the contribution of other acidic organelles stained with the acidotropic probes. C. Senescence-Associated b-Galactosidase (SA b-Gal) Activity Escherichia coli supplied with 5-bromo-4-chloro-indoly-b-D-galactoside (X-Gal) turns blue when grown on a neutral agar plate due to the activity of b-galactosidase, which cleaves the b-galactoside linkage of the chromophore. Meanwhile, eukaryotic cells express b-galactosidase, a lysosomal enzyme that is active at pH near 4.5 but not so when pH becomes neutral. SA b-Gal activity, which is responsible for cleaving X-Gal and staining cells blue at pH 6.0, is frequently found in cultured cells undergoing replicative and induced senescence

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(Dimri et al., 1995) (Fig. 1A and B). SA b-Gal activity is often observed in the tissues of a variety of aged animals from human beings (Dimri et al., 1995) to Caenorhabditis elegans (Dmitrieva and Burg, 2007) or zebrafish (Kishi et al., 2008) and, therefore, has become a critical marker of both senescence and aging. It turns out that SA b-Gal activity originates from lysosomal b-galactosidase activity, which increases high enough in senescent cells to be detected even at a suboptimal pH (6.0) (Lee et al., 2006). Meanwhile, high b-galactosidase activity at pH 6.0 has also been detected in cells under various conditions not related to senescence (summarized in Hwang et al., 2009), suggesting the possibility that SA b-gal activity may be an indicator of high lysosomal activity rather than an exclusive marker of cellular senescence. Conditions characterized by increased lysosomal activity have yet to be uncovered.

1. In situ Determination of SA b-Gal Activity

The SA b-Gal positivity of a culture is usually determined by the assay originally developed by Dimri et al. (1995), which simply involves the incubation of cells in pH 6.0 buffer containing X-Gal. This assay has been cited in thousands of papers and is now the standard method for verifying senescent cells. In middle-passage cultures of human fibroblasts, SA b-Gal-positive cells do exist, albeit at a low frequency, and this number increases with additional passages. Meanwhile, even when the population has stopped doubling and is in a state of senescence, lower than 100% of the constituent cells are stained positive. Cells negative for SA b-Gal activity are usually small and slim, indicating that they are not quite senescent as far as morphology is concerned. This is another manifestation of heterogeneity in a cell population entering a senescent state. In addition, there is no consensus on the quantitative criteria regarding SA b-Gal activity of a culture in senescence. Therefore, it would be wise to track the number of positive cells in the test cultures since it may take many population doublings from a decent amount of positive cells to a very high amount. Considering the above, it may be a common occurrence that certain studies have used a cell population in which a large portion did not reach senescence yet. Meanwhile, in the case of induced senescence, it appears that SA b-Gal positivity remains low during the first several days after initiation of the treatment that causes senescence but increases rapidly thereafter (Sohee Cho, unpublished data).

2. Flow Cytometry for SA b-Gal-Positive Cells and Quantification of Activity There is room for controversy as to whether or not cells are sufficiently stained positive for SA b-Gal activity, due to the subjective nature of judging color. A method to objectively verify a change in SA b-Gal activity in a population of cells was developed by Kurz et al. (2000) based on the ‘‘FACS-Gal assay’’ initially designed by Nolan et al. (1988) and Fiering et al. (1991). This flow cytometric analysis detects the hydrolysis of 5-dodecanoylaminofluorescein di-b-D-galactopyranoside (C12FDG), a membrane-permeable and nonfluorescent substrate of

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b-galactosidase, which, upon cleavage, remains within the cytosol and emits green fluorescence. A histogram of the fluorescence levels allows estimation of the enzyme activity and also provides quantitative information on the population size of SA b-Gal-positive cells.

V. Changes Associated with Mitochondria Hundreds to thousands of mitochondria are present in a single mammalian cell. They undergo qualitative, quantitative, and morphological changes during senescence and aging, and under certain pathological conditions. Deterioration in mitochondrial respiratory chain function and accumulation of mutations and large deletions in mitochondrial DNA accompany senescence and aging as well as certain agerelated diseases (Boffoli et al., 1994; Lezza et al., 1994; Trifunovic et al., 2004). However, an increase in the size of individual mitochondria and total mitochondrial mass is one of the most prominent changes exhibited as cells progress toward replicative senescence or undergo induced senescence. Mitochondria suffer from a decrease in the activities of fusion and fission and thereby remain enlarged during senescence. A. Increased Mitochondrial Mass Mitochondrial mass as well as the lengths of individual mitochondria increase during replicative and oncogene or stress-induced senescence in human fibroblasts (Lee and Wei, 2001; Lee et al., 1998, 2002; Passos et al., 2007; Moiseeva et al., 2009; Yoon et al., 2006; and Fig. 6). Increases in mitochondrial protein and DNA content as well as mass have also been observed in tissues of aged animals (Beregi and Regius, 1987; O’Connell and Ohlendieck, 2009; Sachs et al., 1977). It seems that at least two different causes underlie the increase in mitochondrial mass: an increase in biogenesis and a decrease in mitochondrial turnover. First, cells may make more mitochondria in an attempt to compensate for the decline in mitochondrial function caused by ROS-induced damage (Lee et al., 1998; Moiseeva et al., 2009; Wei et al., 2001). Oxidative stress and inhibition of mitochondrial electron transport chain function have been shown to cause an increase in mRNA levels of mitochondrial proteins (Fu et al., 2008; Lee et al., 2000; Miranda et al., 1999). It also has been reported that DNA damage (including that caused by H2O2 treatment) activates AMP-activated protein kinase (AMPK), which in turn induces the mitochondrial biogenesis factors PGC-1a, NRF-1, and TFAM (Fu et al., 2008). The levels of TFAM and NRF-1 and their DNA binding activities were also found substantially increased in aged subjects (Lezza et al., 2001). Second, mitochondrial mass may also increase as a consequence of decreased mitochondria turnover, which is, in large part, mediated by autophagy (Bota and Davies, 2001). A decrease in autophagy activity has been observed in cells of aged animals (Cuervo and Dice, 2000) and likely contributes to the accumulation of

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Fig. 6 Increase in mitochondrial mass and formation of elongated mitochondria in senescent cells. Mv1Lu cells (a mink lung epithelial line) were induced to undergo senescence by treatment with 2 ng/mL of TGF-b1 (A and B) or 0.8 mM H2O2 followed by chase (C). Electron photomicrography (A and C) shows elongation and enlargement of mitochondria in cells at senescence caused by either treatment. (B) Progressive increase in mitochondrial mass in cells undergoing senescence is shown by flow cytometric analysis following staining with MitoTrackerTM Red. (Reprinted with permission from Yoon et al., 2006. Copyright 2006 John Wiley & Sons.)

altered mitochondria in aged tissues as well as senescent cells (Bergamini et al., 2007; Yen and Klionsky, 2008). The decline in autophagy activity may be caused by a decrease in autophagosome formation and/or functional lysosomes (Terman, 1995). In addition, the efficiency of autophagic removal of mitochondria (sometimes called mitophagy) also depends on the structure of the substrate mitochondria. For example, filamentous mitochondria would not be easily enwrapped by autophagosomes. Decreased mitochondrial fission has been observed in senescent cells (see below), and this may contribute to the increase in mitochondrial mass (Lee et al., 2007).

1. Quantification of Changes in Mitochondria Mass Cellular content of mitochondria can be quantified by measuring the fluorescence emitted from cells stained with a fluorescent probe specific to mitochondria by flow

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cytometry or fluorometry. Conventional mitochondrial probes such as rhodamine 123 and tetramethylrosamine are mostly lipophilic, and therefore permeate membranes, but they also contain one positive charge, and get retained in the inner mitochondrial membrane facing the negatively charged matrix. However, these probes are susceptible to photobleaching, and more importantly, are easily lost during fixation, which inevitably reduces the MMP (Chen, 1989). Meanwhile, a family of chemicals that contain a thiol-reactive chloromethyl moiety (CMXRos) has been developed (Poot et al., 1996). Like the conventional probes, these are taken up into mitochondria. However, they then interact with thiol groups of membrane proteins, which prevents their loss during fixation. Such probes have been commercially named ‘‘MitoTrackerTM’’ (Molecular Probes, Eugene, OR, USA), and they are most heavily used as fluorescent mitochondrial probes. Quantification of MitoTracker fluorescence in conjunction with changes in cellular FSC and side scattering (SSC) by flow cytometry provides reliable information on changes in the mitochondrial contents of cell subgroups during senescence. A comprehensive report on these mitochondrial probes by Poot et al. (1996) was published. It should be noted that most of these mitochondrial probes, including some of the MitoTracker families, are responsive to MMP. In other words, the quantity of fluorescence is subject to changes in the membrane potential of the particular mitochondrion. Therefore, to quantitatively analyze the mitochondrial content of cells in which a change in MMP is expected, the choice of mitochondrial probe should be made carefully. MitoTrackerTM Green FM is claimed by the manufacturer to not be affected by MMP (MitoTracker and Mitofluor Mitochondrion-Selective Probes, Molecular Probes). Meanwhile, 10-n-nonyl acridinium-orange chloride (NAO) binds to cardiolipin, a special type of phospholipid present only in the mitochondrial inner membrane, and thereby localizes to the inner membranes (Petit et al., 1992). NAO has been reported to not respond to MMP-altering drugs such as dinitrophenol (DNP) or carbonyl m-chlorophenylhydrazone (CCCP), and therefore it is considered a genuine probe for detection of mitochondrial mass (Benel et al., 1989; Ferlini et al., 1995; Ratinaud et al., 1988; Septinus et al., 1985). However, the MMP-independence of NAO has been challenged (Keij et al., 2000). It seems that NAO fluorescence may also be affected by changes in MMP, albeit to a much lesser degree compared to other probes. Furthermore, the emission of green fluorescence (through 530/30 nm band path filter, for example) by NAO may be less sensitive to MMP changes. Practically, mitochondrial content may be better determined by assaying both MTG and NAO fluorescence, although, in most cases, quantifications made using these two chemicals provide similar results (Fig. 7A). The best way, but not an easy way, to determine mitochondrial mass independent of MMP is to permeabilize cells and label mitochondria using a fluorescently tagged antibody specific for mitochondrial proteins, which would be detected by flow cytometry or fluorospectrometry. Other independent measures such as quantification of mitochondrial DNA by real-time PCR or protein bands of electron transport chain complexes by Western blotting can provide supplementary documentation of changes in mitochondrial mass (see Fig. 7A–C for an example).

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Fig. 7 Changes in cellular mitochondrial content demonstrated by several independent methods. Human fibroblasts were cultured for different durations in medium containing nicotinamide, which has been reported to cause a decrease in mitochondrial mass. (A) Cells were fixed, stained with either 10-nonylacridine-orange bromide (NAO) or MitoTrackerTM Red (MTR), and analyzed by flow cytometry. The mean fluorescence was divided by that of the mock-treated cells, and the relative values were plotted. Fluorescence of both chemicals rapidly decreased during the first 7 days. (B) Western blotting analysis was carried out for the proteins of the electron transport chain complexes of the cells incubated as in (A). (C) Total DNA from an equal number of cells was isolated and applied to real-time PCR for relative quantification of mitochondrial DNA. The levels of both mitochondrial proteins and DNA changed in a pattern similar to that of the mitochondrial fluorescence in (A), supporting the decrease in mitochondrial content in nicotinamide-treated cells. (D) Relative levels of mitochondrial protein mRNAs quantified by real-time PCR. (Reprinted with permission from Kang and Hwang, 2009. Copyright 2009 John Wiley & Sons.) (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

B. Altered Structural Dynamics of Mitochondria In human cells undergoing replicative senescence or stress-induced senescence, mitochondria frequently change their morphology from short fragments or dots to long, thin, and irregularly shaped reticula (Jendrach et al., 2005; Lee et al., 2007; Yoon et al., 2006) (Fig. 6A and C). This filamentous degeneration is largely attributed to a decrease in the structural dynamics of mitochondria, especially fission activity (Lee et al., 2007; Yoon et al., 2006). In early-passage cells, mitochondria rapidly change their structure through frequent fission and fusion, which are mediated by special proteins; Fis1 and DrpI for fission and OpaI and MfnI (and II) for fusion (Hyde et al., 2010). However, the fission and fusion activities of late-passage HUVEC cells were substantially lower, and mitochondria existed as huge aggregates (Jendrach et al., 2005). Meanwhile, the expression of Fis1 and the fission activity in

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cellular senescence were found to be substantially downregulated (Yoon et al., 2006). And, subtoxic doses of H2O2 caused a state of cellular senescence while also decreasing mitochondrial fission and inducing elongated giant mitochondria (Yoon et al., 2006). Meanwhile, the alteration of mitochondrial structural dynamics may also play a causative role in senescence and aging. Inhibition of fission activity not only caused the formation of elongated mitochondria but also elevated the level of ROS and induced a state of senescence (Lee et al., 2007; Yoon et al., 2006). Conversely, overexpression of Fis1 delayed expression of the senescence phenotype (Yoon et al., 2006).

1. Visualization of Altered Mitochondria Structure The shift from short and fragmentary mitochondria to a thin reticulum is a change easily noticeable by confocal microscopy of cells after staining with a variety of the mitochondria-selective probes mentioned above (Jendrach et al., 2005; Yoon et al., 2006). Since most of these chemicals depend on high MMP and are washed out of the cells once MMP drops, they should be used without cell fixation. Meanwhile, the MitoTracker dyes based on CMXRos mentioned earlier can be used with other fluorescent probes that require cell fixation for coimaging of mitochondria. However, Minamikawa et al. (1999) raised a concern for using CMXRos in live cell imaging. CMXRos may cause loss of MMP, mitochondrial swelling, and release of cytochrome c. In addition, they warned that repeated laser scanning of CMXRosstained cells during confocal microscopy can damage mitochondria, leading to both substantial alteration in the image and MMP and severe phototoxity-induced cell death. It is noteworthy that staining of mitochondria using antibodies specific for mitochondrial proteins after fixing gives rise to a consistently better mitochondrial image (Sohee Cho, unpublished result). Meanwhile, visualization and quantification of mitochondrial fusion are possible with the use of mitochondrial matrix-targeted photoactivatable GFP (mtPA-GFP), which emits fluorescence upon irradiation with a laser at 750 nm (Twig et al., 2008). Mitochondrial fusion events can be deduced by determining the decrease in the GFP fluorescence intensity through the spread of fluorescence to mitochondria that are previously unlabeled.

C. Decreased Membrane Potential Mitochondria in senescent cells (Chen et al., 2005; Jendrach et al., 2005) and tissues of aged animals have low membrane potential (Navarro and Boveris, 2007). More precisely, the number of mitochondria with low MMP increases as cells are continuously passaged or induced to undergo senescence. The decrease in MMP may be attributed to the accumulation of defects in electron transport chain function, which is, in turn, brought about by either ROS-induced oxidative damage or expression of mutant proteins from damaged mtDNA (Lee and Wei, 2001).

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1. Quantitative Comparison of MMP The mitochondria matrix is negatively charged and certain lipophilic cations are trapped in the mitochondria inner membrane. Therefore, the extent of their uptake could reflect the potential across the membrane. By using such fluorescently labeled cations, a change in MMP can be either visualized in the individual mitochondria of a living cell via confocal microscopy or quantitatively presented by flow cytometry. Although there are a variety of fluorescent probes that respond to changes in MMP as mentioned above, JC-1 (5,50 ,6,60 -tetrachloro-1,10 ,3,30 tetraethylbenzimidazolylcarbocyanine iodide) is the most frequently used and favored probe since it is highly sensitive and responds consistently to MMP changes and is not sensitive to changes in plasma membrane potential (Salvioli et al., 1997). JC-1 exists as a monomer in the cytosol or mitochondria with low MMP, and emits green fluorescence (510/527 nm). However, JC-1 can accumulate in mitochondria with high MMP as J-aggregates emitting red fluorescence (585/590 nm) when excited at 490 nm (Cossarizza et al., 1993). Therefore, confocal imaging using JC-1 can identify mitochondria with high or low MMP (Kang and Hwang, 2009; Fig. 8) by

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Fig. 8 JC-1 staining of mitochondria. Human fibroblasts were stained with JC-1, washed with PBS, and visualized by confocal microscopy. To detect fluorescence from JC-1 monomers, samples were excited by an argon laser at 488 nm (green), and to detect fluorescence from JC-1 aggregates, a helium/neon laser at 543 nm (red) were used, respectively. The right bigger image is an overlap of the two left images. It is apparent that the green fluorescence localized mostly in the background of the cytosol, although some localized to form filaments, suggesting that a certain part of the filamentous mitochondria with low MMP was visualized. Meanwhile, all of the red fluorescence formed short filaments. This indicates that mitochondria in these cells are a mixed population of those with high MMP and others with low MMP. (Reprinted with permission from Kang and Hwang, 2009. Copyright 2009 John Wiley & Sons.) (See plate no. 6 in the color plate section.)

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utilizing two different optical filters (one for fluorescein and another for tetramethylrhodamine) for separate visualization of green and red emissions. For flow cytometric determination of the MMP level (but not an absolute voltage), both the FL1 (using a bandpass filter of 530/30) and FL2 (using a bandpass filter of 585/ 42) measures of individual cells are collected, after which the FL2 value is normalized to the FL1 value and the ratio is plotted to produce a histogram of the population. This way, possible error due to variation in the levels of JC-1 uptake or in the sizes among cells can be minimized. It is important to plot a histogram using the FL2/FL1 values from individual cells in order to obtain a mean or pik value (not of the population’s mean FL2 value normalized to the population’s mean FL1 value). A histogram of individual cell FL2/FL1 values can be drawn by running the raw data in a flow cytometry data analysis program such as Weasle (freeware developed by Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia). In addition, alteration in MMP is sometimes accompanied by a change in mitochondrial content (as in cells undergoing senescence). In such a case, the FL2/FL1 ratio might not correctly reflect changes in MMP. Therefore, mitochondrial quantity needs to be simultaneously but independently measured (by using NAO, for example) and used to normalize the mean FL2 values in place of the JC-1 FL1 values. A minimal concentration of JC-1 would most likely minimize red fluorescence emission from the cytosol, although an amount of about 1 mM is used in typical flow cytometric analysis. Treatment of cells with carbonyl cyanide m-chlorophenylhydrazone (CCCP), a potent uncoupler that causes rapid depolarization of mitochondria (Heytler and Prichard, 1962), reduces the emission of red fluorescence and therefore can serve as a good negative control. Tetramethylrhodamine ethyl ester (TMRE) and 2-(4-(dimethylamino)styryl)-1methylpyridinium iodide (DASPMI) are rapidly and reversibly taken up by live cells (Loew et al., 1994) and then used to demonstrate a shift in MMP. Such compounds are sequestered by functioning mitochondria in an MMP-dependent manner. Therefore, their relative intensities could highlight differences in MMP. Like JC-1, these voltage-sensitive fluorescence dyes should be applied to unfixed live cells. Quantitative data can also be produced by analyzing cells with digitalized confocal imaging. However, this method raises concerns in that mitochondria are not static or fixed structures. Mitochondria migrate and undergo rapid fusion and fission (Twig et al., 2008), resulting in movement out of the focal plane and decreased fluorescence intensity. Therefore, identification of changes in MMP based upon the fluorescence intensity of the confocal image requires costaining of mitochondria using an MMP-independent mitochondrial probe and determination of the ratio between the fluorescence intensities. Finally, it is important to remember that MMP measurement using these fluorescent probes is sensitive to the measurement conditions. Cells are to be measured at the same pH, temperature, and for the same time between the probe treatment and measurement.

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D. Decreased Autophagy The increase in mitochondrial mass in cells from aged individuals as well as in those undergoing senescence may be due to a decline in autophagy as mentioned above (Bergamini et al., 2007; Cuervo and Dice, 2000; Yen and Klionsky, 2008). The reason for this decline is not known in detail, but a decrease in autophagosome formation combined with a delay in autophagosome elimination may conceivably lead to decreased turnover of mitochondria and other damaged organelles. Recent evidence indicates that SIRT1, an NAD+-dependent deacetylase involved in a variety of physiological processes such as metabolism, cell survival, and senescence plays an active role in autophagosome formation by deacetylating and thereby activating several key autophagy proteins such as Atg5, Atg7, and Atg8 (Lee et al., 2008). A progressive decrease in SIRT1 activity during the cellular replicative life span has been reported (Sasaki et al., 2006), and this in combination with the degeneration of mitochondria may contribute to the senescence-associated decline in mitophagy. Meanwhile, autophagosome elimination may decline in part due to lysosomal dysfuction. Either lipofuscin overload or AGE-induced permeabilization of the lysosomal membrane (Patschan et al., 2008) may cause a decrease in lysosomal enzyme activity or autophagolysosomes formation.

1. Determination of Autophagy Activity LC3 protein is a key factor in the process of autophagosome formation. During the early stage of autophagosome formation, the C-terminus of LC3 is removed by the action of a cysteine protesase forming a smaller species (LC3-II), and the exposed glycine residue is conjugated to phosphatidylethanolamine on the autophagosome membrane. LC3-II is easily separated from the inactive full-length LC3 protein, and increases in its level can be monitored by SDS-PAGE to confirm autophagy activation. However, LC3 protein levels are difficult to predict due to variations between cells in their levels as wells as in the rates of autophagosome formation and elimination. Therefore, an increase in the ratio of LC3-II molecule/LC3 molecule rather than a change in LC3-II protein level alone can be considered a supportive evidence for autophagy activation (Fig. 9 and see Proikas-Cezanne et al., 2007 for an example). Frequently, GFP (or other types of fluorescence protein)-tagged LC3 is transfected into test cells, and increased formation of GFP puncta in the cytosol can be considered an indicator of increased autophagosome formation (and autophagy activity) (Proikas-Cezanne et al., 2007, for an example). Puncta formation is also observed with endogenous molecules by immunofluorescence (Kang and Hwang, 2009). LC3 puncta colocalization with or localization near mitochondria is also presented as supportive data for autophagy activation (Kim and Lemasters, 2011). However, LC3 proteins in autophagosomes are eventually removed through lysosome-mediated degradation, which makes detection of LC3 puncta formation unreliable. Sometimes, treatment with an inhibitor of autophagosome–lysosome fusion (bafilomycin A1) or lysosome acidification (monensin) results in higher levels of

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Fig. 9 WIPI-1 puncta formation in cells with active autophagy. Human G361 cells were treated with rapamycin or incubated in EBSS medium (as an amino acid-deprivation treatment) to induce autophagy in the presence or absence of wortmannin (an inhibitor of autophagy). (A) WIPI-1 puncta formation was detected in the rapamycin-treated cells by immunofluorescence (antiWIPI-1 antiserum/anti-rabbit IgG Alexa 488) and confocal microscopy. (B and C) The percentage of puncta-positive cells and the ratio of puncta/nonpuncta cells are presented. These graphs demonstrate that WIPI-1 puncta formation is attenuated by wortmannin treatment. (d) LC3-I and LC3-II proteins were detected by Western blotting using anti-LC3 antibody, and the LC3II/LC3-I ratios, which have been used as a major indication of autophagy activation, are presented in the bar graph. Different from WIPI-1 puncta formation, the increase in LC3-II/LC3-I ratio was not completely attenuated by wortmannin treatment. (Reprinted with permission from Proikas-Cezanne et al., 2007. Copyright 2007 Elsevier.)

LC3 puncta and helps to demonstrate autophagy activation. GFP-LC3 has been used in flow cytometry for quantification of autophagy activation. Different from LC3 puncta, GFP-LC3 fluorescence decreases during active autophagy due to rapid autophagolysis. In a study, the mean cellular GFP fluorescence decreased by 50% after 3 h of starvation (a known inducer of autophagy) in a manner sensitive to inhibitors of autophagy or lysosome activity (Shvets et al., 2008). Meanwhile, WDrepeat protein interacting with phosphoinositides (WIPI-1), another component of autophagosome, has been reported to accumulate upon autophagy stimulation, forming puncta that overlap with those of LC3 (Fig. 9A–C). WIPI-1 puncta appear to form more reliably than LC3 puncta do (Proikas-Cezanne et al., 2007), implying they may be better suited for quantitative analysis of autophagy.

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VI. Changes in the Level of Reactive Oxygen Species (ROS) Various ROS and nitrogen species (RNS) are produced in cells. Superoxide (O2
), hydroxyl (OH), peroxyl (RO2
), alkoxyl (RO), hydroperoxyl (HO2) radicals, and nonradical species such as hydrogen peroxide (H2O2), hypochlorous acid (HOCl), ozone (O3), singlet oxygen (1O2), and peroxynitrite (ONOO
) are those. ROS production in tissues increases with age (Perez-Campo et al., 1998; Sohal and Sohal, 1991) and in cells as they proliferate toward the end of their replicative life span (Hutter et al., 2002; Kang et al., 2006). ROS production also increases during Ras-induced senescence in human fibroblasts (Lee et al., 1999) and DNA damageinduced senescence in human fibroblasts and cancer cells (Song et al., 2005). ROS not only increase during senescence but also function as a common trigger of cellular senescence. Human fibroblasts grown in low oxygen proliferate for a longer time (Packer and Fuehr, 1977), whereas those grown in high oxygen have a shorter life span with accelerated telomere-shortening rates (Von Zglinicki et al., 1995). Treatment of primary fibroblasts with nonlethal doses of H2O2 induces the senescence phenotype (Chen and Ames, 1994). Meanwhile, Ras-induced senescence could be attenuated either by reducing ambient oxygen or by treatment with an antioxidant (Lee et al., 1999). Superoxide, hydrogen peroxide, and hydroxyl radical are directly associated with oxidative damage in cells. The levels of both superoxide and hydroxyl radical increase in mitochondria and cytosol during senescence (Fig. 10). Especially, a large quantity of superoxide anion is produced and serves as a major source of elevated levels of hydrogen peroxide and hydroxyl radical. Most superoxide anions are produced through electron leaking during oxidative phosphorylation in mitochondria, especially at complex I (NADH dehydrogenase) and complex III (ubiquinone– cytochrome c reductase) (Turrens, 1997). In proliferating human cells, approximately 0.1–4% of electrons are estimated to leak from the electron transport chain and interact with molecular oxygen to form superoxide anion (Batandier et al., 2002; Kudin et al., 2004). ROS production from mitochondria and its role in senescence and aging support the ‘‘mitochondria theory of aging’’ hypothesis (Linnane et al., 1989; Wei and Lee, 2002). Oxidative stress is an important contributor to mitochondrial dysfunction during aging and senescence. An increase in mitochondrial ROS production was shown to directly impair mitochondrial respiratory function (Melov et al., 1999). mtDNA, which lack protective histones, are direct targets of ROS attack (GarciaRuiz et al., 1995; Wei, 1998). And, damage-induced mtDNA mutations would lead to expression of defective proteins in electron transport chain complexes, which augments ROS production. Thereby, it is predicted that ROS are produced by dysfunctional mitochondria and aggravate the problem by causing more damage to mitochondria and mtDNA, thus leading to even more ROS production (Balaban et al., 2005; Harman, 1972). Although plausible, this hypothesis still lacks certain critical data (e.g., data that rule out association of mitochondrial dysfunction with other factors involved in aging or senescence).

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Fig. 10

Increase in levels of superoxide and hydroxyl radicals in mitochondria and the cytosol of senescent cells. MCF-7 cells were induced to undergo senescence by a pulse of 0.5 mM adriamycin as described in Fig. 1 (left). At day 6 postadriamycin pulse, cells were incubated with 0.1 mM MitoSox (Invitrogen), 5 mM DHE, 15 mM DHR123, or 10 mM DCF for 30 min, followed by flow cytometry to determine the levels of mitochondrial and cytosolic superoxide and of mitochondrial and cytosolic hydroxyl radical, respectively. Note that the increase in cytosolic and mitochondrial superoxide levels is more prominent than that of hydroxyl radical in this type of DNA damage-induced senescence. (Sohee Cho, unpublished data.)

It is not easy to correctly quantify the presence of ROS in cells due to their short lifetime and the presence of a variety of antioxidant chemicals and enzymes at different levels in cells (Khan et al., 1992). Quantitative analysis can be further hindered by the high intracellular concentration of glutathione, which can form thiyl or sulfinyl radicals while at the same time reducing oxygen species (Winterbourn and Metodiewa, 1994). In addition, metal ions that are present at variable concentrations can either promote or inhibit radical reactions (Khan et al., 1992). Among the many ROS, superoxide and hydroxyl radicals are exclusively assayed since they are the major species that cause the most damage to cellular macromolecules and due to their well-established methods for detection and quantification.

A. Changes in Level of Superoxide Hydroethidine (HE), more frequently called dihydroethidium (DHE), is one of the most widely used fluorogenic probes for detection of intracellular superoxide. DHE, a reduced form of ethidium (Etd+), is rapidly taken up by live cells and oxidized to

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Etd+, which then intercalates into nucleic acids and emits fluorescence at 610 nm when excited at 535 nm (Bucana et al., 1986)). However, DHE can be oxidized by H2O2 (Rothe and Valet, 1990) or by cellular processes involving peroxidases, oxidases, or cytochrome C to yield Etd+ (Benov et al., 1998; Bucana et al., 1986; Henderson and Chappell, 1993; LeBel et al., 1992; Zhu et al., 1994). Consequently, an increase in Etd+ fluorescence may not necessarily prove the increase in superoxide production. However, it was recently found that DHE oxidation by O2
is a two-step mechanism in which DHE is further oxidized by O2
to yield hydroxylated ethidium (HO-Etd+) (Zhao et al., 2003). HO-Etd+ is also a red fluorescent product, but it also has a distinct excitation wavelength (at 396 nm) (Robinson et al., 2006). Therefore, the cellular superoxide level can be specifically quantified and analyzed by fluorometry and flow cytometry (and visualized by fluorescence microscopy) by measuring the fluorescence intensity of HO-Etd+ using excitation and emission wavelengths of 400 nm and 590 nm, respectively. Recently, DHE was modified to allow detection of superoxide within mitochondria. DHE was covalently attached to hexyl triphenylphosphonium. The three lipophilic phenyl groups facilitate penetration of the molecule through the membranes, and the positive charge of phosphonium also facilitates its accumulation in the mitochondrial matrix, which has negative membrane potential (Ross et al., 2005). In mitochondria, this compound is then oxidized by superoxide and emits bright red fluorescence. This modified molecule has been commercially termed ‘‘MitoSOXTM Red’’ (Molecular Probes) and is used frequently to monitor changes in the O2
level in mitochondria (Fig. 10C). MitoSOXTM Red reagent was shown to be readily oxidized by superoxide but not by other ROS or RNS, and oxidation of the probe was shown to be prevented by superoxide dismutase (Janes et al., 2004).

B. Changes in Level of Hydroxyl Radicals In cells, hydroxyl radical is mostly derived from superoxide in a reaction called the Fenton reaction, which is catalyzed by Fe2+ or other transition metals (Chevion, 1988). The hydroxyl radical is very reactive and has a lifetime of about 2 ns in aqueous solution, and induces peroxidation of molecules present only in very close proximity. Consequently, quantitative detection of hydroxyl radical requires high sensitivity. Fluorogenic probes used for detection of cellular levels of hydroxyl radicals have been produced by exploiting a property common to fluorescent dyes such as fluorescein and rhodamine. The reduced ‘‘dihydro’’ forms of these molecules are nonfluorescent but freely permeate the lipid bilayer, similar to DHE (aforementioned). However, inside cells, these modified probes are readily oxidized by hydroxyl radicals to emit fluorescence. Nonfluorescent 20 ,70 -dichlorodihydrofluorescein diacetate (H2DCF-DA) crosses the cell membrane and is deacetylated by cellular esterases to 20 ,70 -dichlorodihydrofluorescein (H2DCF), which is then rapidly oxidized to highly fluorescent 20 ,70 -dichlorofluorescein (DCF) by the radicals (Brandt

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and Keston, 1965; LeBel et al., 1992; Zhu et al., 1994). The fluorescence intensity of DCF can be quantitatively detected and analyzed by fluorometry or flow cytometry or visualized by fluorescence microscopy at excitation and emission wavelengths of 480 nm and 530 nm, respectively (or with excitation sources and filters appropriate for fluorescein (FITC)) (Fig. 10). Carboxy or chloromethyl (CM) derivatives of H2DCF-DA (carboxy-H2DCF-DA or CM-H2DCF-DA) and a fluorinated derivative (H2DFFDA) have been developed for better retention and improved photostability, and they are currently commercially available (Molecular Probes). It should be noted that H2DCF is oxidized by peroxynitrite anion (ONOO–), horseradish peroxidase, or Fe2+ (in the absence of H2O2) (Myhre et al., 2003), which means it is not exclusively specific for hydroxyl radical. It is also important to note that regarding the use of these fluorogenic dyes, cells should be washed and resuspended in buffer free of phenol red or other colorimetric dyes prior to and during the measurement. Since the dyes are susceptible to photooxidation, work should be carried out in the dark. Dihydrorhodamine 123 (DHR123), like MitoSOXTM Red (aforementioned), freely passes through membranes and are then selectively trapped in mitochondria due its positive charge (Johnson et al., 1980) and oxidized to rhodamine 123, which emits fluorescence. DHR 123 reacts with H2O2 and peroxynitrite but poorly with superoxide (Henderson and Chappell, 1993). Therefore, DHR 123 provides information on the overall levels of nonsuperoxide ROS in mitochondria. A modified dihydrorhodamine (DHR 6G), whose oxidized form has a longer wavelength spectrum than rhodamine 123, is commercially available (Invitrogen) and can be used in multifluorescent probe analysis as well as for simultaneous measurement of ROS and autofluorescence (Wersto et al., 1996). All these reduced (and nonfluorescent) chemicals are slowly oxidized in air back to the parent fluorescent dyes and therefore should not be stored for too long, especially since this oxidation is accelerated by exposure to light.

VII. Changes Associated with Nucleus and Chromosomes The structure of the nucleus and chromatins also undergo conspicuous changes during cellular senescence. The appearance of SAHF is a well-documented nuclear trait of cellular senescence. This phenotype has not been addressed in a population study and can only be demonstrated in individual cells examined by confocal imaging. However, since SAHF is the most frequently referred to nuclear phenotype of senescence, it is mentioned here in detail. When actively proliferating or early-passage cells are treated with 40 ,6-diamidine20 -phenylindole dihydrochloride (DAPI), a fluorochrome that binds selectively to DNA, nuclei appear as bright ovals with relatively uniform distribution of fluorescence. However, when a senescent cell is stained, numerous bright foci appear in the fluorescent image of the nucleus. These foci represent severely condensed chromatin called SAHF (Narita et al., 2003). SAHF appear in cells in either replicative or

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[(Fig._1)TD$IG]

Fig. 11 Accumulation of heterochromatin foci in the nuclei of senescent cells. IMR90 cells at an early passage were either mock-treated or induced to undergo senescence (by the expression of H-rasV12 (Ras) or MEK1 Q56P (MEK)). High SA b-Gal activity confirms senescence. Along with these, IMR90 cells at a late-passage, those induced to quiescence (low serum) or those expressing both E1A and H-rasV12 (E1A/Ras) (E1A blocks oncogenic Ras-induced senescence) were treated with DAPI 6 days posttreatment. Enlarged images of DAPI-stained nuclei shown in the lower panels indicate that the nuclei in the control and nonsenescent cells produce a rather uniform staining pattern while those in the senescent cells are represented by small fluorescent puncta, which are referred to as senescence-associated heterochromatin foci. Scale bars are equal to 10 mm. (Reprinted with permission from Narita et al., 2003. Copyright 2003 Elsevier.)

induced senescence (Narita et al., 2003) (Fig. 11) and also marks senescent cells found in tumor masses in animal models (Braig et al., 2005). Due to the simplicity of the method (does not even need a confocal microscope), it has great potential to be utilized in the studies on senescence and aging. SAHF may be a mechanism underlying the irreversible growth arrest of senescent cells. In SAHF, proteins such as heterochromatin protein 1 (HP1) and histone H3 methylated on Lys 9 (H3K9m) bind to DNA en masse and seem to cause transcriptional incompetence (Narita et al., 2003; Zhang et al., 2005). Each SAHF is quite large and appears to cover a large portion of, if not the entire, chromosome. Genes that are embedded in SAHF include E2F-responsive genes, most of which are required for cell cycle progression (Narita et al., 2003). The molecular mechanism for SAHF formation is not well understood, although high-mobility group A (HMGA), nonhistone proteins, and a group of chaperones that deposit histones into nucleosomes have been shown to be involved (Narita et al., 2006; Zhang et al., 2005, 2007). It is noteworthy that SAHF formation in WI38 and IMR90 human fibroblasts is quite pronounced, but not in BJ human fibroblasts (Denoyelle et al., 2006; Narita et al., 2003). No solid explanation of this discrepancy has been provided, but this suggests that the activity of factors controlling SAHF formation is not modulated equally in all cell types. Meanwhile, although the occurrence of SAHF-positive cells in tissues of aged

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humans has not yet been shown, the possibility has been suggested by the presence of SAHF in the tissues of aging baboons (Herbig et al., 2006; Smith et al., 2002).

VIII. Use of Flow Cytometry for Analysis of Cellular Senescence As mentioned often above, the occurrence of senescence in a cell population is not a synchronous event and often occurs stochastically. Any culture has a fraction of cells that exhibit certain phenotype(s) of senescent cells. Meanwhile, it is often the case that not all cells exhibit senescence in a population that is actually presumed to be in senescence. In a population approaching the end of its replicative capacity, the exact size of the fraction of cells that exhibit senescence cannot be predicted. Certainly, handling a whole population in a culture en bloc and obtaining quantitative data for senescence would cause inaccuracy and confusion, especially for a first-time researcher of senescence. For example, an increase in the quantity of a phenotype in a population of cells undergoing senescence may occur in two different situations. The portion of the cells that are positive for the activity of interest may increase, or the activity may increase in individual positive cells without an increase in cell number. Increases in both the number of positive cells and the activity of interest in a limited number of cells would result in a similar increase in the mean value of the whole culture. These two cases cannot be easily resolved in assays such as Western blotting or fluorometry, which analyze the whole population. However, differentiation of multiple populations in a test culture in flow cytometry would allow an accurate quantitative assessment of any cell physiological processes including senescence. Second, determination of a culture of cells as being senescent is frequently dependent on a single time point assay for a senescence phenotype such as SA b-Gal activity. However, expression of a single senescence phenotype in a culture sometimes may not prove senescence. For example, SA b-Gal activity also appears in cells that are not in senescence (Coates, 2002; Cristofalo, 2005; Yang and Hum, 2005). Lipofuscin accumulation may also be used as a marker for senescence, but it is also known to occur in cells in a state of temporary growth arrest (Collins and Brunk, 1976). This emphasizes the necessity for assaying multiple phenotypes, which is, in many cases, possible through flow cytometry. Finally, senescence has not been defined quantitatively as of yet. The kinetics and quantitative measurement of changes in the levels of various cellular traits associated with senescence are rarely considered important or subjected to thorough investigation. However, such studies certainly would reveal the interrelationship between those changes and promote understanding of cell biological and molecular networks of senescence.

IX. Conclusion Recently, evidence supports that senescence is not simply a passive state arrived by exhaustion of doubling capacity but instead a condition actively affecting body

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function. Its role in tumor suppression is one example. Furthermore, senescent cells actively secrete cytokines and various molecules, collectively termed the secretome, which are found to be involved in regulation of immune function and tissue remodeling in wounds healing (Adams, 2009; Campisi and d’Adda di Fagagna, 2007; Krizhanovsky et al., 2008; Swann and Smyth, 2007). More physiological roles (and pathological ones as well) for senescence are expected to be uncovered in the future, and this will definitely be accelerated by the understanding of the underlying mechanisms of the various cellular phenotypes, which requires improvement of the detection methods in terms of both sensitivity and diversity. Especially, methods that allow quantitative analysis of cellular changes will be of utmost importance. Furthermore, new senescence markers and methods that allow for quantitative analysis can be utilized as diagnostic and prognostic tools and also potentially used as screening tools for senescence-inducing drugs as anticancer agents. Overall, both flow cytometry and confocal imaging analysis are greatly advantageous over biochemical and cell biological methods. In addition to easiness, they allow quantitative estimates of multiple different phenotypes expressed in multiple cell populations simultaneously. Utilization of flow cytometry and confocal imaging analysis equipped with various fluorogenic probes for a variety of proteins and organelles will dramatically increase in future studies on senescence. The imageassisted cytometric approaches such as provided by laser scanning cytometry (Pozarowski et al., 2005; Zhao et al., 2010) will additionally be expanding analytical capabilities probing populations of cells undergoing senescence. The image-assisted cytometric approaches such as provided by laser scanning cytometry (Pozarowski et al., 2005; Zhao et al., 2010) will additionally be expanding analytical capabilities probing populations of cells undergoing senescence. Acknowledgments This work was supported by the Mid-Career Researcher Program through an NRF grant funded by the MEST (No. 2009-0086432) and the Center for Aging and Apoptosis Research (R11-2002-097-07002-0) funded by the MEST.

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CHAPTER 8

Measurement of Telomere Length Using PNA Probe by Cytometry Maurizio Carbonari,* Marina Cibati,* Nicla Sette,* Angela Catizoney and Massimo Fiorilli* * Clinical Medicine Department, University Sapienza, viale dell’Universit
a, Roma, Italy y Histology and Medical Embryology Department, University Sapienza, via Scarpa, Roma, Italy

Abstract I. Introduction to Telomeres II. Technical Background III. Methods A. Our Protocol Versus the Original Flow-FISH Protocol IV. Results V. Critical Aspects of the Flow-FISH Methodology VI. Conclusion and Perspectives References

Abstract Peptide nucleic acid (PNA) probes hybridize to denatured telomeric sequences in cells permeabilized in hot formamide. In reported protocols, the hybridization was conducted in solutions with high formamide concentrations to avoid the DNA renaturation that can hamper binding of the oligo-PNA probe to specific sequences. We postulated that telomeric DNA, confined in the nuclear microvolume, is not able to properly renature after hot formamide denaturation. Therefore, to improve hybridization conditions between the probe and the target sequences, it might be possible to add probe to sample after the complete removal of formamide.

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0091-679X/10 $35.00 DOI 10.1016/B978-0-12-385493-3.00008-5

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I. Introduction to Telomeres In 1938, Muller used for the first time the term ‘‘telomeres’’ to define the chromosome ends, whereas in 1941 McClintock described their chromosome stabilizing function (McClintock, 1941; Muller, 1938). The discovery of DNA structure (Watson and Crick, 1953), DNA polymerase (Bollum, 1960), and mechanism of DNA replication prompted Olovnikov in 1971 and Watson in 1972 to address the mechanisms involved in telomeres replication (Olovnikov, 1971; Watson, 1972). In 1980s, Elizabeth Blackburn, Carol Greider, and Jack Szostak found out that telomeres are DNA sequences with a structure that protects chromosomes from erosion and that a specific enzyme, telomerase, is involved in their length maintenance after mitosis (Greider and Blackburn, 1985, 1987, 1989; Szostak and Blackburn, 1982). These investigators have been recently awarded the Nobel Prize in Physiology or Medicine for their elucidation of the structure and function of telomeres. Subsequently, De Lange gave the first detailed characterization of the structure and variability of human autosomal chromosome ends (De Lange et al., 1990). The author reported that from these regions, sequences are lost during development leading to shortened and heterogeneously sized telomeres in somatic tissues, primary tumors, and most cell lines. In further studies, Harley demonstrated that the amount and length of telomeric DNA in human fibroblasts does in fact decrease as a function of serial passages during aging in vitro and possibly in vivo (Harley et al., 1990). However, he could not clarify whether this loss of DNA had a causal role in cell senescence. To further explore this hypothesis, Allsop examined the relationship between telomere length and replicative capability of fibroblasts from normal donors and subjects with the Hutchinson–Gilford syndrome of premature aging (Allsop et al., 1992). He determined also the relationship between telomere length in sperm DNA and donor age. Counter focused on populations that had become immortal. In these cells he observed telomerase expression, stabilization of telomeric DNA length and decrease in the frequency of dicentric chromosomes (Counter et al., 1992). Vaziri was the first to postulate that telomere shortening plays a role in the immune system aging and that it could account for some of the morbidity of elderly individuals and Down syndrome patients (Vaziri et al., 1993). Furthermore, the individual differences in mean telomere length seem to be genetically determined (Slagboom et al., 1994). In order to examine the role of telomerase in normal and neoplastic growth, Blasco et al. (1997) studied mice, which do not have the gene encoding the telomerase RNA component. She reported that telomerase is essential for telomere length maintenance but is not needed for tumor formation in mice. In 1999, Rufer carried out a study on 500 individuals ranging in age from 0 to 90 years, including 36 pairs of monozygous and dizygotic twins. For this purpose, she used quantitative fluorescence in situ hybridization and, for the first time on a so wide population, flow-fluorescence in situ hybridization (flow-FISH) technique. She reported that granulocytes and naive T cells have similar aging decline in

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telomere length most likely caused by accumulated cell divisions in hematopoietic stem cells, the common precursors of both cell types (Rufer et al., 1999). In 21st century, the concept that telomeres are simple noncoding protective zones at the ends of linear chromosomes has been extended and other additional telomere functions has been advanced (Gilson and G eli, 2007). According to Verdun’s works, telomeric sequences have to be recognized as DNA damage by the homologous recombination machinery so that their replication is achieved and they acquire the structure essential for their function (Gilson and G eli, 2007; Verdun and Karlseder, 2006) that consists also in cooperating with DNA replication and DNA-damage repair machineries (Verdun and Karlseder, 2007). Even more recent and revolutionary is Azzalin’s idea according to which telomeres are not silent genomic regions, but they are transcribed into TElomeric Repeat containing RNA (TERRA) molecules, which remain associated with telomeric chromatin, suggesting RNA-mediated mechanisms in organizing telomere architecture (Azzalin and Lingner, 2008).

II. Technical Background In this section, we decided to focus on papers dealing with hybridization procedures by sequence-specific probes applied to whole cells in suspension that are subsequently analyzed by flow cytometry. Baumann’s paper published in 1988 is the first to describe the application of flow cytometry and fluorescent in situ hybridization (FC-FISH) to intact cells in suspension, aiming to the detection of specific ribosomal RNA sequences in cells (Baumann and Bentvelzen, 1988). The authors say that in order to obtain good quality results, the probes must not be longer than 100–150 nucleotides. Moreover, the hybridization conditions have to be sufficiently mild (i.e., 50% formamide at 45
C) so that cell subpopulations can be recognized by their physical parameters. Furthermore, it is reported that the most important factor that limits the method sensitivity is the high level of autofluorescence especially of cultured cells. Two years later, Bayer and Baumann (1990) applied FC-FISH to 70% ethanol prefixed cells to measure beta-globin mRNA contents. Fluorescence and phasecontrast microscopy were used respectively to verify the fluorescence localization within the cells and the erythroid characteristics of positive cells and so to validate FC-FISH protocol. In 1991, Hong et al. (1991), for the first time, used as probes directly fluorochrome-conjugated oligodeoxynucleotides complementary to defined regions of the RNA. Nonspecific binding was reported as the major limiting factor of this technique (Hong et al., 1991). Based on fixation and hybridization conditions described by Baumann and Bentvelzen (1988), Cao et al. (1995) developed a FC-FISH technique using as probes sense and antisense biotinylated single-stranded RNA probes prepared by transcription from immunoglobulin heavy chain variable region genes.

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A milestone in FC-FISH development has been the introduction of the synthetic peptide nucleic acid (PNA) oligonucleotide probes to detect telomeric sequences. Even instances of high background fluorescence PNA probes allowed an intense staining of most telomeres. Initially, Lansdorp et al. (1996) used these probes in conjunction with quantitative fluorescence in situ hybridization procedure providing a FITC-labeled (CCCTAA)3 PNA probe and digital imaging microscopy. In 1997, as reported in Lansdorp’s patent (Lansdorp, 1997), the use of PNA probe was extended to telomeric sequences detection in whole cells to be analyzed by flow cytometry. During the next year, Rufer et al. (1998) published the first scientific paper dealing with telomere length measurement in human lymphocytes by flow cytometry and PNA probes (flow-FISH). Two key aspects of this procedure require the use of fluorochrome-labeled PNA probes and cell permeabilization/fixation combined with DNA denaturation performed directly by heating at 80
C in a solution containing 70% formamide. The authors indicate that the fixation step prior to the heat formamide treatment applied in the most of the previous protocols, results in reduced accessibility of target sequences and contributes to variable fluorescence signals. In the same year, Hultdin et al. (1998) published a paper in which they described a quantitative flow-FISH protocol similar to the protocol of Rufer et al. (1998). Nevertheless, it is possible to point out some differences between the two procedures. First, Hultdin et al., reintroduce the prefixation step that is performed by a commercial kit (Fix & Perm, Caltag Laboratories). Second, they use an internal cell line control, giving an automatic compensation for potential differences in the hybridization steps. The control cell population also serves as an internal telomere length standard, which makes it possible to compare different samples with high precision. Finally, total DNA is counterstained by propidium iodide at low concentration (0.1 mg/mL) so that telomere fluorescence signal can be determined in different cell cycle phases, indicating that in human cells the vast majority of telomeric DNA is replicated early in S phase. Based on the protocols described in the last two papers, several authors performed investigations combining telomere analysis with immunophenotyping (Batliwalla et al., 2001; Norrback et al., 2001; Plunkett et al., 2001; Schmid et al., 2002) in order to obtain data about telomere length in cell subpopulations without a preliminary sorting. However, in 2009, Kapoor stated that most traditional organic fluorochromes (i.e., fluorescein, phycoerythrin, etc.) have limited thermal stability even when the antigen–antibody–fluorophore complex is covalently crosslinked. Thus, the author proposed quantum dot antibody conjugates directed against monocyte and T lymphocytes antigens to simultaneous phenotype-specific telomere length measurement (Kapoor et al., 2009). Flow-FISH protocol was also applied to determine telomere length dynamics in proliferating cells belonging to different generations tracked by dye (carboxyfluorescein diacetate succinimidyl ester, CFSE) dilution (Potter and Wener, 2005). The last development of flow-FISH methodology is represented by the protocol that we recently set up. Our aim was to solve, by a different approach, the

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hybridization problem between PNA probes and target sequences in whole cells in suspension (Carbonari et al., 2010). We postulated that the telomeric DNA, confined in the nuclear microvolume, is not able to properly renature after hot formamide denaturation. This would be not only due to secondary intrastrand structures formation but probably also to cross annealing among strands of repeated noncoding (like telomeric sequences) and coding sequences. The repeated sequences represent most of the chromosomes (Faulkner et al., 2009), and thus hamper each other in the attempt to renature. Therefore, to improve hybridization conditions between the probe and the target sequences, it could be possible to add the probe to the sample after the complete removal of formamide. According to this hypothesis, incubation of the sample with the probe and stringency washes can be performed under mild conditions (without formamide and at room temperature), thus increasing the efficiency, specificity, and proportionality of the binding of the probe to the target sequences, not hampered anymore by the presence of formamide. Moreover, our procedure, not requiring hot step in formamide solution for the probe, permits the use of oligo-PNA probes conjugated with thermolabile fluorochromes.

III. Methods A. Our Protocol Versus the Original Flow-FISH Protocol In this section, our protocol (Carbonari et al., 2010) and we define the original flow-FISH one based on the procedure proposed by Lansdorp’s group (Rufer et al., 1998) will be reported step by step. Our aim is to point out the different approaches adopted by these two procedures. Crucial stages characteristic of each protocol are underlined.

1. Our Protocol           

3  105 cells washed with PBS 0.15 M pH 7.2 containing 10% FCS. Resuspend in 300 mL of denaturing solution containing 50% formamide, 10% FCS, and PBS 9 mM. Incubate for 10 min at RT, 15 min at 87
C, and 10 min at RT. Centrifuge at 800g for 3 min at 20
C. Washed twice at 800g for 6 min at 20
C with 1 mL of PBS 10% FCS. Resuspend in 0.5 mL of PBS 10% FCS containing 20 nM PNA probe Cy5-OO(CCCTAA)3. Incubate overnight at RT in the dark with gentle rotation. Washed once at 800g for 6 min at 20
C with 1 mL of PBS 10% FCS. Resuspend in PBS 10% FCS. Incubate for 10 min at RT. Centrifuge at 800g for 6 min at 20
C.

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Resuspend in 0.5 mL of PBS 10% FCS containing ethidium bromide (EB) 1 mg/mL. Incubate for at least 30 min at RT in the dark before acquisition. Negative control sample (autofluorescence + aspecific binding) is treated as described above, but it is not heated at 87
C for 15 min.

2. Original Flow-FISH Protocol  

     

3  105 cells washed with PBS 0.15 M pH 7.2 containing 10% FCS. Resuspend in 300 mL of denaturing/hybridization solution containing 70% formamide, 10% FCS, 20 mM Tris-Cl pH 7.0, and 20 nM PNA probe Cy5-OO(CCCTAA)3. Incubate for 10 min at RT, 10 min at 80
C, and overnight at RT in the dark with gentle rotation. Centrifuge at 800g for 3 min at 20
C. Washed twice at 800g for 6 min at 20
C with 1 mL of wash buffer containing 70% formamide, 10 mM Tris-Cl pH 7.0, 10% FCS, 0.1% Tween 20. Washed once at 800g for 6 min at 20
C with 1 mL of PBS containing 10% FCS and 0.1% Tween 20. Resuspend in 0.5 mL of PBS 10% FCS containing ethidium bromide 1 mg/mL. Incubate for at least 2 h at RT in the dark before acquisition.

Negative control sample (autofluorescence) is treated as described above, but it is not incubated with the probe. In order to obtain the relative telomere length (RTL) in samples treated with both protocols, the Cy5 mean fluorescence intensity (MFI) ratios between G1 + G0 events, subtracted of the corresponding negative control MFI and normalized for the DNA content have to be calculated. It should be noted that the Lansdorp’s group observed that heat treatment (80–87
C) of cells, without prior fixation, in presence of PNA probe and high concentration (70–75%) of formamide, led to both, cell permeabilization and fixation denaturation of DNA allowing efficient hybridization between the probe and the target sequences. The original flow-FISH protocol is based on this fundamental observation. In the attempt to understand the problems with the autofluorescence we encountered carrying out the original procedure, we mixed in a single tube (i) the cells that had been heat treated with PNA probe and (ii) control cells heat treated without probe (autofluorescence). The cytometric analysis of the mixed sample after overnight incubation, surprisingly, showed that fluorescence of control cells significantly increased. We postulated that DNA denatured in situ cannot completely renature after the ionic solvent having been removed and hybridization between the probe and the target sequences is still possible. Since the formamide inevitably reduces the strength of the bonds between oligo-PNA (18 nucleotides) and DNA, giving rise to very limited hybrid formation decreasing the efficiency, specificity, and proportionality of the assay, we decided to add the probe to the sample after complete removal of formamide. Continuing further optimization of our protocol

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we obtained the best results (cell recovery and parameters of structural cell preservation) for the cell models of interest for our laboratory (human lymphocytes and continuous cell lines lymphocyte derived) decreasing formamide concentration to 50%.

IV. Results Fig. 1 shows the typical results obtained by both our and the original flow-FISH protocol. The hypodiploid human Burkitt’s lymphoma B-cell line Ramos scattergrams (a, c) show that physical parameters are better preserved in cells treated with our protocol (a) than in cells treated with the original flow-FISH one (c). Corresponding overlaid histograms of PNA-Cy5 fluorescence, obtained with the same instrument setting, show the staining efficiency and the resolution (distance in channels between the negative control or autofluorescence and the sample peaks) of the two protocols. Although the negative control signal (autofluorescence + nonspecific binding) obtained following our procedure (b, dashed line) is higher than the autofluorescence signal in the flow-FISH original protocol (d, dashed line), the related staining efficiencies give to our protocol a resolution power better than that of the other procedure. In fact, the fluorescence intensity of hybridized Ramos cells treated with our protocol (b, continuous line) is approximately 10-fold higher than the original flow-FISH protocol Ramos cells treated fluorescence intensity (d, continuous line). Our procedure also provides improved results than the original one when human lymphocyte telomeres are analyzed. For this purpose, we used Ramos cells as internal standard since this near-diploid cell line telomere length is comparable to the lymphocyte one and so the fluorescence intensities can be acquired using a linear amplification. Overlaid histograms in panels e and f show the results of a comparative experiment where T lymphocyte telomere length is measured by our (e) and the original flow-FISH (f) protocol. It is noteworthy that FL4-H (PNA Cy5) PMT voltage was set at 660 for the sample treated with our procedure and at 950 for the other one. In order to validate the results obtained by the flow cytometer, we decided to verify cell localization and pattern distribution of the Cy5 PNA probe by confocal laser scanning microscopy. In Fig. 2, the nuclear and cytoplasmic compartments of Ramos cells treated following our hybridization protocol and examined in brightfield appear well preserved (Fig. 2A). Fig. 2B and C shows, respectively, ethidium bromide, staining total DNA, and Cy5 (telomeric DNA) fluorescences at nuclear level in the same cells. It is well evident that the EB fluorescence is distributed throughout nucleus while the Cy5 fluorescence is restricted to fluorescence spots (‘‘foci’’), as shown in Fig. 2D. Following our protocol optimization, the efficiency, specificity, and proportionality of the probe/telomere hybridization and the stoichiometric DNA staining (performed in the same tube) reached a quality level that it makes it possible to

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[(Fig._1)TD$IG]

Fig. 1 Comparison between typical results obtained with our and original flow-FISH procedure. FSC-H versus SSC-H contour graphs (a, c) show the better preservation of the physical parameters of the Ramos cells after our (a) and original flowFISH (c) treatment. Overlaid histograms (b, d) show the better PNA-Cy5 staining efficiency obtained with our (b) and original flow-FISH (d) procedure. Continuous lines correspond to hybridized samples, dashed lines correspond to our negative control (b) and original autofluorescence (d). Both samples were acquired using the same instrument setting. Overlaid histograms (e, f) show the better PNA-Cy5 staining efficiency obtained with our (e) and original flow-FISH (f) procedure on human T lymphocytes and Ramos cells mixed samples. Continuous lines correspond to hybridized Ramos cells and their control, dashed lines correspond to hybridized lymphocytes and their control. With our procedure (e), the FL4-H PMT voltage was 660 and with original flow-FISH (f), the FL4-H PMT voltage was 950.

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[(Fig._2)TD$IG]

Fig. 2 Laser scanning confocal microscopy. The image at this depth represents the section of the nucleus. Brightfield microscopy of Ramos cells treated with the hybridization protocol (A). Spread EB fluorescence (B) and spotted Cy5 fluorescence (C) in nuclei of the same cells. Overlayed EB and Cy5 fluorescences (D) indicating nuclear localization of PNA probe.

visualize different duplication kinetics of the total and telomeric DNA in the three cell lines studied. In Fig. 3, the biparametric plot of the total DNA content (FL2-A: Eth-Br Area) versus telomere fluorescence (FL4-H: PNA Cy5) of a sample containing the three cell lines of interest is shown (upper panel). R1-gated events correspond to the near diploid (with 20% polyploidy) human Burkitt’s lymphoma B-cell line Daudi, Ramos cells are gated in R2, and the near tetraploid human T-cell leukemia line 1301 in R3. The telomere fluorescences plotted in this panel agree with literature data reporting short telomeres for Daudi cells (Mochida et al., 2005), intermediate telomeres for Ramos cells (Mochida et al., 2005), and long telomeres for 1301 cells (Hultdin et al., 1998). In order to point out how the three cell lines duplicate telomeres during their total DNA replication we plotted (Fig. 3, lower panels) the ratio between telomere fluorescence and DNA content (FL4-H/FL2-A) versus this last parameter (FL2-A: Eth-Br Area). Daudi cells show a ratio constantly <1 and slightly decreasing along DNA replication. A more complex telomere duplication kinetic is observable in Ramos cells where the ratio starting from a value approximately equal to 1, after a stationary trend (corresponding to the cells in the S phase), decreases in G2/M cells. Even more complex is 1301 telomere duplication kinetic. In these cells the ratio is always >1 and after a remarkable decrease corresponding to the transition from G1 to early S phase, it increases until to reach a maximum and then decreases along the whole S phase. The ratio keeps decreasing also in the G2/M phase.

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[(Fig._3)TD$IG]

Fig. 3 Upper panel: biparametric plot FL2-A: Eth-Br area versus FL4-H: PNA Cy5 of the three cell lines Daudi (R1), Ramos (R2), and 1301 (R3) treated following our procedure. Lower panels: biparametric plots ratio FL4-H/FL2-Aversus FL2-A: Eth-Br area of R1-gated Daudi cells, R2-gated Ramos cells, and R3-gated 1301 cells.

V. Critical Aspects of the Flow-FISH Methodology The most critical stage of flow-FISH procedure is when the PNA probe (CCCTAA)3 has to be added. According to the original flow-FISH protocol it is essential to denature the DNA in the presence of the PNA probe to provide an access to the telomere before the chromosome strands could reanneal. Nevertheless, a 18base long oligonucleotide at room temperature (20–25
C) and in presence of formamide concentration 70% should have a low hybridization efficiency

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considering that its melting temperature ranges from 45 to 54
C depending upon other chemical/physical parameter values. This implies that formamide removal step, within the original flow-FISH protocol, causes PNA probe removal as well. In light of these observations, the hybridization between PNA probe and telomere sequences would seem difficult to standardize because of PNA propensity to dissociate from DNA. On the contrary, considering genomic DNA complexity, the presence of great number of repeated sequences in the in situ conditions of chromatin it is likely that after denaturation chromosome strands cannot rapidly and completely reanneal. Thus, when PNA probe is added after formamide removal it still effectively hybridizes and efficiency, specificity, and proportionality of the hybridization is improved. The addiction of the probe at this time might be justified also by PNAs ability to invade heat formamide destabilized DNA double strand. After a wide bibliographic research we found only one author attesting, in a total different context, that genomic DNA in whole cells remains denatured after heat formamide treatment and its complete removal (Frankfurt and Krishan, 2001). Other critical aspects of the methodology have been addressed by Baerlocher et al. (2002) who addresses six flow-FISH protocol basic steps individually: (1) cell separation/preparation; (2) DNA denaturation; (3) hybridization with telomere PNA probe; (4) washes to remove excess probe; (5) DNA counterstaining; and (6) acquisition and analysis on flow cytometer. Concerning step 1 we agree with Lansdorp’s group to consider heat formamide treatment adequate to obtain a good cell fixation/permeabilization even when formamide concentration is 50%. Thus, we judge useless and damaging the prior fixation step proposed by Hultdin et al. (1998) since it is performed by a reticulating agent that hampers DNA denaturation, target sequences accessibility, and DNA stoichiometric staining. Preliminary cell fixation performed by coagulant agents has been investigated by Derradji et al. (2005). We have already addressed steps 2–4 earlier in the text. DNA counterstaining used to represent a critical aspect of the flow-FISH original protocol because DNA dyes interfere by spectral overlap/energy transfer or quenching with PNA probe fluorescence (Baerlocher et al., 2002). In our protocol, the increased hybridization efficiency reduces a lot this kind of problem. This allows us to stain total DNA and telomeres in the same tube obtaining DNA stoichiometric staining of good quality (CVs < 5% and mode ratio G2–M/G0–G1 from 1.95 to 2.05). Now we want to focus on some important points about step 6. First, the original flow-FISH protocol provides only autofluorescence as negative control. In our opinion, negative control should include nonspecific PNA probe binding as well. Thus, in our procedure negative control consists of a sample incubated with the PNA probe, permeabilized but not completely denatured. Anyway, if negative control was restricted to autofluorescence only, PNA probe should be conjugated to a fluorochrome different from FITC that emits in a fluorescence range typical of the mammalian cells autofluorescence one (Shapiro, 2003). Finally, as for total DNA content analysis, a linear scale has to be adopted when telomere length is measured in order to increase the assay resolution. This is possible if total DNA content and telomere

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[(Fig._4)TD$IG]

Fig. 4

Lymphocytes hybridization: biparametric plot FL2-A: Eth-Br area (EB) versus FL4-H: PNA Cy5 (PNA Cy5) of T lymphocytes mixed with Ramos cells (a) and B lymphocytes mixed with Ramos cells (b). Samples were acquired with the same instrument setting for all parameters. EB and Cy5 MFIs were measured gating G1 + G0 Ramos cells (R1) and T (R2) and B (R3) lymphocytes.

length of the internal standard and of the cells of interest are not very different. For this reason, in spite of what is reported (Hultdin et al., 1998), our protocol provides both Ramos cell line as internal standard and telomere fluorescence acquisition on linear scale. A typical biparametric plot of total DNA content versus telomere fluorescence of human peripheral blood T and B lymphocytes is shown in Fig. 4.

VI. Conclusion and Perspectives The increasing interest for the telomere length measurement in medicine and biology (Calado and Young, 2009; Lansdorp, 2009) calls for flow-FISH protocol that would be optimal and universally adopted. Although the original flow-FISH protocol was presented over 10 years ago, some of its crucial steps have been revised and modified to this day (Derradji et al., 2005; Kapoor et al., 2009; Wieser et al., 2006). Thus, we hope that our protocol, based on a different experimental approach, after having been tested and validated in other laboratories, will be used to characterize telomere physiopathology with improved accuracy. There are divergent opinions regarding the possibility of accurate analysis telomere length and DNA concurrently with cell immunophenotype (Batliwalla et al., 2001; Kapoor et al., 2009; Norrback et al., 2001; Schmid et al., 2002). On the one hand, both the original flow-FISH protocol and our protocol did not give satisfying results in the multiparametric analysis combining cell phenotypes (surface and internal) and telomere length. On the other hand, the cells subjected to our protocol

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have well preserved physical attributes and their DNA can be stoichiometrically stained. At present, thus, the methodology is best suited to the concurrent analysis of telomere length with respect to the cell cycle phase, and the analysis can be performed in continuous cell lines as well as in primary cell cultures. Our current attempts are directed toward telomere length measurement in conjunction with dye dilution and DNA staining analysis in successive lymphocyte-derived blast cells generations (Potter and Wener, 2005).

References Allsop, R. C., and Vaziri, H., et al. (1992). Telomere length predicts replicative capacity of human fibroblasts. Proc. Natl. Acad. Sci. 89, 10114–10118. Azzalin, C. M., and Lingner, J. (2008). Telomeres. The silence is broken. Cell Cycle 7, 1161–1165. Baerlocher, G. M., and Mak, J., et al. (2002). Telomere length measurement by fluorescence in situ hybridization and flow cytometry: tips and pitfalls. Cytometry 47, 89–99. Batliwalla, F. M., and Damle, R. N., et al. (2001). Simultaneous flow cytometric analysis of cell surface markers and telomere length: analysis of human tonsilar B cells. J. Immunol. Methods 247, 103–109. Baumann, J. G. J., and Bentvelzen, P. (1988). Flow cytometric detection of ribosomal RNA in suspended cells by fluorescent in situ hybridization. Cytometry 9, 517–524. Bayer, J. A., and Baumann, J. G. J. (1990). Flow cytometric detection of beta-globin mRNA in murine haemopoietic tissues using fluorescent in situ hybridization. Cytometry 11, 132–143. Blasco, M. A., and Lee, H., et al. (1997). Telomere shortening and tumor formation by mouse cells lacking telomerase RNA. Cell 91, 25–34. Bollum, F. J. (1960). Calf thymus polymerase. J. Biol. Chem. 235, 2399–2403. Calado, R. T., and Young, N. S. (2009). Telomere diseases. N. Engl. J. Med. 361, 2353–2365. Cao, J., and Vescio, R. A., et al. (1995). Identification of malignant cells in multiple myeloma bone marrow with immunoglobin VH gene probes by fluorescent in situ hybridization and flow cytometry. J. Clin. Invest. 95, 964–972. Carbonari, M., and Mancaniello, D., et al. (2010). Improved procedure for the measurement of telomere length in whole cells by PNA probes and flow cytometry. Cell Prolif. 43, 553–561. Counter, C. M., and Avilion, A. A., et al. (1992). Telomere shortening associated with chromosome instability is arrested in immortal cells which express telomerase activity. EMBO J. 11, 1921–1929. De Lange, T., and Shiue, L., et al. (1990). Structure and variability of human chromosome ends. Mol. Cell. Biol. 10, 518–527. Derradji, H., and Bekaert, S., et al. (2005). Comparison of different protocols for telomere length estimation by combination of quantitative fluorescence in situ hybridization (Q-FISH) and flow cytometry in human cancer cell lines. Anticancer Res. 25, 1039–1050. Faulkner, G. J., and Kimura, Y., et al. (2009). The regulated retrotransposon transcriptome of mammalian cells. Nat. Genet. 41, 563–571. Frankfurt, O. S., and Krishan, A. (2001). Identification of apoptotic cells by formamide-induced DNA denaturation in condensed chromatin. J. Histochem. Cytochem. 49, 369–378. Gilson, E., and G eli, V. (2007). How telomeres are replicated. Nat. Rev. 8, 825–838. Greider, C. W., and Blackburn, E. H. (1985). Identification of a specific telomere terminal transferase activity in Tetrahymena extracts. Cell 43, 405–413. Greider, C. W., and Blackburn, E. H. (1987). The telomere terminal transferase of Tetrahymena is a ribonucleoprotein enzyme with two kinds of primer specificity. Cell 51, 887–898. Greider, C. W., and Blackburn, E. H. (1989). A telomeric sequence in the RNA of Tetrahymena telomerase required for telomere repeat synthesis. Nature 337, 331–337. Harley, C. B., and Futcher, A. B., et al. (1990). Telomeres shorten during ageing of human fibroblasts. Nature 345, 458–460.

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Maurizio Carbonari et al. Hong, Y., and Ernst, L., et al. (1991). Sensitive detection of RNAs in single cells by flow cytometry. Nucleic Acids Res. 20, 83–88. Hultdin, M., and Gr€ onlund, E., et al. (1998). Telomere analysis by fluorescence in situ hybridization and flow cytometry. Nucleic Acids Res. 26, 3651–3656. Kapoor, V., and Hakim, F. T., et al. (2009). Quantum dots thermal stability improves simultaneous phenotype-specific telomere length measurement by FISH-flow cytometry. J. Immunol. Methods 344, 6–14. Lansdorp, P. M. Inentor. (1997). PCT patent application WO 97/14026. Lansdorp, P. M. (2009). Telomeres and disease. EMBO J. 28, 2532–2540. Lansdorp, P. M., and Verwoerd, N. P., et al. (1996). Heterogeneity in telomere length of human chromosomes. Hum. Mol. Genet. 5, 685–691. McClintock, B. (1941). The stability of broken ends of chromosomes in Zea mays. Genetics 26, 234–282. Mochida, A., and Gotoh, E., et al. (2005). Telomere size and telomerase activity in Epstein–Barr virus (EBV)-positive and EBV-negative Burkitt’s lymphoma cell lines. Arch. Virol. 150, 2139–2150. Muller, H. J. (1938). The remaking of chromosomes. Collecting Net. 13, 182–198. Norrback, K. F., and Hultdin, M., et al. (2001). Telomerase regulation and telomere dynamics in germinal centers. Eur. J. Haematol. 67, 309–317. Olovnikov, A. M. (1971). Principles of marginotomy in template synthesis of polynucleotides. Doklady Akad. Nauk. SSSR 201, 1496–1499. Plunkett, F. J., and Soares, M. V. D., et al. (2001). The flow cytometric analysis of telomere length in antigen-specific CD8+ T cells during acute Epstein–Barr virus infection. Blood 97, 700–707. Potter, A. J., and Wener, M. H. (2005). Flow cytometric analysis of fluorescence in situ hybridization with dye dilution and DNA staining (flow-FISH-DDD) to determine telomere length dynamics in proliferating cells. Cytometry Part A 68A, 53–58. Rufer, N., and Brumm€ endorf, T. H., et al. (1999). Telomere fluorescence measurements in granulocytes and T lymphocyte subsets point to a high turnover of hematopoietic stem cells and memory T cells in early childhood. J. Exp. Med. 190, 157–167. Rufer, N., and Dragowska, W., et al. (1998). Telomere length dynamics in human lymphocyte subpopulations measured by flow cytometry. Nat. Biotechnol. 16, 743–747. Schmid, I., and Dagarag, M. D., et al. (2002). Simultaneous flow cytometric analysis of two cell surface markers, telomere length, and DNA content. Cytometry 49, 96–105. Shapiro, H. M. (2003). Practical Flow Cytometry, 4th edn., Wiley, Hoboken, NJ. Slagboom, P. E., and Droog, S., et al. (1994). Genetic determination of telomere size in humans: a twin study of three age groups. Am. J. Hum. Genet. 55, 876–882. Szostak, J. W., and Blackburn, E. H. (1982). Cloning yeast telomeres on linear plasmid vectors. Cell 29, 245–255. Vaziri, H., and Sch€ achter, F., et al. (1993). Loss of telomeric DNA during Aging of normal and trisomy 21 human lymphocytes. Am. J. Hum. Genet. 52, 661–667. Verdun, R. E., and Karlseder, J. (2006). The DNA damage machinery and homologous recombination pathway act consecutively to protect human telomeres. Cell 127, 709–720. Verdun, R. E., and Karlseder, J. (2007). Replication and protection of telomeres. Nature 447, 924–931. Watson, J. (1972). Origin of concatemeric T7 DNA. Nat. New Biol. 239, 197–201. Watson, J. D., and Crick, F. H. C. (1953). Molecular structure of nucleic acids. Nature 171, 737–738. Wieser, M., and Stadler, G., et al. (2006). Nuclear flow FISH: isolation of cell nuclei improves the determination of telomere lengths. Exp. Gerontol. 41, 230–235.

SECTION II

Pre-clinical and clinical applications

CHAPTER 9

Cytometry of Intracellular Signaling: From Laboratory Bench to Clinical Application David W. Hedley,* Sue Chow* and T. Vincent Shankeyy * y

Ontario Cancer Institute/Princess Margaret Hospital, Toronto, Ontario, Canada

Systems Research/Cellular Analysis Business Group, Beckman Coulter, Inc., Miami, Florida, USA

Abstract I. Introduction A. Activation States Are Transient B. Most Signaling Pathways Exist in Their Ground State C. Constitutive Activation of Signaling Pathways Can Occur in Leukemia II. Rationale III. Methods A. Protocol for Whole Blood Fixation and Red Cell Lysis B. Activators and Inhibitors of Signaling Pathways IV. Pathway Activators V. Pathway Inhibitors A. Freezing Samples VI. Materials A. Fixation and Red Cell Lysis B. Pathway Activators C. Pathway Inhibitors D. Antibodies VII. Discussion VIII. Summary References

Abstract The analysis of signaling pathways based on combinations of phospho-specific antibodies is now a well-recognized flow cytometry technique. Despite its wideranging potential in the fields of biology, industry, and medicine, it has been relatively slow to gain widespread use, and is often considered to be technically METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

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challenging. In this chapter, we detail protocols developed in our laboratory for monitoring signaling pathways in blood samples based on combinations of phosphospecific antibodies. Emphasis is placed on clinical application. The assays have a modular design, with a core protocol for whole blood fixation and lysis, a suite of agents that can acutely activate or inhibit the different signaling pathways, and a wide range of phospho-specific antibodies as the readout.

I. Introduction Biological processes are regulated by signaling pathways that sense inputs such as growth factors or the cell’s microenvironment, and result in alterations in gene expression, cell movement, or metabolism. These processes are fundamental to all life forms, and are highly regulated through feedback loops and interactions between signaling pathways. Derangements occur during pathological processes including inflammation and cancer, and in recent years the study of cell signaling has become closely linked to the development of novel agents to treat these conditions by effects on specific signaling elements involved in disease processes. The biochemistry of signal transduction involves reversible protein interactions and modifications including phosphorylation at serine, threonine, or tyrosine sites. The use of phospho-specific antibodies that recognize the transient alterations produced by kinases and phosphatases involved in cell signaling is now well recognized. Their introduction has revolutionized the study of cell signaling, and is responsible for much of our current understanding of how signaling pathways interact to regulate gene expression and cellular metabolism. In recent years, specific and high-affinity monoclonal antibodies have been developed that recognize a wide range of phosphorylation sites on signaling proteins. Many of these antibodies are suitable for cellular analysis, including flow cytometry, and can be used to study cell signaling in clinical samples. The need for robust techniques to measure signaling pathways in patient samples has become particularly pressing in oncology, due to the introduction of novel treatments that target signaling pathways. Increasingly the emphasis of molecular oncology is on personalized medicine, where treatment selection is based on the molecular processes occurring in an individual patient’s cancer cells, rather than on traditional clinicopathological classification. Furthermore, during the early phases of drug development, there is a requirement to optimize dose schedules based on the pharmacodynamic effects of its action on signaling pathways during treatment. Accordingly, the flow cytometry applications detailed in this chapter have been optimized for the analysis of blood samples obtained from cancer patients during treatment with agents that inhibit signaling pathways (Chow et al., 2001, 2005, 2006, 2008; Hedley et al., 2008; Tong et al., 2006). However, with appropriate modifications they can be applied to blood samples from patients with nonmalignant disorders, or to experimental systems based on cell suspension cultures. Furthermore, because signaling pathways are highly conserved, many of the phospho-specific antibodies developed for human application are specific to the corresponding site across a wide range of species.

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A. Activation States Are Transient Although signaling proteins are fairly long-lived, their activation states are typically transient, and depend on the opposing effects of upstream kinases, which add phosphate groups to specific amino acids, and phosphatases, which remove them. Therefore, the magnitude of the signaling response, determined by comparing the basal level to the activated state, is highly dependent on the time point measured following the addition of an activating stimulus such as a growth factor. For these reasons, experiments designed to compare effects between samples, for example, ex vivo drug sensitivity testing or pharmacodynamic monitoring of drug effects in patient samples, demand a high level of consistency in sample handling.

B. Most Signaling Pathways Exist in Their Ground State With notable exceptions, signaling pathways in samples that can be analyzed by flow cytometry show low levels of basal activity. Therefore, a single measurement of unperturbed cell populations is relatively uninformative. However, analysis based on the correlated measurement of multiple signaling elements and their responses to different growth factors provides a very powerful approach to study how signaling pathways interact to regulate biological processes at the single cell level, and to identify critical aberrations in cell signaling that occur in diseased states including cancer and leukemia. This approach, substantially developed by Nolan and coworkers, is now being developed into a standardized test of great potential value in the clinic (Irish et al., 2004; Kotecha et al., 2008; Krutzik and Nolan, 2003; Perez and Nolan, 2002).

C. Constitutive Activation of Signaling Pathways Can Occur in Leukemia In some situations, constitutive activation of signaling pathways can be detected in leukemia samples in the absence of growth factor activation. For example, in chronic myeloid leukemia (CML) cell lines and patient samples, high levels of STAT5 phosphorylation can be detected in the majority of cases (Hedley et al., 2008; Jacobberger et al., 2003). Treatment with the bcr/abl kinase inhibitor imatinib (Gleevec) results in the rapid loss of P-STAT5, consistent with the aberrant activation of this pathway playing an important role in the progression of CML, and suggesting the potential to monitor the pharmacodynamic effects of bcr/abl inhibitors by measuring P-STAT5 levels in CML patient blood samples (Hedley et al., 2008; Jacobberger et al., 2003). Constitutive activation of signaling pathways in acute leukemia patients is less well documented compared to CML. However, with refinements in laboratory technique, and the availability of higher affinity/specificity phospho-specific antibodies, in our hands abnormalities can be detected in peripheral blood samples from the majority of acute leukemia patients (Fig. 1). The patterns of alteration show greater heterogeneity than CML, consistent with acute leukemia being a much more heterogeneous disease. As detailed below, aberrant activation of signaling elements

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Fig. 1 Examples of constitutively activated signaling involving STAT5, ERK, Akt, and S6 in peripheral blood samples from patients with acute myeloid leukemia. Data obtained using a quadruple staining protocol, with the fluorescence conjugates shown for each marker, combined with surface immunophenotype. Individual phosphoepitopes are shown plotted against side scatter, showing effects of the indicated pathway inhibitors. Right panels are single parameter histograms for each signaling marker (after gating on the blast cell population), overlaid to show effects of signaling inhibitors.

can be inferred by comparing the staining intensities of the leukemic and normal blood cell populations in the sample, and by observing a decrease in phosphorylation upon the addition of pharmacological inhibitors of the upstream pathway. Constitutive phosphorylation of STAT5 in AML patients appears to be particularly prominent in cases carrying the internal tandem duplication (ITD) of the Flt-3 receptor, whereas in our hands the S6 ribosomal subunit is the most commonly affected signaling protein, with increased levels of pS6 being detectable in the majority of patients (Chow et al., 2006). In many AML patient samples, the extent of S6 phosphorylation is extremely heterogeneous, as seen in the example in Fig. 1. As illustrated in Fig. 2, S6, which is involved in protein translation, can be activated

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Fig. 2 Schematic of signaling pathways discussed in this chapter, including major interactions, and sites of action of pathway activators and inhibitors. Phosphoepitopes illustrated in Fig. 1 are indicated by -PO4.

in response to upstream signaling via the PI3-kinase/Akt/mTOR and the ERK pathways, which intersect at various points. Interestingly, we have previously published that AML patient samples showing increased pS6 differ with respect to the relative inputs from these two pathways, as shown by differences in their responses to mTOR and ERK pathway inhibitors (Chow et al., 2006). In the example shown in Fig. 1, pS6 appears to be driven substantially via the ERK pathway, since treatment with the MEK inhibitor U0126 strikingly reduces the level of staining. In acute leukemia, cell proliferation occurs in response to signals derived from bone marrow stroma, and circulating blasts are cytokinetically quiescent. Although it might be speculated that constitutively activated signaling seen in peripheral blood samples indicates a degree of autonomy from the bone marrow environment, further work is needed to establish if this is clinically relevant, or if the patterns are correlated with specific genetic/epigenetic changes such as Flt-3 ITD mutations.

II. Rationale At the present time, clinical application is limited to blood samples or bone marrow aspirates (which are processed using the same protocol). We have also studied cell signaling in solid tumors, using fine needle aspiration samples and slide-based cytometry (Schwock et al., 2007), but that is outside the scope of this chapter. Sample preparation involves whole blood processing, rather than initial isolation of mononuclear cells, in order to preserve drug:target equilibrium in pharmacodynamic studies, and constitutive activation of signaling proteins in leukemia patient samples. Positive and negative controls are established by the addition of agents to activate the pathways of interest, or by incubation with appropriate pharmacological inhibitors in order to measure constitutively activated pathways.

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The different populations of cells present in blood can also be used as internal controls for many of the applications. Because this approach is particularly powerful studying signaling interactions within subpopulations of cells defined by phenotypic markers, the protocols described below were developed in order to optimize signal-to-noise ratios for a wide range of signaling targets, while preserving light scatter and surface immunophenotypic markers. Following the addition of pathway activators and inhibitors to sample aliquots, these are fixed by the addition of formaldehyde. As well as preserving light scatter and intracellular antigens, fixation rapidly destroys the enzyme functions of kinases and phosphatases, and therefore stabilizes phosphoepitopes. Because accurate pipetting requires a certain length of time, and the peak signal for some growth factors such as stem cell factor (SCF) is of short duration, a worksheet is needed for experiments involving many sample tubes, so that the additions of growth factor and fixative are staggered in order to allow a consistent activation time (e.g., exactly 5 min stimulation). With extensive cross-linking by formaldehyde, red cells become resistant to lysis. There is a fairly narrow window of fixation in which phosphoepitopes are optimally preserved and red cells remain amenable to lysis, and this is a critical step in the protocols detailed below. We have previously shown that formaldehyde fixation has minimal effect on the efficiency of mononuclear cell isolation by density gradient centrifugation (Tong et al., 2006). Whole blood fixation followed by density gradient centrifugation is therefore an alternative to red cell lysis. Although time-consuming when applied to large numbers of samples, this approach could be considered for experiments requiring the isolation of large numbers of cells from a limited number of samples. To a variable degree, phosphoepitopes can be masked following formaldehyde fixation. This effect is most pronounced with the STAT proteins, occurs to some extent with ERK, but appears minimal for other markers including Akt and S6 ribosomal protein. Following red cell lysis, methanol treatment can be used to unmask antigens if necessary. However, higher concentrations of methanol can result in a decrease (e.g., CD13, CD14, CD15) or loss (CD64) of some surface immunophenotypic markers. Fig. 3 illustrates the effects of increasing methanol concentrations on the recovery of phosphorylated STAT5 in normal donor blood monocytes and granulocytes activated by GM-CSF. It can be seen that a methanol concentration of 80% or greater is needed for full antigen retrieval. Side scatter is well preserved at high alcohol concentrations, but surface expression of the monocyte marker CD14 shows low levels of immunoreactivity at concentrations greater than 70%. Workarounds could involve the selection of alternative surface markers that are less sensitive to alcohol, a compromise alcohol concentration that allows sufficient phosphoepitope unmasking while preserving adequate surface staining, or labeling whole blood samples for surface markers prior to the alcohol treatment or the fixation step. As shown in Fig. 4, staining monocyte surface markers (here, CD14) after fixation and permeabilization, but before treatment with 80% cold methanol results in
30% decrease in the level of expression of CD14, compared with a sample fixed and permeabilized, but not treated with 80% methanol. Although

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Fig. 3

Effects of increasing methanol concentration on the recovery of P-STAT5 in peripheral blood monocytes and granulocytes (identified by side scatter and CD14 staining; left panels), following activation by GM-CSF. Right panels are overlays of side scatter versus P-STAT5 for the unstimulated and GM-CSF-stimulated samples. Note that >70% methanol is needed to resolve these two populations, and the loss of CD14 immunofluorescence with increasing methanol. (See plate no. 7 in the color plate section.)

we have not explored the utility of labeling cell surface markers before fixation and permeabilization for subsequent measurement of cytoplasmic or nuclear signaling markers, we have successfully utilized this approach for labeling cytoplasmic ZAP70 (Shankey et al., 2006). Based on the above considerations, we have used a modular design to describe the protocols. The core component consists of the protocol for fixation and red cell lysis. Prior to this step, there is a wide range of agents to activate or inhibit pathways, and the selection obviously depends on the purpose of the assay. A similar consideration applies to the choice of phospho-specific and immunophenotypic markers for labeling the samples.

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Fig. 4

Comparison of the impact of staining CD14 (here using a PE–Cy7 conjugate) at different steps in the whole blood processing. Whole blood from one normal donor was fixed and permeabilized, and stained immediately after fixation (left panel) without any methanol treatment, or fixed, permeabilized, and treated with 80% methanol before CD14 staining (middle panel), or fixed and permeabilized, washed, then stained with CD14 before subsequent treatment with 80% methanol (right panel). Numbers in each histogram indicate the median fluorescence intensity for the CD14 positive population.

III. Methods A. Protocol for Whole Blood Fixation and Red Cell Lysis

1. Samples Whole blood (or bone marrow aspirate) collected into K2EDTA or sodium heparin. Stored at room temperature until tested.

2. Sample Suitability Sample testing should (ideally) begin within 1–4 h of collection. The blood sample must be used as soon as possible in order to preserve appropriate signaling capabilities. Whole blood samples that display gross hemolysis are unacceptable. The impact of other anticoagulants (e.g., ACD) has not been tested.

3. Addition of Pathway Activators or Inhibitors Dispense the required number of 100 mL aliquots of blood or bone marrow into 12 mm  75 mm polypropylene tubes. Prewarm in a 37  C water bath (or dry bath), start timer, and then add growth factors or pathway inhibitors, as detailed in Section III.B that follows.

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4. Whole Blood Fixation With exact timing, at the appropriate time add 65 mL of 10% methanol-free formaldehyde to each tube, giving a final concentration of 4% formaldehyde. Vortex and place tube in test tube rack and incubate at room temperature for 10 min.

5. Red Cell Lysis After exactly 10 min of incubation with formaldehyde, add 1 mL of Triton X-100 working solution (0.1165%), to give a final concentration of 0.1% Triton X-100, vortex. After Triton X-100 has been added to all tubes, place them back in 37  C water or dry bath. Set timer for 15 min. At the end of the Triton incubation time, inspect tubes for complete red cell lysis (clear, nonturbid red color). If lysis is incomplete, continue incubating at 37  C for up to an additional 15 min.

6. Washing and Alcohol Treatment Remove tubes from 37  C and place in a rack. Add 1 mL ice cold wash buffer (4% FBS/PBS) to each tube, vortex well, then centrifuge at 400g for 4 min. Remove supernatant by aspiration or gently tipping the tubes (Note: At this stage, samples can be resuspended in freezing medium and placed in 20  C or colder freezer for later processing and analysis, as described below). For epitopes such as P-STAT3 and P-STAT5 that require 80% methanol treatment, use an extra wash step prior to methanol treatment with 2 mL wash buffer. Vortex to loosen up the cell pellet, then add 1 mL cold (20  C) 80% methanol (in 0.9% NaCl) while vortexing (to prevent cell aggregation). Place tube on ice, and repeat for all tubes in the test system. After standing in ice for 10 min, centrifuge at 400g for 4 min, remove the supernatant, vortex to loosen the cell pellet, and add 2 mL ice cold wash buffer (4% FBS/PBS). Centrifuge at 400g for 4 min, then remove supernatant.

7. Antibody Staining Add antibodies (see below for further details – concentrations and volumes must have been previously defined) and cold wash buffer to give a final concentration of 100 mL. It would be best to prepare a cocktail of desired antibodies. Predilute all antibodies in order to add a total volume of 100 mL. This ensures that the antibody concentration in each tube is identical. Incubate at room temperature in the dark for 30 min, then add 2 mL wash buffer, centrifuge at 400g for 4 min, remove supernatant, and vortex to loosen up the cell pellet. Resuspend in 350 mL of 0.1% formaldehyde/PBS, vortex, and run on flow cytometer. If samples are to be analyzed on the flow cytometers 8–24 h after antibody labeling, store cell pellets at 4  C until use.

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Analysis of cell signaling by flow cytometry is much more informative when a dynamic assay is performed, comparing the ground state of a phosphoepitope to that when the pathway is either inhibited or fully activated. This is a rich and rapidly evolving field of science, and the number of agents that could be added to samples prior to the fixation/lysis step is large and growing. Pathway inhibitors typically target upstream kinases, so that over time signaling proteins that were constitutively activated in the sample become dephosphorylated under the action of phosphatases. Many of these agents are drugs that, to a variable degree, bind to plasma proteins or cell membranes. The amount needed to produce maximum target inhibition can therefore be greater in whole blood than in tissue culture medium, and for this reason a range of drug concentrations should be tested. Pathway activators are typically growth factors that regulate the pathway under physiological conditions, or agents like phorbol ester that act intracellularly to bypass the growth factor receptor. A third category is phosphatase inhibitors like hydrogen peroxide that promote the accumulation of phosphoepitopes undergoing rapid turnover through the opposing effects of upstream activators and phosphatases (Irish et al., 2006). These agents are added to aliquots of sample at 37  C. Their effects are typically rapid, and particularly with stimulating growth factors, it is critically important to establish the time of maximum signal intensity, and adhere to this rigorously. Treatment with pathway inhibitors typically results in the loss of constitutive phosphorylation within minutes. However, in clinical samples we have found that the rate at which this occurs can vary between patients and cell subpopulations. For this reason, we normally incubate with pathway inhibitors for 30 min (at 37  C) to ensure that ground state is achieved. We do not recommend treating samples for more than 60 min, as small aliquots of whole blood are likely to become metabolically compromised with prolonged incubation at 37  C. The following is a list of agents that are used routinely in our laboratory, with some technical comments. It is not intended to be a comprehensive list, but to give a sense of how these applications can be developed according to specific needs. Their sites of action, and the main signaling pathways involved, are shown in Fig. 2.

IV. Pathway Activators (1) Phorbol myristate acetate (PMA; phorbol ester) activates protein kinase C, which then activates a wide range of signaling pathways, including ERK via effects on the upstream kinase Raf. ERK then activates a wide range of downstream targets, including S6 ribosomal protein. The advantages of PMA relative to growth factors are that it is not dependent on the expression of functional receptors on the cell membrane, and it is less time-critical as its effects are sustained for at least 30 min.

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Protocol: add PMA to whole blood at final concentration of 400 nM (Note: Higher than typically used for tissue culture), incubate at 37  C for 10 min, then fix samples. Readouts are P-ERK and P-S6, with P-ERK peaking around 5 min and P-S6 a few minutes later. (2) Growth factors: SCF and Flt-3 ligand act on closely related, classical receptor tyrosine kinases expressed in early hematopoietic cells and leukemic blast cells. They acutely activate the ERK and PI3-kinase/Akt pathways in cells that express the appropriate receptor. This effect is transient, and typically peaks around 3– 5 min, following which cells fall back to ground state by 10 min. Readouts are PERK, P-Akt, and P-S6. Protocol: SCF 100 ng or Flt-3L 200 ng to 100 mL whole blood. It is strongly recommended that a preliminary time course experiment be run in your own laboratory before establishing, and adhering to, the optimum stimulation time. Growth factors: G-CSF and GM-CSF act via JAK tyrosine kinases to activate STAT proteins. Receptors are expressed in mature peripheral blood granulocytes and monocytes (GM-CSF). GM-CSF generally gives better activation of STAT5, whereas G-CSF activates STAT3. Protocol: G-CSF and GM-CSF are both used at a final concentration of 10 ng to 100 mL, incubate for 5 min at 37  C, then fix with formaldehyde. (3) Lipopolysaccharide (LPS), or bacterial endotoxin, activates an acute inflammatory response in monocytes and macrophages via toll-like receptor 4 (TLR4) in concert with CD14 (Lu et al., 2008). This response includes activation of all three of the MAP kinase pathways (ERK, p38, and SAPK/JNK), PI3-kinase/Akt, and IKK/NFkB (Fig. 2). It is of clinical relevance in areas such as autoimmunity and sepsis, but for our purposes LPS stimulation is a convenient way to test a wide range of signaling pathways in normal donor blood. Because these responses are consistent between individuals, it can be used as a training exercise, and for evaluating new antibody conjugates. It also has potential for development as a surrogate pharmacodynamic marker for testing drug effects in the normal monocytes of patients participating in early phase clinical trials. Protocol: add LPS (from E. coli 0127) to whole blood at final concentration of 1 mg/mL, and monitor effects at time points out to 30 min (Note: The fine kinetics are complex, but typically peak signals are around 5–10 min). Readouts are ERK, p38, SAPK/JNK, and Akt (Fig. 5).

V. Pathway Inhibitors (1) ERK pathway: U0126 (MEK inhibitor), 100 mM final concentration in whole blood. Readouts are ERK and S6. (2) PI3-kinase: LY294002, 500 mM final concentration. We have now switched to the much more potent and selective PI3-kinase inhibitor GDC-0941, final

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Fig. 5 LPS activates multiple signaling pathways in monocytes. Quadruple phospho-specific staining protocol (P-p38, P-Akt, P-ERK, and P-S6), plus CD14–PE–Cy7 to label monocytes. Left panels show each signaling protein versus side scatter and center panels show effects of 10 min stimulation with LPS. Note that this effect is confined to the monocytes, which are readily apparent based on side scatter and CD14. Right panels are overlays.

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concentration 25 mM (Raynaud et al., 2009). GDC-0941 is available from commercial sources. Readouts are Akt and S6. (3) mTOR: rapamycin, 1 mg/mL final concentration. Readout is S6. Note that because the extent to which S6 is constitutively activated via ERK and mTOR pathways (Fig. 3) differs between AML patient samples, it is helpful to include a tube that contains both U0126 and rapamycin in order to establish the ground level for nonphosphorylated S6. (4) Flt-3 ITD mutation: sorafenib 20 mM final concentration (note that higher concentrations of sorafenib are required to inhibit its originally planned target, Raf kinase, due to high plasma protein binding). Readout is STAT5.

A. Freezing Samples Once samples have been prepared by fixation followed by Triton X-100 lysis (Step no. 6 in the protocol for whole blood fixation and red cell lysis), they can be stored at 20  C or lower in a freezing mixture consisting of 10% glycerol, 20% fetal bovine serum, balance tissue culture medium (Chow et al., 2005). We have stored samples for up to 2 years, with minimal loss of phosphoepitope expression or light scatter. This allows samples to be batched for processing or shipped on ice packs. We also maintain large numbers of aliquots of standard cell preparations as quality controls and for evaluating new antibody conjugates.

VI. Materials A. Fixation and Red Cell Lysis Ten percent formaldehyde, methanol free, EM grade (Polysciences catalogue #04018). Store at room temperature in the dark, use within 6 months of opening. Triton X-100, 10% aqueous solution (Pierce catalogue #28314). Prepare working solution by diluting 116 mL stock with 10 mL PBS; store stock and working solution at room temperature. Working solution is stable for 1 month. Wash buffer – 4% fetal bovine serum in calcium- and magnesium-free PBS. Methanol, 100% reagent grade, dilute to 80% in 0.9% NaCl (final concentration), store aliquots at 20  C.

B. Pathway Activators SCF and Flt-3 ligands are available from several sources. We prefer the products from Orf Genetics, Iceland, because they are cloned into barley rather than E. coli, and therefore less likely to contain bacterial endotoxin (LPS). Working solutions diluted to 10 mg/mL (SCF) and 20 mg/mL (Flt-3L) in wash buffer and stored at 4  C. GM-CSF from R&D Systems diluted to 10 mg/mL stock solution, diluted in wash buffer, and store at 20  C. Use 1 mL per 100 mL test (10 ng/100 mL final concentration).

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G-CSF from ORF Genetics diluted to 10 mg/mL stock solution in wash buffer and store at 20  C. Immediately before use, thaw an aliquot, use 1 mL = 10 ng/100 mL test. Phorbol ester (PMA) from Sigma-Aldrich, diluted to 40 mM stock solution in ethanol. Store at 20  C. Lipopolysaccharide (endotoxin) from E. coli 0127 (Sigma catalogue #L4516). Dilute to 100 mg/mL in PBS, store this working solution at 4  C. It is stable for up to 6 months.

C. Pathway Inhibitors U0126 is available from many sources; we use the Cell Signaling Technology (CST) product. Prepared as 10 mM solution in methanol and stored in aliquots at 20  C. LY294002 from Calbiochem, prepared as 50 mM stock solution in ethanol, store at 20  C. The alternative PI3-kinase inhibitor GDC-0941 is available from Medimol (PN: MM1005). Prepare a stock solution (2.5 mM in DMSO) and store at 20  C. Rapamycin from Calbiochem, 1 mM stock solution in dimethylsulfoximine (DMSO), diluted to 100 mM working solution.

D. Antibodies The phospho-specific antibodies used to illustrate this chapter were from CST, with the fluorescence conjugates prepared by either CST or Beckman–Coulter, and the product numbers are included in the figure legends. Equivalent products with similar performance are available from BD Biosciences. It should be noted that ongoing efforts by several companies is resulting in a steady improvement in the range and performance of phospho-specific antibodies developed for flow cytometry. However, the consistency of the commercially available fluorescence conjugates shows greater variability that seen with antibodies sold for routine clinical use. Therefore, we recommend that you search online sources for newer products, and check each antibody with a detailed dilution curve using positive and negative controls described in this chapter.

VII. Discussion The core protocol described in this chapter is the result of a very thorough assessment of the variables involved. It uses readily available material, and the stock solutions are stable over several months. For several years the authors have been using this technique to teach cell signaling at the Annual Los Alamos/Bowdoin College Flow Cytometry Methods Course, and in our experience it can be mastered fairly rapidly, and gives highly consistent results between different laboratories.

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Currently, this field is being driven by a number of forces, including progress in the basic science of cell signaling, improvements in the quality and the scope of phospho-specific antibodies suitable for flow cytometry, the pathobiology of human cancers, and the ever-increasing number of molecular-targeted drugs entering human clinical trial. Although in this chapter we emphasize applications in cancer, the methodology described is broadly applicable to other medical fields, including hematology, autoimmunity, and infectious disease.

VIII. Summary This chapter takes a modular approach to the analysis of cell signaling by flow cytometry. Depending on the experimental requirements, cell signaling can be probed in aliquots of test sample by the addition of agents that activate, or inhibit, the pathway in question. Rapid whole blood fixation followed by red cell lysis is then readily achieved using the core protocol, following which samples can be labeled using a wide range of phospho-specific antibodies, and surface immunophenotypic markers. References Chow, S., Hedley, D., Grom, P., Magari, R., Jacobberger, J. W., Shankey, T. V. (2005). Whole blood fixation and permeabilization protocol with red blood cell lysis for flow cytometry of intracellular phosphorylated epitopes in leukocyte subpopulations. Cytometry A 67(1), 4–17. Chow, S., Hedley, D., and Shankey, T. V. (2008). Whole blood processing for measurement of signaling proteins by flow cytometry. Curr. Prot. Cytom. Chapter 9, Unit 9.27. Chow, S., Minden, M. D., and Hedley, D. W. (2006). Constitutive phosphorylation of the S6 ribosomal protein via mTOR and ERK signaling in the peripheral blasts of acute leukemia patients. Exp. Hematol. 34(9), 1183–1191. Chow, S., Patel, H., and Hedley, D. W. (2001). Measurement of MAP kinase activation by flow cytometry using phospho-specific antibodies to MEK and ERK: potential for pharmacodynamic monitoring of signal transduction inhibitors. Cytometry 46(2), 72–78. Hedley, D. W., Chow, S., Goolsby, C., and Shankey, T. V. (2008). Pharmacodynamic monitoring of molecular-targeted agents in the peripheral blood of leukemia patients using flow cytometry. Toxicol. Pathol. 36(1), 133–139. Irish, J. M., Czerwinski, D. K., Nolan, G. P., and Levy, R. (2006). Kinetics of B cell receptor signaling in human B cell subsets mapped by phosphospecific flow cytometry. J. Immunol. 177(3), 1581–1589. Irish, J. M., Hovland, R., Krutzik, P. O., Perez, O. D., Bruserud, O., Gjertsen, B. T., et al. (2004). Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118(2), 217–228. Jacobberger, J. W., Sramkoski, R. M., Frisa, P. S., Ye, P. P., Gottlieb, M. A., Hedley, D. W., et al. (2003). Immunoreactivity of Stat5 phosphorylated on tyrosine as a cell-based measure of Bcr/Abl kinase activity. Cytometry 54A(2), 75–88. Kotecha, N., Flores, N. J., Irish, J. M., Simonds, E. F., Sakai, D. S., Archambeault, S., et al. (2008). Singlecell profiling identifies aberrant STAT5 activation in myeloid malignancies with specific clinical and biologic correlates. Cancer Cell 14(4), 335–343. Krutzik, P. O., and Nolan, G. P. (2003). Intracellular phospho-protein staining techniques for flow cytometry: monitoring single cell signaling events. Cytometry A 55(2), 61–70. Lu, Y. C., Yeh, W. C., and Ohashi, P. S. (2008). LPS/TLR4 signal transduction pathway. Cytokine 42(2), 145–151.

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David W. Hedley et al. Perez, O. D., and Nolan, G. P. (2002). Simultaneous measurement of multiple active kinase states using polychromatic flow cytometry. Nat. Biotechnol. 20(2), 155–162. Raynaud, F. I., Eccles, S. A., Patel, S., Alix, S., Box, G., Chuckowree, I., et al. (2009). Biological properties of potent inhibitors of class I phosphatidylinositide 3-kinases: from PI-103 through PI-540, PI-620 to the oral agent GDC-0941. Mol. Cancer Ther. 8(7), 1725–1738. Schwock, J., Ho, J. C., Luther, E., Hedley, D. W., and Geddie, W. R. (2007). Measurement of signaling pathway activities in solid tumor fine-needle biopsies by slide-based cytometry. Diagn. Mol. Pathol. 16 (3), 130–140. Shankey, T. V., Forman, M., Scibelli, P., Cobb, J., Smith, C. M., Mills, R., et al. (2006). An optimized whole blood method for flow cytometric measurement of ZAP-70 protein expression in chronic lymphocytic leukemia. Cytom. B Clin. Cytom. 70(4), 259–269. Tong, F. K., Chow, S., and Hedley, D. (2006). Pharmacodynamic monitoring of BAY 43-9006 (Sorafenib) in phase I clinical trials involving solid tumor and AML/MDS patients, using flow cytometry to monitor activation of the ERK pathway in peripheral blood cells. Cytom. B Clin. Cytom. 70(3), 107–114.

CHAPTER 10

Immunophenotypic Pattern of Myeloid Populations by Flow Cytometry Analysis Wojciech Gorczyca, Zhong-Yi Sun, William Cronin, Xiaoyu Li, Sophal Mau and Sorina Tugulea Genzyme Genetics (New York Laboratory), New York, New York, USA

I. II. III. IV. V. VI. VII. VIII. IX. X. XI. XII. XIII. XIV. XV. XVI. XVII. XVIII. XIX.

Abstract Introduction Granulocytes Basophils Eosinophils Monocytes Erythroid Precursors Megakaryocytes Acute Myeloid Leukemia (AML) with Minimal Differentiation and AML without Maturation AML with Maturation AML with t(8;21) Acute Promyelocytic Leukemia (APL) Acute Myelomonocytic Leukemia (AML-M4) Acute Monoblastic Leukemia Acute Erythroid Leukemia (FAB: AML-M6) Acute Megakaryoblastic Leukemia (FAB: AML-M7) Blastic Plasmacytoid Dendritic Cell Neoplasm Chronic Myeloid Leukemia (CML, BCR–ABL+) Granulocytes/Maturing Myeloid Precursors with Dyspoietic Features Associated with Myelodysplastic Syndrome (MDS) Chronic Myelomonocytic Leukemia (CMML) References

Abstract We present our experience with immunophenotypic characteristics of benign and malignant myeloid populations, with emphasis on differential diagnosis especially between eosinophils, dysplastic granulocytes, neoplastic promyelocytes, and METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

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monocytes. Eosinophils are characterized by bright CD45, high side scatter (SSC), very low forward scatter (FSC), positive CD11b, CD11c, CD13, CD15, and CD33. They are negative for CD10, CD14, CD16, CD56, CD64, and HLA-DR. Mature monocytes are positive for CD11b, CD11c, CD13, CD14, CD33, and CD64, and may express CD2 and CD4. Blasts in acute myeloid leukemias (AML) with minimal differentiation have low SSC and moderate CD45 expression and are positive for CD34, CD117, CD13, HLA-DR, and CD33 and may be positive for TdT, CD4, and CD11c. In acute promyelocytic leukemia (APL), four FC patterns can be recognized. The majority of cases represented classical (hypergranular) APL and were characterized by high SSC, positive CD117, usually negative CD34, heterogeneous CD13, and bright CD33 (pattern 1). The second most common type, corresponding to hypogranular (microgranular) variant of APL, differed from classical APL by low SSC and frequent coexpression of CD2 and CD34 (pattern 2). Rare cases of APL (pattern3) showed mixture of neoplastic cells (SSClow/CD2+/CD13+/CD33+/CD34+/ CD117+) and prominent population of benign granulocytes/maturing myeloid precursors (SSChigh/CD10+/
/CD16+/
/CD117
). One case showed two APL populations, one with hypogranular and one with hypergranular characteristics (pattern 4). Detailed phenotypic characteristics of neoplastic monocytes and dysplastic granulocytes with their differential diagnosis are also presented.

I. Introduction Flow cytometry (FC) plays an indispensable role in a multimethodology approach to diagnosis of hematologic tumors, and by providing data on the extent of involvement, prognosis, and posttreatment monitoring (Al-Mawali et al., 2009; Baumgarth and Roederer, 2000; Borowitz, 2000; Borowitz et al., 1993, 1997, 2003; Braylan, 1997, 2004; Braylan and Benson, 1989; Braylan et al., 1993, 1997; Craig and Foon, 2008; D’Archangelo, 2007; Gorczyca, 2004a, 2004b, 2006; Gorczyca et al., 2002; Greig et al., 2007; Jennings and Foon, 1997a, 1997b; Orfao et al., 1999; StetlerStevenson and Braylan, 2001; Vidriales et al., 2003; Weir and Borowitz, 2001; Weir et al., 1999; Weisberger et al., 2000; Wood et al., 2007). We present our experience with immunophenotypic characteristics of benign and malignant myeloid populations, with emphasis on differential diagnosis especially between eosinophils, dysplastic granulocytes, neoplastic promyelocytes, and monocytes.

II. Granulocytes Hematopoietic progenitors are positive for CD34, CD133, CD184, and HLA-DR. Myeloblasts are positive for CD34, CD38, HLA-DR, CD117, CD4, CD13 (dim), and CD33 (CD34 is expressed by all hematopoietic precursors, including early myeloblasts; CD117 expression appears after CD34). The expression of CD13 appears first, followed by acquisition of CD33 and increased expression of both CD13 and CD33. Neutrophilic maturation from blasts through promyelocytes, myelocytes,

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metamyelocytes, bands, and neutrophils is characterized by loss of CD34 and HLADR expression at promyelocytic stage and loss CD117 expression at myelocyte stage and acquisition of CD11b and CD11c expression at myelocytic stage and CD10 expression by neutrophils (Wood, 2004). CD11b intensity increases as the cells mature to late myelocytes and metamyelocytes (at the same time, the expression of CD4 disappears). CD64 is expressed by promyelocytes through metamyelocytes. CD13 and CD33 are expressed at all stages of maturation with CD13 being brightly expressed by blasts and neutrophils and dimly by metamyelocytes, and CD33 showing slight decrease in the intensity of CD33 expression as the cells become more mature. Metamyelocytes start to express CD10 and CD16 that increases in intensity as the cells progress to mature neutrophils. Segmented forms display bright expression of CD11b, CD11c, CD10, CD16, and CD15. Other markers expressed at metamyelocytes/band/segmented stages include CD24, CD32, CD35, and CD65. Minute subset of normal granulocytes shows dim expression of CD64. CD14 and CD64 expression is upregulated in infections and in patients on G-CSF therapy. Granulocytic precursors display dim expression of CD45 and the intensity of expression increases in the final stages of differentiation (based on the side scatter and CD45 neutrophils can be often separated from promyelocytes, myelocytes, metamyelocytes, and bands by having the strongest expression of CD45. Figs. 1 and 2 present phenotypic profile of granulocytes.

[(Fig._1)TD$IG]

Fig. 1

CD45 expression by granulocytes. The expression of CD45 increases as the cells reach final stages of differentiation into neutrophils (A). The direction of maturation is indicated in dot plot A by arrows (benign bone marrow sample from 11 month-old infant). Based on the CD45 versus SSC, two populations of granulocytic cells can be identified: one with variable SSC and dimmer CD45 and the other with slightly lower SSC and brighter CD45. The first populations include promyelocytes, myelocytes, metamyelocytes, and bands and the second population includes mostly mature neutrophils. The distinction between those two populations is more evident in plot B (benign bone marrow with relative T-cell lymphocytosis). Blood sample reactive neutrophilia shows only one population cells with brighter CD45.

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[(Fig._2)TD$IG]

Fig. 2

The expression of CD10, CD11b, CD16, CD11c, and HLA-DR during normal myeloid maturation (normal bone marrow samples; insets show normal blood samples). Myeloblasts gain CD13 as they mature to promyelocytes, lose its intensity as they become myelocytes, and then gain expression along the maturation toward mature neutrophils (A). CD16 expression starts to appear at metamyelocyte stage and is strongest in segmented forms (A and B). CD11b and CD11c expression gradually increases as the cells mature (B and C); neutrophils are brightly positive for both antigens. HLA-DR is positive in blasts and negative in promyelocytes and subsequent stages (C and E). CD33 is positive in all stages of granulocytic maturation, but the expression decreases as the cells mature (D and E). CD10 is positive in segmented forms (F). Pro, promyelocytes; Myel, myelocytes; Neut, neutrophils.

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III. Basophils Basophils show very low side scatter and dim-to-moderate CD45 (blastic gate or hematogones gate). They are negative for CD15 and CD16, and the expression of CD13 is dimmer when compared to neutrophils. Basophils are positive for CD9, CD13, CD22, CD25 (dim), CD33, CD36, CD38 (bright), CD45 (dimmer than lymphocytes and brighter than myeloblasts), and CD123 (bright), and are negative for CD19, CD34, CD64, CD117, and HLA-DR (Pirruccello et al., 2006).

IV. Eosinophils Fifteen cases of eosinophilia were analyzed, including two cases of chronic eosinophilic leukemia (CEL) with clonal chromosomal changes and two cases of myeloproliferative neoplasm (MPN) with FIP1L1–PDGFRA rearrangement. All cases were negative for BCR–ABL fusion (Philadelphia chromosome). The number of eosinophils ranged from 25 to 75% (average: 39%). Eosinophils (Fig. 3) are

[(Fig._3)TD$IG]

Fig. 3

Flow cytometric features of eosinophils. Blood with marked eosinophilia (A; cytospin preparation from flow sample). Eosinophils similar to neutrophils have high SSC and moderate CD45 (B). Neutrophils are HLA-DR negative whereas eosinophils show dim HLA-DR (C). The expression of CD13 (D) and CD33 (E) is dimmer in eosinophils compared to that of neutrophils, and expression of CD11b (G) and CD11c (H) is comparable. Eosinophils are negative for CD10 (F) and CD16 (I), whereas neutrophils are positive.

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characterized by bright CD45, high side scatter (SSC), very low forward scatter (FSC), and positive CD11b, CD11c, CD13, CD15, and CD33. They are negative for CD10, CD14, CD16, CD56 and CD64. Two cases showed CD4 upregulation. Eosinophils differ from neutrophils in FC analysis by very low FSC, negative CD10 and CD16, higher SSC, and dimmer CD11b, CD11c, CD13, CD15, and CD33 (Fig. 4). Table I presents phenotypic profile of eosinophils.

V. Monocytes Monocytic differentiation can be categorized by three stages: monoblasts, promonocytes, and monocytes. Early monoblasts express CD34, CD117, and HLA-DR. Late monoblasts start to lose CD34 and become CD4+. As the cells mature into promonocytes, expression of CD117 start to diminish, CD34 is lost and they start to

[(Fig._4)TD$IG]

Fig. 4

Comparison of phenotypic features of neutrophils (gray dots), monocytes (blue dots), and eosinophils (purple dots). Eosinophils differ from neutrophils by higher SSC (A), lower forward scatter (A–I, y-axis) , slightly dimmer expression of CD13 (B), CD33 (C), and CD11b (F) and negative CD16 (H) and CD10 (I). Monocytes differ from neutrophils and eosinophils by lower SSC (A; x-axis), brighter CD13 (B) and CD33 (C), positive CD14 (D) and CD64 (E). (See plate no. 8 in the color plate section.)

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Table I Immunophenotypic profile of eosinophils (n = 15 cases, including two cases of chronic eosinophilic leukemia and two cases of MPN with PDGFR rearrangement) Marker

% Positive

CD10 CD11b CD11c CD13 CD14 CD16 CD19 CD33 CD34 CD45 CD56 CD64 CD117 HLA-DR Side scatter Forward scatter

0 100 100 100 0 0 0 100 0 100% 0 0 0 0

Comments

Moderate Moderate Usually dim to moderate

Usually dim to moderate Bright

Very high (100%) Very low (100%)

acquire the expression of CD64, CD15, and CD11b and CD11c. Late promonocytes become positive for CD14. The expression of CD11b, CD11c, CD14, CD64, and HLA-DR becomes brighter as the monocytic cells become more mature. The expression of CD14 is strongest by mature cells (monocytes). In contrast to the neutrophils’ differentiation, CD11b expression typically precedes CD15 and HLADR expression is retained. Mature monocytes in blood are positive for CD11b, CD11c, CD13, CD14, CD33, and CD64, and may express CD2 and CD4. Minute subset of monocytes may be CD16+ and CD56+ (CD16+ population may increase in bacterial or viral infections). Monocytes give rise to tissue macrophages, osteoclasts (bone), Langerhans cells (skin, other), Kupfer cells (liver), and dendritic cells (skin, other).

VI. Erythroid Precursors In the erythroid series, blasts are positive for CD34, CD38, CD45, CD117, and HLA-DR (moderate), proerythroblasts are positive for CD36, CD38, CD45, CD71, CD117, glycophorin (CD235a; dim), and HLA-DR (moderate) and may express CD34, basophilic erythroblasts express CD36, CD71, glycophorin (CD235a; bright), and HLA-DR (dim), and polychromatophilic and orthochromatophilic erythroblasts express CD36, CD71, and glycophorin (CD235a; bright) (Wood, 2004). Erythroid precursors progressively lose the expression of CD45.

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VII. Megakaryocytes Megakaryoblasts are positive for CD45, CD34, CD38, HLA-DR (dim), and CD117. As they mature to megakaryocytes, they become positive for CD61 and lose the expression of HLA-DR, CD34, and CD117. Apart from CD61, megakaryocytes are positive for CD41, CD42, and CD29. Platelets lose the expression of CD38 and retain the expression of CD29, CD41, and CD61. They are CD45 negative.

VIII. Acute Myeloid Leukemia (AML) with Minimal Differentiation and AML without Maturation Blasts in AML with minimal differentiation (FAB: AML-M0) have low SSC and moderate CD45 expression and are positive for CD34, CD117, CD13, HLA-DR, and often CD33 (Fig. 5). TdT, CD4, and CD11c may be positive. Markers associated with myeloid or monocytic maturation (e.g., CD14, CD15, CD64, or CD65), B-cell markers (cCD22, cCD79a), and cytoplasmic CD3 are negative. In some cases, there is expression (often partial) of CD2, CD7, CD11b, CD19, CD56, or CD64. Subset of cases is CD34( and occasional cases are negative for HLA-DR. The presence of 3% of blasts expressing MPO and/or SBB indicates AML without maturation (FAB: AML-M1). This subtype shows similar phenotype to AML with minimal differentiation, but more often shows lack of CD34 and TdT expression, and less often is negative for CD33. Acute undifferentiated leukemia expresses HLA-DR, CD34, CD38, and often TdT (Swerdlow et al., 2008). They are negative for markers associated with specific leukemias, mentioned above (e.g., CD3, cCD22, cCD79a, CD19, and CD56). Mixed phenotype acute leukemia is characterized by expression of MPO (or two of the monocytic markers: CD11c, CD14, CD64, and NSE) and either CD3 or B-cell markers (strong CD19 with strong expression of CD79a, cCD22, or CD10; or weak CD19 with strong expression of two of the following: CD79a, cCD22, and CD10) (Swerdlow et al., 2008).

IX. AML with Maturation Phenotyping by flow cytometry reveals blasts with low SSC and moderate CD45 and admixture of maturing myeloid precursors and neutrophils. Blasts are positive for myeloid markers (CD13, CD33, and MPO), CD34, CD117, and HLA-DR in majority of cases. Occasional cases show aberrant expression of maturation markers, CD11b, CD15, and/or CD65. CD11c may be positive (usually dim), and the subset of cases shows CD64 (bright CD11b, CD11c, and CD64 are typical for acute

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[(Fig._5)TD$IG]

Fig. 5

AML with minimal differentiation – flow cytometry. Blasts (A) have scanty cytoplasm with occasional hand-mirror appearance. They are characterized by low side scatter and moderate CD45 (‘‘blast region’’ on CD45 vs. SSC display; B); they are positive for CD13 (C), CD33 (D), HLA-DR (E), CD117 (F), and CD34 (G). CD15 and CD65 are negative (E–H).

monoblastic leukemia). There may be aberrant expression of CD7, CD56, and CD4 and less often CD19. AML with maturation has to be differentiated from acute myelomonocytic leukemia (20% monocytes and their precursors), high-grade MDS, such as refractory anemia with excess blasts-2 (<20% blasts), chronic myeloproliferative neoplasm (CMN) in accelerated phase (<20% blasts), and AML without maturation (<10% maturing myeloid elements).

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X. AML with t(8;21) Acute myeloid leukemia with t(8;21)(q22;q22)/RUNX1–RUNX1T1 (Fig. 6) most often displays the morphology of AML with maturation (FAB: AML-M2). Blasts are large with a variable number of azurophilic cytoplasmic granules and Auer rods. Phenotypically, they are positive for panmyeloid antigens (CD13, CD33), HLA-DR, blastic markers (CD34, CD117), and characteristically for CD19 and often also CD56. Occasional cases may include dim CD33, lack of surface panmyeloid antigens, or aberrant expression of CD15 and/or CD65.

XI. Acute Promyelocytic Leukemia (APL) One-hundred and fourteen cases of APL with t(15;17)/PML-RARA confirmed by conventional cytogenetics and/or FISH studies were analyzed for FC immunophenotypic features. Seventy-nine cases (69.3%) were characterized by high SSC

[(Fig._6)TD$IG]

Fig. 6 AML with t(8;21) – flow cytometry. Myeloblasts have granular cytoplasm and may show perinuclear clearing (A). They are positive for CD13 (B), CD33 (C), CD34 (D), CD19 (E), CD56 (F), and CD117 (G). Cytogenetic (H) and FISH shows t(8;21)/RUNX1–RUNX1T1.

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(pattern 1; classic APL), 30 cases (26.3%) had low SSC (pattern 2; hypogranular APL), 3 cases (2.6%) showed leukemic cells and separate population of benign (residual) granulocytes/maturing myeloid precursors (pattern 3; partial involvement), and 2 cases (1.8%) showed two separate populations of leukemic cells, one with high SSC and one with low SSC (pattern 4; mixed classic/hypogranular APL). Classic APL (pattern 1) was characterized by a predominant population of atypical promyelocytes with markedly increased SSC (leukemic cells distributed in ‘‘granulocytic’’ gate on CD45 vs. SSC dot plot display). Neoplastic cells were positive for CD13 (94%), CD33 (100%), CD64 (68%), CD117 (100%), and negative for HLA-DR, CD10, CD11b, CD11c, and CD14 (see Table II). Subset of cases was positive for CD2 (15%), CD4 (18%), CD34 (4%), and CD56 (13%). The expression of CD13 was dim to moderate, expression of CD33 was bright, and expression of CD64, if present, was usually dim. The expression of CD117 may be very dim. Fig. 7 presents a typical example of classic APL (pattern 1). Hypogranular (microgranular) APL (pattern 2; Fig. 8) was characterized by low SSC and moderate expression of CD45 (leukemic cells distributed in ‘‘blast’’ region on CD45 vs. SSC dot plot display, similar to blasts in non-APL acute myeloid leukemias). Neoplastic cells in this variant were positive for CD2 (80%), CD4 (30%), CD13 (95%), CD33 (100%), CD34 (67%), and CD117 (100%), whereas HLA-DR, CD10, CD11b, and CD11c were always negative (see Table III for details).

Table II Immunophenotypic profile of APL (hypergranular variant; n = 79 cases) Marker

% Positive

Side scatter CD2 CD4 CD7 CD11b CD11c CD13 CD14 CD16 CD19 CD33 CD34

15.2 17.7 1.3 0 0 93.7 0 0 0 100 3.8

CD45 CD56 CD64 CD117 HLA-DR

100 12.6 68.4 100 0

Comments Markedly increased (‘‘granulocytic’’ region) Dim expression

Dim (dim to moderate), 40.5%; moderate, 53.2%

Moderate, 18%; bright, 82% Four additional cases (5%) showed dim expression on subset of neoplastic cells Moderate expression Dim expression Very dim or dim-to-moderate expression

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[(Fig._7)TD$IG]

Fig. 7 Flow cytometry of classic APL (pattern 1): leukemic cells (arrow) have characteristic high SCC (A); they are negative for CD34 (B) and HLA-DR (D), and are positive for CD117 (C), CD13 (dim expression; E), and CD33 (bright expression; F). CD64 is negative to dim on subset (G).

The third pattern by flow cytometry showed a mixture of neoplastic promyelocytes with decreased side scatter and expression of CD117 (Fig. 9; green dots) and significant proportion of benign granulocytes with typical high SSC (Fig. 9; gray dots), negative CD117, and partially positive for CD10, CD11b, and CD16. The immunophenotype of neoplastic cells in pattern 3 was similar to that observed in hypogranular APL. The least common immunophenotypic variant of APL (pattern 4) showed two neoplastic populations, one with high SSC and the other with low SSC. Both populations were positive for CD117 and negative for HLA-DR and CD11c. The cells with high SSC expressed CD13 (dim), CD33 (moderate), and CD64 (dim), and cells with low SSC had brighter CD45 and were positive for CD34 and CD2 (Fig. 10). This pattern was seen in two cases. In flow cytometry analysis, differential diagnosis of hypergranular APL (pattern 1) includes normally maturing myeloid cells, bone marrow with myelodysplasia, CMNs with myeloid leftward shift, benign marrow proliferations (e.g., recovering marrow after treatment), occasional cases of acute monoblastic leukemia, and rare

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[(Fig._8)TD$IG]

Fig. 8

Flow cytometry of hypogranular APL (pattern 2): leukemic cells (arrow) have low SCC (A); they are partially positive for CD34 (B), and strongly positive for both CD117 (C) and CD33 (F). HLA-DR is negative (D). CD13 (E) and CD2 (G) are positive but dimly expressed.

cases of AML with maturation, which have high SSC and lack HLA-DR expression. Differential diagnosis of hypogranular APL (pattern 2) includes acute myeloid leukemia with or without maturation, acute monoblastic leukemia, and MDS with prominent dysgranulopoiesis (e.g., hypogranular cytoplasm). Blasts in non-APL acute myeloid leukemia usually have low SSC and are positive for HLA-DR, whereas atypical hypergranular promyelocytes in APL have high SSC (they are located in the same area as normal granulocytes on CD45 vs. SSC dot plot display; Fig. 11) and lack the expression of HLA-DR. Rare cases of AML with or without maturation may be HLA-DR negative, but they differ from hypogranular APL, by positive CD11c and negative CD2. Rare cases of AML with maturation may be characterized by high SSC (‘‘granulocytic’’ gate); those blasts may lack CD34 and CD117 expression. Expression of HLA-DR, CD11b, and CD11c helps to differentiate phenotypically acute monoblastic leukemia (HLA-DR+/CD11b+/
/CD11c+) from the microgranular variant of APL (HLA-DR
/CD11b
/CD11c
). Moreover, monoblasts may show positive, often variable (smeary) expression of CD14, and positive CD10, CD16,

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Table III Immunophenotypic profile of APL (hypogranular variant; n = 30 cases) Marker

% Positive

Side scatter CD2 CD4 CD7 CD11b CD11c CD13 CD14 CD16 CD19 CD33 CD34

80 30 0 0 0 95 0 0 0 100 67

CD45 CD56 CD64 CD117 HLA-DR

100 13 80 100 0

Comments Low (‘‘blastic’’ region) Dim expression Dim expression

Dim, 30%; moderate, 65%

Moderate expression, 10%; bright expression, 90% 10 cases (33%) were completely negative; >8 cases (27%) had moderate CD34, 2 cases had variable (smeared) expression, 5 cases (16.5%) showed dim-to-moderate expression, and remaining 5 cases (16.5%) showed dim expression on all blasts (3 cases) or significant subset of blasts (2 cases) Moderate expression Dim expression, 55%; moderate expression, 25% Very dim or dim-to-moderate expression

and/or CD23 (those markers are negative in APL). In most cases of acute monoblastic leukemia, the expression of CD45 is brighter than that in APL. Only rare cases of acute monoblastic leukemia are HLA-DR
. Both APL and acute monoblastic leukemia express CD64, but the expression is usually dim in APL and bright in acute monoblastic leukemia. Acute monoblastic leukemia often is CD56+, whereas CD56 is only rarely expressed in APL. Analysis of CD11b versus HLA-DR (Fig. 12) and CD10, CD16, and CD117 (Fig. 13) distinguishes benign process from APL. Neutrophilic maturation from blasts through promyelocytes, myelocytes, metamyelocytes, bands, and neutrophils is characterized by loss of CD34 and HLA-DR expression at promyelocytic stage and loss of CD117 expression at myelocyte stage, and acquisition of CD11b and CD11c expression at myelocytic stage and CD10 expression by neutrophils (Wood, 2004). CD64 is expressed by promyelocytes through metamyelocytes. Granulocytes/ maturing myeloid precursors with dyspoiesis (e.g., MDS) and/or leftward shift (e.g., CML) may display aberrant downregulation of CD10, CD11b, and CD16, but in contrast to neoplastic promyelocytes lack CD117 expression and are (at least partially) CD11c+. Kussick et al. (2004) described HLA-DR
/CD34
phenotype in AML with normal karyotype by conventional cytogenetics and association with FLT-3 gene internal tandem duplication. Albano et al. (2006) reported association

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Fig. 9

Flow cytometry of APL displaying partial involvement (pattern 3), three cases (A, B, and C). Leukemic cells have low SCC (green dots, arrow; A, B, C) and are positive for CD CD117 (A0 , B0 , and C0 ), CD2 (C00 ), and CD34 (C000 ), as seen in hypogranular APL (APLv). In contrast to typical APLv, there is significant admixture of normal (benign) maturing myeloid precursors and granulocytes (gray dots; A, B, C), which are negative for CD117 (A0 , B0 , and C0 ), but express CD10 (B00 ). (See plate no. 9 in the color plate section.)

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Fig. 10

Flow cytometry of APL, mixed variant (pattern 4). In this rare immunophenotypic variant of APL, two populations of leukemic cells are identified (A): one with high SSC (gray dots; arrow) and one with low SSC and brighter CD45 (green dots; *). Both populations are positive for CD117 (B), but only cells with low SSC express CD34 (C) and CD2 (H). CD13 and CD64 (I) are dimly expressed (D), and CD33 expression is bright (E). Both populations are negative for HLA-DR (F) and CD11c (G). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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Fig. 11 SSC (orthogonal, right angle scatter) on y-axis corresponds to granularity of the cytoplasm (xaxis presents CD45 expression). Neutrophils (A; arrow) and atypical promyelocytes in APL (B, arrow) have high SSC, whereas blasts in AML with maturation have low SSC (A, dotted arrow).

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Fig. 12

APL – differential diagnosis: comparison of the expression of CD11b and HLA-DR in APL, non-M3 AML, and acute monocytic leukemia. (A) Normal (control) sample. Granulocytes are brightly positive for CD11b and negative for HLA-DR. (B) APL: promyelocytes lack the expression of CD11b and HLA-DR. (C) AML-non M3: blasts are strongly positive for HLA-DR and negative for CD11b. (D) Monoblasts are positive for both CD11b and HLA-DR.

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Fig. 13 APL – differential diagnosis: flow cytometric differences between promyelocytes (left column) and granulocytes (right column). Promyelocytes and granulocytes are positive for CD13 and CD33 (A, B). In contrast to granulocytes, promyelocytes are negative for CD10 (C, compare with C0 ) and CD16 (D, compare with D0 ), and are positive for CD117 (E, compare with E0 ).

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of CD34 expression with the hypogranular APL variant and higher proportion of CD2+ and HLA-DR+ cases. In the same study, CD34+ APL patients had a significantly higher percentage of peripheral blood leukemic promyelocytes at presentation, were more frequently female, and had a higher proportion of bcr3 expression, but there were no differences between the two groups in terms of complete remission, overall survival, and disease-free survival (Albano et al., 2006).

XII. Acute Myelomonocytic Leukemia (AML-M4) Acute myelomonocytic leukemia shows two distinct neoplastic populations: blasts, with the phenotype similar to AML with/without maturation, and monocytic cells, which are positive for CD11b, CD11c, CD14, CD64, and HLA-DR. Flow cytometry (Fig. 14) reveals two distinct populations: blasts (moderate CD45 and low side scatter) and monocytic cells (bright CD45 and slightly increased side scatter). The monocytic component is positive for CD11b, CD11c, CD13, CD14, CD33, CD64, and HLA-DR and myeloblasts are positive for CD13, CD33, CD34,

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Fig. 14 AML-M4 – flow cytometry. The immunophenotyping shows two abnormal populations: blasts with low side scatter and moderate CD45 expression (A, green dots) and monocytes with low side scatter and bright CD45 expression (A, blue dots). Myeloblasts are positive for CD34 (B), CD117 (C), CD11c (D; dim expression), CD33 (F), and CD64 (G; dim expression). Monocytic population is negative for CD34 (B) and CD117 (C), and has bright expression of CD11c (D), CD14 (E), CD33 (F), and CD64 (G). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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Table IV Immunophenotypic profile of acute myelomonocytic leukemia (n = 40 cases) Marker

% Positive monocytes blasts

CD2 CD4 CD7 CD10 CD11b CD11c CD13 CD14

62.5 87.5 0 12.5 97.5 100 92.5 85

0 47.5 2.5 0 2.5 65 100 0

CD16 CD23 CD33 CD34 CD45 CD56 CD64 CD117 HLA-DR

25 15 100 2.5 100 22.5 100 2.5 82.5

0 0 100 82.5 100 7.5 20 92.5 90

Comments

Usually bright expression; three cases were positive on subset of cells and two cases showed dim expression

One case showed dim expression on minute subset of monocytic cells Bright expression on monocytic cells and moderate on blasts

Two cases showed HLA-DR expression on subset of monocytes

CD117, and HLA-DR. A subset of cases shows expression of CD2, CD7, CD34, and CD56 by atypical monocytes. The expression of CD56 is less common in neoplastic monocytes from acute myelomonocytic leukemia (22%) than in monocytes from either CMML (69%) or acute monoblastic leukemia (78%) (Gorczyca, 2004b). Monocytic population from acute myelomonocytic leukemia less often displays aberrant lack of CD11b, CD14, and HLA-DR, or positivity for CD16, CD23 and CD117 compared to acute monoblastic leukemia or CMML (Gorczyca, 2004b). Table IV presents immunophenotypic profile of acute myelomonocytic leukemia.

XIII. Acute Monoblastic Leukemia Fig. 15 presents typical flow cytometric features of acute monoblastic leukemia and Table V presents details of immunophenotypic profile. Based on the CD45 versus orthogonal SSC display, majority of cases (60%) are characterized by bright CD45 and slightly increased side scatter (‘‘monocytic’’ gate; Fig. 15). Rare cases (5%) display markedly increased side scatter, similar to APL, and placing leukemic cells in ‘‘granulocytic’’ gate. All cases are positive for CD45 and CD33. The CD45 is most often bright, but subset of cases shows moderate expression (similar to blasts in other types of

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Fig. 15

Acute monoblastic leukemia – flow cytometry. CD45 is usually bright and SSC is slightly increased placing monocytic cells in ‘‘monocytic gate’’ (A; arrow). Occasional cases may have higher SSC (A0 ) placing neoplasticcellsin‘‘granulocyticgate’’ or moderateCD45locatingneoplasticcellsin‘‘blasticgate’’(A00 ;arrow). Neoplasticmonocytes,similartobenignmonocytesdisplaybrightCD11b(B),brightCD11c(C),positiveCD64 (D), dim-to-moderate CD13 (E), and moderate-to-bright CD33 (F). Majority of cases are CD56 positive (G). In contrasttobenignmonocytesthatarebrightlypositiveforCD14,neoplasticmonocytesareeitherCD14negative (H) or display variable (smeared) expression of CD14 (H0 ). Only rare cases have bright CD14 (H00 ). HLA-DR is most often positive (I), but occasional cases may be HLA-DR-negative (I0 ). Additional markers that may be expressed by monocytic leukemia is CD4 (J), CD10 (K), CD16 (L), and CD23 (M).

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Table V Immunophenotypic profile of acute monoblastic leukemia (n = 100 cases) Marker

% Positive Comments

CD2 CD4 CD7 CD10 CD11b

14 91 8 11 90

CD11c

99

CD13 CD14

80 54

CD16 CD19 CD23 CD33 CD34 CD45

15 0 23 100 8a 100

CD56 CD64 CD117 HLA-DR High side scatter

78 98 21a 93 5

a

Usually dim Dim or dim-to-moderate; in rare cases only on subset of cells Includes four cases positive on subset Includes five cases positive on subset; bright, 38%; moderate, 21%; variable, 14%; and dim, 11% Usually bright, maybe be moderate; only two cases were dimly positive and two cases showed subset cells positive. One case was negative Includes 16 cases positive on subset Includes 17 cases positive on subset; dim/moderate, 7%; bright, 15%; and variable, 15% Includes four cases positive on subset Includes five cases positive on subset Usually bright Includes two cases positive on subset Most often bright, followed by moderate; only three cases had dim expression Only two cases were negative for CD64 Includes five cases positive on subset

Only two cases coexpressed CD34 and CD117.

AML). CD33 is most often bright. Majority of cases are positive for CD11c (99%), CD64 (98%), HLA-DR (93%), CD4 (91%), and CD11b (90%). CD13 is positive in 80% of cases (with majority of those cases showing either dim expression or only subset of cells positive). Other myeloid markers often positive include CD15 and CD65. In contrast to benign monocytes that have bright CD14 expression, leukemic cells in acute monoblastic leukemia are often negative for CD14 (46%). Among CD14+ cases, the expression often is present only on subset (17%) or has characteristic ‘‘smeared’’ pattern (variable expression; 15%). CD56 is positive in 78%. A subset of cases shows positive expression of CD2, CD7, CD10, and/or CD23. Blastic markers (CD34 and CD117) are rarely expressed (Fig. 16). In our series, CD34 was positive in 8% and CD117 in 21% of cases (only two cases were positive for both markers). Based on FC pattern, differential diagnosis of acute monoblastic leukemia includes APL (microgranular variant), AML with minimal differentiation and AML without maturation, CMML, acute megakaryoblastic leukemia, acute myelomonocytic leukemia, and plasmacytoid dendritic cell tumor. Myeloblasts and

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[(Fig._6)TD$IG]

Fig. 16

Acute monoblastic leukemia with CD117 expression. Monoblasts are strongly positive for NSE (A). They are negative for CD34 (B) and show strong expression of CD117 (C), CD11c (D), CD33 (E), CD64 (F), and CD56 (G).

abnormal promyelocytes are strongly MPO+, whereas monocytes are either weakly positive or negative. Monoblasts and promonocytes usually are positive for nonspecific esterase (NSE), but a significant subset of acute monoblastic leukemias is NSE
. Therefore, the definite diagnosis often requires correlation of CBC data, cytologic features, and cytochemistry with additional techniques such as immunophenotyping by flow cytometry, cytogenetics/FISH, and molecular tests. Expression of HLA-DR, CD11b, and CD11c helps to differentiate phenotypically acute monoblastic leukemia (HLA-DR+/CD11b+/(/CD11c+) from the hypogranular variant of APL (HLA-DR
/CD11b
/CD11c
). Moreover, monoblasts may show positive, often variable (smeary) expression of CD14, and positive CD10, CD16, and/or CD23 (those markers are negative in APL). Only rare cases of acute monoblastic leukemia are HLA-DR
. Both APL and acute monoblastic leukemia express CD64, but the expression is usually dim in APL and bright in acute monoblastic leukemia. Acute monoblastic leukemias often express CD56, whereas CD56 is only rarely positive in APL. Fig. 17 compares FC features of acute monoblastic leukemia and hypogranular APL. Fig. 18 compares FC feature of acute monoblastic leukemia with high SSC and classical APL. CD64, typical for acute monoblastic leukemia, is often positive in other types of AML, including APL; the expression of CD64 in other types of leukemia is usually dim or positive only on subset. Positive CD11b, CD11c, CD14, and CD64 favor acute monoblastic leukemia over AML with or without maturation, especially when expression is strong (myeloblasts may be positive for both CD11c and CD64, but expression is dim). Compared to CMML and acute myelomonocytic leukemia, acute monoblastic leukemias show predominance of monocytic population in flow sample. In CMML,

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Fig. 17

Comparison between acute monoblastic leukemia and hypogranular variant of APL. Monoblasts (left panels; blue dots) are positive for HLA-DR, CD11b, CD11c, CD14 (variable expression), and CD64 (bright expression), while promyelocytes (right panels; green dots) are negative for HLADR, CD11b, CD11c, and CD14; the expression of CD64 is dim on subset. Residual neutrophils (gray dots) express both CD11b and CD11c. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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Fig. 18 Acute monoblastic leukemia with SSC: comparison with APL. Monoblasts with high SSC (left panels) differ from hypergranular APL (right panels) by bright CD64, positive CD11c, positive HLA-DR, and negative CD117.

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[(Fig._9)TD$IG] Fig. 19

Comparison between maturing myeloid cells with dysmaturation (left) and acute monoblastic leukemia (right). Occasional cases of acute monoblastic leukemia with aberrant loss of HLA-DR and CD14 and moderate (not bright) CD45 need to be differentiated from granulocytes displaying dyspoiesis

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monocytes are mature without overt cytological atypia. In our series, neoplastic monocytes comprised (on average) 74% of total events in acute monoblastic leukemia, 37% in CMML, and 34% in acute myelomonocytic leukemia. The aberrant immunophenotype of monocytic population is more often seen in acute monoblastic leukemia than in CMML or acute myelomonocytic leukemia. Loss of CD14 expression in acute monoblastic leukemia, CMML, and acute myelomonocytic leukemia is seen in 46, 5, and 15%, respectively. Aberrant expression of CD56 is seen in a majority of acute monoblastic leukemias (78%) and in 69% of CMML and only 22% of acute myelomonocytic leukemias. Aberrant expression of CD10, CD16, and CD23 is comparable in all three disorders, but only acute monoblastic leukemia shows expression of CD117 (21% of cases) or CD34 (8% of cases). Lack of HLA-DR is noted in 7, 23, and 18% of acute monoblastic leukemia, CMML, and acute myelomonocytic leukemia, respectively. In contrast to acute monoblastic leukemia, acute myelomonocytic leukemia show significant population of myeloblasts, in addition to monocytic cells. The number of myeloblasts in CMML varies but by definition is below 20% (20% blasts qualifies the leukemia to acute myelomonocytic group). Occasionally, granulocytes with phenotypic features of dysmaturation may have similar phenotype to neoplastic monocytes (Fig. 19). Lack of CD4 and CD56 and positive expression of CD10 and CD16 on subset favor granulocytes, whereas expression of CD4 and bright CD56 favor monocytes. Final diagnosis of equivocal cases should be based on cytomorphologic features and cytochemical staining for MPO and NSE. Although CD45 expression is most often bright on monocytic population, subset of acute monoblastic leukemias may display moderate CD45 expression, similar to myeloblasts.

XIV. Acute Erythroid Leukemia (FAB: AML-M6) Erythroblasts (Fig. 20) are positive for glycophorin A (GPHA), CD71 and hemoglobin A. They lack MPO, CD34, CD45, and panmyeloid antigens. CD117, CD43, and epithelial membrane antigen (EMA) are often positive. The early erythroblasts show coarse granular positivity for PAS (periodic acid-Shiff). Myeloblasts in the erythroleukemia are positive for CD34, CD117, and panmyeloid antigens (CD13 and CD33).

(e.g., from MDS or CMN). Similar phenotypic features include decreased side scatter, lack of HLA-DR and CD14, moderate expression of CD64 and CD11c, and moderate expression of CD45 (majority of acute monoblastic leukemias have bright CD45). CD11b is bright on granulocytes and may be negative or dim to moderate on monocytic cells. In contrast to granulocytes, acute monoblastic leukemia display bright CD56 (although CD56 may be expressed on granulocytes in MDS or CMN, the expression is usually dim and present on subset of cells, but occasionally may be prominent – see inset). Subset of granulocytes expresses CD10 and CD16, but those markers may be downregulated in MDS or CMN. Atypical monocytes are often CD4 positive and usually do not express CD10 and CD16 (only fraction of cases may be positive for CD10, CD16, and CD23).

248

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Fig. 20

Acute erythroid leukemia. Neoplastic erythroid precursors (A) are negative for CD45 (B), CD34 (C), CD117 (D), HLA-DR (E), CD13 (F), CD33 (G), and CD64 (H). Immunohistochemistry analysis shows positive CD45 (H), EMA (I), CD117 (J), and CD71 (K).

XV. Acute Megakaryoblastic Leukemia (FAB: AML-M7) Flow cytometry analysis (Figs. 21 and 22) is characteristic and shows blasts with moderate CD34, dim CD117, negative-to-dim HLA-DR, negative CD13, bright CD33, and dim CD64. CD41 and CD61 are expressed (expression of CD41 and CD61 has to be interpreted with caution due to potential nonspecific adsorption of

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Fig. 21 Acute megakaryoblastic leukemia. Although the phenotypic pattern may vary, most cases show the following phenotype: low side scatter (A, arrow), CD34+ (moderate expression; B), negative-todim expression of CD117 (C), negative HLA-DR (D), negative CD13 (E), bright expression of CD33 (F), dim expression of CD64 (G), and coexpression of CD41a and CD61 (H, I).

[(Fig._2)TD$IG]

Fig. 22

Poorly differentiated acute megakaryoblastic leukemia with negative CD45 (A), positive CD41 (B), positive CD61 (C), dim CD34 (B, C), and negative CD13 (D), CD33 (E), and CD117 (F).

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platelets on other cells). Some poorly differentiated acute megakaryoblastic leukemias may be negative for most of the markers except CD41, CD61, and dim CD34.

XVI. Blastic Plasmacytoid Dendritic Cell Neoplasm Blastic plasmacytoid dendritic cell neoplasm (blastic NK-cell lymphoma/leukemia; DC2 acute leukemia; CD4+/CD56+ hematodermic neoplasm) is a highly aggressive neoplasm that involves the skin and often disseminates into other organs with leukemic blood and bone marrow involvement (Aoyama et al., 2001; Chaperot et al., 2001, 2004; Garnache-Ottou et al., 2007; Gopcsa et al., 2005; Jacob et al., 2003; Ng et al., 2006; Petrella et al., 2002; Shapiro et al., 2003). It affects mainly elderly patients who present with isolated skin lesion that rapidly progresses to multiple sites, including bone marrow, blood, CNS, spleen, liver, lungs, and kidneys (Garnache-Ottou et al., 2007; Jacob et al., 2003; Willemze et al., 2005). Due to frequent and extensive bone marrow involvement, most patients have anemia and neutropenia (Garnache-Ottou et al., 2007; Jacob et al., 2003). It is derived from the precursors of plasmacytoid dendritic cells. Typically, tumor cells express CD4, CD43, CD45, and CD56 (Fig. 23), and may be positive for CD7, CD33 (rare cases), CD2 (rare cases), TdT, and HLA-DR. There are no surface or cytoplasmic CD3, CD5, B-cell markers (CD19/CD20/CD79a), CD13, CD34, TCRab, and TCRgd. In reported series, tumor cells expressed CD56, CD43, HLA-DR, and CD4, as well as the following markers: CD45 (dim-to-moderate

[(Fig._3)TD$IG]

Fig. 23

Blastic plasmacytoid dendritic cell neoplasm (bone marrow) – flow cytometry. Blasts have low side scatter (A, green dots) and are positive for CD45 (A), CD56 (B), CD4 (C), CD7 (D), and HLA-DR (F). CD34 (E) and CD3 (G) are negative. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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expression), CD116, CD123 (IL-3a receptor), CD45RA, BDCA-2 (blood dendritic cell antigen-2; CD303), BDCA-4 (CD304), and ILT-3 (immunoglobulin-like transcript-3) (Chaperot et al., 2001, 2004; Feuillard et al., 2002; Garnache-Ottou et al., 2005; Gopcsa et al., 2005; Jacob et al., 2003; Leroux et al., 2002). Cases with lack of CD4 expression have been reported (Ng et al., 2006). Differential diagnosis includes acute monoblastic leukemia, AML with NK-cell differentiation/CD56 expression, acute leukemia of ambiguous lineage, acute undifferentiated leukemia, large B- and T-cell lymphomas, poorly differentiated plasma cell tumors, T-lymphoblastic leukemia/lymphoma, histiocytic sarcoma, NK/T-cell lymphoma/leukemia, and other small ‘‘blue cell’’ tumors.

XVII. Chronic Myeloid Leukemia (CML, BCR–ABL+) FC immunophenotyping has a limited role in the diagnosis of myeloproliferative neoplasms (MPNs). FC can determine the number and phenotype of blasts, thus helping quantify MPN in accelerated phase or blast crisis. In addition, there are subtle phenotypic abnormalities displayed by granulocytes in the chronic phase of MPN, including aberrant expression of CD10, CD11b, CD15, CD16, CD56, CD65, CD117, and HLA-DR similar to those described in MDS. The FC findings in untreated CML are not specific and include marked lymphopenia and predominance of maturing myeloid cells/granulocytes, which may display only subtle phenotypic atypia, such as upregulation of CD56 on subset of cells. In the specimen from the blood, the atypical FC findings are more evident, since usually there is a prominent downregulation of CD10, CD16, and CD45 (the FC pattern resembles sample taken from bone marrow rather than blood). Fig. 24 compares FC pattern between CML and reactive neutrophilia in samples from blood. CML in myeloid blast phase (Fig. 25) shows increased myeloblasts and variable proportion of maturing myeloid elements (granulocytes). Some cases of CML show increased lymphoblasts (Fig. 26) that may herald progression into lymphoid blast phase.

XVIII. Granulocytes/Maturing Myeloid Precursors with Dyspoietic Features Associated with Myelodysplastic Syndrome (MDS) Decreased granularity of maturing myeloid cells/granulocytes, one of the most important features of dysgranulopoiesis observed in MDS, can be identified by FC as a decreased SSC (Fig. 27), occasionally resulting in their overlap with myeloblasts on CD45 versus SSC display. Evaluation of ‘‘blastic’’ markers (CD34 and CD117) and markers associated with maturation (CD10, CD11b, CD15, CD16, and CD65) helps to distinguish blasts from maturing elements with low SSC. Abnormal granulocytes with low SSC have to be differentiated from other neoplastic cells with

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Fig. 24

Comparison of the immunophenotypic features of blood granulocytes in CML (left column) and reactive neutrophilia (leukemoid reaction; right column). The expression of CD33 (A) and CD56 (E) is similar (some cases of CML show aberrant upregulation of CD56). In contrast to benign neutrophils, CML often shows aberrant expression of CD13 (B; arrow), downregulation (arrow), and/or negative (dotted arrow) of CD11b (C), lack of CD10 on subset (D, arrow), and lack of CD16 on subset (F, arrow). In blood sample from CML, the pattern of expression of CD10, CD11b, CD11c, CD13, CD16, CD33, and CD56 often resembles the immunophenotype of myeloid cells in normal BM sample.

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Fig. 25 CML – myeloid blast phase. Aspirate smear (A) shows blasts. FISH studies (B) show increased number of bcr–abl copies. Flow cytometry (C–G) shows blasts (green dots; arrow) expressing CD45 (C), CD34 (D), CD13 (F; dim expression), and CD33 (G; bright expression). CD117 is negative (E). Residual granulocytes (gray dots) have higher side scatter (C) and lower forward scatter (D–G) compared to blasts. (See plate no. 10 in the color plate section.)

moderate CD45 and low SSC including blasts, monocytes (Fig. 28), hypogranular variant of APL (Fig. 29), leftward-shifted maturing precursors, and large-cell lymphomas. The phenotypic abnormalities identified in MDS include aberrant expression of CD10, CD11b, CD13, CD14, CD15, CD16, CD33, CD45, CD56, CD64, and HLADR in granulocytic series and aberrant expression of CD2, CD5, CD7, CD11b,

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Fig. 26

CML with increased number of lymphoblasts. Flow cytometry (bone marrow sample) shows predominance of granulocytes/maturing myeloid cells (gray dots). Lymphoblasts (green dots) have dim CD45 expression (A; arrow); they are negative for CD117 (B), positive CD34 (C; arrow), CD19 (D; arrow), and bright CD10 (E; arrow). Granulocytes do not display aberrant CD56 (F). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

CD11c, CD13, CD14, CD15, CD16, CD33, CD36, CD45, CD56, and HLA-DR by monocytes. The most common phenotypic abnormalities in maturing myeloid precursors/granulocytes include downregulation of CD10, CD11b, and CD16 and upregulation of CD56 and HLA-DR (Fig. 30). Bowen and Davis reported consistently normal pattern of CD11b and CD16 expression in the granulocytic series in healthy individuals, but in MDS patients there was an increased percentage of granulocytic cells with low CD16 or both low CD16 and low CD11b (Bowen and Davis, 1997). Chang and Cleveland (2000) reported significantly lower percentage of CD10+ mature

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Fig. 27

Refractory anemia with excess blasts. Maturing myeloid precursors/granulocytes display low SSC, resulting in their overlapping with blasts (A). Analysis of blastic and maturation markers helps to distinguish between two neoplastic populations. Blasts (green dots) are positive for CD34 (B), CD117 (C), and HLA-DR (D), whereas granulocytes/maturing myeloid cells (gray dots) express CD11b (E), CD15 (F), and CD16 (G). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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Fig. 28 MDS – granulocytes (green dots) display low side scatter (A), which in conjunction with positive CD11b (C) and CD11c (D) mimic monocytic cells. Dysplastic granulocytes differ phenotypically from monocytic cells by strong CD10 (B) and CD16 (E) expression and lack of HLA-DR (F), CD14 (G), and CD64 (H) expression. Cytologic evaluation (A, inset) confirmed dysplastic (agranular) granulocytes. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

granulocytes in patients with MDS than in controls. Maturing myeloid cells in MDS may display dim CD15 without a concomitant shift to the left by CD11b, CD13, CD16, and HLA-DR (asynchronous maturation) (Wells et al., 2003). In a series reported by Stetler-Stevenson et al. (2001), the most common myelomonocytic abnormalities detected by FC in MDS were granulocytic hypogranulation (84%), abnormal CD13/CD16 (78%) or CD11b/CD16 (70%) patterns, and CD64 negativity (66%). Wells et al. (2003) reported that the most common FC abnormalities in MDS patients were the presence of abnormal myeloblasts (62%), abnormal relationship between CD13 and CD16 in maturing myeloid cells and monocytes (23%), asynchronous shift to the left (23%), and the presence of CD56 on maturing myeloid cells (16%) and monocytes (17%). In monocytes, the most frequent immunophenotypic abnormalities include abnormal intensity of CD13, CD14, CD36, CD33, or CD64, abnormal pattern of CD11b versus HLA-DR, the presence of

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Fig. 29

Comparison between APL (left panels) and dysplastic maturing myeloid cells (right panels). Both populations are HLA-DR negative (A–A0 ). Note the different expression of CD11b (A–A0 and E–E0 ; arrow) and CD117 (F–F0 ; arrow). Prominent downregulation of CD10 and CD16 in dysplastic granulocytes mimic neoplastic promyelocytes (B–B0 , C–C0 , D–D0 ; only minute subset of granulocytes shows CD10 and CD16 expression, *).

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Fig. 30

Flow cytometric feature of MDS. A–B, aberrant expression of CD11b and HLA-DR by granulocytes. (A) Normal (control) sample with variable but mostly bright expression of CD11b and negative HLA-DR. B shows prominent downregulation of CD11b (arrow) and upregulation of HLA-DR (dashed arrows), creating characteristic ‘‘window’’ pattern. C–E, aberrant expression of CD16. (C) Normal (control) sample with variable expression of CD16 mature elements are positive (arrow). Panel D shows prominent downregulation of CD16. Only minute subset of granulocytes is positive (arrow). Panel E shows negative CD16 in all granulocytes. F–G, aberrant expression of CD56 by granulocytes (F, mild upregulation; G, marked upregulation).

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Fig. 31

MDS with increased blasts (RAEB-I). Blasts (green dots) are positive for CD34 (A–B) and HLA-DR (B–C). Granulocytes (gray dots) display subtle downregulation of CD11b (C–D), and prominent downregulation of CD16 (E–F). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

CD56 and expression of CD2, CD7, or CD19 (Benesch et al., 2004; Stachurski et al., 2008; Stetler-Stevenson et al., 2001; van de Loosdrecht et al., 2008; Wells et al., 2003). Vikentiou et al. (2009) showed that analyzing CD16/MPO/lactoferrin expression can differentiate between lower and higher risk MDS patients. In a series reported by van de Loosdrecht et al. (2008), 14/50 patients had single immunophenotypic abnormality and 32/50 patients had multiple abnormalities with abnormal relations being most common between CD11b, CD13, CD15, and CD16. MDS patients show increased proportions of the more immature immunophenotypic compartments (CD33+/CD16
; CD45+/CD16
; CD13+/CD16
) of myeloid cells and decreased proportions of mature granulocytes (CD33+/CD16+bright; CD45+/CD16+bright; CD13+/CD16+bright) (Malcovati et al., 2005). Myeloblasts are increased in MDS, regardless of the morphologic subtype of MDS, but number of CD34+/CD10+ B-cell precursors (hematogones) and CD34+ plasmacytoid dendritic cell precursors is decreased. van de Loosdrecht et al. (2008) reported the median percentage of myeloid progenitor cells of 2.4% in MDS patients compared to 1.2% in normal bone marrow samples. Two subtypes of MDS, refractory cytopenia with multilineage dysplasia (RCML) and refractory anemia with excess blasts (RAEBs), show higher number of myeloblasts than other types of MDS. Identification of patients with RAEBs is one of the most straightforward features offered by FC in evaluation of MDS (Fig. 31). Based on the number of blasts, RAEB is further subdivided into RAEB-1 with 5–9% blasts and RAEB-2 with 10–19% of blasts.

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Since blasts may be negative for CD34, the enumeration of blasts in MDS should include CD117, HLA-DR, CD11b, and CD45 (blasts being defined as CD34+/
/CD117+/
/CD11b
/CD45+(dim/moderate)/HLA-DR+/SSClow). It is suggested that the cutoff level for blasts in flow cytometry analysis is 3% rather than 5% used in cytomorphology (Matarraz et al., 2008; Ogata, 2008; van de Loosdrecht et al., 2008; Wells et al., 2003). Circulating CD34+ blasts >10/L predict MDS leukemic evolution independent of WHO categories (Cesana et al., 2008). The abnormal antigen expression by blasts in MDS is similar to changes occurring in acute myeloid leukemias (LAP). The aberrant phenotypic changes of blasts observed in patients with MDS include overexpression of CD34, CD117, and CD38, dim CD34, CD38, and CD45, abnormal expression of CD4, CD11b, CD15, and CD65, lack or aberrantly dim expression of CD13, CD33, and HLA-DR, and cross-lineage expression of CD2, CD5, CD7, CD19, CD56, and/or TdT (Arroyo et al., 2004; Del Canizo et al., 2003; Jilani et al., 2002; Kussick et al., 2005; Matarraz et al., 2008; Monreal et al., 2006; Ogata et al., 2002; Pirruccello et al., 2006; van de Loosdrecht et al., 2008; Wells et al., 2003). Some MDS patients display decreased expression of CD45 (Pirruccello et al., 2006; Wells et al., 2003). Aberrant phenotype of blasts in MDS patients (e.g., CD7 and/or TdT) correlates with poor prognosis (Font et al., 2006; Ogata et al., 2002; van de Loosdrecht et al., 2008). van de Loosdrecht et al. (2008) demonstrated that patient with pure refractory anemia (as defined by WHO classification) with expression of lineage infidelity markers on myeloblasts (e.g., CD5, CD7, and/or CD56) had an adverse clinical outcome.

[(Fig._2)TD$IG]

Fig. 32

Dysmaturation associated with growth factor treatment (G-CSG; Neupogen). Patient with chronic renal failure and recent onset of pancytopenia with significant reduction in the expression of CD10 (A, A0 ), CD11b (B, B0 ), and CD16 (C, C0 ) and aberrant expression of CD56 (D, D0 ) 1 month after Neupogen (filgrastim) treatment (upper panels, before treatment; lower panels, after treatment).

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Patients with MDS have decreased number of hematogones (Ogata, 2008; Ogata et al., 2002; Sternberg et al., 2005; van de Loosdrecht et al., 2008). Hematogones have very low side scatter and can be visualized on CD45 versus SSC display. The most immature hematogones (stage 1) express CD34, TdT, CD10, and usually CD19 and CD22. Ogate et al. recommend quantitation of stage I CD10+/CD34+ hematogones as a percentage of all CD34+ cells in the bone marrow (Ogata, 2008). The immunophenotypic abnormalities observed in patients with MDS are not specific and can be observed in reactive (benign) bone marrow, for example, in postchemotherapy marrow regeneration, viral infections, marrow involvement by lymphoma, and treatment with growth factors (granulocyte colony-stimulating factor; G-CSF), and other myeloid disorders (e.g., MPN). Dysplastic granulocytes with low side scatter need to be differentiated from blasts, monocytes, and leftwardshifted maturing precursors. Regenerating marrow may display marked myeloid shift, indicated by the predominance of granulocytes with low side scatter and low-to-negative expression of CD16, low CD11b, and relatively bright CD33 (Kussick and Wood, 2003). CD56 may be aberrantly expressed on subset of

[(Fig._3)TD$IG]

Fig. 33

CMML-I flow cytometry. Neoplastic monocytes are increased in number, display bright expression of CD45 (A; arrow), and have the phenotype similar to benign monocytes (B–G, I) with the exception of aberrant expression of CD56 (H). Blasts are not increased.

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granulocytes and/or monocytes under reactive conditions, including regenerating marrow, infections, and treatment with G-CSF. The G-CSF-treated marrow displays increased immature cells, dyssynchronous expression of CD13 and CD16 among the maturing granulocytes, decreased side scatter of maturing myeloid cells, and increased CD45 expression on the immature granulocytes (Kussick and Wood, 2003). Fig. 32 shows abnormal phenotype associated with growth factor treatment. Maturing myeloid cells with prominent downregulation of CD10 and CD16 need to be differentiated from neoplastic promyelocytes (Fig. 30). Aberrant lack of expression of CD14 by monocytes is seen in PNH patients.

XIX. Chronic Myelomonocytic Leukemia (CMML) Chronic myelomonocytic leukemia is a mixed myeloproliferative/myelodysplastic neoplasm defined by persistent monocytosis (>1  109/L) in the blood, fewer than 20% blasts, and dysplastic features in one or more myeloid lineages. The monocytes are mature with atypia. On basis of the number of blasts, CMML is divided into two categories: CMML-1 (<5% blasts in blood; <10% blasts in BM) and CMML-2 (5–19% blasts in blood and 10–19% blasts in BM). The neoplastic monocytes have the phenotype of mature monocytes with bright expression of CD11b, CD11c, CD14, CD33, CD45, and CD64 (Fig. 33). A majority of

Table VI Immunophenotypic profile of CMML (n = 39 cases) Marker

% Positive Comments

CD2 CD4 CD7 CD10 CD11b CD11c CD13 CD14

41 79 10 23 100 100 97 95*

CD16 CD23 CD33 CD34 CD45 CD56 CD64

36 26 100 0 100 69 97

CD117 0 HLA-DR 77

Usually dim expression Dim-to-moderate expression Includes one case positive on subset Bright expression Bright expression Includes two cases with dim expression on subset of cells Includes two cases positive on subset; most often bright expression (only one case showed variable expression (smeared) Includes four cases positive on subset Includes four cases positive on subset Bright expression Bright expression One case was negative for both CD14 and CD64 (CD11b and CD11c were positive; NSE was positive by cytochemistry)

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cases express CD13, HLA-DR, and CD4. Lack of HLA-DR, and CD13, aberrant expressionofCD16,CD23,CD56,andCD117,aswellasthepresenceofabnormalgranulocytes (with aberrant expression of CD10, CD11b, CD15, CD16, CD56, and HLA-DR) and increased numbers of blasts (<20%), distinguishes CMML from reactive monocytosis. Table VI presents the phenotype of CMML based on flow cytometry evaluation. References Albano, F., Mestice, A., Pannunzio, A., Lanza, F., Martino, B., Pastore, D., Ferrara, F., Carluccio, P., Nobile, F., Castoldi, G., Liso, V., Specchia, G. (2006). The biological characteristics of CD34+ CD2+ adult acute promyelocytic leukemia and the CD34 CD2 hypergranular (M3) and microgranular (M3v) phenotypes. Haematologica 91, 311–316. Al-Mawali, A., Gillis, D., and Lewis, I. (2009). The role of multiparameter flow cytometry for detection of minimal residual disease in acute myeloid leukemia. Am. J. Clin. Pathol. 131, 16–26. Aoyama, Y., Yamane, T., Hino, M., Ohta, K., Nakamae, H., Yamamura, R., Koh, K. R., Takubo, T., Inoue, T., Tatsumi, Y., Tatsumi, N. (2001). Blastic NK-cell lymphoma/leukemia with T-cell receptor gamma rearrangement. Ann. Hematol. 80, 752–754. Arroyo, J. L., Fernandez, M. E., Hernandez, J. M., Orfao, A., San Miguel, J. F., del Canizo, M. C. (2004). Impact of immunophenotype on prognosis of patients with myelodysplastic syndromes. Its value in patients without karyotypic abnormalities. Hematol. J. 5, 227–233. Baumgarth, N., and Roederer, M. (2000). A practical approach to multicolor flow cytometry for immunophenotyping. J. Immunol. Methods 243, 77–97. Benesch, M., Deeg, H. J., Wells, D., and Loken, M. (2004). Flow cytometry for diagnosis and assessment of prognosis in patients with myelodysplastic syndromes. Hematology 9, 171–177. Borowitz, M. J. (2000). Flow cytometry defended. Am. J. Clin. Pathol. 113, 596–598. Borowitz, M. J., Bray, R., Gascoyne, R., Melnick, S., Parker, J. W., Picker, L., Stetler-Stevenson, M. (1997). U.S.–Canadian Consensus recommendations on the immunophenotypic analysis of hematologic neoplasia by flow cytometry: data analysis and interpretation. Cytometry 30, 236–244. Borowitz, M. J., Guenther, K. L., Shults, K. E., and Stelzer, G. T. (1993). Immunophenotyping of acute leukemia by flow cytometric analysis. Use of CD45 and right-angle light scatter to gate on leukemic blasts in three-color analysis. Am. J. Clin. Pathol. 100, 534–540. Borowitz, M. J., Pullen, D. J., Shuster, J. J., Viswanatha, D., Montgomery, K., Willman, C. L., Camitta, B. (2003). Minimal residual disease detection in childhood precursor-B-cell acute lymphoblastic leukemia: relation to other risk factors. A Children’s Oncology Group Study. Leukemia 17, 1566–1572. Bowen, K. L., and Davis, B. H. (1997). Abnormal pattern of expression of CD16 (FcR-III) and CD11b (CRIII) antigens by developing neutrophils in the bone marrow of patients with myelodysplastic syndrome. Lab. Hematol. 3, 292–298. Braylan, R. C. (1997). Flow cytometry is becoming an indispensable tool in leukemia diagnosis and classification. Cancer Invest. 15, 382–383. Braylan, R. C. (2004). Impact of flow cytometry on the diagnosis and characterization of lymphomas, chronic lymphoproliferative disorders and plasma cell neoplasias. Cytometry A 58, 57–61. Braylan, R. C., Atwater, S. K., Diamond, L., Hassett, J. M., Johnson, M., Kidd, P. G., Leith, C., Nguyen, D. (1997). U.S.–Canadian Consensus recommendations on the immunophenotypic analysis of hematologic neoplasia by flow cytometry: data reporting. Cytometry 30, 245–248. Braylan, R. C., and Benson, N. A. (1989). Flow cytometric analysis of lymphomas. Arch. Pathol. Lab. Med. 113, 627–633. Braylan, R. C., Benson, N. A., and Iturraspe, J. (1993). Analysis of lymphomas by flow cytometry. Current and emerging strategies. Ann. N. Y. Acad. Sci. 677, 364–378.

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Jennings, C. D., and Foon, K. A. (1997a). Flow cytometry: recent advances in diagnosis and monitoring of leukemia. Cancer Invest. 15, 384–399. Jennings, C. D., and Foon, K. A. (1997b). Recent advances in flow cytometry: application to the diagnosis of hematologic malignancy. Blood 90, 2863–2892. Jilani, I., and Estey, E., et al. (2002). Differences in CD33 intensity between various myeloid neoplasms. Am. J. Clin. Pathol. 118, 560–566. Kussick, S. J., Fromm, J. R., Rossini, A., Li, Y., Chang, A., Norwood, T. H., Wood, B. L. (2005). Four-color flow cytometry shows strong concordance with bone marrow morphology and cytogenetics in the evaluation for myelodysplasia. Am. J. Clin. Pathol. 124, 170–181. Kussick, S. J., Stirewalt, D. L., Yi, H. S., Sheets, K. M., Pogosova-Agadjanyan, E., Braswell, S., Norwood, T. H., Radich, J. P., Wood, B. L. (2004). A distinctive nuclear morphology in acute myeloid leukemia is strongly associated with loss of HLA-DR expression and FLT3 internal tandem duplication. Leukemia 18, 1591–1598. Kussick, S. J., and Wood, B. L. (2003). Using 4-color flow cytometry to identify abnormal myeloid populations. Arch. Pathol. Lab. Med. 127, 1140–1147. Leroux, D., Mugneret, F., Callanan, M., Radford-Weiss, I., Dastugue, N., Feuillard, J., Le Mee, F., Plessis, G., Talmant, P., Gachard, N., Uettwiller, F., Pages, M. P., Mozziconacci, M. J., Eclache, V., Sibille, C., Avet-Loiseau, H., Lafage-Pochitaloff, M. (2002). CD4(+), CD56(+) DC2 acute leukemia is characterized by recurrent clonal chromosomal changes affecting 6 major targets: a study of 21 cases by the Groupe Francais de Cytogenetique Hematologique. Blood 99, 4154–4159. Malcovati, L., Della Porta, M. G., Lunghi, M., Pascutto, C., Vanelli, L., Travaglino, E., Maffioli, M., Bernasconi, P., Lazzarino, M., Invernizzi, R., Cazzola, M. (2005). Flow cytometry evaluation of erythroid and myeloid dysplasia in patients with myelodysplastic syndrome. Leukemia 19, 776–783. Matarraz, S., Lopez, A., Barrena, S., Fernandez, C., Jensen, E., Flores, J., Barcena, P., Rasillo, A., Sayagues, J. M., Sanchez, M. L., Hernandez-Campo, P., Hernandez Rivas, J. M., Salvador, C., Fernandez-Mosteirin, N., Giralt, M., Perdiguer, L., Orfao, A. (2008). The immunophenotype of different immature, myeloid and B-cell lineage-committed CD34+ hematopoietic cells allows discrimination between normal/reactive and myelodysplastic syndrome precursors. Leukemia 22, 1175–1183. Monreal, M. B., Pardo, M. L., Pavlovsky, M. A., Fernandez, I., Corrado, C. S., Giere, I., Sapia, S., Pavlovsky, S. (2006). Increased immature hematopoietic progenitor cells CD34+/CD38dim in myelodysplasia. Cytometry B Clin. Cytom. 70, 63–70. Ng, A. P., Lade, S., Rutherford, T., McCormack, C., Prince, H. M., Westerman, D. A. (2006). Primary cutaneous CD4+/CD56+ hematodermic neoplasm (blastic NK-cell lymphoma): a report of five cases. Haematologica 91, 143–144. Ogata, K. (2008). Diagnostic flow cytometry for low-grade myelodysplastic syndromes. Hematol. Oncol. 26, 193–198. Ogata, K., Nakamura, K., Yokose, N., Tamura, H., Tachibana, M., Taniguchi, O., Iwakiri, R., Hayashi, T., Sakamaki, H., Murai, Y., Tohyama, K., Tomoyasu, S., Nonaka, Y., Mori, M., Dan, K., Yoshida, Y. (2002). Clinical significance of phenotypic features of blasts in patients with myelodysplastic syndrome. Blood 100, 3887–3896. Orfao, A., Schmitz, G., Brando, B., Ruiz-Arguelles, A., Basso, G., Braylan, R., Rothe, G., Lacombe, F., Lanza, F., Papa, S., Lucio, P., San Miguel, J. F. (1999). Clinically useful information provided by the flow cytometric immunophenotyping of hematological malignancies: current status and future directions. Clin. Chem. 45, 1708–1717. Petrella, T., Comeau, M. R., Maynadie, M., Couillault, G., De Muret, A., Maliszewski, C. R., Dalac, S., Durlach, A., Galibert, L. (2002). ‘Agranular CD4+ CD56+ hematodermic neoplasm’ (blastic NK-cell lymphoma) originates from a population of CD56+ precursor cells related to plasmacytoid monocytes. Am. J. Surg. Pathol. 26, 852–862. Pirruccello, S. J., Young, K. H., and Aoun, P. (2006). Myeloblast phenotypic changes in myelodysplasia. CD34 and CD117 expression abnormalities are common. Am. J. Clin. Pathol. 125, 884–894.

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CHAPTER 11

Flow Cytometry-Based Pharmacodynamic Monitoring After Organ Transplantation Maja-Theresa Dieterlen,* Katja Eberhardt,* Attila Tarnok,y Hartmuth B. Bittner* and Markus J. Barten* * Department of Cardiac Surgery, Heart Center, University of Leipzig, Leipzig, Germany y

Department of Pediatric Cardiology, Heart Center, University of Leipzig, Leipzig, Germany

I. II. III. IV. V.

Abstract Introduction T Cells Regulatory T Cells Dendritic Cells Conclusions References

Abstract Conventional therapeutic drug monitoring based on measuring immunosupressive drug concentrations in blood is important in the clinical management of immunosuppressive therapy in transplantation medicine. Since rejection or infection occurs at irregular drug concentrations immunosuppressive drug therapy is often empiric and prophylactic in nature. In addition, blood immunosuppressant levels are only indirect predictors of the pharmacologic effects on immune cells, because the genetic heterogeneity the immune systems of transplant recipients are not equally sensitive to drug effects. Therefore, therapeutic drug monitoring requires the application of reliable and effective methods to study the pharmacodynamic variability by direct measurements of drug effects on immune cell functions. Flow cytometry METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

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0091-679X/10 $35.00 DOI 10.1016/B978-0-12-385493-3.00011-5

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offers a multiplicity of quantitative analysis possibilities, from detection of phosphorylated molecules up to complex multicolor analysis of whole blood samples. A large spectrum of flow cytometry-based applications for pharmacodynamic monitoring is available and allows detection and analysis of diverse function of T cells and dendritic cell subsets. By combining several assays, it is possible to generate a broad picture of the immune status of every single transplanted recipient. Furthermore, it is even possible to differentiate between synergistic and antagonistic pharmacodynamic effects of immunosuppressive drug combination therapy in vitro and to predict the pharmacodynamic drug effects in transplanted recipients. Such a pharmacodynamic drug monitoring may offer the opportunity to complete conventional therapeutic drug monitoring and, therefore, to tailor immunosuppressive therapy more individually.

I. Introduction The main problem after organ transplantation stems from immunological reactions against the graft. The graft’s and patient’s survival is significantly dependent on effective and patient-specific immunosuppressive therapies. Currently available immunosuppressive drugs possess narrow therapeutic windows that require a close meshed therapeutic drug monitoring (TDM) to avoid toxic drug levels, to prevent formation of cancer, and to allow immunologic reaction to infections (Barten et al., 2007; Levy, 2010). In clinical daily routine, TDM is performed by measurements of blood or plasma drug concentrations. But this pharmacokinetic drug adjustment comprises to some extent the individual effects of immunosuppressants, in consequence immunosuppressive therapy can result in under- and over-immunosuppression leading to either graft rejection or infection, respectively. The physiological effects and the intersubject variability to drugs can only be determined by pharmacodynamic analysis. Moreover, immunosuppressive drugs that are usually administered as a triple-drug regimen are associated with synergistic and/or antagonistic effects (Barten and Gummert, 2007). Thus, a current goal of transplantation medicine is to explore robust biomarkers and to establish reliable assays to monitor the individual alloreactive and anti-infectious immunity for a more balanced immunosuppressive therapy that prevents under- or over-immunosuppression (Barten and Gummert, 2007; Oellerich et al., 2006). Two different types of pharmacodynamic monitoring strategies can be distinguished: (i) enzymatic strategies, which monitor inhibition of drug-target enzyme activity and (ii) immunologic strategies, which measure cellular responsiveness after in vitro stimulated immunologic responses (van Rossum et al., 2010). Enzymatic assays determine drug-type specific markers but fail to detect the effects on immune cell function or the effects on immune system caused by combination therapies (Barten and Gummert, 2007; van Rossum et al., 2010). Immunologic strategies measure immune responsiveness at several levels, such as intracellular concentrations or excretion of cytokines, expression of surface activation markers, and cell proliferation (van Rossum et al., 2010).

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Flow cytometry is one of the most common analysis technologies in research and diagnostics and affords the simultaneous multiparametric detection of different cell surface and intracellular markers. Irrespectively whether cytokines, surface or intracellular markers, and phosphorylated proteins are being detected, flow cytometric analysis offers certain advantages such as (i) it is easy to perform, (ii) requires small cell numbers, (iii) measures the frequency and phenotype of cells, and (iv) demonstrates coexpression of different molecules in individual cells (Barten and Gummert, 2007; Benitez and Najafian, 2008). Thus, it was possible to develop up to 12-color reagent panels to identify the different types of immune cells and their subsets (Autissier et al., 2010; McLaughlin et al., 2008a, 2008b). The disadvantages of this method include that for some assays, such as the detection of activation markers of Tcells or intracellular cytokines-specific activation procedures, the use of inhibitors of intracellular transport, which can be toxic to the cells, is required (Benitez and Najafian, 2008; Berry et al., 2006; B€ ohler et al., 2007; Lindsey et al., 2007). Additionally, it was documented that flow cytometry is less sensitive than Elispot (Benitez and Najafian, 2008). Nevertheless, multicolor flow-cytometry-based assays are a valid addition to the currently available monitoring assays and display the method of choice for multiparametric whole blood analysis and the ideal tool for pharmacodynamic immune cell monitoring. Pharmacodynamic flow cytometry-based assays that were developed in the past 10 years are based on measurements of immune cells from whole blood, purified peripheral blood mononuclear cells (PBMCs), or cell lines. Whole blood is the matrix of choice to reflect the effects of immunosuppressive drugs on immune cell function by flow cytometric analysis because: (i) smaller sample volumes and shorter preparation times are required compared to techniques that rely on purified cells or cell lines; (ii) nonuniform cell loss and/or dissociation of drugs from cellular receptors caused by separation techniques do not occur; (iii) it provides more realistic distribution of drugs due to the presence of serum components and red blood cells (Barten and Gummert, 2007). A large number of protocols were established and validated that ensure lysis and fixation of whole blood samples even for detection of such critical epitopes like phosphorylated proteins (Chow et al., 2005; Dahl et al., 2008; Lin et al., 2010; Quaedackers et al., 2009). Furthermore, it was found that blood storage for up to 24 h is possible without any change in biomarker expression for lymphocytes surface marker and intracellular cytokines (B€ ohler et al., 2007). The possibility for storing blood over a longer period allows the shipping of samples and the design of multicenter studies for pharmacodynamic analysis. Here, we reviewed the current progress of pharmacodynamic monitoring after organ transplantation with the focus on flow cytometric analysis in the last 5 years.

II. T Cells Organ transplantation elicits a complex series of immunologic processes, but T cells are the critical initiators and mediators of the alloimmune response. T cells are

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central to the process of transplant rejection through allorecognition of foreign antigens, have the ability to directly cause graft damage through various mechanisms, and play an important role in providing help to B cells in the generation of the humoral immune response to donor antigens (Benitez and Najafian, 2008; Issa et al., 2010). T-cell activation and proliferation results in consequence of a direct cell-tocell contact of naive T cells with antigen-presenting cells (APCs). A large spectrum of the cellular and molecular changes that are connected to T-cell activation and proliferation can be detected and quantified by flow cytometry. T-cell proliferation is measured by simultaneous analysis of proliferating nuclear antigen (PCNA) and DNA content. Several clinical studies (Barten et al., 2005a, 2005b, 2007; B€ ohler et al., 2008) showed that PCNA detection in T cells serves as pharmacodynamic biomarker for different immunosuppressive drugs in kidney-, lung-, and hearttransplanted patients (Table I). Activated T cells could be detected by the expression of surface antigens, all of which have potential roles in costimulation (CD25, CD71), adhesion (CD134, CD154), and apoptosis (CD95) of the immune response (Barten and Gummert, 2007). Furthermore, T-cell activation is detected by the accumulation and secretion of intracellular cytokines. Cytokines are a category of small signaling proteins and glycoproteins that are used extensively in cellular communication and are crucial to the functioning of both innate and adaptive immunity (Benitez and Najafian, 2008). T helper (Th) cells are a subpopulation of lymphocytes that are involved in activating and directing other immune cells. Proliferating helper T cells that develop into effector T cells differentiate into two major subtypes of cells known as Th1 and Th2 cells. Th1 cells produce IL-2, IFN-g and TNF-a, which have been described as important cytokines for acute rejection. These cytokines mediate the T-cell-dependent cytotoxic effects in allograft rejection. IL-4, IL-5, IL-6, and IL-10 are cytokines produced by Th2 cells that are responsible for the stimulation of B cell that help in antibody production (Benitez and Najafian, 2008). Cytokine analysis of Th1 and Th2 cells to predict immunosuppression was performed in whole blood of heart-transplanted recipients by Barten et al. (2005b, 2006b, 2007), of lung-transplanted patients by Hodge et al. (2005), and of kidney-transplanted recipients by B€ ohler et al. (2008), Couzi et al. (2008), and Panigrahi et al. (2006). These studies validated that cytokines produced by Th1 and Th2 cells play a foremost role in graft tolerance and rejection. IL-17 is a cytokine produced by another Th subpopulation, the Th17 cells. This cell population has been linked to renal and cardiac graft rejection, which has originally thought to be Th1 mediated (Cunnusamy et al., 2010). In general, monitoring cytokine production is problematic because of its restriction to certain cell cycle phases, the varying half-life of circulating cytokines and the variations in up- and downregulation of cytokine gene expression. Thus, technical differences, such as time of collecting and the assay procedure could influence and bias the results (Barten et al., 2006b). Several commercially available multiplex bead arrays enable the simultaneous detection of different Th1, Th2, and Th17 interleukins. Not only the cytokines that are expressed and secreted by helper T cells display a meaningful tool for transplantation monitoring but also the frequency and the ratio

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11. Flow Cytometry-Based Pharmacodynamic Monitoring After Organ Transplantation

Table I Clinical application of flow cytometry-based pharmacodynamic T-cell assays after organ transplantation Author

Aim of the study

Key findings

Reference

M. J. Barten

Monitoring of T-cell cytokines (IL-2, TNF-a), proliferation (PCNA), and activation (CD25, CD95) at IS therapy after heart Tx Monitoring of intracellular cytokines (IL-2, IL-4, TGF-b, TNF-a) subsets (CD4+, CD8+) after lung Tx Detection of Th1/Th2 cell cytokines (IFN-g , TNF-a, IL-2, IL-4, IL-6, IL-10) after heart Tx

Correlation between PD and PK and dose were higher for CsA than for MMF

Transplant Proc. (2005)

After lung Tx increased levels of IL-4 and TGF-b, decreased levels of IL-2 and TNF-a

Clin. Exp. Immunol. (2005)

After Tx increased levels of TNF-a, IL6, decreased levels of IL-2, IL-4; decreased cytokine levels 2 h after drug administration CD3, HLA-DR, Fas-L, ICAM-1, and CD25 useful monitoring markers after Tx

Cytometry A (2006b)

G. Hodge

M. J. Barten

N. Z. Galante

A. Panigrahi

M. J. Barten

T. B€ ohler

L. Couzi

Monitoring of CD3, CD4, CD8, HLA-DR, Fas-L, ICAM-1, and CD25 in renal Tx patients with AR Analysis of intracellular cytokines (IL-2, IFN-g ) in T cells after renal Tx in AR patients Analysis of T-cell function (PCNA, IL-2, IL-4, IFN-g , TNF-a, CD25, CD95, CD134) after heart Tx

Analysis of T-cell proliferation (PCNA+PIhigh), activation (CD25, CD71), function and lymphocyte subsets (CD2, CD3, CD4, CD8, CD19, NK cells) after renal Tx at drug conversion therapy Measurement of cytokine (IL-2, IFN-g , TNF-a) after renal Tx

R. Alonso-Arias

Expression analysis of CD127 in CD4+CD25(high) T cells after renal Tx

S. Bremer

Analysis of T-cell subsets (CD3, CD4, CD8, CD45RA, CD45RO)

Transplant Immunol. (2006)

IFN-g expression in T cells higher in AR patients

Clin. Transplant. (2006)

Correlation between CsA dose and [CsA] and PD marker inhibition; TRL higher inhibition of CD25, CD95, IL-2, IFN-g , TNF-a; individual [SRL] not reflected in degree of PD marker inhibition No significant differences of MMF versus EC-MPS on lymphocyte function

Cell Prolif. (2007)

IS decreased all T-cell-produced cytokines; cytokine monitoring is no predictor for CMVinfection after Tx Tx patients: increased levels of CD127(high), lower frequency of CD127(low); quantification of CD127(low)CD4+ T cells is new monitoring tool for outcome after renal Tx No difference between betalacept and CsA IS-treatment groups

Transplantation (2008)

Int. Immunopharmacol. (2008)

Transplantation (2009)

J. Trans. Med. (2009)

(Continued)

Maja-Theresa Dieterlen et al.

272 Table I

(Continued)

Author

N. Kamar

M. E. Quaedackers

G. Serban

F. Xue

Aim of the study after drug intake in renal Tx patients Analysis of MMF effect on T cell cytokine expression (IL-2, TNFa), activation (CD25, CD71), and proliferation (PCNA+PIhigh) before renal Tx Phospho-flow cytometric (p-STAT5) characterization of T-cell subsets in heart Tx patients after CP-690550 intake Quantification of CD4+ T cells phenotype and frequency of renal Tx patients with thymoglobulin therapy Quantification of T cells (Th (CD3+CD4+), Ts (CD3+CD8+), and Th/Ts ratio in lung Tx patients with post-Tx infection

Key findings

Reference

T-cell proliferation, CD25 and CD71 expression decreased after MMF intake

Clin. J. Am. Soc. Nephrol. (2009)

p-STAT5 reduced in CD3+, CD3+CD4+, and CD3+CD8+ cells after CP-690550 intake

Transplantation (2009)

CD4+ T cells with activated/ memory phenotype; CD25+FOXP3+ cells detected; CD4+ T cell counts increased during first 5 months after Tx No correlation of Th/Ts ratio to infection and ImmuKnowTM ATP values; specificity of Th/Ts ratio 75.5%

Hum. Immunol. (2009)

Transplantation (2010)

AR, acute rejection; CMV, cytomegalovirus; CsA, cyclosporine A; [CsA], concentration of CsA; CP-690550, small molecule JAK3-inhibitor; ECMPS, enteric-coated-mycophenolate sodium; IS, immunosuppression; MMF, mycophenolate mofetil; PD, pharmacodynamic; PK, pharmacokinetic; SRL, sirolimus; [SRL], concentration of SRL; Th, T helper cell; TRL, tacrolimus; Ts, T suppressor cell; Tx, transplantation.

of Th cells to suppressor T (Ts) cells provide a tool for monitoring posttransplant risks and outcomes (Serban et al., 2009; Xue et al., 2010). Furthermore, apoptotic T cells can be quantified using the fluorescent Annexin V binding assay. Alternatively, DNA breaks that are caused by apoptotic events could be detected by TUNEL assay (Boldt et al., 2006). In the past 5 years, clinical studies that focused on T-cell characterization for pharmacodynamic monitoring after organ transplantation were performed by using flow cytometry. Therefore, the different proliferation, activation, and apoptosis markers were monitored to identify pharmacodynamic biomarkers. In many cases, combinations of flow cytometric analysis, ELISA, PCR, and enzymatic assays were accomplished (Bremer et al., 2009; Galante et al., 2006; Kamar et al., 2009; Kim et al., 2009; Nickel et al., 2009; Panigrahi et al., 2006). This combination of different assays may provide an insight into the individual immunological status. B€ ohler et al. (2008) investigated the pharmacodynamic effects of two formulations of the immunosuppressive drug mycophenolic acid, mycophenolate mofetil, and enteric-coated mycophenolate sodium, on lymphocyte proliferation, activation, and function. Both formulations seem to have different pharmacokinetic profiles, and the clinical study of B€ ohler on kidney-transplanted patients revealed that EC-MPS trend to a lower immunosuppression compared to equivalent doses of mycophenolate mofetil. Other studies assessed the effects of the first dose of immunosuppressive drugs

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before transplantation by a number of different pharmacodynamic assays (Kamar et al., 2009) or investigated the pharmacokinetic and pharmacodynamic relation of immunosuppressive drugs in combination therapies (Berry et al., 2006; Bremer et al., 2009). In summary, multiple pharmacodynamic assays are available for a broad characterization of T-cell responses after organ transplantation. Furthermore, these assays can be exerted as helpful tools for development and assessment of new immunosuppressive drugs.

III. Regulatory T Cells Immune tolerance, which is defined as the lack of an immune response toward a specific antigen without additional immunosuppression was first demonstrated in animal models in the 1950s (Billingham et al., 1953). In the following decades, immune tolerance was linked to a specialized subpopulation of T lymphocytes, the regulatory T cells (Tregs), formerly known as suppressor T cells. Tregs have been described as the most potent immunosuppressive cells in the human body (Trzonkowski et al., 2009). These cells act to suppress activation of other immune cells and maintain immune system homeostasis, self-tolerance, as well as prevent excessive immune response to foreign antigens (Le and Chao, 2007). In addition to CD4, CD25, and Fox-P3 antigens, several immunological markers such as CD127, CD45RA, CD28, CD152, CD184 have been found to be expressed by different subpopulations of Tregs (Lanza, 2009). Two main subsets of Tregs, the naturally occurring (nTregs) and the adaptive Tregs, are characterized in humans. These subsets differ in their targets and the mechanism of action by which they regulate the immune response. nTregs develop in the thymus and express the cytokines IL-4, IL-10, and TGF-b after stimulation. The suppression of immune function by nTregs is mediated by direct cell-to-cell contacts with CD4+ and CD8+ effector cells. Interactions and suppression with other cell subsets such as NK cells, monocytes, dendritic cells, and granulocytes are also assumed (Lewkowicz et al., 2006; Taams et al., 2005; Trzonkowski et al., 2006). Besides suppressing self-reactive cells of the immune system, nTregs suppressive activity can regulate allo- and xenoresponses, provided that a given alloantigen is presented together with a previously tolerated antigen. These findings have a large relevance to develop a strategy for adoptive therapy after organ transplantation (Table II). The second subset of Tregs, the adaptive Tregs develop in the periphery and express the cytokines IL-10, IFN-g , TGF-b and IL-15. The immunoregulatory function of adaptive Tregs is permitted by secretion of these cytokines. Beside these two main subsets three other subpopulations of Tregs were identified and characterized: (i) CD8+CD28
T suppressor cells (Ts), the CD4
CD8
double negative Tregs, and (iii) NK Tregs. Treg adoptive therapy holds promise to induce and maintain donor-specific tolerance to the transplant without the need for life-long immunosuppression and

Maja-Theresa Dieterlen et al.

274

Table II Clinical application of flow cytometry-based pharmacodynamic assays for regulatory (Treg) detection after organ transplantation Author

Aim of the study

Key findings

Reference

A. Demirkiran

Monitoring of Tregs within the first year after lung Tx

Transplant Proc. (2005)

D. S. Segundo

Analysis of Tregs under IS after renal Tx Analysis of Tregs under IS after renal Tx

Tregs and CD4+ T cells reduced within the first year after Tx; CNI treatment reduced Tregs (%) CNI treatment, but not SRL treatment reduced Tregs after Tx No influence on Tregs under SRL treatment; CsA therapy reduced Tregs (%) Tregs (%) decreased after Tx; high CNI treatment reduced Tregs (%) Decrease of CD8+CD28
T cells after Tx; no changes of CD4+CD25(high) or CD4+FoxP3+ Tregs; no correlation of CNI level/dose with Tregs (%) Decreased frequency of Tregs before liver Tx; decreased frequency of Tregs at AR patients; CNI and anti-CD25, but not SRL/ERL treatment decreased frequency of Tregs Tregs (%) decreased after Tx; Tregs (%) decreased under MMF treatment compared to other drugs; Tregs (%) increased in monotherapy compared to combined IS

G. Korczak-Kowalska

D. San Segundo M. Calvo-Turrubiartes

Analysis of Tregs under IS after liver Tx Analysis of Tregs and cytotoxic T cells (CD8, CD28) under CNI therapy after renal Tx

S. H. Kim

Monitoring of Tregs before and after renal Tx and liver Tx

E. Ram
ırez

Analysis of Tregs after renal Tx

Transplantation (2006) Transplant Proc. (2007) Transplant Proc. (2007) Transplant Immunol. (2009)

J. Korean Med. Sci. (2009)

Transplant Proc. (2009)

AR, acute rejection; CNI, calcineurin inhibitors; CsA, cyclosporine A; ERL, everolimus; IS, immunosuppression; MMF, mycophenolate mofetil; SRL, sirolimus; Tregs, regulatory T cells; Tx, transplantation.

therefore to serve as an alternative to immunosuppressants. Thus, it is of great importance for transplantation medicine to use the capacity of Tregs to induce tolerance against any antigen and to monitor Treg cell populations. But little is known about the dynamics of Tregs in relation to rejection, tolerance, and immunosuppression (Demirkiran et al., 2005). The ideal tool for characterization of Treg subpopulations and for monitoring the effects of immunosuppressive drugs on Treg cell therapy is multiparametric flow cytometry. A major stumbling block in the clinical application of Tregs is related to their phenotype and the very limited number of these cells in the periphery, not exceeding 1–5% of total CD4+ T cells. Recent progress in multicolor flow cytometry and cell sorting as well as cellular immunology has found ways of overcoming these obstacles, and has opened the doors to the clinical application of Tregs (Trzonkowski et al., 2009). Protocols for

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phenotype analysis of Tregs by flow cytometry were proposed and validated (Battaglia et al., 2005; Grant et al., 2009; Miroux et al., 2009). Additionally, clinical studies investigating Treg subpopulations in transplantated recipients by flow cytometric analysis were performed (Calvo-Turrubiartes et al., 2009; Demirkiran et al., 2005; Kim et al., 2009; Korczak-Kowalska et al., 2007; Ram
ırez et al., 2009; San Segundo et al., 2007; Segundo et al., 2006). One strategy for clinical application of Treg-based immunotherapy includes the infusion of ex vivo-selected naturally occurring Tregs (Peters et al., 2009). This procedure includes a sorting step to enrich the hosts Tregs. For this enrichment of cells the fluorescence-activated cell sorting (FACS) is the method of choice compared to alternative sorting methods like immunomagnetic sorting (Baecher-Allan, 2006; Trzonkowski et al., 2009). FACS bears the great advantage to isolate Treg populations with a high purity and consistency (Baecher-Allan, 2006). Contaminations with effector cells are extensively foreclosed (Trzonkowski et al., 2009). An alternative aim for tolerance therapy is based on the in vivo expansion of Treg numbers or function. This type of therapy requires a close meshed monitoring and quantification of blood Tregs that could be accomplished by flow cytometric analysis. Immunosuppressive drugs, such as the calcineurin inhibitors (cyclosporine A and tacrolimus), the mTOR inhibitors (sirolimus and everolimus), or the mycophenolatebased drugs (mycophenolate mofetil and mycophenolic sodium) are widely used to prevent allograft rejection and graft versus host disease, and therefore it is essential to determine the effects of those drugs on Tregs before any clinical application in transplantation (Albert et al., 2006). Recent studies in animal models and clinical studies showed that mycophenolate mofetil and cyclosporine A at their full therapeutic doses exerted an inhibiting effect of alloantigen-stimulated proliferation, function, and survival of Tregs (Coenen et al., 2006; Demirkiran et al., 2005; Kawai et al., 2005; Korczak-Kowalska et al., 2007; Lim et al., 2010; Miroux et al., 2009; Ram
ırez et al., 2009; San Segundo et al., 2007; Segundo et al., 2006). In contrast, mTOR inhibitors like sirolimus and its derivate everolimus selectively expand CD4+CD25+FOXP3+ Tregs and preserve the functions of Tregs for transplant tolerance (Battaglia et al., 2005; Chen et al., 2010; Coenen et al., 2006; Segundo et al., 2006). In spite of promising results that account Tregs as inductors for immune tolerance after organ transplantation the safe application of these cells in clinical studies was not documented sufficiently until now. Intensive preclinical and clinical research activity is necessary to enhance the possibilities for Treg immunotherapy in transplantation.

IV. Dendritic Cells Two distinct lineages of DCs are known, the myeloid DCs (mDC or DC1) originated from myeloid precursor cells and the plasmacytoid DCs (pDC or DC2), originated from lymphoid precursor cells (Gerrit et al., 2007). Plasmacytoid DCs display a more plasma cell-like morphology and express high levels of IL-3 receptor a-chain, also known as CD123 (CD11c
CD123high), which pDCs require for their

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survival and differentiation. On the other hand mDCs (CD11c+CD123low) express markers of myeloid cells like CD13 and CD33 and need exogenous granulocytemacrophage colony-stimulating factor for their survival (Womer et al., 2005). DCs are in lymphoid and in nonlymphoid tissues, but also in peripheral blood. The function of DCs varies with their maturation state. Hence, immature DCs present chemokine receptors such as CCR1, CCR2, CCR5, CXCR3, and CXCR4, but low levels of MHC class I and II as well as costimulatory molecules (CD83, CD54, CD80/CD86), and so have tolerogenic properties. Immature DCs capture and further process antigens to mature. Additionally, DCs mature by other environmental stimuli as alarmins, tissue factors, and cytokines. Expression of costimulatory molecules (CD83, CD54, CD80/86, MHC class I and II), chemokine receptors (CCR7), and MHC class I and II molecules mature DCs homing to secondary lymphoid tissue organs to activate T cells through cytokines (Thomson and Robbins, 2008). The DC analysis is technically demanding due to their rarity as a minor population in peripheral blood and their ex vivo fragility (Magyarics et al., 2008). For flow cytometry analysis of DCs a cocktail of DC Lineage (Lin1-method) marker is available, which contains negative monoclonal antibodies (mAbs) against T cells, B cells, NK cells, and monocytes to discriminate against DCs and mAbs against HLA-DR, CD123, and CD11c to detect DCs. Another possibility of flow cytometric detection of DCs is to use CD33 mAbs to detect mDCs or CMRF-44 mAbs to identify peripheral blood DCs. The monocyte markers CD11c and the IL-3 receptor CD123 serve as differentiation criteria for mDCs or rather DC1 (CD11c+CD123
) and pDCs or rather DC2 (CD11c
CD123+) allowing to analyze the whole fraction of DCs (Solari and Thomson, 2008) (Table III). Lately, a new set of mAbs became available for detecting both mDCs and pDCs: the blood dendritic cell antigen (BDCA)-1 mAbs are utilized for mDCs and BDCA-3 mAbs for a very small fraction of mDCs, which lack the expression of BDCA-1. BDCA-2 mAbs and BDCA-4 mAbs serve for identification of pDCs. With the help of BDCA antibodies, it became possible to define pDCs and mDCs without the usage of a large number of Lineage-negative markers and anti-HLA-DR (Gerrit et al., 2007; Magyarics et al., 2008). Currently available methods have some limitations in multiparametric DCs analysis, because they are based on 3- or 4-color assays. Subsequently, the use of a singleplatform 6-color method is preferred to analyze multiple parameters of both mDCs and pDCs simultaneously like HLA-DR or costimulatory molecules (CD80, CD83, CD86, or CD40). Thus, such multiparametric assay allows to detect numerical and immunophenotypic changes of DCs in particular physiological and pathological conditions (Giannelli et al., 2008). The ability to use only small volumes of whole blood to monitor changes in the phenotyping of lymphocyte, monocyte, and DC subsets is another important advantage of flow cytometry (Autissier et al., 2010). Another nomenclature that includes the surface marker CD1c, which is also known as BDCA-1, defines mDC1 as CD14
CD19
BDCA-1+BDCA-2
, mDC2 as CD14
CD19
BDCA-1
BDCA2
BDCA-3+, and pDC as CD14
CD19
BDCA-1
BDCA-2+ (Daniel et al., 2005).

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Table III Clinical application of flow cytometry-based pharmacodynamic assays for dendritic cell detection after organ transplantation Author

Aim of the study

Key findings

Reference

P. Athanassopoulos

Examination of DC subsets (incl. CD83, CCR7) in CHF and heart Tx

Eur. J. Cardiothorac. Surg. (2004)

P. Athanassopoulos

Examination of PBDC incidence and DC subset reconstitution in relation to histological diagnosis after heart Tx Quantification of IL-producing DCs after renal Tx

CHF patients: higher DCs and mDCs, CD83+ and CCR7+ mDCs (%) increased; post-Tx: DCs decreased Inverse correlation of mDCs with rejection grade; PBDCs and mDC/pDC ratio decreased during AR episode IL-10/IL-12-producing DCs decreased after RTx and increased with time post-Tx PBDC levels reduced in ESRD patients pre-Tx; decrease of DCs after Tx; return of PBDC levels to pre-Tx values mDCs (%) increased after IS; ratio pDC/mDC decreased after Tx; CD83 unchanged, IL-12 and IL-1b levels drug dependent DCs decreased after Tx; mDCs with upregulated CD62L and CD86 expression; DC levels alone not a predictable marker for rejection episodes

V. Daniel

K. L. Womer

Quantification of PBDC levels in patients with ESRD undergoing renal Tx

M. J. Barten

Quantification of DC subsets, CD83 and cytokines (IL-1b, TNF-a, IL-8, IL-12) after heart Tx

J. Fangmann

Quantification of DC subsets, CD62L, CD80, and CD83 expression after renal Tx

Eur. J. Cardiothorac. Surg. (2005) Transplantation (2005) Clin. Transplant. (2005b)

Int. Immunopharmacol. (2006a) Transplant Proc. (2007)

AR, acute rejection; CCR7, chemokine receptor 7; CHF, chronic heart failure; CKR, chemokine receptor; DC, dendritic cell; ESRD, end stage renal disease; IL, interleukin; IS, immunosuppression; mDC, myeloid dendritic cell; PBDC, peripheral blood dendritic cells; pDC, plasmacytoid dendritic cell; Tx, transplantation.

DC analysis has taken into account several influences on DCs, which are sensitive to the preservation techniques, for example, ficoll-isolation or cryopreservation. Thus, the use of whole blood is preferred compared to PMBCs for flow cytometric analysis, but for a more realistic interpretation of the DC status of transplant recipients human blood has to be analyzed within 4 h after harvesting (Gerrit et al., 2007; Magyarics et al., 2008). Other factors that affect DCs analysis are patient age or the clinical conditions such as exercise, stress, surgery (Gerrit et al., 2007), or the pathogenesis of cardiomyopathy (Athanassopoulos et al., 2004). Importantly, mDC and pDC levels are significantly reduced by renal failure, often induced to drug toxicity in the maintenance phase after renal, liver, or heart transplantation, with or without hemodialysis (Womer et al., 2005). Further analyses show a relation between loss of glomerular filtration rate and a decrease in numbers of pDCs (Hesselink et al., 2005) while mDCs are more reduced by dialysis (Daniel et al., 2005). In general immunosuppression

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leads to render mDC and pDC subsets into an immature state, suggesting a potent effect not only on maturation but also on the migration characteristics of the mDCs and pDCs (Athanassopoulos et al., 2004). Immunosuppressive agents influence DCs in different ways. First, the agent antithymocyte globulin (ATG), often used to induce immunosuppression or to treat severe rejection, induces apoptosis in DCs regardless of the status of maturation, and inhibits the uptake of antigen by binding lectins to surface molecules like CD206 and CD209 (Daniel et al., 2005). Absolute DC levels but not percentages of total DCs were significantly lower after ATG therapy compared to patients not receiving ATG (Womer et al., 2005). Second, corticosteroids inhibit the differentiation and maturation of DCs (Hackstein and Thomson, 2004), and third the mTOR inhibitor sirolimus limits antigen capture and maturation of DCs. Fourth, mycophenolic acid, the active compound of mycophenolate mofetil, influences the phenotype and the function of DCs during their maturation. Finally, the calcineurin inhibitors cyclosporine A and tacrolimus suppress the gene transcription of regulating proteins like nuclear factor of activated T cells, and cyclosporine A restrains the migration of DCs (Daniel et al., 2005). Over the past years clinical studies show the usefulness of monitoring peripheral blood DCs in transplant patients as the number of mDCs and pDCs reflect the degree of immunosuppression, for example, tolerance or rejection. Hence, after renal transplantation both DCs subsets decrease, with a more prominent reduction of pDCs, followed by return of peripheral blood mDCs and pDCs back to pretransplant values simultaneously with the overall reduction of immunosuppression in the maintenance phase (Daniel et al., 2005; Womer et al., 2005). Furthermore, the expression of CD62L was significantly higher, while CD86 was significantly downregulated on mDCs but not on pDCs. Neither infection nor rejection episodes showed any correlation to DC levels (Fangmann et al., 2007). These results are in contrast to a study in liver transplant patients where patients at risk for acute rejection differ significantly in their mDC/pDC ratio from patients who successfully withdrawn from immunosuppression (Athanassopoulos et al., 2005). Studies in patients many years after liver transplantation showed significantly lower pDCs and higher mDC values between patients without immunosuppressive drugs, so called ‘‘tolerant,’’ and patients with immunosuppressive drug therapy (Daniel et al., 2005). Similarly, studies showed a preferential loss of mDCs in heart-transplanted patients during acute rejection as a sign of a negative association of mDCs with the rejection grad. Hence, a regular DC-monitoring could identify patients with a higher risk of rejection after heart transplantation (Athanassopoulos et al., 2005; Daniel et al., 2005). Moreover, immunosuppressive drugs differ in their affect on DCs subsets in the early postoperative phase as well as in the maintenance phase after heart transplantation. One week post-heart transplantation total DC numbers, especially numbers of pDCs, were lower than preoperatively in the pretransplant (Athanassopoulos et al., 2004). In the maintenance phase mDCs in peripheral blood of heart-transplanted patients were higher in comparison to values of healthy controls. On the other hand,

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percentages of positive pDCs were higher in patients treated with mTOR inhibitors compared to patients treated with calcineurin inhibitors (Barten et al., 2006a). Not only the assessment of DCs subsets but also the different production of intracellular cytokines of mDCs and pDCs could be valuable for an enhanced TDM (Barten et al., 2006a). The full cytokine production of DCs requires a microbial stimulus (e.g., LPS) and a crosstalk between DCs and T cells by linking CD40 ligands. Human pDCs fail to express toll-like receptor (TLR) 4 and do not react to LPS. In contrast, mDCs show a high sensitivity to LPS stimulation even if they only express low levels of TLR4 (Barten et al., 2006a). High expression of TLR7 and TLR9 is a marker of pDCs and B cells while monocytes and mDCs display TLR2 and TLR4. The expression of different TLR is in support of the hypothesis of independent differentiation pathways of mDCs and pDCs. Flow cytometric analysis of intracellular cytokine production of DCs revealed that mature, tolerogenic DCs show either low or lack of production of proinflammatory cytokines IL-1b, TNF-a, IL-8, and especially IL-12 (Thomson and Robbins, 2008). Upon bacterial infection, mDCs produce high levels of IL-12 and interact strongly with Th1 cells and cytotoxic T cells (Solari and Thomson, 2008). Whereas in consequence of viral infection, pDCs cause Th2 cell responses and excrete high levels of IFN-a and TNF-a, which prime naı¨ve CD4+ T cells to produce INF-g and IL-10. Additionally, pDCs recognize viral nucleic acids through TLR, for example, TLR7 and TLR9, in response to viral infections (Gerrit et al., 2007; Womer et al., 2005). Clinical investigations on heart-transplanted recipients revealed that calcineurin inhibitors and sirolimus treatment increased the percentage of IL-12-expressing DCs and decreased the percentage of IL-1b-expressing DCs, while IL-8 and TNF-a expression levels remain unchanged (Barten et al., 2006a).

V. Conclusions Immunological responses to transplant allografts are the main drivers of rejection. T cells subsets, including Tregs, and DCs play a crucial role in rejection-associated immunological processes such as inflammation and tolerance. Thus, the knowledge about these immune cells, their characterization, and the possibility of long-term monitoring after organ transplantation is of great advantage for to individualize immunosuppressive drug therapy after organ transplantation. Conventionally, determination of optimal exposure levels for new immunosuppressive drugs, or for existing drugs within novel combinations, has relied on clinical experience and population-based regular pharmacokinetic measurements. However, the wide variation between patients in the immunological response to immunosuppressive drugs – even when receiving identical drug exposure – has limited the effectiveness of therapeutic monitoring strategies (Barten and Gummert, 2007). It has long been recognized that pharmacodynamic monitoring, whereby biologically relevant events are measured with the aim of predicting drug efficacy in vivo,

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would be highly advantageous when attempting to identify the best exposure level in individual patients to provide adequate antirejection prophylaxis while minimizing drug-related complications (Oellerich et al., 2006). During the last decade a large number of new monitoring strategies have been developed for quantification of T-cell and DC responses. Ideally, immune cell assays are able to identify key steps in the process of rejection or infection. Thus, predicting such events prior to their clinical manifestation makes it possible to intervene at an earlier stage which benefits the patient (Hernandez-Fuentes and Salama, 2006). For routine monitoring of T-cell reactivity in transplanted patients immune assays such as IFN-g -Elispot, multiparameter flow cytometry, and measurement of intracellular ATP, all are well accepted (Nickel et al., 2009). But for a definitive picture of alloimmune response the assessment of multiple accurate, reproducible, and clinically relevant markers is required (van Rossum et al., 2010). Thus, cell types that appear to a smaller percentage in whole blood, like Tregs or DCs, are currently in the focus of clinicians, because of their potential to identify risk of rejection and infection as well as to enhance therapeutic monitoring of immunosuppressive drugs. Compared to other diagnostic tools, flow cytometry offers the advantage of analyzing only microliters of whole blood, for example, to simultaneously assess expression of different immune cell function parameters circulating Tregs and DCs (Barten et al., 2007). In the future, TDM should include patient-specific pharmacodynamic analysis. Rather than a single assay, it is likely that a battery of serially performed assays would be best suited to determine the broad picture of T-cell and DC alloreactivity in a given patient (Benitez and Najafian, 2008). In spite of the described advantages of the combination of the technologies whole blood and flow cytometry for assessment of biomarkers of immune cell functions, the increasing innovative technology in the field of flow and image cytometry will yield more information of biomarkers of many more functions in different immune cell lineages from minute volume of a single sample of whole blood to predict pharmacodynamic drug effects (Barten and Gummert, 2007). For example, T-cell activation status can be alternatively identified by Raman spectroscopy (Brown et al., 2009), detection of low-copy molecules that could serve as new biomarkers was made possible through tyramide signal amplification (TSA) (Clutter et al., 2010), and high-content flow cytometric screening (FC-HCS) was automated to improve diagnosis and biomarker identification (Naumann and Wand, 2009). In the future, robust flow cytometric assays to monitor pharmacodynamic drug effects will show the accuracy and reliability to decrease probability of rejection and enhance safety. However, flow cytometry is technically demanding and many clinical transplant centers are not equipped with the necessary technology or expertise. Thus, future investigations may also be aimed to develop assays utilizing specimens freezing which would allow the application of flow cytometric analysis of T cells and DCs in peripheral blood after organ transplantation in large multicenter laboratories specializing in such analyses.

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CHAPTER 12

Clinical Cytometry and Progress in HLA Antibody Detection Robert A. Bray*, Christine Tarsitaniy, Howard M. Gebel* and Jar-How Leey * y

Department of Pathology, Emory University, Atlanta, Georgia, USA

Research Department, One Lambda, Inc., Canoga Park, California, USA

Abstract I. Introduction II. Cell-Based Assays A. Lymphocytotoxicity B. Modified Lymphocytotoxicity Assays C. Flow Cytometry III. Antigen-Based Assays A. Solid-Phase Immunobinding Assays B. Multiplex Platform for Microparticle Analysis C. Recombinant HLA Antigens D. Determining Antibody Specificity IV. Current Frontiers in Transplant-Related Antibody Testing A. Extended HLA Loci B. Conundrums in HLA Antibody Assignment C. Functional Assessments of HLA Alloantibody by Flow Cytometry V. Summary References

Abstract For most solid organ and selected stem cell transplants, antibodies against mismatched HLA antigens can lead to early and late graft failure. In recognition of the clinical significance of these antibodies, HLA antibody identification is one of the most critical functions of histocompatibility laboratories. Early methods employed cumbersome and insensitive complement-dependent cytotoxicity assays with a visual read-out. A little over 20 years ago flow cytometry entered the realm of METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

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antibody detection with the introduction of the flow cytometric crossmatch. Cytometry’s increased sensitivity and objectivity quickly earned it popularity as a preferred crossmatch method especially for sensitized recipients. Although a sensitive method, the flow crossmatch was criticized as being ‘‘too sensitive’’ as false positive reactions were a know drawback. In part, the shortcomings of the flow crossmatch were due to the lack of corresponding sensitive and specific HLA antibody screening assays. However, in the mid 1990s, solid phase assays, capable of utilizing standard flow cytometers, were developed. These assays used microparticles coated with purified HLA molecules. Hence, the era of solid-phase, microparticle technology for HLA antibody detection was born permitting the sensitive and specific detection of HLA antibody. It was now possible to provide better correlation between HLA antibody detection and the flow cytometric crossmatch. This flow-based technology was soon followed by adaptation to the Luminex platform permitting a mutltiplexed approach for the identification and characterization of HLA antibodies. It is hoped that these technologies will ultimately lead to the identification of parameters that best correlate with and/or predict transplant outcomes.

I. Introduction The importance of detecting antibody to human leukocyte antigens (HLA) has been known since the early years of clinical organ transplantation, when the association of hyperacute rejection of kidney grafts with preexisting humoral antibody to donor cells was identified (Kissmeyer-Nielsen et al., 1966; Patel and Terasaki, 1969; Starzl et al., 1968; Williams et al., 1968). Analysis of transplant outcome for patients in the UCLA Kidney Transplant Registry revealed that graft survival was lower for patients with panel reactive antibodies (PRA), with an even greater effect for patients undergoing second transplants (Terasaki and Mickey, 1971). Later studies confirmed the involvement of both HLA Class I- and Class II-directed antibodies in hyperacute organ rejection, as well as in acute rejection episodes and immunological chronic rejection, as reviewed by Terasaki (Cai and Terasaki, 2005; Terasaki, 2003). NonHLA markers on endothelial or monocytic cells may also play a role in antibodymediated rejection (Alheim et al., 2010; Zwirner et al., 2000); however, they will not be the focus of this discussion. Similarly, acute HLA antibody-mediated rejection has been recognized as a significant clinical entity and has been added to the updated Banff 97 renal allograft rejection classification (Racusen et al., 2003). In addition, as shown in an international prospective trial on 4763 patients form 36 centers, chronic rejection may be predicted by the development of HLA-specific antibody following transplant (Terasaki and Ozawa, 2004). It should be noted that HLA antibody has been linked with acute rejection episodes, graft loss, and decreased patient survival in lung, liver–kidney, and heart transplantation (Cai and Terasaki, 2005), as well as with platelet transfusion refractoriness (Sato et al., 2005) and transplant-related acute lung injury (TRALI) (Kleinmanm et al., 2010). Rejection may be prevented

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by immunosuppressive drugs such as mycophenolate mofetil (MMF) in combination with tacrolimus or neoral, which can decrease the production of donor-specific HLA antibody (Theruvath et al., 2001). More recently, the drug Bortezomib, which attacks active plasma cells, has shown promise in reversing acute antibody-mediated rejection (Everley et al., 2009; Perry et al., 2009). However, while 1 year graft survival has dramatically improved, the long-term graft survival rate has not changed substantially in the past 20 years (Meier-Kriesche et al., 2004; Woodroffe et al., 2005), suggesting that further refinements in either testing or treatment procedures may be warranted. Outside the realm of solid organ transplantation, stem cell transplantation has also benefited from flow cytometric assessments. In studies sponsored by the National Marrow Donor Program (Spellman et al., 2010) and others (Ciurea et al., 2009) it was shown that the incidence of graft rejection in mismatched, unrelated stem cell transplants is significantly increased if the recipient possesses HLA antibodies directed against the mismatched antigens/alleles of the donor. The primary goal of HLA antibody testing for transplant patients is to assess a given patient’s potential risk for graft loss by determining immunological status before/after transplantation. Armed with this information, clinicians are better able to make informed decisions regarding donor selection, immunosuppression regimens, and posttransplant patient care. The selection of an appropriate donor–recipient pair or the posttransplant therapeutic approach can then be tailored for an individual patient according to his/her HLA antibody status. It is therefore important to have accurate and sensitive detection of these antibodies and to unambiguously assign HLA specificity. Several methodologies were developed that improved the sensitivity of the original cell-based cytotoxicity assay. The newer solid-phase antigen-based techniques have refined the sensitivity and the degree of specific HLA antibody assignment. Importantly, evaluation of assay results depends on understanding the particular characteristics of each assay, the theoretical principles involved, and the technological or biophysical limitations of the reagents, instrumentation, and analytical methods employed. To this end, cytometry has played a key role during the past 20 years. This chapter will consider the progress that has occurred over two decades and highlight the technological advances that shaped the current HLA antibody detection assays.

II. Cell-Based Assays A. Lymphocytotoxicity Until recently, the complement-dependent lymphocytotoxicity (CDC) assay has been the ‘‘Gold Standard’’ to detect and identify HLA antibodies (Terasaki and McClelland, 1964). The CDC assay is a biological assay that requires antibody binding, complement activation, and cellular damage to register a positive reaction. Isolated lymphocytes from the (potential) donor are first incubated with the

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recipient’s serum, followed by the addition of rabbit complement. If antibodies in the test serum bind to the target cells, activation of the complement cascade is initiated, leading to lymphocyte death. The percentage of dead cells is determined by either the exclusion of a vital dye such as trypan blue or eosin or uptake of a DNA staining fluorescent dye such as ethidium bromide. The results are scored using a standardized ranking system introduced by Terasaki and later adopted by the NIH (Terasaki et al., 1974). The CDC assay (and all other HLA antibody detection methods) is used in two distinct clinical applications: (1) as a crossmatch method to determine if a patient has antibodies against a specific organ donor and (2) as a screening assay to determine if a patient has antibodies against one or more HLA antigens (to determine the level of sensitization). Historically, for the screening assay, many laboratories utilized cell panels composed of local volunteer donors to define the antibody specificity of patients’ sera. Later, commercial cell panels became available providing a limited degree of consistency in testing. The percentage of panel cells that reacted with a patient’s serum (%PRA) was calculated. For example, if a given patient’s serum reacted with 25/100 panel cells, the %PRA is 25%. Obviously, the higher the PRA value the greater the level of sensitization, and consequently, the less likely that the patient would have a negative crossmatch with a random organ donor. The cell panels were initially composed of randomly selected HLA typed donor lymphocytes in an effort to mimic the natural distribution of HLA antigens within a population. Sometimes panels were modified to obtain a representative antigen frequency for specific ethnic populations. Thus, the %PRA for a given serum sample could vary significantly depending on the antigen composition of the test panel. Fig. 1 shows a sample cell panel, with the individual Class I HLA typing information. In heterozygous individuals, two HLA antigens are expressed per locus and each target cell will express an array of HLA antigens. Thus, the constellation of HLA phenotypes of any given panel will affect antibody analysis. A high %PRA could be obtained with a serum containing several different specificities or just a single specificity corresponding to an over - represented antigen on the test panel. Importantly, non-HLA antibodies, such as those commonly found in patients with autoimmune diseases could produce a positive result in the CDC but be clinically irrelevant. For these reasons, cell-based PRA assessments have several important limitations:







Individual HLA antigen frequency is not independent of other HLA antigens. PRA does not reflect the complex array of antibodies that could be present. PRA does not reflect the strength (i.e., titer) of the antibody. PRA does not indicate the specificity of the antibody. PRA does not accurately predict a given patient’s probability of having a positive crossmatch with any given donor. Non-HLA antibodies can skew the %PRA result.

The CDC assay also cannot detect antibodies that are either noncomplement binding or at a titer that does not activate complement. Such antibodies have been shown to

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Fig. 1

Example of a lymphocyte test panel for determination of the percent of panel reactive antibody (% PRA). Serum having anti-HLA A1 would react with 10/24 cells (42% PRA), while a serum having anti-HLA-A2 antibody would react with 15/24 cells (63% PRA). Serum with A23 antibody would be 4% PRA. A combination of A1 and A2 antibodies in a sample serum would test as 100% PRA for this panel. (See plate no. 11 in the color plate section.)

be clinically significant. Other disadvantages of the CDC assay include the difficulty to prepare and maintain viable target cells, possible cell death due to complement sensitivity, and presence of anticomplementary factors in a patient’s serum. B. Modified Lymphocytotoxicity Assays In an attempt to circumvent some of the known drawbacks of the standard CDC assay, several modifications to the original method have been introduced. Such modifications have included additional washes, extended incubation, or the addition of an antihuman globulin (AHG) to increase the sensitivity of the assay (Cross et al., 1977; Fuller et al., 1978; Ross et al., 1975; Van Rood et al., 1976; Zachary et al., 1995a, 1995b). Table I shows a list of the common CDC methodologies. CDC assays can detect both IgM and IgG classes of antibodies. Current clinical practice has put a greater emphasis on IgG antibodies rather than IgM (Kerman et al., 1991). Therefore, an accepted laboratory practice is to pretreat the test serum with heat or reducing agents such as dithiothreitol (DTT) in order to reduce IgM pentamers to monomers, rendering them incapable of fixing complement. The subsequent CDC assay then detects only IgG antibody. Although

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Method details

Standard CDC (NIH) Extended incubation Amos Amos modified Antihuman globulin enhanced (AHG) DTT/DTE treatment

30 min serum/cells; 60 min C0 ; no washes 60 min serum/cells; 120 min C0 ; no washes Standard CDC with one wash prior to C0 Standard CDC with three or four washes prior to C0 Amos-modified technique with addition of antihuman globulin prior to C0 Treatment of serum prior to any CDC testing to remove IgM antibodies

CDC, complement-dependent lymphocytotoxicity; NIH, National Institutes of Health; C0 , complement.

considered not to be clinically significant, IgM antibodies can interfere with the detection of IgG antibodies. Undoubtedly, the most widely utilized modification to the CDC assay is the use of antihuman globulin (AHG-CDC) to enhance the complement fixing ability of the alloantibody. Initially introduced by Johnson et al. in 1972, this assay played a significant role in reducing early allograft rejection episodes by detecting low levels of HLA antibody or high titer antibodies that could not fix complement (Johnson et al., 1972; Rodey and Fuller, 1987). The term CYNAP describes this latter group of antibodies. CYNAP means CYtotoxicity Negative, Adsorption Positive, derived from early experiments describing this phenomenon (Johnson et al., 1972). In brief, antibody reactivity of a serum could be removed by adsorption using a cell that did not apparently possess the target antigen. For example, a serum with presumed specificity against HLA-A2 could have the HLA-A2 reactivity completely removed by adsorbing the serum with an HLA-A24 expressing cell that was not killed in the CDC assay (i.e., cytotoxicity negative, adsorption positive). Subsequently, AHG-CDC testing explained the observation by showing that the serum actually did react with the A24 cell but required AHG in order to trigger cytotoxicity. Hence, CYNAP antibodies could only be detected by the more sensitive AHG-CDC test method. In clinical practice, AHG-CDC testing emerged as an important test. Results from several centers showed that patients whose sera tested positive in the AHG-CDC assay had a greater likelihood of severe accelerated allograft failure compared to patients with a negative AHG-CDC result (Kerman et al., 1991). The exceptions were patients who tested positive with an even more sensitive method (flow cytometry). Such AHG-negative/flow positive results may have additional clinical implications (see below for further discussion). Although AHG-CDC testing represented an improvement over standard cytotoxicity testing it still suffers from the same drawbacks common to all cell-based assays. Moreover, for certain patients, even a negative AHG-CDC assay did not guarantee an event-free posttransplant course.

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C. Flow Cytometry

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Why did some patients experience early antibody-mediated graft failures when transplanted following a negative AHG-CDC crossmatch? An answer emerged when flow cytometric crossmatch (FCXM) was introduced (Garavoy et al., 1983). Subsequent studies by many groups clearly showed the significance of flow cytometry for improving allograft survival (Cook et al., 1987; Lazda et al., 1988; Mahoney et al., 1990; Ogura et al., 1993). The technique involves incubation of purified donor mononuclear cells with patient’s serum. After washing away unbound immunoglobulin and serum components, the cells are incubated with fluoresceinconjugated, goat (Fab0 )2 antihuman IgG to detect IgG antibody in the patient’s serum bound to the cellular targets. A subsequent refinement employed three-color flow cytometry to permit the simultaneous evaluation of T- and B-cell reactivities (Bray et al., 1989, 2004a, 2004b) (Fig. 2). This technique incorporated an additional incubation with anti-CD3 PerCP/CD19-PE to identify the T- and B-cell populations, respectively (Bray and Gebel, 2007). Pretreatment of the target cells with the proteolytic enzyme Pronase has been reported to further increase the sensitivity of the assay by eliminating nonspecific binding of IgG to Fc receptors on B-lymphocytes

Fig. 2 Example plots from a typical three-color flow cytometric crossmatch. (A) CD3 versus CD19 plot illustrating the ability to simultaneously identify both T- and B-cell populations. (B) Overlay examples from a negative and a positive serum. The delta value (D) indicates the fluorescence difference between the two populations. Staining is performed using a polyclonal, FITC-conjugated antihuman IgG. (C) Histogram plot of B cells. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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(Lobo et al., 1995; Vaidya et al., 2001). Flow cytometry also afforded the flexibility to assess other classes of antibody. For example, by varying the type of secondary antibody used, one can test for IgG, IgM, or IgA immunoglobulin isotypes. The fact that the FCXM is a binding assay and not dependent on the activation of complement, reduces the chance of technical failures or inconsistencies (e.g., due to weak complement or poor viability of cells in CDC). More importantly, the FCXM is a more sensitive crossmatch method and it has been demonstrated that patients with a negative cytotoxicity crossmatch, yet a positive FCXM, are at risk for early antibodymediated rejection and graft loss (Nelson et al., 1996; Talbot et al., 1992). In a parallel fashion, recent studies have shown increased survival among patients transplanted with organs from donors with a negative FCXM (Ilham et al., 2008; Lindemann et al., 2010). In addition to the selection of appropriate donor/recipient pairs via flow cytometry crossmatching, the sensitive HLA antibody assessments afforded by flow cytometry have been instrumental in helping promote a new approach to living donor transplantation, namely ‘‘kidney paired donor’’ exchange (KPD) (Rees et al., 2009; Segev et al., 2008). KPD programs are gaining popularity rapidly throughout the world, with national programs in Europe, Canada, and Australia. The United States is exploring the possibility of a national KPD program. Briefly, incompatible pairs are identified and entered into a database. Several types of data are collected (ABO type, HLA types, %PRA, HLA antibody specificities). Sophisticated computer algorithms are then used to determine potential compatible pairs among the clusters of incompatible patient/donor combinations. Central to the matching algorithm is the use of sensitive HLA antibody identification methods to predict crossmatch compatible pairs, so-called ‘‘virtual crossmatching’’ (Amico et al., 2009; Bray et al., 2006). Flow cytometry is integral in this regard. Sensitive antibody assessments combined with sensitive crossmatching (as afforded by flow cytometry) ensures that compatible pairs will be selected. Even though the FCXM is the most sensitive crossmatch method, it too suffers from the same drawbacks as all other cell-based assays. Most notably, nonspecific immunoglobulin binding to Fc receptors can be mistaken as a positive result. NonHLA antibodies (e.g., autoantibody) may also bind to the cells producing a positive result (Lyon et al., 2002). In both situations, the positive result is a ‘‘false’’ positive. Monoclonal anti-lymphocyte antibodies such as Thymoglobulin1, Rituxan1, and Campath1 (used for immunosuppressive therapy) (Ettenger et al., 1983; Wagenknecht et al., 2004), can also interfere with the FCXM and produce a ‘‘false’’ positive result. High dose IVIG, used in some desensitization protocols, can also interfere with the FCXM.

III. Antigen-Based Assays A. Solid-Phase Immunobinding Assays Various formats for immunobinding assays have been developed over the past 20 years. Since intact cells have other membrane proteins in addition to HLA, assigning

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HLA specificity via membrane-dependent methods can be quite challenging, particularly when the patient’s sera contains more than just HLA antibodies. In contrast, solid-phase assays utilize purified HLA proteins as targets. As such, these products are devoid of other cellular components that contribute to false-positive reactions in membrane-dependent assays. In early studies, Kao et al. (1993) developed an enzyme-linked immunosorbent assay (ELISA) using affinity purified HLA Class I from platelets as the target antigens for the detection of HLA antibodies in patient sera. While this assay discriminated HLA from non-HLA antibodies, the specificity of the positive sera could not be determined due to the use of admixed HLA antigens derived from the platelets of many different donors. Subsequently, several ELISA assays were introduced in the 1990s, and all were more sensitive than any CDC assay, but less sensitive than the FCXM (Buelow et al., 1995; Quillen et al., 2011; Sumitran-Karuppan and M€ oller, 1996; Uboldi de Capei et al., 2002; Zachary et al., 1995a). More recently, fixed-matrix technologies such as the microchip have been introduced and manufactures are attempting to utilize this new platform for HLA antibody identification (Fulton et al., 1997). Although ELISA was a significant step forward it was cumbersome to perform and was less sensitive than cytometry-based testing. Moreover, separate tests needed to be performed to discriminate Class I (A, B, and C) from Class II (DR, DQ, and DP) antibodies. With these issues in mind, Pei et al. (1999) described the isolation and purification of HLA Class I and Class II proteins from EBV transformed cell lines and their subsequent attachment to microparticles. These HLA-coated microparticles could easily be analyzed on a standard flow cytometer that provided many advantages over earlier membrane-based assays and ELISA methods. In addition to increased sensitivity was the ability to simultaneously assay for antibodies against HLA Class I and Class II. Additional advantages of flow cytometric assessments are as follows:








Reactivity is specific for HLA antigens (Class I and/or Class II). Lack of significant interference from non-HLA antibodies. Sensitivity comparable to the FCXM. Consistent source of antigen from stable cell lines. Not affected by subjective microscopic readings or cell viability. Semiquantitative assessment of antibody binding. May lead to better standardization between laboratories.

A diagrammatic representation of HLA antigen purification is shown in Fig. 3. The first iteration of a cytometric assay for detecting HLA antibodies was the FlowPRA1 (One Lambda, Inc, Canoga Park, CA). This assay combined 30 individual beads for Class I and 30 individual beads for Class II with each bead coated with either a Class I (A, B, C) or a Class II (DR, DQ, DP) phenotype. The HLA proteins that comprised the phenotype were obtained from individual EBV transformed cell lines. After completing the conjugation process of antigen to bead, individual beads are mixed together to create a bead pool used for the test (Fig. 4). Bead mixtures containing either Class I or Class II antigens can then be combined. Discrimination of beads expressing Class I

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Fig. 3

Diagram illustrating the production of antigen-based immunoassays.

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Fig. 4

Diagram illustrating the construction of the FlowPRA screening test. Individual beads are coated with either an HLA Class I or Class II phenotype as obtained from individual EBV transformed cell lines. Individual beads (N = 30) are combined to create a screening pool of HLA antigens. The percent bead reactivity allows for the calculation of a percent of panel reactive antibody (PRA).

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from beads expressing Class II antigens is based on two distinct physical properties easily assessed by a standard flow cytometer namely, bead size and inherent bead fluorescence. The Class I beads are smaller and nonfluorescent while the Class II beads are slightly larger and are impregnated with a fluorescent dye that has excitation/ emission properties similar to phycoerythrin. A positive reaction is determined by the number and fluorescence intensity of beads. Beads that exhibit increased fluorescence (FITC antihuman, IgG) compared to background controls are considered positive. These differences permit simultaneous testing for both Class I and Class II HLA antibodies (Fig. 5). The %PRA is calculated as the percentage of beads that demonstrate a significant positive fluorescence shift above background. Examples of FlowPRA results are shown in Fig. 6. Moreover, since an individual bead represents 1/30 (3%) of the total number of events, a true-positive reaction must demonstrate a distinct peak of fluorescence above that of the negative control (NC) and must contain at lease 3% of the total gated events. For comparison, the conceptual differences among the different solid-phase assays are illustrated in Fig. 7. Although this assay is specific for HLA antibody and represented a significant improvement over cell-based assays, it could not determine individual HLA

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Fig. 5

FlowPRA test. Beads coated with HLA Class I or Class II antigens are distinguished by the FL2 channel signal of the red dye marker on the Class II beads. Antibody to either bead is indicated by a fluorescent shift on the FL1 channel, due to binding of the antihuman IgG indicator reagent whenever antiHLA antibody reacts with HLA antigens on the beads. Additionally, a third bead is used as a background fluorescence control. This bead is coated with human albumin and is used to detect the presence for antibead (plastic) antibodies. Such antibodies are observed in approximately 1% of samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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Fig. 6

Example of test results of a serum with antibody to both HLA Class I and Class II antigens. (A) Represents the FSC (linear) versus SSC (log) graph of the beads. (B) Shows the FSC versus FL2 plot. The different types of beads are labeled, respectively. The %PRA is determined by the area under the peaks in the M2-gated region. The M1-gated region represents the boundaries of the negative control serum. Events shifted to the right of the unreacted or negative control (NC) serum are considered ‘‘positive.’’

specificities. Elucidating individual HLA specificities present in a patient’s serum is critical for selecting potentially compatible donors. For this determination, a different type of bead array was needed. Taking advantage of the flexibility of the flow cytometer and the fact that manufacturers could isolate a unique HLA phenotype, a new set of multiplexing beads were developed. For this assay, HLA phenotypes (Class I or Class II) were attached to individual bead populations in the exact manner as described above. However, rather than all beads having the same physical properties, each bead was identified by its stable but unique level of red fluorescence. As such, up to 11 different beads could be identified on a single fluorescence axis. With this reagent panel, assessment of individual antibody specificities was possible. By using multiple different phenotypes distributed over several arrays, individual HLA specificities present in a patient’s serum could be determined. Fig. 8 shows the concept of the FlowPRA II specific assay. While this flow cytometric assay was very useful, there were patients with broadly reactive antibodies in whom individual HLA specificities could not be assigned. Hence, the next iteration of the cytometry-based assay was based on using purified individual

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Fig. 7

Alternate platforms for solid-phase antigen-based immunoassays.

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Fig. 8

Example of a FlowPRA II specific test illustrating a DR10 alloantibody.

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HLA proteins for adherence to the microparticles. Pei et al. (2003) conjugated individual HLA proteins to single microparticles that could then be run on standard flow cytometers. This assay used panels of 8 or 11 individual beads each identified by their unique fluorescence signature. With such panels, even the most complex sera could be dissected into definable specificities. Hence, a broadly sensitized patient could have all his/her HLA antibodies identified thereby making it possible to predict compatibility with specific donors (i.e., crossmatch negative). HLA antigens to which the patient lacked reactivity would be considered as safe or ‘‘compatible’’ antigens. By comparing the HLA type of a potential donor to the list of ‘‘unacceptable’’ or ‘‘avoid’’ antigens of the recipient, compatibility could be assessed. This concept became known as the ‘‘virtual crossmatch’’ (Amico et al., 2009; Stehlik et al., 2009). Although extremely useful, the assay was cumbersome requiring up to 15 tubes per patient, with each tube containing 8 or 11 individual, single HLA-antigen-coated beads.

B. Multiplex Platform for Microparticle Analysis While flow cytometry provided an approach in determining the HLA specificities contained in patient’s serum, there were still two major drawbacks: (1) the large number of polymorphisms present in the MHC made array design quite challenging and (2) the limitation of running only 11 beads per tube by classic flow cytometry proved to be a significant technical handicap. As mentioned above, complete analysis of both Class I and Class II required up to 15 different tubes per patient. Thus, while standard cytometry provided a significant breakthrough in the detection of HLA antibody, the high degree of HLA diversity and the challenges of highly sensitized patients made it use limited. Recently, a new type flow cytometry platform was invented that greatly facilitated HLA antibody determination by allowing up to 100 individual beads to be evaluated in a single multiplexed assay (Luminex1 Corp, Austin, TX). A unique bead signature was created by incorporating two different fluorescent tags into a single bead population. By adjusting the concentration of the two dyes, a set of 100 identifiable beads was developed. Each could be coated with a different antigen, allowing positive immunoassay results to be automatically correlated to a unique HLA specificity. The concept is based on a simple permutation model. Color #1 is incorporated into different batches of beads at a dose of 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100%. Then each of these bead populations is further modified to include color #2 in a similar dosage series, giving rise to 100 separate bead populations (10  10), as depicted in Fig. 9. Theoretically, a third color could be added to generate 1000 color combinations, and a fourth to generate 10,000 combinations, if the detection instrument is modified to read additional wavelengths. Alternatively, beads of different sizes could also be incorporated into the current fluorescence schema and thereby increase the number of analytes.

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Fig. 9 Principle of Luminex platform for multiplex bead assay. Diagram shows a 100-bead array using varying fluorescence intensity for the identification and addressing of individual beads within the test matrix.

Currently, the optical system uses red and infrared light to identify the color-coded microspheres (classifier parameters) and orange light to measure the strength of the individual reactions (identifier). A phycoerythrin-conjugated antihuman IgG reagent is used to detect the binding of test serum antibodies to the various antigen-coated microspheres. Interestingly, the Luminex instrument utilizes a YAGlaser to excite the PE-conjugated identifier. PE has a bimodal excitation curve and the YAG-laser excites the secondary peak yet still allows emission at 570 nm. The fluidics of the Luminex cytometer introduces the microspheres in single file as occurs with a flow cytometer. The optics then focuses on each bead to generate and collect the fluorescent signals, while the electronics convert and digitize the signals for computer analysis. The advantage to the end-user is that color separation replaces physical separation. Hence, the technology allows simultaneous measurement of multiple analytes in a single reaction vessel. Fig. 10A shows the ‘‘gating’’ of the addressable antigencoated beads and Fig. 10B a histogram of the reaction pattern of a Class I HLA alloserum using the LABScreen1 PRA I assay. For HLA antibody detection, the 100

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Fig. 10 Sample of Luminex-100 report generation. (A) Identification of a set of test beads in different analysis windows according to their dye signature/address. (B) Histogram of mean fluorescent signals for a specific HLA serum test using Class I single antigen beads.

beads can be utilized for expansion of the target antigen panel or for detection of Class I and Class II simultaneously (Pei et al., 1998). Even with all these tools available, it was still difficult to determine the specificity of high PRA sera when multiple phenotypic HLA antigens were coated on

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Fig. 11

Genetic engineering of recombinant HLA molecules.

the same beads. Creation of single antigen beads (see below) recently addressed this problem.

C. Recombinant HLA Antigens Assignment of antibody specificity has gradually become more sophisticated. The first ELISA assays attempted to correlate results with serology; however, with advances in molecular typing of HLA antigens and the use of monoclonal antibodies, additional immunogenic sites began to be defined (Claas et al., 1999). In addition, broad antibody reactivities defined as ‘‘cross-reactive groups’’ or CREG antigens initially defined by Fuller et al. (1990a, 1990b) were more prominent and far more complex than originally considered. The technological advance to distinguish antibody to antigen specific (private) from crossreactive (public) epitopes was made possible with the development of recombinant (r) HLA antigens by genetic engineering (Bernabeu et al., 1983). Fig. 11 shows a diagram of rHLA production from plasmid design to the final antigen purification step. This complicated process has to be repeated, and sometimes modified, to transfect an appropriate host cell, and clone and culture a separate cell line, for each HLA allelic protein.

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A significant proportion of patients possess very broadly reactive HLA antibodies. That is, when using a phenotype panel, all beads will be positive. Most of these patients do not have a 100% PRA. However, they usually do possess several antibodies that react against common HLA specificities. As a result, most if not all beads carrying a complete HLA phenotype will be positive. With the implementation of recombinant technology it has become possible to prepare purified single HLA proteins for attachment to microparticles. Using single antigen bead panels, complex HLA sera can be dissected to clearly delineate the antibody reactivity of a patient. Results of a FlowPRA assay are shown in Fig. 12 comparing the use of phenotypic HLA antigen-coated beads to single HLA antigens bound to the beads (Bray et al., 2004a). Assignment of HLA antibody specificity is generally more accurate and reliable using the single antigen microparticles, although exceptions

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Fig. 12 Flow cytometry beads for the determination of Class II HLA antibody specificity. (A and B) Latex beads classified by different levels of red indicator dye are coated with a complete HLA Class I or Class II phenotype from EBV-transformed lymphoblast cell lines. Note that, with the exception of the control beads, all HLA beads are positive. (C and D) FlowPRA single antigen bead assessment of the same sample. Note the ability to discern unique HLA specificities. The black vertical line indicates the maximum fluorescence position of the beads when using a negative control serum. The ‘‘control bead’’ is coated with human serum albumin. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of the chapter.)

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Fig. 13 Example of a clinical test sample that was negative by cytotoxicity, but positive on FlowPRA specific Class I beads. The antibody specificity was identified to be against the closely related molecules HLA B57 and B58.

have been noted. One such example involved undetected allele-specific antibody (when the target allele was not represented in the panel). In another instance, recognition of additional epitopes (if the orientation of the purified antigens on the beads differs from their presentation on the cell membrane) was postulated (Gebel et al., 2002). Not only is there an improvement in specificity of the assay with single antigens but there is also a general increase in sensitivity (even for phenotypic antigen bead arrays), due to the greater number of particular antigen molecules per bead. Studies published by Gebel and Bray (2000) demonstrated the increased sensitivity of flow cytometric bead-based assays over other conventional assays. An example of increased sensitivity of flow-based testing is given in Fig. 13.

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IV. Current Frontiers in Transplant-Related Antibody Testing A. Extended HLA Loci There has been recent interest in extending the coverage of antibody detection assays to include new target antigens. Single antigen-expressing cell lines and purified recombinant antigen coated on beads or microplate wells have made it possible to identify antibodies to HLA loci that were not amenable to detection by traditional cell-based assays. These include HLA-C antigens that are poorly expressed on the lymphocyte surface, DQ and DP antigens, as well as DRB3, 4, and 5 that were difficult to distinguish from DRB1 antigens coexpressed on the surface of B cells. Antibodies to these HLA antigens may very well be detrimental to graft survival. Anti-C-locus antibodies were observed in 24/51 patients (45%) with failed kidney grafts (Duquesnoy and Marrari, 2010) when a high degree of C-locus mismatches (67%) were permitted by the prevailing kidney allocation policy.

B. Conundrums in HLA Antibody Assignment Algorithms for assignment of specificity may take advantage of modern computer database tools to exclude the patient’s own typing from consideration for antibody assignment. However, since new evidence has emerged documenting the existence of allele-specific antibodies in patients who type for a different allele within the same parent HLA antigen, additional consideration must be given to identifying such antibodies. Single antigen test panels have been expanded to include the more common alleles of certain antigens, in an effort to detect allele-specific antibodies. Mapping of these allele-specific epitopes should aid in our understanding of the immunogenicity of HLA molecules and may have ramifications in stem cell transplantation. Identification of allele-specific epitopes that elicit a humoral immune response may help define ‘‘immuno-dominant’’ regions of the HLA molecule. Studies have clearly shown that patients can make antibodies against unique allelic differences. In some instances, these differences may only be single amino acid substitutions, as in the case of a patient who types as B*44:02 and makes an antibody against B*44:03. These two alleles differ by only a single amino-acid (aspartic acid for leucine at codon 156) yet they can elicit a distinct humoral response (Duquesnoy and Marrari, 2010). Many other examples have been published (Gebel et al., 2009). More recently, an interesting observation has been made that some individuals may possess ‘‘naturally’’ occurring antibodies against HLA antigens (MoralesBuenrostro et al., 2009). While the etiology of these antibodies is unknown and puzzling, one such speculative explanation may be via exposure to nonclassical HLA molecules such as HLA-E (Ravindranath et al., 2010). Some evidence suggests that there may be cross-reactive epitopes between HLA-E and classical HLA molecules

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that may explain this reactivity. Whether these ‘‘naturally’’ occurring antibodies have real clinical significance is yet to be determined.

C. Functional Assessments of HLA Alloantibody by Flow Cytometry The binding of HLA-specific IgG antibody to appropriate targets may not be sufficient to assess the complete biological function of the immunoglobulin. Since different IgG isotypes can have different biological functions, there may be clinical relevance to differentiating the isotypes. It is well documented that complementmediated destruction of allografts is a major factor in allograft loss. Studies from several groups have suggested that assessing the IgG isotype composition of an alloantibody may lead to better correlation between IgG detection and clinical outcomes. To this end, Wahrmann et al. (2005) first demonstrated the ability to measure, via flow cytometry, complement deposition on to microparticles. Subsequently, a FCXM method using viable cells was developed that could simultaneously determine complement-mediated cell killing and quantitative antibody binding (Saw et al., 2008). Lastly, in studies published by Heinemann et al. (2007) and H€ onger et al. (2010), noncomplement fixing IgG subclasses, as assessed by cytometric technologies, were not associated with early allograft failure. However, studies in cardiac transplant recipients clearly showed the significance of cytometric complement activation and poor outcomes (Rose and Smith, 2009). Also, a C1q binding assay has recently been used in conjunction with single antigen beads to predict antibody-mediated rejection (Tyan et al., 2009; Yabu et al., 2011; Zoet et al., 2005). Although still in the clinical research stage, such specialized flow cytometry assays may be useful for assessing the potential clinical significance of bound HLA-specific alloantibody.

V. Summary For the transplant patient, formation of antibody to any mismatched donor antigens or alleles may signal an impending rejection episode, or the slow progression of chronic rejection/allograft nephropathy. Since modern modalities of immunosuppressive therapy may be effective in either of these outcomes, specific and sensitive detection of any deleterious antibody is critical for posttransplant patient management. Advances in molecular biology and protein chemistry have contributed to the recent development of antigen-based assays that have rapidly replaced the classical lymphocytotoxicity method of anti-HLA antibody detection. Flow cytometric technology has provided a very sensitive method for the final crossmatch assay using donor lymphocytes and via microparticles, contributed significantly to our understanding of HLA-specific alloantibody. As determined by cytometric methods (classic flow cytometry and Luminex), the best described mechanism for both acute and

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chronic rejection continues to involve antibody formation against HLA Class I and Class II antigens. Thus, the development of recombinant single antigens as targets for antibody detection assays has been the most important advancement in this field. Single antigen-coated multiplexed flow cytometric bead arrays and Luminex bead assays now allow concurrent detection of antibody to a number of HLA Class I and Class II antigens and alleles. Analysis of these test results has been automated and simplified, offering a great advantage for testing high PRA sera, for which assignment of antibody specificity has been particularly challenging. Single antigen panels can distinguish allele-specific reactivities and will ultimately lead to the definition of epitope-specific antibodies. These cytometric-based HLA-bead assays offer high sensitivity that is comparable to that obtained with the flow cell crossmatch, bringing alloantibody detection to a new level of sophistication. The combination of highly sensitive antibody assessments combined with flow cytometric crossmatching is resulting in more transplants to sensitized individuals with a greater degree of safety. Finally, the potential to better correlate the biological function of immunoglobulins with clinical outcomes will also contribute to better selection of donor/recipient pairs. References Alheim, M., Johansson, S. M., Hauzenberger, D., Grufman, P., and Holgersson, J. (2010). A flow cytometric crossmatch test for the simultaneous detection of antibodies against donor lymphocytes and endothelial precursor cells. Tissue Antigens 75, 269–277. Amico, P., Honger, G., Steiger, J., and Schaub, S. (2009). Utility of the virtual crossmatch in solid organ transplantation. Curr. Opin. Organ Tranplant. 14, 656–661. Bernabeu, C., Finlay, D., van de Rijn, M., Maziarz, R. T., Biro, P. A., Spits, H., de Vries, J., Terhorst, C. P. (1983). Expression of the major histocompatibility antigens HLA-A2 and HLA-B7 by DNA-mediated gene transfer. J. Immunol. 131, 2032–2037. Bray, R. A., and Gebel, H. M. (2007). Clinical utility of flow cytometry in allogeneic transplantation. In ‘‘Clinical Flow Cytometry,’’ (and D. Keren, ed.), pp. 275–292. ASCP Press, Chicago, IL Chapter 14. Bray, R. A., Gebel, H. M., and Ellis, T. M. (2004a). Flow cytometric assessment of HLA alloantibodies. In ‘‘Current Protocols in Cytometry,’’ (J.P. Robinson, ed.), pp. 1–23. Wiley Publishing, Hoboken, NJ Supplement 27, Chapter 6.16. Bray, R. A., Lebeck, L. K., and Gebel, H. M. (1989). The flow cytometric crossmatch. Dual-color analysis of T cell and B cell reactivities. Transplantation 49, 834–840. Bray, R. A., Nickerson, P. W., Kerman, R. H., and Gebel, H. M. (2004b). Evolution of antibody detection. Technology emulating biology. Immunol. Res. 29(1–3), 41–53. Bray, R., Nolen, J. D., Larsen, C., Pearson, T., Newell, K., Kokko, K., Guasch, A., Tso, P., Mendel, J., Gebel, H. (2006). Transplanting the highly sensitized patient: the Emory algorithm. Am. J. Transplant. 6, 2307–2315. Buelow, R., Mercier, I., Glanville, L., Regan, J., Ellingson, L., Janda, G., Claas, F., Colombe, B., Gelder, F., Grosse-Wilde, H., Orosz, C., Westhoff, U., Voegeler, U., Montiero, F., Pouletty, P. (1995). Detection of panel-reactive anti-HLA class I antibodies by enzyme-linked immunosorbent assay or lymphocytotoxicity. Results of a blinded controlled multicenter study. Hum. Immunol. 44(1), 1–11. Cai, J., and Terasaki, P. (2005). Human leukocyte antigen antibodies for monitoring transplant patients. Surg. Today 35(8), 605–612.

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Ciurea, S. O., deLima, M., Cano, P., Korbling, M., Giralt, S., Shpall, E. J., Wang, X., Thall, P. F., Champlin, R. E., Fernandez-Vina, M. (2009). High risk of graft failure in patients with anti-HLA antibodies undergoing haploidentical stem-cell transplantation. Transplantation 88, 1019–1024. Claas, F. H., De Meester, J., Witvliet, M. D., Smits, J. M., Persijn, G. G., Doxiadis, I. I. (1999). Acceptable HLA mismatches for highly sensitized patients. Rev. Immunogen. 1(3), 351–358. Cook, D. J., Terasaki, P. I., and Iwaki, Y. (1987). An approach to reducing early kidney transplant failure with a positive flow cytometric crossmatch. Clin. Transplant. 1, 253–257. Cross, D. E., Whittier, F. C., Weaver, P., and Foxworth, J. (1977). A comparison of the antiglobulin versus extended incubation time crossmatch: results in 223 renal transplants. Transplant Proc. 9(4), 1803–1806. Duquesnoy, R. J., and Marrari, M. (2010). Detection of antibodies against HLA-C epitopes in patients with rejected kidney transplants. Transplantation 90(Suppl.), 374. Ettenger, R. B., Jordan, S. C., and Fine, R. N. (1983). Cadaver renal transplant outcome in recipients with auto-lymphocytotoxic antibodies. Transplantation 35, 429–431. Everley, M. J., Eerly, J. J., Arend, L. J., Brailey, P., Suskind, B., Govil, A., Rike, A., Roy-Chaudhury, P., Mogilishetty, G., Alloway, R. R., Tevar, A., Woodle, E. S. (2009). Reducing de novo donor-specific antibody levels during acute rejection diminishes renal allograft loss. Am. J. Transplant. 9, 1063–1071. Fuller, T. C., Cosimi, A. B., and Russell, P. S. (1978). Use of antiglobulin-ATG reagent for detection of low levels of alloantibody-improvement of allograft survival in presensitized recipients. Transplant Proc. 10(2), 463–464. Fuller, A. A., Rodey, G. E., Parham, P., and Fuller, T. C. (1990a). Epitope map of the HLA-B7 CREG using affinity-purified human alloantibody probes. Hum. Immunol. 28(3), 306–325. Fuller, A. A., Trevithich, J. A., Rodey, G. E., Parham, P., and Fuller, T. C. (1990b). Topographic map of the HLA-A2 CREG epitopes using human alloantibody probes. Hum. Immunol. 28(3), 284–306. Fulton, R. J., McDade, R. L., Smith, P. L., Kienker, L. J., and Kettman Jr., J. R. (1997). Advanced multiplexed analysis with the FlowMetrixTM system. Clin. Chem. 43(9), 1749–1756. Garavoy, M. R., Rheinschmidt, M. A., Bigos, M., Perkins, H. A., and Colombe, B. (1983). Flow cytometry analysis: a high technology crossmatch technique facilitating transplantation. Transplant Proc. 15, 1939–1944. Gebel, H. M., and Bray, R. A. (2000). Sensitization and sensitivity: defining the unsensitized patient. Transplantation 69, 1370–1374. Gebel, H. M., Harris, S. B., and Bray, R. A. (2002). Conundrums with FlowPRA beads. Clin. Transplant. 16, 24–29. Gebel, H. M., Moussa, O., Eckels, D. D., and Bray, R. A. (2009). Donor-reactive HLA antibodies in renal allograft recipients: considerations, complications, and conundrums. Hum. Immunol. 70(8), 610–617. Heinemann, F. M., Roth, I., Rebmann, V., Arnold, M. L., Witzke, O., Wilde, B., Spriewald, B. M., GrosseWilde, H. (2007). Immunoglobulin isotype-specific characterization of anti-human leukocyte antigen antibodies eluted from explanted renal allografts. Hum. Immunol. 68(6), 500–506. H€ onger, G., Wahrmann, M., Amico, P., Hopfer, H., B€ ohmig, G. A., Schaub, S. (2010). C4d-fixing capability of low-level donor-specific HLA antibodies is not predictive for early antibody-mediated rejection. Transplantation 89(12), 1471–1475. Ilham, M. A., Winkler, S., Coates, E., Rizzello, A., Rees, T. J., Asderakis, A. (2008). Clinical significance of a positive flow crossmatch on the outcomes of cadaveric renal transplants. Transplant Proc. 40, 1839–1843. Johnson, A. H., Rossen, R. D., and Butler, W. T. (1972). Detection of alloantibodies using a sensitive antiglobulin microcytotoxicity test. Tissue Antigens 2, 215–221. Kao, K. J., Scornik, J. C., and Small, S. J. (1993). Enzyme-linked immunoassay for anti-HLA antibodies – an alternative to panel studies by lymphocytotoxicity. Transplantation 55, 192–196. Kerman, R. H., Kimball, P. M., Van Buren, C. T., Lewis, R. M., DeVera, V., Baghdahsarian, V., Heydari, A., Kahan, B. D. (1991). AHG and DTE/AHG procedure identification of crossmatch-appropriate donorrecipient parings that result in improved graft survival. Transplantation 51(2), 316–320.

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CHAPTER 13

Clinical Utility of Flow Cytometry in the Study of Erythropoiesis and Nonclonal Red Cell Disorders Alden Chesney,*,y,z David Good*,y,z and Marciano Reis*,y,z * y z

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada

Department of Clinical Pathology, Sunnybrook Health Sciences Centre, Toronto, Canada Department of Laboratory Hematology, University Health Network, Toronto, Canada

Abstract I. Introduction II. Normal Erythroid Development A. Flow Cytometry for Reticulocyte Enumeration B. Flow Cytometry for the Detection of Fetal–Maternal Hemorrhage C. Flow Cytometry for Quantitation of HbF in Sickle Cell Disease D. Flow Cytometry in the Evaluation of Hereditary Spherocytosis E. Measurement of Red Cell Survival and Red Cell Volume F. Cell Cycle Studies G. RBC Surface Abnormalities References

Abstract Erythropoiesis involves proliferation and differentiation of small population of hematopoietic stem cells resident in the bone marrow into mature red blood cells. The determination of the cellular composition of the blood is a valuable tool in the diagnosis of diseases and monitoring of therapy. Flow cytometric analysis is increasingly being used to characterize the heterogeneous cell populations present in the blood and the hematopoietic cell differentiation and maturation pathways of the bone marrow. Here we discuss the role of flow cytometry in the study of erythropoiesis and nonclonal red blood cell disorders. First, we discuss flow cytometric analysis of reticulocytes. Next, we review salient quantitative methods that can be METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

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used for detection of fetal–maternal hemorrhage (FMH). We also discuss flow cytometric analysis of high hemoglobin F (HbF) in Sickle Cell Disease (SCD), hereditary spherocytosis (HS), red cell survival and red cell volume. We conclude by discussing cell cycle of erythroid cells. Keywords: Erythropoiesis; Reticulocytes; Sickle Cell Disease (SCD); Fetal– maternal hemorrhage (FMH); Flow cytometry; Red blood cell; Hereditary spherocytosis (HS); Hematopoietic stem cells; Aldehyde dehydrogenase (ALDH); Immature reticulocyte fraction (IRF)

I. Introduction The determination of the cellular composition of the blood has been a valuable tool both in the diagnosis of many diseases and in the monitoring of therapy. Measurements performed on blood cells are among the earliest diagnostic tests and blood counts remain the most requested of all tests in general medicine. Multiparametric flow cytometric analysis is a powerful technique now firmly established in clinical practice and increasingly being applied to the analysis of red blood cells. The sensitivity and accuracy of the technique strongly favor it for rare event analysis and it is increasingly employed to dissect and characterize the heterogeneous cell populations that exist in the blood and the hematopoietic cell differentiation and maturation pathways of the bone marrow. This chapter discusses the role of flow cytometry in the study of erythropoiesis and nonclonal red blood cell disorders emphasizing the clinical utility of the available assays.

II. Normal Erythroid Development Human erythropoiesis is an orderly, continuous process whereby a small population of hematopoietic stem cells resident in the bone marrow proliferate and differentiate into mature red blood cells (Cumano and Godin, 2007). Erythropoiesis is first detected within the blood islands of the murine yolk sac on embryonic days 7 to 7.5 (Ferkowicz and Yoder, 2005). This first wave of primitive erythropoiesis is transient, is erythropoietin independent (Lin et al., 1996), and is succeeded by a second wave of definitive erythropoiesis that takes place first in the fetal liver and later in the bone marrow and spleen (Cumano and Godin, 2007). Unlike primitive erythropoiesis, definitive erythropoiesis depends on erythropoietin, although terminal differentiation and enucleation of the late basophilic and orthochromatic erythroblasts are independent of erythropoietin. Erythropoietin levels regulate red cell production by regulating the rate of apoptosis within developing erythroblasts (Kelley et al., 1993). Hematopoietic stem cells are a rare population comprising only 0.01% of nucleated bone marrow cells (Rizo et al., 2006), but they possess the potential for both self-

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renewal and differentiation into all lineages of blood cells (Nielsen, 1999; Rizo et al., 2006; Suda et al., 2005). Two distinct erythroid progenitors have been functionally defined in colony assays, namely, the early-stage burst-forming unit-erythroid (BFUE) and the later stage colony-forming unit-erythroid (CFU-E) progenitor (Gregory and Eaves, 1978). The earliest morphologically identifiable erythroblast in hematopoietic tissues, the pronormoblast, results from cell division of the CFU-E. A series of progressive physical, functional, biochemical, and antigenic changes accompany the differentiation of immature erythroid cell precursors into mature red blood cells (Palis and Segel, 1998). Thus, morphologically distinct populations of erythroblasts are produced by each successive mitosis, beginning with proerythroblasts and followed by basophilic, polychromatic, and orthochromatic erythroblasts. Nuclear condensation and extrusion (enucleation) are key events marking late-stage erythropoiesis and the generation of reticulocytes and mature red blood cells (Testa, 2004). Differentiation of progenitor cells into the erythroid lineage and maturation along the erythroid lineage can be assessed by simultaneous analysis of cell surface antigens and light scatter parameters. This was first described by Loken and colleagues who sorted bone marrow cells on the basis of their light scattering features identifying an RBC light scattering population composed exclusively of mature erythrocytes and reticulocytes, late stages of developing erythroid cells in the LYMPH region and most of the less mature erythroid cells (morphologic erythroblasts) in the BLAST window. Multiparameter flow cytometry was then used to map the expression of the cell surface antigens – transferrin receptor (CD71), glycophorin A (CD235), and CD45/HLe1 – on erythroid cells in marrow aspirate preparations from normal volunteers (Loken et al., 1987a, 1987b). By correlating the expression of these three cell surface molecules with the light scatter properties of human marrow cells, the maturation of the erythroid lineage could be traced, identifying cells from the BFU-E to the mature erythrocyte. These early findings, confirmed by others, demonstrated that the CD71 antigen appears on the cell surface upon commitment to the erythroid lineage concurrently with a loss of CD34 and CD33 antigens and a decrease in CD45 (Loken et al., 1987b; Terstappen et al., 1990, 1993). The increasing availability of multicolor, multiparameter flow cytometric analysis has made it possible to obtain a more precise description of the continuous maturational sequence of erythroid cells by the simultaneous quantitative analysis and correlation of several parameters demonstrating quantitative and qualitative differences in antigen expression at different developmental stages. The sequence of cell surface antigen expression during erythroid maturation is schematically represented in Fig. 1. The observations gleaned from flow cytometric analysis allowed new and detailed insights into the genesis of red cell membrane function during erythroblast differentiation and a deeper understanding of stage-specific alterations in erythroid maturation occurring in various inherited and acquired red cell disorders and bone marrow failure syndromes. Thus, on the basis of the knowledge of the normal maturation patterns, flow cytometric aberrances in the erythroid lineage in MDS were reported in 2001 (Stetler-Stevenson et al., 2001). Defining the erythroid compartment by lack of CD45

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[(Fig._1)TD$IG]

Fig. 1 Cell surface antigen expression during the process of erythroid maturation. (Modified from: Loken et al., 1987b; Civin and Loken, 1987; Fornas et al., 2002).

expression and by light scatter (Loken et al., 1987a), the expression profile of glycophorin A (CD235) and CD71 within this subpopulation is considered most informative for dysplasia. For immature erythropoietic cells, endoglin (CD105) and CD117 as well as CD36 can be employed as markers in normal marrow and probably may also be useful markers in the assessment of erythroid dysplasia in MDS (Ogata et al., 2009; Matarraz et al., 2008). In patients with MDS, up to 71% show decreased reactivity for CD36 and significant alterations of CD71 and CD235 expression. A recent European LeukemiaNet conference on flow cytometry in myelodysplastic syndromes recognized that the limited number of antibodies available to study erythroid dysplasia precludes a comprehensive assessment of the spectrum of abnormalities in the erythroid compartment. However, consensus was reached that at least the expression of CD45, CD71, CD235a, CD117, and CD105 should be analyzed with emphasis on an abnormal pattern of CD71 in relation to CD235a. Table I lists antigens recommended in a screening panel for four-color flow cytometry of erythroid lineage cells. The transferrin receptor is a transmembrane, homodimeric glycoprotein with a molecular mass of approximately 180 kD (Testa et al., 1993). The main function of the transferrin receptor is delivering iron into cells via endocytosis of transferrin. The expression of transferrin is strictly related to cell proliferation (Cazzola et al., 1990; Neckers, 1991; Trowbridge et al., 1982) as both normal and malignant proliferating cells synthesize elevated levels of transferrin receptors compared to nonproliferating counterparts. The number of transferrin receptors on erythroid cells is markedly

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Table I Example of a screening panel for four-color flow cytometry of erythroid cells

1 2 3

FL-1

FL-2

FL-3

FL-4

CD71 HLA-DR

CD235 CD117

CD45 CD45 CD45

CD36 CD34

higher than on the other cell types (Glass et al., 1975) and is directly related to hemoglobin production (Iacopetta et al., 1982; Nunez et al., 1977). Sposi et al. (2000) demonstrated differential transferrin receptor expression and normal hematopoiesis with elevated expression in the erythroid lineage and sharp down-modulation in the granulopoietic, monocytopoietic, and megakaryocytic series. On the basis of its expression pattern, Ke Chen and colleagues selected CD44 as a cell surface marker that would best discriminate between erythroblasts at different stages of maturation as its surface expression decreased by no less than 30-fold in a stepwise manner, passing from the proerythroblast through the orthochromatic erythroblast. The choice of CD44 was validated by demonstrating the ability to obtain, by cell sorting, highly purified populations of erythroblasts at all stages of maturation. By contrast, they found that the more commonly used CD71 expression changed only fourfold and not in a progressive manner during terminal erythroid differentiation suggesting a lesser degree of effectiveness of this marker to discriminate between erythroblasts at different maturation stages (Chen et al., 2009). Aldehyde dehydrogenase (ALDH) is a cytosolic enzyme highly expressed in hematopoietic precursors from cord blood and G-CSF-mobilized peripheral blood, as well as in bone marrow. Mirabelli et al. (2008) compared surface antigen expression of ALDH+/CD34+, ALDH
/CD34+ and ALDH+/CD34
progenitor cell subsets in human bone marrow and demonstrated that the ALDH+/CD34
cells are mainly committed toward erythropoiesis. This novel finding holds promise as an additional marker of hematopoietic differentiation by employing ALDH in flow cytometric analysis of erythroid cells. Other markers of potential informative value for assessment of erythroid dysplasia include intracellular expression of H-ferritin and M-ferritin (MtF). M-ferritin expression is closely linked to the presence of ringed sideroblasts in bone marrow (98% sensitivity, 100% specificity) and a strong correlation between MtF and Perls staining has been reported (r = 0.89) (Della Porta et al., 2006). However, these antibodies are not commercially available at present. A. Flow Cytometry for Reticulocyte Enumeration Reticulocytes are immature red blood cells, representing the stage of red cell development immediately following expulsion of the nucleus. They comprise approximately 1% of circulating red cells in the body. They are distinguished from mature

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red cells by their relatively high RNA composition, exhibiting a bluish tinge on Romanowsky stained peripheral blood smears. However, examination of reticulocytes in this fashion is not quantitative and leaves it to the observer to determine whether they appear increased or normal. Accurate quantitation of reticulocytes is clinically important as the reticulocyte count is a reflection of the erythropoietic activity of the bone marrow and the rate of delivery into the peripheral blood. Reticulocytosis, reflecting an increased percentage of reticulocytes in the peripheral blood, is expected when patients become anemic, as the bone marrow attempts to compensate for the reduction in hemoglobin by increasing erythropoietic activity and subsequent release of reticulocytes into the peripheral blood. As such, an elevated reticulocyte count in an anemic patient is used clinically to confirm an appropriate bone marrow response and can also be used diagnostically to help confirm a clinical suspicion of hemolysis. If the reticulocyte count is inappropriately low in comparison to the degree of anemia, this suggests an inappropriate marrow response, likely a result of ineffective erythropoiesis. The reticulocyte count can also be used to monitor bone marrow regenerative activity following chemotherapy or bone marrow transplantation. The detection and enumeration of reticulocytes in the laboratory are based on the principle of high levels of RNA in the reticulocytes in comparison to mature red cells. The earliest method involved staining a peripheral blood slide with a supravital dye, which precipitates and stains RNA, allowing the reticulocytes to be counted under a microscope (Brecher, 1949; Deiss and Kurth, 1970). However, manual counting of a large number of cells leads to a high degree of inaccuracy and imprecision and a large inter- and intraobserver variability (Koepke and Koepke, 1986; Savage et al., 1985). As such, automated methods including flow cytometric techniques have been developed for reticulocyte counting. There are many advantages to reticulocyte enumeration by flow cytometry over conventional light microscopy and slide-based, manual counting techniques. Flow cytometry is technically easy to perform and is semiautomated, making it more rapid, less labor intensive, and more cost-effective than manual slide-based techniques (Riley et al., 2001). Being semiautomated and counting a higher number of cells, it is less subjective, providing higher accuracy and precision. It also offers the ability to assess the age distribution of the reticulocyte population. Reticulocyte enumeration by flow cytometry is not without its limitations. Due to the use of immunofluorescent techniques, there can be spuriously high reticulocyte counts as a result of interfering RBC inclusions, such as nucleated RBCs and Howell–Jolly bodies, as these contain high levels of RNA. For small laboratories with low volumes, the requirement for expensive, complex instrumentation and highly trained technologists may not be practical (Corash et al., 1988; Davis and Bigelow, 1990). However, given the significant advantages, flow cytometry has become the reference technique for measuring absolute reticulocyte counts and parameters of reticulocyte maturation (Davis and Bigelow, 1989; Ferguson et al., 1990; Tanke et al., 1983). Flow cytometric identification of reticulocytes relies on the trait of their high RNA content compared to mature red cells. A variety of fluorescent dyes may be used that bind to ribosomal RNA [thiazole orange (TO), acridine orange, thioflavin,

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pyronin Y, and auramine-O], although TO has become the most common dye employed due to its counting accuracy and practicality for clinical laboratories with commercial clinical flow cytometers with relatively low-powered visible lasers (Lee et al., 1986). The degree of fluorescence emission caused by the excitation (at 488 nm with an argon-laser light) of the bound dye is proportional to the amount of RNA in the erythrocyte. A detailed description of the flow cytometry method for reticulocytes is beyond the scope of this chapter, but has been published (Koepke et al., 1997). In short, fresh anticoagulated blood is mixed with a solution of TO dye and incubated in the dark at room temperature for a brief period of time. Flow cytometric analysis is then performed. Precise gating is needed in order to achieve accurate enumeration of the reticulocytes. Based on the size of mature red cells and reticulocytes being intermediate between platelets and white cells, this population can be gated on to remove interfering fluorescence from the other blood cells. Once the red cell gate is determined by the size of the cells, the intensity of fluorescence will correlate with the age of the red cell. Young, immature reticulocytes will be brightly fluorescent due to their high RNA content, while maturing reticulocytes show intermediate fluorescence intensity, and older reticulocytes show dim fluorescence due to their low RNA content. Therefore, reticulocytes are first identified by size and then enumerated within the gated RBC population on the basis of fluorescence intensity. Because there is a continuum of fluorescence intensity from immature reticulocytes to mature red cells, a cutoff point needs to be established for accurate enumeration. Many methods have been described but the simplest method determines a threshold value in which events with greater fluorescence intensity than the threshold value are determined to be reticulocytes, and events not reaching the required threshold value are considered mature red cells (Sage et al., 1983; Tanke et al., 1983). The reticulocyte percentage is calculated as the number of events above the threshold divided by the total number of events in the red cell gate. There are many clinical indications for obtaining a reticulocyte count. In patients with a normally functioning bone marrow, an elevated reticulocyte count is expected when anemia occurs such as in hemolysis or acute blood loss. This situation also occurs after treatment of other correctable causes of anemia. In cases of anemia where there is impaired marrow function, the reticulocyte count is inappropriately low for the degree of anemia. This includes nutritional deficiencies (iron, folate, and vitamin B12), myelosuppressive medications, renal failure leading to decreased erythropoietin production, bone marrow infiltration or fibrosis, and myelodysplastic syndromes leading to ineffective erythropoiesis. Newer parameters have been described that provide additional clinical information beyond the absolute reticulocyte count. Flow cytometric methods are able to determine the reticulocyte RNA content, based on quantitation of the fluorescence intensity. Numerous groups have evaluated the mean fluorescence of TO-stained reticulocytes as clinical parameters, using the terms reticulocyte maturation index (RMI), RNA index, and RNA content (Davis and Bigelow, 1989, 1990, 1994a, 1994b). The reticulocyte mean channel fluorescence has been found to be significantly elevated in

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iron-deficient or iron-depleted patients but not in patients with anemia of chronic disease, compared to reticulocytes of normal controls (Wells et al., 1992). The immature reticulocyte fraction (IRF) was introduced to indicate the less mature reticulocyte fraction (Davis, 1996; Procopio et al., 1996). However, there are various expressions of this fraction depending on the analyzer used. Some analyzers divide the reticulocytes into three distinct populations and others into only two on the basis of their RNA content. The IRF is the sum of reticulocyte fractions with medium and high fluorescence. Irrespective of the way in which the IRF is achieved, it is an early and sensitive index of erythropoiesis (Buttarello et al., 2002). An elevated IRF has been identified as the first sign of hematologic recovery in patients receiving induction chemotherapy and first sign of engraftment in patients undergoing bone marrow transplantation. In both autologous and in allogeneic transplantation, an increase in the IRF predicts the success of the transplantation even before the increase in absolute neutrophil and total reticulocyte counts (Noronha et al., 2003; Torres et al., 2001). In situations where there is anemia associated with increased erythropoiesis, such as an acquired hemolytic process or acute blood loss, both the reticulocyte count and the IRF are elevated. However, when the anemia results from decreased bone marrow activity such as in chronic renal disease, both the reticulocyte count and the IRF are decreased (Buttarello and Plebani, 2008; Chang and Kass, 1997). In myelodysplastic syndromes, the reticulocyte count may be normal or decreased but the IRF is usually increased (Lesesve et al., 2004; Torres Gomez et al., 2003). When assessing the efficacy of therapy, an increase in the IRF usually precedes an increase in the absolute reticulocyte count. B. Flow Cytometry for the Detection of Fetal–Maternal Hemorrhage During the course of pregnancy, there may be loss of integrity of the normal physiological barrier between fetal and maternal circulation. As a result, the positive arteriovenous pressure gradient between the fetus and the mother causes fetal blood to enter the maternal circulation, which is referred to as a fetal–maternal hemorrhage (FMH). Known causes for FMH include trauma to the pregnant woman, especially abdominal trauma, vascular malformations in the placenta, and abruptio placentae. However, in the majority of cases, no cause is found (Sebring and Polesky, 1990). FMH can lead to a range of consequences to the fetus. On one end of the spectrum, it may simply be an uneventful laboratory observation. However, it may result in fetal demise with hydrops fetalis and eventually circulatory collapse, leading to cerebral infarction (Giacoia, 1997). The classical cause for this fetal demise is the process of maternal chimerism, where there is immunization of the mother against fetal alloantigens. This commonly occurs when red cells from a Rhesus factor positive (Rh+) fetus may pass into the circulation of an Rh
mother, resulting in the formation of Rh antibodies in the mother. In subsequent pregnancies, the Rh antibodies that were previously formed and remain in the mother are transmitted through the placenta and into the fetal circulation. These Rh antibodies have the potential to react with the Rh + cells in the fetus, causing a disease process referred to as hemolytic disease of the

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newborn (Bowman, 1998). The passage of fetal cells into the maternal circulation may occur early in pregnancy, observed as early as the first trimester, and may in fact increase as gestational age increases. Although small amounts of fetal blood entering the maternal circulation is relatively common, larger volume bleeds are much less frequent. Approximately 3 in 1000 pregnancies result in an FMH of 30 mL or more (Sebring and Polesky, 1990). Massive hemorrhages of more than 150 mL are less common, reported as one in 2800 pregnancies (de Almeida and Bowman, 1994). Unexpected prenatal death of fetuses has been reported to be due to FMH in 13.8% of cases. Due to the uncertain clinical significance of small volumes of FMH, standard medical practice has been to administer Rh immune globulin (RhIg) to all Rh
women at 28 weeks of gestation and within 72 h of delivery of an Rh+ infant (Kumpel, 2006; Lee et al., 1999; Urbaniak and Greiss, 2000). In order to better guide decisions on the amount of RhIg to administer, tests have been developed to measure the amount of fetal hemoglobin in the maternal circulation. The earliest test developed is known as the Kleihauer–Betke (KB) test (Kleihauer et al., 1957). This test is based on the principle that fetal red cells with high hemoglobin F (HbF) have greater stability in an acid solution compared to adult red cells. Fetal red cells will stand out as densely colored cells with the maternal red cells appearing as ghost cells in the background. Both fetal and maternal cells are counted under the microscope and the quantity of fetal blood in the maternal circulation is calculated from the estimated total maternal blood volume. Although relatively easy to perform at all hours of the day with little equipment, the KB acid elution method is limited to a detection sensitivity of approximately 0.5% of fetal cells and errors frequently arise due to interobserver subjectivity, as well as the need to manually count a substantial number of maternal and fetal red cells to determine the percentage of fetal cells present (Corsetti et al., 1987). Because of the limitations of the KB acid elution test, flow cytometric methods have been developed as a more sensitive way to detect and quantify the amount of FMH. As a result of automated detection and the ability to count significantly more cells, flow cytometry can circumvent the subjectivity of the KB method (Corsetti et al., 1987). It has also proved effective in detecting very small fetal bleeds in the range of 1 fetal cell per 100,000 maternal red cells, has been proven to be more accurate in quantitating fetal cells, and has demonstrated good precision at fetal cell frequencies of approximately 0.1% (Nance and Garratty, 1987; Nance et al., 1989). Flow cytometry for FMH uses the principle of anti-HbF detection to distinguish fetal cells with high levels of fetal hemoglobin from adult red cells (Davis et al., 1998). A monoclonal anti-HbF antibody is conjugated to a fluorochrome in order to detect fetal hemoglobin inside permeabilized RBCs. Red cells containing fetal hemoglobin may be found in individuals of any age, but with lower amounts of fetal hemoglobin compared to fetal cells. These cells have been termed F cells (Chen et al., 2000). High levels of F cells may also exist in adults with a heterogeneous group of genetic disorders of uncertain etiology, referred to as hereditary persistence of fetal hemoglobin (HPFH). Other clinical conditions causing significant levels of anemia may also result in elevated levels of F cells including hereditary

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diseases such as sickle cell anemia and thalassemia major (Chen et al., 2000; Mundee et al., 2000; Thorpe et al., 1994). These F cells exhibit lower fluorescence intensity than fetal cells, allowing them to be differentiated and excluded from the fetal cells for analysis and subsequent calculations. Methods and approved guidelines for the detection of fetal red cells have been published (Davis, 2001a, 2001b). In short, analysis is performed on an EDTA anticoagulated peripheral blood sample drawn from the mother and the red cell count is determined by a complete blood count (CBC) analysis. The red cells are then fixed, washed, and permeabilized with a detergent to allow antibodies to penetrate the red cell membrane. The red cells are then incubated with an anti-HbF antibody, washed, and analyzed on a flow cytometer. At least 50,000 events should be collected for analysis. As with all flow cytometry tests, positive and negative control samples are run with the samples to establish optimal performance of reagents and to establish that the positive fluorescence attributed to antibody stained fetal cells is distinguished from unstained normal red cells, white cells, and cellular debris. Two levels of positive control are used, usually consisting of 1 and 5% fetal erythrocytes in normal adult blood. A negative control sample usually consists of a normal male or nonpregnant adult female blood sample. In order to accurately quantify the number of fetal cells present, meticulous gating is necessary for each series of analyses and to that end, gating and controls are repeated daily. Examples of gating on the two levels of positive control and negative control are shown in Fig. 2. The 1% positive control should be used to properly define the fluorescence intensity region for the detection of fetal cells and to exclude any F cells that may also be present in the sample. Fig. 3A depicts a positive FMH patient sample with increased fetal cells detected. Fig. 3B depicts a patient with HPFH showing an increased number of F cells. The value achieved by flow cytometry reflects the percentage of fetal cells among all red cells in the mother’s blood sample. In order to determine the quantity of fetal hemorrhage (in mL of fetal blood) into the maternal circulation, the percentage of fetal cells in the maternal circulation is multiplied by a factor of 50 (quantity of fetal hemorrhage into maternal circulation (mL) = (% fetal cells in maternal circulation  50)). This assumes that the maternal blood volume is 5.0 L. This assumption is not universally accurate and the potential impact of this assumption on the imprecision of the quantification of FMH by flow cytometry may, in fact, be more important than the coefficients of variation described for the various techniques (Dziegiel et al., 2005, 2006). This assumption will underestimate the true volume of FMH in women with higher red cell volumes and therefore, recognition of this fact is critical, especially when the FMH volume is close to the value covered by the standard dose of prophylactic RhIg. In an ideal situation, the actual maternal body weight or RBC volume should accompany the requisition for FMH flow cytometry detection to allow for a more accurate quantitation but this may not be practical. As with all laboratory tests, there are limitations to the flow cytometry method for detecting FMH. The presence of F cells was alluded to earlier in this section. In most situations, the percentage of F cells is not extremely high and can be distinguished

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[(Fig._2)TD$IG]

Fig. 2

Gating for Hemoglobin F. Panel A. Increased HbF in pregnant woman with hereditary persistence of fetal hemoglobin (HPFH). Panel B. Negative control for fetal HbF. Panels C and D. Positive controls for fetal HbF, levels 1 and 2 respectively.

from fetal cells with precise gating (Davis et al., 1998). However, cases have been described where the F cells in patients with sickle cell disease (SCD) and HPFH were indistinguishable from fetal cells (Marcus et al., 1997). Hemagglutination can be another issue in this method with the agglutinated red cells falling outside the range of detection. To reduce the occurrence of agglutination, fixation of the cells is recommended as well as the use of directly fluorescent-labeled antibodies (Davis et al., 2001). Potential interfering substances may be present in the blood sample leading to autofluorescence. This may include fibrin, platelet aggregates, and dead cells in unfixed specimens and white blood cells and nucleated red cells in fixed specimens. Proper gating and data analysis should be conducted to exclude these interferences from fetal cell analysis. One final consideration is the organization of the diagnostic service for detection and quantitation of FMH. In most centers, flow cytometry laboratories do not operate 24 h a day and are not available on weekends. In addition, the cost of flow cytometry detection of FMH is considerably higher than earlier methods. Therefore, it is advisable to employ a screening method such as the KB test, which is simple,

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[(Fig._3)TD$IG]

Fig. 3 (A) Positive FMH patient sample with increased fetal cells. (B) Patient with HPFH showing an increased number of F cells.

inexpensive, semiquantitative with good negative predictive value and can be performed in small laboratories at any hour of the day. Positive samples should then be referred to a centralized flow cytometry facility for confirmation and thorough investigation of all cases of suspected FMH (Dziegiel et al., 2006). C. Flow Cytometry for Quantitation of HbF in Sickle Cell Disease SCD is a severe progressive disease resulting from homozygosity for a point mutation in codon 6 of the b-globin gene. In the United States, it affects mainly

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those of Sub-Saharan African descent with increased disease prevalence in Africa, and parts of the Mediterranean, India, and the Middle East (Kwiatkowski, 2005; Roseff, 2009). In order for red cells to normally function, they need to be able to flow freely through blood vessels and to be flexible enough to move in and out of vessels and tissue where oxygen delivery occurs. In sickle cells, especially in low-oxygen tension states, the hemoglobin S (HbS) will polymerize leading to rigidity of the sickle cells and subsequent loss of the normal functional properties of red cells. These chronic physiologic changes can precipitate vaso-occlusion crises as well as chronic hemolytic anemia and immunologic impairment. Over time, this will lead to problems in all organ systems with the major involved organs including heart, lungs, brain, and kidneys. Of note, when a patient with SCD has a higher percentage of HbF, the course of this disease tends to be milder with a subsequent increase in life expectancy (Mukherjee et al., 1997; Platt et al., 1994). This is likely due to the lower percentage of HbS in these patients and the fact that red cells with HbF do not form polymers. As such, many patients with SCD are given hydroxyurea (HU), as it has been found to reduce mortality as a result of induction of HbF production and a subsequent reduction in vaso-occlusive events. As a result, there is further reduction in the incidence of acute chest syndrome, transfusion requirements, episodes of hospitalization, pain crisis, and reduces blood flow through intracranial blood vessels, as measured by transcranial Doppler in children (Charache et al., 1995; Steinberg et al., 2003; Ware et al., 1999). As was mentioned in the section on the flow cytometry measurement of FMH, red cells containing an increased level of HbF are termed F cells. Being able to accurately quantify the number of F cells in a patient with SCD will help to monitor the effect of HU therapy and the resulting clinical response (Ballas et al., 2006). There are many methods for the measurement of HbF levels including highperformance liquid chromatography (HPLC) and capillary electrophoresis. Although more expensive, flow cytometry using an anti-HbF antibody can be used to quantify the percentage of F cells in circulation (Italia et al., 2007; Navenot et al., 1998; Thorpe et al., 1994). These F cells are characterized by lower fluorescence intensity allowing them to be distinguished from the higher fluorescence intensity of fetal hemoglobin in fetal cells. Flow cytometry has the advantage over HPLC analysis in that HPLC will demonstrate only an overall increase or decrease in total HbF but cannot differentiate between fetal HbF cells and adult F cell numbers. This is clinically important when investigating the effect of therapy in modulating adult F cell expression in sickle cell disorders (Italia et al., 2007). D. Flow Cytometry in the Evaluation of Hereditary Spherocytosis Hereditary spherocytosis (HS) is an inherited hemolytic anemia characterized by a broad spectrum of clinical severity that ranges from an asymptomatic condition to compensated hemolysis to severe hemolytic anemia requiring frequent transfusions

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(Eber and Lux, 2004). This phenotypic variability is a result of different underlying molecular defects and subsequent bone marrow compensation. Mutations have been identified in numerous genes that encode for transmembrane proteins (i.e., band 3), membrane skeletal proteins (i.e., a- and b-spectrin) and proteins mediating the attachment of the latter to the former (i.e., protein 4.2 and ankyrin) (Iolascon et al., 1998). Regardless of the defect, the abnormal red cell membrane leads to osmotically fragile red cells that become entrapped and are destroyed in the spleen. Numerous screening tests have been employed for the diagnosis of HS. The traditional test is the osmotic fragility (OF) test, which measures the fragility of red cells when subjected to varying osmotic pressure by suspending the cells in a series of hypotonic solutions (Parpart et al., 1947). Further modifications of this method include the acidified glycerol lysis test (Zanella et al., 1980) and hypertonic cryohemolysis test (Streichman and Gescheidt, 1998) that use severe conditions such as glycerol and cold, respectively, to investigate the fragility of the red cells. However, these tests, especially the OF test, tend to have a low sensitivity and specificity and may fail to detect atypical and mild cases of HS (BoltonMaggs et al., 2004). They may also be affected by factors unrelated to red cell cytoskeletal defects including iron deficiency, obstructive jaundice, and an elevated reticulocyte count (Korones and Pearson, 1989). Another method to investigate for HS is to directly measure the red cell membrane proteins using sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE) (Palek and Jarolim, 1993). This method separates membrane proteins that remain attached to the membrane lipid bilayer after hypotonic lysis of the red cells. However, it lacks sensitivity in detecting a minimal protein deficiency in mild HS cases containing a mixture of normal and abnormal red cell proteins, but still remains the confirmatory test for HS (King et al., 2000). A flow cytometry-based test using eosin-5-maleimide (EMA) has been developed with high sensitivity for the diagnosis of HS (Kar et al., 2010; Kedar et al., 2003; King et al., 2000; Stoya et al., 2006). This test is based on the principle that EMA, a fluorescent dye, has a high affinity to bind to band3 protein with different aspects of the EMA molecule binding to different regions of the band3 protein (King et al., 2000). The red cells are washed and incubated with EMA for 1 h in the dark. After centrifugation, the labeled red cells are washed twice in a phosphate-buffered saline– bovine serum albumin (PBS–BSA) solution and then suspended in PBS–BSA for flow analysis. At least 15,000 events are captured in order to measure the mean fluorescence intensity, which has been found to be lower in HS than other hemolytic and nonhemolytic anemias. The sensitivity for detection of HS has been reported as ranging from 92.7 to 96.6% with the specificity ranging from 94.2 to 99.1% (Kar et al., 2010; King et al., 2000; Stoya et al., 2006). Although EMA binds to band3, protein deficiencies in HS other than band3 can also lead to impairment of EMA binding (King et al., 2000; Stoya et al., 2006). This may be a result of a defective membrane cytoskeletal protein other than band3 deficiency causing a modulation effect on the dye-binding site in band3.

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The EMA MFI values are highly variable in normal controls and HS patients and, therefore, it is important for every laboratory to establish its own reference range for normal samples and to determine the optimum cutoff point for diagnosis of HS (Kar et al., 2010).

E. Measurement of Red Cell Survival and Red Cell Volume Normal human red blood cells survive in the circulation for approximately 120 days after they are released from the bone marrow. While mild to moderate decreases in RBC life span can be compensated by an erythropoietin-dependent increase in RBC production, a more severe shortening leads to hemolytic anemia. The first valid estimates of RBC survival showing transfused RBCs survived over 100 days instead of the much shorter time previously believed were published by Winifred Ashby who used a differential agglutination assay after transfusing Type O cells into Type A or B subjects (Ashby, 1919, 1921a, 1921b). Most techniques to estimate RBC survival require that a label be placed on the cells that can be followed while the RBCs age in the circulation. A number of radioactive isotopes have been used to label RBCs and estimate their survival. The most commonly used is 51Cr that was introduced in 1950 for the measurement of RBC volume (Gray and Sterling, 1950) and soon after was used to determine RBC survival (Ebaugh et al., 1953). However, increasing concern about radiation effects motivated the development of flow cytometric techniques using cellular biotinylation to determine BCV and RBC life span in animals and humans (Franco, 2009; Mock et al., 2008). In animals, all the circulating RBCs may be labeled by IV administration of biotin. The rate of disappearance of the labeled cells in postinfusion blood samples is followed by reacting red cells with fluorochrome-labeled avidin or streptavidin followed by flow cytometric quantitation of labeled cells as a percentage of the total cells. For human studies, the red cells are labeled ex vivo and then analyzed the same way. Compared to both (14C)cyanate and 51Cr reference methods, biotinylation yields equivalent values for RBC life span and offers several advantages in addition to the absence of radioactivity. As biotin forms a covalent bond with surface membrane and cytosolic proteins, there is little elution from cells or loss of cells during analysis. The ability to isolate the labeled cells by magnetic separation is an additional important advantage especially in the clinical research setting. On the other hand, biotin labeling requires more manipulation of the cells with multiple washes to remove excess reagent and reaction byproducts. Also, as flow cytometric analysis determines labeled cells as a percentage of the total cells, it assumes that the total number of RBCs is at a steady state during life span measurement.

F. Cell Cycle Studies During development, erythroid cells show a distinct cell cycle profile ranging from an almost purely G0/G1 population of CD34+ cells through rapidly cycling

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cells to cells that become arrested in G0/G1 when terminally differentiating. Zhang et al. (2001) showed that in vivo the most primitive hematopoietic stem cells are quiescent in G0 phase and Furukawa (1998) demonstrated that the majority of committed progenitors are rapidly cycling for effective expansion. Mobilized peripheral blood hematopoietic stem cells have been shown to be predominantly found in G0/G1 phase of the cell cycle and contain fewer cycling cells than bone marrow CD34 cells (Jetmore et al., 2002; Sanchez-Garcia et al., 2006; Uchida et al., 1997). Lataillade et al. (2002) found about 28% of freshly isolated peripheral blood CD34+ cells to be in G0 while 70% were in G1 and only 2% were in S and G2/M phase. As erythroid cells progress through late stages of development, their cycling potential decreases and they ultimately withdraw from the cell cycle (Matushansky et al., 2000; Tamir et al., 2000). The work of Dirlam et al. (2007) suggests that the G0/G1 arrest is closely tied to chromatin condensation occurring in terminal erythroid differentiation. The process of red cell engraftment following homologous bone marrow or peripheral stem cell transplantation may be monitored provided there is a suitable mismatch of antigens. Continuing quantitation of the differences can give early warning of loss of the graft or confirmation of a stable chimera (Arnold et al., 1986; Blanchard et al., 1995; Lazarus et al., 1992). Utility in disease states characterized complete or partial loss of specific antigens such as the GPI-linked antigens in PNH. There are further advantages in that only a few hundred cells are needed for analysis, and this has been utilized in the determination of fetal blood group antigens on the RBC contained in fragments of chorionic villus (Nelson et al., 1996).

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CHAPTER 14

Immunophenotypic Characterization of Bone Marrow Mast Cells in Mastocytosis and Other Mast Cell Disorders Laura S
anchez-Muñoz,* Cristina Teodósio,y Jos
e M. Morgado* * and Luis Escribano * Instituto de Estudios de Mastocitosis de Castilla La Mancha, Hospital Virgen del Valle, Toledo, Spain y

Servicio General de Citometr
ıa, Instituto de Biolog
ıa Molecular y Celular del C
ancer, Centro de Investigación del C
ancer/IBMCC (CSIC-USAL) and Departamento de Medicina, Universidad de Salamanca, Salamanca, Spain

Abstract I. Introduction II. Background III. Methods A. Collection of BM Samples B. Staining of BM Samples C. Panel of Monoclonal Antibodies D. Data Acquisition and Analysis E. Critical Parameters and Troubleshooting IV. Results A. Identification, Enumeration, and Characterization of BM MC by Flow Cytometry B. Immunophenotypic Features of Normal BM MC C. Immunophenotypic Characteristics of BM MC in Systemic Mastocytosis V. Immunophenotypic Analysis of MC from Biological Specimens Other than BM A. Identification of Circulating MC in Peripheral Blood B. Identification of MC in Organic Fluids or Tissues VI. Medical Indications VII. Summary Acknowledgments References

METHODS IN CELL BIOLOGY, VOL 103 Copyright 2011, Elsevier Inc. All rights reserved.

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0091-679X/10 $35.00 DOI 10.1016/B978-0-12-385493-3.00014-0

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Abstract Mastocytosis is a term used to designate a heterogeneous group of disorders characterized by an abnormal proliferation and accumulation of mast cells (MCs) in one or multiple tissues including skin, bone marrow (BM), liver, spleen, and lymph nodes, among others. Recent advances in our understanding of mast cell biology and disease resulted in the identification of important differences in the expression of mast cell surface antigens between normal and neoplastic mast cells. Most notably, detection of aberrant expression of CD25 and CD2 on the surface of neoplastic mast cells but not on their normal counterparts lead to the inclusion of this immunophenotypic abnormality in the World Health Organization diagnostic criteria for systemic mastocytosis. Aberrant mast cell surface marker expression can be detected in the bone marrow aspirate by flow cytometry, even in patients lacking histopathologically detectable aggregates of mast cells in bone marrow biopsy sections. These aberrant immunophenotypic features are of great relevance for the assessment of tissue involvement in mastocytosis with consequences in the diagnosis, classification, and follow-up of the disease and in its differential diagnosis with other entities. In this chapter, we provide the reader with information for the objective and reproducible identification of pathologic MCs by using quantitative multiparametric flow cytometry, for their phenotypic characterization, and the criteria currently used for correct interpretation of the immunophenotypic results obtained.

I. Introduction From a historical point of view, in 1869 a paper reporting a 2-year-old child with typical lesions of mastocytosis described as a rare type of urticaria was published (Nettleship and Tay, 1869). In 1887, mast cells (MC) were identified within skin lesions of urticaria pigmentosa (Unna, 1887). The first report on mastocytosis was published in 1936 (S
ezary et al., 1936) and the systemic nature of mastocytosis was first recognized in 1949 (Ellis, 1949). The term mastocytosis includes a group of heterogeneous diseases characterized by an abnormal expansion and accumulation of MC in different tissues. The symptoms and signs of the disease are usually related to the release of MC mediators and, in aggressive forms, to the tissue MC burden. The clonal nature of mastocytosis can be established in virtually all cases through the demonstration of gain-of-function mutations involving the tyrosine kinase domain of KIT in lesional skin and/or bone marrow (BM) cells (Garcia-Montero et al., 2006; Longley et al., 1996, 1999; Nagata et al., 1995; Pardanani et al., 2003; Yavuz et al., 2002). In the great majority (>90%) of adult cases with systemic mastocytosis (SM), mutations in the activation loop of KIT (most frequently D816V) are detected in MC in association with an aberrant CD25+ phenotype (Garcia-Montero et al., 2006). Interestingly, D816V-negative SM patients frequently carry other KIT mutations in the activation loop involving codons

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815, 816, 817, 820, and 839 (reviewed in reference Orfao et al., 2007). Of note, mutation of KIT in mastocytosis has also been associated with decreased expression of kit (CD117) on the cell surface, which could probably be due to an increased cleavage and release of the mutated kit molecule into the extracellular compartment, leading to increased soluble levels of CD117 (Akin et al., 2000). In 2001, a consensus classification of mastocytosis was proposed and adopted by the World Health Organization (WHO) (Horny et al., 2008; Valent et al., 2001a, 2007). Diagnosis of SM was based on the presence of one major criterion such as multifocal dense aggregates of
15 MC in BM and/or other extracutaneous tissues and four minor criteria: (a) presence of morphologically atypical MC in smears or biopsy sections of BM or other extracutaneous organ, (b) aberrant expression of CD25 and/or CD2 by BM MC, (c) presence of activating D816V KIT mutation in BM, blood, or other extracutaneous organs, and (d) serum tryptase levels over 20 mg/L in the absence of other pathological conditions associated to increased serum tryptase (Klion et al., 2003; Sperr et al., 2001, 2002; Valent et al., 2008). Diagnosis of SM requires one major and one minor criterion or three minor criteria. In addition, other parameters based on clinical, biological, and image probes were included in order to establish the exact subtype of the disease (Horny et al., 2008; Valent et al., 2001a, 2007). On the basis of the above-mentioned criteria, seven categories of mastocytosis are defined in the WHO classification (Table I) (Horny et al., 2008; Valent et al., 2001a, 2007). Despite the fact that BM MCs represent only a very small proportion of all nucleated cells present in normal BM, it has recently been shown that they can be specifically identified and accurately enumerated using multiparameter flow cytometry. In the authors’ experience, BM MCs are clearly identifiable on the basis of their light-scatter properties and their CD117/CD45 pattern of expression (Escribano et al., 1999, 2002, 2004, 2006; Orfao et al., 1996). The low frequency at which MC are usually present in human BM, and the lack of MC-specific antigens have been important stumbling blocks to MC identification. Despite the fact that different techniques can be used for the analysis of the expression of antigens by MC from mastocytosis, flow cytometry is the preferred method because it allows sensitive detection and quantitative evaluation of antigen

Table I WHO diagnostic categories of mastocytosis (Horny et al., 2008) Cutaneous mastocytosis Indolent systemic mastocytosis Systemic mastocytosis associated with clonal hematological nonmast-cell lineage disease Aggressive systemic mastocytosis Mast cell leukemia Mast cell sarcoma Extracutaneous mastocytoma

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expression in large numbers of MC, even when they are present in a sample at very low frequencies. In recent years, it has been shown that combined usage of multiple stainings and flow cytometry allows sensitive detection of rare cells. In this sense, it has also been shown that the use of a double-acquisition procedure allows the detection of small populations of leukemic cells in complete remission human BM samples, even when their frequency is as low as one leukemic cell in 106 normal cells. This methodological approach also allows a systematic analysis of the immunophenotypic characteristics of normal human BM MC. During the past few years, multiparametric flow cytometry has shown to be a powerful tool for the study of mastocytosis, revealing the existence of an aberrant immunophenotype on BM MC that go beyond the classically used CD2 and CD25 (Cervero´ et al., 1999; Diaz-Agustin et al., 1999; Escribano et al., 1998c, 1998d, 2002; Nu´n˜ez et al., 2003; Valent et al., 2001a) (Table II). Table II Relevant findings in mastocytosis research Date

Authors

Event

Reference

1869

J. Nettleship and W. Tay

Nettleship and Tay, 1869

1879 1887

Paul Ehrlich Paul Gerson Unna

1936 1949 1991

Albert S
ezary J. Ellis Metcalfe et al.

1995 1996

H. Nagata et al. A. Orfao et al.

1998

L. Escribano et al.

1998 2001

Horny et al. P. Valent et al.

2007

P. Valent et al.

2009 2010

L. Escribano et al. C. Teodosio et al.

2010

I. Alvarez-Twose et al.

Description of a clinical picture in a 2-year-old child with typical lesions of mastocytosis as a rare type of urticaria First description of mast cells Demonstration of mast cells in skin lesions of urticaria pigmentosa First report on mastocytosis Recognition of the systemic nature of mastocytosis Classification of mast cell diseases based on clinical and morphological criteria Identification of the D816V c-kit mutation in mastocytosis Identification and enumeration of human mast cells in both normal and pathological bone marrow samples using flow cytometry Immunophenotypic characterization of BM MC from systemic mast cell disease in adults: aberrant expression of CD2 and CD25 as a hallmark for SM Diagnostic value of IHC tryptase staining in mastocytosis Consensus classification of mastocytosis adopted by the World Health Organization (WHO) Consensus on standardization in mastocytosis diagnosis and treatment Prognosis in adult indolent systemic mastocytosis Immunophenotypic patterns associated with different molecular and prognostic subtypes of systemic mastocytosis Clinical, biological, immunophenotypical, and molecular characterization of clonal mast cell activation disorders

Ehrlich, 1879 Unna, 1887 S
ezary et al., 1936 Ellis, 1949 Metcalfe, 1991 Nagata et al., 1995 Orfao et al., 1996 Escribano et al., 1998c Horny et al., 1998 Valent et al., 2001a Valent et al., 2007 Escribano et al., 2009 Teodosio et al., 2010 Alvarez-Twose et al., 2010

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In the following sections, we will provide the reader with information required for the objective and reproducible identification of pathological MC in hematological samples, as well as for their phenotypic characterization, together with the criteria currently used for a correct interpretation of the immunophenotypic results obtained.

II. Background Fluorescence-based flow cytometry is the method of choice for the quantification, characterization, and purification of cells in suspension, allowing rapid analysis of thousands of cells and, therefore, the study of very rare cell subsets such as MC (Gross et al., 1993, 1995; Leary, 1994; Leary et al., 1994). Prior to the onset of immunophenotyping techniques, the ability to distinguish between different cell types was dependent on morphological or functional differences between them. The staining with mixtures of dyes to examine cells under the microscope, frequently lead to morphological classification of cells but with a great degree of subjectivity. Using these principles, Paul Ehrlich (1879) described for the first time in his doctoral thesis, a type of cells that he believed had the function of feeding the surrounding tissue, this explaining why he named them Mastzellen (from the German: Mast, ‘‘feed’’). Since then, quantitative evaluation of MCs has been attempted in several tissues and pathological conditions using different techniques. In any case, the enumeration of MCs present in a sample has never been achieved with accuracy, despite the fact that existence of increased numbers of MCs in different tissues, including BM, may have clinical significance in several pathologic conditions (McKenna, 1994; Prokocimer and Polliack, 1981; Sale and Marmont, 1981; Yoo and Lessin, 1982; Yoo et al., 1978). The granules of those cells described by Ehrlich presented metachromasia, a color ‘‘shift’’ observed in some components, like proteoglycans, when stained with basic dyes such as toluidine blue, in acidic conditions. However, this metachromasia is not exclusive of MC, and it is also characteristic of basophils. For this reason, correct identification of MC in a tissue stained with basic dyes, was far from easy and soon, components of MC secretory granules other than proteoglycans seemed to offer advantages as markers for MCs. In this regard, detection of histamine by immunohistochemical procedures was successfully reported (Johansson et al., 1992); however, with the production of monoclonal antibodies that specifically detect MC proteases, such as tryptase, chymase, MC carboxypeptidase, and cathepsin G, immunostaining techniques employing such antibodies became the method of choice for the detection of MC in human tissues. Notwithstanding its importance, these approaches may present specific problems. Accordingly, while in most cases MC degranulation may be partial, under specific conditions, a substantial degree of degranulation may occur resulting in ‘‘phantom MC’’ that fail to stain with basic dyes (Claman et al., 1986) and, most likely, with immunostaining techniques against granule proteases. Besides this, the extent to which

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basophils may be detected as MC using this approach remains an important issue. Early studies suggested ‘‘undetectable’’ levels of tryptase and chymase in basophils, albeit more recent studies provide evidence of substantial quantities of these proteases in peripheral blood (PB) basophilic cells from certain allergic subjects (Li et al., 1998). In 1987, Mayrhofer et al. reported that a mouse monoclonal antibody (mAb) (YB5.B8), raised against acute myeloid leukemia cells, reacted with MC without distinguishing mucosal from connective tissue MC, but it did not bind basophils (Mayrhofer et al., 1987). A few years later it was demonstrated that the YB5.B8 antibody was specific for the c-kit protooncogen product (Lerner et al., 1991) (kit or CD117), which proved to be expressed not only by MC but also by hematopoietic precursors in BM (Ashman et al., 1991). Through usage of appropriate multiparameter flow cytometry approaches, that allow the exclusion of unwanted cells and a two-step gating strategy to increase the number of MC to be analyzed, we have demonstrated that BM MC may be clearly identifiable by flow cytometry on the basis of their light scatter properties and strong CD117 expression; these cells are negative for the CD34, CD38, and CD138 antigens. In addition, they are CD33+ and display a high reactivity for anti-IgE mAb (Orfao et al., 1996). In 1995, the presence of the ‘‘lymphoid-associated antigen-2’’ (CD2) as assessed by multiparameter flow cytometry, in both BM and PB MC from a patient with MC leukemia (MCL) was described (Escribano et al., 1995) confirming a previous report in a case of MCL using immunocytochemistry (Dalton et al., 1986). One year later, we reported that MC from mastocytosis express abnormally low levels of CD117, together with unexpectedly high light scatter and autofluorescence characteristics (Orfao et al., 1996). In 1998, it was reported that BM MC from patients with SM display unique aberrant immunophenotypic characteristics, once compared to normal MC (Escribano et al., 1998c). Among other features, pathological MC showed aberrant expression of CD25 and CD2 together with abnormally high levels of the CD11c and CD35 complement receptors (Nun˜ez et al., 2002), the CD59 complement regulatory molecule (Nun˜ez et al., 2002), the CD63 lysosomal membrane antigen (Escribano et al., 1998b), and the CD69 early activation antigen (Diaz-Agustin et al., 1999). Recently, three clearly different maturation-associated immunophenotypic profiles were found for BM MC in SM. These different profiles are related with both genetic markers of the disease and its clinical behavior. Accordingly, BM MC from poor-prognosis categories of SM (aggressive SM and MCL) typically showed an immature phenotype with clonal involvement of all myeloid lineages by the D816V KIT mutation. In turn, a mature activated versus resting BM MC immunophenotype is commonly found among patients with good-prognosis subtypes of SM, depending on whether they carry (ISMs+ and ISMs) or not (well-differentiated SM, WDSM) the D816V KIT mutation, respectively. These aberrant immunophenotypic features are of great relevance for the assessment of tissue involvement in mastocytosis with direct consequences in the diagnosis, classification, and follow-up of the disease as well as in its differential diagnosis with other entities.

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III. Methods A. Collection of BM Samples Diagnosis of SM systematically requires BM aspiration and biopsy as different techniques have to be applied in both specimens. Heparinized or ethylenediaminetetraacetic acid (EDTA) anticoagulated BM aspirates should be used for flow cytometry studies on MC, and specimens should be processed within the first 24 h after sample collection. BM aspiration should be performed in posterior iliac spina, using an 11- to 8-G biopsy needle in order to obtain enough BM particles. A minimum of 1.5–2 mL of BM should be collected and the aspirate passed two or three times through a 25-G gauge needle in order to disaggregate the BM particles. It is also advisable to assess the nucleated cell count in a conventional hematology analyzer or a flow cytometer (Escribano et al., 2001).

B. Staining of BM Samples Staining for membrane antigens is performed according to a stain-and-then-lyse technique previously described (Escribano et al., 2001). Briefly, approximately 1.5–2.5  106 cells are incubated with, previously titrated, fluorochrome-conjugated monoclonal antibody reagents, for 15 min (room temperature), protected from light. Lysis of the red blood cells (RBC) should be performed by adding 2 mL of a lysis solution, for example, FACS lysing solution (BD Biosciences, San Jose, CA) diluted 1/10 (v/v) in distilled water, and incubated for another 10 min at room temperature, protected from light. afterward, a 5 min centrifugation (540g) and a washing step with 4 mL of PBS should be performed and the cell pellet resuspended in 500 mL PBS, prior to data acquisition in the flow cytometer. Samples should be kept at 4  C for a maximum of 24 h before analysis. For staining of intracellular proteins an alternative protocol must be used, since fixation and permeabilization are required (Escribano et al., 2001). Briefly, following the incubation with the mAb directed to membrane proteins, a washing step with 4 mL of PBS and a centrifugation for 5 min (540g) is performed. The cell pellet should be incubated with 100 mL of a fixative solution (e.g., Fix&Perm Solution A – An Der Grub, Viena, Austria) for 15 min (room temperature), protected from light, followed by a washing step with 4 mL of PBS and a 5 min of centrifugation step (540g). After removal of the supernatant, 100 mL of a permeabilization solution (e.g., Fix&Perm Solution B – An Der Grub) together with the mAb used to stain cytoplasmic epitopes are incubated for 15 min at room temperature in the dark. A washing step with PBS, as previously described for membrane staining procedure, is necessary prior to sample acquisition in the flow cytometer.

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Each laboratory should develop its own panel depending on their own flow cytometer facilities and the specific mAb reagents available. As a general rule, a short screening panel for mastocytosis is recommended in combination with a larger panel to be applied for further MC characterization, when aberrant MC are detected in the sample (Tables III and IV). In Table V, a list of fluorochrome-conjugated mAb reagents and their commercial source that can be used in these panels as reference reagents is provided. Despite CD117 can also be expressed on CD34+ HPC, myeloid and erytroid precursors, and neoplastic cells from different malignancies, MC express uniquely higher amounts of CD117 versus all other hematopoietic cells (Escribano et al., 1998a). Despite this, simultaneous staining for CD45 and CD117 is desirable for adequate identification of the MC population (Escribano et al., 1998c, 2004); such double staining is particularly useful in those subtypes of SM in which MC display decreased CD117 expression (Escribano et al., 1998c, 2002; Teodosio et al., 2010). Whenever required, counterstaining with CD33, FceRI, or CD203c might be used, in order to improve the identification of MC, as these cells highly express these three proteins (Escribano et al., 2004; Hauswirth et al., 2008).

D. Data Acquisition and Analysis For data acquisition, instrument set up, calibration, and quality control should follow rules identical to those described for the immunophenotypic analysis of other nucleated blood cells (Roederer, 2002). According to the REMA’s recommendations Table III Four-color monoclonal antibody combinations recommended for the immunophenotypic analysis of bone marrow mast cells Tube no.

FITC

PE Screening panel Control CD25 CD69

1 2 3

Control CD2 FceRIa

4 5 6 7 8 9

Further mast cell characterization panel CD35 CD59 CD63 CD32 HLA-DR CD123 cy Control cy Control cy Total tryptase (B12) CD34 cy CPA CD203c

PerCP Cy5.5

APC

CD45 CD45 CD45

CD117 CD117 CD117

CD45 CD45 CD45 CD45 CD45 CD45

CD117 CD117 CD117 CD117 CD117 CD117

Control, unstained negative control; CPA, carboxypeptidase A3; cytoplasmic markers are preceded by ‘‘cy’’, otherwise we refer to membrane markers. a Indirectly observed by anti-IgE staining.

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Table IV Eight-color monoclonal antibody combinations recommended for the immunophenotypic analysis of bone marrow mast cells Tube no. Pacific Blue Pacific Orange FITC

PE Screening panel Control CD25

PerCP Cy5.5 PECy7 APC

APCH7

CD34 CD34

CD117 Control CD117 CD203c

Control CD69

CD117 CD117 CD117 CD117

CD25b CD25b CD25b CD25b

1 2

Control CD2

CD45 CD45

Control FceRIa

3 4 5 6

HLA-DR HLA-DR HLA-DR HLA-DR

CD45 CD45 CD45 CD45

Further mast cell characterization panel CD35 CD59 CD34 CD63 CD32 CD34 cy Control cy Control CD34 cy Total tryptase (B12) cy Chymase CD34

CD123 CD123 cy Control cy CPA

Control, unstained negative control; CPA, carboxypeptidase A3; cytoplasmic markers are preceded by ‘‘cy’’, otherwise we refer to membrane markers. a Indirectly observed by anti-IgE staining. b To be included when a double MC population (CD25+ and CD25 MC) is observed with the screening panel.

(Escribano et al., 2004), the use of a double-step data acquisition procedure is frequently convenient (especially for those cases with low BM MC load). Briefly, in a first step, 2–5  104 events, corresponding to the whole sample cellularity, should be acquired and stored; in a second step, a minimum of 102–103 events falling in a CD117+ electronic live gate should be stored (Escribano et al., 2001). E. Critical Parameters and Troubleshooting Since BM MCs are closely attached to the stroma and stromal cells, BM aspiration should be performed firmly and quickly in order to obtain a sufficient number of BM fragments. The harvest of higher volumes of sample (>2 mL) in a single aspirate will typically not increase the number of MCs collected due to increased dilution with blood; BM samples obtained without particles could be adequate for other immunophenotypical studies but not for MC enumeration. For the enumeration of BM MC, fresh samples with high cell viability (>95%) should be used. MC counts in samples processed more than 3 h after a BM aspirate was performed should be considered as potentially worthless. In cases in which it is not expected to perform sample processing within the first 24 h following a BM puncture, a stabilizing solution should be used to avoid deterioration of cells [e.g., Transfix (Cytomark, Buckingham, UK)]. Prior to immunophenotyping, staining of a BM smear containing BM particles with 1% toluidine blue (pH 0.5) is of utility to morphologically control for the relative BM MC content of the sample and get an overall impression on whether it is low, normal, or high; in those cases in which the BM MC load is expected to be very low it is advisable to perform duplicates or even triplicates of the staining tubes, in order to have enough MC analyzed.

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Table V List of reference monoclonal antibody reagents for the immunophenotypic analysis of bone marrow mast cells Antibody conjugate

Clone

Source

CD2–FITC CD25–PE CD32–PE CD35–FITC CD59–PE CD63–FITC CD69–PE CD45–PerCP Cy5.5 CD117–APC CD123–PE CD203c–PE Carboxypeptidase A3 FceRI–FITC HLA-DR–FITC Total tryptase CD2–Pacific Blue CD25–APCH7 CD34–PerCP Cy5.5 CD45–Pacific Orange CD69–APCH7 CD117–PECy7 CD203c–APC HLA-DR–Pacific Blue CD123-APC

S5.2 2A3 AT-10 E11 p282 (H19) CLBGran/12 L78 2D1 YB5.B8 9F5 97A6 CA2 Polyclonal L234 B12 TS1/8 M-A251 8G12 2D1 FN50 104D2D1 FR3-16A11 L243 6H6

BD Biosciencesa BD Biosciencesa Cytognosb BD Pharmingenc BD Biosciencesa Beckman Coulterd BD Biosciencesa BD Biosciencesa DAKOe BD Biosciencesa Beckman Coulterd AF Wallsf Invitrogeng BD Biosciencesa LB Schwartzh Biolegendi BD Pharmingenc BD Biosciencesa BD Biosciencesa BD Pharmingenc Beckman Coulterd Miltenyij Biolegendi Immunostepk

a

BD Biosciences (BDB, San Jos
e, CA, USA). Cytognos (Cytognos SL, Salamanca, Spain). c BD Pharmingen (BDB, San Jos
e, CA, USA). d Beckman Coulter (Beckman Coulter, Brea, CA, USA). e Dako (Dako, Glostrup, Denmark). f This mAb was a kind gift from AF Walls (Southampton, UK). g Invitrogen (Invitrogen, Carlsbad, CA, USA). h mAb kindly provided by LB Schwartz (Richmond, VA, USA). i Biolegend (San Diego, CA, USA). j Miltenyi Biotec (Miltenyi Biotec, Bergisch Gladbach, Germany). k Immunostep (Salamanca, Spain) b

In some cases where red cell lysis is not adequate, it is recommended to incubate the sample with the lysing solution for longer periods of time (e.g., 20 min), in a horizontal position with rotation movement, to homogenize cell concentration throughout the lysing solution and, consequently, improve the lysing process. Assessment of the basal MC autofluorescence levels should always be performed, by using either an unstained tube or an appropriate fluorochrome-matched isotype controls. To assess the statistical reliability of MC counts obtained by flow cytometry, it is important to acquire a minimum number of events corresponding to MC. As MCs

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are usually rare events, the authors recommend acquiring and storing information on at least 100 events corresponding to MCs. For that purpose, the total number of events to be acquired may vary between 0.3  106 and 3.5  106, depending on the proportion of MC present in the sample. With this approach, the coefficient of variation for replicates in the authors’ experience is always lower than 10%.

IV. Results A. Identification, Enumeration, and Characterization of BM MC by Flow Cytometry MCs strongly express CD117, CD203c, and the high affinity receptor for immunoglobulin E (FceRI) (reviewed in references Escribano et al., 2002, 2004, 2006; Hauswirth et al., 2008). Nevertheless, none of them is a specific marker for MC; for instance, CD117 is also detected on a major fraction of hematopoietic precursor cells (HPC), CD56+bright NK cells, and neoplastic cells from patients diagnosed with monoclonal gammopathies, acute leukemias, and myelodysplastic syndromes (MDS), among other hematologic malignancies, and in cells derived from other nonhematopoietic tissues (reviewed in reference Escribano et al., 1998a); CD203c and FceRI are also expressed on basophils (Gane et al., 1995; Valent and Bettelheim, 1992; Valent et al., 1990). In early studies, the identification of MC was based on their strong reactivity for CD117 in the absence of expression for CD38, CD34, and CD138, to discriminate MC from CD34 HPC and CD117+ plasma cells; in turn, coexpression of FceRI and CD33 together with strong positivity for CD117 allows discrimination between MC and basophils (Orfao et al., 1996). More recently, it has been suggested that the use of a CD117high/CD45low combination could accurately identify BM MC (Escribano et al., 2004; Teodosio et al., 2010). MCs are present at low frequencies in both normal and SM BM samples, usually representing less than 0.2% of all BM nucleated cells (Escribano et al., 1998c, 1998d; Orfao et al., 1996). Despite the fact that in systemic MC disorders (e.g., SM) these values are significantly increased, low and overlapping frequencies are still found on these patients, compared to normal BM [0.21  0.27% (range: 0.001–1.7%) vs. 0.02  0.02% (range: 0.001–0.09%), respectively] (Escribano et al., 2006; Orfao et al., 1996). Furthermore, it is also known that increased BM MC can also be detected in reactive BM [0.087  0.12% (range: 0.0021–0.54%)] (Escribano et al., 2006) or in patients with other hematological disorders like Waldenstr€ om macroglobulinemia [0.095  0.11% (range: 0.01–0.47%)] or MDS [0.099  0.12% (range: 0.002–0.47%)] (Escribano et al., 2006).

B. Immunophenotypic Features of Normal BM MC MCs arise from pluripotent BM-derived hematopoietic precursors (Kirshenbaum et al., 1991) and only become fully differentiated in those tissues where they home at the host’s interfaces with the environment and play a key role in the

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recognition of pathogens or other signs of infection (Abraham and St John, 2010). It is well known that MC display distinct immunophenotypical features depending on their stage of maturation, tissue microenvironment, and activation status (Schernthaner et al., 2001), making it essential to know the normal MC immunophenotypic profile in each situation, so as to allow for the correct interpretation of the distinct immunophenotypic profiles that can be detected in MC-related disorders.

1. BM MC-Committed Precursors and Normal MC Maturation Patterns MCs are hematopoietic cells that derive from hematopoietic stem cell precursors (Agis et al., 1993, 1996). Using in vitro MC-differentiation models it has been shown that human MC arise from CD34+ progenitor cells (Kirshenbaum et al., 1991) that are CD34+, CD117+, CD14, CD17 (Agis et al., 1993, 1996), FceRI (Rottem et al., 1994), CD38 often positive, HLA-DR often negative (Kempuraj et al., 1999), and CD13+ (Kirshenbaum et al., 1999). More recent, ex vivo studies using BM from both healthy controls and patients with MDS, suggest that MC-committed precursors may be identified, within the BM CD34+ HPC compartment, as being CD117hi/HLA-DR/int (Matarraz et al., 2008). Nevertheless, these precursors are extremely infrequent in normal BM [0.000  0.005% (range: 0–0.02%)] (Matarraz et al., 2008) and, consequently, our knowledge about the normal pattern of MC maturation is mainly based on in vitro differentiated MC (Dahl et al., 2004; Schernthaner et al., 2005; Tedla et al., 2008; Yokoi et al., 2006). However, this methodology has several drawbacks, since it is known that the MC immunophenotype, morphology, and function vary depending on (i) the source of the CD34+ HPC (Iida et al., 2001; To et al., 1994), (ii) the culture medium used (Kambe et al., 2000), and/or (iii) the culture conditions (Dahl et al., 2002); such factors may contribute to explain some of the contradictory results reported in the literature (Dahl et al., 2004; Schernthaner et al., 2005). More recently, it has been reported that both immature MC-committed progenitors and mature MC express CD117, CD58, CD63, CD147, CD151, CD203c, and CD172a independently of the growth factors used, but that the expression of IL-3Ra (CD123) and the granulocyte-macrophage colony-stimulating factor receptor (GM-CSFR; CD116) is restricted to early MC precursors (Schernthaner et al., 2005). General consensus exists in the literature, which indicates that proteins typically associated with MC, like the FceRI, chymase, tryptase, and histamine, are only expressed at late stages of the maturation process while absent in the CD34+ MC precursors (Rottem et al., 1994; Shimizu et al., 2002, 2004; Tedla et al., 2008). The exception seems to be tryptase that, based on unpublished observations of our group from ex vivo studies, may be dimly expressed in a small proportion of CD34+ MC committed precursors.

2. Immunophenotype of Mature Resting Normal and Reactive BM MC Normal MC display high light scatter and autofluorescence levels and they express a broad set of proteins (Table VI), including myeloid-associated antigens

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[(Fig._1)TD$IG]

Fig. 1 Schematic representation of the phenotypic changes that occur during normal MC differentiation (Escribano et al., 2001; Matarraz et al., 2008; Rottem et al., 1994; Schernthaner et al., 2005) and activation of mature MC (Escribano et al., 2001; Galli et al., 2005; Henz et al., 2001; Marshall, 2004) and their correlation with the different immunophenotypic profiles of BM MC observed among patients with different subtypes of SM (Teodosio et al., 2010). (See plate no. 12 in the color plate section.)

(e.g., CD33), proteins related with the initiation of the MC inflammatory response (e.g., FceRI) (Escribano et al., 1998d; Orfao et al., 1996), or MC proteases [e.g., cytoplasmic carboxypeptidase (CyCPA) and cytoplasmic (total) tryptase (CyB12)]; they display relatively high amounts of cytoplasmic immature tryptase (high CyB12/ CyG5 ratio) (Teodosio et al., 2010). Other proteins, reported as present in the majority of normal/reactive MC, include bcl2 (94%) and cytoplasmic mature tryptase (G5) (75%) (Teodosio et al., 2010) or they are only detected in a restricted number of cases, like the IgG Fc receptors CD16 or CD64 (Escribano et al., 1998c, 1998d, 2006; Orfao et al., 1996). Interestingly, reactivity for these later two proteins, like for HLA-DR and HLA-DQ, has been described to be dim and/or restricted to a fraction of the whole MC population (Teodosio et al., 2010). Furthermore, normal/ reactive BM MC constantly lack the expression of several other proteins, including some that are relevant for the diagnosis of SM, such as CD2 and CD25 (Escribano et al., 1998c, 1998d; Orfao et al., 1996). The complement receptor 1 – CD35 – has been described to be absent in normal BM MC, but expressed in reactive BM and in BM MC from 80% of all MDS cases; in turn, CD63, despite being constitutively expressed on BM MC, is overexpressed in MDS when compared with MC from both healthy controls and reactive BM (Escribano et al., 2001).

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Table VI Qualitativeandsemiquantitativeantigenexpressionprofilesofbonemarrowmastcellsfrom controls and patients with mastocytosis, as analyzed by multiparameter flow cytometry Functional group

Fc receptors

Molecules associated with antigen presentation

Integrins

Tetraspanins and other associated proteins

Cell adhesion molecules

Proteases

Cytokine receptors

Protein

Phenotypic profiles Controls

Systemic mastocytosis

CD16 CD23 CD32 CD64 FceRI CD1a

/+(13%)  + /+(5%) + 

/+ (69%)

HLA-I HLA-DR HLA-DQ CD11a CD11b CD11c CD18 CD29 CD41a CD49d CD49e CD51 CD54 CD9

+ /+(25%) /+(13%) /+ (20%) /+ (50%) /+ (71%) /+ (65%) +  + + + /+ (75%) +

+ /+ (85%) /+ (58%)  /+ (50%) +/++ /+ (44%) + /+ (45%) + (80%) /+ (30%) /+ (45%) ++ (100%) +

CD37 CD53 CD81 CD82 CD151 CD2 CD6 CD15 CD22 CD33 CD34 CD44 CD61 CD66b CD146 CD13 Carboxypeptidase A3 (CPA) Total tryptase (B12) Mature tryptase (G5) CD25

 + + + +    /+ (50%) +  + /+ (66%)  + /+ (33%) + + /+ (75%) 

a

a

+/++ /+ (84%) +b a

a a a a

/+ (72%)   /+ (96%) +/++  + /+ (22%) a a

/+ (75%) +/++b +/++b +/++ /+ (93%) (Continued)

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Table VI

(Continued)

Functional group

Complement-associated molecules

Tumor necrosis factor receptor family Immune response associated markers

Ectoenzymes Activation markers

Other markers

Protein

Phenotypic profiles Controls

Systemic mastocytosis

CD117 CD123 CD124 CD21

+  +/ 

+b /+ (73%)

CD35 CD59 CD88 CD30

 + /+ (18%) 

+ +/++ /+ (54%)

CD40 CD3

/+ (65%) 

/+ (65%)

CD4 CD5 CD8 CD14 CD19 CD20 CD43 CD38 CD157 CD63 CD69 CD203c CD10 CD42b CD45 CD65 CD71 CD116 CD138 CD147 Cybcl2

/+ (60%)      +  + + /+ (88%) +   +  +   + /+ (94%)

/+ (60%)

a a

a

a

a

    +  a

+/++ +/++ +/++  /+ (45%) + a

/+ (38%)

a

 a

/+ (87%)

/+, expressed in a subset of patients (percentage of positive cases); +, expressed in 100% of the cases analyzed; , absent in all cases analyzed; ++, increased expression compared to normal/reactive BM MC. (Source: Bodni et al., 2003; Diaz-Agustin et al., 1999; Escribano et al., 1995, 1997, 1998b, 1998c, 1998d, 1999, 2001, 2002, 2006; Nun˜ez et al., 2002; Orfao et al., 1996; Pardanani et al., 2004; Teodosio et al., 2010; Valent et al., 2001b. a Expression has not been systematically described for BM MC from SM. b In some SM patients, expression is decreased compared to normal/reactive BM MC.

3. Activated MC Immunophenotype Mast cells undergo the final stages of their differentiation/maturation after the migration of their precursors into those vascularized tissues or serosal cavities in which they ultimately home (Galli et al., 2005). Mature tissue MC are

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heterogeneous, and specific subpopulations with variable mediator profiles and different functional properties are observed in distinct tissues (Marshall, 2004) where they have an important role in the recognition of pathogens or other signs of infection (Abraham and St John, 2010). A variety of stimuli can activate MC to release a diverse array of biologically active products (reviewed in reference Galli et al., 2005) and MC activation by IgE-dependent stimuli or other agonists is associated with significant changes in their antigen expression profiles. These antigens include b2-integrins, cytokine receptors, complement receptors, and members of the tetraspanin adhesion molecules (Valent et al., 2001b), such as the CD63 lysosomal glycoprotein that is constitutively expressed on MC, and upregulated by IgE receptor crosslinking (Furuno et al., 1996; Valent et al., 2001b). Likewise, both the ectonucleotide pyrophosphatase/phosphodiesterase 3 (CD203c) and the CD69 activation linked cell surface antigen are upregulated upon IgE activation (Agis et al., 1996; B€ uhring et al., 1999; Ghannadan et al., 1998; Valent et al., 2001b). Other proteins, such as MHC class II, are not expressed on resting MC, but they are upregulated on MC isolated from pathogen infected tissues and/or tissues stimulated by tumor necrosis factor (TNFa), INFg, or bacterial lipopolysaccharide (LPS), supporting a role for MC in adaptative immune responses, through antigen presentation to T cells (Frandji et al., 1993; Henz et al., 2001; Wong et al., 1982). Although MC express multiple direct receptors for pathogens and their products, the indirect activation of MC during infection allows them to respond to a wider range of microorganisms (Marshall, 2004). As referred above, MC constitutively express the high-affinity IgE receptor – FceRIand the IgG receptor Fcg RII – CD32. Conversely, expression for Fcg RI (CD64) is only induced after exposure to INFg (Woolhiser et al., 2001). Fc-receptor-mediated activation of MC generally leads to production of lipid mediators, various cytokines and chemokines, and degranulation, reflected by a decreased cytoplasmic content on MC proteases (e.g., tryptase) (Marshall, 2004). C. Immunophenotypic Characteristics of BM MC in Systemic Mastocytosis Early studies on the immunophenotypic features of BM MC in SM reported several aberrant phenotypes that allow a clear discrimination from normal and reactive MC (Bodni et al., 2003; Escribano et al., 1995, 1997, 1998c, 1999, 2001, 2002; Pardanani et al., 2004) (Table VI). These early studies reported aberrant expression for CD2 and CD25 as a hallmark for SM, since both these proteins were absent in normal/reactive BM MC, but expressed in the great majority of SM patients (Escribano et al., 1995, 1997, 1998c). From these two markers, CD25 has been described to be the most sensitive, specific, and stable while positivity for CD2 has been described to vary depending on the sensitivity of the fluorochrome-conjugated mAb used in the study (Escribano et al., 2004). Apart from the aberrant expression of CD2 and CD25, BM MC from SM patients also display abnormally high expression of CD33 (Escribano et al., 1999, 2001) and

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of the CD2 ligand – CD58 – (Escribano et al., 2002). Increased expression for activation markers such as CD69 (Diaz-Agustin et al., 1999) or CD63 (Escribano et al., 1998b), and complement-associated molecules, such as CD11c, CD35 (Escribano et al., 1998b), CD59, and/or CD88 (Nu´n˜ez et al., 2003) are also commonly found in BM MC from SM. In contrast, expression of CD117 (Escribano et al., 2002), the CD71 transferrin receptor and CD29 integrin is abnormally downregulated on BM MC from these patients (Escribano et al., 1998c) (Table VI). Despite the fact that SM is a group of heterogeneous diseases regarding both their clinical behavior and prognosis (Akin et al., 2004a, 2004b; Alvarez-Twose et al., 2009, 2010; Bonadonna et al., 2009; Escribano et al., 2009; Lim et al., 2009; Valent et al., 2001a, 2007), only recently the immunophenotypic profiles of the distinct subtypes of SM were studied individually (Teodosio et al., 2010); in such analysis, three different patterns were identified, which correlate with distinct prognostic and molecular subtypes of SM at the same time they reflect the maturation status of clonal MC (Teodosio et al., 2010) (Fig. 1).

1. Good-Prognosis SM: Indolent Systemic Mastocytosis (ISM) with or without Skin Lesions The most frequently detected immunophenotypic profile of BM MC in ISM is compatible with an activated MC immunophenotype, both in patients that show (s+) or do not show (s) skin lesions (Alvarez-Twose et al., 2010), usually associated with a good prognosis (Escribano et al., 2009). Mast cells from these patients show a mature phenotype (CD34, CD117hi, FceRIhi) associated with the typical (CD2þ CD25þ) aberrant SM phenotype. (Teodosio et al., 2010). Furthermore, an activated phenotype with increased expression of CD63, CD69, and CD203c proteins, associated with expression of both CD64 and MHC class II molecules – HLA-DR and HLA-DQ – is detected in these patients (Fig. 2). Such aberrant overexpression of some proteins could be related, at least in part, to the constitutive activation induced by the D816V KIT mutation, which is virtually carried by all these SM patients (Garcia-Montero et al., 2006; Teodosio et al., 2010). Interestingly, the BM MC burden can be extremely low among ISM patients [0.09% (range: 0.0006–0.5%) on ISMs and 0.1% (range: 0.003–1.47%) on ISMs+ cases], and the observation of coexisting normal and pathological MCs in the same patient is a relatively frequent finding (33% of ISMs patients and 18% of ISMs+ cases) (Alvarez-Twose et al., 2009). Furthermore, aberrant MC can be as few as only 20% of all BM MC (personal observation), which raises the need for the investigation of large numbers of cells in order to avoid false negative results.

2. Well-Differentiated Systemic Mastocytosis In contrast to ISMs+ and ISMs, WDSM patients do not show an aberrant CD2+/ CD25+ phenotype, since WDSM BM MC are typically CD2/CD25, except for few cases that are CD2+/CD25 or CD2/CD25lo (Akin et al., 2004a, 2004b; Teodosio et al., 2010). As these patients typically do not carry the D816V KIT

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[(Fig._2)TD$IG]

Fig. 2

Representative bivariate dot plots illustrating the light scatter (panel A) and immunophenotypical characteristics (panels C–L) of BM MC from healthy donors (black dots) and BM MC from patients with ISM (blue dots in column I), WDSM (green dots in column II), and ASM (red dots in column III) identified on the basis of their strong reactivity for CD117 and positivity for CD45 (panel B). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this chapter.)

mutation (Garcia-Montero et al., 2006) and BM MC display a mature (CD34, CD117hi, FceRIhi) resting immunophenotype (Figs. 1 and 2) in the absence of other activation-associated aberrant phenotypes typically described for SM (Teodosio et al., 2010), it could be speculated that this could be related to lack of D816V activating KIT mutation. In fact, BM MC from these cases only show a few immunophenotypic changes versus normal/reactive BM MC, consisting of overexpression of bcl2, CPA, or total tryptase (Teodosio et al., 2010), in association with increased light scatter and autofluorescence features (Fig. 2). The lack of a welldefined aberrant phenotype, as clear as that consisting on CD25 expression in ISM patients, and the fact that their pattern of antigen expression resembles that of normal

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BM MC, leads to a lower sensitivity in the detection of WDSM cases by flow cytomery (Teodosio et al., 2010), in fact, diagnosis of WDSM frequently requires confirmation of the clonal nature of BM MC by molecular techniques such as the HUMARA X-chromosome inactivation assay or the detection of KIT mutations other than D816V (Akin et al., 2004b; Garcia-Montero et al., 2006), together with a careful clinical, cytological, and histological evaluation of the patient.

3. Poor-Prognosis Systemic Mastocytosis: Aggressive Systemic Mastocytosis and Mast Cell Leukemia In contrast to the other groups of patients, poor-prognosis SM patients typically show a CD2/CD25+ phenotype, associated with decreased expression of antigens acquired during MC maturation/differentiation (e.g., CD117, FceRI, and/or HLA-I), increased positivity for antigens present at early stages of MC differentiation (e.g., CD123, HLA-DR, and/or HLA-DQ) and low levels of cytoplasmic tryptase and CPA together with low light scatter features (Teodosio et al., 2010) (Figs. 1 and 2). This antigen profile reflects a more immature phenotype (Escribano et al., 2006; Matarraz et al., 2008; Rottem et al., 1994; Schernthaner et al., 2005; Teodosio et al., 2010) (Fig. 1), which could be explained on the basis of the degree of clonal hematopoiesis and the potential concurrence of molecular changes other than the D816V KIT mutation, since, in contrast to ISM patients – who usually carry the D816V KIT mutation restricted to MC compartment – these patients typically display multilineage involvement (Garcia-Montero et al., 2006; Teodosio et al., 2010).

V. Immunophenotypic Analysis of MC from Biological Specimens Other than BM Identification of MC in tissues other than BM may be required in specific cases in order to more precisely define the subtype of the disease or to evaluate the presence of MC infiltration in other tissues. The same approach explained above can be applied and adapted to a wide range of specimens, including peripheral blood, adenoids, and ascitic fluid, among others.

A. Identification of Circulating MC in Peripheral Blood The same approach used for the analysis of BM MC can be applied for the analysis of the presence of circulating MC in PB. Nevertheless, counterstaining with CD203c or FceRI and CD25 is highly recommended in such cases, in order to detect the presence of circulating mature pathological MC. In the authors’ experience, demonstration of circulating MC is associated with aggressive subtypes of the disease, supporting the need for further studies in which this test is performed in the followup of mastocytosis patients, to confirm our preliminary findings.

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Regarding the specific technical approaches used to study MC in PB samples, it is recommended to stain
300 mL of sample per tube with appropriate saturating amounts of mAb and to systematically stain tubes in triplicates in order to increase the sensitivity of the technique. B. Identification of MC in Organic Fluids or Tissues In the case of body fluids samples other than PB or BM, for example, ascitic fluid or pleural effusions, a first step of centrifugation is frequently necessary to concentrate the sample. The cell pellet must then be resuspended in a relatively smaller volume (1 mL) of PBS/tube. Before staining with the appropriate mAb, assess the nucleated cell count of the sample and follow the same protocol as described for BM. A modified protocol can be used for the identification of MC in solid tissues such as lymph nodes and tonsils. In order to obtain an appropriate single cell suspension for flow cytometric analysis, the fragment of fresh tissue must be cut into small (1–2 mm3) pieces by using surgical blades and tweezers. Although enzymatic disaggregation procedures can be applied, mechanical disaggregation is preferred, especially in lymphoid tissues, since the later procedures typically yield high number of single cells in a few minutes. After assessing the nucleated cell count, stain samples as described for BM aspirate specimens. Time is critical for the enumeration of MCs from ascitic fluid as well as from lymph nodes, for which immunophenotypic studies of MC should be performed immediately after the drainage of ascitic fluid or the lymph node biopsy. Samples obtained from tonsils or other solid tissues could be considered as adequate for the identification and immunophenotypical characterization of MC but probably not for their enumeration. However, it should be noted that at present, information on this subject is lacking in the literature.

VI. Medical Indications Flow cytometry immunophenotyping has demonstrated to have the highest sensitivity and specificity for the identification of phenotypically aberrant MC in BM mainly when they are present at very low frequencies. As a consequence, flow cytometry allows the diagnosis of BM involvement in SM even at very early stages of the disease in patients who still show normal serum tryptase values and in the absence of BM MC aggregates. In addition, combined use of flow cytometry-based cell sorting and molecular probes applied to purified BM MC and other hematopoietic cell lineages, allows to specifically identify, enumerate and sort aberrant MC on the basis of the expression of CD25 and investigate the presence of KIT mutations in multiple different BM cell populations. This contributes to establish the long-term prognosis of SM patients, even before an overt ISM develops; such information is of major interest for patients and doctors and can help in deciding specific follow-up measures adapted to individual patients as well as to take decisions related to their treatment.

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According to the REMA’s recommendations (Escribano et al., 2004), flow cytometry immunophenotyping of MC should be applied in the following situations: (i) all adult patients with mastocytosis or suspected of having mastocytosis at diagnosis, (ii) all mastocytosis cases suspected of having involvement of PB and/or other tissues by pathological MC, (iii) the analysis of potential changes occurring during follow-up of adult mastocytosis patients in whom disease progression is suspected due to significant changes in the total blood cell counts and/or the leukocyte differential, appearance of PB dysplasia, presence of circulating MC, increase in serum tryptase levels, or the size of the liver and/or the spleen, (iv) pediatric mastocytosis cases when the disease persists without significant regression after puberty or, before it, if changes suggesting progression into an adult form develop (i.e., increase in serum tryptase levels and/or organomegalies), (v) in the follow-up of minimal residual disease in patients undergoing cytoreductive or targeted therapies, and (vi) in patients who have an incidental finding of mastocytosis with no clinical symptoms of systemic disease, if they have an associated hematological non-mast cell lineage disease. In addition, analysis of large series of patients with mastocytosis allowed the identification of three different maturation-related immunophenotypic profiles of BM MC that are associated with both genetic markers of the disease and its clinical behavior (Escribano et al., 2004; Teodosio et al., 2010) with relevant prognostic implications: aggressive forms of the disease correlate with an immature phenotype and clonal involvement of all myeloid lineages by the D816V KIT mutation; in turn, a mature activated versus resting BM MC immunophenotype is commonly found among patients with good-prognosis subtypes of SM depending on whether they carry (indolent SM) or not (WDSM) the D816V KIT mutation.

VII. Summary Multiparameter flow cytometry immunophenotyping has revealed as a very powerful tool for the analysis of rare cell populations as it is the case of BM MC in SM. During the last decades, many advances in the field of this disease have increased our knowledge about the most sensitive and specific criteria to accurate diagnose SM patients. In this study, we provide the guidelines to perform BM MC immunophenotyping with a detailed protocol, including a recommended mAb panel for four and eightcolor flow cytometer users. Furthermore, information is also provided about normal BM MC precursor maturation profiles and the three different immunophenotypic patterns associated with different subtypes of the disease and long-term prognosis. Acknowledgments This work was supported by grants from the Fondo de Investigaciones Sanitarias (FIS) of the Ministerio de Ciencia e Innovacio´n of Spain (PS09/00032 and RETICS RD06/0020/0035-FEDER); Junta de

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anchez-Muñoz et al. Comunidades de Castilla La Mancha (FISCAM 2007/36, FISCAM 2008/46); Junta de Castilla y Leo´n (Grant SAN1778/2009, Ayuda al Grupo GR37 de Excelencia de Castilla y Leo´n); C.T. was supported by a grant from the Funda¸ca~o para a Ci^ encia e Tecnologia (FCT) of Portugal (SFRH/BD/17545/2004) and by a grant from the Fondo de Investigaciones Sanitarias (FIS) of the Ministerio de Ciencia e Innovacio´n of Spain (PI08/90881).

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376

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Volume 55 (1997) Laser Tweezers in Cell Biology Edited by Michael P. Sheetz Volume 56 (1998) Video Microscopy Edited by Greenfield Sluder and David E. Wolf Volume 57 (1998) Animal Cell Culture Methods Edited by Jennie P. Mather and David Barnes Volume 58 (1998) Green Fluorescent Protein Edited by Kevin F. Sullivan and Steve A. Kay Volume 59 (1998) The Zebrafish: Biology Edited by H. William Detrich III, Monte Westerfield, and Leonard I. Zon Volume 60 (1998) The Zebrafish: Genetics and Genomics Edited by H. William Detrich III, Monte Westerfield, and Leonard I. Zon Volume 61 (1998) Mitosis and Meiosis Edited by Conly L. Rieder Volume 62 (1999) Tetrahymena thermophila Edited by David J. Asai and James D. Forney Volume 63 (2000) Cytometry, Third Edition, Part A Edited by Zbigniew Darzynkiewicz, J. Paul Robinson, and Harry Crissman Volume 64 (2000) Cytometry, Third Edition, Part B Edited by Zbigniew Darzynkiewicz, J. Paul Robinson, and Harry Crissman Volume 65 (2001) Mitochondria Edited by Liza A. Pon and Eric A. Schon Volume 66 (2001) Apoptosis Edited by Lawrence M. Schwartz and Jonathan D. Ashwell Volume 67 (2001) Centrosomes and Spindle Pole Bodies Edited by Robert E. Palazzo and Trisha N. Davis

377

Volumes in Series

Volume 68 (2002) Atomic Force Microscopy in Cell Biology Edited by Bhanu P. Jena and J. K. Heinrich H€orber Volume 69 (2002) Methods in Cell–Matrix Adhesion Edited by Josephine C. Adams Volume 70 (2002) Cell Biological Applications of Confocal Microscopy Edited by Brian Matsumoto Volume 71 (2003) Neurons: Methods and Applications for Cell Biologist Edited by Peter J. Hollenbeck and James R. Bamburg Volume 72 (2003) Digital Microscopy: A Second Edition of Video Microscopy Edited by Greenfield Sluder and David E. Wolf Volume 73 (2003) Cumulative Index Volume 74 (2004) Development of Sea Urchins, Ascidians, and Other Invertebrate Deuterostomes: Experimental Approaches Edited by Charles A. Ettensohn, Gary M. Wessel, and Gregory A. Wray Volume 75 (2004) Cytometry, 4th Edition: New Developments Edited by Zbigniew Darzynkiewicz, Mario Roederer, and Hans Tanke Volume 76 (2004) The Zebrafish: Cellular and Developmental Biology Edited by H. William Detrich, III, Monte Westerfield, and Leonard I. Zon Volume 77 (2004) The Zebrafish: Genetics, Genomics, and Informatics Edited by William H. Detrich, III, Monte Westerfield, and Leonard I. Zon Volume 78 (2004) Intermediate Filament Cytoskeleton Edited by M. Bishr Omary and Pierre A. Coulombe Volume 79 (2007) Cellular Electron Microscopy Edited by J. Richard McIntosh Volume 80 (2007) Mitochondria, 2nd Edition Edited by Liza A. Pon and Eric A. Schon

378

Volumes in Series

Volume 81 (2007) Digital Microscopy, 3rd Edition Edited by Greenfield Sluder and David E. Wolf Volume 82 (2007) Laser Manipulation of Cells and Tissues Edited by Michael W. Berns and Karl Otto Greulich Volume 83 (2007) Cell Mechanics Edited by Yu-Li Wang and Dennis E. Discher Volume 84 (2007) Biophysical Tools for Biologists, Volume One: In Vitro Techniques Edited by John J. Correia and H. William Detrich, III Volume 85 (2008) Fluorescent Proteins Edited by Kevin F. Sullivan Volume 86 (2008) Stem Cell Culture Edited by Dr. Jennie P. Mather Volume 87 (2008) Avian Embryology, 2nd Edition Edited by Dr. Marianne Bronner-Fraser Volume 88 (2008) Introduction to Electron Microscopy for Biologists Edited by Prof. Terence D. Allen Volume 89 (2008) Biophysical Tools for Biologists, Volume Two: In Vivo Techniques Edited by Dr. John J. Correia and Dr. H. William Detrich, III Volume 90 (2008) Methods in Nano Cell Biology Edited by Bhanu P. Jena Volume 91 (2009) Cilia: Structure and Motility Edited by Stephen M. King and Gregory J. Pazour Volume 92 (2009) Cilia: Motors and Regulation Edited by Stephen M. King and Gregory J. Pazour Volume 93 (2009) Cilia: Model Organisms and Intraflagellar Transport Edited by Stephen M. King and Gregory J. Pazour

379

Volumes in Series

Volume 94 (2009) Primary Cilia Edited by Roger D. Sloboda Volume 95 (2010) Microtubules, in vitro Edited by Leslie Wilson and John J. Correia Volume 96 (2010) Electron Microscopy of Model Systems Edited by Thomas M€ ueller-Reichert Volume 97 (2010) Microtubules: In Vivo Edited by Lynne Cassimeris and Phong Tran Volume 98 (2010) Nuclear Mechanics & Genome Regulation Edited by G.V. Shivashankar Volume 99 (2010) Calcium in Living Cells Edited by Michael Whitaker Volume 100 (2010) The Zebrafish: Cellular and Developmental Biology, Part A Edited by: H. William Detrich III, Monte Westerfield and Leonard I. Zon Volume 101 (2011) The Zebrafish: Cellular and Developmental Biology, Part B Edited by: H. William Detrich III, Monte Westerfield and Leonard I. Zon Volume 102 (2011) Recent Advances in Cytometry, Part A: Instrumentation, Methods Edited by Zbigniew Darzynkiewicz, Elena Holden, Alberto Orfao, William Telford and Donald Wlodkowic

[(Plate_1)TD$FIG]

Plate 1

(Chapter 4, Fig. 9 on page 87 of this volume).[(Plate_1)TD$FIG]

Plate 2

(Chapter 6, Fig. 6 on page 128 of this volume).

[(Plate_1)TD$FIG]

Plate 3

(Chapter 6, Fig. 10 on page 136 of this volume).

[(Plate_1)TD$FIG]

Plate 4

(Chapter 7, Fig. 1 on page 151 of this volume).[(Plate_1)TD$FIG]

Plate 5

(Chapter 7, Fig. 3 on page 156 of this volume).

[(Plate_1)TD$FIG]

Plate 6

(Chapter 7, Fig. 8 on page 169 of this volume).[(Plate_1)TD$FIG]

Plate 7

(Chapter 9, Fig. 3 on page 211 of this volume).[(Plate_1)TD$FIG]

Plate 8

(Chapter 10, Fig. 4 on page 226 of this volume).[(Plate_1)TD$FIG]

Plate 9

(Chapter 10, Fig. 9 on page 235 of this volume).[(Plate_1)TD$FIG]

Plate 10

(Chapter 10, Fig. 25 on page 253 of this volume).[(Plate_1)TD$FIG]

Plate 11

(Chapter 12, Fig. 1 on page 289 of this volume).

Plate 12

(Chapter 14, Fig. 1 on page 345 of this volume).

[(Plate_1)TD$FIG]