Flow Cytometry in Drug Discovery and Development E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 631

Veröffentlichungsjahr: 2011

Beschreibung

This book covers the unique application of flow cytometry in drug discovery and development. The first section includes two introductory chapters, one on flow cytometry and one on biomarkers, as well as a chapter on recent advances in flow cytometry. The second section focuses on the unique challenges and added benefits associated with the use of flow cytometry in the drug development process. The third section contains a single chapter presenting an in depth discussion of validation considerations and regulatory compliance issues associated with drug development.

Leseprobe

Contents

Cover

Title Page

Copyright

Dedication

Preface

Foreword

Acknowledgments

Contributors

Part I: Introduction

Chapter 1: Introduction to Flow Cytometry

1.1 Introduction

1.2 Basic Principles of How a Flow Cytometer Works

1.3 Fluidics

1.4 Optics

1.5 Types and Choice of Fluorochromes

1.6 Compensation

1.7 Threshold and Gates

1.8 Analysis

1.9 Sorting

1.10 Conclusion

References

Chapter 2: Recent Advances in Flow Cytometry: Platforms, Tools, and Challenges for Data Analysis

2.1 Introduction and Overview

2.2 Current Instrumentation: Advances in Flexibility, Performance, and Throughput

2.3 Advances in Operating Principles: Object Manipulation, Separation, Analysis, and Light Sources

2.4 Advances in Platform Formats: Microflow and New Light Sources

2.5 Reagents and Applications

2.6 Facing the Challenge of High-Throughput Flow Cytometry Data Analysis

References

Chapter 3: Introduction to Biomarkers

3.1 Introduction

3.2 Types of Biomarkers

3.3 Technology Platforms

3.4 Biomarkers in Discovery

3.5 Analytical Validation of Biomarker Assays

3.6 Biomarker Qualification

3.7 Biomarker in Clinical Development

3.8 Conclusions

References

Part II: Flow Cytometry in the Drug Development Process

Chapter 4: HTS Flow Cytometry, Small-Molecule Discovery, and the NIH Molecular Libraries Initiative

4.1 Introduction and Overview

4.2 NIH as an Engine in Discovery Research

4.3 The UNMCMD

4.4 Plate-Based Flow Cytometry

4.5 Operational Issues

4.6 Future Opportunities

Acknowledgments

References

Chapter 5: A Multiparameter Approach to Cell Cycle Analysis as a Standard Tool in Oncology Drug Discovery

5.1 Introduction

5.2 Cell Cycle Profile Components

5.3 Specific Activities Evaluated

5.4 Experimental Examples

5.5 Conclusions

Acknowledgment

References

Chapter 6: Flow Cytometry in Preclinical Toxicology/Safety Assessment

6.1 Introduction

6.2 Flow Cytometry and Biomarkers

6.3 Cell Sorting and High Content Analysis

6.4 Challenges

6.5 In Vitro Oxidative Stress Assay

6.6 Developing a Biomarker-Based Assay for Drug-Induced Vascular Damage

6.7 Mouse Bone Marrow Assessment

6.8 Summary

Acknowledgments

References

Chapter 7: Use of Flow Cytometry to Study Drug Target Inhibition in Laboratory Animals and in Early-Phase Clinical Trials

7.1 Overview

7.2 Stages in the Development of In Vivo Testing for Novel Agents

7.3 Other Targets and Applications

7.4 Future Prospects

References

Chapter 8: CD4 T Cell Assessments in Evaluation of HIV Therapeutics

8.1 Introduction

8.2 Historical Background of the Human Immunodeficiency Virus

8.3 First AZT Clinical Trials

8.4 Importance of CD4 Monitoring in Antiretroviral Therapy

8.5 Evolution of CD4 Testing and Methodology

8.6 Future of CD4 T Cell Assessment Methodologies and Technologies

References

Chapter 9: Monitoring the Cellular Components of the Immune System During Clinical Trials: A Translational Medicine Approach

9.1 Translational Medicine in Drug Development

9.2 Basic Research: Cellular Components of the Immune System

9.3 Clinical Research: Characterization of Abnormalities of the Cellular Components of the Immune System

9.4 Clinical Drug Development I: Monitoring the Cellular Components of the Immune System to Establish the Effect of Therapeutic Intervention

9.5 Clinical Drug Development II: Risk/Benefit of Monitoring Cellular Components of the Immune System During Clinical Trials

9.6 Summary

Abbreviations

9.8 Acknowledgment

References

Chapter 10: Immunogenicity Testing Using Flow Cytometry

10.1 Introduction

10.2 Selection of Assay Reagents

10.3 Assay Optimization, Development, and Validation

10.4 Clinical Application

10.5 Summary

Acknowledgments

References

Chapter 11: Pharmacokinetics by Flow Cytometry: Recommendations for Development and Validation of Flow Cytometric Method for Pharmacokinetic Studies

11.1 Introduction

11.2 Assay Development

11.3 Assay Validation

11.4 Conclusions and discussion

References

Part III: Validation and Regulatory Compliance

Chapter 12: Regulatory Compliance and Method Validation

12.1 Introduction

12.2 Regulatory Compliance

12.3 Method Development and Validation for Drug Development

12.4 Summary

References

Chapter 13: Instrument Validation for Regulated Studies

13.1 Introduction

13.2 Attributes of CLIA, GLP, and GCP Instrument Validations

13.3 Hybrid Solutions

13.4 Feeding and Care of Your Validated System: Change Control

13.5 In Closing: Validation Means Validation

References

Part IV: Future Directions

Chapter 14: Probability State Modeling: A New Paradigm for Cytometric Analysis

14.1 Introduction

14.2 Parametric Equations

14.3 Showing Coordinated Expressions

14.4 Equal Probability States

14.5 Time-Dependent Progression

14.6 Metainformation

14.7 Traditional Gating Strategies and Metainformation

14.8 Decreasing Need for Metainformation

14.9 Optimal Panel Design

14.10 The Music Analogy

14.11 The Future of Cytometric Analysis

14.12 Back to Parametric Equations

References

Chapter 15: Phospho Flow Cytometry: Single-Cell Signaling Networks in Next-Generation Drug Discovery and Patient Stratification

15.1 Introduction

15.2 Insight Gained Through Multiparameter Measurements

15.3 Understanding Disease Mechanism: Autoimmunity as Case Study

15.4 High-Throughput Screening

15.5 Secondary Screening and Preclinical Testing

15.6 Patient Stratification and Diagnostics

15.7 Next-Generation Cytometry

15.8 Summary

References

Color Plates

Index

Copyright © 2011 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey

Published simultaneously in Canada

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ISBN: 978-0-470-43356-0

Dedication

Philip Marder (1948–2010)

This book is dedicated to my dear friend and coeditor, Philip Marder.

Phil was one of the early leaders in the use of flow cytometry pertaining to drug discovery and development. He participated in ISAC, GLIFCA, Indy Flow, and AAPS, and was a key contributor to both the flow cytometry and the Pharma communities. In 1973, he joined Eli Lilly where he went on to build a strong team of research scientists: biomarker assays developed by this team supported the full spectrum of the drug development process, including in vitro studies, toxicology, and clinical trials.

Midway through this editing project, Phil was diagnosed with glioblastoma multiforme. To say that he approached this illness with grace, intelligence, and courage would be a considerable understatement. Those of us who followed his blog were impressed by his ability to continue to enjoy so many different aspects of his life throughout the treatment process. Among these would be the arrival of a much loved puppy by the name of Chaim, and the professional quality reviews he provided for many of the fine (and not so fine) restaurants from Indianapolis to Chicago with many stops in between. Most impressive of all, however, was the connection he maintained with his strong and loving family, and, of course, they with him.

Completely in character, Phil continued to be involved with the details of this project until November 2009. It was truly a pleasure to collaborate with him on this book that should be seen as a fitting tribute to his substantial contributions to flow cytometry and drug development.

Our very best wishes to his family and friends.

Preface

The recent contraction within the pharmaceutical sector and the push to decrease the timelines and costs associated with drug development have, fortuitously, coincided with a marked advancement in the field of flow cytometry. As a result, flow cytometry is now a critically important analytical tool for drug development. Flow Cytometry in Drug Discovery and Development chronicles the unique application of flow cytometry in drug development and provides examples of how it can be applied along the drug development pathway from drug discovery to clinical testing.

The Part I of this book provides the reader with essential background information regarding both flow cytometry and drug development. In the chapters in Part II, the focus is on the way in which flow cytometry serves as an essential tool for each stage of the drug development life cycle. The organization of this part follows along the lines of the drug development process: drug discovery/preclinical toxicology/clinical testing. In addition, the reader will be introduced to distinct therapeutic areas such as oncology, anti-infectives, and autoimmunity/inflammation that are highlighted in several chapters.

During the drug discovery phase, the life cycle of a new drug begins with the identification of a therapeutic need and potential molecular targets that may interfere with the disease process. The next steps in the long and costly process of bringing a new drug to market are target validation, high-throughput compound screening, and multiple rounds of compound selection followed by characterization. With each successive round of selection and characterization, more rigorous standards are applied until, ultimately, a small pool of lead compounds, or potential drug candidates, is identified from the initial compound library. Chapters 4–7 address the application of flow cytometry in these early stages of drug development. The development of high-throughput flow cytometry and its application in compound screening are addressed in Chapter 4. During the earlier stages of compound selection and characterization in oncology drug discovery, cell cycle analysis using flow cytometry plays a critical role, as illustrated in Chapter 5. A wider variety of flow cytometric methods are critical during the later stages of preclinical compound selection and characterization as described in Chapter 6. The process of conducting formal GLP toxicology studies with a small pool of highly characterized potential drug candidates is also introduced in Chapter 6. The transition from preclinical testing to clinical testing is highlighted in Chapter 7 that emphasizes the use of panels of phosphospecific antibodies in cancer therapeutics.

Clinical trial testing of novel therapeutic compounds includes biomarker measurements for efficacy and pharmacodynamic (PD) effect, safety assessment, and pharmacokinetic (PK) evaluation. Chapters 8–11 describe how flow cytometry is used to achieve each of these objectives. Chapter 8 reviews the historical and continued importance of CD4 T-cell monitoring as an efficacy marker in the evaluation of antiretroviral therapy for HIV infections. Chapter 9 outlines the translational medicine approach to PD biomarker evaluation with an emphasis on inflammatory and autoimmune disorders. Chapter 10 focuses on safety concerns associated with the clinical administration of biotherapeutic compounds and the advantages of flow cytometric analysis in determining antidrug antibody responses. Flow cytometric methods can also be used for PK analysis of protein- and peptide-based therapeutics as described in Chapter 11.

The importance of validation and regulatory compliance in drug development is highlighted by the fact that an entire section of the book is dedicated to this topic (Part III, Validation and Regulatory Compliance). Different stages of the drug development life cycle are subjected to different regulatory requirements. For data to be acceptable for submission to regulatory agencies, laboratory instrumentation and analytical methods must be validated according to applicable standards.

The last, Part IV, focuses on how multiparametric flow cytometry data will be analyzed and applied in near future. At present, one of the most pressing needs in flow cytometry is for advanced data analysis tools, capable of rapidly evaluating the large, complex data sets generated by multiparametric methods. Chapter 14 presents a new paradigm for data analysis, called probability state modeling, which is capable of visualizing and analyzing multiparametric files. The last chapter brings together one of the newest, potentially most valuable applications of flow cytometry, phospho-flow, with one of the most exciting and potentially most valuable aspects of patient treatment, personalized medicine.

The contributors to the book include both flow cytometry thought leaders from academia and industry and flow cytometry users from the pharmaceutical sector. This diverse mix of expertise is typical of the collaboration among disciplines that increasingly has been a hallmark of the flow cytometry community, and part of what makes working in this challenging and exciting discipline a rewarding experience.

Foreword

Almost 10 years ago, Phil Marder asked me how ISAC could expand its interest in high-content screening and in particular the role that flow cytometry could play in this emerging area. Over the next decade, Phil Marder was responsible for developing and advancing high-content screening tools within the pharma industry. This book is the result of that drive of Phil Marder to show how much impact flow cytometry can have on this arena. It is with great sadness that Phil did not live to see this volume published. Without doubt, it reflects the knowledge, skills, and foresight of Phil Marder who saw the impact of flow cytometry on screening well before anyone else.

What this book does effectively is to establish a well-defined set of assays, processes, and regulations that must be considered in the performance of robust screening by flow cytometry. Specific assays are identified and discussed with sufficient details to be reproduced in screening mode. The variety of screens presented covers a wide spectrum from cell culture assays, to human and animal studies and to the role that management and manipulation of data play in today's scientific world.

Flow cytometry has been used for over 40 years to define intricate pathways and responses from single cells. Its power as the leader in single cell analysis is unquestioned by most, but many scientists fail to see its capability at the systems level. Indeed, it is this capacity that is now opening up new opportunities that were previously not possible. The ability to multiplex both cells and beads has made it to possible to track cellular phenotype and function in multiple dimensions simultaneously. In drug screening, very complex assays can be automated and analyzed in minutes rather than days. The need now is to advance the analytical tools required for the very complex opportunities.

This book brings out an entirely new set of opportunities for this mature technology. It opens up a Pandora's box of assay systems and approaches in a way that may well expand the field well beyond what was imaginable a few years ago.

J. Paul Robinson

Acknowledgments

The editors wish to offer sincere thanks to all the contributors for the expertise they provided during the writing and compilation of this book. Thanks also to Jonathan Rose at John Wiley & Sons, Inc. for approaching us about the project, to the artist Glenn Vilppu for the drawing of Phil Marder, and Ira Schieren for the cover design.

Contributors

Fatima Abbasi, CBER, FDA, NIH, Bethesda, MD, USA

James Andahazy, Partec North America, Swedesboro, NJ, USA

C. Bruce Bagwell, Verity Software House, Topsham, ME, USA

Sean C. Bendall, Stanford University, Stanford, CA, USA

Thomas N. Denny, Duke University Medical Center, Durham, NC, USA

Roy Edward, Biostatus Ltd., Shepshed, Leicester, UK

Bruce S Edwards, University of New Mexico Center for Molecular Discovery, Albuquerque, NM, USA

Rachel J. Errington, Cardiff University, Cardiff, UK

John Ferbas, Amgen Inc., Thousand Oaks, CA, USA

Matthew B. Hale, Stanford University, Stanford, CA, USA

Kristi R. Harkins, Harkins Strategic Consulting, LLC, Madrid, IA, USA

David W. Hedley, Princess Margaret Hospital/Ontario Cancer Institute, Toronto, ON, Canada

Carla G Hill, ICON Central Laboratories, Farmingdale, NY, USA

Jonathan M. Irish, Stanford University, Stanford, CA, USA

Siddha Kasar, NJMS/UMDNJ, Newark, NJ, USA

Peter O. Krutzik, Stanford University, Stanford, CA, USA

Virginia Litwin, Covance Central Laboratory Services, Indianapolis, IN, USA

Byron H. Long, Doylestown, PA, USA

Raul Louzao, Duke University Medical Center, Durham, NC, USA

Philip Marder, Redram Consulting, LLC, Indianapolis, IN, USA

Gerald E. Marti, CBER, FDA, NIH, Bethesda, MD, USA

David McFartand, iCyt, a Sony Company, Champaign, IL, USA

Garry P. Nolan, Stanford University, Stanford, CA, USA

Denise M. O'Hara, Pfizer (formerly Wyeth), Andover, MA, USA

Elizabeth Raveche, NJMS/UMDNJ, Newark, NJ, USA

Carmen Raventos-Suarez, Bristol-Myers Squibb Company, Princeton, NJ, USA

Manjula P. Reddy, Ortho Biotech, Unit of Centocor R&D, Johnson and Johnson, Radnor, PA, USA

Susan M. Richards, Genzyme, Framingham, MA, USA

Erica Salerno, NJMS/UMDNJ, Newark, NJ, USA

Larry A. Sklar, University of New Mexico, Albuquerque, NM, USA

Paul J. Smith, Cardiff University, Cardiff, UK

Michelle J. Schroeder, Amgen Inc., Thousand Oaks, CA, USA

Valerie Theobald, Genzyme, Framingham, MA, USA

Ole Vesterqvist, Covance Central Laboratory Services Inc., Indianapolis, IN, USA

Brooke Walker, Duke University Medical Center, Durham, NC, USA

John Wong, Duke University Medical Center, Durham, NC, USA

Dianna Y. Wu, Bristol-Myers Squibb Company, Pennington, NJ, USA

Yuanxin Xu, Genzyme, Framingham, MA, USA

Yao Yuan, NJMS/UMDNJ, Newark, NJ, USA

Part I

Introduction

Philip Marder and Virginia Litwin

The discovery and development of novel therapeutic compounds is a lengthy, difficult, and expensive process with recent estimates of more than 1.2 billion dollars required for each new drug brought to market. As a result, the standard processes of the pharmaceutical industry are being reevaluated and modified in order to increase efficiencies in the drug development process. One approach in process transformation is to promote more informed decision making by incorporating advanced technologies such as flow cytometry.

A wide variety of flow cytometric methods are employed during various stages of the drug development life cycle. This book explores many of the benefits and complexities associated with this unique application of the technology. Part I is intended to provide the reader with essential background information regarding both flow cytometry (Chapters 1 and 2) and drug development (Chapter 3).

Chapter 1

Introduction to Flow Cytometry

Elizabeth Raveche, Fatima Abbasi, Yao Yuan, Erica Salerno, Siddha Kasar, and Gerald E. Marti

1.1 Introduction

This chapter presents, in basic terms, the concepts and principles of flow cytometry. Numerous books and articles describing flow cytometers and their use in a clinical and biomedical research setting have been published [1–7]. In this chapter, flow cytometers will be discussed from their infancy arriving at the current instrumentation that allows for detection of numerous features of individual cells or particles, including determination of size and granularity, surface marker expression, DNA content, intracellular protein expression, and function. The key to flow cytometers is that the analysis is done on cells in suspension [8–10]. The analysis of individual cells (or particles) rather than the whole population allows for detection of multiple properties measured on the same cell. The detection is rapid (as fast as the cell in the fluid sheath passes through the laser beam). In addition to analysis of individual cells, some types of flow cytometers can physically sort cells based on signals associated with the parameters being detected. The term fluorescence-activated cell sorter or FACS has been adopted to refer to this type of analysis [11]. Flow cytometry is a very useful tool for both clinical diagnosis and scientific research. The history of flow cytometers has been the subject of numerous reviews [12–20]. The first flow cytometers were introduced in the mid-1970s and first used for DNA analysis and leukemia immunophenotyping [7, 21–25]. A further impetus to bring flow cytometers to the forefront of clinical labs came in the early 1980s with the discovery that individuals infected with the HIV virus developed AIDS, which could be monitored by enumerating the number of CD4+ T cells by flow cytometric analysis [26–30]. Currently, there are emerging areas with flow cytometric applications including the enumeration of CD34+ hematopoietic stem cells [29, 31, 32], detection of circulating metastatic tumor cells [33–37], determination of antigen-specific T cells [38–40], and identification of pathogens [41–45], to list a few. Combination of sorting with molecular analysis represents an important use of the sorting aspects of flow cytometers. There are over 100,000 flow cytometers in use and the employment of this instrument in clinical diagnostics has increased dramatically, particularly with the increase in FDA-approved fluorochrome reagents for in vitro diagnostics (fluorochrome-conjugated antibodies). However, in third-world countries, access to clinical flow cytometers is not optimal [46, 47]. The use of flow cytometers and the impact of this instrument on biomedical and clinical studies can be appreciated by looking at the increase in publications in which the word “flow cytometry” appeared in the abstract or title with time (Figure 1.1).

Figure 1.1 A bar graph showing the number of publications having “flow cytometry” in their title/abstract since 1970 to present. There is almost a 150% increase since 1980–1989.

Improvements in instrumentation and computer-assisted analysis have made the flow cytometer a critical instrument in biomedical research, clinical diagnostics, and drug discovery. Herzenberg was honored for his work in flow cytometry by the American Association for Clinical Chemistry with the Ullman Award in 2002 and some of the history described in this chapter comes from his lecture and the accompanying article [19]. The original description of the first flow cytometer was provided in Scientific American [48]. This instrument consisted of one laser and two light detectors, one for forward scatter to measure cell size and the other for fluorescence. This meant that one was restricted to measuring a single marker. When one of the authors of this article used that prototype instrument, the LASL, we were measuring the DNA content of individual cells. This was one of the first uses of these early flow cytometers since reagents were available that not only bound specifically to DNA (e.g., ethidium bromide developed by Dittrich and Gohde in 1969 [49]) but also emitted fluorescence when excited with a laser. Much of the essentials of the modern-day FACS are the same as those in the early flow cytometers. However, these early flow cytometers were cumbersome and required an on-site engineer. The laser was water cooled and alignment issues were critical. In addition, no computer was attached to these early flow cytometers, nor were programs available for data analysis [50]. At one point, we took Polaroid pictures of oscilloscopes and sent data to a DEC10 supercomputer and wrote our own programs for cell cycle analysis.

Although the development of FACS depended on many advances in various disciplines including dye chemistry, electronics, and computers, one important breakthrough that was critical for the development of flow cytometers was the principle of measuring cells or particles in liquid suspension. Advances in the flow principle began in 1940 with Crosland-Taylor using the flow principle and light scatter to measure blood cells [51]. The breakthrough technology was first developed by Coulter and the Coulter principle describes changes in the electrical conductivity of a small saline-filled orifice as a cell passes through it. In 1953, Wallace Coulter and his brother Joe obtained a U.S. patent for the Coulter counter that automated counting of particles, particularly cells in the blood [52]. The use of a liquid stream (or a sheath) to which a sample is introduced allows individual cells to be distributed in the sheath that then passes through a nozzle (detecting electrical conductivity changes) to generate a trigger, which indicates the presence of a signal that exceeds the threshold level.

Many of the applications for FACS analysis involve the identification of membrane markers via the use of fluorochrome-tagged antibodies, which recognize these markers. Many of these membrane markers are surface proteins or surface antigens, which help to define the cell. These antigens are used to classify the cells and are often assigned a cluster of differentiation number or a CD number. Antibodies (which are normally produced by B lymphocytes) can be made that specifically bind to these CD molecules. There are more than 200 CD molecules that have been identified and specific antibodies have been produced that recognized these CD markers [53–55]. In addition, many of these antibodies are commercially available as labeled antibodies with different fluorochromes.

1.2 Basic Principles of How a Flow Cytometer Works

The basic components of a flow cytometer (Figure 1.2) consist of (1) a flow cell that forces single cells into the middle of a fluidic sheath, (2) a laser source of light, (3) optical components to focus light of different wavelengths (colors) onto a detector, (4) a photomultiplier to amplify the signal, and (5) a computer.

Figure 1.2 Diagrammatic representation of a basic flow cytometer. The fluorescently labeled cells are hydrodynamically focused into a single file in the flow cell. Individual cells are excited by the laser light source and the fluorescence emissions, FSC, and SSC are detected. The cells can then be given a particular charge based on their fluorescence profile and deflected toward the oppositely charged plates. In the figure, light grey cells and dark grey cells are given negative and positive charges, respectively, and are thus deflected toward two different tubes.

In a basic flow cytometer, the sample (containing the cells tagged with fluorochromes in a liquid) is drawn up and pumped into the flow cell through tubing. The cells flow through the flow chamber rapidly and singly and are passed through one or more laser light beams. As the laser beam hits the cells, the light beam is scattered in a forward direction and a side direction. Fluorescence emission can also be detected. Scatter or fluorescence is captured, filtered (based on the wavelength), and directed to the appropriate photodetectors for conversion to electronic signals. The electronics in the flow cytometer amplify the signal and convert the analog data to digital data, which can then be analyzed by computer software programs.

1.3 Fluidics

1.3.1 Flow Cells

In order to perform flow cytometric analysis, the sample must be in a suspension and the cell in the sample stream must be centered in the laminar flow [49]. Hydrodynamic focusing induces cells to orient with their long axis parallel to the flow. The end result is that the introduced sample passes by the laser with each cell oriented in the center of the sample stream in a particular manner in three dimensions.

1.4 Optics

Flow cytometers depend on the laws of optics, such as reflection, refraction, and other principles, which are not new but based on works established centuries ago [56]. Optics are present on both the excitation and the emission side. The excitation optics encompass the lasers and the lenses that focus the laser beam. The emission optics are involved in collecting the emission following excitation. These involve lenses to collect emitted light and mirrors and filters to route specified wavelengths of the collected light to designated optical detectors. Light coming out of a laser may be considered a beam but fluorescence must be considered as a photon.

1.4.1 Light Scatter

Due to differences between the refractive indices of cells and the surrounding sheath fluid, light impinging upon the cells is scattered. The forward light scatter (FSC) provides empirical information on cell size. Light scattered in an orthogonal direction or side scatter (SSC), which is collected by a different detector, provides information about granularity.

1.4.2 Types of Lasers

Laser stands for light amplification by stimulated emission of radiation. Gas lasers have mirrors at each end of a cylinder or plasma tube filled with an inert gas. The gas is ionized to a higher energy state by a high-voltage electric current. When these excited atoms return to the ground state, they give off photons of a characteristic wavelength. The photons can be reflected by the mirrors and the excitation of the atoms in the plasma can be amplified but the wavelengths of the emission still are the characteristic wavelengths for that gas [57]. In the front of the laser there is a small optic that allows the transmitted light to form a laser beam of desired output wavelengths. The light from lasers is a stimulated emission and it has uniform characteristics. For current stream-in-air instrumentation, it is desirable to have at least 50 mW of power for each laser line in use, since the fluorescence signal (and thus sensitivity) increases with laser power. Cytometers use multiple lasers that are positioned spatially such that there is a time delay for each laser beam intercept with the cell. Newer solid-state diode lasers [58–60] are becoming prevalent and these are significantly cheaper than the older gas ion lasers. Diode lasers are pumped by input of electric current. A partial list of different lasers is presented in Table 1.1.

Table 1.1 Partial List of Laser with Their Excitation Wavelength Line and the Fluorochromes which Can be Detected.

LaserExcitation Line (nm)FluorochromeUV355Hoescht 33342, 33250He–Cd325DAPI, ELF-97, AMCA (AlexaFluor 350), INDO-1Mercury lampViolet405Pacific Blue, CasBKrypton ion435CasY, AlexaFluor 405 (AF405)Blue (argon)488PE-TR, 6FP, FITC, PE, AF488, PE-Cy7, PerCP, PE-Cy5, SYTO 9, PerCP-Cy5.5Red (solid state)640APC, APC-Cy7, SYTO 59–61He–Ne633AF647, APC-Cy7Red diode635APC-Cy5.5, AF700Yellow/green561PE-Texas Red, PerCP, PE

The most common lasers for flow cytometers are the argon ion lasers that run at 488 nm. The lasing medium in an ion laser is plasma. A high-voltage pulse is used to ionize the gas to start the plasma. Ion lasers require a high current to maintain the plasma discharge. In addition to the 488 nm emission, argon ion lasers also emit at 515 nm (green) and 457 nm (violet-blue). Other emissions can be obtained using specially coated mirrors. The new low-power, air-cooled argon laser gives out 25 mW at 488 nm. To obtain other lines of emission, large lasers capable of giving 100 mW in UV must be used.

Krypton lasers can give out strong blue-green lines and UV and violet lines. Krypton lasers need to be water cooled and optimized and the alignment is very difficult. Another type of laser is a dye laser and the lasing medium in a dye laser is a fluorescent dye. The selection of dye depends on the wavelength at which the operation is desired. Helium–neon (He–Ne) lasers are also small, air cooled, and stable. The most common lasers emit at 633 nm and have power outputs ranging from 1 to 50 mW. He–Ne lasers are available at 633, 543, 594, and 611 nm. Helium–cadmium (He–Cd) lasers emit 5–200 mW in blue (441 nm) and 1–50 mW in UV (325 nm). They plug into the wall and do not require water cooling.

1.4.3 Filters for Emission

All signals that are emitted from fluorochromes that are excited as the cells to which they are bound are interrogated by the laser beams are routed to detectors via a system of mirrors and optical filters. In addition, beam splitters direct light of different wavelengths in different directions. The most commonly used filters are short-pass filters (which transmit wavelengths of light equal to or shorter than the specified wavelength), long-pass filters (which transmit wavelengths of light equal to or larger than the specified wavelength), and band-pass filters (which allow a narrow range of wavelengths to reach the detector). An example of these types of filters is presented in Figure 1.3. Because each fluorochrome has an emission spectrum, the choice of filters optimizes detection of the specific fluorochrome by one detector or photomultiplier tube (PMT).

Figure 1.3 An example of fluorescence emission of various wavelengths (top) as it passes through different types of optical filters (bottom).

Detection of fluorochromes requires selection of appropriate filters that are placed before each detector or PMT. The type of filter selected must collect as much emitted light from the primary fluorochrome for high sensitivity, but as little as possible from other fluorochromes to reduce the compensation required. A partial list of filters is presented in Table 1.2.

Table 1.2 Partial List of Filters Typically Employed with Various Fluorochromes.

FluorochromeFilterPacific Blue, BD Horizon V450, CasB440/40AmCyan525/40FITC, AlexaFluor 488 (AF488)530/30CasY, AF430545/90PE585/40PE-Texas Red, AF595625/40APC, AF647660/20PE-Cy5, PerCP-Cy5.5, PerCP695/40APC-Cy5.5705/50AF700720/45PE-Cy7, BD-APC H7780/60

1.5 Types and Choice of Fluorochromes

A fluorochrome is a fluorescent marker that emits a particular wavelength when a laser light hits it. Fluorescence occurs when a molecule, which is excited by light from a laser at one wavelength, loses its energy and emits light of a longer wavelength. The emitted wavelength is what is detected. The excited and emitted light are of different wavelengths. The fluorescence intensity that is emitted is proportional to the quantity of binding sites for the fluorescent compound on the cell. Therefore, the more the fluorescence that is emitted the more the binding sites on the cell. For instance, for an antibody tagged with FITC (fluorescein isothiocyanate, which is excited by a 488 nm argon laser but emits in the 520 nm (green) range) that recognizes and binds to CD4, the more the 520 nm emission the more the CD4 on the cell (Figure 1.4).

Figure 1.4 Excitation and emission spectra of FITC and phycoerythrin (PE). Fluorescent molecules absorb light of a characteristic wavelength and emit light of a longer wavelength. FITC and PE that are commonly used for flow cytometry absorb at 488 and 488–560 nm, respectively, but emit at 520 and 590 nm, respectively. Thus, they can be excited by the same laser line and used together in the same tube [10].

The fluorochrome label for a reagent depends on instrument configuration (type and number of lasers and type of optical filters and detectors), which determines if a given instrument can excite a given fluorochrome and detect the emission. While it is not possible to uniformly state the best fluorochrome combination, there are a few guidelines that can help in this choice. The first issue is to determine what is the reagent brightness, which takes into account the resolvable signal associated with the presence of the marker being detected by comparing a negative and a positive sample. The negative population emission is the background emission. Background is signal (emission) due to electronic noise (dark current), cell autofluorescence, nonspecific staining, and background emission that is a spillover from another fluorochrome [61, 62]. The rule of thumb is to use the brightest reagents possible [63, 64]. There is a caveat to this statement. The spillover problems increase as the number of colors to be resolved (different emissions) increases. Compensation can help prevent the spillover contribution, but as a rule of thumb, one should use fluorochromes whose emissions have the least amount of spectral overlap [65, 66]. In addition, logically the markers with the least amount of expression on a given population should be detected with a reagent that is labeled with the brightest fluorochrome. However, this weakly expressed antigen should be stained with a fluorochrome-tagged reagent that does not have spillover issues with another fluorochrome reagent recognizing a cell marker that is highly expressed [64]. A final word of caution is to take into account that a fluorochrome as a single reagent may give different results when employed in a multicolor reagent cocktail and this is a fidelity issue. To determine if this is a problem, one can compare the antibody–fluorochrome conjugate by itself and compare the results with the results obtained for this reagent when it is in the multicolor reagent cocktail. One should try and use reagent combinations that have good fidelity when used in a multicolor reagent cocktail.

1.6 Compensation

Due to overlap in the emission spectra of different dyes, it is often not possible to choose emission filters that uniquely measure only one of the dyes in a multicolor experiment. Due to this spectral overlap, one fluorochrome can contribute a signal to several detectors; therefore, the contribution in detectors not assigned to that fluorochrome must be removed from the total signal in those detectors. Compensation is an artificial means of eliminating spectral overlap between two different fluorochromes by mathematical means and is not just a subtraction process [65, 66]. Compensation between detectors can be performed either by hardware, after signal detection but before logarithmic conversion and/or digitization, or by uncompensated data that are analyzed post-collection by software (Figure 1.5).

Figure 1.5 Compensation controls (a–c) human PBMCs stained with a single FITC-labeled antibody. (a) Cells are gated on the lymphoid gate based on forward scatter (x-axis) and low side scatter (y-axis). (b) Uncompensated data demonstrating that the FITC signal is “spilling” into the PE channel with 65.4% of the cells demonstrating dual positivity incorrectly. (c) Data after compensation with only single positive cells expressing FITC and no PE. Note that the PE mean for the FITC negative and FITC positive cells is nearly identical. (a′–c′) Two-color data of human PBMCs stained with anti-CD19 labeled with PerCP-Cy5.5 (B-cell marker) and anti-CD3 labeled with APC (T-cell marker). There should be no dual positive cells. Data were collected as listmode and compensation was added after data collection: (a′) lymphoid gate; (b′) uncompensated data; (c′) data after compensation. (See the color version of the figure in the Color Plates section.)

1.7 Threshold and Gates

An electronic threshold is defined as a gate for acquiring signals. Only events with intensity greater than the threshold will be processed and analyzed. Thresholding is most often used to eliminate debris. A gate is a boundary that can be used to identify subpopulations and this limits the number of events that are analyzed (note that these events are acquired in the listmode data file but not analyzed). Gates are often used to identify the lymphoid cells for analysis.

1.8 Analysis

The electric pulses that are detected by the PMTs are amplified (log amplification is most often used to measure fluorescence). These amplified signals are converted from analog to digital. Data can be stored as a listmode file, which consists of a complete listing of all events and parameters that were measured [67]. One can take a listmode file and subject the data to analysis such as regions and gating but one cannot adjust amplification or fluorescence [68].

1.8.1 Flow Histogram

For single-color analysis, the events can then be plotted as a single parameter such as a histogram, in which the x-axis is the measurement and the y-axis is the number of events. Usually the x-axis corresponds to channels (typically, 1024 channels); the brighter the specific fluorescence the higher the channel number. A new “Logicle” display method (also known as biexponential method) when analyzing flow data enables the close to zero signals to be shown on the plot graph that combines both the logarithmic and linear scales, providing a more complete way of interpretation of data [11, 69, 70]. Multicolor flow analysis is often displayed as two-color analysis. In Figure 1.6, an idealized phenotype of cells in two-color analysis is shown.

Figure 1.6 Idealized two-parameter quadrant analysis. A population of cells is stained for two markers labeled with PE indicated with a diamond (y-axis) and FITC indicated with a solid small circle (x-axis). Lower left quadrant: cells lacking both the markers, and hence double negative. Lower right quadrant: cells FITC and shown as cells with solid circles. Upper left quadrant: cells PE and shown as cells with diamonds. Upper right quadrant: cells expressing both markers, also called double positive, and shown as individual cells (diamonds and solid circles) together.

1.8.2 Thresholding and Doublet Discrimination

The flow cytometer parameters can be set such that only events whose intensity is greater than a particular threshold value are recorded. This is called thresholding and it can be used to eliminate debris (Figure 1.7a), that is, cells having a very low FSC and SSC. Cells of interest can be gated based on fluorescence parameters; for example, expression of CD45 with low side scatter predominantly identifies lymphoid cells (Figure 1.7b and c). Although hydrodynamic focusing streams the cells into a single file, occasionally two cells stick together. A doublet made of two single positive cells (each one positive for a different fluorochrome) can be erroneously recorded as a double positive cell. Hence, doublet discrimination is crucial. Doublets will have a higher FSC height/FSC area ratio as compared to singlets. One can set a gate around the events in which there is a linear relationship between FSC-H and FSC-A (Figure 1.7d) and these cells can then be analyzed for different markers (Figure 1.7e–h).

Figure 1.7 Typical gating for lymphoid cells. (a) The different populations of cells present in the sample are visualized based on their size (FSC) and complexity (SSC). Thresholding was performed to remove debris (arrow). (b) The cells are stained with a FITC-labeled antibody that recognizes CD45, a hematopoietic lineage marker (referred to as CD45-FITC). The circled population indicates lymphoid gate (high FSC but very low SSC). (c) CD45-gated lymphoid cells are reevaluated to exclude large CD45+ cells. (d) Doublets that are distinguished from singlets on the basis of FSC-H/FSC-A ratio are excluded from the area of interest indicated with the circle. Detection of macrophages (CD45+CD14+) (e), B cells (CD45+CD19+) (f), and T cells (CD45+CD3+) (g). (h) Two subpopulations, B and T cells, can be easily visualized on the same dot plot.

1.8.3 Two-Color Dot Plot Versus Contour Plot

The fluorescence of two different markers can be represented in 2D using the two-color dot plot (Figure 1.8a–d) or a contour plot (Figure 1.8b′–d′). There are advantages to data displayed in either mode and data are amenable to either type of analysis. In Figure 1.8, identical data are displayed as a dot plot and a contour plot. In a dot plot, each dot represents one or more events (events are usually cells that have passed the criteria of thresholding and gating) that are determined by the user. The density of events can be color coded (e.g., red implying highest density). The events are gated on the lymphoid gate on the basis of FSC and SSC (Figure 1.8a) with a second gate to include only lymphoid cells that are CD3 positive (Figure 1.8c′). There exists a small population of cells positive for both CD4 and CD8, which is visible in the dot plot (Figure 1.8d). Contour plots show the same data in which rings represent a defined percentage of total events with a particular combination of fluorescence intensities (Figure 1.8b′–d′). This type of data display removes the outliers allowing one to clearly see subpopulations of cells. In addition, one can smoothen the contour plots making it even more difficult to visualize the rare population (Figure 1.8d′, CD4+/CD8+ cells). Using contour plots as a formatting option, other display options include linear density and log density contour plots in which the contour lines are defined as a percentage of the maximum numbers. There is an additional option in which one can combine a contour plot and a dot plot in which dots are displayed below the lowest contour line allowing the observation of the rare population in a contour plot. Despite different display options, the data quadrant statistics would be identical in both the dot plot and the contour plot.

Figure 1.8 Dot plot versus contour plot of the same data. Cells were stained with CD3-FITC, CD16 and CD56-PE, CD4-APC, and CD8-PerCP-Cy5.5. Plots (b)–(d) and (b′)–(d′) show similar results by both dot plot and contour plot. However, the rare population of double positive cells can be visualized in the dot plot (d) but not in the contour plot (d′). For details refer to the text.

1.9 Sorting

Cells (or particles) of interest (expression of desired markers) can be purified or sorted [71]. In most flow cytometers equipped with sorting capabilities, the liquid sheath stream is regularly broken into droplets by the vibration of the piezoelectric crystal attached to the flow chamber. A cell passing through the laser meeting the selection criteria based on the fluorescence pattern is electrically charged in the droplet. These droplets containing the charged cells are then deflected and collected into awaiting tubes/wells or onto slides. Depending on the further downstream applications of these sorted cells, they may have to be collected in appropriate buffers or under sterile conditions. The temperature of the sheath fluid and sample collection tubes may need to be controlled. In the example shown, mouse spleen cells to be sorted are shown on the left (Figure 1.9a–d) and analysis of the cells following sorting is shown on the right (Figure 1.9 a′–d′). The live cell gate (Figure 1.9a and a′) indicates that following sorting the cells that were gated on CD4 and the live gate resulted in 99% of the gated cells meeting the gating criteria (live, singlets, and CD4+).

Figure 1.9 Sorting of CD4+ cells from cells stained with CD4-APC and CD25-PE. (a) After thresholding, the live cells were gated. (b) Doublet discrimination was performed on the live cells. (c) Single-color histogram showing the CD staining before sorting (left side). (d) Two-color dot plot representing the different subpopulations present after thresholding and gating but before sorting. The indicated subpopulation was sorted. (a′–d′) The sorted population was reacquired to assess the efficiency of sorting. The FSC and SSC of the sorted cells (a′) were limited to the cells present in the gate region of (a). (b′) The sorted cells were predominantly singlets. (c′) The single-color histogram of CD4 showed predominantly CD4+ population after sorting. (d′) Two-color dot plot representing the different subpopulations present after sorting. The quadrant statistics indicate that the sorted cells are a 99% pure population of CD4+CD25− cells.

1.10 Conclusion

Since their introduction in the 1970s, the design and applications of flow cytometers have undergone tremendous change. Current flow cytometers are rapid, use multiple lasers (five lasers on several instruments), and can detect more than 20 different fluorochrome tags. Some of the novel applications include fluorescence in situ hybridization (FISH) using flow cytometer and Amnis ImageStream, which is a blend of flow cytometry and microscopy and allows the visualization of single cells. The field of flow cytometry holds great promise for research and clinical diagnostics.

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Chapter 2

Recent Advances in Flow Cytometry: Platforms, Tools, and Challenges for Data Analysis

Paul J. Smith, Roy Edward, and Rachel J. Errington

2.1 Introduction and Overview

An expanding range of both new and refined technologies is being applied at all stages of modern drug discovery by the pharmaceutical industries. There is also a growing involvement of the academic sector in the early phases of target identification, elucidation of mechanisms of action, invention of discovery technologies, and creation of molecular probes and reporter systems. In parallel, there is a drive to advance the means of extraction of knowledge from acquired data through bioinformatics. Flow cytometry lends itself to exploitation within this discovery and development pathway due to its unparalled capacity to analyze heterogeneous cellular systems, provide multiparameter functional information at the single-cell level, offer flexibility for different reporter systems, and acquire data in standard structures. The working context for flow cytometry in discovery and development essentially reflects the emphasis and expectation placed upon cell-based analyses at different stages within the pathway. Fundamentally, the cell-based assay provides an opportunity to examine the target within a minimally relevant environment and to perceive complex outcomes such as molecular translocation events as part of a downstream pharmacodynamic response that may have a stochastic element to the timing of expression or be driven by other permissive events such as cell cycle progression.

The high impact of flow cytometry on clinical practice and research, together with the availability of instrumentation, has fostered the view that flow cytometry is a mature technology. This belies the continued appearance of advances in analysis capacity, improvements in sorting, integration of sample handling solutions, and enhanced event detection and resolution. Such developments carry burdens for platform evaluation and cross-platform comparisons particularly when data from trials depend on multicenter analyses. For example, in the discrete area of hematology analyzers, the introduction of new platforms (e.g., LH 750, Beckman Coulter; Advia 120, Bayer Diagnostics; XE 2100, Sysmex; Excell 2280, Drew Scientific Inc.) required cross-platform evaluations for analyte sample stability [1] or operation within a given workload environment [2].

An emerging feature of the advance in the flow cytometry platform is the expansion of multiparameter capacity and the provision of supporting reagent technologies as addressed elsewhere in this book. The success of the technology has also primed awareness in microfluidics and the search for new modalities, beyond light scatter and fluorescence, for both cell sensing and analysis. For the drug discovery sector, innovation in cytometry presents issues for platform selection when coping with biological/chemical diversity, the need for improved robotic handling, sample throughput, and choice within an expanding range of screening applications that require validation on a given platform [3]. This is a particular challenge when faced with new and highly informative model systems, such as zebrafish (Danio rerio), for discovery and drug development in the screening of lead compounds, target identification, target validation, and physiology-based assays including toxicity testing [4]. High-throughput multifactorial analysis has previously promised an enhanced efficiency with which novel bioresponse-modifying drugs may be identified and characterized [5]. Thus, in the drug discovery and development (DD&D) area, the demand remains for higher throughput performance and “high-content” screening applications [6, 7].