A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> List of Tables and Figures. Preface to the Fourth Edition: Why You Should Read the Book - Or Not. Foreword to the Third Edition. Preface to the Third Edition. Preface to the Second Edition. Foreword to the First Edition. Preface to the First Edition. 1. Overture. 2. Learning Flow Cytometry. 3. History. 4. How Flow Cytometers Work. 5. Data Analysis. 6. Flow Sorting. 7. Parameters and Probes. 8. Buying Flow Cytometers. 9. Building Flow Cytometers. 10. Using Flow Cytometers: Applications, Extensions, and Alternatives. 11. Sources of Supply. 12. Afterword. <s> BIB001 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Analysis of flow cytometry histogram data by the subjective selection of an integration window can be a tedious and time-consuming task and is often inaccurate. A new method for automated calculation of the percent positive from immunofluorescence histograms is presented. This new method is a modification of the currently used method of channel-by-channel histogram subtraction. Its accuracy is compared to that of the channel-by-channel histogram subtraction method and to another currently used automated method, which selects an integration window by finding the channels that contain the most fluorescent 2% of a control histogram. The new histogram subtraction method is objective, easy to use, and is more accurate than other currently used automated analysis methods. PASCAL source code is given for each method of analysis. <s> BIB002 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> The identification of distinct T helper lymphocyte subsets (Th1/2) with polarised cytokine production has opened up new fields in immunobiology. Of the several alternative methods of monitoring cytokine production, flow cytometric analysis of intracellular staining has distinct advantages and pitfalls. It allows high throughput of samples and multiparameter characterisation of cytokine production on a single cell basis without the need for prolonged in vitro culture and cloning. However, these methods may cause important changes in cell surface phenotype which can make interpretation difficult. <s> BIB003 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> The term apoptosis or programmed cell death defines a genetically encoded cell death program, which is morphologically and biochemically distinct from necrosis or accidental cell death. The characteristic morphological signs of apoptosis (cellular shrinkage, membrane blebbing, nuclear condensation and fragmentation) are the final results of a complex biochemical cascade of events which is an integral part of physiological homeostasis. Techniques designed to identify, quantitate and characterize apoptosis are numerous, but flow cytometry (FCM) remains the methodology of choice to study the apoptotic cascade in relation to cell type, trigger and time. This review outlines the main stages of the apoptotic cascade together with current FCM methods. All FCM apoptosis assays described have a solid experimental basis and have been used successfully in basic research on molecular and biochemical mechanisms of apoptosis. In various clinical settings the ability to follow the apoptotic process in patient samples may offer the rationale for optimal treatment schedules. <s> BIB004 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Defects in phagocyte function or in the interactions between phagocytes, microorganisms and serum factors are associated with increased susceptibility to infection. Flow cytometry (FCM) offers rapid and reproducible measurements of single cells in suspension and, following staining with one or more fluorochromes, simultaneous biochemical and functional examinations of the complex process of phagocytosis. FCM techniques have been used for more than two decades to evaluate phagocyte cellular defects, as well as species-specific serum opsonic activities during disease and after vaccination. Recently, multiparameter assays have been developed to reveal the antigen-specificity of opsonophagocytic responses. This review presents basic methodological principles of FCM quantitation of phagocytosis and intracellular oxidative burst, and assays to evaluate species-specific and antigen-specific opsonophagocytosis. The calculations performed to present opsonophagocytosis results, as well as technical and methodological challenges are discussed, and examples of applications are presented. <s> BIB005 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Flow cytometry has played an invaluable role in recent advances made against HIV and AIDS, and there is every reason to expect it will continue to do so. Newer-generation machines, using three- and four-color panels, make measurements of lymphocyte subsets ever more accurate and potentially cost-effective. Similarly, recent single-platform techniques promise even more efficiencies, freeing CD4 enumeration from parallel determination of the CBC. In other developments, quantitative reporting of immunophenotype may well change the way subsets traditionally defined by qualitative determinations are viewed. The most promising initial candidates for such changes are surrogates of immune activation, such as CD38 expression on CD8 cells. But technical demonstration of reproducibility and reliability, and clinical trial evidence of utility--especially as measured against such established laboratory parameters as CD4 cell and HIV RNA determinations--are needed before a change from qualitative to quantitative immunophenotype measurement can be widely accepted. Although too soon to tell if they will emerge from the research arena into the clinical arena, tetramer binding, intracellular cytokine detection, and assays of cell division are rapidly advancing understanding of HIV-disease, and indeed understanding of all of human immunology. Some of these assays provide often subtly different information, and together they will help determine the most important surrogate measures of clinical immunity. Identifying such surrogates is critical to developing a rational HIV vaccine. The power of flow cytometry is its ability simultaneously to analyze data in four, five, six, or more dimensions. For longitudinal experiments, time adds yet another dimension. Humans struggle to think past three dimensions; interpreting results from experimental permutations and combinations that expand geometrically with each additional parameter studied requires considerable effort. Ultimately computer programs might come to our aid, but for now we have to live with the information overload if we hope to gain insight into the workings of complex biologic systems. In a world of limited resources, and with limited capacity to conceptualize in multiple dimensions, insight, inspiration, and perhaps a little luck are needed to ask the right questions and use the right assays if the recent string of advances against AIDS is to continue. <s> BIB006 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Background ::: ::: In multivariate distributions (for example, in 3- or more color flow cytometric datasets), it can become difficult or impossible to identify populations that differ between samples based only on a combination of univariate or bivariate displays. Indeed, it is possible that such differences can only be identified in “n”-dimensional space, where “n” is the number of parameters measured. Therefore, computer assisted identification of such differences is necessary. Such a method could be used to identify responses (i.e., by comparing cell samples before and after stimulation) in exquisite detail by allowing complete analysis of the collected data on only those events which have responded. ::: ::: ::: ::: Methods ::: ::: Multivariate Probability Binning can be used to compare different datasets to identify the distance and statistical significance of a difference between the distributions. An intermediate step in the algorithm provides access to the actual locations within the n-dimensional comparison which are most different between the distributions. Gates based on collections of hyper-rectangular bins can then be applied to datasets, thereby selecting those events (or clusters of events) that are different between samples. We term this process Frequency Difference Gating. ::: ::: ::: ::: Results ::: ::: Frequency Difference Gating was used in several test scenarios to evaluate its utility. First, we compared PBMC subsets identified by solely by immunofluorescence staining: based on this training data set, the algorithm automatically generated an accurate forward and side-scatter gate to identify lymphocytes. Second, we applied the algorithm to identify subtle differences between CD4 memory subsets based on 8-color immunophenotyping data. The resulting 3-dimensional gate could resolve cells subsets much more frequent in one subset compared to the other; no combination of two-dimensional gates could accomplish this resolution. Finally, we used the algorithm to compare B cell populations derived from mice of different ages or strains, and found that the algorithm could find very subtle differences between the populations. ::: ::: ::: ::: Conclusion ::: ::: Frequency Difference Gating is a powerful tool that automates the process of identifying events comprising underlying differences between samples. It is not a clustering tool; it is not meant to identify subsets in multidimensional space. Importantly, this method may reveal subtle changes in small populations of cells, changes that only occur simultaneously in multiple dimensions in such a way that identification by univariate or bivariate analyses is impossible. Finally, the method may significantly aid in the analysis of high-order multivariate data (i.e., 6-12 color flow cytometric analyses), where identification of differences between datasets becomes so time-consuming as to be impractical. Cytometry 45:56–64, 2001. Published 2001 Wiley-Liss, Inc. <s> BIB007 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> BACKGROUND ::: In multicolor flow cytometric analysis, compensation for spectral overlap is nearly always necessary. For the most part, such compensation has been relatively simple, producing the desired rectilinear distributions. However, in the realm of multicolor analysis, visualization of compensated often results in unexpected distributions, principally the appearance of a large number of events on the axis, and even more disconcerting, an inability to bring the extent of compensated data down to "autofluorescence" levels. ::: ::: ::: MATERIALS AND METHODS ::: A mathematical model of detector measurements with variable photon intensities, spillover parameters, measurement errors, and data storage characteristics was used to illustrate sources of apparent error in compensated data. Immunofluorescently stained cells were collected under conditions of limiting light collection and high spillover between detectors to confirm aspects of the model. ::: ::: ::: RESULTS ::: Photon-counting statistics contribute a nonlinear error to compensated parameters. Measurement errors and log-scale binning error contribute linear errors to compensated parameters. These errors are most apparent with the use of red or far-red fluorochromes (where the emitted light is at low intensity) and with large spillover between detectors. Such errors can lead to data visualization artifacts that can easily lead to incorrect conclusions about data, and account for the apparent "undercompensation" previously described for multicolor staining. ::: ::: ::: CONCLUSIONS ::: There are inescapable errors arising from imperfect measurements, photon-counting statistics, and even data storage methods that contribute both linearly and nonlinearly to a "spreading" of a properly compensated autofluorescence distribution. This phenomenon precludes the use of "quadrant" statistics or gates to analyze affected data; it also precludes visual adjustment of compensation. Most importantly, it is impossible to properly compensate data using standard visual graphical interfaces (histograms or dot plots). Computer-assisted compensation is required, as well as careful gating and experimental design to determine the distinction between positive and negative events. Finally, the use of special staining controls that employ all reagents except for the one of interest (termed fluorescence minus one, or "FMO" controls) becomes necessary to accurately identify expressing cells in the fully stained sample. <s> BIB008 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> The Fluorescence Activated Cell Sorter (FACS) was invented in the late 1960s by Bonner, Sweet, Hulett, Herzenberg, and others to do flow cytometry and cell sorting of viable cells. Becton Dickinson Immunocytometry Systems introduced the commercial machines in the early 1970s, using the Stanford patent and expertise supplied by the Herzenberg Laboratory and a Becton Dickinson engineering group under Bernie Shoor. Over the years, we have increased the number of measured FACS dimensions (parameters) and the speed of sorting to where we now simultaneously measure 12 fluorescent colors plus 2 scatter parameters. In this history, I illustrate the great utility of this state-of-the-art instrument, which allows us to simultaneously stain, analyze, and then sort cells from small samples of human blood cells from AIDS patients, infants, stem cell transplant patients, and others. I also illustrate analysis and sorting of multiple subpopulations of lymphocytes by use of 8-12 colors. In addition, I review single cell sorting used to clone and analyze hybridomas and discuss other applications of FACS developed over the past 30 years, as well as give our ideas on the future of FACS. These ideas are currently being implemented in new programs using the internet for data storage and analysis as well as developing new fluorochromes, e.g., green fluorescent protein and tandem dyes, with applications in such areas as apoptosis, gene expression, cytokine expression, cell biochemistry, redox regulation, and AIDS. Finally, I describe new FACS methods for measuring activated kinases and phosphatases and redox active enzymes in individual cells simultaneously with cell surface phenotyping. Thus, key functions can be studied in various subsets of cells without the need for prior sorting. <s> BIB009 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Abstract Objective.—To review the advances in clinically useful molecular biologic techniques and to identify their applications in clinical practice, as presented at the 11th Annual William Beaumont Hospital DNA Symposium. Data Sources.—The 8 manuscripts submitted were reviewed, and their major findings were compared with literature on the same or related topics. Study Selection.—Manuscripts address the use of molecular techniques in microbiology to evaluate infectious disease and epidemiology; molecular microbiology methods, including rapid-cycle real-time polymerase chain reaction; peroxisome proliferator–activated receptor γ as a potential therapeutic target in inflammatory bowel disease or colon cancer; the effect of nonapoptotic doses of the bisbenizamide dye Hoechst 33342 on luciferase expression in plasmid-transfected BC3H-1 myocytes; the routine use of cystic fibrosis screening and its challenges; and the use of flow cytometry and/or chromosomal translocation in the diagnostic evaluation of hemat... <s> BIB010 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> BackgroundCytokine flow cytometry (CFC) provides a multiparameter alternative to ELISPOT assays for rapid quantitation of antigen-specific T cells. To increase the throughput of CFC assays, we have optimized methods for stimulating, staining, and acquiring whole blood or PBMC samples in 96-well or 24-well plates.ResultsWe have developed a protocol for whole blood stimulation and processing in deep-well 24- or 96-well plates, and fresh or cryopreserved peripheral blood mononuclear cell (PBMC) stimulation and processing in conventional 96-well round-bottom plates. Samples from both HIV-1-seronegative and HIV-1-seropositive donors were tested. We show that the percent response, staining intensity, and cell recovery are comparable to stimulation and processing in tubes using traditional methods. We also show the equivalence of automated gating templates to manual gating for CFC data analysis.ConclusionWhen combined with flow cytometry analysis using an automated plate loader and an automated analysis algorithm, these plate-based methods provide a higher throughput platform for CFC, as well as reducing operator-induced variability. These factors will be important for processing the numbers of samples required in large clinical trials, and for epitope mapping of patient responses. <s> BIB011 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> +T cells’. Instead, investigation of ‘fine’ lymphocyte subsets is necessary to understand the mechanisms of immunological disease and protection. To distinguish among these specific cell types, it is necessary to measure simultaneously as many as six different T-cell-associated ‘markers’ (typically cell surface proteins); further functional characterization requires the additional measurement of responses (such as cytokine production, proliferative capacity or apoptotic potential). This has necessitated the development of flow cytometric technologies capable of detecting considerably more parameters than the typical 3- or 4-color cytometers prevalent to date. Current technology is capable of measuring 2 scatter and 12 fluorescence parameters simultaneously and individually for each cell. Because this technology comes with unique problems (and solutions), we term it polychromatic flow cytometry (PFC), which we use to refer in general to flow cytometric analyses encompassing 6 or more colors. Instrument manufacturers have recognized the growing demand for PFC, and most now provide the hardware necessary to carry out these types of measurements. During the past few years, several research groups have employed custom-built instruments with the capacity to quantify as many as 12 fluorescences. In this paper we explore how this multicolor cytometric technology is substantially refining extant methods to allow new experimental designs. Together, these types of studies are providing a completely new view of the immune system that encompasses a far greater heterogeneity of cell type and function than previously imagined. <s> BIB012 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Lymphocytes can become neoplastic at any stage of their development. When the neoplastic change occurs early on in their ontogeny, they act aggressively, producing a disease designated as lymphoblastic leukemia, invading the marrow, the blood, and sometimes, solid tissues (the latter are called lymphoblastic lymphomas). The antigen profile of these neoplastic cells recapitulates that of lymphoid precursors. When the oncogenic change takes place after lymphocytes have acquired maturation, the neoplastic progeny exhibit the phenotype of adult elements. If these more differentiated lymphocytes infiltrate lymph nodes or other lymphoid tissues or organs, we use the term lymphoma. When mature neoplastic lymphocytes circulate in the blood, they usually produce indolent ailments, generally designated as chronic lymphoproliferative disorders (CLPD). Differences between lymphomas and CLPD are not always distinct, since malignant lymphocytes may simultaneously involve tissues and blood. The term myeloma applies to systemic neoplasias of the bone marrow that are composed of plasma cells, the latest stage in the life of the B-lymphocyte. The nomenclature of lymphoid neoplasias has evolved over time and this process is certain to continue, as new biological advances are made. Until recently, characterization of the lymphomas and lymphoproliferative disorders was largely descriptive and based exclusively on microscopic observations. While useful, this approach is very limited, since the identity of neoplastic cells cannot always be established, nor can their clonal nature be recognized. Descriptive terms, such as large or small, follicular or diffuse, granular, intravascular, anaplastic, plasmacytic, etc., which were prevalent in earlier classifications, are still being used to characterize malignant lymphoid cells, even in modern taxonomies, like the World Health Organization (WHO) classification (1). However, this classification scheme now incorporates more meaningful biological properties for their category definitions, including extensive descriptions of the immunophenotype of the neoplastic cells. Flow cytometry (FCM) has been used in the analysis of human lymphomas since the late 1970s. The earlier studies focused mainly on the measurement of cell cycle phases and ploidy, exploiting the simplicity of measuring DNA content by this technology. In the 1980s, we witnessed a rapid increase in the use of FCM analysis of lymphoid tumors, as the understanding of the biology of the immune cells became much clearer and the production of specific markers such as monoclonal antibodies expanded rapidly. Today, FCM analysis is an accepted and essential medical practice in the clinical evaluation of lymphoid neoplasia. This technology assists in the diagnosis and characterization of lymphomas and CLPD, and in the detection of low-level disease. In patients with plasma cell dyscrasias, FCM is playing an ever-growing role, providing diagnostic support and prognostic information. <s> BIB013 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Abstract Context.—To review the applications of flow cytometry in the diagnosis and management of primary immunodeficiency disease. Data Sources.—Articles describing the use of flow cytometry in th... <s> BIB014 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> As a cell biologist, one experiences some ambivalencewith an enigmatic sensation on the occasion of the silverjubilee of Clinical Cytometry. This signiﬁcant milestone iscelebrated with ominous feelings because of the remark-able symbiosis that was forged between the birth of a newclinical diagnostic tool and the emergence of one of themost devastating infectious diseases of the 20th century. Itis less then 25 years ago that the ﬁrst reports surfacedabout clusters of aggressive opportunistic infections ap-pearing mysteriously in young gay men in some metropol-itan areas in the United States (1,2). The causative agent ofthe epidemic was unknown, yet the disease was wellcharacterized within a very short time span as the ac-quired immunodeﬁciency syndrome (AIDS). The initialCDC deﬁnition of AIDS incorporated leukocyte subsetnumbers from peripheral blood. AIDS’ deﬁning conditionincluded CD4 T-cell counts of 200 or less (3,4). Initially, inmost immunology laboratories, immunophenotyping wasperformed with ﬂuorescent microcopy. But quickly, thechoice of assay method shifted to ﬂow cytometry. As theworld-wide apocalyptic devastation began, cellular immu-nologists engaged in research had at their disposal anarsenal of reagents and instruments just waiting for anopportunity for qualitative assessment of immunodeﬁ-ciency. Therefore, in a very short time, instruments thatcould count T-helper cells were in great demand. Theepidemic had profound impact throughout the Westernworld, but those initial human losses paled in comparisonwith the carnage AIDS delivered and still is delivering inresource-poor regions of the globe. It is estimated that in2003, over 40 million individuals live with AIDS; about10% of this population lives in South Africa.Most of the time, signiﬁcant scientiﬁc progress occursin dull obscurity, but occasionally human curiosity pre-vails and the odd science story proceeds with signiﬁcantfanfare. The discoverers of the structure of DNA did re-ceive immediate accolades. However, in most instances oftechnical breakthroughs in diagnostics, the assessment ofthe day falls short of appropriate recognition. Invariablythe press selects trivia to report as newsworthy science.For example, the postmortem analysis of the space shuttledisaster received phenomenal coverage. Polemical reportswere bombarding us about how scientists were able todemonstrate that a piece of the foam debris at a certainvelocity could have or could not have damaged criticalceramic tiles attached to the wing on that doomed andmost unfortunate space vehicle. At the same time, thispast July, a subcommittee of the International Union ofPure and Applied Chemists identiﬁed the 110th element.The new chart recognizes darmstadtium (Ds) as a uniqueelement. It was formally added to the Periodic Chart. Thislatter event received virtually no publicity, while the sig-niﬁcance of the ﬁnding is relatively easy to convey to thepublic, as we all learned about the elements while takinghigh school chemistry. The low proﬁle coverage that isoften associated with our discipline was altered forever 25years ago. Events in cytology during the early 1980s weregoing to unfold in a most uneventful fashion. However,with a newly discovered incredibly devastating lethal dis-ease on the horizon, the role of the underrated ﬂowcytometer changed forever. Because reliable diagnosing ofimmunodeﬁciency was an essential requirement, clinicalcytometry was born. With ﬂow, rapid diagnosis of AIDSwas possible as early as 1981 (1). Clinical immunopheno-typing was possible just a few years after the discovery ofthe hybridoma technology. It opened the possibility forthe production of mAbs with high afﬁnity and avidity.Between 1978 and 1981, Milstein, Reinhertz, Schlossman,Goldstein, Janossy, and many others were diligently crank-ing out antibody-producing murine clones and were de-ﬁning and assigning function-speciﬁc roles to markers onhuman T- and B-cells. Flow cytometry, a laser-based tech-nology, was photogenic and there were no serologicaltests available to feature. Therefore, ﬂow cytometry prof-ited from seemingly spontaneous and persistent presscoverage. The virtuosity and veracity of the investigators’productivity a quarter-century ago expedited research ca-pacity in immunology beyond any expectations. Unfortu-nately, to date the regulatory role of various T-cell subsetshas not been fully resolved. The scholarly struggle to gainmore knowledge in this area, including oncology, hasadvanced, but it is far from over.Thereisalwaysasilverliningtoanyhumancatastrophe.The story of the AIDS pandemic is no exception. In thiscase, 25 years ago we had instrumentation to analyzeperipheral blood cells, but there was no apparent wide-scale clinical application beyond differentiating various <s> BIB015 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Alberto Orfao,* Francisco Ortuno, Maria de Santiago, Antonio Lopez, and Jesus San Miguel Servicio General de Citometria, Universidad de Salamanca, Salamanca, Spain Centro de Investigacion del Cancer y Departamento de Medicina, Universidad de Salamanca, Salamanca, Spain Servicio de Hematologia y Oncologia Medica, Hospital General Universitario J.M. Morales Meseguer, Murcia, Spain Servicio de Hematologia, Hospital Universitario de Salamanca, Salamanca, Spain <s> BIB016 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> The combination of DNA ploidy and S-phase is one of the strongest general prognostic indicators for node-negative breast cancer (1). The costs associated with this prognostic power are relatively minimal; laboratory total processing time is usually less than 10 min and reagents are relatively inexpensive. The test can be standardized world-wide as long as strict guidelines are followed, and the test’s result can be presented in a way that is easily understood by oncologists. DNA ploidy and S-phase prognostic strength can be further augmented by other wellknown prognostic indicators, such as menopausal status and primary tumor size. Figures 1 and 2 provide evidence for the above statements (1). Figure 1A shows highly significant relapse-free survival (RFS) stratifications for over 935 node-negative patients in the Baylor study. The stratifications are based on a prognostic model composed of an optimal combination of adjusted DNA ploidy and S-phase. When the same prognostic model and stratification boundaries are applied to other independent studies (see Fig. 1B and C), similar highly-significant patient stratifications are observed. Figure 2 demonstrates how the prognostic model can be further augmented by primary size and menopausal status. These patient stratifications are potentially important for managing this prevalent and deadly disease. In 1999, the incidence of breast cancer in the United States was over 170,000; greater than leukemia, lymphoma, and AIDS combined. In 2003, it is predicted to be over 200,000. Most of these cases are discovered before the tumor has infiltrated into the patient’s nodes (node-negative); therefore, this diagnostic test can potentially impact the strategy for optimal treatment for this large patient group. The DNA histogram test can be divided into four processing steps: sample preparation, DNA histogram acquisition, cell-cycle analysis, and final processing. In order for cytometrists to perform this test to its full capability, the following guidelines for each of these steps are recommended. <s> BIB017 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> In the late 1950s, Thomas et al. (1) successfully trans-planted bone marrow cells into supralethally irradiatedrecipients, and until the 1990s, the majority of autologousand allogeneic hematopoietic stem/progenitor cell trans-plants were performed utilizing bone marrow as a sourceof stem cells. In the mid-1980s, reports demonstrated thefeasibility of obtaining clinically useful numbers of periph-eral blood stem/progenitor cells (HPSC) from cancer pa-tients recovering from chemotherapy (2). By the early1990s, the availability of a number of hematopoietic cyto-kines used either singly or in combination and/or withchemotherapy facilitated the harvesting of peripheralblood stem cells (PBSC) (3). This development, coupledwith clear data that time to hematopoietic reconstitutionis signiﬁcantly shorter with PBSC compared to bone mar-row, has led to the widespread use of PBSC for autologousand, increasingly, allogeneic transplantation (reviewed in4). More recently, cord blood has provided a source ofHPSC, with an estimate of over 2,500 transplants per-formed worldwide since the ﬁrst one in 1988 (5). Tradi-tionally, the absolute mononuclear count in relation topatient body weight was used to predict the engraftmentpotential of bone marrow and, more recently, cord blood.However, due to the variable HPSC content of peripheralblood, this number is unreliable. Initially, colony-formingcell (CFC) assays were used as a surrogate for PBSC, butthis test has the limitation of requiring 10–14 days toperform, making it unsuitable for planning apheresisschedules. In addition, CFC assays are subjective and lackstandardization in both methodology and reagents. <s> BIB018 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Analysis of protein phosphorylation with flow cytometric techniques has emerged as a powerful tool in the field of immunological signaling, allowing cellular subsets in complex populations to be analyzed accurately and rapidly. In this review, we examine the development of phospho-epitope, or phospho-specific, flow cytometry and the premises upon which the technique is based. Phospho-specific flow cytometry is compared to traditional biochemical methods, and its advantages, such as single cell analysis, multiparameter data acquisition, rapid protocols, and the ability to analyze rare cell subsets, are detailed. We also discuss the many technical considerations that must be addressed when developing new antibodies or analyzing new epitopes including antigen accessibility, stability of the phospho-epitope, fluorophore selection, surface phenotype integrity, and antibody suitability for staining epitopes inside fixed and permeabilized cells. The methods that have been used to date are described in light of these technical considerations. The importance of developing bioinformatic platforms in parallel with these techniques is emphasized due to the large, multiparameter datasets that are rapidly accumulated and which require more efficient data viewing and complex clustering methods than currently available for flow cytometric data. Finally, we discuss the potential clinical applications of phospho-specific flow cytometry in analyzing immune cell development and antigen-specific immune responses, as well as pharmacodynamic profiling of disease states or drug efficacy and specificity against particular signaling proteins. <s> BIB019 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Described are four widely used procedures to analyze the cell cycle by flow cytometry. The first two are based on univariate analysis of cellular DNA content following cell staining with either propidium iodide (PI) or 4',6'-diamidino-2-phenylindole (DAPI) and deconvolution of the cellular DNA content frequency histograms. This approach reveals distribution of cells in three major phases of the cycle (G1 vs S vs G2/M) and makes it possible to detect apoptotic cells with fractional DNA content. The third approach is based on the bivariate analysis of DNA content and proliferation-associated proteins. The expression of cyclin D, cyclin E, cyclin A, or cyclin B1 vs DNA content is presented as an example. This approach allows one to distinguish, for example, G0 from G1 cells, identify mitotic cells, or relate expression of other intracellular proteins to the cell cycle position. The fourth procedure relies on the detection of 5'-bromo-2'-deoxyuridine (BrdU) incorporation to label the DNA-replicating cells. <s> BIB020 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> The increasing need for polychromatic approaches to flow cytometry, coupled with rapid technological advances, has pushed the frontiers of flow cytometry beyond 12-colour systems. Recent breakthroughs have allowed the design and implementation of instruments that measure 19 parameters (17 fluorescent colours and 2 physical parameters). This article describes the instrumentation and considers the reagents, analysis and applications for this powerful, new extension of flow-cytometric technology. <s> BIB021 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> PREHISTORY: THE PAINS OF THE PRE–FLOW CYTOMETRY ERA For young investigators who presently enter their scientific pursuit focused on cell cycle and have flow cytometry (FCM) at their disposal, it may be difficult to visualize the hardship of experimental procedures during the precytometry era. Autoradiography (1) was then the predominant method for cell cycle studies. It was a cumbersome and time-consuming methodology. The radioisotope-labeled cells deposited on microscope slides had to be fixed and covered with photographic emulsion in nearly total darkness. This was particularly tricky when using the “stripping film” approach, and required the preparer to either be on a carrot diet or to consume large quantities of vitamin A to enhance his or her night vision. After careful air-drying, the autoradiographs had to be left in light-proof boxes for several days’ and sometimes weeks’ exposure. Then, again in the dark, the autoradiographs had to be processed through developer, rinse, and fixer, followed by drying. Subsequently, the cells had to be counterstained through the emulsion (which also was tricky, because the emulsion had a tendency to detach, ruining the specimen) and mounted under a coverslip. Quantitative analysis of autoradiographs was painful as well. One had to identify labeled cells located below the silver grains of emulsion by microscopy, and score by eye the labeling index (LI) by counting hundreds of cells per each sample. Sometimes it was necessary to count individual silver grains, to estimate the intensity of cell labeling. Such analysis could take a long time, leaving the investigator with painful eyes and phantom images of the silver grains residing in his or her retina for hours. Attempts to develop semiautomatic or automatic screening of autoradiographs based on backward light scattering by silver grains of emulsion were generally unsuccessful (1). Despite the hardship, important discoveries were made, and numerous autoradiographic techniques, designed to assess the cell cycle and kinetics of cell proliferation, were developed. In fact, the evidence that DNA replication is discontinuous during the cycle, occurring within the distinct time interval during the interphase, was obtained by autoradiography (2,3). It was observed that the radioisotope (P or H)-labeled DNA precursor thymidine was incorporated into nuclei by a fraction of the interphase cells only, leaving many cells with unlabeled nuclei. This finding provided the foundation for subdivision of the cell cycle into four major phases: preDNA synthetic interphase or postmitotic gap (G1), DNA synthesis phase (S), postsynthetic interphase or premitotic gap (G2), and mitosis (M). Perhaps the most elegant technique to measure kinetics of cell progression through the cycle was based on pulse-labeling cells with H-thymidine, followed by analysis of the percentage of labeled mitotic cells (3,4). Analysis of the kinetics of progression of the cohort of cells labeled during the short pulse in S phase through the narrow time-window of the M phase provided an accurate estimate of the duration of each phase of the cycle and of the whole cell cycle (Tc) (4). In vitro and in vivo applications of H-thymidine autoradiography yielded a wealth of information about cell cycle and the kinetics of cell proliferation of several normal and cancer cell models (4–6). Microspectrophotometry and microfluorometry were also applied in studies of the cell cycle, as techniques complementary to autoradiography. They were used to measure the content of DNA, RNA, and protein in individual cells. However, only few laboratories could afford such instruments, which were then generally homemade. Their development and maintenance required a significant investment and close collaboration of biologists with optical and mechanic engineers. During 1967–1968, one of us (ZD) had an opportunity to use these instruments at the Institute for Medical Cell Research and Genetics at the Karolinska Institute in Stockholm. Directed by Torbjorn Caspersson, the “grandfather” of cytometry (7), this laboratory had the most advanced microspectrophotometers and microfluorometers at the time, and was the Mecca for scientists from all over the world who were seeking a possibility to quantify DNA, RNA, or total protein in individual cells. Reservations to use the microspectrophotometer had to be made weeks ahead, as there was a long waiting line of investigators eager to measure cells. After some experience was gained, approximately 40 cells could be measured per hour. This number was then considered be adequate for statistical analyses in most publications. Needless to say, the cell analysis had to be biased <s> BIB022 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> FLOW CYTOGENETICS Flow cytometric analyses of individual chromosomes were made possible by the development of techniques for isolating intact chromosomes (1) and by procedures for staining chromosomes with DNA-specific fluorescent dyes. The term flow cytogenetics (2) was coined in the early 1980s to describe the application of flow cytometry and sorting to chromosome classification and purification. Two publications that appeared in 1975 described highresolution measurements of single mammalian chromosomes in a flow cytometer (3,4) and illustrated the use of sorting for chromosome purification (3). Although singlecolor measurements were an exciting development and a portent of what was to come, the development of twocolor staining provided the resolution needed to separate most of the human chromosomes. The introduction of Hoechst and chromomycin staining of mammalian chromosomes in 1979 (5) provided the breakthrough that allowed flow cytogenetics to achieve the successes described below. The ability to distinguish among individual chromosome types provided the first opportunities to distinguish and purify them in quantities suitable for visual, chemical, and molecular analyses. Initial sorting studies allowed chromosome types to be associated with flow karyotype peaks and provided chromosomes in a form that could be used for multiple purposes. <s> BIB023 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> The plant kingdom is divided into the lower plants (blue-green algae, green algae, the bryophytes consisting of mosses and liverworts, and the euglenaphytes) and the higher plants (which mainly comprise the vascular plants). Higher plants are grouped into the pteridophytes (ferns) and their relatives, and the two classifications of seed plants, the angiosperms and the gymnosperms. The former comprises the monocotyledons and the dicotyledons, and the latter comprises the conifers and cycads (1). In this retrospective, I have restricted my coverage to the seed plants. Plants differ fundamentally from animals in a few general ways, one of which is the almost universal presence of a cellulosic cell wall. This is coupled to a method of cell division that involves partitioning of the daughter nuclei after mitosis by a phragmoplast, which expands and elaborates to join the parental cell walls, thereby effecting cytokinesis. Consequently, the cell walls of daughter cells remain topologically continuous with one another. Given that the entire mature form of a plant is achieved through regulated cell division and cell expansion, all cell walls can be viewed as a single continuum, termed the apoplast. The concept of flow cytometry and cell sorting arose in the late 1960s and 1970s from the study of natural single-cell suspensions, particularly those of the hematopoietic system. At first glance, these technologies did not seem applicable to higher plants, comprising complex three-dimensional tissue architectures of interlinked cells. In fact, only in the last few weeks did I become aware of what appears to be the first report, in German, of the use of flow cytometry for analysis of fluorescence signals from higher plant nuclei prepared from fixed tissues (1a), which involved use of ISAC member Wolfgang Gohde’s flow cytometer (2). My interest in flow cytometry and cell sorting started in 1976, when I took up a NATO postdoctoral fellowship at Stanford University. I was working in the laboratory of Dr. Peter Ray in the Department of Biological Sciences, about 300 yards away from the medical school. One of the topical interests of plant biologists at that time was the production from plant tissues of single-cell suspensions (termed protoplasts, prepared by enzymatic hydrolysis and solubilization of the cell wall) and their use for somatic hybridization. Kao and coworkers at Saskatoon had described the use of polyethylene glycol for the induction of high-frequency fusion of protoplasts (3), an observation that translated to animal cells and became a major technical basis for the emerging hybridoma technology. One persistent problem for plant biologists interested in somatic cell fusion was how to recognize the two parental sets of protoplasts that would be employed as fusion partners. Genetic mutants and transgenic lines resistant to various chemicals were not at that time available. In part of my graduate work at Cambridge, I had explored the use of generating antibodies directed against plant protoplasts with the hope of being able to find antibodies directed against plasma membrane proteins. At Stanford I became aware that Len Herzenberg had developed an interesting machine that was capable of recognizing and sorting cells based on surface fluorescence. I remember walking over to his laboratory to see a version of the fluorescenceactivated cell sorter (I think a FACS IV) installed there. I recognized the possibilities offered by the instrument, but also realized that it would be necessary to devise means to label specific plant cells by using fluorescent tags before the fluorescence-activated cell sorter could be used for sorting hybrid protoplasts. There also was a variety of technical questions that would require solving, including how to accommodate protoplasts having diameters close to, or in some cases greater than, that of the flow tips. To label protoplasts, it would be necessary to find two pairs of fluorochromes that had distinct absorption and emission spectra. Keller et al. (4) in 1977 had described the synthesis of lipid-linked derivatives of fluorescein and rhodamine. I and my colleagues synthesized these molecules and found that they could be used to prelabel cell cultures from which fluorescent protoplasts could subsequently be produced (5). Alternatively, we found it was possible to label protoplasts after preparation by using fluorescein isothiocyanate and rhodamine isothiocyanate (6), which are required for protoplasts prepared from tissues other than suspension cultures. It therefore seemed feasible that flow cytometry and cell sorting might be used for heterokaryon identification and cell sorting for their purification (7,8). For the work to be successfully accomplished, of course, we would need a flow sorter. In 1979, I put together a multi-user equipment proposal to the National Science Foundation requesting funds to purchase a cell <s> BIB024 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Because this article is a bit of history rather than ascientiﬁc paper, we will depart from the formality of ajournalarticle.Thisarticlehastwopurposes.Theﬁrstistogive credit to two deceased individuals, Albert Castro,M.D., and Howard Gratzner, Ph.D. (1). Castro was the keyperson who was able to produce a polyclonal antibodythat was speciﬁc for bromodeoxyuridine (BrdU). For 10years, Gratzner took the lead in converting an idea into afunctional assay that affected DNA analysis and led to thedevelopment of many other assays with similar method-ologies. The second is to inform young scientists that (a)inventions or creations have utility in ﬁelds other than thepurpose for which they were created; (b) it is possible todo useful work in an institution that certainly would nothave been classiﬁed as one of the best; and (c) the pres-ence of talented people who are willing to collaborate isinvaluable.Scientiﬁcally, Gratzner is best known for the develop-ment of the ﬁrst monoclonal antibody (mAb) to BrdU andiododeoxyuridine. This work was the culmination of pre-vious efforts with Robert Leif to develop polyclonal anti-bodies, which detected BrdU with variable speciﬁcity.Collaborations with scientists at the Lawrence LivermoreLaboratory encouraged the development of a widely usedﬂow cytometric test for the simultaneous measurementsof DNA and BrdU. The use of mAbs to halogenated pyri-midines was a signiﬁcant scientiﬁc advance and currentlyis an integral tool for the analysis by ﬂow and digitalmicroscopy of the cell cycle.On a personal level, Gratzner’s enthusiasm for scienceand his intelligence, curiosity, loyal interactions with fel-low collaborators, and friendship are sorely missed by allwhoknewhim.Hewasgenuinelyawarmandnicepersonwho was liked by almost everyone with whom he came incontact (1).Castro was a jovial man who interacted well with hiscollaborators. He was generous and very knowledgeable.He ran an immunodiagnostics laboratory at the universi-ties of Oregon and Miami and was an expert in the pro-duction of antibodies in animals. His warm smile, gener-ous giving of his knowledge, common sense, enthusiasmfor scientiﬁc research, and ability to get the job done wereessential to this project.Because this is a story of events that happened approx-imately 30 years ago and the existing records are limited,the accuracy of this article is limited by our memory andthose of our colleagues who have kindly helped with thisproject. Of course, this is our version of the events.Cell division and DNA replication are fundamental tobiology and speciﬁcally to the biology of cancer. Thisarticledescribeshowasimpleprocedureforthedetectionand quantitation of DNA synthesis was developed. Theprecise determination of when S phase occurs in the cellcycle is of use to maximize the selective killing of tumorcells with cycle-speciﬁc chemotherapeutic drugs. Ofgreater signiﬁcance, studies of the control of the cellcycle, including its detour into apoptosis, can provideextremely useful insights into the creation of new thera-peutic regimens.The original process to detect S-phase cells included theuse of tritiated thymidine with autoradiography to mea-sure the labeling index (percentage of S-phase cells)and/or the fraction of labeled mitotic cells. Before and atthe infancy of ﬂow cytometry, it was believed that thequantitative DNA analysis of normal and neoplastic cellsmight provide an objective marker for the diagnosis ofneoplasia. These measurements were performed with amicroscope on Feulgen-stained cells. In a normal popula-tion, it was established that there were two predominantpeaks in the DNA distribution, with a ratio of 1:2 in DNAcontent, and that cells that were synthesizing DNA (Sphase) were scattered in between, with a relative DNAcontent between 1 and 2. The detection of tritiated thy- <s> BIB025 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Until the second half of the 1990s, quantification of MHC-restricted, peptide-specific T lymphocytes required cell culture– based techniques of mononuclear cell suspensions that were often laborious, cumbersome, and prone to bias towards cellular subsets with growth advantage. Initially, limiting dilution assays were used. These assays include multiple days of culturing T lymphocytes in the presence of antigen and antigen-presenting cells, and the functional read-outs produced were T-cell proliferation (1) or cytolytic activity (2). However, such assays may not accurately reflect the function and frequency of T cells in vivo, due to selective proliferation and apoptosis that occur over time in culture. Alternatively, assays for T-cell activation have been developed based on the upregulation of the early activation marker CD69 (3) and/or the production of cytokines upon specific stimulation. The cytokines thus being secreted by the T cells can be measured <s> BIB026 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Flow cytometry-based immunophenotyping assays have become increasingly multiparametric, concomitantly analyzing multiple cellular parameters. To maximize the quality of the information obtained, antibody conjugate panels need to be developed with care, including requisite controls at every step. Such an optimization procedure for multicolor immunophenotyping assays is time consuming, but the value of having a reliable antibody conjugate panel that provides for sensitive detection of all molecules of interest justifies this time investment. This article outlines important considerations and procedures to undertake for the successful design and development of multicolor flow cytometry panels. <s> BIB027 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Introduction <s> Scientists today collect samples of curves and other functional observations. This monograph presents many ideas and techniques for such data. Included are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drwan from growth analysis, meterology, biomechanics, equine science, economics, and medicine. The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, to applied data analysts, and to experienced researchers; it will have value both within statistics and across a broad spectrum of other fields. Much of the material is based on the authors' own work, some of which appears here for the first time. Jim Ramsay is Professor of Psychology at McGill University and is an international authority on many aspects of multivariate analysis. He draws on his collaboration with researchers in speech articulation, motor control, meteorology, psychology, and human physiology to illustrate his technical contributions to functional data analysis in a wide range of statistical and application journals. Bernard Silverman, author of the highly regarded "Density Estimation for Statistics and Data Analysis," and coauthor of "Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach," is Professor of Statistics at Bristol University. His published work on smoothing methods and other aspects of applied, computational, and theoretical statistics has been recognized by the Presidents' Award of the Committee of Presidents of Statistical Societies, and the award of two Guy Medals by the Royal Statistical Society. <s> BIB028
Flow cytometry (FCM) is widely used in health research and treatment for a variety of tasks, such as providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, in the diagnosis and monitoring of leukemia and lymphoma patients, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases BIB013 BIB006 BIB014 BIB010 BIB015 BIB016 BIB017 BIB018 . The technology is also used in crossmatching organs for transplantation, research involving stem cells, vaccine development, apoptosis, phagocytosis, and a wide range of cellular properties including phenotype, cytokine expression, and cell-cycle status BIB019  BIB020 BIB003 BIB004 BIB005 . Clinically, FCM is also used to analyze a wide array of immunological parameters in disease and to study the humoral and cellular response to vaccines. FCM traditionally has been a tube-based technique limited to small-scale laboratory and clinical studies BIB027 . Due to recent hardware advances it is now possible to analyze thousands of samples per day. This has dramatically increased the efficiency and use of this technique and allowed the adoption of FCM to high-throughput settings. It is widely recognized that data analysis is by far one of the most challenging and time-consuming aspects of FCM experiments as well as being a primary source of variation in clinical tests BIB017 BIB019  [16] BIB007 BIB011 BIB028 BIB009 BIB002 BIB008 BIB012 BIB021  . Investigators have traditionally relied on intuition rather than on standardized statistical inference in the analysis of FCM data. The increased volume and complexity of FCM data resulting from the increased throughput greatly boosts the demand for reliable statistical methods and accompanying software implementations, for the analysis of these data [1-6, 16, 20, 23, 26-31] . This is because the ability to analyze FCM data is lagging far behind the ability to collect samples and to run FCM analyses, to the detriment of health research. This article reviews published approaches for FCM data analysis in the context of a framework created to facilitate the reporting and review process.  BIB001 . This technology can be used to examine many cellular parameters on live or fixed cells, including surface, cytoplasmic, and nuclear proteins, DNA, RNA, reactive-oxygen species, intracellular pH, and calcium flux. Measurement of the expression of cellular-activation markers, intracellular cytokines, immunological signaling, and cytoplasmic and nuclear cell cycle and transcription factors can also be readily performed BIB019 BIB020 BIB003 BIB022 BIB023 BIB024 BIB025 BIB026 .