Computational analysis of high-throughput flow cytometry data

Comparative assessment of cytometry by time-of-flight and full spectral flow cytometry based on a 33-color antibody panel

Mass cytometry and full spectrum flow cytometry have recently emerged as new promising single cell proteomic analysis tools that can be exploited to decipher the extensive diversity of immune cell repertoires and their implication in human diseases. In this study, we evaluated the performance of mass cytometry against full spectrum flow cytometry using an identical 33-color antibody panel on four healthy individuals. Our data revealed an overall high concordance in the quantification of major immune cell populations between the two platforms using a semi-automated clustering approach. We further showed a strong correlation of cluster assignment when comparing manual and automated clustering. Both comparisons revealed minor disagreements in the quantification and assignment of rare cell subpopulations. Our study showed that both single cell proteomic technologies generate highly overlapping results and substantiate that the choice of technology is not a primary factor for successful biological assessment of cell profiles but must be considered in a broader design framework of clinical studies.

Comparative analysis between zebrafish and an automated live-cell assay to classify developmental neurotoxicant chemicals

Modern toxicology's throughput has dramatically increased due to alternative models, laboratory automation, and machine learning. This has enabled comparative studies across species and assays to prioritize chemical hazard potential and to understand how different model systems might complement one another. However, such comparative studies of high-throughput data are still in their infancy, with more groundwork needed to firmly establish the approach. Therefore, this study aimed to compare the bioactivity of the NIEHS Division of Translational Toxicology's (DTT) 87-compound developmental neurotoxicant (DNT) library in zebrafish and an in vitro high-throughput cell culture system. The early life-stage zebrafish provided a whole animal approach to developmental toxicity assessment. Chemical hits for abnormalities in embryonic zebrafish morphology, mortality, and behavior (ZBEscreen™) were compared with chemicals classified as high-risk by the Cell Health Index (CHI™), which is an outcome class probability from a machine learning classifier using 12 parameters from the SYSTEMETRIC® Cell Health Screen (CHS). The CHS was developed to assess human toxicity risk using supervised machine learning to classify acute cell stress phenotypes in a human leukemia cell line (HL60 cells) following a 4-h exposure to a chemical of interest. Due to the design of the screen, the zebrafish assays were more exhaustive, yielding 86 total bioactive hits, whereas the SYSTEMETRIC® CHS focusing on acute toxicity identified 20 chemicals as potentially toxic. The zebrafish embryonic and larval photomotor response assays (EPR and LPR, respectively) detected 40 of the 47 chemicals not found by the zebrafish morphological screen and CHS. Collectively, these results illustrate the advantages of using two alternative models in tandem for rapid hazard assessment and chemical prioritization and the effectiveness of CHI™ in identifying toxicity within a single multiparametric assay.

A Guide to Flow Cytometry: Components, Basic Principles, Experimental Design, and Cancer Research Applications

Flow cytometry (FCM) is a state-of-the-art technique for the qualitative and quantitative assessment of cells and other particles' physical and biological properties. These cells are suspended within a high-velocity fluid stream and pass through a laser beam in single file. The main principle of the FCM instrument is the light scattering and fluorescence emission upon the interaction of the fluorescent particle with the laser beam. It also allows for the physical sorting of particles depending on different parameters. A flow cytometer comprises different components, including fluidic, optics, and electronics systems. This article briefly explains the mechanism of all components of a flow cytometer to clarify the FCM technique's general principles, provides some useful guidelines for the proper design of FCM panels, and highlights some general applications and important applications in cancer research. © 2023 Wiley Periodicals LLC.

Computational methods for characterizing and learning from heterogeneous cell signaling data

Heterogeneity in cell signaling pathways is increasingly appreciated as a fundamental feature of cell biology and a driver of clinically relevant disease phenotypes. Understanding the causes of heterogeneity, the cellular mechanisms used to control heterogeneity, and the downstream effects of heterogeneity in single cells are all key obstacles for manipulating cellular populations and treating disease. Recent advances in genetic engineering, including multiplexed fluorescent reporters, have provided unprecedented measurements of signaling heterogeneity, but these vast data sets are often difficult to interpret, necessitating the use of computational techniques to extract meaning from the data. Here, we review recent advances in computational methods for extracting meaning from these novel data streams. In particular, we evaluate how machine learning methods related to dimensionality reduction and classification can identify structure in complex, dynamic datasets, simplifying interpretation. We also discuss how mechanistic models can be merged with heterogeneous data to understand the underlying differences between cells in a population. These methods are still being developed, but the work reviewed here offers useful applications of specific analysis techniques that could enable the translation of single-cell signaling data to actionable biological understanding.

The effects of incubation media on the assessment of the shape of human erythrocytes by flow cytometry: a contribution to mathematical data interpretation to enable wider application of the method

Flow cytometry (FC) analysis of erythrocyte shape and related biomechanical properties, such as osmotic fragility, have not moved from a research tool to regular clinical testing. The main reason is existing evidence that various pre-analytical factors influence the mathematical interpretation of the data obtained. With an aim to contribute to the standardization and broaden the use of FC for human erythrocyte shape assessment, freshly prepared peripheral blood erythrocytes isolated from healthy donors were incubated in iso and hypo-osmotic solutions (pure saline, saline with potassium and calcium, and phosphate buffered saline) and examined by FC using values of forward scatter (FSC) and side scatter (SSC). Kurtosis, skewness, Pearson's second skewness coefficient of dissymmetry (PCD), and spherical index, calculated from FSC distributions, were used for the erythrocyte shape evaluation. In all isotonic media FSC distribution and FSC-based morphology parameters showed huge inter-individual and inter-medium variation. With decreasing osmolality, in all media and samples, the size of the erythrocytes increased, and swelling index and kurtosis decreased. However, changes in skewness and PCD were influenced by the medium used and the sample tested. Compared to FSC, SSC signal in isotonic and its change in hypotonic media showed lower inter-individual variation and was not influenced by the type of medium. We propose a spherical index and kurtosis as FSC-based indicators of erythrocyte shape. As more resistant to the influence of the preanalytical treatment, SSC data appeared to be unfairly neglected for the assessment of erythrocyte shape, in comparison to the usually employed FSC data.

Diagnosis of Malaria Parasites Plasmodium spp. in Endemic Areas: Current Strategies for an Ancient Disease

Fast and effective detection of the causative agent of malaria in humans, protozoan Plasmodium parasites, is of crucial importance for increasing the effectiveness of treatment and to control a devastating disease that affects millions of people living in endemic areas. The microscopic examination of Giemsa-stained blood films still remains the gold-standard in Plasmodium detection today. However, there is a high demand for alternative diagnostic methods that are simple, fast, highly sensitive, ideally do not rely on blood-drawing and can potentially be conducted by the patients themselves. Here, the history of Plasmodium detection is discussed, and advantages and disadvantages of diagnostic methods that are currently being applied are assessed.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Single-cell technologies in reproductive immunology

The maternal-fetal interface represents a unique immune privileged site that maintains the ability to defend against pathogens while orchestrating the necessary tissue remodeling required for placentation. The recent discovery of novel cellular families (innate lymphoid cells, tissue-resident NK cells) suggests that our understanding of the decidual immunome is incomplete. To understand this complex milieu, new technological developments allow reproductive immunologists to collect increasingly complex data at a cellular resolution. Polychromatic flow cytometry allows for greater resolution in the identification of novel cell types by surface and intracellular protein. Single-cell RNA-seq coupled with microfluidics allows for efficient cellular transcriptomics. The extreme dimensionality and size of data sets generated, however, requires the application of novel computational approaches for unbiased analysis. There are now multiple dimensionality reduction (tSNE, SPADE) and visualization tools (SPICE) that allow researchers to efficiently analyze flow cytometry data. Development of computational tools has also been extended to RNA-seq data (including scRNA-seq), which requires specific analytical tools. Here, we provide an overview and a brief primer for the reproductive immunology community on data acquisition and computational tools for the analysis of complex flow cytometry and RNA-seq data.

From big flow cytometry datasets to smart diagnostic strategies: The EuroFlow approach

The rise in the analytical speed of mutiparameter flow cytometers made possible by the introduction of digital instruments, has brought up the possibility to manage progressively higher number of parameters simultaneously on significantly greater numbers of individual cells. This has led to an exponential increase in the complexity and volume of flow cytometry data generated about cells present in individual samples evaluated in a single measurement. This increase demands for new developments in flow cytometry data analysis, graphical representation, and visualization and interpretation tools to address the new big data challenges, i.e. processing data files of ≥10-25 parameters per cell in samples with >5-10 million cells (= up to 250 million data points per cell sample) obtained in a few minutes. Here, we present a comprehensive review of some of the tools developed by the EuroFlow consortium for processing flow cytometric big data files in diagnostic laboratories, particularly focused on automated EuroFlow approaches for: i) identification of all cell populations coexisting in a sample (automated gating); ii) smart classification of aberrant cell populations in routine diagnostics; iii) automated reporting; together with iv) new tools developed to visualize n-dimensional data in 2-dimensional plots to support expert-guided automated data analysis. The concept of using reference data bases implemented into software programs, in combination with multivariate statistical analysis pioneered by EuroFlow, provides an innovative, highly efficient and fast approach for diagnostic screening, classification and monitoring of patients with distinct hematological and immune disorders, as well as other diseases.

Data-Driven Flow Cytometry Analysis

The emergence of flow and mass cytometry technologies capable of generating 40-dimensional data has spurred research into automated methodologies that address bottlenecks across the entire analysis process from quality checking, data transformation, and cell population identification, to biomarker identification and visualizations. We review these approaches in the context of the stepwise progression through the different steps, including normalization, automated gating, outlier detection, and graphical presentation of results.

Of Cytometry, Stem Cells and Fountain of Youth

Outlined are advances of cytometry applications to identify and sort stem cells, of laser scanning cytometry and ImageStream imaging instrumentation to further analyze morphometry of these cells, and of mass cytometry to classify a multitude of cellular markers in large cell populations. Reviewed are different types of stem cells, including potential candidates for cancer stem cells, with respect to their "stemness", and other characteristics. Appraised is further progress in identification and isolation of the "very small embryonic-like stem cells" (VSELs) and their autogenous transplantation for tissue repair and geroprotection. Also assessed is a function of hyaluronic acid, the major stem cells niche component, as a guardian and controller of stem cells. Briefly appraised are recent advances and challenges in the application of stem cells in regenerative medicine and oncology and their future role in different disciplines of medicine, including geriatrics.

Antibody Engineering for Pursuing a Healthier Future

Since the development of antibody-production techniques, a number of immunoglobulins have been developed on a large scale using conventional methods. Hybridoma technology opened a new horizon in the production of antibodies against target antigens of infectious pathogens, malignant diseases including autoimmune disorders, and numerous potent toxins. However, these clinical humanized or chimeric murine antibodies have several limitations and complexities. Therefore, to overcome these difficulties, recent advances in genetic engineering techniques and phage display technique have allowed the production of highly specific recombinant antibodies. These engineered antibodies have been constructed in the hunt for novel therapeutic drugs equipped with enhanced immunoprotective abilities, such as engaging immune effector functions, effective development of fusion proteins, efficient tumor and tissue penetration, and high-affinity antibodies directed against conserved targets. Advanced antibody engineering techniques have extensive applications in the fields of immunology, biotechnology, diagnostics, and therapeutic medicines. However, there is limited knowledge regarding dynamic antibody development approaches. Therefore, this review extends beyond our understanding of conventional polyclonal and monoclonal antibodies. Furthermore, recent advances in antibody engineering techniques together with antibody fragments, display technologies, immunomodulation, and broad applications of antibodies are discussed to enhance innovative antibody production in pursuit of a healthier future for humans.

Automated Assessment of Disease Progression in Acute Myeloid Leukemia by Probabilistic Analysis of Flow Cytometry Data

OBJECTIVE

Flow cytometry (FC) is a widely acknowledged technology in diagnosis of acute myeloid leukemia (AML) and has been indispensable in determining progression of the disease. Although FC plays a key role as a posttherapy prognosticator and evaluator of therapeutic efficacy, the manual analysis of cytometry data is a barrier to optimization of reproducibility and objectivity. This study investigates the utility of our recently introduced nonparametric Bayesian framework in accurately predicting the direction of change in disease progression in AML patients using FC data.

METHODS

The highly flexible nonparametric Bayesian model based on the infinite mixture of infinite Gaussian mixtures is used for jointly modeling data from multiple FC samples to automatically identify functionally distinct cell populations and their local realizations. Phenotype vectors are obtained by characterizing each sample by the proportions of recovered cell populations, which are, in turn, used to predict the direction of change in disease progression for each patient.

RESULTS

We used 200 diseased and nondiseased immunophenotypic panels for training and tested the system with 36 additional AML cases collected at multiple time points. The proposed framework identified the change in direction of disease progression with accuracies of 90% (nine out of ten) for relapsing cases and 100% (26 out of 26) for the remaining cases.

CONCLUSIONS

We believe that these promising results are an important first step toward the development of automated predictive systems for disease monitoring and continuous response evaluation.

SIGNIFICANCE

Automated measurement and monitoring of therapeutic response is critical not only for objective evaluation of disease status prognosis but also for timely assessment of treatment strategies.

Bovine viral diarrhea virus structural protein E2 as a complement regulatory protein

Bovine viral diarrhea virus (BVDV) is a member of the genus Pestivirus, family Flaviviridae, and is one of the most widely distributed viruses in cattle worldwide. Approximately 60 % of cattle in endemic areas without control measures are infected with BVDV during their lifetime. This wide prevalence of BVDV in cattle populations results in significant economic losses. BVDV is capable of establishing persistent infections in its host due to its ability to infect fetuses, causing immune tolerance. However, this cannot explain how the virus evades the innate immune system. The objective of the present work was to test the potential activity of E2 as a complement regulatory protein. E2 glycoprotein, produced both in soluble and transmembrane forms in stable CHO-K1 cell lines, was able to reduce complement-mediated cell lysis up to 40 % and complement-mediated DNA fragmentation by 50 %, in comparison with cell lines not expressing the glycoprotein. This work provides the first evidence of E2 as a complement regulatory protein and, thus, the finding of a mechanism of immune evasion by BVDV. Furthermore, it is postulated that E2 acts as a self-associated molecular pattern (SAMP), enabling the virus to avoid being targeted by the immune system and to be recognized as self.

Analyses of 123 Peripheral Human Immune Cell Subsets: Defining Differences with Age and between Healthy Donors and Cancer Patients Not Detected in Analysis of Standard Immune Cell Types

Recent advances in human immunology have led to the identification of novel immune cell subsets and the biological function of many of these subsets has now been identified. The recent US Food and Drug Administration approval of several immunotherapeutics for the treatment of a variety of cancer types and the results of ongoing immunotherapy clinical studies requires a more thorough interrogation of the immune system. We report here the use of flow cytometry-based analyses to identify 123 immune cell subsets of peripheral blood mononuclear cells. The use of these panels defines multiple differences in younger (< 40 years) vs. older (≥ 40 years) individuals and between aged-matched apparently healthy individuals and metastatic cancer patients, aspects not seen in the analysis of the following standard immune cell types: CD8, CD4, natural killer, natural killer-T, regulatory T, myeloid derived suppressor cells, conventional dendritic cells (DCs), plasmacytoid DCs and B cells. The use of these panels identifying 123 immune cell subsets may aid in the identification of patients who may benefit from immunotherapy, either prior to therapy or early in the immunotherapeutic regimen, for the treatment of cancer or other chronic or infectious diseases.

Deep profiling of multitube flow cytometry data

MOTIVATION

Deep profiling the phenotypic landscape of tissues using high-throughput flow cytometry (FCM) can provide important new insights into the interplay of cells in both healthy and diseased tissue. But often, especially in clinical settings, the cytometer cannot measure all the desired markers in a single aliquot. In these cases, tissue is separated into independently analysed samples, leaving a need to electronically recombine these to increase dimensionality. Nearest-neighbour (NN) based imputation fulfils this need but can produce artificial subpopulations. Clustering-based NNs can reduce these, but requires prior domain knowledge to be able to parameterize the clustering, so is unsuited to discovery settings.

RESULTS

We present flowBin, a parameterization-free method for combining multitube FCM data into a higher-dimensional form suitable for deep profiling and discovery. FlowBin allocates cells to bins defined by the common markers across tubes in a multitube experiment, then computes aggregate expression for each bin within each tube, to create a matrix of expression of all markers assayed in each tube. We show, using simulated multitube data, that flowType analysis of flowBin output reproduces the results of that same analysis on the original data for cell types of >10% abundance. We used flowBin in conjunction with classifiers to distinguish normal from cancerous cells. We used flowBin together with flowType and RchyOptimyx to profile the immunophenotypic landscape of NPM1-mutated acute myeloid leukemia, and present a series of novel cell types associated with that mutation.

ISAC's classification results file format

Identifying homogenous sets of cell populations in flow cytometry is an important process for sorting and selecting populations of interests for further data acquisition and analysis. Many computational methods are now available to automate this process, with several algorithms partitioning cells based on high-dimensional separation versus the traditional pairwise two-dimensional visualization approach of manual gating. ISAC's classification results file format was developed to exchange the results of both manual gating and algorithmic classification approaches in a standardized way based on per event based classifications, including the potential for soft classifications expressed as the probability of an event being a member of a class. © 2014 International Society for Advancement of Cytometry.

An automated high-throughput cell-based multiplexed flow cytometry assay to identify novel compounds to target Candida albicans virulence-related proteins

Although three major classes of systemic antifungal agents are clinically available, each is characterized by important limitations. Thus, there has been considerable ongoing effort to develop novel and repurposed agents for the therapy of invasive fungal infections. In an effort to address these needs, we developed a novel high-throughput, multiplexed screening method that utilizes small molecules to probe candidate drug targets in the opportunistic fungal pathogen Candida albicans. This method is amenable to high-throughput automated screening and is based upon detection of changes in GFP levels of individually tagged target proteins. We first selected four GFP-tagged membrane-bound proteins associated with virulence or antifungal drug resistance in C. albicans. We demonstrated proof-of-principle that modulation of fluorescence intensity can be used to assay the expression of specific GFP-tagged target proteins to inhibitors (and inducers), and this change is measurable within the HyperCyt automated flow cytometry sampling system. Next, we generated a multiplex of differentially color-coded C. albicans strains bearing C-terminal GFP-tags of each gene encoding candidate drug targets incubated in the presence of small molecules from the Prestwick Chemical Library in 384-well microtiter plate format. Following incubation, cells were sampled through the HyperCyt system and modulation of protein levels, as indicated by changes in GFP-levels of each strain, was used to identify compounds of interest. The hit rate for both inducers and inhibitors identified in the primary screen did not exceed 1% of the total number of compounds in the small-molecule library that was probed, as would be expected from a robust target-specific, high-throughput screening campaign. Secondary assays for virulence characteristics based on null mutant strains were then used to further validate specificity. In all, this study presents a method for the identification and verification of new antifungal drugs targeted to fungal virulence proteins using C. albicans as a model fungal pathogen.

A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects

Background

Flow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.

The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way.

Results

We demonstrate the performance of the proposed method in identifying anomalous samples in two different FC data sets, one of which represents a set of samples including acute myeloid leukemia (AML) cases, and the other a generic 5-parameter peripheral-blood immunophenotyping. Results are evaluated in terms of the area under the receiver operating characteristics curve (AUC). ASPIRE achieved AUCs of 0.99 and 1.0 on the AML and generic blood immunophenotyping data sets, respectively.

Conclusions

These results demonstrate that anomalous samples can be identified by ASPIRE with almost perfect accuracy without a priori access to samples of anomalous subtypes in the training set. The ASPIRE approach is unique in its ability to form generalizations regarding normal and anomalous states given only very weak assumptions regarding sample characteristics and origin. Thus, ASPIRE could become highly instrumental in providing unique insights about observed biological phenomena in the absence of full information about the investigated samples.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-314) contains supplementary material, which is available to authorized users.

How to build a time-gated luminescence microscope

The sensitivity of filter-based fluorescence microscopy techniques is limited by autofluorescence background. Time-gated detection is a practical way to suppress autofluorescence, enabling higher contrast and improved sensitivity. In the past few years, three groups of authors have demonstrated independent approaches to build robust versions of time-gated luminescence microscopes. Three detailed, step-by-step protocols are provided here for modifying standard fluorescent microscopes to permit imaging time-gated luminescence.

Single cell spectroscopy: noninvasive measures of small-scale structure and function

The advancement of spectroscopy methods attained through increases in sensitivity, and often with the coupling of complementary techniques, has enabled real-time structure and function measurements of single cells. The purpose of this review is to illustrate, in light of advances, the strengths and the weaknesses of these methods. Included also is an assessment of the impact of the experimental setup and conditions of each method on cellular function and integrity. A particular emphasis is placed on noninvasive and nondestructive techniques for achieving single cell detection, including nuclear magnetic resonance, in addition to physical, optical, and vibrational methods.

Developments in the immunophenotypic analysis of haematological malignancies

Immunophenotyping is the method by which antibodies are used to detect cellular antigens in clinical samples. Although the major role is in the diagnosis and classification of haematological malignancies, applications have expanded over the past decade. Immunophenotyping is now used extensively for disease staging and monitoring, to detect surrogate markers of genetic aberrations, to identify potential immuno-therapeutic targets and to aid prognostic prediction. This expansion in applications has resulted from developments in antibodies, methodology, automation and data handling. In this review we describe recent advances in both the technology and applications for the analysis of haematological malignancies. We highlight the importance of the expanding repertoire of testing capability for diagnostic, prognostic and therapeutic applications. The impact and significance of immunophenotyping in the assessment of haematological neoplasms are evident.

Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery

Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.

Multiparameter flow cytometry: advances in high resolution analysis

Over the past 40 years, flow cytometry has emerged as a leading, application-rich technology that supports high-resolution characterization of individual cells which function in complex cellular networks such as the immune system. This brief overview highlights advances in multiparameter flow cytometric technologies and reagent applications for characterization and functional analysis of cells modulating the immune network. These advances significantly support high-throughput and high-content analyses and enable an integrated understanding of the cellular and molecular interactions that underlie complex biological systems.

Computational analysis of high-dimensional flow cytometric data for diagnosis and discovery

Recent technological advancements have enabled the flow cytometric measurement of tens of parameters on millions of cells. Conventional manual data analysis and bioinformatics tools cannot provide a complete analysis of these datasets due to this complexity. In this chapter we will provide an overview of a general data analysis pipeline both for automatic identification of cell populations of known importance (e.g., diagnosis by identification of predefined cell population) and for exploratory analysis of cohorts of flow cytometry assays (e.g., discovery of new correlates of a malignancy). We provide three real-world examples of how unsupervised discovery has been used in basic and clinical research. We also discuss challenges for evaluation of the algorithms developed for (1) identification of cell populations using clustering, (2) identification of specific cell populations, and (3) supervised analysis for discriminating between patient subgroups.

High-throughput secondary screening at the single-cell level

We have developed an automated system for drug screening using a single-cell-multiple functional response technology. The approach uses a semiautomated preparatory system, high-speed sample collection, and a unique analytical tool that provides instantaneous results for compound dilutions using 384-well plates. The combination of automation and rapid robotic sampling increases quality control and robustness. High-speed flow cytometry is used to collect single-cell results together with a newly defined analytical tool for extraction of IC(50) curves for multiple assays per cell. The principal advantage is the extreme speed of sample collection, with results from a 384-well plate being completed for both collection and data processing in less than 10 min. Using this approach, it is possible to extract detailed drug response information in a highly controlled fashion. The data are based on single-cell results, not populations. With simultaneous assays for different functions, it is possible to gain a more detailed understanding of each drug/compound interaction. Combined with integrated advanced data processing directly from raw data files, the process from sampling to analytical results is highly intuitive. Direct PubMed links allow review of drug structure and comparisons with similar compounds.