Per-channel basis normalization methods for flow cytometry data

Potential and challenges of clinical high-dimensional flow cytometry: A call to action

Clinical biomarker strategies increasingly integrate translational research to gain new insights into disease mechanisms or to define better biomarkers in clinical trials. High-dimensional flow cytometry (HDFCM) holds the promise to enhance the exploratory potential beyond traditional, targeted biomarker strategies. However, the increased complexity of HDFCM poses several challenges, which need to be addressed in order to fully leverage its potential and to align with current regulatory requirements in clinical flow cytometry. These challenges include among others extended timelines for assay development and validation, the necessity for extensive knowledge in HDFCM, and sophisticated data analysis strategies. However, no guidelines exist on how to manage such challenges in adopting clinical HDFCM. Our CYTO 2024 workshop "Potential and challenges of clinical high-dimensional flow cytometry" aimed to find consensus across the pharmaceutical industry and broader scientific community on the overall benefits and most urgent challenges of HDFCM in clinical trials. Here, we summarize the insights we gained from our workshop. While this report does not provide a blueprint, it is a first step in defining and summarizing the most pressing challenges in implementing HDFCM in clinical trials. Furthermore, we compile current efforts with the goal to overcome some of these challenges. As such we bring the scientific community and health authorities together to build solutions, which will accelerate and simplify the full adoption of HDFCM in clinical trials.

FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational tools in Julia

As the dimensionality, throughput and complexity of cytometry data increases, so does the demand for user-friendly, interactive analysis tools that leverage high-performance machine learning frameworks. Here we introduce FlowAtlas: an interactive web application that enables dimensionality reduction of cytometry data without down-sampling and that is compatible with datasets stained with non-identical panels. FlowAtlas bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community, eliminating the need for coding and bioinformatics expertise. New population discovery and detection of rare populations in FlowAtlas is intuitive and rapid. We demonstrate the capabilities of FlowAtlas using a human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings. FlowAtlas is available at https://github.com/gszep/FlowAtlas.jl.git.

ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets

CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier.

Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues

Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and dissecting transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins, as in cells of the immune system. Cellular Indexing of Transcriptomes and Epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell type annotation requires a classifier that integrates multimodal data. Here, we describe MultiModal Classifier Hierarchy (MMoCHi), a marker-based approach for classification, reconciling gene and protein expression without reliance on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal novel subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.

Fat-to-blood recirculation of partially dysfunctional PD-1+CD4 Tconv cells is associated with dysglycemia in human obesity

Obesity is characterized by the accumulation of T cells in insulin-sensitive tissues, including the visceral adipose tissue (VAT), that can interfere with the insulin signaling pathway eventually leading to insulin resistance (IR) and type 2 diabetes. Here, we found that PD-1+CD4 conventional T (Tconv) cells, endowed with a transcriptomic and functional profile of partially dysfunctional cells, are diminished in VAT of obese patients with dysglycemia (OB-Dys), without a concomitant increase in apoptosis. These cells showed enhanced capacity to recirculate into the bloodstream and had a non-restricted TCRβ repertoire divergent from that of normoglycemic obese and lean individuals. PD-1+CD4 Tconv were reduced in the circulation of OB-Dys, exhibited an altered migration potential, and were detected in the liver of patients with non-alcoholic steatohepatitis. The findings suggest a potential role for partially dysfunctional PD-1+CD4 Tconv cells as inter-organ mediators of IR in obese patients with dysglycemic.

Murine Bone Marrow Erythroid Cells Have Two Branches of Differentiation Defined by the Presence of CD45 and a Different Immune Transcriptome Than Fetal Liver Erythroid Cells

Mouse erythropoiesis is a multifaceted process involving the intricate interplay of proliferation, differentiation, and maturation of erythroid cells, leading to significant changes in their transcriptomic and proteomic profiles. While the immunoregulatory role of murine erythroid cells has been recognized historically, modern investigative techniques have been sparingly applied to decipher their functions. To address this gap, our study sought to comprehensively characterize mouse erythroid cells through contemporary transcriptomic and proteomic approaches. By evaluating CD71 and Ter-119 as sorting markers for murine erythroid cells and employing bulk NanoString transcriptomics, we discerned distinctive gene expression profiles between bone marrow and fetal liver-derived erythroid cells. Additionally, leveraging flow cytometry, we assessed the surface expression of CD44, CD45, CD71, and Ter-119 on normal and phenylhydrazine-induced hemolytic anemia mouse bone marrow and splenic erythroid cells. Key findings emerged: firstly, the utilization of CD71 for cell sorting yielded comparatively impure erythroid cell populations compared to Ter-119; secondly, discernible differences in immunoregulatory molecule expression were evident between erythroid cells from mouse bone marrow and fetal liver; thirdly, two discrete branches of mouse erythropoiesis were identified based on CD45 expression: CD45-negative and CD45-positive, which had been altered differently in response to phenylhydrazine. Our deductions underscore (1) Ter-119's superiority over CD71 as a murine erythroid cell sorting marker, (2) the potential of erythroid cells in murine antimicrobial immunity, and (3) the importance of investigating CD45-positive and CD45-negative murine erythroid cells separately and in further detail in future studies.

CTLA-4 Pathway Is Instrumental in Giant Cell Arteritis

BACKGROUND

Giant cell arteritis (GCA) causes severe inflammation of the aorta and its branches and is characterized by intense effector T-cell infiltration. The roles that immune checkpoints play in the pathogenesis of GCA are still unclear. Our aim was to study the immune checkpoint interplay in GCA.

METHODS

First, we used VigiBase, the World Health Organization international pharmacovigilance database, to evaluate the relationship between GCA occurrence and immune checkpoint inhibitors treatments. We then further dissected the role of immune checkpoint inhibitors in the pathogenesis of GCA, using immunohistochemistry, immunofluorescence, transcriptomics, and flow cytometry on peripheral blood mononuclear cells and aortic tissues of GCA patients and appropriated controls.

RESULTS

Using VigiBase, we identified GCA as a significant immune-related adverse event associated with anti-CTLA-4 (cytotoxic T-lymphocyte-associated protein-4) but not anti-PD-1 (anti-programmed death-1) nor anti-PD-L1 (anti-programmed death-ligand 1) treatment. We further dissected a critical role for the CTLA-4 pathway in GCA by identification of the dysregulation of CTLA-4-derived gene pathways and proteins in CD4+ (cluster of differentiation 4) T cells (and specifically regulatory T cells) present in blood and aorta of GCA patients versus controls. While regulatory T cells were less abundant and activated/suppressive in blood and aorta of GCA versus controls, they still specifically upregulated CTLA-4. Activated and proliferating CTLA-4+ Ki-67+ regulatory T cells from GCA were more sensitive to anti-CTLA-4 (ipilimumab)-mediated in vitro depletion versus controls.

CONCLUSIONS

We highlighted the instrumental role of CTLA-4 immune checkpoint in GCA, which provides a strong rationale for targeting this pathway.

TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS

In immunology studies, flow cytometry is a commonly used multivariate single-cell assay. One key goal in flow cytometry analysis is to detect the immune cells responsive to certain stimuli. Statistically, this problem can be translated into comparing two protein expression probability density functions (pdfs) before and after the stimulus; the goal is to pinpoint the regions where these two pdfs differ. Further screening of these differential regions can be performed to identify enriched sets of responsive cells. In this paper, we model identifying differential density regions as a multiple testing problem. First, we partition the sample space into small bins. In each bin, we form a hypothesis to test the existence of differential pdfs. Second, we develop a novel multiple testing method, called TEAM (Testing on the Aggregation tree Method), to identify those bins that harbor differential pdfs while controlling the false discovery rate (FDR) under the desired level. TEAM embeds the testing procedure into an aggregation tree to test from fine- to coarse-resolution. The procedure achieves the statistical goal of pinpointing density differences to the smallest possible regions. TEAM is computationally efficient, capable of analyzing large flow cytometry data sets in much shorter time compared with competing methods. We applied TEAM and competing methods on a flow cytometry data set to identify T cells responsive to the cytomegalovirus (CMV)-pp65 antigen stimulation. With additional downstream screening, TEAM successfully identified enriched sets containing monofunctional, bifunctional, and polyfunctional T cells. Competing methods either did not finish in a reasonable time frame or provided less interpretable results. Numerical simulations and theoretical justifications demonstrate that TEAM has asymptotically valid, powerful, and robust performance. Overall, TEAM is a computationally efficient and statistically powerful algorithm that can yield meaningful biological insights in flow cytometry studies.

Ensuring Full Spectrum Flow Cytometry Data Quality for High-Dimensional Data Analysis

Full spectrum flow cytometry (FSFC) allows for the analysis of more than 40 parameters at the single-cell level. Compared to the practice of manual gating, high-dimensional data analysis can be used to fully explore single-cell datasets and reduce analysis time. As panel size and complexity increases so too does the detail and time required to prepare and validate the quality of the resulting data for use in downstream high-dimensional data analyses. To ensure data analysis algorithms can be used efficiently and to avoid artifacts, some important steps should be considered. These include data cleaning (such as eliminating variable signal change over time, removing cell doublets, and antibody aggregates), proper unmixing of full spectrum data, ensuring correct scale transformation, and correcting for batch effects. We have developed a methodical step-by-step protocol to prepare full spectrum high-dimensional data for use with high-dimensional data analyses, with a focus on visualizing the impact of each step of data preparation using dimensionality reduction algorithms. Application of our workflow will aid FSFC users in their efforts to apply quality control methods to their datasets for use in high-dimensional analysis, and help them to obtain valid and reproducible results. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Data cleaning Basic Protocol 2: Validating the quality of unmixing Basic Protocol 3: Data scaling Basic Protocol 4: Batch-to-batch normalization.

Platelet Phenotyping by Full Spectrum Flow Cytometry

Platelets play key roles in hemostasis, immunity, and inflammation, and tests of platelet phenotype and function are useful in studies of disease biology and pathology. Full spectrum flow cytometry offers distinct advantages over standard tests and enables the sensitive and simultaneous detection of many biomarkers. A typical assay provides a wealth of information on platelet biology and allows the assessment of in vivo activation and in vitro reactivity, as well as the discovery of novel phenotypes. Here, we describe the analysis of platelets by full spectrum flow cytometry and discuss a range of controls and methods for interpreting results. © 2023 Wiley Periodicals LLC. Basic Protocol: Platelet phenotyping by full spectrum flow cytometry Support Protocol 1: Spectral unmixing Support Protocol 2: Data preprocessing.

Multiparameter flow cytometry in the evaluation of myelodysplasia: Analytical issues: Recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group

Multiparameter flow cytometry (MFC) is one of the essential ancillary methods in bone marrow (BM) investigation of patients with cytopenia and suspected myelodysplastic syndrome (MDS). MFC can also be applied in the follow-up of MDS patients undergoing treatment. This document summarizes recommendations from the International/European Leukemia Net Working Group for Flow Cytometry in Myelodysplastic Syndromes (ELN iMDS Flow) on the analytical issues in MFC for the diagnostic work-up of MDS. Recommendations for the analysis of several BM cell subsets such as myeloid precursors, maturing granulocytic and monocytic components and erythropoiesis are given. A core set of 17 markers identified as independently related to a cytomorphologic diagnosis of myelodysplasia is suggested as mandatory for MFC evaluation of BM in a patient with cytopenia. A myeloid precursor cell (CD34⁺ CD19^- ) count >3% should be considered immunophenotypically indicative of myelodysplasia. However, MFC results should always be evaluated as part of an integrated hematopathology work-up. Looking forward, several machine-learning-based analytical tools of interest should be applied in parallel to conventional analytical methods to investigate their usefulness in integrated diagnostics, risk stratification, and potentially even in the evaluation of response to therapy, based on MFC data. In addition, compiling large uniform datasets is desirable, as most of the machine-learning-based methods tend to perform better with larger numbers of investigated samples, especially in such a heterogeneous disease as MDS.

Single-cell transcriptomics in bone marrow delineates CD56dimGranzymeK+ subset as intermediate stage in NK cell differentiation

Human natural killer (NK) cells in lymphoid tissues can be categorized into three subsets: CD56^brightCD16⁺, CD56^dimCD16⁺ and CD69⁺CXCR6⁺ lymphoid tissue-resident (lt)NK cells. How the three subsets are functionally and developmentally related is currently unknown. Therefore, we performed single-cell RNA sequencing combined with oligonucleotide-conjugated antibodies against CD56, CXCR6, CD117 and CD34 on fresh bone marrow NK cells. A minor CD56^dimGzmK⁺ subset was identified that shared features with CD56^bright and CD56^dimGzmK^- NK cells based on transcriptome, phenotype (NKG2A^highCD16^lowKLRG1^highTIGIT^high) and functional analysis in bone marrow and blood, supportive for an intermediate subset. Pseudotime analysis positioned CD56^bright, CD56^dimGzmK⁺ and CD56^dimGzmK^- cells in one differentiation trajectory, while ltNK cells were developmentally separated. Integrative analysis with bone marrow cells from the Human Cell Atlas did not demonstrate a developmental connection between CD34⁺ progenitor and NK cells, suggesting absence of early NK cell stages in bone marrow. In conclusion, single-cell transcriptomics provide new insights on development and differentiation of human NK cells.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.

Flow Cytometric Characterization of Pluripotent Cell Protein Markers in Naïve, Formative, and Primed Pluripotent Stem Cells

Here we describe methodologies to characterize, delineate, and quantify pluripotent cells between naïve, formative, and primed pluripotent state mouse embryonic stem cell (mESCs) populations using flow cytometric analysis. This methodology can validate pluripotent states, sort individual cells of interest, and determine the efficiency of transitioning naïve mESCs to a primed-like state as mouse epiblast-like cells (mEpiLCs) and onto fully primed mouse epiblast stem cells (mEpiSCs). Quantification of the cell surface markers; SSEA1(CD15) and CD24 introduces an effective method of distinguishing individual cells from a population by their respective positioning in the pluripotent spectrum. Additionally, this protocol can be used to demarcate and sort cells via fluorescently activated cell sorting for downstream applications. Flow cytometric analysis within mESCs, mEpiLCs, and mEpiSCs can be efficiently completed using these optimized protocols.

CytoPy: An autonomous cytometry analysis framework

Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.

Mass Cytometry of CSF Identifies an MS-Associated B-cell Population

Objective

To identify an MS-specific immune cell population by deep immune phenotyping and relate it to soluble signaling molecules in CSF.

Methods

We analyzed surface expression of 22 markers in paired blood/CSF samples from 39 patients using mass cytometry (cytometry by time of flight). We also measured the concentrations of 296 signaling molecules in CSF using proximity extension assay. Results were analyzed using highly automated unsupervised algorithmic informatics.

Results

Mass cytometry objectively identified a B-cell population characterized by the expression of CD49d, CD69, CD27, CXCR3, and human leukocyte antigen (HLA)-DR as clearly associated with MS. Concentrations of the B cell–related factors, notably FCRL2, were increased in MS CSF, especially in early stages of the disease. The B-cell trophic factor B cell activating factor (BAFF) was decreased in MS. Proteins involved in neural plasticity were also reduced in MS.

Conclusion

When analyzed without a priori assumptions, both the soluble and the cellular compartments of the CSF in MS were characterized by markers related to B cells, and the strongest candidate for an MS-specific cell type has a B-cell phenotype.

Computational Analysis of Microbial Flow Cytometry Data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

Specific Immune Response and Cytokine Production in CD70 Deficiency

Collective clinical and immunologic findings of defects in the CD27-CD70 axis indicate a primary immunodeficiency associated with terminal B-cell development defect and immune dysregulation leading to autoimmunity, uncontrolled viral infection, and lymphoma. Since the molecular mechanism underlying this entity of primary immunodeficiency has been recently described, more insight regarding the function and profile of immunity is required. Therefore, this study aimed to investigate stimulated antibody production, polyclonal vs. virus-specific T-cell response, and cytokine production of a CD70-deficient patient reported previously with early-onset antibody deficiency suffering from chronic viral infections and B-cell lymphoma. The patient and her family members were subjected to clinical evaluation, immunological assays, and functional analyses. The findings of this study indicate an impaired ability of B cells to produce immunoglobulins, and a poor effector function of T cells was also associated with the severity of clinical phenotype. Reduced proportions of cells expressing the memory marker CD45RO, as well as T-bet and Eomes, were observed in CD70-deficient T cells. The proportion of 2B4⁺ and PD-1⁺ virus-specific CD8⁺ T cells was also reduced in the patient. Although the CD70-mutated individuals presented with early-onset clinical manifestations that were well-controlled by using conventional immunological and anticancer chemotherapies, with better prognosis as compared with CD27-deficient patients, targeted treatment toward specific disturbed immune profile may improve the management and even prevent secondary complications.

A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry

The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning-based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician-scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics. © 2020 International Society for Advancement of Cytometry.

Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry

High-dimensional, single-cell cell technologies revolutionized the way to study biological systems, and polychromatic flow cytometry (FC) and mass cytometry (MC) are two of the drivers of this revolution. As up to 30-50 dimensions respectively can be measured per single-cell, they allow deep phenotyping combined with cellular functions studies, like cytokine production or protein phosphorylation. In parallel, the bioinformatics field develops algorithms that are able to process incoming data and extract the most useful and meaningful biological information. However, the success of automated analysis tools depends on the generation of high-quality data. In this review we present the most recent FC and MC computational approaches that are used to prepare, process and interpret high-content cytometry data. We also underscore proper experimental design as a key step for obtaining good quality data.

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.

CytoNorm: A Normalization Algorithm for Cytometry Data

High‐dimensional flow cytometry has matured to a level that enables deep phenotyping of cellular systems at a clinical scale. The resulting high‐content data sets allow characterizing the human immune system at unprecedented single cell resolution. However, the results are highly dependent on sample preparation and measurements might drift over time. While various controls exist for assessment and improvement of data quality in a single sample, the challenges of cross‐sample normalization attempts have been limited to aligning marker distributions across subjects. These approaches, inspired by bulk genomics and proteomics assays, ignore the single‐cell nature of the data and risk the removal of biologically relevant signals. This work proposes CytoNorm, a normalization algorithm to ensure internal consistency between clinical samples based on shared controls across various study batches. Data from the shared controls is used to learn the appropriate transformations for each batch (e.g., each analysis day). Importantly, some sources of technical variation are strongly influenced by the amount of protein expressed on specific cell types, requiring several population‐specific transformations to normalize cells from a heterogeneous sample. To address this, our approach first identifies the overall cellular distribution using a clustering step, and calculates subset‐specific transformations on the control samples by computing their quantile distributions and aligning them with splines. These transformations are then applied to all other clinical samples in the batch to remove the batch‐specific variations. We evaluated the algorithm on a customized data set with two shared controls across batches. One control sample was used for calculation of the normalization transformations and the second control was used as a blinded test set and evaluated with Earth Mover's distance. Additional results are provided using two real‐world clinical data sets. Overall, our method compared favorably to standard normalization procedures. The algorithm is implemented in the R package “CytoNorm” and available via the following link: http://www.github.com/saeyslab/CytoNorm © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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.

flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry

Motivation

Identification of cell populations in flow cytometry is a critical part of the analysis and lays the groundwork for many applications and research discovery. The current paradigm of manual analysis is time consuming and subjective. A common goal of users is to replace manual analysis with automated methods that replicate their results. Supervised tools provide the best performance in such a use case, however they require fine parameterization to obtain the best results. Hence, there is a strong need for methods that are fast to setup, accurate and interpretable.

Results

flowLearn is a semi-supervised approach for the quality-checked identification of cell populations. Using a very small number of manually gated samples, through density alignments it is able to predict gates on other samples with high accuracy and speed. On two state-of-the-art datasets, our tool achieves median(F1)-measures exceeding 0.99 for 31%, and 0.90 for 80% of all analyzed populations. Furthermore, users can directly interpret and adjust automated gates on new sample files to iteratively improve the initial training.

Availability and implementation

FlowLearn is available as an R package on https://github.com/mlux86/flowLearn. Evaluation data is publicly available online. Details can be found in the Supplementary Material.

Supplementary information

Supplementary data are available at Bioinformatics online.

Characterization of immune cell subtypes in three commonly used mouse strains reveals gender and strain-specific variations

The lack of consensus on bone marrow (BM) and splenic immune cell profiles in preclinical mouse strains complicates comparative analysis across different studies. Although studies have documented relative distribution of immune cells from peripheral blood in mice, similar studies for BM and spleen from naïve mice are lacking. In an effort to establish strain- and gender-specific benchmarks for distribution of various immune cell subtypes in these organs, we performed immunophenotypic analysis of BM cells and splenocytes from both genders of three commonly used murine strains (C57BL/6NCr, 129/SvHsd, and BALB/cAnNCr). Total neutrophils and splenic macrophages were significantly higher in C57BL/6NCr, whereas total B cells were lower. Within C57BL/6NCr female mice, BM B cells were elevated with respect to the males whereas splenic mDCs and splenic neutrophils were reduced. Within BALB/cAnNCr male mice, BM CD4⁺ Tregs were elevated with respect to the other strains. Furthermore, in male BALB/cAnNCr mice, NK cells were elevated with respect to the other strains in both BM and spleen. Splenic CD4⁺ Tregs and splenic CD8⁺ T cells were reduced in male BALB/c mice in comparison to female mice. Bone marrow CD4⁺ T cells and mDCs were significantly increased in 129/SvHsd whereas splenic CD8⁺ T cells were reduced. In general, males exhibited higher immature myeloid cells, macrophages, and NK cells. To our knowledge, this study provides a first attempt to systematically establish organ-specific benchmarks on immune cells in studies involving these mouse strains.

Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery

CITRUS is a supervised machine learning algorithm designed to analyze single cell data, identify cell populations, and identify changes in the frequencies or functional marker expression patterns of those populations that are significantly associated with an outcome. The algorithm is a black box that includes steps to cluster cell populations, characterize these populations, and identify the significant characteristics. This chapter describes how to optimize the use of CITRUS by combining it with upstream and downstream data analysis and visualization tools.

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.

DNA immunization site determines the level of gene expression and the magnitude, but not the type of the induced immune response

Optimization of DNA vaccine delivery improves the potency of the immune response and is crucial to clinical success. Here, we inquired how such optimization impacts the magnitude of the response, its specificity and type. BALB/c mice were DNA-immunized with two model immunogens, HIV-1 protease and reverse transcriptase by intramuscular or intradermal injections with electroporation. DNA immunogens were co-delivered with DNA encoding luciferase. Delivery and expression were monitored by in vivo bioluminescence imaging (BLI). The endpoint immune responses were assessed by IFN-γ/IL-2 FluoroSpot, multiparametric flow cytometry and antibody ELISA. Expression and immunogenicity were compared in relation to the delivery route. Regardless of the route, protease generated mainly IFN-γ, and reverse transcriptase, IL-2 and antibody response. BLI of mice immunized with protease- or reverse transcriptase/reporter plasmid mixtures, demonstrated significant loss of luminescence over time. The rate of decline of luminescence strongly correlated with the magnitude of immunogen-specific response, and depended on the immunogenicity profile and the immunization route. In vitro and in vivo BLI-based assays demonstrated that intradermal delivery strongly improved the immunogenicity of protease, and to a lesser extent, of reverse transcriptase. Immune response polarization and epitope hierarchy were not affected. Thus, by changing delivery/immunogen expression sites, it is possible to modulate the magnitude, but not the type or fine specificity of the induced immune response.

DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.

Codon optimization and improved delivery/immunization regimen enhance the immune response against wild-type and drug-resistant HIV-1 reverse transcriptase, preserving its Th2-polarity

DNA vaccines require a considerable enhancement of immunogenicity. Here, we optimized a prototype DNA vaccine against drug-resistant HIV-1 based on a weak Th2-immunogen, HIV-1 reverse transcriptase (RT). We designed expression-optimized genes encoding inactivated wild-type and drug-resistant RTs (RT-DNAs) and introduced them into mice by intradermal injections followed by electroporation. RT-DNAs were administered as single or double primes with or without cyclic-di-GMP, or as a prime followed by boost with RT-DNA mixed with a luciferase-encoding plasmid (“surrogate challenge”). Repeated primes improved cellular responses and broadened epitope specificity. Addition of cyclic-di-GMP induced a transient increase in IFN-γ production. The strongest anti-RT immune response was achieved in a prime-boost protocol with electroporation by short 100V pulses done using penetrating electrodes. The RT-specific response, dominated by CD4+ T-cells, targeted epitopes at aa 199–220 and aa 528–543. Drug-resistance mutations disrupted the epitope at aa 205–220, while the CTL epitope at aa 202–210 was not affected. Overall, multiparametric optimization of RT strengthened its Th2- performance. A rapid loss of RT/luciferase-expressing cells in the surrogate challenge experiment revealed a lytic potential of anti-RT response. Such lytic CD4+ response would be beneficial for an HIV vaccine due to its comparative insensitivity to immune escape.

Removal of batch effects using distribution-matching residual networks

Motivation

Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq (scRNA-seq), are plagued with systematic errors that may severely affect statistical analysis if the data are not properly calibrated.

Results

We propose a novel deep learning approach for removing systematic batch effects. Our method is based on a residual neural network, trained to minimize the Maximum Mean Discrepancy between the multivariate distributions of two replicates, measured in different batches. We apply our method to mass cytometry and scRNA-seq datasets, and demonstrate that it effectively attenuates batch effects.

Availability and Implementation

our codes and data are publicly available at https://github.com/ushaham/BatchEffectRemoval.git.

Contact

yuval.kluger@yale.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Single Cell and Population Level Analysis of HCA Data

High Content Analysis instrumentation has undergone tremendous hardware advances in recent years. It is now possible to obtain images of hundreds of thousands to millions of individual objects, across multiple wells, channels, and plates, in a reasonable amount of time. In addition, it is possible to extract dozens, or hundreds, of features per object using commonly available software tools. Analyzing this data provides new challenges to the scientists. The magnitude of these numbers is reminiscent of flow cytometer, where practitioners have long been taking what effectively amounted to very low resolution, multi-parametric measurements from individual cells for many decades. Flow cytometrists have developed a wide range of tools to effectively analyze and interpret these types of data. This chapter will review the techniques used in flow cytometry and show how they can easily and effectively be applied to High Content Analysis.

Luciferase Expression Allows Bioluminescence Imaging But Imposes Limitations on the Orthotopic Mouse (4T1) Model of Breast Cancer

Implantation of reporter-labeled tumor cells in an immunocompetent host involves a risk of their immune elimination. We have studied this effect in a mouse model of breast cancer after the orthotopic implantation of mammary gland adenocarcinoma 4T1 cells genetically labelled with luciferase (Luc). Mice were implanted with 4T1 cells and two derivative Luc-expressing clones 4T1luc2 and 4T1luc2D6 exhibiting equal in vitro growth rates. In vivo, the daughter 4T1luc2 clone exhibited nearly the same, and 4T1luc2D6, a lower growth rate than the parental cells. The metastatic potential of 4T1 variants was assessed by magnetic resonance, bioluminescent imaging, micro-computed tomography, and densitometry which detected 100-μm metastases in multiple organs and bones at the early stage of their development. After 3-4 weeks, 4T1 generated 11.4 ± 2.1, 4T1luc2D6, 4.5 ± 0.6; and 4T1luc2, <1 metastases per mouse, locations restricted to lungs and regional lymph nodes. Mice bearing Luc-expressing tumors developed IFN-γ response to the dominant CTL epitope of Luc. Induced by intradermal DNA-immunization, such response protected mice from the establishment of 4T1luc2-tumors. Our data show that natural or induced cellular response against the reporter restricts growth and metastatic activity of the reporter-labelled tumor cells. Such cells represent a powerful instrument for improving immunization technique for cancer vaccine applications.

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals).

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

High-dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high-throughput interrogation and characterization of cell populations. Here, we present an updated R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signalling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models or linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g., multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g., plots of aggregated signals).

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals).

Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities

Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general.

CD4+ T cells with an activated and exhausted phenotype distinguish immunodeficiency during aviremic HIV-2 infection

HIV type 2 (HIV-2) represents an attenuated form of HIV, in which many infected individuals remain ‘aviremic’ without antiretroviral therapy. However, aviremic HIV-2 disease progression exists, and in the current study, we therefore aimed to examine if specific pathological characteristics of CD4⁺ T cells are linked to such outcome.

flowVS: channel-specific variance stabilization in flow cytometry

Background

Comparing phenotypes of heterogeneous cell populations from multiple biological conditions is at the heart of scientific discovery based on flow cytometry (FC). When the biological signal is measured by the average expression of a biomarker, standard statistical methods require that variance be approximately stabilized in populations to be compared. Since the mean and variance of a cell population are often correlated in fluorescence-based FC measurements, a preprocessing step is needed to stabilize the within-population variances.

Results

We present a variance-stabilization algorithm, called flowVS, that removes the mean-variance correlations from cell populations identified in each fluorescence channel. flowVS transforms each channel from all samples of a data set by the inverse hyperbolic sine (asinh) transformation. For each channel, the parameters of the transformation are optimally selected by Bartlett’s likelihood-ratio test so that the populations attain homogeneous variances. The optimum parameters are then used to transform the corresponding channels in every sample. flowVS is therefore an explicit variance-stabilization method that stabilizes within-population variances in each channel by evaluating the homoskedasticity of clusters with a likelihood-ratio test.

With two publicly available datasets, we show that flowVS removes the mean-variance dependence from raw FC data and makes the within-population variance relatively homogeneous. We demonstrate that alternative transformation techniques such as flowTrans, flowScape, logicle, and FCSTrans might not stabilize variance. Besides flow cytometry, flowVS can also be applied to stabilize variance in microarray data. With a publicly available data set we demonstrate that flowVS performs as well as the VSN software, a state-of-the-art approach developed for microarrays.

Conclusions

The homogeneity of variance in cell populations across FC samples is desirable when extracting features uniformly and comparing cell populations with different levels of marker expressions. The newly developed flowVS algorithm solves the variance-stabilization problem in FC and microarrays by optimally transforming data with the help of Bartlett’s likelihood-ratio test. On two publicly available FC datasets, flowVS stabilizes within-population variances more evenly than the available transformation and normalization techniques. flowVS-based variance stabilization can help in performing comparison and alignment of phenotypically identical cell populations across different samples. flowVS and the datasets used in this paper are publicly available in Bioconductor.

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

BayesFlow: latent modeling of flow cytometry cell populations

Background

Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another.

Results

We introduce BayesFlow, a pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations.

BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation.

Conclusions

Modeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable. BayesFlow is freely available at GitHub.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0862-z) contains supplementary material, which is available to authorized users.

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes

The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.

Studying Cellular Signal Transduction with OMIC Technologies

In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.

Receptor occupancy assessment by flow cytometry as a pharmacodynamic biomarker in biopharmaceutical development

Receptor occupancy (RO) assays are designed to quantify the binding of therapeutics to their targets on the cell surface and are frequently used to generate pharmacodynamic (PD) biomarker data in nonclinical and clinical studies of biopharmaceuticals. When combined with the pharmacokinetic (PK) profile, RO data can establish PKPD relationships, which are crucial for informing dose decisions. RO is commonly measured by flow cytometry on fresh blood specimens and is subject to numerous technical and logistical challenges. To ensure that reliable and high quality results are generated from RO assays, careful assay design, key reagent characterization, data normalization/reporting, and thorough planning for implementation are of critical importance during development. In this article, the authors share their experiences and perspectives in these areas and discuss challenges and potential solutions when developing and implementing a flow cytometry‐based RO method in support of biopharmaceutical drug development. © 2015 The Authors Cytometry Part B: Clinical Cytometry Published by Wiley Periodicals, Inc.

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.

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation

A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, then splitting and merging stages use a criterion of unimodality to optimize the detection of rare subpopulations, to converge on a consistent cluster number, and to describe non-Gaussian distributions. Probabilistic assignment of cells to clusters, visualization, and manipulation of clusters by their cluster medians, facilitate application of expert knowledge using standard flow cytometry programs. The dual problems of rigorously comparing similar complex samples, and enumerating absent or very rare cell subpopulations in negative controls, were solved by assigning cells in multiple samples to a cluster template derived from a single or combined sample. Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells. A sensitivity of better than one part per million was attained in very large samples. Results were highly consistent on biological replicates, yet the analysis was sensitive enough to show that multiple samples from the same subject were more similar than samples from different subjects. A companion manuscript (Part 1) details the algorithmic development of SWIFT. © 2014 The Authors. Published by Wiley Periodicals Inc.