Nima Aghaeepour

Critical assessment of automated flow cytometry data analysis techniques

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

Hematopoietic Stem Cell Subtypes Expand Differentially during Development and Display Distinct Lymphopoietic Programs

Adult hematopoietic stem cells (HSCs) with serially transplantable activity comprise two subtypes. One shows a balanced output of mature lymphoid and myeloid cells; the other appears selectively lymphoid deficient. We now show that both of these HSC subtypes are present in the fetal liver (at a 1:10 ratio) with the rarer, lymphoid-deficient HSCs immediately gaining an increased representation in the fetal bone marrow, suggesting that the marrow niche plays a key role in regulating their ensuing preferential amplification. Clonal analysis of HSC expansion posttransplant showed that both subtypes display an extensive but variable self-renewal activity with occasional interconversion. Clonal analysis of their differentiation programs demonstrated functional and molecular as well as quantitative HSC subtype-specific differences in the lymphoid progenitors they generate but an indistinguishable production of multipotent and myeloid-restricted progenitors. These findings establish a level of heterogeneity in HSC differentiation and expansion control that may have relevance to stem cell populations in other hierarchically organized tissues.

Rapid cell population identification in flow cytometry data

We have developed flowMeans, a time‐efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K‐means clustering. Unlike traditional K‐means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub‐populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state‐of‐the‐art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.

Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium

Standardization of immunophenotyping requires careful attention to reagents, sample handling, instrument setup, and data analysis, and is essential for successful cross-study and cross-center comparison of data. Experts developed five standardized, eight-color panels for identification of major immune cell subsets in peripheral blood. These were produced as pre-configured, lyophilized, reagents in 96-well plates. We present the results of a coordinated analysis of samples across nine laboratories using these panels with standardized operating procedures (SOPs). Manual gating was performed by each site and by a central site. Automated gating algorithms were developed and tested by the FlowCAP consortium. Centralized manual gating can reduce cross-center variability, and we sought to determine whether automated methods could streamline and standardize the analysis. Within-site variability was low in all experiments, but cross-site variability was lower when central analysis was performed in comparison with site-specific analysis. It was also lower for clearly defined cell subsets than those based on dim markers and for rare populations. Automated gating was able to match the performance of central manual analysis for all tested panels, exhibiting little to no bias and comparable variability. Standardized staining, data collection, and automated gating can increase power, reduce variability, and streamline analysis for immunophenotyping.

Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays*

MOTIVATIONnPolychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted.nnnRESULTSnTo address this problem, we developed a computational approach that automatically reveals all possible cell subsets. From tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. We demonstrate the utility of this approach in a large (n=466), retrospective, 14-parameter PFC study of early HIV infection, where we identify three T-cell subsets that strongly predict progression to AIDS (only one of which was identified by an initial manual analysis).nnnAVAILABILITYnThe flowType: Phenotyping Multivariate PFC Assays package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/nnnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.nnnCONTACTnrbrinkman@bccrc.ca.

Flow Cytometry Bioinformatics

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

RchyOptimyx: Cellular hierarchy optimization for flow cytometry

Analysis of high‐dimensional flow cytometry datasets can reveal novel cell populations with poorly understood biology. Following discovery, characterization of these populations in terms of the critical markers involved is an important step, as this can help to both better understand the biology of these populations and aid in designing simpler marker panels to identify them on simpler instruments and with fewer reagents (i.e., in resource poor or highly regulated clinical settings). However, current tools to design panels based on the biological characteristics of the target cell populations work exclusively based on technical parameters (e.g., instrument configurations, spectral overlap, and reagent availability). To address this shortcoming, we developed RchyOptimyx (cellular hieraRCHY OPTIMization), a computational tool that constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels. RchyOptimyx can assess and graphically present the trade‐offs between marker choice and population specificity in high‐dimensional flow or mass cytometry datasets. We present three proof‐of‐concept use cases for RchyOptimyx that involve 1) designing a panel of surface markers for identification of rare populations that are primarily characterized using their intracellular signature; 2) simplifying the gating strategy for identification of a target cell population; 3) identification of a non‐redundant marker set to identify a target cell population. Published 2013 Wiley‐Periodicals, Inc.

Thinking Outside the Gate: Single-Cell Assessments in Multiple Dimensions

Present address: Immuno-Oncology & Combinations DPU, Oncology RDBendall et al., 2011), allowing an oppor-tunity to better understand the immuno-logical mechanisms underlying disease.Complex flow cytometry (FCM) data arenow surpassing our ability to fully analyzeand interpret all information via currentstandard approaches, such as 2D dotplots and Boolean gates. Indeed, thenumber of potential cell subpopulationsincreases exponentially with the numberof parameters assessed, making it diffi-cult to decipher all possible combina-tions included in the raw data (e.g., 512potential subsets with nine markers) viathe traditional approaches (Bendall andNolan, 2012). This could limit the transla-tion of technical advances into new diag-nostics or therapies. Newly developedbioinformatics tools that have the poten-tial to bridge this gap are now available.The aim of this letter is to foster theimplementation and adoption of thesenovel computational methodologies forunbiased analysis of complex cytometrydata.In recent years, a host of new data-analysis tools have emerged, creatingworkflows for processing and analyzingcomplex FCM datasets; however, thesehave gone mostly unnoticed by immunol-ogists. Table S1 provides an overview ofmany of the currently available tools andtheir specific applications. They can beassigned to specific categories arrangedin a ‘‘FCM data-analysis workflow’’ fromcompensated data as input to biologi-cally interpretable results as output. Thevast majority of the listed tools for FCMdata processing, analysis, and visualiza-tion are made available by the bio-informaticians at no cost and includeopen source code and unrestrictive soft-ware licensing, opening up these compu-tational approaches to broad use by theresearch community. Many of the toolshave been developed to address similaranalysis objectives via quite different ap-proaches. They might provide optimal re-sultsfordifferentdatasets,suchthatthereis no ‘‘right’’ or ‘‘best’’ tool, and usingseveral algorithms in combination mightyield even better results and exceedthe possibilities offered by manual anal-ysis.Comprehensivecomparativestudiesby the Flow Cytometry: Critical Assess-mentofPopulationIdentificationMethods(FlowCAP) project have shown that manyof these tools have reached a level ofmaturitythatmatches,orevensurpasses,the results produced by human experts(Aghaeepour et al., 2013).The development of computational ap-proaches addresses many needs asso-ciated with high-dimensional datasets.However,fortheimmunologycommunity,threemain challengeshave surfaced, andtackling them will facilitate a paradigmshift in the analysis of FCM data. First,despite the focused efforts by bio-informaticians to develop novel tools foranalyzingFCMdata,onlyaminorityofim-munologists are aware of the advantagesofferedtothefield.Thesetoolsneedtobepresented in immunology forums ratherthan limited to bioinformatics journalsand conferences. Second, even thoughthe vast majority of the computationalImmunity 42, April 21, 2015 a2015 Elsevier Inc. 591

Patient-specific Immune States before Surgery Are Strong Correlates of Surgical Recovery.

Background:Recovery after surgery is highly variable. Risk-stratifying patients based on their predicted recovery profile will afford individualized perioperative management strategies. Recently, application of mass cytometry in patients undergoing hip arthroplasty revealed strong immune correlates of surgical recovery in blood samples collected shortly after surgery. However, the ability to interrogate a patient’s immune state before surgery and predict recovery is highly desirable in perioperative medicine. Methods:To evaluate a patient’s presurgical immune state, cell-type–specific intracellular signaling responses to ex vivo ligands (lipopolysaccharide, interleukin [IL]-6, IL-10, and IL-2/granulocyte macrophage colony-stimulating factor) were quantified by mass cytometry in presurgical blood samples. Selected ligands modulate signaling processes perturbed by surgery. Twenty-three cell surface and 11 intracellular markers were used for the phenotypic and functional characterization of major immune cell subsets. Evoked immune responses were regressed against patient-centered outcomes, contributing to protracted recovery including functional impairment, postoperative pain, and fatigue. Results:Evoked signaling responses varied significantly and defined patient-specific presurgical immune states. Eighteen signaling responses correlated significantly with surgical recovery parameters (|R| = 0.37 to 0.70; false discovery rate < 0.01). Signaling responses downstream of the toll-like receptor 4 in cluster of differentiation (CD) 14+ monocytes were particularly strong correlates, accounting for 50% of observed variance. Immune correlates identified in presurgical blood samples mirrored correlates identified in postsurgical blood samples. Conclusions:Convergent findings in pre- and postsurgical analyses provide validation of reported immune correlates and suggest a critical role of the toll-like receptor 4 signaling pathway in monocytes for the clinical recovery process. The comprehensive assessment of patients’ preoperative immune state is promising for predicting important recovery parameters and may lead to clinical tests using standard flow cytometry.

Deletions in the cytoplasmic domain of iRhom1 and iRhom2 promote shedding of the TNF receptor by the protease ADAM17.

Without the N terminus, iRhom proteins cannot properly limit ADAM17 activity, resulting in impaired cancer cell death. Tumor susceptibility from truncated rhomboids Tumor necrosis factor (TNF) is an extracellular signal that can trigger cell death through its receptor. The protease ADAM17 has a dual role in regulating TNF signaling: ADAM17 promotes TNF signaling by cleaving and releasing TNF from the cell surface, and ADAM17 dampens TNF signaling by cleaving and releasing TNF receptors from the surface. The rhomboid proteins iRhom1 and iRhom2, which lack catalytic activity, mediate the maturation and delivery of ADAM17 to the cell surface. Maney et al. found that deletions in the cytoplasmic region of iRhom1 or iRhom2, which mimic mutations in the N-terminal cytoplasmic tail of iRhom2 in some patients with susceptibility to esophageal cancer, reduced TNF signaling, despite increasing ADAM17 activity. Expression of N-terminally truncated iRhoms in mouse fibrosarcoma cells increased the abundance of ADAM17 at the surface and the subsequent shedding of the TNF receptors, thereby suppressing TNF-induced intracellular signaling and cell death. The protease ADAM17 (a disintegrin and metalloproteinase 17) catalyzes the shedding of various transmembrane proteins from the surface of cells, including tumor necrosis factor (TNF) and its receptors. Liberation of TNF receptors (TNFRs) from cell surfaces can dampen the cellular response to TNF, a cytokine that is critical in the innate immune response and promotes programmed cell death but can also promote sepsis. Catalytically inactive members of the rhomboid family of proteases, iRhom1 and iRhom2, mediate the intracellular transport and maturation of ADAM17. Using a genetic screen, we found that the presence of either iRhom1 or iRhom2 lacking part of their extended amino-terminal cytoplasmic domain (herein referred to as ΔN) increases ADAM17 activity, TNFR shedding, and resistance to TNF-induced cell death in fibrosarcoma cells. Inhibitors of ADAM17, but not of other ADAM family members, prevented the effects of iRhom-ΔN expression. iRhom1 and iRhom2 were functionally redundant, suggesting a conserved role for the iRhom amino termini. Cells from patients with a dominantly inherited cancer susceptibility syndrome called tylosis with esophageal cancer (TOC) have amino-terminal mutations in iRhom2. Keratinocytes from TOC patients exhibited increased TNFR1 shedding compared with cells from healthy donors. Our results explain how loss of the amino terminus in iRhom1 and iRhom2 impairs TNF signaling, despite enhancing ADAM17 activity, and may explain how mutations in the amino-terminal region contribute to the cancer predisposition syndrome TOC.