The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis.

Despite recent discoveries of genetic variants associated with autoimmunity and infection, genetic control of the human immune system during homeostasis is poorly understood. We undertook a comprehensive immunophenotyping approach, analyzing 78,000 immune traits in 669 female twins. From the top 151 heritable traits (up to 96% heritable), we used replicated GWAS to obtain 297 SNP associations at 11 genetic loci, explaining up to 36% of the variation of 19 traits. We found multiple associations with canonical traits of all major immune cell subsets and uncovered insights into genetic control for regulatory T cells. This data set also revealed traits associated with loci known to confer autoimmune susceptibility, providing mechanistic hypotheses linking immune traits with the etiology of disease. Our data establish a bioresource that links genetic control elements associated with normal immune traits to common autoimmune and infectious diseases, providing a shortcut to identifying potential mechanisms of immune-related diseases.

Single-cell technologies for monitoring immune systems.

The complex heterogeneity of cells, and their interconnectedness with each other, are major challenges to identifying clinically relevant measurements that reflect the state and capability of the immune system. Highly multiplexed, single-cell technologies may be critical for identifying correlates of disease or immunological interventions as well as for elucidating the underlying mechanisms of immunity. Here we review limitations of bulk measurements and explore advances in single-cell technologies that overcome these problems by expanding the depth and breadth of functional and phenotypic analysis in space and time. The geometric increases in complexity of data make formidable hurdles for exploring, analyzing and presenting results. We summarize recent approaches to making such computations tractable and discuss challenges for integrating heterogeneous data obtained using these single-cell technologies.

Single cell multiomic analysis of T cell exhaustion in vitro.

T-cell activation is a key step in the amplification of an immune response. Over the course of an immune response, cells may be chronically stimulated, with some proportion becoming exhausted; an enormous number of molecules are involved in this process. There remain a number of questions about the process, namely: (1) what degree of heterogeneity and plasticity do T-cells exhibit during stimulation? (2) how many unique cell states define chronic stimulation? and (3) what markers discriminate activated from exhausted cells? We addressed these questions by performing single-cell multiomic analysis to simultaneously measure expression of 38 proteins and 399 genes in human T cells expanded in vitro. This approach allowed us to study -with unprecedented depth-how T cells change over the course of chronic stimulation. Comprehensive immunophenotypic and transcriptomic analysis at day 0 enabled a refined characterization of T-cell maturational states and the identification of a donor-specific subset of terminally differentiated T-cells that would have been otherwise overlooked using canonical cell classification schema. As expected, activation downregulated naïve-cell markers and upregulated effector molecules, proliferation regulators, co-inhibitory and co-stimulatory receptors. Our deep kinetic analysis further revealed clusters of proteins and genes identifying unique states of activation, defined by markers temporarily expressed upon 3 days of stimulation (PD-1, CD69, LTA), markers constitutively expressed throughout chronic activation (CD25, GITR, LGALS1), and markers uniquely up-regulated upon 14 days of stimulation (CD39, ENTPD1, TNFDF10); expression of these markers could be associated with the emergence of short-lived cell types. Notably, different ratios of cells expressing activation or exhaustion markers were measured at each time point. These data reveal the high heterogeneity and plasticity of chronically stimulated T cells. Our study demonstrates the power of a single-cell multiomic approach to comprehensively characterize T-cells and to precisely monitor changes in differentiation, activation, and exhaustion signatures during cell stimulation.

A Cytometrist's Guide to Coordinating and Performing Effective COVID-19 Research.

Cytometry is playing a crucial role in addressing the COVID-19 pandemic. In this commentary-written by a variety of stakeholders in the cytometry, immunology, and infectious disease communities-we review cytometry's role in the COVID-19 response and discuss workflow issues critical to planning and executing effective research in this emerging field. We discuss sample procurement and processing, biosafety, technology options, data sharing, and the translation of research findings into clinical environments. © 2020 International Society for Advancement of Cytometry.

Simultaneous epitope and transcriptome measurement in single cells.

High-throughput single-cell RNA sequencing has transformed our understanding of complex cell populations, but it does not provide phenotypic information such as cell-surface protein levels. Here, we describe cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), a method in which oligonucleotide-labeled antibodies are used to integrate cellular protein and transcriptome measurements into an efficient, single-cell readout. CITE-seq is compatible with existing single-cell sequencing approaches and scales readily with throughput increases.

Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells.

CD4 T cells harboring HIV-1/SIV represent a formidable hurdle to eradicating infection, and yet their detailed phenotype remains unknown. Here we integrate two single-cell technologies, flow cytometry and highly multiplexed quantitative RT-PCR, to characterize SIV-infected CD4 T cells directly ex vivo. Within individual cells, we correlate the cellular phenotype, in terms of host protein and RNA expression, with stages of the viral life cycle defined by combinatorial expression of viral RNAs. Spliced RNA+ infected cells display multiple memory and activation phenotypes, indicating virus production by diverse CD4 T cell subsets. In most (but not all) cells, progressive infection accompanies post-transcriptional downregulation of CD4 protein, while surface MHC class I is largely retained. Interferon-stimulated genes were also commonly upregulated. Thus, we demonstrate that combined quantitation of transcriptional and post-transcriptional regulation at the single-cell level informs in vivo mechanisms of viral replication and immune evasion.

flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data.

Modern flow cytometry systems can be coupled to plate readers for high-throughput acquisition. These systems allow hundreds of samples to be analyzed in a single day. Quality control of the data remains challenging, however, and is further complicated when a large number of parameters is measured in an experiment. Our examination of 29,228 publicly available FCS files from laboratories worldwide indicates 13.7% have a fluorescence anomaly. In particular, fluorescence measurements for a sample over the collection time may not remain stable due to fluctuations in fluid dynamics; the impact of instabilities may differ between samples and among parameters. Therefore, we hypothesized that tracking cell populations (which represent a summary of all parameters) in centered log ratio space would provide a sensitive and consistent method of quality control. Here, we present flowClean, an algorithm to track subset frequency changes within a sample during acquisition, and flag time periods with fluorescence perturbations leading to the emergence of false populations. Aberrant time periods are reported as a new parameter and added to a revised data file, allowing users to easily review and exclude those events from further analysis. We apply this method to proof-of-concept datasets and also to a subset of data from a recent vaccine trial. The algorithm flags events that are suspicious by visual inspection, as well as those showing more subtle effects that might not be consistently flagged by investigators reviewing the data manually, and out-performs the current state-of-the-art. flowClean is available as an R package on Bioconductor, as a module on the free-to-use GenePattern web server, and as a plugin for FlowJo X. © 2016 International Society for Advancement of Cytometry.

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.

Mixture models for single-cell assays with applications to vaccine studies.

Blood and tissue are composed of many functionally distinct cell subsets. In immunological studies, these can be measured accurately only using single-cell assays. The characterization of these small cell subsets is crucial to decipher system-level biological changes. For this reason, an increasing number of studies rely on assays that provide single-cell measurements of multiple genes and proteins from bulk cell samples. A common problem in the analysis of such data is to identify biomarkers (or combinations of biomarkers) that are differentially expressed between two biological conditions (e.g. before/after stimulation), where expression is defined as the proportion of cells expressing that biomarker (or biomarker combination) in the cell subset(s) of interest. Here, we present a Bayesian hierarchical framework based on a beta-binomial mixture model for testing for differential biomarker expression using single-cell assays. Our model allows the inference to be subject specific, as is typically required when assessing vaccine responses, while borrowing strength across subjects through common prior distributions. We propose two approaches for parameter estimation: an empirical-Bayes approach using an Expectation-Maximization algorithm and a fully Bayesian one based on a Markov chain Monte Carlo algorithm. We compare our method against classical approaches for single-cell assays including Fisher's exact test, a likelihood ratio test, and basic log-fold changes. Using several experimental assays measuring proteins or genes at single-cell level and simulations, we show that our method has higher sensitivity and specificity than alternative methods. Additional simulations show that our framework is also robust to model misspecification. Finally, we demonstrate how our approach can be extended to testing multivariate differential expression across multiple biomarker combinations using a Dirichlet-multinomial model and illustrate this approach using single-cell gene expression data and simulations.

A deep profiler's guide to cytometry.

In recent years, advances in technology have provided us with tools to quantify the expression of multiple genes in individual cells. The ability to measure simultaneously multiple genes in the same cell is necessary to resolve the great diversity of cell subsets, as well as to define their function in the host. Fluorescence-based flow cytometry is the benchmark for this; with it, we can quantify 18 proteins per cell, at >10 000 cells/s. Mass cytometry is a new technology that promises to extend these capabilities significantly. Immunophenotyping by mass spectrometry provides the ability to measure >36 proteins at a rate of 1000 cells/s. We review these cytometric technologies, capable of high-content, high-throughput single-cell assays.

Holoendemic malaria exposure is associated with altered Epstein-Barr virus-specific CD8(+) T-cell differentiation.

Coinfection with Plasmodium falciparum malaria and Epstein-Barr virus (EBV) is a major risk factor for endemic Burkitt lymphoma (eBL), still one of the most prevalent pediatric cancers in equatorial Africa. Although malaria infection has been associated with immunosuppression, the precise mechanisms that contribute to EBV-associated lymphomagenesis remain unclear. In this study, we used polychromatic flow cytometry to characterize CD8(+) T-cell subsets specific for EBV-derived lytic (BMFL1 and BRLF1) and latent (LMP1, LMP2, and EBNA3C) antigens in individuals with divergent malaria exposure. No malaria-associated differences in EBV-specific CD8(+) T-cell frequencies were observed. However, based on a multidimensional analysis of CD45RO, CD27, CCR7, CD127, CD57, and PD-1 expression, we found that individuals living in regions with intense and perennial (holoendemic) malaria transmission harbored more differentiated EBV-specific CD8(+) T-cell populations that contained fewer central memory cells than individuals living in regions with little or no (hypoendemic) malaria. This profile shift was most marked for EBV-specific CD8(+) T-cell populations that targeted latent antigens. Importantly, malaria exposure did not skew the phenotypic properties of either cytomegalovirus (CMV)-specific CD8(+) T cells or the global CD8(+) memory T-cell pool. These observations define a malaria-associated aberration localized to the EBV-specific CD8(+) T-cell compartment that illuminates the etiology of eBL.

Q and B values are critical measurements required for inter-instrument standardization and development of multicolor flow cytometry staining panels.

Much of the complexity of multicolor flow cytometry experiments lies within the development of antibody staining panels and the standardization of instruments. In this article, we propose a theoretical metric and describe how measurements of sensitivity and resolution can be used to predict the success of panels, and ensure that performance across instruments is standardized (i.e., inter-instrument standardization). Sensitivity can be determined by summing two major contributors of background, background originating from the instrument (optical noise and electronic noise) and background due to the experimental conditions (i.e., Raman scatter, and spillover spreading arising from other fluorochromes in the panel). The former we define as Bcal and the latter we define as Bsos . The combination of instrument and experiment background is defined as Btot . Importantly, the Btot will affect the degree of panel separation, therefore the greater the degree of Btot the lower the separation potential. In contrast, resolution is a measure of separation between populations. Resolution is directly proportional to the number of photoelectrons generated per molecule of excited fluorochrome and is known as the "Q" value. Q and Btot values can be used to define the performance of each detector on an instrument and together they can be used to calculate a separation index. Hence, detectors with known Q and Btot values can be used to evaluate panel success based on the detector specific separation index. However, the current technologies do not enable measurements of Q and Btot values for all parameters, but new technology to allow these measurements will likely be introduced in the near future. Nonetheless, Q and Btot measurements can aid in panel development, and reveal sources of instrument-to-instrument variation in panel performance. In addition, Q and B values can form the basis for a comprehensive and versatile quality assurance program.

Molecular cytometry for comprehensive immune profiling.

Molecular cytometry refers to a group of high-parameter technologies for single-cell analysis that share the following traits: (1) combined (multimodal) measurement of protein and transcripts, (2) medium throughput (10-100K cells), and (3) the use of oligonucleotide-tagged antibodies to detect protein expression. The platform can measure over 100 proteins and either hundreds of targeted genes or the whole transcriptome, on a cell-by-cell basis. It is currently one of the most powerful technologies available for immune monitoring. Here, we describe the technology platform (which includes CITE-Seq, REAP-Seq, and AbSeq), provide guidance for its optimization, and discuss advantages and limitations. Finally, we provide some vignettes from studies that demonstrate the application and potential insight that can be gained from molecular cytometry studies.

terraFlow, a high-parameter analysis tool, reveals T cell exhaustion and dysfunctional cytokine production in classical Hodgkin's lymphoma.

Immune cells express an incredible variety of proteins; by measuring combinations of these, cell types influencing disease can be precisely identified. We developed terraFlow, a platform that defines cell subsets exhaustively by combinatorial protein expression. Using high-parameter checkpoint-focused and function-focused panels, we studied classical Hodgkin's lymphoma (cHL), where systemic T cells have not been investigated in detail. terraFlow revealed immune perturbations in patients, including elevated activated, exhausted, and interleukin (IL)-17+ phenotypes, along with diminished early, interferon (IFN)γ+, and tumor necrosis factor (TNF)+ T cells before treatment; many perturbations remained after treatment. terraFlow identified more disease-associated differences than other tools, often with better predictive power, and included a non-gating approach, eliminating time-consuming and subjective manual thresholds. It also reports a method to identify the smallest set of markers distinguishing study groups. Our results provide mechanistic support for past reports of immune deficiency in cHL and demonstrate the value of terraFlow in immunotherapy and biomarker studies.

A Phase 1/2 multicenter trial of DKN-01 as monotherapy or in combination with docetaxel for the treatment of metastatic castration-resistant prostate cancer (mCRPC).

BACKGROUND: Dickkopf-related protein 1 (DKK1) is a Wingless-related integrate site (Wnt) signaling modulator that is upregulated in prostate cancers (PCa) with low androgen receptor expression. DKN-01, an IgG4 that neutralizes DKK1, delays PCa growth in pre-clinical DKK1-expressing models. These data provided the rationale for a clinical trial testing DKN-01 in patients with metastatic castration-resistant PCa (mCRPC). METHODS: This was an investigator-initiated parallel-arm phase 1/2 clinical trial testing DKN-01 alone (monotherapy) or in combination with docetaxel 75 mg/m2 (combination) for men with mCRPC who progressed on ≥1 AR signaling inhibitors. DKK1 status was determined by RNA in-situ expression. The primary endpoint of the phase 1 dose escalation cohorts was the determination of the recommended phase 2 dose (RP2D). The primary endpoint of the phase 2 expansion cohorts was objective response rate by iRECIST criteria in patients treated with the combination. RESULTS: 18 pts were enrolled into the study-10 patients in the monotherapy cohorts and 8 patients in the combination cohorts. No DLTs were observed and DKN-01 600 mg was determined as the RP2D. A best overall response of stable disease occurred in two out of seven (29%) evaluable patients in the monotherapy cohort. In the combination cohort, five out of seven (71%) evaluable patients had a partial response (PR). A median rPFS of 5.7 months was observed in the combination cohort. In the combination cohort, the median tumoral DKK1 expression H-score was 0.75 and the rPFS observed was similar between patients with DKK1 H-score ≥1 versus H-score = 0. CONCLUSION: DKN-01 600 mg was well tolerated. DKK1 blockade has modest anti-tumor activity as a monotherapy for mCRPC. Anti-tumor activity was observed in the combination cohorts, but the response duration was limited. DKK1 expression in the majority of mCRPC is low and did not clearly correlate with anti-tumor activity of DKN-01 plus docetaxel.

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

MOTIVATION: Polychromatic 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. RESULTS: To 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). AVAILABILITY: The 'flowType: Phenotyping Multivariate PFC Assays' package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: rbrinkman@bccrc.ca.