Cytomegalovirus-specific T-cell reconstitution following letermovir prophylaxis after hematopoietic cell transplantation.

Decreased cytomegalovirus (CMV)-specific immunity after hematopoietic cell transplantation (HCT) is associated with late CMV reactivation and increased mortality. Whether letermovir prophylaxis-associated reduction in viral exposure influences CMV-specific immune reconstitution is unknown. In a prospective cohort of allogeneic HCT recipients who received letermovir, we compared polyfunctional CMV-specific T-cell responses to those of controls who received PCR-guided preemptive therapy before the introduction of letermovir. Thirteen-color flow cytometry was used to assess T-cell responses at 3 months after HCT following stimulation with CMV immediate early-1 (IE-1) antigen and phosphoprotein 65 (pp65) antigens. Polyfunctionality was characterized by combinatorial polyfunctionality analysis of antigen-specific T-cell subsets. Use of letermovir and reduction of viral exposure were assessed for their association with CMV-specific T-cell immunity. Polyfunctional T-cell responses to IE-1 and pp65 were decreased in letermovir recipients and remained diminished after adjustment for donor CMV serostatus, absolute lymphocyte count, and steroid use. Among letermovir recipients, greater peak CMV DNAemia and increased viral shedding were associated with stronger CD8+ responses to pp65, whereas the CMV shedding rate was associated with greater CD4+ responses to IE-1. In summary, our study provided initial evidence that letermovir may delay CMV-specific cellular reconstitution, possibly related to decreased CMV antigen exposure. Evaluating T-cell polyfunctionality may identify patients at risk for late CMV infection after HCT.

Redistribution of CD8+ T cell subsets in metastatic renal cell carcinoma patients treated with anti-PD-1 therapy.

Renal-cell carcinoma (RCC) is responsible for the majority of tumors arising from the kidney parenchyma. Although a progressive improvement in median overall survival has been observed after the introduction of anti-PD-1 therapy, many patients do not benefit from this treatment. Therefore, we have investigated T cell dynamics to find immune modification induced by anti-PD-1 therapy. Here, we show that, after therapy, RCC patients (5 responders and 14 nonresponders) are characterized by a redistribution of different subsets across the memory T cell compartment.

ggCyto: next generation open-source visualization software for cytometry.

Motivation: Open source software for computational cytometry has gained in popularity over the past few years. Efforts such as FlowCAP, the Lyoplate and Euroflow projects have highlighted the importance of efforts to standardize both experimental and computational aspects of cytometry data analysis. The R/BioConductor platform hosts the largest collection of open source cytometry software covering all aspects of data analysis and providing infrastructure to represent and analyze cytometry data with all relevant experimental, gating and cell population annotations enabling fully reproducible data analysis. Data visualization frameworks to support this infrastructure have lagged behind. Results: ggCyto is a new open-source BioConductor software package for cytometry data visualization built on ggplot2 that enables ggplot-like functionality with the core BioConductor flow cytometry data structures. Amongst its features are the ability to transform data and axes on-the-fly using cytometry-specific transformations, plot faceting by experimental meta-data variables and partial matching of channel, marker and cell populations names to the contents of the BioConductor cytometry data structures. We demonstrate the salient features of the package using publicly available cytometry data with complete reproducible examples in a Supplementary Material. Availability and implementation: https://bioconductor.org/packages/devel/bioc/html/ggcyto.html. Supplementary information: Supplementary data are available at Bioinformatics online.

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.

Safety and immunogenicity of a mRNA rabies vaccine in healthy adults: an open-label, non-randomised, prospective, first-in-human phase 1 clinical trial.

BACKGROUND: Vaccines based on mRNA coding for antigens have been shown to be safe and immunogenic in preclinical models. We aimed to report results of the first-in-human proof-of-concept clinical trial in healthy adults of a prophylactic mRNA-based vaccine encoding rabies virus glycoprotein (CV7201). METHODS: We did an open-label, uncontrolled, prospective, phase 1 clinical trial at one centre in Munich, Germany. Healthy male and female volunteers (aged 18-40 years) with no history of rabies vaccination were sequentially enrolled. They received three doses of CV7201 intradermally or intramuscularly by needle-syringe or one of three needle-free devices. Escalating doses were given to subsequent cohorts, and one cohort received a booster dose after 1 year. The primary endpoint was safety and tolerability. The secondary endpoint was to determine the lowest dose of CV7201 to elicit rabies virus neutralising titres equal to or greater than the WHO-specified protective antibody titre of 0·5 IU/mL. The study is continuing for long-term safety and immunogenicity follow-up. This trial is registered with ClinicalTrials.gov, number NCT02241135. FINDINGS: Between Oct 21, 2013, and Jan 11, 2016, we enrolled and vaccinated 101 participants with 306 doses of mRNA (80-640 µg) by needle-syringe (18 intradermally and 24 intramuscularly) or needle-free devices (46 intradermally and 13 intramuscularly). In the 7 days post vaccination, 60 (94%) of 64 intradermally vaccinated participants and 36 (97%) of 37 intramuscularly vaccinated participants reported solicited injection site reactions, and 50 (78%) of 64 intradermally vaccinated participants and 29 (78%) of 37 intramuscularly vaccinated participants reported solicited systemic adverse events, including ten grade 3 events. One unexpected, possibly related, serious adverse reaction that occurred 7 days after a 640 µg intramuscular dose resolved without sequelae. mRNA vaccination by needle-free intradermal or intramuscular device injection induced virus neutralising antibody titres of 0·5 IU/mL or more across dose levels and schedules in 32 (71%) of 45 participants given 80 µg or 160 µg CV7201 doses intradermally and six (46%) of 13 participants given 200 µg or 400 µg CV7201 doses intramuscularly. 1 year later, eight (57%) of 14 participants boosted with an 80 µg needle-free intradermal dose of CV7201 achieved titres of 0·5 IU/mL or more. Conversely, intradermal or intramuscular needle-syringe injection was ineffective, with only one participant (who received 320 µg intradermally) showing a detectable immune response. INTERPRETATION: This first-ever demonstration in human beings shows that a prophylactic mRNA-based candidate vaccine can induce boostable functional antibodies against a viral antigen when administered with a needle-free device, although not when injected by a needle-syringe. The vaccine was generally safe with a reasonable tolerability profile. FUNDING: CureVac AG.

CytoML for cross-platform cytometry data sharing.

CytoML is an R/Bioconductor package that enables cross-platform import, export, and sharing of gated cytometry data. It currently supports Cytobank, FlowJo, Diva, and R, allowing users to import gated cytometry data from commercial platforms into R. Once data are available in R, the data can be further manipulated. For example it can be combined with other computational and analytic approaches, and the results can be exported to FlowJo or Cytobank to be explored by researchers using those platforms. We demonstrate how CytoML and related R packages can be used as a tool to import, modify and export several samples stained with the T cell panel from the FlowCAP IV Lyoplate data set. Once imported, the gating is modified using computational approaches, and exported for visualization in Cytobank and FlowJo. We further show how CytoML can be used to import gated data from a publicly accessible mass cytometry experiment from Cytobank. CytoML is the only tool that allows such sharing of gated cytometry data between researchers working across different platforms, and it will serve as a useful tool for validating and verifying the reproducibility of analyses. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Higher T-Cell Responses Induced by DNA/rAd5 HIV-1 Preventive Vaccine Are Associated With Lower HIV-1 Infection Risk in an Efficacy Trial.

Background: It is important to identify vaccine-induced immune responses that predict the preventative efficacy of a human immunodeficiency virus (HIV)-1 vaccine. We assessed T-cell response markers as correlates of risk in the HIV Vaccine Trials Network (HVTN) 505 HIV-1 vaccine efficacy trial. Methods: 2504 participants were randomized to DNA/rAd5 vaccine or placebo, administered at weeks 0, 4, 8, and 24. Peripheral blood mononuclear cells were obtained at week 26 from all 25 primary endpoint vaccine cases and 125 matched vaccine controls, and stimulated with vaccine-insert-matched peptides. Primary variables were total HIV-1-specific CD4+ T-cell magnitude and Env-specific CD4+ polyfunctionality. Four secondary variables were also assessed. Immune responses were evaluated as predictors of HIV-1 infection among vaccinees using Cox proportional hazards models. Machine learning analyses identified immune response combinations best predicting HIV-1 infection. Results: We observed an unexpectedly strong inverse correlation between Env-specific CD8+ immune response magnitude and HIV-1 infection risk (hazard ratio [HR] = 0.18 per SD increment; P = .04) and between Env-specific CD8+ polyfunctionality and infection risk (HR = 0.34 per SD increment; P < .01). Conclusions: Further research is needed to determine if these immune responses are predictors of vaccine efficacy or markers of natural resistance to HIV-1 infection.

T Cell Responses against Mycobacterial Lipids and Proteins Are Poorly Correlated in South African Adolescents.

Human T cells are activated by both peptide and nonpeptide Ags produced by Mycobacterium tuberculosis. T cells recognize cell wall lipids bound to CD1 molecules, but effector functions of CD1-reactive T cells have not been systematically assessed in M. tuberculosis-infected humans. It is also not known how these features correlate with T cell responses to secreted protein Ags. We developed a flow cytometric assay to profile CD1-restricted T cells ex vivo and assessed T cell responses to five cell wall lipid Ags in a cross-sectional study of 19 M. tuberculosis-infected and 22 M. tuberculosis-uninfected South African adolescents. We analyzed six T cell functions using a recently developed computational approach for flow cytometry data in high dimensions. We compared these data with T cell responses to five protein Ags in the same cohort. We show that CD1b-restricted T cells producing antimycobacterial cytokines IFN-γ and TNF-α are detectable ex vivo in CD4(+), CD8(+), and CD4(-)CD8(-) T cell subsets. Glucose monomycolate was immunodominant among lipid Ags tested, and polyfunctional CD4 T cells specific for this lipid simultaneously expressed CD40L, IFN-γ, IL-2, and TNF-α. Lipid-reactive CD4(+) T cells were detectable at frequencies of 0.001-0.01%, and this did not differ by M. tuberculosis infection status. Finally, CD4 T cell responses to lipids were poorly correlated with CD4 T cell responses to proteins (Spearman rank correlation -0.01; p = 0.95). These results highlight the functional diversity of CD1-restricted T cells circulating in peripheral blood as well as the complementary nature of T cell responses to mycobacterial lipids and proteins. Our approach enables further population-based studies of lipid-specific T cell responses during natural infection and vaccination.

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.

flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.

SUMMARY: flowDensity facilitates reproducible, high-throughput analysis of flow cytometry data by automating a predefined manual gating approach. The algorithm is based on a sequential bivariate gating approach that generates a set of predefined cell populations. It chooses the best cut-off for individual markers using characteristics of the density distribution. The Supplementary Material is linked to the online version of the manuscript. AVAILABILITY AND IMPLEMENTATION: R source code freely available through BioConductor (http://master.bioconductor.org/packages/devel/bioc/html/flowDensity.html.). Data available from FlowRepository.org (dataset FR-FCM-ZZBW). CONTACT: rbrinkman@bccrc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.

An important aspect of immune monitoring for vaccine development, clinical trials, and research is the detection, measurement, and comparison of antigen-specific T-cells from subject samples under different conditions. Antigen-specific T-cells compose a very small fraction of total T-cells. Developments in cytometry technology over the past five years have enabled the measurement of single-cells in a multivariate and high-throughput manner. This growth in both dimensionality and quantity of data continues to pose a challenge for effective identification and visualization of rare cell subsets, such as antigen-specific T-cells. Dimension reduction and feature extraction play pivotal role in both identifying and visualizing cell populations of interest in large, multi-dimensional cytometry datasets. However, the automated identification and visualization of rare, high-dimensional cell subsets remains challenging. Here we demonstrate how a systematic and integrated approach combining targeted feature extraction with dimension reduction can be used to identify and visualize biological differences in rare, antigen-specific cell populations. By using OpenCyto to perform semi-automated gating and features extraction of flow cytometry data, followed by dimensionality reduction with t-SNE we are able to identify polyfunctional subpopulations of antigen-specific T-cells and visualize treatment-specific differences between them.

Modeling bi-modality improves characterization of cell cycle on gene expression in single cells.

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.

OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis.

Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.

Hypoxia promotes ligand-independent EGF receptor signaling via hypoxia-inducible factor-mediated upregulation of caveolin-1.

Caveolin-1 (CAV1) is an essential structural constituent of caveolae, specialized lipid raft microdomains on the cell membrane involved in endocytosis and signal transduction, which are inexplicably deregulated and are associated with aggressiveness in numerous cancers. Here we identify CAV1 as a direct transcriptional target of oxygen-labile hypoxia-inducible factor 1 and 2 that accentuates the formation of caveolae, leading to increased dimerization of EGF receptor within the confined surface area of caveolae and its subsequent phosphorylation in the absence of ligand. Hypoxia-inducible factor-dependent up-regulation of CAV1 enhanced the oncogenic potential of tumor cells by increasing the cell proliferative, migratory, and invasive capacities. These results support a concept in which a crisis in oxygen availability or a tumor exhibiting hypoxic signature triggers caveolae formation that bypasses the requirement for ligand engagement to initiate receptor activation and the critical downstream adaptive signaling during a period when ligands required to activate these receptors are limited or are not yet available.

Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments.

MOTIVATION: Cell populations are never truly homogeneous; individual cells exist in biochemical states that define functional differences between them. New technology based on microfluidic arrays combined with multiplexed quantitative polymerase chain reactions now enables high-throughput single-cell gene expression measurement, allowing assessment of cellular heterogeneity. However, few analytic tools have been developed specifically for the statistical and analytical challenges of single-cell quantitative polymerase chain reactions data. RESULTS: We present a statistical framework for the exploration, quality control and analysis of single-cell gene expression data from microfluidic arrays. We assess accuracy and within-sample heterogeneity of single-cell expression and develop quality control criteria to filter unreliable cell measurements. We propose a statistical model accounting for the fact that genes at the single-cell level can be on (and a continuous expression measure is recorded) or dichotomously off (and the recorded expression is zero). Based on this model, we derive a combined likelihood ratio test for differential expression that incorporates both the discrete and continuous components. Using an experiment that examines treatment-specific changes in expression, we show that this combined test is more powerful than either the continuous or dichotomous component in isolation, or a t-test on the zero-inflated data. Although developed for measurements from a specific platform (Fluidigm), these tools are generalizable to other multi-parametric measures over large numbers of events. AVAILABILITY: All results presented here were obtained using the SingleCellAssay R package available on GitHub (http://github.com/RGLab/SingleCellAssay).

High-throughput flow cytometry data normalization for clinical trials.

Flow cytometry datasets from clinical trials generate very large datasets and are usually highly standardized, focusing on endpoints that are well defined apriori. Staining variability of individual makers is not uncommon and complicates manual gating, requiring the analyst to adapt gates for each sample, which is unwieldy for large datasets. It can lead to unreliable measurements, especially if a template-gating approach is used without further correction to the gates. In this article, a computational framework is presented for normalizing the fluorescence intensity of multiple markers in specific cell populations across samples that is suitable for high-throughput processing of large clinical trial datasets. Previous approaches to normalization have been global and applied to all cells or data with debris removed. They provided no mechanism to handle specific cell subsets. This approach integrates tightly with the gating process so that normalization is performed during gating and is local to the specific cell subsets exhibiting variability. This improves peak alignment and the performance of the algorithm. The performance of this algorithm is demonstrated on two clinical trial datasets from the HIV Vaccine Trials Network (HVTN) and the Immune Tolerance Network (ITN). In the ITN data set we show that local normalization combined with template gating can account for sample-to-sample variability as effectively as manual gating. In the HVTN dataset, it is shown that local normalization mitigates false-positive vaccine response calls in an intracellular cytokine staining assay. In both datasets, local normalization performs better than global normalization. The normalization framework allows the use of template gates even in the presence of sample-to-sample staining variability, mitigates the subjectivity and bias of manual gating, and decreases the time necessary to analyze large datasets.