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

IL-10 normalizes aberrant amygdala GABA transmission and reverses anxiety-like behavior and dependence-induced escalation of alcohol intake

Alcohol elicits a neuroimmune response in the brain contributing to the development and maintenance of alcohol use disorder (AUD). While pro-inflammatory mediators initiate and drive the neuroimmune response, anti-inflammatory mediators provide an important homeostatic mechanism to limit inflammation and prevent pathological damage. However, our understanding of the role of anti-inflammatory signaling on neuronal physiology in critical addiction-related brain regions and pathological alcohol-dependence induced behaviors is limited, precluding our ability to identify promising therapeutic targets. Here, we hypothesized that chronic alcohol exposure compromises anti-inflammatory signaling in the central amygdala, a brain region implicated in anxiety and addiction, consequently perpetuating a pro-inflammatory state driving aberrant neuronal activity underlying pathological behaviors. We found that alcohol dependence alters the global brain immune landscape increasing IL-10 producing microglia and T-regulatory cells but decreasing local amygdala IL-10 levels. Amygdala IL-10 overexpression decreases anxiety-like behaviors, suggesting its local role in regulating amygdala-mediated behaviors. Mechanistically, amygdala IL-10 signaling through PI3K and p38 MAPK modulates GABA transmission directly at presynaptic terminals and indirectly through alterations in spontaneous firing. Alcohol dependence-induces neuroadaptations in IL-10 signaling leading to an overall IL-10-induced decrease in GABA transmission, which normalizes dependence-induced elevated amygdala GABA transmission. Notably, amygdala IL-10 overexpression abolishes escalation of alcohol intake, a diagnostic criterion of AUD, in dependent mice. This highlights the importance of amygdala IL-10 signaling in modulating neuronal activity and underlying anxiety-like behavior and aberrant alcohol intake, providing a new framework for therapeutic intervention.

Generation of highly activated, antigen-specific tumor-infiltrating CD8+ T cells induced by a novel T cell-targeted immunotherapy

The induction of tumor-targeted, cytotoxic T lymphocytes has been recognized as a key component to successful immunotherapy. DPX-based treatment was previously shown to effectively recruit activated CD8⁺ T cells to the tumor. Herein, we analyze the unique phenotype of the CD8⁺ T cells recruited into the tumor in response to DPX-based therapy, and how combination with checkpoint inhibitors impacts T cell response. C3-tumor-bearing mice were treated with cyclophosphamide (CPA) for seven continuous days every other week, followed by DPX treatment along with anti-CTLA-4 and/or anti-PD-1. Efficacy, immunogenicity, and CD8⁺ T cells tumor infiltration were assessed. The expression of various markers, including checkpoint markers, peptide specificity, and proliferation and activation markers, was determined by flow cytometry. tSNE analysis of the flow data revealed a resident phenotype of CD8⁺ T cells (PD-1⁺TIM-3⁺CTLA-4⁺) within untreated tumors, whereas DPX/CPA treatment induced recruitment of a novel population of CD8⁺ T cells (PD-1⁺TIM-3⁺CTLA-4⁻) within tumors. Combination of anti-CTLA-4 (ipilimumab) with DPX/CPA versus DPX/CPA alone significantly increased survival and inhibition of tumor growth, without changing overall systemic immunogenicity. Addition of checkpoint inhibitors did not significantly change the phenotype of the newly recruited cells induced by DPX/CPA. Yet, anti-CTLA-4 treatment in combination with DPX/CPA enhanced a non-antigen specific response within the tumor. Finally, the tumor-recruited CD8⁺ T cells induced by DPX/CPA were highly activated, antigen-specific, and proliferative, while resident phenotype CD8⁺ T cells, seemingly initially exhausted, were reactivated with combination treatment. This study supports the potential of combining DPX/CPA with ipilimumab to further enhance survival clinically.

Validation of a hybrid approach to standardize immunophenotyping analysis in large population studies: The Health and Retirement Study

Traditional manual gating strategies are often time-intensive, place a high burden on the analyzer, and are susceptible to bias between analyzers. Several automated gating methods have shown to exceed performance of manual gating for a limited number of cell subsets. However, many of the automated algorithms still require significant manual interventions or have yet to demonstrate their utility in large datasets. Therefore, we developed an approach that utilizes a previously published automated algorithm (OpenCyto framework) with a manually created hierarchically cell gating template implemented, along with a custom developed visualization software (FlowAnnotator) to rapidly and efficiently analyze immunophenotyping data in large population studies. This approach allows pre-defining populations that can be analyzed solely by automated analysis and incorporating manual refinement for smaller downstream populations. We validated this method with traditional manual gating strategies for 24 subsets of T cells, B cells, NK cells, monocytes and dendritic cells in 931 participants from the Health and Retirement Study (HRS). Our results show a high degree of correlation (r ≥ 0.80) for 18 (78%) of the 24 cell subsets. For the remaining subsets, the correlation was low (<0.80) primarily because of the low numbers of events recorded in these subsets. The mean difference in the absolute counts between the hybrid method and manual gating strategy of these cell subsets showed results that were very similar to the traditional manual gating method. We describe a practical method for standardization of immunophenotyping methods in large scale population studies that provides a rapid, accurate and reproducible alternative to labor intensive manual gating strategies.

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.

Sensitive and specific detection of tumour cells based on a multivalent DNA nanocreeper and a multiplexed fluorescence supersandwich

A self-assembled DNA nanostructure based on a DNA nanocreeper and multiplexed fluorescence supersandwich was designed for the sensitive and specific detection of tumour cells. This nanostructure could improve the binding affinity of current aptamers and trigger signal amplification, which provide potential for the discrimination of low abundant target cells in liquid biopsy.

High-speed automatic characterization of rare events in flow cytometric data

A new computational framework for FLow cytometric Analysis of Rare Events (FLARE) has been developed specifically for fast and automatic identification of rare cell populations in very large samples generated by platforms like multi-parametric flow cytometry. Using a hierarchical Bayesian model and information-sharing via parallel computation, FLARE rapidly explores the high-dimensional marker-space to detect highly rare populations that are consistent across multiple samples. Further it can focus within specified regions of interest in marker-space to detect subpopulations with desired precision.

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to produce clear representations of datasets when millions of cells are projected. We develop opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Leibler divergence evaluation in real time to tailor the early exaggeration and overall number of gradient descent iterations in a dataset-specific manner. The precise calibration of early exaggeration together with opt-SNE adjustment of gradient descent learning rate dramatically improves computation time and enables high-quality visualization of large cytometry and transcriptomics datasets, overcoming limitations of analysis tools with hard-coded parameters that often produce poorly resolved or misleading maps of fluorescent and mass cytometry data. In summary, opt-SNE enables superior data resolution in t-SNE space and thereby more accurate data interpretation.

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.

CytoBinning: Immunological insights from multi-dimensional data

New cytometric techniques continue to push the boundaries of multi-parameter quantitative data acquisition at the single-cell level particularly in immunology and medicine. Sophisticated analysis methods for such ever higher dimensional datasets are rapidly emerging, with advanced data representations and dimensional reduction approaches. However, these are not yet standardized and clinical scientists and cell biologists are not yet experienced in their interpretation. More fundamentally their range of statistical validity is not yet fully established. We therefore propose a new method for the automated and unbiased analysis of high-dimensional single cell datasets that is simple and robust, with the goal of reducing this complex information into a familiar 2D scatter plot representation that is of immediate utility to a range of biomedical and clinical settings. Using publicly available flow cytometry and mass cytometry datasets we demonstrate that this method (termed CytoBinning), recapitulates the results of traditional manual cytometric analyses and leads to new and testable hypotheses.

Single cell analysis of Crohn's disease patient-derived small intestinal organoids reveals disease activity-dependent modification of stem cell properties

BACKGROUND

Intestinal stem cells (ISCs) play indispensable roles in the maintenance of homeostasis, and also in the regeneration of the damaged intestinal epithelia. However, whether the inflammatory environment of Crohn's disease (CD) affects properties of resident small intestinal stem cells remain uncertain.

METHODS

CD patient-derived small intestinal organoids were established from enteroscopic biopsy specimens taken from active lesions (aCD-SIO), or from mucosa under remission (rCD-SIO). Expression of ISC-marker genes in those organoids was examined by immunohistochemistry, and also by microfluid-based single-cell multiplex gene expression analysis. The ISC-specific function of organoid cells was evaluated using a single-cell organoid reformation assay.

RESULTS

ISC-marker genes, OLFM4 and SLC12A2, were expressed by an increased number of small intestinal epithelial cells in the active lesion of CD. aCD-SIOs, rCD-SIOs or those of non-IBD controls (NI-SIOs) were successfully established from 9 patients. Immunohistochemistry showed a comparable level of OLFM4 and SLC12A2 expression in all organoids. Single-cell gene expression data of 12 ISC-markers were acquired from a total of 1215 cells. t-distributed stochastic neighbor embedding analysis identified clusters of candidate ISCs, and also revealed a distinct expression pattern of SMOC2 and LGR5 in ISC-cluster classified cells derived from aCD-SIOs. Single-cell organoid reformation assays showed significantly higher reformation efficiency by the cells of the aCD-SIOs compared with that of cells from NI-SIOs.

CONCLUSIONS

aCD-SIOs harbor ISCs with modified marker expression profiles, and also with high organoid reformation ability. Results suggest modification of small intestinal stem cell properties by unidentified factors in the inflammatory environment of CD.

Characterization of T-cell immune responses in clinical trials of the candidate RTS,S malaria vaccine

The candidate malaria vaccine RTS,S has demonstrated 45.7% efficacy over 18 months against all clinical disease in a phase-III field study of African children. RTS,S targets the circumsporozoite protein (CSP), which is expressed on the Plasmodium sporozoite during the pre-erythrocyte stage of its life-cycle; the stage between mosquito bite and liver infection.

Early in the development of RTS,S, it was recognized that CSP-specific cell-mediated immunity (CMI) was required to complement CSP-specific antibody-mediated immunity. In reviewing RTS,S clinical studies, associations between protection and various types of CMI (CSP-specific CD4⁺ T cells and INF-γ ELISPOTs) have been identified, but not consistently. It is plausible that certain CD4⁺ T cells support antibody responses or co-operate with other immune-cell types to potentially elicit protection. However, the identities of vaccine correlates of protection, implicating either CSP-specific antibodies or T cells remain elusive, suggesting that RTS,S clinical trials may benefit from additional immunogenicity analyses that can be informed by the results of controlled human malaria infection studies.

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).

Comparative exploration of multidimensional flow cytometry software: a model approach evaluating T cell polyfunctional behavior

Advancement in flow cytometry reagents and instrumentation has allowed for simultaneous analysis of large numbers of lineage/functional immune cell markers. Highly complex datasets generated by polychromatic flow cytometry require proper analytical software to answer investigators' questions. A problem among many investigators and flow cytometry Shared Resource Laboratories (SRLs), including our own, is a lack of access to a flow cytometry-knowledgeable bioinformatics team, making it difficult to learn and choose appropriate analysis tool(s). Here, we comparatively assess various multidimensional flow cytometry software packages for their ability to answer a specific biologic question and provide graphical representation output suitable for publication, as well as their ease of use and cost. We assessed polyfunctional potential of TCR-transduced T cells, serving as a model evaluation, using multidimensional flow cytometry to analyze 6 intracellular cytokines and degranulation on a per-cell basis. Analysis of 7 parameters resulted in 128 possible combinations of positivity/negativity, far too complex for basic flow cytometry software to analyze fully. Various software packages were used, analysis methods used in each described, and representative output displayed. Of the tools investigated, automated classification of cellular expression by nonlinear stochastic embedding (ACCENSE) and coupled analysis in Pestle/simplified presentation of incredibly complex evaluations (SPICE) provided the most user-friendly manipulations and readable output, evaluating effects of altered antigen-specific stimulation on T cell polyfunctionality. This detailed approach may serve as a model for other investigators/SRLs in selecting the most appropriate software to analyze complex flow cytometry datasets. Further development and awareness of available tools will help guide proper data analysis to answer difficult biologic questions arising from incredibly complex datasets.

Using Visualization of t-Distributed Stochastic Neighbor Embedding To Identify Immune Cell Subsets in Mouse Tumors

High-dimensional flow cytometry is proving to be valuable for the study of subtle changes in tumor-associated immune cells. As flow panels become more complex, detection of minor immune cell populations by traditional gating using biaxial plots, or identification of populations that display small changes in multiple markers, may be overlooked. Visualization of t-distributed stochastic neighbor embedding (viSNE) is an unsupervised analytical tool designed to aid the analysis of high-dimensional cytometry data. In this study we use viSNE to analyze the simultaneous binding of 15 fluorophore-conjugated Abs and one cell viability probe to immune cells isolated from syngeneic mouse MB49 bladder tumors, spleens, and tumor-draining lymph nodes to identify patterns of anti-tumor immune responses. viSNE maps identified populations in multidimensional space of known immune cells, including T cells, B cells, eosinophils, neutrophils, dendritic cells, and NK cells. Based on the expression of CD86 and programmed cell death protein 1, CD8⁺ T cells were divided into distinct populations. Additionally, both CD8⁺ T cells and CD8⁺ dendritic cells were identified in the tumor microenvironment. Apparent differences between splenic and tumor polymorphonuclear cells/granulocytic myeloid-derived suppressor cells are due to the loss of CD44 upon enzymatic digestion of tumors. In conclusion, viSNE is a valuable tool for high-dimensional analysis of immune cells in tumor-bearing mice, which eliminates gating biases and identifies immune cell subsets that may be missed by traditional gating.