Identifying therapeutic candidates for endometriosis through a transcriptomics-based drug repositioning approach.

Existing medical treatments for endometriosis-related pain are often ineffective, underscoring the need for new therapeutic strategies. In this study, we applied a computational drug repurposing pipeline to stratified and unstratified disease signatures based on endometrial gene expression data to identify potential therapeutics from existing drugs, based on expression reversal. Of 3,131 unique genes differentially expressed by at least one of six endometriosis signatures, only 308 (9.8%) were in common; however, 221 out of 299 drugs identified, (73.9%) were shared. We selected fenoprofen, an uncommonly prescribed NSAID that was the top therapeutic candidate for further investigation. When testing fenoprofen in an established rat model of endometriosis, fenoprofen successfully alleviated endometriosis-associated vaginal hyperalgesia, a surrogate marker for endometriosis-related pain. These findings validate fenoprofen as a therapeutic that could be utilized more frequently for endometriosis and suggest the utility of the aforementioned computational drug repurposing approach for endometriosis.

Homeostatic cytokines reciprocally modulate the emergence of prenatal effector PLZF+CD4+ T cells in humans.

The development of human prenatal adaptive immunity progresses faster than previously appreciated, with the emergence of memory CD4+ T cells alongside regulatory T cells by midgestation. We previously identified a prenatal specific population of promyelocytic leukemia zinc finger-positive (PLZF+) CD4+ T cells with heightened effector potential that were enriched in the developing intestine and accumulated in the cord blood of infants exposed to prenatal inflammation. However, the signals that drive their tissue distribution and effector maturation are unknown. Here, we define the transcriptional and functional heterogeneity of human prenatal PLZF+CD4+ T cells and identify the compartmentalization of T helper-like (Th-like) effector function across the small intestine (SI) and mesenteric lymph nodes (MLNs). IL-7 was more abundant in the SI relative to the MLNs and drove the preferential expansion of naive PLZF+CD4+ T cells via enhanced STAT5 and MEK/ERK signaling. Exposure to IL-7 was sufficient to induce the acquisition of CD45RO expression and rapid effector function in a subset of PLZF+CD4+ T cells, identifying a human analog of memory phenotype CD4+ T cells. Further, IL-7 modulated the differentiation of Th1- and Th17-like PLZF+CD4+ T cells and thus likely contributes to the anatomic compartmentalization of human prenatal CD4+ T cell effector function.

Cyclone: an accessible pipeline to analyze, evaluate, and optimize multiparametric cytometry data.

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.

Cyclone: an accessible pipeline to analyze, evaluate and optimize multiparametric cytometry data.

In the past decade, high-dimensional single cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation which are computationally intense and difficult to evaluate and optimize. Here we present Cyclone, an analysis pipeline integrating dimensionality reduction, clustering, evaluation and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification, but also enables the unsupervised identification of lymphocytes and mononuclear phagocytes subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on variety of cytometry datasets which will further power immunology research and provide a scaffold for biological discovery.

HIV silencing and cell survival signatures in infected T cell reservoirs.

Rare CD4 T cells that contain HIV under antiretroviral therapy represent an important barrier to HIV cure1-3, but the infeasibility of isolating and characterizing these cells in their natural state has led to uncertainty about whether they possess distinctive attributes that HIV cure-directed therapies might exploit. Here we address this challenge using a microfluidic technology that isolates the transcriptomes of HIV-infected cells based solely on the detection of HIV DNA. HIV-DNA+ memory CD4 T cells in the blood from people receiving antiretroviral therapy showed inhibition of six transcriptomic pathways, including death receptor signalling, necroptosis signalling and antiproliferative Gα12/13 signalling. Moreover, two groups of genes identified by network co-expression analysis were significantly associated with HIV-DNA+ cells. These genes (n = 145) accounted for just 0.81% of the measured transcriptome and included negative regulators of HIV transcription that were higher in HIV-DNA+ cells, positive regulators of HIV transcription that were lower in HIV-DNA+ cells, and other genes involved in RNA processing, negative regulation of mRNA translation, and regulation of cell state and fate. These findings reveal that HIV-infected memory CD4 T cells under antiretroviral therapy are a distinctive population with host gene expression patterns that favour HIV silencing, cell survival and cell proliferation, with important implications for the development of HIV cure strategies.

Multi-Modal Single-Cell Sequencing Identifies Cellular Immunophenotypes Associated With Juvenile Dermatomyositis Disease Activity.

Juvenile dermatomyositis (JDM) is a rare autoimmune condition with insufficient biomarkers and treatments, in part, due to incomplete knowledge of the cell types mediating disease. We investigated immunophenotypes and cell-specific genes associated with disease activity using multiplexed RNA and protein single-cell sequencing applied to PBMCs from 4 treatment-naïve JDM (TN-JDM) subjects at baseline, 2, 4, and 6 months post-treatment and 4 subjects with inactive disease on treatment. Analysis of 55,564 cells revealed separate clustering of TN-JDM cells within monocyte, NK, CD8+ effector T and naïve B populations. The proportion of CD16+ monocytes was reduced in TN-JDM, and naïve B cells and CD4+ Tregs were expanded. Cell-type differential gene expression analysis and hierarchical clustering identified a pan-cell-type IFN gene signature over-expressed in TN-JDM in all cell types and correlated with disease activity most strongly in cytotoxic cell types. TN-JDM CD16+ monocytes expressed the highest IFN gene score and were highly skewed toward an inflammatory and antigen-presenting phenotype at both the transcriptomic and proteomic levels. A transitional B cell population with a distinct transcriptomic signature was expanded in TN-JDM and characterized by higher CD24 and CD5 proteins and less CD39, an immunoregulatory protein. This data provides new insights into JDM immune dysregulation at cellular resolution and serves as a novel resource for myositis investigators.

Bioinformatics Analysis of Publicly Available Single-Nuclei Transcriptomics Alzheimer's Disease Datasets Reveals APOE Genotype-Specific Changes Across Cell Types in Two Brain Regions.

Alzheimer's Disease (AD) is a complex neurodegenerative disease that gravely affects patients and imposes an immense burden on caregivers. Apolipoprotein E4 (APOE4) has been identified as the most common genetic risk factor for AD, yet the molecular mechanisms connecting APOE4 to AD are not well understood. Past transcriptomic analyses in AD have revealed APOE genotype-specific transcriptomic differences; however, these differences have not been explored at a single-cell level. To elucidate more complex APOE genotype-specific disease-relevant changes masked by the bulk analysis, we leverage the first two single-nucleus RNA sequencing AD datasets from human brain samples, including nearly 55,000 cells from the prefrontal and entorhinal cortices. In each brain region, we performed a case versus control APOE genotype-stratified differential gene expression analysis and pathway network enrichment in astrocytes, microglia, neurons, oligodendrocytes, and oligodendrocyte progenitor cells. We observed more global transcriptomic changes in APOE4 positive AD cells and identified differences across APOE genotypes primarily in glial cell types. Our findings highlight the differential transcriptomic perturbations of APOE isoforms at a single-cell level in AD pathogenesis and have implications for precision medicine development in the diagnosis and treatment of AD.

Sex-Stratified Single-Cell RNA-Seq Analysis Identifies Sex-Specific and Cell Type-Specific Transcriptional Responses in Alzheimer's Disease Across Two Brain Regions.

Alzheimer's disease (AD) is a pervasive neurodegenerative disorder that disproportionately affects women. Since neural anatomy and disease pathophysiology differ by sex, investigating sex-specific mechanisms in AD pathophysiology can inform new therapeutic approaches for both sexes. Previous bulk human brain RNA sequencing studies have revealed sex differences in dysregulated molecular pathways related to energy production, neuronal function, and immune response; however, the sex differences in disease mechanisms are yet to be examined comprehensively on a single-cell level. We leveraged nearly 74,000 cells from human prefrontal and entorhinal cortex samples from the first two publicly available single-cell RNA sequencing AD datasets to perform a case versus control sex-stratified differential gene expression analysis and pathway network enrichment in a cell type-specific manner for each brain region. Our examination at the single-cell level revealed sex differences in AD prominently in glial cells of the prefrontal cortex. In the entorhinal cortex, we observed the same genes and networks to be perturbed in opposing directions between sexes in AD relative to healthy state. Our findings contribute to growing evidence of sex differences in AD-related transcriptomic changes, which can fuel the development of therapies that may prove more effective at reversing AD pathophysiology.

Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19.

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated 16 of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19.

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated sixteen of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

Single-Cell Mapping of Progressive Fetal-to-Adult Transition in Human Naive T Cells.

Whereas the human fetal immune system is poised to generate immune tolerance and suppress inflammation in utero, an adult-like immune system emerges to orchestrate anti-pathogen immune responses in post-natal life. It has been posited that cells of the adult immune system arise as a discrete ontological "layer" of hematopoietic stem-progenitor cells (HSPCs) and their progeny; evidence supporting this model in humans has, however, been inconclusive. Here, we combine bulk and single-cell transcriptional profiling of lymphoid cells, myeloid cells, and HSPCs from fetal, perinatal, and adult developmental stages to demonstrate that the fetal-to-adult transition occurs progressively along a continuum of maturity-with a substantial degree of inter-individual variation at the time of birth-rather than via a transition between discrete waves. These findings have important implications for the design of strategies for prophylaxis against infection in the newborn and for the use of umbilical cord blood (UCB) in the setting of transplantation.

dittoSeq: universal user-friendly single-cell and bulk RNA sequencing visualization toolkit.

SUMMARY: A visualization suite for major forms of bulk and single-cell RNAseq data in R. dittoSeq is color blindness-friendly by default, robustly documented to power ease-of-use and allows highly customizable generation of both daily-use and publication-quality figures. AVAILABILITY AND IMPLEMENTATION: dittoSeq is an R package available through Bioconductor via an open source MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Whole-Tissue Deconvolution and scRNAseq Analysis Identify Altered Endometrial Cellular Compositions and Functionality Associated With Endometriosis.

The uterine lining (endometrium) exhibits a pro-inflammatory phenotype in women with endometriosis, resulting in pain, infertility, and poor pregnancy outcomes. The full complement of cell types contributing to this phenotype has yet to be identified, as most studies have focused on bulk tissue or select cell populations. Herein, through integrating whole-tissue deconvolution and single-cell RNAseq, we comprehensively characterized immune and nonimmune cell types in the endometrium of women with or without disease and their dynamic changes across the menstrual cycle. We designed metrics to evaluate specificity of deconvolution signatures that resulted in single-cell identification of 13 novel signatures for immune cell subtypes in healthy endometrium. Guided by statistical metrics, we identified contributions of endometrial epithelial, endothelial, plasmacytoid dendritic cells, classical dendritic cells, monocytes, macrophages, and granulocytes to the endometrial pro-inflammatory phenotype, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue.

Multiple Origins of Virus Persistence during Natural Control of HIV Infection.

Targeted HIV cure strategies require definition of the mechanisms that maintain the virus. Here, we tracked HIV replication and the persistence of infected CD4 T cells in individuals with natural virologic control by sequencing viruses, T cell receptor genes, HIV integration sites, and cellular transcriptomes. Our results revealed three mechanisms of HIV persistence operating within distinct anatomic and functional compartments. In lymph node, we detected viruses with genetic and transcriptional attributes of active replication in both T follicular helper (TFH) cells and non-TFH memory cells. In blood, we detected inducible proviruses of archival origin among highly differentiated, clonally expanded cells. Linking the lymph node and blood was a small population of circulating cells harboring inducible proviruses of recent origin. Thus, HIV replication in lymphoid tissue, clonal expansion of infected cells, and recirculation of recently infected cells act together to maintain the virus in HIV controllers despite effective antiviral immunity.