Implementing and Applying Multiplexed Single Cell RNA-sequencing to Reveal Context-specific Effects in Systemic Lupus Erythematosus

Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each cell and detect droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 SNPs per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of 8 pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We also apply demuxlet to assess cell type-specific changes in gene expression in 8 pooled lupus patient samples treated with IFN- and perform eQTL analysis on 23 pooled samples.Systemic lupus erythematosus (SLE) is an autoimmune disease defined by a broad range of symptoms that disproportionately affects women. Our knowledge of which immune cells mediate the etiology and pathogenesis of the disease remains incomplete. Identifying pathogenic cells using bulk gene expression analysis is confounded by the functional overlap and frequency variation of immune cell types. Here, we used multiplexed single-cell RNA-seq (scRNA-seq) to profile ~1 million peripheral blood mononuclear cells from 134 SLE cases and 58 healthy controls. Cases were marked by a reduction of naive CD4+ T cells, clonal restriction of effector memory CD8+ T cells, and elevated expression of interferon-stimulated genes in classical monocytes. An additional 15 cases experiencing active disease flares displayed increased expansion of effector memory CD8+ T cells and the presence of macrophages not seen in managed disease. Although cell-type-specific expression contributed most to inter-individual expression variability across all cells, cell composition accounted for more variability in genes differentially expressed in cases. We integrated dense genotyping data to map thousands of genetic variants, including SLE-associations, whose effects on expression are modified by cell type or interferon activation. Population-scale scRNA-seq analysis reveals changes in cell composition and state associated with SLE, and when integrated with genetic data, ascribes function to disease-associated and disease-modified variants

Functional interpretation of single cell similarity maps.

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration

Quantifying the Reproducibility of Cell-Perturbation Experiments

Experiments adhering to the same protocol can nonetheless lead to different conclusions, for instance, due to batch effects or lab effects. A statistical test applied to measurements from one experiment may yield a vanishingly small $p$-value, yet applying the same test to measurements from a replicate experiment may yield a large $p$-value. Recent work has highlighted this lack of reproducibility in cell-perturbation experiments. We introduce the Reproducible Sign Rate (RSR), a new reproducibility metric for settings in which each hypothesis test has two alternatives (e.g., upregulation and downregulation of gene expression). The RSR identifies the proportion of discoveries that are expected to reproduce in a future replicate. We provide conditions under which the RSR can be estimated accurately -- even when as few as two experimental replicates are available. We also provide conditions under which high RSR implies a low Type S error rate. We demonstrate the uses of RSR with experiments based on several high-throughput technologies, including L1000, Sci-Plex, and CRISPR.Comment: Submitted to AoA

Genetic and environmental perturbations lead to regulatory decoherence

Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among N MR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

Implementing and Applying Multiplexed Single Cell RNA-sequencing to Reveal Context-specific Effects in Systemic Lupus Erythematosus

Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each cell and detect droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 SNPs per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of 8 pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We also apply demuxlet to assess cell type-specific changes in gene expression in 8 pooled lupus patient samples treated with IFN- and perform eQTL analysis on 23 pooled samples.Systemic lupus erythematosus (SLE) is an autoimmune disease defined by a broad range of symptoms that disproportionately affects women. Our knowledge of which immune cells mediate the etiology and pathogenesis of the disease remains incomplete. Identifying pathogenic cells using bulk gene expression analysis is confounded by the functional overlap and frequency variation of immune cell types. Here, we used multiplexed single-cell RNA-seq (scRNA-seq) to profile ~1 million peripheral blood mononuclear cells from 134 SLE cases and 58 healthy controls. Cases were marked by a reduction of naive CD4+ T cells, clonal restriction of effector memory CD8+ T cells, and elevated expression of interferon-stimulated genes in classical monocytes. An additional 15 cases experiencing active disease flares displayed increased expansion of effector memory CD8+ T cells and the presence of macrophages not seen in managed disease. Although cell-type-specific expression contributed most to inter-individual expression variability across all cells, cell composition accounted for more variability in genes differentially expressed in cases. We integrated dense genotyping data to map thousands of genetic variants, including SLE-associations, whose effects on expression are modified by cell type or interferon activation. Population-scale scRNA-seq analysis reveals changes in cell composition and state associated with SLE, and when integrated with genetic data, ascribes function to disease-associated and disease-modified variants

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Not AvailableThe mirid bug, Creontiodes biseratense (Distant) (Hemiptera: Miridae) is as a serious pest of cotton crop. Forecasting model by linking the pest incidence with season, crop phenology, biotic and abiotic factors enable to understand the dynamics of pest occurrence likely to occur. A data mining technique decision tree induction model is proposed for forecasting the pest incidence and study the population dynamics of mirid bug, C. biseratense in relation to its natural enemies viz., spider Lycosa sp. and coccinellid Cheilomenes sexmaculata Fabricius and abiotic factors. The results of the decision tree agreed well with statistical analysis.Not Availabl

Functional interpretation of single cell similarity maps.

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration