Deep generative modeling for single-cell transcriptomics.

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task

Selective Impairment of TH17-Differentiation and Protection against Autoimmune Arthritis after Overexpression of BCL2A1 in T Lymphocytes

The inhibition of apoptotic cell death in T cells through the dysregulated expression of BCL2 family members has been associated with the protection against the development of different autoimmune diseases. However, multiple mechanisms were proposed to be responsible for such protective effect. The purpose of this study was to explore the effect of the Tcell overexpression of BCL2A1, an anti-apoptotic BCL2 family member without an effect on cell cycle progression, in the development of collagen-induced arthritis. Our results demonstrated an attenuated development of arthritis in these transgenic mice. The protective effect was unrelated to the suppressive activity of regulatory T cells but it was associated with a defective activation of p38 mitogen-activated protein kinase in CD4+ cells after in vitro TCR stimulation. In addition, the in vitro and in vivo TH17 differentiation were impaired in BCL2A1 transgenic mice. Taken together, we demonstrated here a previously unknown role for BCL2A1 controlling the activation of CD4+ cells and their differentiation into pathogenic proinflammatory TH17 cells and identified BCL2A1 as a potential target in the control of autoimmune/inflammatory diseases

The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation

Type 2 innate lymphoid cells (ILC2s) both contribute to mucosal homeostasis and initiate pathologic inflammation in allergic asthma. However, the signals that direct ILC2s to promote homeostasis versus inflammation are unclear. To identify such molecular cues, we profiled mouse lung-resident ILCs using single-cell RNA sequencing at steady state and after in vivo stimulation with the alarmin cytokines IL-25 and IL-33. ILC2s were transcriptionally heterogeneous after activation, with subpopulations distinguished by expression of proliferative, homeostatic and effector genes. The neuropeptide receptor Nmur1 was preferentially expressed by ILC2s at steady state and after IL-25 stimulation. Neuromedin U (NMU), the ligand of NMUR1, activated ILC2s in vitro, and in vivo co-administration of NMU with IL-25 strongly amplified allergic inflammation. Loss of NMU-NMUR1 signalling reduced ILC2 frequency and effector function, and altered transcriptional programs following allergen challenge in vivo. Thus, NMUR1 signalling promotes inflammatory ILC2 responses, highlighting the importance of neuro-immune crosstalk in allergic inflammation at mucosal surfaces

T cell fate and clonality inference from single-cell transcriptomes.

We developed TraCeR, a computational method to reconstruct full-length, paired T cell receptor (TCR) sequences from T lymphocyte single-cell RNA sequence data. TraCeR links T cell specificity with functional response by revealing clonal relationships between cells alongside their transcriptional profiles. We found that T cell clonotypes in a mouse Salmonella infection model span early activated CD4(+) T cells as well as mature effector and memory cells

JunB is essential for IL-23-dependent pathogenicity of Th17 cells

CD4+ T-helper cells producing interleukin-17 (IL-17), known as T-helper 17 (TH17) cells, comprise heterogeneous subsets that exhibit distinct pathogenicity. Although pathogenic and non-pathogenic TH17 subsets share a common RORγt-dependent TH17 transcriptional programme, transcriptional regulatory mechanisms specific to each of these subsets are mostly unknown. Here we show that the AP-1 transcription factor JunB is critical for TH17 pathogenicity. JunB, which is induced by IL-6, is essential for expression of RORγt and IL-23 receptor by facilitating DNA binding of BATF at the Rorc locus in IL-23-dependent pathogenic TH17 cells, but not in TGF-β1-dependent non-pathogenic TH17 cells. Junb-deficient T cells fail to induce TH17-mediated autoimmune encephalomyelitis and colitis. However, JunB deficiency does not affect the abundance of gut-resident non-pathogenic TH17 cells. The selective requirement of JunB for IL-23-dependent TH17 pathogenicity suggests that the JunB-dependent pathway may be a therapeutic target for autoimmune diseases

A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.

RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology-the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation

A general and flexible method for signal extraction from single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) data provides information on transcriptomic heterogeneity within cell populations. Here, Risso et al develop ZINB-WaVE for low-dimensional representations of scRNA-seq data that account for zero inflation, over-dispersion, and the count nature of the data