Single-cell Genomics Unveils Critical Regulators of Th17 cell Pathogenicity

By Jellert T. Gaublomme, Nir Yosef, Youjin Lee, Rona S. Gertner, Li V. Yang, Chuan Wu, P. Pandolfi, T. Mak, Rahul Satija, A. Shalek and 3 more. 2015

552 citations recorded by the source corpus.

SUMMARY Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or differentiated in vitro under either pathogenic or non-pathogenic polarization conditions. Computational analysis relates a spectrum of cellular states in vivo to in vitro differentiated Th17 cells, and unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four new genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while potentially sparing non-pathogenic tissue-protective ones.

Subfields: Immunology

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Citations in the corpus

A general and flexible method for signal extraction from single-cell RNA-seq data (2017, unknown): “Dimensionality reduction is used in the literature as a preliminary step prior to clustering [3, 13, 14], the inference of developmental trajectories [15–18], spatio-temporal ordering of the cells [5, 19], and, of course, as a visualization tool [20, 21].”
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Single cell genomics: from phenomenology to mechanism (2017, methodology): “…was also used to recover a spectrum of transcriptional states that correspond to the pathogenicity and autoimmunogenic potential of T-helper cells that express interleukin-17, identifying gene modules and putative regulators associated with this spectrum, which were validated in animal models 43 .”
Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution (2017, result): “…average expression profile across single cells and the matching population profile were tightly correlated (Figures 1B and S1H), with an average Pearson product-moment correlation coefficient (r = 0.86) comparable with that observed in previous studies (Gaublomme et al., 2015; Shalek et al., 2014).”
A deep generative model for gene expression profiles from single-cell RNA sequencing (2017, background): “From that ensemble of problems, the biologist can then answer biological questions like identifying new cell types [11] or finding new regulators of autoimmunity [12].”
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, background): “Single-cell RNA sequencing (scRNA-seq) is an increasingly popular tool that opens the way for studying cellular heterogeneity at a high resolution [1], thus shedding new light on fundamental questions in areas such as development [2], autoimmunity [3], and cancer [4].”