Single cell RNA Seq reveals dynamic paracrine control of cellular variation

By A. Shalek, Rahul Satija, Joe Shuga, John J. Trombetta, D. Gennert, Diana Lu, Peilin Chen, Rona S. Gertner, Jellert T. Gaublomme, Nir Yosef and 12 more. Nature, 2014

990 citations recorded by the source corpus.

High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a ‘core’ module of antiviral genes is expressed very early by a few ‘precocious’ cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secretion and extracellular signalling, we show that this response is coordinated by interferon-mediated paracrine signalling from these precocious cells. Notably, preventing cell-to-cell communication also substantially reduces variability between cells in the expression of an early-induced ‘peaked’ inflammatory module, suggesting that paracrine signalling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c644a12fe605d44619d2bc4906c611f3e2b9ea0e

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Citations in the corpus

The Human Cell Atlas (2017, background): “…stage), the inherent noise in molecular systems (Eldar and Elowitz, 2010; Kharchenko et al., 2014; Kim et al., 2015; Shalek et al., 2013), and measurement errors (Buettner et al., 2015; Kharchenko et al., 2014; Kim et al., 2015; Shalek et al., 2013; Shalek et al., 2014; Wagner et al., 2016).”
Modelling dropouts for feature selection in scRNASeq experiments (2016, methodology): “To facilitate the identification of true positive DE genes only the two replicates of Unstimulated and after 4h LPS stimulation were considered for the Shalek dataset; in addition technical artefacts as described by the authors were removed [15].”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, background): “Similarly, assessments of transcriptional differences between individual cells have been used to identify rare cell populations that would otherwise go undetected in analyses of pooled cells [18], for example malignant tumour cells within a tumour mass [19], or hyperresponsive immune cells within a ”
ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis (2015, methodology): “…single cells exhibiting null expression [15]. b Heat maps showing the relationship between dropout rate and mean non-zero expression level for three published single-cell data sets [3, 5, 14] sum of the coordinates in the original high-dimensional space and no non-linear transformations are used.”
Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution (2017, methodology): “To quantify the activation of these two gene sets within individual cells, we computed a module score as previously described (Shalek et al., 2014; see Experimental Procedures).”
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets (2015, background): “By comparison, previous studies that used FACS (Jaitin et al., 2014) or a commercial microfluidics platform (Shalek et al., 2014) to isolate single cells reported doublet rates of 2.3% and 11% respectively, based upon examining microscopy images of captured cells.”
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq (2017, methodology): “The covariance matrix used for PCA was generated using an approach previously outlined (28) to decrease the weight of less reliable “missing” values in the data.”
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor (2016, neutral): “## [1] "total_counts" "log10_total_counts" "filter_on_total_counts" ## [4] "total_features" "log10_total_features" "filter_on_total_features"”
Single-cell Genomics Unveils Critical Regulators of Th17 cell Pathogenicity (2015, methodology): “Deng Q, Ramskold D, Reinius B, Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells.”
Computational and analytical challenges in single-cell transcriptomics (2015, methodology): “…example, by using such a strategy, a recent study discovered that when dendritic cells are challenged by pathogenic stimuli, a set of antiviral genes is upregulated in only a small subset of cells immediately post-stimulation; this set of genes is upregulated in all cells at later time points 36 .”