Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin

By Sai Ma, Bing Zhang, Lindsay M. LaFave, Zachary D. Chiang, Yan Hu, Jiarui Ding, Alison Brack, Vinay K. Kartha, T. Law, Caleb A. Lareau and 3 more. bioRxiv, 2020

976 citations recorded by the source corpus.

Cell differentiation and function are regulated across multiple layers of gene regulation, including the modulation of gene expression by changes in chromatin accessibility. However, differentiation is an asynchronous process precluding a temporal understanding of the regulatory events leading to cell fate commitment. Here, we developed SHARE-seq, a highly scalable approach for measurement of chromatin accessibility and gene expression within the same single cell. Using 34,774 joint profiles from mouse skin, we develop a computational strategy to identify cis-regulatory interactions and define Domains of Regulatory Chromatin (DORCs), which significantly overlap with super-enhancers. We show that during lineage commitment, chromatin accessibility at DORCs precedes gene expression, suggesting changes in chromatin accessibility may prime cells for lineage commitment. We therefore develop a computational strategy (chromatin potential) to quantify chromatin lineage-priming and predict cell fate outcomes. Together, SHARE-seq provides an extensible platform to study regulatory circuitry across diverse cells within tissues.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/118c0d1fd2186be29eff704a9678d823870f8190

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Citations in the corpus

Differential abundance testing on single-cell data using K-nearest neighbour graphs (2021, background): “Milo might also be applicable to single-cell assays other than scRNA-seq, including multi-omic assays 35–39 .”
Large Scale Foundation Model on Single-cell Transcriptomics (2023, neutral): “Furthermore, the emerging field of single-cell multiomics data 67,68 presents opportunities for developing models that can delineate multilevel complex laws of cells.”
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding (2022, background): “8g–o and 9 and Methods), indicating reliable alignment.”
Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender (2019, background): “Examples include CITE-seq [5], Perturb-Seq [6], scCAT-seq [7], SNARE-seq [8], SHARE-seq [9], ECCITE-seq [10], 10x Multiome, among many other recently introduced droplet-based assays.”
Methods and applications for single-cell and spatial multi-omics (2023, methodology): “For example, gene expression changes have been shown to be foreshadowed by chromatin priming, as seen in cell cycling via SHARE-seq 38 .”
Microglia states and nomenclature: A field at its crossroads (2022, positive): “122,123 New bulk and single-cell epigenetic approaches 75,124–129 will help answer these questions and ultimately may provide a means to toggle microglial states at will, enabling the ﬁeld to ﬁnally understand the function of distinct microglial states and their impact in different contexts.”
Gene regulatory network inference in the era of single-cell multi-omics (2023, background): “For this reason, and with the introduction of multimodal profiling technologies 26–28 , there has been a recent explosion of novel GRN inference methods.”
Deep learning and alignment of spatially-resolved single cell transcriptomes with Tangram (2021, positive): “Finally, Tangram correctly predicted the expression of genes that were not measured by STARmap, including markers of cortical layers ( Tenm3 , Cdh12 , Kcng1 , Igf2 ) or subcortical tissue ( Opalin and Enpp6 ), as assessed by their consistency with the Allen Brain ISH Atlas (Fig.”
Mapping Transcriptomic Vector Fields of Single Cells (2022, positive): “Incorporating datasets from the recent developments of single-cell multi-omics (Cao, 2020; Ma et al., 2020), spatial transcriptomics (Chen et al.”
Computational principles and challenges in single-cell data integration (2021, background): “In the past several years, highly scalable approaches to simultaneously measure chromatin accessibility and gene expression in thousands of cells 66 have increased the power to identify cis -regulatory interactions.”
Single-cell chromatin state analysis with Signac (2021, methodology): “The measurement of both gene expression and DNA accessibility in the same cell creates an opportunity to link noncoding DNA elements to their potential regulatory targets through the correlation between DNA accessibility and the expression of a nearby gene 12,17,20 .”