Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Zhang, Shilu; Pyne, Saptarshi; Pietrzak, Stefan; Halberg, Spencer; McCalla, Sunnie Grace; Siahpirani, Alireza Fotuhi; Sridharan, Rupa; Roy, Sushmita

doi:10.1038/s41467-023-38637-9

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Journal Article · Sat May 27 00:00:00 EDT 2023 · Nature Communications

DOI:https://doi.org/10.1038/s41467-023-38637-9· OSTI ID:1975415

Zhang, Shilu; Pyne, Saptarshi; Pietrzak, Stefan; Halberg, Spencer; McCalla, Sunnie Grace; Siahpirani, Alireza Fotuhi; Sridharan, Rupa;

Abstract

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

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Research Organization:: Univ. of Wisconsin, Madison, WI (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0021052

OSTI ID:: 1975415

Alternate ID(s):: OSTI ID: 2421704

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 14; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets Zhang, Shilu; Pyne, Saptarshi; Pietrzak, Stefan Zenodo https://doi.org/10.5281/zenodo.7834742	dataset	January 2023

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