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

Autor: Zhang S; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA., Pyne S; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA., Pietrzak S; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.; Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI, USA., Halberg S; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA., McCalla SG; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.; Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, 53706, USA., Siahpirani AF; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.; Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran., Sridharan R; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.; Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI, USA., Roy S; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA. sroy@biostat.wisc.edu.; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA. sroy@biostat.wisc.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2023 May 27; Vol. 14 (1), pp. 3064. Date of Electronic Publication: 2023 May 27.
DOI: 10.1038/s41467-023-38637-9
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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