Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 2023 May 27;14(1):3064
Date
05/28/2023Pubmed ID
37244909Pubmed Central ID
PMC10224950DOI
10.1038/s41467-023-38637-9Scopus ID
2-s2.0-85160374592 (requires institutional sign-in at Scopus site) 72 CitationsAbstract
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.
Author List
Zhang S, Pyne S, Pietrzak S, Halberg S, McCalla SG, Siahpirani AF, Sridharan R, Roy SAuthor
Stefan Joseph Pietrzak PhD, BS Postdoctoral Researcher in the Physiology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Cell LineageChromatin
Gene Regulatory Networks
Single-Cell Analysis
Transcription Factors
