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

• Published in: Nature communications 2023-05, Vol.14 (1), p.3064-3064, Article 3064
• Main Authors: Zhang, Shilu, Pyne, Saptarshi, Pietrzak, Stefan, Halberg, Spencer, McCalla, Sunnie Grace, Siahpirani, Alireza Fotuhi, Sridharan, Rupa, Roy, Sushmita
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/2114; 631/114/2401; 631/1647/48; 639/705/794; BASIC BIOLOGICAL SCIENCES; bioinformatics; Cell differentiation; Cell fate; Cell lineage; Cell Lineage - genetics; Chromatin; Chromatin - genetics; data integration; Datasets; Gene expression; Gene regulation; Gene Regulatory Networks; Gene sequencing; Genes; Humanities and Social Sciences; Inference; Learning; machine learning; multidisciplinary; Networks; Proteins; Science; Science (multidisciplinary); Signaling; Single-Cell Analysis; software; Transcription factors; Transcription Factors - genetics; Transcription Factors - metabolism; Transposase
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-38637-9

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. Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Here, the authors present single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer cell type-specific GRN dynamics from scRNA-seq and scATAC-seq datasets collected for diverse cell fate specification trajectories.