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)
• 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: Science & Technology - Other Topics
• ISSN: 2041-1723; 2041-1723

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.