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

Shilu Zhang, Saptarshi Pyne, Stefan Pietrzak, Spencer Halberg, Sunnie Grace McCalla, Alireza Fotuhi Siahpirani, Rupa Sridharan and Sushmita Roy ()
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Shilu Zhang: University of Wisconsin-Madison
Saptarshi Pyne: University of Wisconsin-Madison
Stefan Pietrzak: University of Wisconsin-Madison
Spencer Halberg: University of Wisconsin-Madison
Sunnie Grace McCalla: University of Wisconsin-Madison
Alireza Fotuhi Siahpirani: University of Wisconsin-Madison
Rupa Sridharan: University of Wisconsin-Madison
Sushmita Roy: University of Wisconsin-Madison

Nature Communications, 2023, vol. 14, issue 1, 1-25

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-38637-9

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