Functional inference of gene regulation using single-cell multi-omics.

Area of Science:

• Genomics
• Immunology
• Computational Biology

Background:

• Cellular responses to environmental stimuli rely on precise gene expression control.
• Understanding the regulatory mechanisms of immune responses at a single-cell level is crucial.

Purpose of the Study:

• To develop a computational framework for integrating single-cell multi-omics data (scATAC-seq and scRNA-seq).
• To investigate the cis-regulatory landscape and gene regulatory networks (GRNs) underlying immune stimulation in human blood cells.
• To identify dynamic changes in chromatin accessibility and gene expression during immune responses.

Main Methods:

• Application of single-cell Assay for Transposase-Accessible Chromatin sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) on human blood cells.
• Development and utilization of the functional inference of gene regulation (FigR) framework to pair multi-omics data.
• Computational inference of gene regulatory networks (GRNs) and identification of cis-regulatory elements.

Main Results:

• Generation of ~91,000 single-cell profiles enabling high-resolution analysis of immune responses.
• Identification of domains of regulatory chromatin (DORCs) associated with immune stimulation.
• Observation of rapid alterations in chromatin accessibility and gene expression within minutes of stimulation.
• Elucidation of transcription factor (TF) activity within disease-associated DORCs through GRN construction.

Conclusions:

• The FigR framework effectively integrates scATAC-seq and scRNA-seq data to infer gene regulation at a single-cell level.
• Immune stimulation induces rapid and dynamic changes in chromatin accessibility and gene expression.
• This approach provides novel insights into cellular functions and regulatory interactions within tissues.