Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms

Area of Science:

• Computational Biology
• Genomics
• Systems Biology

Background:

• Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is crucial for understanding cell-type-specific gene regulation.
• Existing methods often struggle with computational complexity and the accurate reconstruction of regulatory logic.

Purpose of the Study:

• To introduce scGATE (single-cell gene regulatory gate), a novel computational tool for inferring transcription factor (TF)-gene interaction networks and Boolean logic gates.
• To develop a computationally efficient method that reduces time complexity for logic-based GRN inference.

Main Methods:

• scGATE utilizes a Bayesian framework to infer Boolean rules directly from scRNA-seq data, avoiding individual rule formulation and likelihood calculations.
• Integration of assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter non-relevant TFs.
• Incorporation of single-cell clustering with external data for context-specific network inference.

Main Results:

• scGATE demonstrates superior performance in reconstructing TF-gene networks compared to existing tools, validated on synthetic and real single-cell multi-omics data.
• The tool successfully infers context-specific networks by integrating scRNA-seq, scATAC-seq, and TF motif information.
• scGATE efficiently reconstructs Boolean logic gates, revealing complex combinatorial and cooperative TF relationships.

Conclusions:

• scGATE offers a significant advancement in computational tools for GRN inference from scRNA-seq data.
• The Bayesian approach and integration of multi-omics data enable robust and efficient reconstruction of context-specific regulatory logic.
• scGATE provides a flexible framework for dissecting intricate TF regulatory mechanisms in cellular systems.