A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility...

Area of Science:

• Molecular Biology
• Systems Biology
• Genomics

Background:

• Eukaryotic gene regulation is complex, involving combinatorial, dynamic, and quantitative processes crucial for development and disease.
• Gene regulatory networks (GRNs) model these processes, with recent advances driven by multi-omics data, especially at the single-cell level.
• Enhancer GRNs (eGRNs) specifically model interactions between transcription factors, regulatory elements, and target genes.

Purpose of the Study:

• To review key components for successful GRN and eGRN inference using transcriptomics and chromatin accessibility data.
• To highlight state-of-the-art methods, challenges, and future directions in the field.
• To emphasize the integration of multi-omics data for mechanistic network inference.

Main Methods:

• Utilizing single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data.
• Applying computational strategies for omics pairing, transcription factor binding site detection, and chromatin interaction analysis.
• Exploring linear and 3D approaches for identifying regulatory element interactions.

Main Results:

• Combinations of transcriptomics and chromatin accessibility enable fine-grained regulatory program prediction beyond simple expression correlation.
• eGRNs provide a detailed model of molecular interactions within regulatory pathways.
• The review covers preprocessing, metacell generation, and omics integration techniques.

Conclusions:

• The integration of transcriptomics and epigenomics data at the single-cell level represents the new standard for mechanistic network inference.
• Future advancements lie in integrating additional omics layers, spatiotemporal data, and shifting towards quantitative and causal modeling.
• This approach is vital for a deeper understanding of gene regulation in biological processes and diseases.