Single-cell gene regulation network inference by large-scale data integration

Nucleic Acids Research, 2022 · DOI: https://doi.org/10.1093/nar/gkac819 · Published: September 26, 2022

Simple Explanation

This paper introduces SCRIP, a new computational method that integrates single-cell ATAC-seq data with a large database of ChIP-seq data to infer gene regulatory networks at the single-cell level. SCRIP helps to understand how transcription regulators (TRs) bind to DNA and control gene expression in individual cells. SCRIP uses a comprehensive reference dataset of TR ChIP-seq and motif information to evaluate TR activity in single cells. It then models the regulatory potential of TRs to identify their target genes and construct gene regulatory networks. The method was tested on several biological systems, including PBMCs, HSC differentiation, human fetal organ development, and BCC tumor microenvironments. Results showed that SCRIP can accurately predict TR activity, trace cell lineages, and reveal disease-associated gene regulatory networks.

Study Duration

Not specified

Participants

Not specified

Evidence Level

Not specified

Key Findings

1
SCRIP shows improved performance in evaluating TR binding activity compared to motif-based methods, with higher consistency with matched TR expressions.
2
SCRIP enables identifying TR target genes and constructing GRNs at single-cell resolution based on a regulatory potential model.
3
SCRIP demonstrates utility in accurate cell-type clustering, lineage tracing, and inferring cell-type-specific GRNs in multiple biological systems.

Research Summary

The study introduces SCRIP, a computational method for inferring single-cell gene regulation networks by integrating scATAC-seq data with a large-scale ChIP-seq reference. SCRIP evaluates TR activity and constructs GRNs at single-cell resolution using a regulatory potential model. SCRIP includes a high-quality TR reference covering 1,252 human TRs and 997 mouse TRs, showing superior performance in evaluating single-cell TR activity, performing TR-based clustering, and lineage tracing analyses. SCRIP accurately reconstructs single-cell GRNs based on imputed ChIP-seq peaks and demonstrates usability on multiple biological systems, including PBMC, HSC differentiation, human organ development, and basal cell carcinomas.

Practical Implications

Improved understanding of gene regulation

SCRIP provides a more accurate method for understanding gene regulation at the single-cell level, enabling researchers to identify key transcription factors and their target genes.

Enhanced cell-type identification and lineage tracing

The method facilitates more accurate cell-type clustering and lineage tracing analyses, providing insights into cellular differentiation processes.

Disease-specific GRN discovery

SCRIP allows for the identification of disease-specific gene regulatory networks in complex biological systems, potentially leading to new therapeutic targets.

Study Limitations

1
Reliance on the data quality of ChIP-seq datasets.
2
Potential batch effects between the TR ChIP-seq data with the scATAC-seq data.
3
The bulk-level ChIP-seq datasets have the probability of losing the signals on rare populations.

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