Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes

Area of Science:

• Genomics
• Molecular Biology
• Computational Biology

Background:

• Linking cell-type-specific enhancers to their target genes is crucial for understanding gene regulation.
• Gene co-expression patterns can lead to false positive associations in enhancer-gene linkage studies, similar to linkage disequilibrium in GWAS.
• Single-cell sequencing offers high resolution but is susceptible to confounding factors.

Purpose of the Study:

• To develop a robust statistical method for accurate enhancer-gene association studies (EGAS) using single-cell sequencing data.
• To overcome the challenge of gene co-expression confounding enhancer-gene linkage.
• To enable genome-wide prediction of enhancer-gene associations.

Main Methods:

• Developed a non-parametric permutation-based procedure to establish stringent statistical criteria.
• Applied the procedure to large-scale transcriptome and epigenome data from multiple tissues and species (mouse and human brain).
• Validated predictions using chromatin conformation data and causal enhancer perturbation experiments.

Main Results:

• Successfully controlled the risk of false-positive associations in enhancer-gene association studies.
• Enabled robust genome-wide prediction of enhancer-gene associations.
• Demonstrated functional validity of predicted associations through experimental and conformation data.

Conclusions:

• Controlling for gene co-expression is essential for robust enhancer-gene linkage using single-cell sequencing.
• The developed method provides a reliable framework for deciphering gene regulatory networks.
• This approach advances our understanding of gene expression regulation at a single-cell level.