Correlated gene modules uncovered by high-precision single-cell transcriptomics.

Area of Science:

• Genomics
• Molecular Biology
• Systems Biology

Background:

• Gene expression correlations infer gene relationships but are confounded by cell type and perturbations.
• Existing methods often yield spurious correlations between unrelated genes.

Purpose of the Study:

• To develop a method for accurately capturing correlated gene modules by measuring steady-state gene expression fluctuations in single cells.
• To identify cell-type specific and functionally enriched correlated gene modules.
• To assess the predictive power of these modules for gene regulation and response to perturbations.

Main Methods:

• Developed MALBAC-DT, a high-precision single-cell RNA-sequencing method.
• Measured correlations of steady-state gene expression fluctuations within a homogenous cell population.
• Validated module predictions using gene knockdown experiments and compared them to ChIP-seq data.

Main Results:

• Identified numerous cell-type specific and functionally enriched correlated gene modules.
• A p53 signaling module predicted p53 regulatory targets more accurately than consensus ChIP-seq studies.
• Steady-state correlations predicted transcriptome-wide response patterns to perturbations.

Conclusions:

• Measuring steady-state gene expression fluctuations in single cells effectively captures functional gene modules.
• MALBAC-DT provides a powerful approach for advancing functional genomics and understanding genome-wide regulatory networks.