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Characterizing gene perturbations in single cells via network divergence analysis.

Chao Huang^1,2, Yuhan Li^1,2, Botao Fa^1,2

¹Institute of Molecular and Translational Medicine (IMTM), Department of Biochemistry and Molecular Biology (DBMB), Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.

Nature Communications

|April 11, 2026

Summary

We developed scDNS, a new method to detect gene functional changes in single cells by analyzing gene network rewiring. This approach identifies key regulators and cell populations affected by perturbations, even without significant expression changes.

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Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Gene functional perturbations can alter cellular behavior without changing gene expression levels.
Existing single-cell RNA sequencing (scRNA-seq) analysis methods struggle to link gene functional shifts to specific cell populations.
Identifying context-specific gene regulatory activity is crucial for understanding cellular responses.

Purpose of the Study:

To introduce scDNS, a novel framework for quantifying gene-specific functional perturbations in single cells.
To associate inferred gene perturbations with specific cell populations.
To provide a tool for uncovering dynamic gene regulation and cellular responses.

Main Methods:

scDNS quantifies gene perturbations by measuring information-theoretic divergence between condition-specific gene interaction networks.
The framework analyzes scRNA-seq data to detect network rewiring and functional shifts.
Simulated datasets and experimental data were used for validation.

Main Results:

scDNS successfully prioritizes key regulators and perturbed cell populations, even with minimal gene expression changes but pronounced network rewiring.
Applications revealed hidden regulatory programs and heterogeneous responder cell states in various biological contexts.
In pancreatic cancer, scDNS identified TIMM44 as a potential sensitizer for gemcitabine therapy.

Conclusions:

scDNS offers a powerful new approach for inferring dynamic gene perturbations at the single-cell level.
The framework enhances the ability to link gene function alterations to specific cell types and states.
This tool has broad applications in understanding complex biological processes and disease mechanisms.