Abstract
Functional perturbations of genes do not always cause expression changes, but can manifest through network rewiring or context-specific shifts in regulatory activity. However, inferring functional shifts in genes and linking them to the specific cell populations remains challenging, as most current scRNA-seq data analysis focuses either on differential gene expression or on cell abundance/state changes, but rarely associate gene perturbations with particular cell populations. Here we present scDNS, a framework that quantifies gene-specific functional perturbations by measuring information-theoretic divergence between condition-specific gene interaction network configurations. In simulated stress tests and multiple experimental datasets, scDNS prioritizes key regulators and perturbed cell populations, even when expression changes are minimal but network rewiring is pronounced. Applications to immunodeficiency mutations, stimulus responses, and viral infection reveal hidden regulatory programs and heterogeneous responder cell states. In pancreatic cancer, scDNS nominates TIMM44 as a mitochondrial sensitizer enhancing gemcitabine efficacy. Together, scDNS provides a powerful tool for inferring dynamic gene perturbations in single cells.
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Data availability
scRNA-seq data from cells carrying triple mutations (TERTC228T, TP53G743A, and PDGFRAD842V)31 were obtained from the NCBI Gene Expression Omnibus (GEO) under accession ID GSE229901. scRNA-seq data of DSP-knockout cells29 were obtained from GEO under accession GSE189068. scRNA-seq data of IRF4p.T95R B cells and PBMCs3 were retrieved from GEO under accessions GSE215936 and GSE215938, respectively. PBMC scRNA-seq following IFN-β treatment38 were accessed from NCBI GEO database accession GSE96583. COVID-19 single-nucleus RNA-seq (snRNA-seq) datasets42 were accessed from GEO under accession GSE171524, and PBMC bulk RNA-seq datasets47 were accessed under GSE217948. COVID-19 spatial transcriptomics data45 were obtained from the crost database66 (accession ID VISDS000628). scRNA-seq data of PANC-1 cell lines treated with gemcitabine30 were obtained from GEO under accession GSE186960. Bulk RNA-seq data of PANC-1 cells treated with gemcitabine, MitoBlock-10, or their combination, as well as the phosphoproteomics data of gemcitabine-treated PANC-1 cells, have been deposited in the Genome Sequence Archive under project PRJCA055925. An example dataset has been deposited in Zenodo and is available at https://doi.org/10.5281/zenodo.1876766367. Source data are provided with this paper.
Code availability
scDNS is implemented as an R package and is available on GitHub (https://github.com/xiaolab-xjtu/scDNS), under the CC BY 4.0 license. The specific version of the code associated with this publication is archived in Zenodo and is accessible via https://doi.org/10.5281/zenodo.1876095168.
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Acknowledgements
We thank Drs. Xuerui Yang, Xianwen Ren, Shiquan Sun, and Zhenhai Du for their constructive discussions. This work was supported by the following funding: National Natural Science Foundation of China (32370706 to Z.X., 32200522 to C.H., 82541006 to Y.X.); “Pioneer” and “Leading Goose” R&D Program of Zhejiang (2025C02060 to Q.X.); Zhejiang Provincial Natural Science Foundation of China (LRG26H160003 to Q.X.); Zhejiang Provincial Special Fund for Supporting Provincial-Level Scientific Research Institutes (00004ACYS202403 to Q.X.); China Postdoctoral Science Foundation (2023M732809 to C.H.) and Shaanxi Province Postdoctoral Support Program (2023BSHEDZZ68 to C.H.); Z.X. acknowledges support from the Youth Innovation Team Project of Xi’an Jiaotong University (xtr052025013, xtr052023008, and xtr052025012), the Qinchuangyuan High-level Innovative Entrepreneurial Talent Project (QCYRCXM-2022-337), and Key Research and Development Plan of Shaanxi Province of China (2024SF-ZDCYL-02-04). We thank OE Biotech Co., Ltd. for their assistance with the phosphoproteomics analysis.
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Z.X. conceived and supervised the study. C.H. implemented the algorithm and performed data analyses with substantial assistance from Y.L., B.F., Y.M., and Z.Z.; Y.L. developed the GAT-based deep learning module under the supervision of Y.X.; J.Z. and Z.L. carried out the experimental validation under the supervision of Q.X.; Q.X. contributed to the revision of the manuscript and provided funding and materials for the experiments. C.H. and Z.X. wrote and revised the manuscript, and all authors reviewed and approved the final version.
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Huang, C., Li, Y., Fa, B. et al. Characterizing gene perturbations in single cells via network divergence analysis. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71507-8
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DOI: https://doi.org/10.1038/s41467-026-71507-8
