Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution

Jingyang Qian, Xin Shao (), Hudong Bao, Yin Fang, Wenbo Guo, Chengyu Li, Anyao Li, Hua Hua () and Xiaohui Fan ()
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Jingyang Qian: Zhejiang University
Xin Shao: Zhejiang University
Hudong Bao: Zhejiang University
Yin Fang: Zhejiang University
Wenbo Guo: Zhejiang University
Chengyu Li: Zhejiang University
Anyao Li: Zhejiang University
Hua Hua: SiChuan Institute for Translational Chinese Medicine
Xiaohui Fan: Zhejiang University

Nature Communications, 2025, vol. 16, issue 1, 1-21

Abstract: Abstract Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, we present scNiche, a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution. We benchmark scNiche with both simulated and biological datasets, and demonstrate that scNiche can effectively and robustly identify cell niches while outperforming other existing methods. In spatial proteomics data from human triple-negative breast cancer, scNiche reveals the influence of the microenvironment on cellular phenotypes, and further dissects patient-specific niches with distinct cellular compositions or phenotypic characteristics. By analyzing mouse liver spatial transcriptomics data across normal and early-onset liver failure donors, scNiche uncovers disease-specific liver injury niches, and further delineates the niche remodeling from normal liver to liver failure. Overall, scNiche enables decoding the cellular microenvironment in tissues from single-cell spatial omics data.

Date: 2025
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DOI: 10.1038/s41467-025-57029-9

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