Xin Shao

2 months ago | genomebiology.biomedcentral.com | Xin Shao |Lingqi Yu |Chengyu Li |Jingyang Qian |Xinyu Yang |Haihong Yang | +4 more

For the analyses of human ovarian cancer investigations, patient participants with platinum-free intervals exceeding 20 days and corresponding peritoneum and omentum samples containing a minimum of 100 epithelial ovarian cancer cells in the scRNA-seq data were retained. The genes were revised in accordance with the National Center for Biotechnology Information gene database (https://www.ncbi.nlm.nih.gov/gene/), in which unmatched and duplicated genes were removed.

Feb 16, 2025 | nature.com | Jingyang Qian |Xin Shao |Yin Fang |Wenbo Guo |Chengyu Li |Xiaohui Fan | +1 more

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. Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, the authors develop scNiche, a computational framework to identify and characterise cell niches from spatial omics data at single-cell resolution.