CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data.

ABSTRACT: Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine gran

SUBMITTER: Zhu B 

PROVIDER: S-EPMC12317664 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature