CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics

Recent advances in spatial transcriptomics (ST) have achieved subcellular spatial resolution, yet existing platforms either capture sparse transcript counts per spot or measure only a limited number of genes, complicating the extraction of comprehensive single-cell information. Here we introduce CellART, a unified framework for extracting single-cell information across diverse high-resolution ST platforms. By leveraging multimodal data, including staining images, ST data and single-cell RNA sequencing references, CellART simultaneously performs cell segmentation and cell-type annotation through integration of deep learning and probabilistic modeling. CellART is efficient, generalizable and robust, and its outputs are compatible with widely used community tools, facilitating a variety of downstream analyses.