AI and the World's Hard Problems · Life sciences · Nature (machine learning) · 2026-10-05
CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics
High-resolution spatial transcriptomics platforms either capture sparse transcript counts per spot or measure only a limited number of genes, which makes single-cell information hard to extract. CellART combines staining images, spatial transcriptomics data and single-cell RNA-seq references, using deep learning and probabilistic modelling to segment cells and annotate cell types simultaneously. The authors report that it is efficient, generalizable and robust, and that its outputs are compatible with widely used community tools.