Three weeks ago we followed the RNA map of a tissue; today it’s the protein map. Multiplexed imaging like CODEX stains one slice for dozens of proteins at once — ‘antibody binding events’ read out ‘at a single-cell and cellular neighborhood’ level — turning a piece of tissue into a ~40-channel image where every pixel carries a molecular fingerprint. The AI job is a three-step relay: find every cell (Mesmer, trained on TissueNet’s ‘more than 1 million manually labeled cells,’ hit human-level segmentation across tissue types); name every cell from its protein profile (STELLAR, ‘a geometric deep learning method for cell-type discovery,’ applied straight to CODEX data); then read the neighborhoods. And the honest wall: a 2024 Nature Methods benchmark of ‘more than 50 diverse biological experiments’ found methods ‘often tailored to specific modalities or require manual interventions’ — generalization, not accuracy on one panel, is the frontier. It’s the imaging×omics intersection at the lab’s core, and the protein half of the spatial map a virtual cell still needs.
