METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics

Abstract The recent advance of spatial transcriptomics (ST) technique provides valuable insights into the organization and interactions of cells within the tumor microenvironment (TME). While various analytical tools have been developed for tasks such as spatial clustering, spatially variable gene identification, and cell type deconvolution, most of them are general methods lacking consideration of histological features in spatial data analysis. This limitation results in reduced performance and interpretability of their results when studying the TME. Here, we present a computational framework named, M orphology- E nhanced Spatial T ranscriptome Analysis Integrator (METI) to address this gap. METI is an end-to-end framework capable of spatial mapping of both cancer cells and various TME cell components, robust stratification of cell type and transcriptional states, and cell co-localization analysis. By integrating both spatial transcriptomics, cell morphology and curated gene signatures, METI enhances our understanding of the molecular landscape and cellular interactions within the tissue, facilitating detailed investigations of the TME and its functional implications. The performance of METI has been evaluated on ST data generated from various tumor tissues, including gastric, lung, and bladder cancers, as well as premalignant tissues. Across all these tissues and conditions, METI has demonstrated robust performance with consistency.