sMMC-22M: A Context-Aware Dataset and Benchmark for Single-Cell Spatial Transcriptomics

Spatial transcriptomics has created a compelling opportunity to test whether tissue morphology can predict molecular state, but existing benchmarks are constrained by limited scale, spot-level supervision, and incomplete biological context. We present sMMC-22M, a cell-aligned multimodal resource comprising over 20 mil- lion cells across 25 organ categories, 66 studies, and multiple spatial-transcriptomic assays. We organize the benchmark around three data-centric axes that determine whether histology-to-molecular modeling can move beyond local interpolation. First, sMMC-22M provides scale: broad organ and study coverage enables con- trolled encoder benchmarking and reveals a scaling trend in which larger pathology foundation models improve morphology–gene correspondence under matched evaluation. Second, sMMC-22M provides resolution: by decomposing assays into aligned cell-level records, our framework converts spot-level histology–omics pipelines into single-cell predictors and evaluates them under strict in-domain and cross-patient splits. Third, sMMC-22M provides rich context: each cell is paired with spatial, molecular, and sample-level metadata, allowing analyses such as age-band shift in ovarian samples, where age-mismatched transfer sharply reduces prediction quality despite misleading global-distance summaries. Together, sMMC- 22M and STBoost establish a practical framework for single-cell histology-to-gene prediction while showing that robust generalization still depends on scale, cellular resolution, and explicit biological context.