Alpha Spatial: Language-guided prediction of single-cell spatial gene expression from histology

Authors

YANG, X., GUO, Z., GUO, X., WANG, W., LIU, X., Zhang, C., Bai, B., He, Y., LIU, Y.

Abstract

Routine histology preserves tissue architecture but leaves its molecular organization largely unmeasured. Recovering this information requires learning across heterogeneous gene panels and constraining a biologically ill-posed morphology-to-expression mapping. Here we present Alpha Spatial (AS), a language-conditioned framework that predicts cell-level gene expression from histology while jointly learning from different tissues and panels. Matched biological context improved prediction over omitted context, whereas incompatible context reduced accuracy. Multi-tissue learning further improved breast-tissue prediction, and AS outperformed the evaluated methods while preserving molecular-state organization. In breast cancer, AS recovered POSTN-associated stromal programmes and HER2-associated molecular and spatial differences. Exploratory patient-level analyses identified a CXCL14-associated spatial state whose abundance was associated with non-response to trastuzumab-containing treatment and larger residual invasive tumours. These findings establish a framework for reusing heterogeneous molecular observations to investigate tissue organization and treatment-associated microenvironments in archived histology.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 16 Oct 2026.

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