NexuST: A Hierarchical Foundation Model for Spatial Transcriptomics

Authors

Liu, H., Zhao, Q., Lin, L., Zou, Z., Cai, W., Sun, J., Zhou, Y., Alvarez, M. A., Gilmore, A., Rattray, M., Frangi, A. F., Zhou, H.

Abstract

Spatial transcriptomics captures molecular states within cells and their organisation in tissue. However, integrating fine-grained gene information with spatial context at scale remains challenging for existing foundation models. Here we present NexuST, a hierarchical foundation model that repeatedly interleaves gene-level molecular modelling with cell-level spatial modelling, allowing the two levels to refine one another during end-to-end pretraining. For pretraining, we curated HumanST-46M, comprising 45.7 million human cells from 72 datasets across 11 organs and three imaging-based platforms. Across four held-out datasets totalling approximately 2.6 million cells, NexuST achieved state-of-the-art or competitive performance in cell-type annotation, region prediction, gene recovery and neighbourhood-composition prediction. We find that cell-intrinsic expression remains informative even for spatial tasks, as shown by an expression-only PCA baseline, while NexuST shows particularly strong gains where spatial context is essential. Overall, NexuST establishes a hierarchical framework that can serve as a general backbone for future spatial transcriptomics foundation models.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 29 Sep 2026.

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