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bioRxiv · 10.1101/2025.07.04.663102

AIDO.Tissue: Spatial Cell-Guided Pretraining for Scalable Spatial Transcriptomics Foundation Model

Gong, J.·Wang, Y.·Ho, N.·Cheng, X.·Song, L.·Xing, E.
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Abstract

Single-cell spatial transcriptomics enables high-resolution insights into tissue organization and cell-cell interactions, yet poses significant computational and modeling challenges due to its scale and complexity. Here we introduce AIDO.Tissue, a spatially-informed pretraining framework. The design employs multiple cells as input and an asymmetric encoder-decoder architecture, making it effectively encodes cross-cell dependencies while scaling to large data. Systematic evaluation shows that our method scales with neighboring size and achieves state-of-the-art performance across diverse downstream tasks, including spatial cell type classification, cell niche type prediction and cell density estimation. These results highlight the importance of spatial context in building general-purpose foundation models for tissue-level understanding.

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Gong, J., Wang, Y., Ho, N., Cheng, X., Song, L., Xing, E.. 2025-07-07. AIDO.Tissue: Spatial Cell-Guided Pretraining for Scalable Spatial Transcriptomics Foundation Model. https://doi.org/10.1101/2025.07.04.663102

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