Spatial Cell-Guided Pretraining for Scalable Spatial Transcriptomics Foundation Model

Jing Gong, Yixuan Wang, Nicholas Ho, Xingyi Cheng, Le Song, Eric P. Xing

Keywords: LLM, Tissue, Cell

TL;DR: SpaFoundation is a scalable, spatially-informed pretraining framework that captures cross-cell dependencies and achieves state-of-the-art performance on multiple tissue-level spatial transcriptomics tasks.

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 multi-scale spatial context in building general-purpose foundation models for tissue-level understanding.

Submission Number: 67