ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics

• URL: http://arxiv.org/abs/2411.16793v1
• Date: Mon, 25 Nov 2024 09:15:07 GMT
• Title: ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics
• Abstract summary: Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic expression profiles at individual spots across whole-slide scales. ST-Align is the first foundation model designed for ST that deeply aligns image-gene pairs by incorporating spatial context. ST-Align employs specialized encoders tailored to distinct ST contexts, followed by an Attention-Based Fusion Network (ABFN) for enhanced multimodal fusion.
• Score: 6.680197957317297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic expression profiles at individual spots across whole-slide scales. This setting makes it an ideal data source to develop multimodal foundation models. Although recent studies attempted to fine-tune visual encoders with trainable gene encoders based on spot-level, the absence of a wider slide perspective and spatial intrinsic relationships limits their ability to capture ST-specific insights effectively. Here, we introduce ST-Align, the first foundation model designed for ST that deeply aligns image-gene pairs by incorporating spatial context, effectively bridging pathological imaging with genomic features. We design a novel pretraining framework with a three-target alignment strategy for ST-Align, enabling (1) multi-scale alignment across image-gene pairs, capturing both spot- and niche-level contexts for a comprehensive perspective, and (2) cross-level alignment of multimodal insights, connecting localized cellular characteristics and broader tissue architecture. Additionally, ST-Align employs specialized encoders tailored to distinct ST contexts, followed by an Attention-Based Fusion Network (ABFN) for enhanced multimodal fusion, effectively merging domain-shared knowledge with ST-specific insights from both pathological and genomic data. We pre-trained ST-Align on 1.3 million spot-niche pairs and evaluated its performance through two downstream tasks across six datasets, demonstrating superior zero-shot and few-shot capabilities. ST-Align highlights the potential for reducing the cost of ST and providing valuable insights into the distinction of critical compositions within human tissue.

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