SPADE: Spatial Transcriptomics and Pathology Alignment Using a Mixture of Data Experts for an Expressive Latent Space

• URL: http://arxiv.org/abs/2506.21857v1
• Date: Fri, 27 Jun 2025 02:20:51 GMT
• Title: SPADE: Spatial Transcriptomics and Pathology Alignment Using a Mixture of Data Experts for an Expressive Latent Space
• Authors: Ekaterina Redekop, Mara Pleasure, Zichen Wang, Kimberly Flores, Anthony Sisk, William Speier, Corey W. Arnold,
• Abstract summary: We introduce SPADE, a foundation model that integrates histopathology with spatial transcriptomics data to guide image representation learning.<n> SPADE is evaluated on 14 downstream tasks, demonstrating significantly superior few-shot performance compared to baseline models.
• Score: 8.576634724266228
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of digital pathology and advances in self-supervised deep learning have enabled the development of foundational models for various pathology tasks across diverse diseases. While multimodal approaches integrating diverse data sources have emerged, a critical gap remains in the comprehensive integration of whole-slide images (WSIs) with spatial transcriptomics (ST), which is crucial for capturing critical molecular heterogeneity beyond standard hematoxylin & eosin (H&E) staining. We introduce SPADE, a foundation model that integrates histopathology with ST data to guide image representation learning within a unified framework, in effect creating an ST-informed latent space. SPADE leverages a mixture-of-data experts technique, where experts, created via two-stage feature-space clustering, use contrastive learning to learn representations of co-registered WSI patches and gene expression profiles. Pre-trained on the comprehensive HEST-1k dataset, SPADE is evaluated on 14 downstream tasks, demonstrating significantly superior few-shot performance compared to baseline models, highlighting the benefits of integrating morphological and molecular information into one latent space.

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