SPADE: Spatial Transcriptomics and Pathology Alignment Using a Mixture of Data Experts for an Expressive Latent Space
Journal:
arXiv
Published Date:
Jun 27, 2025
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.
