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CellSymphony: Deciphering the molecular and phenotypic orchestration of cells with single-cell pathomics

arXiv 2508.10232 ↗First posted 13 Aug 2025cs.CVcited by 0

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Abstract (arXiv)

Xenium, a new spatial transcriptomics platform, enables subcellular-resolution profiling of complex tumor tissues. Despite the rich morphological information in histology images, extracting robust cell-level features and integrating them with spatial transcriptomics data remains a critical challenge. We introduce CellSymphony, a flexible multimodal framework that leverages foundation model-derived embeddings from both Xenium transcriptomic profiles and histology images at true single-cell resolution. By learning joint representations that fuse spatial gene expression with morphological context, CellSymphony achieves accurate cell type annotation and uncovers distinct microenvironmental niches across three cancer types. This work highlights the potential of foundation models and multimodal fusion for deciphering the physiological and phenotypic orchestration of cells within complex tissue ecosystems.

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3 of 25 references.

References · 3

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  2. 2023Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learningcited by 79
  3. 2024M2OST: Many-to-one Regression for Predicting Spatial Transcriptomics from Digital Pathology Imagescited by 13

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