DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology

Abstract excerpt

Spatial transcriptomics remains costly and low-throughput, limiting it to a small fraction of routine histology and leaving the molecular state of disease unmeasured in most patients. Predicting spatial expression from histology could address this gap, but existing methods are restricted to predefined genes and small cohorts. We present DeepSpot-M, a multimodal foundation model that predicts spatial expression by...

Topics

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• Biomarkers, Tumor
• Gene Expression
• Gene Expression Profiling
• Machine Learning
• Neoplasms
• Phenotype
• Single-cell and spatial transcriptomics
• Transcriptome

Identifiers and source

Literature Corpus work

c029334f-83d7-5eb2-9b0c-829d0457f535

DOI

10.64898/2026.06.19.26356060