ArticleNature communications2025
A deep learning-based multiscale integration of spatial omics with tumor morphology.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Towards 3D-oid models that integrate molecular, morphological and spatial cellular features.Nature reviews. Molecular cell biology · 2026Article
- Explainable AI-Derived Spatial Pathological Features of Tumor, Necrosis, and Lymphocytes Identify Key Histological Signatures for Residual Cancer Burden Assessment in Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- The spatial revolution in immuno-oncology: artificial intelligence decoding NK cell niches to predict therapeutic response.Frontiers in immunology · 2026Review
- Deciphering immunosuppressive niches by spatial single-cell proteomics: spatial interaction networks and translational opportunities.Frontiers in immunology · 2026Review
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Decoding cardiac homeostasis and injury: the evolving landscape of spatial transcriptomics.Frontiers in cell and developmental biology · 2026Review
- Biophysical simulation enables segmentation and nervous system atlas mapping for image first spatial omics.NPJ systems biology and applications · 2025Article
- A deep learning-based multiscale integration of spatial omics with tumor morphology.Nature communications · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial Transcriptomics (spTx) offers unprecedented insights into the spatial arrangement of the tumor microenvironment, tumor initiation/progression and identification of new therapeutic target candidates. However, spTx remains unlikely to be routinely used in the near future. Hematoxylin and eosin (H&E) stained histological slides, on the other hand, are routinely generated for a large fraction of cancer patients. Here, we present a deep learning-based approach for multiscale integration of spTx with tumor morphology (MISO). We train MISO to predict spTx from H&E and validate it on a dataset of 72 10X Genomics Visium samples. We further validate our approach on 348 samples from five indications from the MOSAIC consortium and show that MISO significantly outperforms competing methods in extensive benchmarks. We demonstrate that MISO enables near single-cell-resolution, spatially-resolved gene expression prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.