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Related Experiment Video

Updated: Jan 8, 2026

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
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Query-driven generative AI synthesizes multi-modal spatial omics from histology.

Minxing Pang1, Tarun Kanti Roy2, Xiaodong Wu3

  • 1Applied Mathematics & Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.

Biorxiv : the Preprint Server for Biology
|December 19, 2025
PubMed
Summary
This summary is machine-generated.

TissueCraftAI uses generative AI to predict spatial omics from histology images, bridging the gap between pathology and molecular data. This computational pathology tool enhances cell annotation and patient survival predictions.

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Area of Science:

  • Computational pathology
  • Artificial intelligence in medicine
  • Spatial omics

Background:

  • Spatial omics provides tissue cellular organization insights but lacks clinical scalability.
  • Histological staining images are foundational for diagnosis but lack molecular data.
  • A computational gap exists in inferring molecular data from histology alone.

Purpose of the Study:

  • Introduce TissueCraftAI, a generative AI framework to predict multi-modal spatial omics maps from histology images.
  • Develop a method to bridge the gap between standard histology and molecular data.
  • Enable query-driven in silico spatial molecular analysis.

Main Methods:

  • Developed TissueCraftAI, a generative artificial intelligence framework.
  • Created PRISM-12M, a large-scale dataset of 12 million histology and spatial omics image patches.
  • Utilized natural language prompts for predicting spatial omics data.

Main Results:

  • TissueCraftAI outperforms existing methods in generating realistic histology images.
  • Accurately predicts spatial proteomics and transcriptomics data with high fidelity.
  • Demonstrated utility in improving cell type annotation and patient survival predictions.

Conclusions:

  • TissueCraftAI enables accurate spatial molecular data inference from routine histology images.
  • Opens new research avenues in computational pathology and precision medicine.
  • Facilitates flexible, query-driven in silico spatial molecular analysis.