Researchers developed PRTS, a deep learning model that infers single-cell spatial transcriptomics from standard H&E-stained tissue images.
Key Details
- 1PRTS stands for Pathology-driven Reconstruction of Transcriptomic States.
- 2It predicts an expression matrix for 1,820 genes at single-cell resolution from H&E images.
- 3The method provides 27x higher resolution than traditional spatial transcriptomics spot technologies.
- 4Validated on mouse brain, human lung cancer, and breast cancer datasets.
- 5PRTS identified 21 cell subtypes and matched key gene expression patterns to ground truth.
- 6Potential applications include diagnostics, cancer subtyping, and drug discovery.
Why It Matters
PRTS could enable cost-effective, scalable spatial transcriptomics directly from routinely collected pathology slides, advancing both research and diagnostics where gene expression mapping was previously prohibitively expensive or impractical.
Source
EurekAlert
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