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Related Experiment Video

Updated: Jan 18, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data.

Xiao Tan^1,2, Onkar Mulay^1,2, Jacky Xie³

¹Genomics and Machine Learning Lab, Institute for Molecular Bioscience, St Lucia, QLD, Australia.

Nature Communications

|January 16, 2026

Summary

STimage predicts spatial gene expression and cell types from standard histology images. This deep learning tool enhances digital pathology by improving robustness and interpretability for clinical applications.

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Area of Science:

Computational biology
Digital pathology
Genomics

Background:

Spatial transcriptomics (ST) integrates tissue morphology with gene expression, advancing digital pathology.
Deep learning models show promise for gene expression prediction and cell classification from images, but need enhanced interpretability and robustness.

Purpose of the Study:

To present STimage, a suite of models for predicting spatial gene expression and classifying cell types directly from standard H&E histology images.
To improve the robustness and interpretability of deep learning models in spatial transcriptomics analysis.

Main Methods:

Utilized an ensemble approach with foundation models for STimage.
Estimated gene expression distributions and quantified aleatoric and epistemic uncertainty.
Integrated attribution analysis at single-cell resolution with histopathological annotations and functional genes.

Main Results:

Validated STimage across diverse datasets and platforms.
Demonstrated STimage's ability to predict gene expression and classify cell types from H&E images.
Showcased STimage-predicted gene expression for stratifying patient survival and predicting drug response.

Conclusions:

STimage provides a robust and interpretable deep learning framework for spatial gene expression and cell type prediction from routine histology.
Enables molecular and cellular insights from standard histopathology, significantly advancing digital pathology and clinical applications.