Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A scoping review of explainable artificial intelligence for medical multimodal data.

NPJ digital medicine·2026
Same author

Fairness in multimodal machine learning applications in clinical decision support: a systematic review.

NPJ digital medicine·2026
Same author

Prediction of incident atrial fibrillation from retinal fundus images using a multimodal foundation model.

NPJ digital medicine·2026
Same author

Capturing Finer-grained Long-range Dependency for Dense Prediction in Medical Images: An Empirical Investigation of MLPs.

IEEE journal of biomedical and health informatics·2026
Same author

Hybrid-CMLP: Hybrid CNN-MLP Networks for Low-to-standard-dose PET Synthesis.

IEEE journal of biomedical and health informatics·2026
Same author

Potential treatment targets in the whole-tissue proteome of triple negative breast cancer.

NPJ breast cancer·2026

Related Experiment Video

Updated: Jan 17, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

661

Spatial gene expression at single-cell resolution from histology using deep learning with GHIST.

Xiaohang Fu1,2,3,4,5, Yue Cao1,3,4,5, Beilei Bian1,3,4

  • 1School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia.

Nature Methods
|September 15, 2025
PubMed
Summary

GHIST, a deep learning framework, predicts single-cell spatial gene expression from histology images. This method enhances spatial transcriptomics data for scalable multi-omics analysis and biomarker discovery.

More Related Videos

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

5.4K
Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

812

Related Experiment Videos

Last Updated: Jan 17, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

661
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

5.4K
Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

812

Area of Science:

  • Genomics
  • Computational Biology
  • Biomedical Imaging

Background:

  • Spatially resolved transcriptomics offers insights into disease mechanisms but faces cost and complexity barriers.
  • Current methods predicting gene expression from histology images lack accuracy and spatial resolution, limiting translational applications.
  • There is a need for advanced computational tools to overcome limitations in spatial transcriptomics data generation and analysis.

Purpose of the Study:

  • To introduce GHIST, a novel deep learning framework for predicting single-cell resolution spatial gene expression.
  • To leverage subcellular spatial transcriptomics and multi-layered biological information for enhanced prediction accuracy.
  • To demonstrate the utility of in silico generation of spatial gene expression data for multi-omics analysis and biomarker identification.

Main Methods:

  • Development of a deep learning-based framework (GHIST) integrating subcellular spatial transcriptomics.
  • Utilizing synergistic relationships between multiple biological information layers for gene expression prediction.
  • Validation of GHIST using public datasets and The Cancer Genome Atlas (TCGA) data across various spatial resolutions.

Main Results:

  • GHIST demonstrates superior performance in predicting spatial gene expression at single-cell resolution.
  • The framework shows flexibility across different spatial resolutions, outperforming existing methodologies.
  • Successful validation using diverse public datasets and TCGA data confirms GHIST's robustness.

Conclusions:

  • GHIST enables the in silico generation of high-resolution spatial gene expression measurements, overcoming current technological limitations.
  • The framework can enrich existing datasets, facilitating scalable multi-omics analyses.
  • GHIST holds significant potential for advancing biomarker identification and understanding disease mechanisms through spatial omics data.