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Related Experiment Video

Updated: Aug 16, 2025

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TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics.

Yiran Shan1, Qian Zhang1, Wenbo Guo1

  • 1MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China.

Genomics, Proteomics & Bioinformatics
|December 22, 2022
PubMed
Summary
This summary is machine-generated.

We developed TIST, a new method integrating spatial transcriptomics (ST) and histopathology images. TIST enhances gene expression patterns and identifies spatial clusters by reducing technical noise in ST data.

Keywords:
Gene expression enhancementMultimodal information integrationNetwork-based analysisSpatial cluster identificationSpatial transcriptomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Sequencing-based spatial transcriptomics (ST) enables in situ gene expression analysis.
  • ST data analysis faces challenges with high technical noise and low resolution.
  • Histopathology images offer complementary cellular information to improve ST data.

Purpose of the Study:

  • To present TIST, a novel method for integrative analysis of ST and histopathology data.
  • To identify spatial clusters (SCs) and enhance spatial gene expression patterns.
  • To mitigate technical noise in ST data using complementary imaging information.

Main Methods:

  • TIST integrates transcriptomic data, location information, and histopathological features.
  • Histopathological features are extracted using a Markov random field (MRF) model.
  • A network, TIST-net, is constructed for integrative analysis, enabling SC identification via random walks and gene expression enhancement via neighborhood smoothing.

Main Results:

  • TIST demonstrates robustness against technical noise in ST data analysis.
  • The method effectively identifies spatial clusters and enhances spatial gene expression patterns.
  • TIST successfully reveals biological microstructures across various scenarios in simulated and real datasets.

Conclusions:

  • TIST provides a powerful approach for analyzing noisy sequencing-based ST data.
  • Integrating histopathology images significantly improves the resolution and accuracy of ST data analysis.
  • TIST facilitates deeper insights into spatial gene expression and cellular organization within tissues.