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Related Concept Videos

Modern Molecular Taxonomy01:29

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Related Experiment Video

Updated: Aug 15, 2025

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

Alona Levy-Jurgenson1, Xavier Tekpli2,3, Vessela N Kristensen2,3,4

  • 1Department of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel.

Methods in Molecular Biology (Clifton, N.J.)
|December 31, 2022
PubMed
Summary
This summary is machine-generated.

Digital pathology and deep learning reveal molecular traits and spatial tumor heterogeneity from whole-slide images (WSIs). These methods aid cancer diagnosis, prognosis, and treatment by analyzing tumor characteristics.

Keywords:
CancerGene expressionHeterogeneityPathologySpatial transcriptomicsWhole-slide images

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Area of Science:

  • Computational pathology
  • Oncology
  • Bioinformatics

Background:

  • Digital analysis of whole-slide images (WSIs) is advancing cancer diagnosis and treatment.
  • Deep learning (DL) methods are identifying novel molecular traits in H&E-stained WSIs.
  • Tumor heterogeneity, both spatial and overall, is crucial for cancer prognosis and treatment response.

Purpose of the Study:

  • To describe methods for analyzing spatial and overall tumor heterogeneity using digital pathology.
  • To introduce an end-to-end deep learning pipeline for spatial transcriptomics from WSIs.
  • To present an algorithm for quantifying spatial tumor heterogeneity.

Main Methods:

  • Developed an end-to-end deep learning pipeline to generate spatial transcriptomics from WSIs.
  • Integrated WSIs with associated bulk gene expression data.
  • Created an algorithm to quantify spatial tumor heterogeneity based on pipeline outputs.

Main Results:

  • The deep learning pipeline successfully produced limited spatial transcriptomics from WSIs.
  • The developed algorithm effectively quantified spatial tumor heterogeneity.
  • These methods support the analysis of key determinants in cancer prognosis and treatment.

Conclusions:

  • Digital pathology combined with deep learning offers powerful tools for cancer research.
  • The described methods advance the understanding and quantification of spatial tumor heterogeneity.
  • These approaches have the potential to improve cancer diagnosis and treatment strategies.