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Related Experiment Video

Updated: Jun 25, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.

Maria Schmidt1, Susanna Avagyan2, Kristin Reiche3,4

  • 1Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany.

Current Issues in Molecular Biology
|May 24, 2024
PubMed
Summary
This summary is machine-generated.

We developed a web tool for analyzing spatial transcriptomics (ST) data, using machine learning to visualize gene expression landscapes. This tool aids in understanding cellular ecosystems and their interactions within tissues.

Keywords:
10x Visium technologyintra-tumoral heterogeneity microanatomymelanomamolecular biologymouse brainreceptor–ligand interactionsself-organizing map (SOM) machine learningspatial gene set analysis

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Life's organization spans molecular to tissue levels, with spatial transcriptomics (ST) revolutionizing biological insights.
  • ST generates complex data, necessitating advanced computational tools for analysis and interpretation.

Purpose of the Study:

  • To develop an interactive web tool, the ST browser, for analyzing spatial transcriptomics data.
  • To enable exploration of single gene expression, gene sets, and cell-cell interactions in ST images.
  • To integrate machine learning for high-resolution spatial data portrayal.

Main Methods:

  • Developed the ST browser web tool for interactive ST data analysis.
  • Applied self-organizing map (SOM) machine learning to ST data for gene expression landscape generation.
  • Utilized the tool to analyze melanoma intra-tumoral heterogeneity and mouse brain microarchitecture.

Main Results:

  • The ST browser provides interactive discovery of ST images, focusing on gene expression and spatial patterns.
  • SOM-based data portrayal generates high-resolution gene expression landscapes for each spot.
  • Demonstrated effectiveness in analyzing complex biological systems like melanoma and brain tissues.

Conclusions:

  • The ST browser, with its integrated machine learning, offers novel perspectives for analyzing cellular ecosystems.
  • Facilitates comprehensive knowledge mining of tissue organization and cell interactions.
  • Advances the interpretation of complex spatial transcriptomics data.