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Updated: May 3, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.

Limin Chen¹, Darwin Chang², Bishal Tandukar¹

¹Department of Dermatology, University of California, San Francisco, San Francisco, USA.

|December 1, 2023

Summary

This summary is machine-generated.

New software, STmut, visualizes genetic mutations in spatial transcriptomic data. It integrates genomic alterations with gene expression for comprehensive tumor analysis, despite some data type limitations.

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

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

Published on: July 6, 2022

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Spatial transcriptomics, like Visium, maps gene expression within tissues.
Understanding genetic alterations alongside spatial gene expression is crucial for cancer research.

Purpose of the Study:

Introduce STmut, a novel software for visualizing genetic alterations in spatial transcriptomic data.
Assess STmut's performance across different Visium data types, including fresh-frozen and FFPE samples.
Propose methods to integrate genomic data with spatial transcriptomics.

Main Methods:

Developed STmut software to analyze somatic point mutations, allelic imbalance, and copy number alterations.
Tested STmut on fresh-frozen and FFPE Visium datasets, with and without matched DNA sequencing data.
Inferred copy number alterations across all tested conditions.

Main Results:

STmut successfully visualizes genetic alterations in spatial transcriptomic data.
Copy number alterations were inferred across fresh-frozen and FFPE Visium data.
Single nucleotide variant analysis was not feasible on FFPE Visium data due to chemical limitations.

Conclusions:

STmut provides a solution for integrating the genetic dimension into spatial transcriptomic datasets.
The study highlights the capabilities and limitations of analyzing different Visium data types for genetic alterations.
Future research can build upon these findings to enhance multi-omic spatial analyses.