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Related Experiment Video

Updated: Jul 13, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Semla: a versatile toolkit for spatially resolved transcriptomics analysis and visualization.

Ludvig Larsson1, Lovisa Franzén1,2, Patrik L Ståhl1

  • 1Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden.

Bioinformatics (Oxford, England)
|October 17, 2023
PubMed
Summary
This summary is machine-generated.

The semla R package simplifies the analysis and visualization of spatial transcriptomics data from the Visium platform. It integrates gene expression with histology for enhanced biological insights.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatially resolved transcriptomics provides gene expression data with spatial context from tissue sections.
  • Integrating spatial information into data analysis and visualization is crucial for biological interpretation.
  • Existing computational tools may not fully leverage the histological context accompanying spatial transcriptomics data.

Purpose of the Study:

  • To introduce semla, an R package designed for processing, analysis, and visualization of Visium spatial transcriptomics data.
  • To facilitate the integration of spatial information and histological context into data analysis workflows.
  • To provide interactive tools for exploring spatial transcriptomics data and annotating tissue features.

Main Methods:

  • Development of an R package, semla, leveraging R for data manipulation and visualization.
  • Implementation of interactive web applications for user-friendly data exploration.
  • Integration of gene expression data with corresponding histological images.

Main Results:

  • The semla package enables comprehensive analysis of spatial transcriptomics data.
  • Interactive visualization tools allow for intuitive exploration of gene expression patterns within tissue architecture.
  • The package supports tissue annotation, aiding in the biological interpretation of spatial data.

Conclusions:

  • semla offers a robust and accessible platform for researchers working with Visium spatial transcriptomics data.
  • The package enhances the ability to derive biological insights by integrating gene expression with spatial and histological information.
  • semla promotes efficient data exploration and analysis, making spatial transcriptomics more tractable.