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Updated: Aug 6, 2025

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The spatial landscape of gene expression isoforms in tissue sections.

Kevin Lebrigand1, Joseph Bergenstråhle2, Kim Thrane2

  • 1Université Côte d'Azur, CNRS, Institut de Pharmacologie Moléculaire et Cellulaire, F06560 Sophia Antipolis, France.

Nucleic Acids Research
|March 17, 2023
PubMed
Summary

Spatial isoform transcriptomics (SiT) reveals gene expression differences across mouse brain regions. This new method profiles splicing variants and RNA editing with spatial resolution, enhancing our understanding of complex biological systems.

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Area of Science:

  • Genomics
  • Neuroscience
  • Molecular Biology

Background:

  • In situ capturing technologies provide tissue context for gene expression but cannot characterize splicing variants or sequence heterogeneity at spatial resolution.
  • Current transcriptome profiling methods lack the ability to analyze full-length sequence heterogeneity and splicing variants in a spatial context.

Purpose of the Study:

  • To introduce spatial isoform transcriptomics (SiT), a novel method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing.
  • To demonstrate SiT's capability in profiling isoform expression and sequence heterogeneity in different areas of the mouse brain.

Main Methods:

  • Spatial isoform transcriptomics (SiT) utilizing long-read sequencing.
  • Profiling isoform expression and sequence heterogeneity in mouse brain tissue.
  • Integration with external single-cell data for cell type nomination.
  • In situ sequencing for independent validation.

Main Results:

  • SiT revealed regional isoform switching of the Plp1 gene in the olfactory bulb layers.
  • Differential isoform usage was identified for key brain function genes (Snap25, Bin1, Gnas), validated by in situ sequencing.
  • SiT generated the first in-depth A-to-I RNA editing map of the adult mouse brain.
  • An online resource (https://www.isomics.eu) was developed for spatial visualization of isoform expression and RNA editing data.

Conclusions:

  • SiT enables spatial profiling of isoform expression and RNA editing, offering new insights into complex biological systems.
  • The method facilitates the identification of cell types associated with specific isoforms through integration with single-cell data.
  • SiT significantly advances the characterization of transcriptomic heterogeneity in a spatial context, particularly in the brain.