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Related Experiment Video

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Protocol for acquiring high-quality fresh mouse lung spatial transcriptomics data.

Yujia Jiang1, Yixin Li2, Mengnan Cheng3

  • 1BGI Research, Hangzhou 310030, China; Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark.

STAR Protocols
|January 27, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a new protocol for spatial transcriptomics in mouse lungs. It overcomes challenges with lung cavities to enable high-quality single-cell resolution data acquisition.

Keywords:
GeneticsGenomicsMolecular BiologySequencing

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Area of Science:

  • Molecular Biology
  • Genomics
  • Anatomy

Background:

  • Spatial transcriptomics offers single-cell resolution of lung tissue characteristics and spatial distribution.
  • Alveolar cavities in lungs present significant challenges for sample preparation in spatial transcriptomics.
  • Existing methods struggle to yield high-quality data from lung tissues due to these anatomical complexities.

Purpose of the Study:

  • To develop and present a robust protocol for obtaining high-quality fresh mouse lung spatial transcriptomics data.
  • To address the technical difficulties associated with sectioning lung tissues containing alveolar cavities.
  • To facilitate advanced research into lung biology at a single-cell, spatially resolved level.

Main Methods:

  • Lung perfusion technique to preserve tissue integrity.
  • Cryosectioning of fresh mouse lungs to obtain precise tissue slices.
  • Complementary DNA (cDNA) collection from prepared lung sections.
  • Detailed bioinformatic analysis pipeline for processed data.

Main Results:

  • Successful acquisition of high-quality spatial transcriptomics data from fresh mouse lungs.
  • Demonstration of a protocol that effectively handles lung tissue with alveolar cavities.
  • Generation of spatially resolved gene expression profiles at single-cell resolution.

Conclusions:

  • The presented protocol enables high-quality spatial transcriptomics analysis of mouse lungs.
  • This method overcomes previous limitations related to lung alveolar structure.
  • Facilitates deeper understanding of lung cellular heterogeneity and spatial organization.