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Related Experiment Video

Updated: Jan 9, 2026

Application of Laser Microdissection to Uncover Regional Transcriptomics in Human Kidney Tissue
05:46

Application of Laser Microdissection to Uncover Regional Transcriptomics in Human Kidney Tissue

Published on: June 9, 2020

4.3K

Streamlining single-cell spatial transcriptomics for human kidney tissue.

Son Vo1, Kieran Meadows2, Han Do1

  • 1BioTuring, San Diego, California, USA.

Biotechniques
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

A new workflow simplifies spatial transcriptomics analysis for kidney disease research. This method accurately identifies kidney cell types and reveals significant changes in cell proportions and gene expression in diabetic kidney disease (DKD).

Keywords:
Spatial transcriptomicsdiabetic kidney diseasehuman biopsyimmune cellstubular injury

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Area of Science:

  • Single-cell spatial transcriptomics
  • Kidney disease research
  • Bioinformatics workflow development

Background:

  • Spatial transcriptomics offers high-resolution tissue architecture analysis.
  • Computational analysis of spatial data is a significant bottleneck.
  • Bioinformatics expertise is often required for spatial transcriptomics data analysis.

Purpose of the Study:

  • To develop a novel cell area normalization method and workflow for spatial transcriptomics data.
  • To enable accurate annotation of kidney cell types.
  • To facilitate comparison of healthy and diseased kidney tissue.

Main Methods:

  • Utilized NanoString's CosMx spatial transcriptomic platform.
  • Developed a cell area normalization method and bioinformatics workflow.
  • Performed gene expression analysis for validation and comparison.

Main Results:

  • Successfully annotated 15 kidney cell types with high accuracy.
  • Demonstrated increased sensitivity and consistency with pathological changes in diabetic kidney disease (DKD).
  • Observed significant alterations in podocyte and immune cell proportions, immune cell enrichment, and differential gene expression in DKD.

Conclusions:

  • The developed workflow provides accessible spatial data analysis for users without formal bioinformatics training.
  • The method accurately identifies cellular and molecular changes in DKD.
  • This approach enhances the discovery potential of spatial transcriptomics in kidney disease research.