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Related Experiment Video

Updated: Jul 16, 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

Multi-task spatial distillation reveals cell-type-resolved programmed cell death landscapes in the human kidney.

Chunling Wu1, Xiaomeng Luo1, Yuansong Zhao2

  • 1Department of Rheumatology and Immunology, The First Hospital of China Medical University, No.155 Nanjing Street of Heping District, 110001 Liaoning Province, China.

Briefings in Bioinformatics
|July 14, 2026
PubMed
Summary

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CoDeST maps kidney cell types and programmed cell death (PCD) activity in spatial transcriptomics data. This framework enhances understanding of kidney injury and disease progression by revealing spatial relationships.

Area of Science:

  • Spatial transcriptomics
  • Computational biology
  • Renal pathology

Background:

  • Kidney injury and chronic kidney disease involve spatially varied programmed cell death (PCD).
  • Current methods struggle to simultaneously analyze cell composition, PCD activity, and spatial distribution in spatial transcriptomics (ST) data.

Purpose of the Study:

  • To develop CoDeST, a novel multi-task spatial inference framework.
  • To jointly predict kidney cell-type composition and PCD program activity (apoptosis, pyroptosis, necroptosis, ferroptosis) within ST data.

Main Methods:

  • CoDeST employs a three-stage pseudo-to-real training strategy.
  • This includes self-supervised pretraining, supervised deconvolution on pseudo-spots, and real-ST domain adaptation using teacher-student distillation and spatial regularization.
Keywords:
deconvolutionkidneyknowledge distillationprogrammed cell deathspatial transcriptomicsweak supervision

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Related Experiment Videos

Last Updated: Jul 16, 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

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Whole-Kidney Three-Dimensional Staining with CUBIC
04:31

Whole-Kidney Three-Dimensional Staining with CUBIC

Published on: July 18, 2022

Main Results:

  • CoDeST demonstrated competitive cell-type proportion recovery in pseudo-spot benchmarks.
  • Analysis on real kidney ST data confirmed its ability to transfer deconvolution signals.
  • The framework achieves stable deconvolution and PCD mapping, balancing spatial coherence and boundary contrast.

Conclusions:

  • CoDeST offers a specialized framework for kidney research.
  • It enables joint spatial mapping of cell composition and PCD programs.
  • This facilitates deeper insights into kidney injury and disease progression.