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Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro.

Zhen Miao1,2,3, Yilong Qu1, Sijia Huang4

  • 1Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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Summary

We developed CoPro, a computational framework to map coordinated gene expression changes in tissues. This tool helps understand how cellular states progress spatially and interact between cell types in health and disease.

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics reveals cellular coordination within tissues.
  • Identifying spatially varying and cell-type-coordinated gene programs is challenging.

Purpose of the Study:

  • Introduce CoPro, a computational framework for detecting spatially coordinated cellular state progression.
  • Enable analysis of gene programs co-varying within or between cell types.
  • Disentangle multiple overlapping spatial patterns in complex tissues.

Main Methods:

  • CoPro framework for spatial transcriptomics data analysis.
  • Operates in supervised and unsupervised modes.
  • Applicable to various single-cell spatial transcriptomics datasets (MERFISH, SeqFISH+, Xenium, imputed data).

Main Results:

  • CoPro successfully analyzed colon, brain, liver, and kidney tissues.
  • Separated epithelial differentiation from inflammation in colon.
  • Identified superimposed aging-associated programs in liver.
  • Decoupled gene expression gradients in the brain.
  • Revealed tubule-vasculature coordination in the kidney.

Conclusions:

  • CoPro is a versatile tool for spatial gene expression analysis.
  • Effectively disentangles anatomical organization and disease-associated variation.
  • Provides insights into cellular coordination in complex biological systems.