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Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper.

Chloé B Steen1,2,3, Bogdan A Luca1,4, Ash A Alizadeh3,5,6,7

  • 1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|March 17, 2023
PubMed
Summary

EcoTyper is a machine learning tool that identifies cell types and states within tissues. It analyzes gene expression data to discover cellular ecosystems in healthy and diseased specimens.

Keywords:
Cell statesEcosystemsEcotypesSingle-cell RNA sequencingSpatial transcriptomicsTissue heterogeneityTranscriptomicsTumor microenvironment

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Tissues comprise diverse cell types and states forming functional ecosystems.
  • Understanding tissue composition is crucial for biological and medical research.

Purpose of the Study:

  • Introduce EcoTyper, a machine learning toolkit for analyzing cellular ecosystems.
  • Demonstrate EcoTyper's application in discovering cell states from gene expression data.

Main Methods:

  • Utilized machine learning algorithms for large-scale delineation of cellular ecosystems.
  • Applied EcoTyper to bulk, single-cell, and spatially resolved gene expression data.
  • Analyzed gene expression data from healthy and diseased tissue specimens.

Main Results:

  • Successfully delineated distinct cellular ecosystems and cell states.
  • Recovered known and identified novel cell states within tissue samples.
  • Demonstrated the tool's utility across different data types and conditions.

Conclusions:

  • EcoTyper provides a robust framework for dissecting tissue cellular heterogeneity.
  • Facilitates the discovery and characterization of cellular ecosystems in various biological contexts.
  • Offers a valuable resource for researchers studying tissue organization and function.