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Related Experiment Video

Updated: Jun 18, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Large scale transcriptome data integration across multiple tissues to decipher stem cell signatures.

Ghislain Bidaut1, Christian J Stoeckert2

  • 1Inserm, UMR891, CRCM, Integrative Bioinformatics, Marseille, France; Institut Paoli-Calmettes, Marseille, France; Univ Méditerranée, Marseille, France.

Methods in Enzymology
|November 10, 2009
PubMed
Summary
This summary is machine-generated.

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Researchers identified a common 63-gene stemness signature by integrating DNA microarray data and training an artificial neural network (ANN). This signature helps understand stem cell differentiation across species and tissues.

Area of Science:

  • Stem cell biology
  • Bioinformatics
  • Computational biology

Background:

  • Adult stem cells renew various tissues, prompting research into a common molecular differentiation program, or stemness signature.
  • Understanding this signature is crucial for regenerative medicine and developmental biology.

Purpose of the Study:

  • To identify a conserved stemness signature across different species and tissues.
  • To develop a computational method for analyzing stem cell differentiation states.

Main Methods:

  • Data integration of multiple DNA microarray datasets from the Stem Cell Genome Anatomy Project (SCGAP).
  • Application of a single-layer artificial neural network (ANN) trained on differentiation labels (totipotent to differentiated).
  • Cross-organism compendium generation and leave-one-out cross-validation for ANN training and testing.

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

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Last Updated: Jun 18, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Main Results:

  • An artificial neural network successfully identified a conserved 63-gene stemness signature.
  • The signature accurately predicted differentiation capabilities in uncharacterized adult stem cells from human prostate and mouse stomach progenitors.
  • The Stem Cell Analysis and characterization by Neural Networks (SCANN) project provides available scripts for analysis.

Conclusions:

  • A common stemness signature exists and can be computationally identified.
  • The developed methodology enables automated detection of differentiation potential in stem cells.
  • This research advances the understanding of stem cell biology and differentiation processes.