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A computational strategy for predicting lineage specifiers in stem cell subpopulations.

Satoshi Okawa1, Antonio del Sol1

  • 1Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.

Stem Cell Research
|September 15, 2015
PubMed
Summary

This study introduces a computational method to identify key genes, called lineage specifiers, that control stem cell differentiation. It reconstructs subpopulation-specific transcriptional regulatory networks (TRNs) to predict these specifiers, aiding stem cell research.

Keywords:
Lineage specifierSeesaw model of differentiationSingle-cell gene expressionStem cell differentiationTranscriptional regulatory network

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Area of Science:

  • Stem cell biology
  • Computational biology
  • Genomics

Background:

  • Stem cell differentiation is complex, hindered by cellular heterogeneity and varying gene expression states.
  • Transcriptional regulatory networks (TRNs) drive these states, but identifying subpopulation-specific TRNs and lineage specifiers remains challenging.
  • Existing computational and experimental workflows lag behind single-cell profiling technologies.

Purpose of the Study:

  • To develop a computational method for predicting lineage specifiers in binary-fate stem cell differentiation.
  • To reconstruct subpopulation-specific TRNs for a more accurate representation of cellular heterogeneity.
  • To identify genes crucial for lineage commitment and understand their regulatory mechanisms.

Main Methods:

  • Reconstruction of subpopulation-specific transcriptional regulatory networks (TRNs).
  • Modeling of stable states in parental stem cell subpopulations maintained by TRN stability cores.
  • Development of a statistical metric to identify opposing lineage specifier pairs with significant ratio changes during differentiation.

Main Results:

  • The computational method successfully predicted known and novel lineage specifiers in three distinct stem cell systems.
  • The approach does not require pre-selection of candidate genes.
  • The method is compatible with single-cell RT-PCR and RNA-seq data for binary-fate differentiation.

Conclusions:

  • The proposed computational method effectively identifies lineage specifiers and their associated TRN stability cores.
  • This approach enhances the understanding of stem cell differentiation and lineage commitment.
  • It offers a valuable tool for stem cell biology and regenerative medicine research using single-cell gene expression data.