Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Quantifying Waddington's epigenetic landscape: a comparison of single-cell potency measures.

Jifan Shi1, Andrew E Teschendorff2, Weiyan Chen3

  • 1School of Mathematical Sciences, Peking University, Beijing, China.

Briefings in Bioinformatics
|October 6, 2018
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics.

Nature communications·2026
Same author

Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches.

Annual review of statistics and its application·2026
Same author

Guidelines on optimizing DNA methylation reference panels for cell-type deconvolution.

Communications biology·2026
Same author

Dynamical Causality Under Latent Confounders for Biological Network Reconstruction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Detecting dynamical causality by intersection cardinal concavity.

Fundamental research·2025
Same author

Machine Learning Prediction of Short Cervix in Mid-Pregnancy Based on Multimodal Data from the First-Trimester Screening Period: An Observational Study in a High-Risk Population.

Biomedicines·2025

Integrating protein interaction networks with single-cell RNA sequencing data significantly enhances the accuracy of cell differentiation potency models. This approach identifies ribosomal and mitochondrial proteins as potential universal markers for cell potency.

Area of Science:

  • Systems biology
  • Single-cell genomics
  • Stem cell biology

Background:

  • Estimating single-cell differentiation potency is crucial for identifying normal and cancer stem cells.
  • Current single-cell potency models lack robust validation across independent studies.

Purpose of the Study:

  • To compare the performance of four distinct single-cell potency models.
  • To assess the impact of integrating protein interaction networks with RNA sequencing data on potency estimation.

Main Methods:

  • Comparative analysis of four single-cell potency models using nine independent single-cell RNA sequencing experiments.
  • Network entropy measures integrating RNA sequencing data with protein interaction networks were employed.
  • Correlation analysis between differentiation potency and gene expression patterns.
Keywords:
Waddingtondifferentiationentropynetworkpotencysingle-cell RNA-Seq

Related Experiment Videos

Main Results:

  • Integration of RNA sequencing data with protein interaction networks significantly improved the robustness and reliability of single-cell potency estimates.
  • High differentiation potency positively correlates with the overexpression of network hub genes.
  • Overexpressed network hubs are enriched for ribosomal and mitochondrial proteins, suggesting their potential as universal potency markers.

Conclusions:

  • Protein interaction network integration offers a more reliable method for assessing single-cell differentiation potency.
  • Overexpression of ribosomal and mitochondrial proteins may serve as a universal indicator of cell potency.
  • This study provides a foundation for developing improved models for stem cell and progenitor cell identification.