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 Concept Videos

Overview of Cell-Matrix Interactions01:24

Overview of Cell-Matrix Interactions

7.2K
The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
7.2K

You might also read

Related Articles

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

Sort by
Same author

[Research progress in mechanisms of Tripterygium wilfordii and its active ingredients in treatment of inflammatory bowel disease].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2026
Same author

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

Nature communications·2026
Same author

Correlation between tumor mutational burden and CT radiographic features in EGFR exon 19 deletion-mutated lung adenocarcinoma: a diagnostic accuracy study.

Frontiers in medicine·2026
Same author

Multiscale learning of gene network-driven phenotypic dynamics of single cells.

Molecular systems biology·2026
Same author

Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.

Nature communications·2026
Same author

Robust identification of cell-cell communication heterogeneity in single cells.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Jun 30, 2025

Induction and Analysis of Epithelial to Mesenchymal Transition
10:37

Induction and Analysis of Epithelial to Mesenchymal Transition

Published on: August 27, 2013

35.8K

Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data.

Manuel Barcenas1, Federico Bocci2, Qing Nie2

  • 1Department of Mathematics, University of California Irvine, Irvine, California.

Biophysical Journal
|March 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a computational method to identify cell fate tipping points using single-cell RNA sequencing. The approach reveals unstable intermediate states and dynamic gene regulatory networks during cell transitions like epithelial-mesenchymal transition.

More Related Videos

Induction of Mesenchymal-Epithelial Transitions in Sarcoma Cells
11:42

Induction of Mesenchymal-Epithelial Transitions in Sarcoma Cells

Published on: April 7, 2017

9.4K
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

18.5K

Related Experiment Videos

Last Updated: Jun 30, 2025

Induction and Analysis of Epithelial to Mesenchymal Transition
10:37

Induction and Analysis of Epithelial to Mesenchymal Transition

Published on: August 27, 2013

35.8K
Induction of Mesenchymal-Epithelial Transitions in Sarcoma Cells
11:42

Induction of Mesenchymal-Epithelial Transitions in Sarcoma Cells

Published on: April 7, 2017

9.4K
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

18.5K

Area of Science:

  • Computational biology
  • Molecular and Cell Biology
  • Genomics

Background:

  • Cell fate decisions are crucial in development and disease, but identifying transition states remains difficult.
  • High-resolution single-cell sequencing offers new insights into cellular heterogeneity and dynamics.
  • Characterizing 'tipping points' where cells commit to new fates is a key challenge.

Purpose of the Study:

  • To develop a computational method for inferring cell-state stability and gene regulatory networks (GRNs) from single-cell transcriptomics data.
  • To identify and characterize unstable intermediate cell states during lineage transitions.
  • To analyze the dynamic changes in GRNs that govern cell fate decisions.

Main Methods:

  • Utilized unspliced and spliced counts from single-cell RNA sequencing data.
  • Employed cell ordering along lineage trajectories to train an RNA splicing multivariate model.
  • Inferred cell-state stability via spectral analysis of the model's Jacobian matrix.
  • Reconstructed GRNs and their variations along cell lineages.

Main Results:

  • Predicted a saddle-node transition during epithelial-mesenchymal transition (EMT) in cancer cell lines.
  • Identified an unstable, intermediate cell state between epithelial and mesenchymal phenotypes.
  • Observed rearrangement of GRNs during EMT, leading to denser, less modular networks in intermediate states.

Conclusions:

  • The developed computational method effectively infers cell lineage dynamics and GRN alterations.
  • The findings provide a deeper understanding of tipping points and intermediate states in cell fate decisions.
  • This approach offers a flexible tool for integrating theory-driven modeling with single-cell transcriptomics data.