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

Rab Cascades01:25

Rab Cascades

3.8K
Rab GTPases act in a regulated cascade during membrane fusion, helping the lipid bilayers mix. The Rab family of proteins are active when bound to GTP, and inactive when bound to GDP. Hence, they act as guanine nucleotide-dependent molecular switches. Rab-GTP recognizes and binds to long or short-range tethering proteins to capture the target vesicle. These tethers coordinate with SNAREs on the vesicle and the target membrane to assemble the trans SNARE complex that locks the mixing bilayers.
3.8K

You might also read

Related Articles

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

Sort by
Same author

Evaluating large language models in biomedical data science challenges through a classroom experiment.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Pseudotimecascade visualizes gene expression cascade in pseudotime analysis.

bioRxiv : the preprint server for biology·2025
Same author

Smmit: A pipeline for integrating multiple single-cell multi-omics samples.

Computational and structural biotechnology journal·2025
Same author

Vascular adhesion molecule 1<sup>+</sup> fibro-adipogenic progenitors mark fatty infiltration in chronic limb-threatening ischemia.

JVS-vascular science·2025
Same author

Advancing biological understanding of cellular senescence with computational multiomics.

Nature genetics·2025
Same author

Comparative analysis of gene regulation in single cells using Compass.

Cell reports methods·2025
Same journal

From Pixels to Patterns: A Multidimensional Framework to Decode Cytoskeletal Organization.

Computational and structural biotechnology journal·2026
Same journal

A Large Concept Model for Mechanistic Simulation of Disease Trajectories: A Hypothesis-Generating Exemplar for Pediatric Acute Lymphoblastic Leukemia.

Computational and structural biotechnology journal·2026
Same journal

Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.

Computational and structural biotechnology journal·2026
Same journal

High-Throughput Prediction of Protein-Protein Interactions Uncovers Hidden Molecular Networks in Biosynthetic Gene Clusters.

Computational and structural biotechnology journal·2026
Same journal

A Region-Aware Structured Framework Improves Prediction of Gene Expression from DNA Methylation.

Computational and structural biotechnology journal·2026
Same journal

Ensemble Machine Learning Approaches Predict Survival in Lower-Grade Glioma Based on Glycosphingolipid Gene Expression and Metabolic Modeling.

Computational and structural biotechnology journal·2026
See all related articles

Related Experiment Video

Updated: Apr 7, 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

19.2K

Pseudotimecascade Visualizes Gene Expression Cascade in Pseudotime Analysis.

Changxin Wan1,2, Beijie Ji3, Zhicheng Ji1,2

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.

Computational and Structural Biotechnology Journal
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

Pseudotimecascade visualizes multi-gene expression cascades during cell differentiation. This tool reveals coordinated gene programs and biological functions driving cell fate decisions, enhancing single-cell analysis.

More Related Videos

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.6K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.8K

Related Experiment Videos

Last Updated: Apr 7, 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

19.2K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.6K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.8K

Area of Science:

  • Single-cell transcriptomics
  • Systems biology
  • Computational biology

Background:

  • Single-cell transcriptomics reveals dynamic biological processes like cell development and differentiation.
  • Existing pseudotime methods analyze temporal gene expression but overlook coordinated gene programs driving cellular transitions.

Purpose of the Study:

  • Introduce Pseudotimecascade, a novel computational tool for visualizing and comparing multi-gene expression cascades along pseudotime.
  • Link gene expression cascades to biological functions by identifying stage-specific pathways.
  • Provide a deeper understanding of gene programs governing cell fate decisions.

Main Methods:

  • Development of the Pseudotimecascade tool for multi-gene expression cascade analysis.
  • Application of Pseudotimecascade to hematopoietic stem cell differentiation data.
  • Identification of stage-specific pathways and regulatory hierarchies.

Main Results:

  • Pseudotimecascade effectively visualizes and compares multi-gene expression patterns during cellular transitions.
  • The tool successfully links gene cascades to biological functions and stage-specific pathways.
  • Analysis of hematopoietic stem cell differentiation revealed key regulatory hierarchies and processes.

Conclusions:

  • Pseudotimecascade offers a powerful approach to study coordinated gene programs in dynamic biological processes.
  • The tool enhances the interpretation of single-cell transcriptomic data by integrating gene expression with functional pathways.
  • Pseudotimecascade advances the understanding of cell fate determination and developmental trajectories.