Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cell Migration

Cell Migration

Cell migration, the process by which cells move from one location to another, is essential for the proper development and viability of organisms throughout their life. When cells are not able to migrate properly to their ordained locations, various disorders may occur. For example, disruption in cell migration causes chronic inflammatory diseases such as arthritis.

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Cell Migration

Cell Migration

Cell migration is a process by which the cells move from one location to another, playing an essential role in embryological development, repair and regeneration, immune response, and metastasis. Cells migrate in response to chemical or mechanical signals generated by specific organs or tissues. The overall mechanism includes three steps - polarization, protrusion, and release. Polarization involves the formation of a distinct cell front and rear, which determines the direction of movement.

You might also read

Related Articles

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

Sort by

Same author

An Integrated Skin Cell Atlas Decodes the Pilosebaceous Unit.

The Journal of investigative dermatology·2026

Same author

New insights into progenitor exhausted T cell populations.

Nature reviews. Immunology·2026

Same author

Integrative multi-omics analysis of growth plate regulation underlying body size in miniature pigs.

Communications biology·2026

Same author

Utilizing Routine Care Data of Rare Diseases: Challenges, Chances and Call for Collaboration.

Studies in health technology and informatics·2026

Same author

Drugst.One DREAM-Drug repurposing through expert annotation and modification.

British journal of pharmacology·2026

Same author

Transferability of a US claims-based machine learning model for ATTRwt-CM identification: a retrospective evaluation in a German setting.

Scientific reports·2026

Same journal

Computing inspired by the brain: a journey from algorithms to organoids.

Nature computational science·2026

Same journal

Systematic bottom-up coarse-graining of hydrated excess proton transport across scales.

Nature computational science·2026

Same journal

Gaining biological insights through supervised data visualization.

Nature computational science·2026

Same journal

The inequalities of GPU access.

Nature computational science·2026

Same journal

Social technologies need societal alignment.

Nature computational science·2026

Same journal

The Quantum Optimization Benchmarking Library.

Nature computational science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 30, 2026

Target Cell Pre-enrichment and Whole Genome Amplification for Single Cell Downstream Characterization

Target Cell Pre-enrichment and Whole Genome Amplification for Single Cell Downstream Characterization

Published on: May 15, 2018

Enabling single-cell trajectory network enrichment.

Alexander G B Grønning^1,2, Mhaned Oubounyt^3,4, Kristiyan Kanev⁵

¹Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark. alexander.groenning@sund.ku.dk.

Nature Computational Science

|January 13, 2024

Summary

This summary is machine-generated.

Scellnetor is a new tool for single-cell RNA sequencing (scRNA-seq) data analysis. It enables systems biology-level insights into cellular differentiation by analyzing gene expression networks across developmental trajectories.

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

Published on: October 29, 2019

Related Experiment Videos

Last Updated: Jun 30, 2026

Target Cell Pre-enrichment and Whole Genome Amplification for Single Cell Downstream Characterization

Target Cell Pre-enrichment and Whole Genome Amplification for Single Cell Downstream Characterization

Published on: May 15, 2018

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

Published on: October 29, 2019

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into cellular differentiation.
Existing scRNA-seq analysis tools lack robust systems biology approaches for trajectory analysis.
This gap hinders deep understanding of molecular mechanisms in development.

Purpose of the Study:

To develop a novel computational tool for systems biology-based scRNA-seq trajectory analysis.
To enable the extraction of temporal gene expression network patterns differentiating developmental paths.
To facilitate hypothesis generation for understanding biological regulation.

Main Methods:

Development of Scellnetor, a network-constrained time-series clustering algorithm.
Application of Scellnetor to analyze differential gene expression network modules.
Validation using experimental model systems for hematopoiesis and T-cell development.

Main Results:

Scellnetor successfully identifies temporal gene expression modules differentiating developmental trajectories.
The tool generated testable hypotheses for mechanisms in hematopoiesis.
Scellnetor revealed interpretable subnetworks in dysfunctional CD8 T-cell development.

Conclusions:

Scellnetor enhances scRNA-seq analysis by integrating systems biology principles.
The algorithm facilitates deeper understanding of molecular control in cellular differentiation.
Scellnetor advances the field of computational systems biology for single-cell data.