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

Cells of the Adaptive Immune Response01:23

Cells of the Adaptive Immune Response

The T and B lymphocytes of the adaptive immune system develop from common lymphoid progenitor cells in the bone marrow. These progenitors give rise to precursors that eventually develop into both T and B lymphocytes. As these precursors mature, they gain the ability to detect and respond to foreign antigens in the body, a process known as immunocompetence. Additionally, these precursors acquire self-tolerance, a process that ensures they do not react to self-antigens. This intricate system...
T Cell Types and Functions01:24

T Cell Types and Functions

When T cells with CD4 markers are activated, they give rise to two types of effector cells: helper T cells and regulatory T cells. Meanwhile, T cells with CD8 markers differentiate into effector cytotoxic T cells. The differentiation of CD4 T cells into helper T cell subsets, such as Th1, Th2, and Th17 cells, is dependent on the antigen type, antigen-presenting cell, and regulatory cytokines.
Th1 cells stimulate dendritic cells to express necessary co-stimulatory molecules on their surfaces for...
Cell-matrix's Response to Mechanical Forces01:13

Cell-matrix's Response to Mechanical Forces

In animal cells, the extracellular matrix allows cells within tissues to withstand external stresses and transmits signals from the outside of the cell to the inside. The extracellular matrix is extensive, and its composition varies between different types of tissues. For example, the reticular fibers and ground substance make up the ECM in loose connective tissue, while collagen and bone minerals make up the ECM of bone tissue. 
Anchoring junctions mechanically attach a cell to the...
Overview of Cell-Matrix Interactions01:24

Overview of Cell-Matrix Interactions

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...
B Cell Activation and Differentiation01:24

B Cell Activation and Differentiation

The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
When naive B cells encounter a specific antigen that can bind to the B cell receptor (BCR) on their surface, they undergo sensitization to respond to the antigen's presence. Sensitization begins with...
Intracellular Signaling Affects Focal Adhesions01:17

Intracellular Signaling Affects Focal Adhesions

Integrins act both as extracellular input receivers and as intracellular processing activators. As their name suggests, integrins are entirely integrated into the membrane structure. Their hydrophobic membrane-spanning regions interact with the phospholipid bilayer's hydrophobic region. These membrane receptors provide extracellular attachment sites for effectors like hormones and growth factors. They activate intracellular response cascades when their effectors are bound and active.
Some...

You might also read

Related Articles

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

Sort by
Same author

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same author

Transcriptional repression by TGIF2 coordinates neurogenic priming and neural stem cell maintenance.

Science advances·2026
Same author

UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.

Nucleic acids research·2026
Same author

RegVelo: Gene-regulatory-informed dynamics of single cells.

Cell·2026
Same author

Glial multicellular programs reveal distinct patient stratification in Parkinson's disease.

Research square·2026
Same author

TarDis: Achieving robust and structured disentanglement of multiple covariates.

Cell systems·2026

Related Experiment Video

Updated: Jun 6, 2026

Microfluidic Approach to Resolve Simultaneous and Sequential Cytokine Secretion of Individual Polyfunctional Cells
09:43

Microfluidic Approach to Resolve Simultaneous and Sequential Cytokine Secretion of Individual Polyfunctional Cells

Published on: March 8, 2024

Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation.

Andreas Kowarsch1, Florian Blöchl, Sebastian Bohl

  • 1Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. andreas.kowarsch@helmholtz-muenchen.de

BMC Bioinformatics
|December 2, 2010
PubMed
Summary
This summary is machine-generated.

We developed a new algorithm, graph-decorrelation (GraDe), to analyze complex gene expression changes over time. GraDe uses prior biological knowledge to reveal how IL-6 stimulation affects cell division and metabolism in hepatocytes.

More Related Videos

Real-time Live Imaging of T-cell Signaling Complex Formation
10:31

Real-time Live Imaging of T-cell Signaling Complex Formation

Published on: June 23, 2013

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Related Experiment Videos

Last Updated: Jun 6, 2026

Microfluidic Approach to Resolve Simultaneous and Sequential Cytokine Secretion of Individual Polyfunctional Cells
09:43

Microfluidic Approach to Resolve Simultaneous and Sequential Cytokine Secretion of Individual Polyfunctional Cells

Published on: March 8, 2024

Real-time Live Imaging of T-cell Signaling Complex Formation
10:31

Real-time Live Imaging of T-cell Signaling Complex Formation

Published on: June 23, 2013

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Cellular responses to external stimuli involve complex, multi-layered temporal gene expression changes.
  • Traditional methods like clustering and gene set enrichment offer limited insights into these dynamic responses.
  • Matrix factorization techniques are suitable for temporal analysis but require data with natural ordering for correlation functions, which is often lacking in biological data.

Purpose of the Study:

  • To develop a novel computational framework for analyzing large-scale, time-resolved 'omics' data.
  • To integrate prior biological knowledge into matrix factorization for improved temporal analysis of gene expression.
  • To introduce the graph-decorrelation (GraDe) algorithm for robust and efficient decomposition of complex biological data.

Main Methods:

  • Developed the concept of graph-decorrelation, encoding prior knowledge (e.g., transcriptional regulation) into a weighted directed graph.
  • Defined a graph-delayed correlation function based on the graph's structure to introduce partial ordering.
  • Applied the graph-decorrelation framework as a constraint in matrix factorization to create the GraDe algorithm.
  • Utilized time-course microarray data from IL-6 stimulated primary mouse hepatocytes.

Main Results:

  • The GraDe algorithm successfully analyzed time-resolved gene expression profiles.
  • IL-6 stimulation was shown to activate genes related to cell cycle progression and division.
  • Genes involved in metabolic and apoptotic processes were downregulated, indicating a shift towards proliferation and reduced energy expenditure.

Conclusions:

  • GraDe offers a novel and effective framework for decomposing large-scale 'omics' data.
  • Incorporating prior biological knowledge significantly enhances the structured and detailed separation of time-dependent responses.
  • The GraDe algorithm provides a more insightful analysis of cellular responses compared to standard methods.