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-matrix's Response to Mechanical Forces

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...

Actin Treadmilling

Actin Treadmilling

Actin filaments undergo polymerization and depolymerization from either end. The polymerization and depolymerization rates depend on the cytosolic concentration of free G-actins. The polymerization rate is generally higher at the plus or barbed end, while the depolymerization rate is higher at the minus or pointed end. At a steady state, critical concentration describes the concentration of free G-actin monomers at which the polymerization rate at the plus end is equal to that of the...

You might also read

Related Articles

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

Sort by

Same author

Single-cell Multiomic and Spatiotemporal Dissection of the Liver Circadian Clock.

Genomics, proteomics & bioinformatics·2026

Same author

Cumulative incidence and prognostic factors for leukemic transformation in chronic myelomonocytic leukemia: a competing risk analysis.

BMC cancer·2026

Same author

XBP1-mediated mitochondrial damage activates the mtDNA/STING/NLRP3 pathway to delay diabetic wound healing.

Chinese medical journal·2026

Same author

PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Comparative and integrated application of flow cytometry and next-generation sequencing for minimal residual disease monitoring in acute myeloid leukemia.

Annals of hematology·2026

Same author

DMS-MaPseq and DREEM Analyses Implicate the Critical Role of RNA Structural Dynamics in Turnip Yellow Mosaic Virus Pathogenicity.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes.

ArXiv·2026

Same journal

A Positron Range Correction with Texture Preservation Framework in PET Imaging.

ArXiv·2026

Same journal

Automated optimization of force field parameters against ensemble-averaged measurements with Bayesian Inference of Conformational Populations.

ArXiv·2026

Same journal

Droplet Fusion as a Relaxation Process: Comparison with Shape Recovery of Newtonian and Viscoelastic Droplets.

ArXiv·2026

Same journal

Ridge-filter crosstalk in conformal proton FLASH planning: dependence on beamlet pitch and iterative mitigation.

ArXiv·2026

Same journal

Electrochemical DNA Hairpin Sensors for Differentiating Small Molecule Intercalation from Minor Groove Binding.

ArXiv·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 31, 2025

Pattern Generation for Micropattern Traction Microscopy

Pattern Generation for Micropattern Traction Microscopy

Published on: February 17, 2022

Machine learning traction force maps of cell monolayers.

Changhao Li¹, Luyi Feng¹, Yang Jeong Park^2,3

¹Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA.

|May 3, 2023

Summary

This summary is machine-generated.

We developed a generative adversarial network (GAN) to create high-resolution cellular traction force maps. This AI tool enhances understanding of cell mechanics and mechanotransduction in cell monolayers.

More Related Videos

Traction Microscopy Integrated with Microfluidics for Chemotactic Collective Migration

Traction Microscopy Integrated with Microfluidics for Chemotactic Collective Migration

Published on: October 13, 2019

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

Related Experiment Videos

Last Updated: Jul 31, 2025

Pattern Generation for Micropattern Traction Microscopy

Pattern Generation for Micropattern Traction Microscopy

Published on: February 17, 2022

Traction Microscopy Integrated with Microfluidics for Chemotactic Collective Migration

Traction Microscopy Integrated with Microfluidics for Chemotactic Collective Migration

Published on: October 13, 2019

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

Area of Science:

Cellular mechanobiology
Biophysics
Computational biology

Background:

Cellular force transmission is crucial for mechanobiological responses.
Existing cellular force microscopy techniques lack throughput and resolution.
Understanding cell-matrix interactions requires advanced force mapping methods.

Approach:

Introduced and trained a generative adversarial network (GAN) for image-to-image translation of traction force maps.
Cross-trained GAN's generative and discriminative networks using hybrid experimental and numerical datasets.
Developed a digital traction force microscopy (TFM) tool for high-fidelity cellular force mapping.

Key Points:

GAN accurately captures colony-size and substrate-stiffness dependent traction force maps.
Predicts asymmetric traction forces in multicellular monolayers on stiffness gradients, suggesting collective durotaxis.
Extracts hidden relationships between substrate stiffness and cell contractility, crucial for mechanotransduction.
Demonstrates extrapolation to other cell types with a single scaling factor.

Conclusions:

The digital TFM offers a high-throughput method for mapping cellular forces in monolayers.
This AI-driven approach facilitates data-driven discoveries in cell mechanobiology.
Enables deeper insights into collective cell behavior and mechanotransduction.