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

You might also read

Related Articles

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

Sort by
Same author

The association between fibrotic diseases and treatment-resistant hypertension in England.

European journal of preventive cardiology·2025
Same author

Does value-based prioritization at working memory enhance long-term memory?

Memory & cognition·2024
Same author

Classifying the unclassifiable-a Delphi study to reach consensus on the fibrotic nature of diseases.

QJM : monthly journal of the Association of Physicians·2023
Same author

Stochastic Methods for Inferring States of Cell Migration.

Frontiers in physiology·2020
Same author

Combined Atomic Force Microscope and Volumetric Light Sheet System for Correlative Force and Fluorescence Mechanobiology Studies.

Scientific reports·2020
Same author

Calcium Sensitive Fluorescent Dyes Fluo-4 and Fura Red under Pressure: Behaviour of Fluorescence and Buffer Properties under Hydrostatic Pressures up to 200 MPa.

PloS one·2016

Related Experiment Video

Updated: May 8, 2026

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading
10:54

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

Automated line scan analysis to quantify biosensor activity at the cell edge.

R J Allen1, D Tsygankov1, J S Zawistowski1

  • 1Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.

Methods (San Diego, Calif.)
|September 3, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces automated software for analyzing cell edge dynamics using biosensors. It quantifies cell edge velocity and biosensor activity, enabling new biological insights from large datasets.

Keywords:
ActinBiosensorGUILinescanMotilitySoftware

More Related Videos

Use of Label-free Optical Biosensors to Detect Modulation of Potassium Channels by G-protein Coupled Receptors
10:59

Use of Label-free Optical Biosensors to Detect Modulation of Potassium Channels by G-protein Coupled Receptors

Published on: February 10, 2014

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions
09:09

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

Related Experiment Videos

Last Updated: May 8, 2026

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading
10:54

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

Use of Label-free Optical Biosensors to Detect Modulation of Potassium Channels by G-protein Coupled Receptors
10:59

Use of Label-free Optical Biosensors to Detect Modulation of Potassium Channels by G-protein Coupled Receptors

Published on: February 10, 2014

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions
09:09

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

Area of Science:

  • Cell Biology
  • Biophysics
  • Biotechnology

Background:

  • Biosensors are crucial for visualizing subcellular protein activity in live cells.
  • Analyzing cell edge dynamics, vital for cell motility, is often limited by data heterogeneity.
  • Automated analysis is needed for robust statistical interpretation of biosensor data.

Purpose of the Study:

  • To develop an automated method for analyzing biosensor activity at the cell edge.
  • To correlate cell edge velocity with localized biosensor signals.
  • To facilitate systematic data extraction for statistical analysis in cell motility studies.

Main Methods:

  • Automated processing of time-series biosensor images to define cell edge position.
  • Calculation of biosensor activity profiles ('line scans') perpendicular to the cell edge.
  • Utilizing an energy minimization method to determine velocity at the cell edge and correlating it with line scan data.

Main Results:

  • Developed 'LineScan' software with a graphical user interface (GUI).
  • Demonstrated the ability to generate large datasets for statistical analysis.
  • Revealed novel biological insights by sorting line scans based on proximal velocity, exemplified with Src merobody and RhoC FLARE biosensors.

Conclusions:

  • Automated analysis overcomes limitations of manual quantification in heterogeneous cell signaling.
  • The 'LineScan' software enables deeper understanding of cell edge dynamics and protein activity.
  • This approach facilitates discoveries not possible with qualitative or manual quantitative techniques.