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

Actomyosin rings constrain CD40 mobility to organize the dendritic cell immunological synapse.

Cell communication and signaling : CCS·2026
Same author

Fluorescent GLP1R/GIPR dual agonist probes reveal cell targets in the pancreas and brain.

Nature metabolism·2025
Same author

Measuring the similarity of single-molecule localization microscopy derived marked point clouds.

Biophysical journal·2025
Same author

Mapping membrane biophysical nano-environments.

Nature communications·2024
Same author

Machine learning in microscopy - insights, opportunities and challenges.

Journal of cell science·2024
Same author

Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy.

Biological imaging·2024
Same journal

RNApedia: a database of structural protein-RNA interactions.

Frontiers in bioinformatics·2026
Same journal

Hydrogen sulfide modulates gene networks in hypoxia/reoxygenation-stressed trophoblasts: insights from transcriptome profiling.

Frontiers in bioinformatics·2026
Same journal

Molecular Dynamics-Based validation of a quinazoline-based KRAS inhibitor (C9) identified through QSAR-guided discovery.

Frontiers in bioinformatics·2026
Same journal

Real-world chronic recordings from implantable adaptive deep brain stimulation systems for Parkinson's disease motor state classification.

Frontiers in bioinformatics·2026
Same journal

A foundational quantum framework for multi-pattern string matching in k-mer detection.

Frontiers in bioinformatics·2026
Same journal

Explainable machine learning-based identification of transcriptomic biomarkers in CD1c+ dendritic cells for non-infectious uveitis: an integrative analysis of bulk RNA-seq data.

Frontiers in bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jul 8, 2025

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans
06:49

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

Published on: December 17, 2021

2.9K

Cluster analysis for localisation-based data sets: dos and don'ts when quantifying protein aggregates.

Luca Panconi1, Dylan M Owen1, Juliette Griffié2

  • 1School of Mathematics, Centre of Membrane Proteins and Receptors (COMPARE), Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom.

Frontiers in Bioinformatics
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

Single Molecule Localisation Microscopy (SMLM) reveals protein clustering on cell surfaces. This guide offers best practices for SMLM data analysis, focusing on accurate cluster quantification for biological insights.

Keywords:
bioinformacticscluster analysisimage quantificationprotein aggregatessingle molecule localisation microscopy (SMLM)spatial point pattern (SPP)

More Related Videos

Monitoring Cell-to-cell Transmission of Prion-like Protein Aggregates in Drosophila Melanogaster
10:26

Monitoring Cell-to-cell Transmission of Prion-like Protein Aggregates in Drosophila Melanogaster

Published on: March 12, 2018

8.1K
Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ
08:44

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Published on: August 3, 2018

10.0K

Related Experiment Videos

Last Updated: Jul 8, 2025

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans
06:49

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

Published on: December 17, 2021

2.9K
Monitoring Cell-to-cell Transmission of Prion-like Protein Aggregates in Drosophila Melanogaster
10:26

Monitoring Cell-to-cell Transmission of Prion-like Protein Aggregates in Drosophila Melanogaster

Published on: March 12, 2018

8.1K
Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ
08:44

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Published on: August 3, 2018

10.0K

Area of Science:

  • Cell Biology
  • Microscopy
  • Biophysics

Background:

  • Proteins on cell surfaces exhibit non-random distributions, forming structures from dimers to large aggregates.
  • These distributions are critical for regulating protein interactions and cellular signalling pathways.
  • Conventional microscopy lacks the resolution to observe these nanoscale organizations.

Purpose of the Study:

  • To provide guidance on performing cluster analysis on Single Molecule Localisation Microscopy (SMLM) data.
  • To improve the extraction of biological insights from SMLM datasets.
  • To standardize methods for quantifying protein clustering.

Main Methods:

  • Utilizing Single Molecule Localisation Microscopy (SMLM) to achieve nanometre precision localization of surface proteins.
  • Processing SMLM data, which is in the form of point-patterns (x, y coordinates).
  • Applying cluster analysis techniques to SMLM point-pattern data.

Main Results:

  • SMLM enables high-resolution mapping of protein distributions below the diffraction limit.
  • Cluster analysis quantifies parameters such as cluster size and monomer percentage.
  • The study outlines optimal approaches for SMLM clustering analysis.

Conclusions:

  • Accurate cluster analysis of SMLM data is essential for understanding protein organization and function.
  • Standardized SMLM clustering methods enhance the reliability of biological interpretations.
  • This guidance facilitates deeper insights into cell surface protein dynamics.