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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

You might also read

Related Articles

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

Sort by
Same author

Mechanics of knee meniscus results from precise balance between material microstructure and synovial fluid viscosity.

PloS one·2025
Same author

Morphogen gradients are regulated by porous media characteristics of the developing tissue.

Development (Cambridge, England)·2025
Same author

Regularized Gradient Statistics Improve Generative Deep Learning Models of Super Resolution Microscopy.

Small methods·2025
Same author

Loss of bimolecular reactions in reaction-diffusion master equations is consistent with diffusion limited reaction kinetics in the mean field limit.

The Journal of chemical physics·2024
Same author

Automated quantification of photoreceptor outer segments in developing and degenerating retinas on microscopy images across scales.

Frontiers in molecular neuroscience·2024
Same author

STENCIL-NET for equation-free forecasting from data.

Scientific reports·2023
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 11, 2026

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Beyond co-localization: inferring spatial interactions between sub-cellular structures from microscopy images.

Jo A Helmuth1, Grégory Paul, Ivo F Sbalzarini

  • 1Institute of Theoretical Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland.

BMC Bioinformatics
|July 9, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical framework for analyzing sub-cellular structure interactions using spatial point processes. The developed methods offer a more robust way to infer interaction strengths and significance compared to classical co-localization measures.

More Related Videos

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
11:06

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
12:51

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

Related Experiment Videos

Last Updated: Jun 11, 2026

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
11:06

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
12:51

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

Area of Science:

  • Cell Biology
  • Biophysics
  • Statistical Modeling

Background:

  • Sub-cellular structures interact through direct and indirect mechanisms to perform cellular functions.
  • Co-localization analysis in microscopy infers potential interactions based on spatial correlations.
  • Existing methods include intensity correlation and object-based approaches, each with limitations.

Purpose of the Study:

  • To develop a generalized statistical framework for object-based co-localization analysis.
  • To connect co-localization with spatial interaction of sub-cellular structures.
  • To provide flexible methods for inferring and quantifying interaction strengths.

Main Methods:

  • Reinvestigation of classical co-localization measures using spatial point pattern analysis.
  • Generalization of object-based co-localization to a spatial statistics framework based on spatial point processes.
  • Modeling interactions as position co-dependencies using effective pairwise interaction potentials and a null hypothesis.

Main Results:

  • Unraveled implicit assumptions and confounding factors of classical co-localization measures.
  • Demonstrated that classical measures often under-exploit available data.
  • Validated the new framework on synthetic data and a live-cell virus entry case study.

Conclusions:

  • Established a link between co-localization and spatial interaction via a spatial statistics framework.
  • Provided procedures for inferring interaction strengths and statistical significance from discrete objects.
  • Enabled flexible modeling of interaction potentials, aiding in parameter quantification and discovery of functional relations.