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

Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

You might also read

Related Articles

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

Sort by

Same author

Movement-stabilized three-dimensional optical recordings of membrane potential changes and calcium dynamics in hippocampal CA1 dendrites.

Neuron·2026

Same author

A transcriptomic axis aligns with in vivo functional dynamics in hippocampal inhibitory circuits.

bioRxiv : the preprint server for biology·2026

Same author

2P-NucTag: On-demand phototagging for molecular analysis of functionally identified cortical neurons.

Neuron·2026

Same author

Movement-stabilized three-dimensional optical recordings of membrane potential changes and calcium dynamics in hippocampal CA1 dendrites.

Neuron·2026

Same author

Disorganized Inhibitory Dynamics in Hippocampal area CA1 of 22q11.2 Deletion Mutant Mice.

bioRxiv : the preprint server for biology·2026

Same author

A generic non-invasive neuromotor interface for human-computer interaction.

Nature·2025

Same journal

Predicting vasovagal syncope during head-up tilt test: three machine learning approaches.

Frontiers in neuroinformatics·2026

Same journal

Decoding basal ganglia motor circuit dysfunction from handwriting: a physics-informed neural signal interpretation framework for Parkinson's disease screening.

Frontiers in neuroinformatics·2026

Same journal

FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.

Frontiers in neuroinformatics·2026

Same journal

A 3D-printed phantom to validate subject orientation in 3D imaging and recordings.

Frontiers in neuroinformatics·2026

Same journal

IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.

Frontiers in neuroinformatics·2026

Same journal

Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.

Frontiers in neuroinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 22, 2026

Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes

Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes

Published on: November 23, 2021

SIMA: Python software for analysis of dynamic fluorescence imaging data.

Patrick Kaifosh¹, Jeffrey D Zaremba¹, Nathan B Danielson¹

¹Department of Neuroscience, Columbia University in the City of New York New York, NY, USA.

Frontiers in Neuroinformatics

|October 9, 2014

Summary

This summary is machine-generated.

SIMA is a new open-source Python package designed to simplify the analysis of dynamic fluorescence imaging data. It offers tools for motion correction, region segmentation, and signal extraction, making neuroscience research more accessible.

Keywords:

Python language analysis software calcium imaging fluorescence imaging in vivo GECI imaging motion correction multi-photon microscopy segmentation

More Related Videos

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Published on: January 18, 2017

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

Related Experiment Videos

Last Updated: Apr 22, 2026

Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes

Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes

Published on: November 23, 2021

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Published on: January 18, 2017

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

Area of Science:

Neuroscience
Bioimaging

Background:

Dynamic fluorescence imaging is crucial for monitoring neural activity.
Analyzing this data is challenging due to a lack of specialized software tools.

Purpose of the Study:

To develop an open-source software package, SIMA, to streamline fluorescence imaging data analysis.
To provide tools for motion correction, ROI segmentation, and signal extraction.

Main Methods:

Developed SIMA, a Python package with functionalities for motion artifact correction, ROI segmentation, and signal extraction.
Created a graphical user interface (GUI) for manual ROI editing and automated registration across datasets.

Main Results:

SIMA offers efficient tools for common fluorescence imaging analysis tasks.
The accompanying GUI, ROI Buddy, allows for user-friendly manual editing and automated ROI registration.

Conclusions:

SIMA provides a flexible and extensible solution for analyzing dynamic fluorescence imaging data.
The open-source availability of SIMA and ROI Buddy promotes wider adoption and further development in neuroscience research.