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

Cell-Type-Specific Circadian Bioluminescence Rhythms in <i>Dbp</i> Reporter Mice.

Journal of biological rhythms·2022
Same author

Methods for Detecting PER2:LUCIFERASE Bioluminescence Rhythms in Freely Moving Mice.

Journal of biological rhythms·2021
Same author

Weekend Light Shifts Evoke Persistent <i>Drosophila</i> Circadian Neural Network Desynchrony.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2021
Same author

CIRCADA: Shiny Apps for Exploration of Experimental and Synthetic Circadian Time Series with an Educational Emphasis.

Journal of biological rhythms·2020
Same author

Recurring circadian disruption alters circadian clock sensitivity to resetting.

The European journal of neuroscience·2018
Same author

Multi-attribute, multi-alternative models of choice: Choice, reaction time, and process tracing.

Cognitive psychology·2017
Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.5K

Computational Analysis of PER2::LUC Imaging Data.

Tanya L Leise1

  • 1Department of Mathematics and Statistics, Amherst College, Amherst, MA, USA. tleise@amherst.edu.

Methods in Molecular Biology (Clifton, N.J.)
|December 7, 2020
PubMed
Summary
This summary is machine-generated.

Automated processing of PER2::LUC imaging data from mouse suprachiasmatic nuclei (SCN) slices generates spatiotemporal maps of circadian rhythms. This method aids in studying the SCN

Keywords:
Automated ROI identificationBioluminescenceCircadian oscillationsImage processingSCNSpatiotemporal maps

More Related Videos

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
06:25

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

Published on: January 19, 2024

1.3K
Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples
08:18

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Published on: April 7, 2023

2.0K

Related Experiment Videos

Last Updated: Nov 27, 2025

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.5K
Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
06:25

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

Published on: January 19, 2024

1.3K
Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples
08:18

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Published on: April 7, 2023

2.0K

Area of Science:

  • Neuroscience
  • Chronobiology
  • Molecular Biology

Background:

  • Circadian rhythms are regulated by core clock genes within the suprachiasmatic nuclei (SCN).
  • Bioluminescent reporters and advanced imaging allow visualization of SCN circadian dynamics.
  • The PERIOD2::luciferase (PER2::LUC) mouse model provides a tool for studying SCN heterogeneity.

Purpose of the Study:

  • To describe an automated method for processing PER2::LUC imaging data from SCN slices.
  • To generate spatiotemporal maps of circadian parameters (phase, period, amplitude).
  • To enable automated detection of cell-like regions for neural network analysis within the SCN.

Main Methods:

  • Utilizing PER2::LUC knockin mouse models for bioluminescence imaging.
  • Automating the processing of imaging data from SCN slices.
  • Developing algorithms for generating spatiotemporal circadian maps.
  • Implementing automated cell-like region detection for neural network studies.

Main Results:

  • Generated spatiotemporal maps of circadian parameters from SCN slices.
  • Created composite maps by aligning and averaging data from multiple slices.
  • Developed automated methods for analyzing SCN neural networks.

Conclusions:

  • Automated processing of PER2::LUC imaging data facilitates detailed analysis of SCN circadian rhythms.
  • The developed methods support the investigation of the SCN's heterogeneous circadian network.
  • This approach enhances the study of neural networks involved in circadian timing.