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

Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

4.6K
4.6K

You might also read

Related Articles

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

Sort by
Same author

Analyzing Co-expression Networks with Network Skeleton Extraction.

Research square·2026
Same author

VIST: variational inference for single cell time series.

Genome biology·2026
Same author

Copula modeling of gene coexpression in single-cell RNA sequencing data.

bioRxiv : the preprint server for biology·2025
Same author

Identification of Distinct Topological Structures From High-Dimensional Data.

bioRxiv : the preprint server for biology·2025
Same author

Author Correction: Profibrotic monocyte-derived alveolar macrophages are expanded in patients with persistent respiratory symptoms and radiographic abnormalities after COVID-19.

Nature immunology·2025
Same author

Transcriptomic DN3 clock neuron subtypes regulate <i>Drosophila</i> sleep.

Science advances·2025
Same journal

The Ontogeny of Rhythms in Circadian Clock Gene Expression in Mouse Brain and Neuroimmune Tissues.

Journal of biological rhythms·2026
Same journal

Biological Rhythms in Coronavirus Pneumonia Pathogenesis.

Journal of biological rhythms·2026
Same journal

On Fealty and Fencers in Science.

Journal of biological rhythms·2026
Same journal

GIGEM (Group Isolation Gauge Effect Metrics), a Software Suite for Analyzing Social Isolation-induced Sleep Loss and Multi-batch Experiments in <i>Drosophila</i>.

Journal of biological rhythms·2026
Same journal

Defining Clock Neurons Within Distributed Circadian Circuits Through Multiscale Technologies.

Journal of biological rhythms·2026
Same journal

Light-induced Desynchrony of a Multi-oscillatory Circadian System in <i>Per2</i>-deficient Mice.

Journal of biological rhythms·2026
See all related articles
  1. Home
  3. Detecting Rhythmic Gene Expression In Single-cell Transcriptomics.
  1. Home
  3. Detecting Rhythmic Gene Expression In Single-cell Transcriptomics.

Related Experiment Video

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

6.6K

Detecting Rhythmic Gene Expression in Single-cell Transcriptomics.

Bingxian Xu1,2, Dingbang Ma3,4, Katharine Abruzzi5,6

  • 1Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA.

Journal of Biological Rhythms
|October 8, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers evaluated methods for detecting circadian rhythm genes in single-cell RNA sequencing data. A subsampling and harmonic regression approach offers an efficient strategy for identifying these crucial biological rhythms.

Keywords:
benchmarkingcircadian rhythmcycling detectionreproducibilitysingle-cell RNA sequencing

More Related Videos

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

16.4K
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

29.8K

Related Experiment Videos

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

6.6K
Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

16.4K
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

29.8K

Area of Science:

  • Chronobiology
  • Genomics
  • Computational Biology

Background:

  • Circadian rhythms, driven by cellular transcription-translation feedback loops, regulate daily gene expression in multicellular organisms.
  • Understanding circadian gene control is vital for elucidating physiological coordination.
  • Single-cell RNA sequencing (scRNA-seq) offers a powerful tool for studying cell-level circadian clocks.

Purpose of the Study:

  • To benchmark existing circadian detection algorithms for their reliability and efficiency on single-cell RNA sequencing data.
  • To provide guidance on adapting bulk transcriptomic methods for circadian gene detection in the single-cell domain.
  • To identify opportunities for improving rhythm detection in single-cell datasets.

Main Methods:

  • Benchmarking of commonly used circadian detection algorithms on scRNA-seq data.
  • Evaluation of algorithm performance in terms of reliability and efficiency.
  • Proposal and testing of a subsampling procedure combined with harmonic regression for circadian gene detection.

    • Main Results:

    • Established the performance characteristics of various circadian detection methods when applied to scRNA-seq data.
    • Demonstrated that existing methods require adaptation for effective use with single-cell resolution.
    • Identified a subsampling and harmonic regression strategy as a robust and efficient approach for single-cell circadian rhythm detection.

    Conclusions:

    • Guidance is provided for adapting established circadian detection algorithms to the single-cell RNA sequencing context.
    • The proposed subsampling and harmonic regression method enhances the efficiency and robustness of circadian gene discovery in single-cell data.
    • This work facilitates deeper understanding of cell-specific circadian regulation and its physiological implications.