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

Circadian Rhythms and Gene Regulation02:19

Circadian Rhythms and Gene Regulation

The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent years,...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

You might also read

Related Articles

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

Sort by
Same author

The Salmon Oil OmeGo Reduces Viability of Colorectal Cancer Cells and Potentiates the Anti-Cancer Effect of 5-FU.

Marine drugs·2023
Same author

Postnatal persistence of hippocampal Cajal-Retzius cells has a crucial role in the establishment of the hippocampal circuit.

Development (Cambridge, England)·2023
Same author

TiSA: TimeSeriesAnalysis-a pipeline for the analysis of longitudinal transcriptomics data.

NAR genomics and bioinformatics·2023
Same author

Genome-wide hydroxymethylation profiles in liver of female Nile tilapia with distinct growth performance.

Scientific data·2023
Same author

NEIL1 and NEIL2 DNA glycosylases modulate anxiety and learning in a cooperative manner in mice.

Communications biology·2021
Same author

MicroRNA profiling of psoriatic skin identifies 11 miRNAs associated with disease severity.

Experimental dermatology·2021

Related Experiment Video

Updated: Jun 14, 2026

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry
11:23

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry

Published on: May 22, 2012

21.2K

Using pseudotime derivative on single-cell RNA sequencing data to identify genes undergoing cell cycle regulation.

Yohan Lefol1,2,3,4, Geir Amund Svan Hasle1,5, Siv Anita Hegre1

  • 1Department of Clinical and Molecular Medicine, NTNU-Norwegian University of Science and Technology, Trondheim, NO-7491, Norway.

Bioinformatics Advances
|July 14, 2025
PubMed
Summary

This study introduces a novel single-cell RNA sequencing method to map cell cycle gene velocities without synchronization. This approach enhances understanding of cell cycle dynamics and gene regulation.

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.7K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K

Related Experiment Videos

Last Updated: Jun 14, 2026

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry
11:23

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry

Published on: May 22, 2012

21.2K
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.7K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K

Area of Science:

  • Molecular Biology
  • Genomics
  • Cell Biology

Background:

  • The cell cycle is fundamental to cellular life and is typically studied via synchronization or selection experiments.
  • These traditional methods involve chemical modifications or cell sorting, which can introduce artifacts and bypass natural cellular processes.

Purpose of the Study:

  • To develop a method for studying the cell cycle using single-cell RNA sequencing (scRNA-seq).
  • To circumvent the need for cell synchronization or selection experiments in cell cycle research.
  • To provide a robust approach for mapping gene velocities throughout cell cycle phases.

Main Methods:

  • Utilized a pseudotime method to calculate gene velocity based on predicted and real gene expression.
  • Applied statistical analysis to identify genes with significant velocity shifts within pseudotime.
  • Incorporated a method for merging technical replicates to enhance robustness and account for variations.
  • Demonstrated observation of gene regulatory behaviors like mRNA splicing and degradation rates.

Main Results:

  • Developed a robust computational approach to map gene velocities across cell cycle phases using scRNA-seq.
  • Identified biologically and statistically significant genes exhibiting dynamic velocity changes.
  • Showcased the ability to infer gene regulatory dynamics, including splicing and degradation.
  • Successfully merged technical replicates for improved analysis of cell line experiments.

Conclusions:

  • The developed method offers a powerful, non-invasive way to study cell cycle progression at single-cell resolution.
  • This approach provides deeper insights into gene regulation and dynamics during the cell cycle.
  • The methodology is robust and applicable to cell line experiments, with data and code publicly available.