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 Experiment Video

Updated: Aug 26, 2025

Reusable Single Cell for Iterative Epigenomic Analyses
10:28

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

1.4K

Profiling epigenetic age in single cells.

Alexandre Trapp1, Csaba Kerepesi1, Vadim N Gladyshev1

  • 1Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.

Nature Aging
|October 10, 2022
PubMed
Summary
This summary is machine-generated.

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

Magnetic resonance imaging based radiomics for predicting pathogenetic features and survival in rectal cancer.

Pathology oncology research : POR·2026
Same author

Foundations of Gerophysics.

Aging·2026
Same author

Improving Face Age Prediction by Using Multiple-Angle Photos.

Computational and structural biotechnology journal·2026
Same author

Physical Fitness Is Negatively Associated With DNA Methylation-Based Risk of Aging-Related Diseases.

Aging cell·2026
Same author

Autonomous AI Agents Discover Aging Interventions from Millions of Molecular Profiles.

bioRxiv : the preprint server for biology·2025
Same author

Human Brain Cell-Type-Specific Aging Clocks Based on Single-Nuclei Transcriptomics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

We developed scAge, a new method to measure biological age in single cells using DNA methylation. This framework tracks aging in various cell types and reveals rejuvenation events during development.

Area of Science:

  • Epigenetics
  • Genomics
  • Computational Biology

Background:

  • DNA methylation is a key biomarker for mammalian aging.
  • Multivariate machine learning models, known as epigenetic clocks, measure biological age in bulk tissues.
  • Assessing aging in single-cell data has been challenging due to sparse methylation profiles.

Purpose of the Study:

  • Introduce scAge, a statistical framework for epigenetic age profiling at single-cell resolution.
  • Validate the scAge approach in mouse models.
  • Enable exploration of epigenetic aging trajectories at the single-cell level.

Main Methods:

  • Developed a novel statistical framework, scAge, for single-cell epigenetic age estimation.
  • Applied scAge to mouse tissues, including hepatocytes, muscle stem cells, and embryonic stem cells.

More Related Videos

Single-cell Profiling of Developing and Mature Retinal Neurons
10:20

Single-cell Profiling of Developing and Mature Retinal Neurons

Published on: April 19, 2012

14.2K
Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR
09:03

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Published on: May 29, 2014

11.6K

Related Experiment Videos

Last Updated: Aug 26, 2025

Reusable Single Cell for Iterative Epigenomic Analyses
10:28

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

1.4K
Single-cell Profiling of Developing and Mature Retinal Neurons
10:20

Single-cell Profiling of Developing and Mature Retinal Neurons

Published on: April 19, 2012

14.2K
Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR
09:03

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Published on: May 29, 2014

11.6K
  • Validated scAge's ability to recapitulate chronological age and reveal cellular heterogeneity.

    • Main Results:

    • scAge accurately recapitulates chronological age in various cell types.
    • Identified attenuated epigenetic aging in muscle stem cells.
    • Tracked age dynamics in hepatocytes and embryonic stem cells.
    • Revealed a natural, stratified rejuvenation event during early embryogenesis at the single-cell level.

    Conclusions:

    • scAge provides a powerful tool for single-cell epigenetic age profiling.
    • The framework uncovers cellular heterogeneity in aging processes.
    • scAge facilitates the study of aging and rejuvenation dynamics at unprecedented resolution.