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

The PRECISE European initiative for cancer-vulnerability mapping and prediction.

Nature genetics·2026
Same author

Exploratory Analysis of Genetic Variants in BDNF, GABA Receptors, and Dopaminergic Pathways with Alcohol Use Disorder in a Spanish Cohort.

International journal of molecular sciences·2026
Same author

RPS20 as a colorectal cancer predisposition gene: an integrated review of the literature and evaluation in 9738 cases and 161,403 controls.

Familial cancer·2026
Same author

Gene mutant dosage is associated with prognosis and metastatic tropism in 60,000 clinical cancer samples.

medRxiv : the preprint server for health sciences·2026
Same author

PHYFUM: Phylogenetic Reconstruction of Normal and Pre-malignant Tissue Evolution Using Fluctuating Methylation.

bioRxiv : the preprint server for biology·2026
Same author

The evolution of polyclonal competition in aging hematopoiesis.

Cancer discovery·2026

Related Experiment Video

Updated: Dec 27, 2025

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

19.0K

Measuring single cell divisions in human tissues from multi-region sequencing data.

Benjamin Werner1,2, Jack Case3,4, Marc J Williams5,6,7

  • 1Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. b.werner@qmul.ac.uk.

Nature Communications
|February 27, 2020
PubMed
Summary

Researchers developed a new method to quantify somatic evolution by analyzing genomic data from healthy and cancerous tissues. This approach reveals mutation rates and cell survival dynamics during development and cancer, showing significantly higher mutation rates in tumors.

More Related Videos

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

379
Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells
10:20

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

2.1K

Related Experiment Videos

Last Updated: Dec 27, 2025

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

19.0K
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

379
Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells
10:20

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

2.1K

Area of Science:

  • Genomics
  • Developmental Biology
  • Cancer Biology

Background:

  • Intra-tissue genetic heterogeneity arises from cell division and mutation accumulation during normal development and cancer growth.
  • Quantifying somatic evolution in human tissues presents significant challenges.
  • Genomic data from multiple samples at a single time point can reveal insights into single-cell divisions.

Purpose of the Study:

  • To develop a theoretical framework for inferring mutation and cell survival/death rates per division from genomic data.
  • To apply this framework to whole-genome sequencing data from healthy and cancerous tissues.
  • To quantify somatic evolution dynamics in human development and cancer.

Main Methods:

  • Development of a novel theoretical framework to analyze multi-sample genomic data.
  • Application of the framework to whole-genome sequencing data of healthy and cancer tissues.
  • Inference of mutation rates and cell division-associated survival/death rates.

Main Results:

  • Cells accumulate an average of 1.14 mutations per division in healthy hematopoiesis and 1.37 mutations per division in brain development.
  • Cell survival rates were highest during early developmental stages in both healthy tissues.
  • Cancer tissues exhibited 4 to 100 times higher mutation rates than healthy development, with significant inter-patient variation in cell survival/death rates.

Conclusions:

  • Multi-sample genomic data from a single time point can effectively quantify somatic evolution.
  • The developed framework allows for the inference of crucial parameters of cellular evolution.
  • Cancer development is characterized by significantly elevated mutation rates and variable cell survival dynamics compared to normal development.