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Related Concept Videos

Replicative Cell Senescence02:15

Replicative Cell Senescence

Replicative cell senescence is a property of cells that allows them to divide a finite number of times throughout the organism's lifespan while preventing excessive proliferation. Replicative senescence is associated with the gradual loss of the telomere — short, repetitive DNA sequences found at the end of the chromosomes. Telomeres are bound by a group of proteins to form a protective cap on the ends of chromosomes. Embryonic stem cells express telomerase — an enzyme that adds the telomeric...
Replicative Cell Senescence02:15

Replicative Cell Senescence

Replicative cell senescence is a property of cells that allows them to divide a finite number of times throughout the organism's lifespan while preventing excessive proliferation. Replicative senescence is associated with the gradual loss of the telomere — short, repetitive DNA sequences found at the end of the chromosomes. Telomeres are bound by a group of proteins to form a protective cap on the ends of chromosomes. Embryonic stem cells express telomerase — an enzyme that adds the telomeric...

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Techniques to Induce and Quantify Cellular Senescence
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Machine learning-based morphological quantification of replicative senescence in human fibroblasts.

Emma M Welter1, Sofia Benavides1, Trevor K Archer2

  • 1Department of Psychiatry, University of North Carolina at Chapel Hill, 438 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC, 27599, USA.

Geroscience
|November 20, 2023
PubMed
Summary

Aging cells undergo significant morphological changes, including increased size and altered shape, as they reach replicative senescence. This study quantifies these cellular changes using a machine learning pipeline.

Keywords:
Cell morphologyFibroblastsMachine learningMicroscopyReplicative senescence

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Area of Science:

  • Cellular biology
  • Aging research
  • Computational biology

Background:

  • Aging is extensively studied at organismal and cellular levels.
  • Morphological changes in individual cells during their lifespan remain poorly quantified.
  • Quantifying cellular morphology provides insights into aging processes.

Purpose of the Study:

  • To develop and apply a machine learning pipeline for quantifying cellular morphological changes during replicative lifespan.
  • To investigate the morphological alterations in human fibroblasts as they age in culture.
  • To assess the pipeline's applicability across diverse human fibroblast populations.

Main Methods:

  • Utilized a machine learning-based pipeline with standard fluorescence microscopy.
  • Employed open-access software for high-throughput cell phenotyping.
  • Analyzed morphological changes in IMR-90 fibroblast cell line and primary human dermal fibroblasts.

Main Results:

  • Quantified significant increases in cell surface area, perimeter, pseudopodia, and nuclear surface area with replicative aging.
  • Observed decreased cell circularity correlating with replicative senescence.
  • Demonstrated recapitulation of these changes in primary fibroblasts from diverse donors.
  • Successfully classified and quantified senescent-like cells, showing their increased numbers with aging.

Conclusions:

  • Established a readily accessible computational pipeline for high-throughput cell phenotyping in aging research.
  • Provided quantitative insights into the morphological hallmarks of replicative senescence.
  • Highlighted the utility of machine learning in dissecting cellular aging processes.