Digital telomere measurement by long-read sequencing distinguishes healthy aging from disease.

Journal: Nature communications
PMID: 38890274

Abstract

Telomere length is an important biomarker of organismal aging and cellular replicative potential, but existing measurement methods are limited in resolution and accuracy. Here, we deploy digital telomere measurement (DTM) by nanopore sequencing to understand how distributions of human telomere length change with age and disease. We measure telomere attrition and de novo elongation with up to 30 bp resolution in genetically defined populations of human cells, in blood cells from healthy donors and in blood cells from patients with genetic defects in telomere maintenance. We find that human aging is accompanied by a progressive loss of long telomeres and an accumulation of shorter telomeres. In patients with defects in telomere maintenance, the accumulation of short telomeres is more pronounced and correlates with phenotypic severity. We apply machine learning to train a binary classification model that distinguishes healthy individuals from those with telomere biology disorders. This sequencing and bioinformatic pipeline will advance our understanding of telomere maintenance mechanisms and the use of telomere length as a clinical biomarker of aging and disease.

Authors

  • Santiago E Sanchez
    Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
  • Yuchao Gu
    Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Anudeep Golla
    Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
  • Annika Martin
    Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
  • William Shomali
    Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Dirk Hockemeyer
    Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
  • Sharon A Savage
    Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
  • Steven E Artandi
    Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA. sartandi@stanford.edu.

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