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

Santiago E. Sanchez, Yuchao Gu, Yan Wang, Anudeep Golla, Annika Martin, William Shomali, Dirk Hockemeyer, Sharon A. Savage and Steven E. Artandi ()
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Santiago E. Sanchez: Stanford University School of Medicine
Yuchao Gu: Stanford University School of Medicine
Yan Wang: Stanford University School of Medicine
Anudeep Golla: Stanford University School of Medicine
Annika Martin: University of California
William Shomali: Stanford University School of Medicine
Dirk Hockemeyer: University of California
Sharon A. Savage: National Cancer Institute
Steven E. Artandi: Stanford University School of Medicine

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49007-4

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