Sanchez Santiago E, Gu Jessica, Golla Anudeep, Martin Annika, Shomali William, Hockemeyer Dirk, Savage Sharon A, Artandi Steven E
Stanford Cancer Institute, Stanford University School of Medicine; Stanford, CA, USA.
Cancer Biology Program, Stanford University School of Medicine; Stanford, CA, USA.
bioRxiv. 2023 Dec 1:2023.11.29.569263. doi: 10.1101/2023.11.29.569263.
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 by nanopore sequencing to understand how distributions of human telomere length change with age and disease. We measure telomere attrition and elongation with unprecedented 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.
端粒长度是生物体衰老和细胞复制潜能的重要生物标志物，但现有的测量方法在分辨率和准确性方面存在局限性。在此，我们采用纳米孔测序进行数字端粒测量，以了解人类端粒长度分布如何随年龄和疾病而变化。我们以前所未有的分辨率测量了基因定义的人类细胞群体、健康供体血细胞以及端粒维持存在基因缺陷的患者血细胞中的端粒损耗和延长情况。我们发现，人类衰老伴随着长端粒的逐渐丢失和短端粒的积累。在端粒维持存在缺陷的患者中，短端粒的积累更为明显，且与表型严重程度相关。我们应用机器学习训练了一个二元分类模型，以区分健康个体和患有端粒生物学障碍的个体。这种测序和生物信息学流程将推动我们对端粒维持机制的理解，以及将端粒长度用作衰老和疾病临床生物标志物的应用。
