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

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.

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

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 de novo 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.

Cigarette smoke-induced changes in the murine vocal folds: a Raman spectroscopic observation.

Raman spectroscopic methods are being projected as novel tools to study the early invisible molecular level changes in a label-free manner. In the present study, we have used Raman spectroscopy to explore the earliest biochemical changes in murine vocal folds in response to time-bound cigarette smoke exposure. Mice were exposed to cigarette smoke for 2 or 4-weeks through a customized smoke inhalation system. The larynx was collected and initial evaluations using standard methods of analysis such as histopathology and immunofluorescence was performed. Concurrent unstained sections were used for Raman imaging. Two common pathological features of vocal fold disorders including alterations in collagen content and epithelial hypercellularity, or hyperplasia, were observed. The mean spectra, principal component analysis, and Raman mapping also revealed differences in the collagen content and hypercellularity in the smoke exposed tissues. The differences in 2-week exposed tissues were found to be more prominent as compared to 4-week. This was attributed to adaptive responses and the already reported biphasic effects, which suggest that collagen synthesis is significantly reduced at higher cigarette smoke concentrations. Overall findings of the study are supportive of the prospective application of Raman imaging in monitoring changes due to cigarette smoke in the vocal folds.

Nanoscopic Characterisation of Individual Endogenous Protein Aggregates in Human Neuronal Cells.

The aberrant misfolding and subsequent conversion of monomeric protein into amyloid aggregates characterises many neurodegenerative disorders, including Parkinson's and Alzheimer's diseases. These aggregates are highly heterogeneous in structure, generally of low abundance and typically smaller than the diffraction limit of light (≈250 nm). To overcome the challenges these characteristics pose to the study of endogenous aggregates formed in cells, we have developed a method to characterise them at the nanometre scale without the need for a conjugated fluorophore. Using a combination of DNA PAINT and an amyloid-specific aptamer, we demonstrate that this technique is able to detect and super-resolve a range of aggregated species, including those formed by α-synuclein and amyloid-ß. Additionally, this method enables endogenous protein aggregates within cells to be characterised. We found that neuronal cells derived from patients with Parkinson's disease contain a larger number of protein aggregates than those from healthy controls.