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Engel, N. L.

Publications and source records attributed to Engel, N. L..

2 recordsLinked to original sources

Nanopore whole-genome sequencing reveals conserved chromosome-specific telomere architecture across tissues and populations

Telomeres are repetitive nucleoprotein structures that cap the ends of linear chromosomes and are essential for maintaining genomic stability. While individual chromosome ends maintain distinct telomere lengths, the extent of this conservation across tissues and populations remains unclear due to the difficulty of analyzing repetitive telomeric sequences. Here, we show that high-coverage whole-genome Nanopore sequencing enables robust measurement of telomere length at the level of individual chromosomes. Nanopore reads yield reproducible telomere length estimates across replicates, in contrast to PacBio HiFi reads. Across > 250 individuals from 1000 Genomes and the SMaHT projects, chromosome-specific telomere length patterns are conserved across individuals and tissues, with tissues from the same individual showing highly similar patterns. This conserved landscape suggests coordinated regulation, whose disruption may contribute to genomic instability. Nanopore sequencing also allows simultaneous detection of structural variants, including disruption of TERT and NHP2 that drive global telomere shortening. Furthermore, our quantification of telomere variant repeats in positional context indicates active telomerase-mediated elongation. Our integrated profiling of telomere length and structural variation enables inference of variant effects on chromosome-specific telomere dynamics and may uncover risk factors for short telomere syndromes and cancer. Importantly, positionally fully resolved telomeric variant repeat patterns may predict activated telomere maintenance mechanisms with high accuracy. SignificanceResolving chromosome-specific telomere length and variant-repeat architecture across tissues and individuals provides a framework to dissect coordinated telomere maintenance, its disruption by genetic variants, and how this shapes telomere mosaicism and disease risk.

genomics↗

Quantifying immune cell telomere content at single-cell resolution in context of PD-1 checkpoint immunotherapy

Biological processes such as aging, carcinogenesis, and immune responses depend on the ability of cells to maintain or rapidly expand populations. This capacity is constrained by a cells replicative potential, which is reflected in its telomere content. Despite the central role of telomeres in cancer and immunity, their analysis at single-cell resolution across diverse cell types remains challenging. Here we show that scATAC-seq data enables quantitative telomeromics when key technical and biological confounders are accounted for. We present a computational framework that addresses read sparsity, telomere representation, and chromatin-state-dependent competition for sequencing signal, enabling robust estimation of telomere content and telomeric variant repeat composition from scATAC-seq data. By inferring global chromatin condensation directly from scATAC-seq profiles, our approach corrects for cell-cycle-associated biases while simultaneously capturing chromatin-state dysregulation in cancer. Applied to a large cancer atlas, this framework reveals patient-specific telomere maintenance phenotypes, including telomerase-associated and alternative lengthening of telomeres (ALT)-like profiles preserved across subclonal populations. Extending beyond cancer cells, we observe that telomere content in exhausted T cell subpopulations prior to immunotherapy is predictive for effective response to PD-1 checkpoint blockade. Together, these results establish scATAC-seq as a robust platform for single-cell telomeromics in cancer and immunity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/609339v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@4f9498org.highwire.dtl.DTLVardef@a4b787org.highwire.dtl.DTLVardef@d1e25org.highwire.dtl.DTLVardef@136196b_HPS_FORMAT_FIGEXP M_FIG C_FIG Created in BioRender. Popp, F. (2026) https://BioRender.com/mi2jove

immunology↗