Evidence Hub

Single-molecule nanopore sequencing reveals spatial coordination of rRNA modifications in human ribosomes

Ettenger G, Fleming AM

2026

Abstract

Ribosomal RNA has a high density of epitranscriptomic modifications essential for faithful translation, and their levels vary across cell types and in disease. Typically, rRNA modifications are quantified in bulk, and therefore, coordination on individual RNA molecules has remained poorly understood. Herein, modification-aware, single-molecule nanopore sequencing enabled detection of the co-occurrence of rRNA modifications on individual transcripts, resolving coordination invisible to ensemble methods. An analytical framework was established that separates co-occurrence from read-quality, false-positive, calling-artifact, near-saturation, and global modification-level confounds, using human rRNAs from HEK293T cells as the test dataset. The method was applied to four human cell lines to find that co-occurrence is not generally explained by a shared small nucleolar RNA (snoRNA) guide; instead, coordinated modifications cluster locally, within ∼100 nucleotides and 35-40 Å in the folded ribosome. For one shared-guide pair, coordination increased as guide levels fell across cell lines, possibly indicating an all-or-nothing mode of modification per molecule under limiting guide availability. Further, the results revealed rRNA heterogeneity between the cells in overall modification levels. Finally, levofloxacin remodeled specific modification sites and their local co-occurrence networks, showing that the approach can reveal small-molecule perturbation of rRNA modification networks.

How strong is the evidence?

Evidence Hub reads this as a preprint, done in cell cultures (in vitro), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

Read the paper

Related studies

Go deeper in Evidence Hub

Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.

Open this study Sign up free