B-T.40: Super-Resolution Modeling of 4sU Labeling Data Enhances Temporal Resolution
Author
Keywords
metabolic labeling, gene regulation, transcriptomics
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Metabolic RNA labeling has enabled the study of transcriptional regulation and RNA dynamics at scale, leveraging nucleotide analog incorporation into nascent transcripts to quantify newly synthesized and pre-existing RNA fractions. Current analysis frameworks account for the stochastic nature of incorporation but assume constant incorporation rates over labeling intervals. However, a growing body of studies suggests that incorporation rates are not constant but increase monotonically over the labeling interval.
Our improved GRAND3 framework does not only address this issue by modeling nucleotide incorporation kinetics as a function of labeling time. It exploits the increasing frequency of 4sU induced nucleotide substitutions to achieve temporal super-resolution differentiating transcriptional events that occur early or late during the labeling time in single cells.
We used GRAND3 to analyze scSLAM-seq data of human fibroblasts infected with human cytomegalovirus (HCMV) sampled in 2-hour-intervals over the first 8 hours of infection. The model estimates precisely captured the highly coordinated viral transcriptional program during early infection and revealed previously unrecognized temporal coordination in the host defense response. Single-cell trajectories further revealed that temporal heterogeneity in this response is associated with divergent infection outcomes.
Enhancing the temporal resolution of metabolic labeling data computationally without the need to modify existing protocols, establishes GRAND3 as a broadly applicable tool to study RNA dynamics and transcriptional regulation.
Co-authors: Eva Maria Borst, Tobias Krammer, Antoine-Emmanuel Saliba, Bhupesh K Prusty, Martin Messerle, Lars Dölken, Florian Erhard
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