Improving the study of RNA dynamics through advances in RNA-seq with metabolic labeling and nucleotide-recoding chemistry.

RNA metabolic labeling using 4-thiouridine (s U) captures the dynamics of RNA synthesis and decay. The power of this approach is dependent on appropriate quantification of labeled and unlabeled sequencing reads, which can be compromised by the apparent loss of s U-labeled reads in a process we refer to as dropout. Here we show that s U-containing transcripts can be selectively lost when RNA samples are handled under sub-optimal conditions, but that this loss can be minimized using an optimized protocol. We demonstrate a second cause of dropout in nucleotide recoding and RNA sequencing (NR-seq) experiments that is computational and downstream of library preparation. NR-seq experiments involve chemically converting s U from a uridine analog to a cytidine analog and using the apparent T-to-C mutations to identify the populations of newly synthesized RNA. We show that high levels of T-to-C mutations can prevent read alignment with some computational pipelines, but that this bias can be overcome using improved alignment pipelines. Importantly, kinetic parameter estimates are affected by dropout independent of the NR chemistry employed, and all chemistries are practically indistinguishable in bulk, short-read RNA-seq experiments. Dropout is an avoidable problem that can be identified by including unlabeled controls, and mitigated through improved sample handing and read alignment that together improve the robustness and reproducibility of NR-seq experiments.