Gaining insight into transcriptome-wide RNA population dynamics through the chemistry of 4-thiouridine

Abstract

Cellular RNA levels are the result of a juggling act between RNA transcription, processing, and degradation. By tuning one or more of these parameters, cells can rapidly alter the available pool of transcripts in response to stimuli. While RNA sequencing (RNA-seq) is a vital method to quantify RNA levels genome-wide, it is unable to capture the dynamics of different RNA populations at steady-state or distinguish between different mechanisms that induce changes to the steady-state (i.e., altered rate of transcription vs. degradation). The dynamics of different RNA populations can be studied by targeted incorporation of noncanonical nucleosides. 4-Thiouridine (s⁴ U) is a commonly used and versatile RNA metabolic label that allows the study of many properties of RNA metabolism from synthesis to degradation. Numerous experimental strategies have been developed that leverage the power of s⁴ U to label newly transcribed RNA in whole cells, followed by enrichment with activated disulfides or chemistry to induce C mutations at sites of s⁴ U during sequencing. This review presents existing methods to study RNA population dynamics genome-wide using s⁴ U metabolic labeling, as well as a discussion of considerations and challenges when designing s⁴ U metabolic labeling experiments. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Turnover and Surveillance > Regulation of RNA Stability.

Keywords: RNA metabolism; RNA turnover; nucleoside recoding; transcriptome dynamics; transient transcription.

Figure 1:

(A) Overview of s⁴U metabolic labeling experiments. Different options are indicated for each module, along with references for representative examples. In the case where two references are listed, the first applies to yeast and the second to mammalian cells, as the time of s⁴U labeling can vary widely between organisms. (B) Representative examples of two s⁴U metabolic labeling experiments that leverage different module options: 4sUDRB-seq to measure RNAPII elongation and TT-TimeLapse-seq to detect unstable RNAs.

Figure 2:

(A) Estimated range of RNA half-lives in mammalian cells based on data from (Schofield et al., 2018; Schwalb et al., 2016; Yang et al., 2003). (B) Simulation of RNA decay rates for a s⁴U pulse-chase experiment for three different RNA half-lives, represented as a fraction of the total amount of s⁴U-labeled RNA at the start of the chase. (C) Simulation of s⁴U approach-to-equilibrium kinetics for three different RNA half-lives. (D) Kinetics of s⁴U incorporation during 5 min of s⁴U labeling for a transient RNA (t_1/2 = 5 min) and two mRNAs (t_1/2 = 0.5h and 4h).

Figure 3:

Significance of inefficient chemistry toward length bias in s⁴U metabolic labeling experiments. (A) Chemistry of activated disulfide reactivity with s⁴U. When HPDP-biotin is used as the activated disulfide, any biotin-s⁴U (bio-s⁴U) product results in a more activated disulfide due to the electron-poor pyrimidine ring of s⁴U, therefore favoring the reverse rather than the forward biotinylation reaction. In contrast, MTS-biotin reacts irreversibly with s⁴U to form the bio-s⁴U product under the reaction conditions, and the covalent disulfide bond can only be reversed under reducing conditions. (B) Schematic of s⁴U-RNA enrichment with HPDP- and MTS-biotin. Efficient activated disulfide reactivity of MTS-biotin results in greater yields of s⁴U-RNA and alleviates potential biases toward longer RNAs with more uridines.

Figure 4:

Methods to normalize s⁴U-RNA data after high-throughput sequencing. (A) After cells are metabolically labeled with s⁴U and total RNA is extracted, exogenous s⁴U-RNA (s⁴U-labeled RNA from S. pombe, or in vitro-transcribed s⁴U-RNAs) are added to each sample and then enriched. Alternatively or in addition, exogenous RNA without s⁴U can also be added to samples after enrichment. Samples are then analyzed by high-throughput sequencing and normalized based on the number of reads that align to the spike-in sample. (B) Samples can also be enriched without RNA spike-ins and normalized based on the relative proportion of introns in each sample (Lugowski et al., 2018). First, intron coverage for each transcript is determined based on the number of reads that map to introns in the longest isoform of a given transcript without overlapping exons or any other isoform. Next, introns are filtered for coverage and time-dependent changes in expression. Reads that map to exons are finally normalized to the sum of all reads mapping to the well-behaved intron set.

Figure 5:

(A) Hydrogen bonding pattern of modified or recoded s⁴U nucleosides following enrichment-free chemistry. (B) Example TimeLapse-seq tracks adapted from Schofield et al. 2018 for faster turnover (Ier3) and slower turnover (Srsf3) transcripts in MEF cells treated with 1 mM s⁴U for 1h.