A Protocol for Transcriptome-Wide Inference of RNA Metabolic Rates in Mouse Embryonic Stem Cells

Abstract

The relative ease of mouse Embryonic Stem Cells (mESCs) culture and the potential of these cells to differentiate into any of the three primary germ layers: ectoderm, endoderm and mesoderm (pluripotency), makes them an ideal and frequently used ex vivo system to dissect how gene expression changes impact cell state and differentiation. These efforts are further supported by the large number of constitutive and inducible mESC mutants established with the aim of assessing the contributions of different pathways and genes to cell homeostasis and gene regulation. Gene product abundance is controlled by the modulation of the rates of RNA synthesis, processing, and degradation. The ability to determine the relative contribution of these different RNA metabolic rates to gene expression control using standard RNA-sequencing approaches, which only capture steady state abundance of transcripts, is limited. In contrast, metabolic labeling of RNA with 4-thiouridine (4sU) coupled with RNAsequencing, allows simultaneous and reproducible inference of transcriptome wide synthesis, processing, and degradation rates. Here we describe, a detailed protocol for 4sU metabolic labeling in mESCs that requires short 4sU labeling times at low concentration and minimally impacts cellular homeostasis. This approach presents a versatile method for in-depth characterization of the gene regulatory strategies governing gene steady state abundance in mESC.

Translation is required for miRNA‐dependent decay of endogenous transcripts

“…Of the three processes (transcription, processing and degradation) that determine steady‐state transcript abundance, only the rate of degradation is directly influenced by miRNAs. To determine transcriptome‐wide differences in degradation rate between miRNA‐depleted and wild‐type mESCs, we performed, in duplicate, 4‐thio‐uridine (4sU, 200 µM) metabolic labelling of RNA for 10 and 15 min, as previously described (Biasini & Marques, 2020) 8 days after induction of DICER loss of function, as well as in uninduced control mESCs. We sequenced pre‐existing and newly synthesized RNA and quantified intron and exon expression transcriptome‐wide in both RNA fractions (Fig 2A, Methods).…”

Dynamics of transcriptional and post-transcriptional regulation

“…Multiple approaches are available that profile nascent transcription [ 26 ] by relying on either RNA metabolic labeling or the isolation of chromatin associated RNAs or of transcripts associated to transcriptionally active polymerase complexes ( Figure 3A ). Among these approaches, those employing RNA metabolic labeling have proved to be particularly successful [ 27 ]. Indeed, the enrichment of chromatin-associated RNAs also captures stably associated pre-existing transcripts.…”

“…In fact, it leads to the contamination of the labeled fraction with unlabeled transcripts (up to 30% for short labeling pulses, [ 3 , 35 ]), and it requires sequencing of both labeled and unlabeled fractions thus increasing costs, especially when time course experiments are performed. Moreover, given the small amount of nascent RNA within the total population of transcripts (especially when short labeling times are used), substantial RNA has to be obtained to retrieve enough nascent RNA for its subsequent sequencing [ 27 ].…”

Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic labeling experiment

“…After addition of 4sU to the growth medium, cells were incubated at 37C for 10 minutes (10 minutes labeling pulse). RNA was then extracted and processed according to the protocol described in [4]. Reads that did not map to mouse ribosomal RNA sequences were aligned to intronic and exonic sequences using STAR V2.5 and quantified using RSEM V1.1.17, yielding intron and exon expression levels for unlabeled and labeled RNA.…”

“…For comparison, [13] reports correlations around 70% by using the same data, but changing only the method of analysis. Using three replicates, [4] reports a 26% correlation using the INSPEcT package.…”