TimeLapse-seq: adding a temporal dimension to RNA sequencing through nucleoside recoding

Abstract

RNA sequencing (RNA-seq) offers a snapshot of cellular RNA populations, but not temporal information about the sequenced RNA. Here we report TimeLapse-seq, which uses oxidative-nucleophilic-aromatic substitution to convert 4-thiouridine into cytidine analogs, yielding apparent U-to-C mutations that mark new transcripts upon sequencing. TimeLapse-seq is a single-molecule approach that is adaptable to many applications and reveals RNA dynamics and induced differential expression concealed in traditional RNA-seq.

Fig.1

TimeLapse-seq uses a convertible nucleoside approach to identify new transcripts in a sequencing experiment. (a) Scheme of TimeLapse-seq. Metabolically labeled RNAs are isolated and treated with TimeLapse chemistry, converting s⁴U into a modified cytosine (C*) that is identified through increased numbers of T-to-C mutations upon sequencing (increasingly dark colors of red). s⁴U is transformed into a convertible nucleoside intermediate through oxidation, which is then converted to C* through aminolysis. (b)Results from a restriction enzyme digestion assay indicating efficient (∼80%) T-to-C* conversion with optimized TimeLapse chemistry

Fig.2

Global analysis of steady state and transient RNA dynamics using TimeLapse-Seq. (a) (left) Tracks depicting coverage from all reads (gray) for transcripts with slow (Ybx1), moderate (Dhx9) or fast (Fosl2) rates of turnover. (right) Tracks from reads with increasing numbers of T-to-C mutations (see scale) displaying mutational content provided by TimeLapse chemistry (right, y-axis zoom 3x). (b) Distribution of reads with each number of T-to-C mutations (points) overlaid on a model of the estimated distribution of reads from new transcripts (red) and pre-existing transcripts (gray) for Ybx1, Dhx9, and Fosl2. The estimated fraction of new reads is indicated for each plot. Light gray: 95%CI. (c) Distribution of T-to-C mutations found in reads mapping to Ybx1, Dhx9, and Fosl2, separated by total, exonic, or intronic reads. (d) TT-TimeLapse-seq and RNA-Seq tracks of DHX9. (e) Cumulative distribution plot of reads containing splice-junctions in RNA-seq, and TT-TimeLapse-seq. (f) Cumulative distribution plot of intron-only reads in RNA-seq and TT-TimeLapse-seq with the same scale as in e. (g) Using TimeLapse-seq to distinguish new RNAs after heat shock. Log2 fold changes after heat shock in total RNA-seq counts and new RNA counts for the top RNAs identified in b as significantly changed upon heat shock (p_adj < 0.01). (h) RNA-seq and TimeLapse-seq tracks of Hsph1 (top) and Hsp90aa1 (bottom) upon heat shock.

Fig. 3

TimeLapse-seq reveals differential transcript isoform stability of the ASXL1 transcript. (a) ASXL1 tracks from TimeLapse-seq (4h s⁴U treatment) with exon-containing regions expanded (lower panel). (b) Exonic T-to-C mutation distributions for ASXL1 in comparison with three transcripts with different stabilities, ACTB, CDK1, FOSL1.