scSLAM-seq reveals core features of transcription dynamics in single cells

Occasionally, these GEITP pages will discuss a technique that clearly will advance the field of gene-environment interactions. The technique to be discussed here is “single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labeling / sequencing” (scSLAM). This is <> — so bear with me. ☹

SLAM-seq involves briefly exposing cells to the nucleoside analog 4-thiouridine (4sU). 4sU is incorporated into newly transcribed RNA, during transcription, and is converted to a cytosine analog, using iodoacetamide (IAA) before RNA sequencing. Sequencing the reads, originating from the new RNA, can be identified within the pool of total RNA reads — on the basis of characteristic U- to-C conversions. Authors [see attached article] applied the SLAM-seq technique to resolve the onset of mouse cytomegalovirus (CMV) infection at the single-cell level. After optimization for single-cell sequencing (scSLAM-seq), authors performed scSLAM-seq on 107 single mouse fibroblast cells and, in parallel, analyzed global transcriptional changes of matched, larger (1.0 x 10–5) populations of cells, using (bulk) SLAM-seq. After quality-filtering for cells with >2,500 detectable genes, the remaining samples (49 CMV-infected, 45 uninfected cells) displayed all the characteristics of high-quality scSLAM-seq libraries, including U-to-C conversion rates of between 4% and 6%. Therefore, incorporation of 4sU is both efficient and uniform at the single-cell level.

Owing to rates of 4sU incorporation of about 1 in 50–200 nucleotides, as much as half of all SLAM-seq reads that originate from new RNA may not contain U-to-C conversions. To overcome this problem, authors developed “globally refined analysis of newly transcribed RNA and decay rates using SLAM-seq” (GRAND-SLAM) — a Bayesian method to compute the ratio of new-to-total RNA in a fully quantitative manner including credible intervals. The accuracy of quantification is further improved by analyzing long reads (150 nucleotides) in paired-end mode, which allows 4sU conversions to be reliably distinguished from sequencing errors within the overlapping sequences. Authors obtained accurate measurements (90% credible interval <0.2) for thousands of genes per cell, thereby approaching the overall sensitivity of scRNA-seq and achieving high correlation (r >0.73) with bulk SLAM-seq.

Therefore, this scSLAM-seq method both visualizes, and explains, differences in transcriptional activity at the single-cell level. Furthermore, it detects ‘on–off’ switches and transcriptional-burst kinetics, in host gene expression with extensive gene-specific differences that correlate with promoter-intrinsic features (TBP–TATA-box interactions and DNA methylation) (the TBP gene encodes a protein called the ‘TATA box-binding protein’). Thus, gene-specific, and not cell-specific, features — explain better the heterogeneity in transcriptomes — between individual cells and the transcriptional response to (e.g. environmental & endogenous signal) perturbations. 😊

Nature 18 July 2019; 571: 419-423

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