Clocking In: Timestamped RNA Charts Transcriptional History

Single-cell RNA sequencing—a methodology very much in vogue these days—captures a snapshot of gene expression; alas, it misses all of the temporal dynamics of how long ago the different genes became activated. After all, scientists have to kill a cell to measure its ribonucleotides, so they can’t watch RNA being made in real time. In the October 19 Nature Biotechnology, scientists led by Edward Boyden at Massachusetts Institute of Technology and Fei Chen at the Broad Institute of Harvard and MIT, both in Cambridge, Massachusetts, debut a technique that allows researchers to “timestamp” RNA. They modified a reporter RNA to recruit editing machinery that gradually altered the RNA over time. In this way, the transcript acted as a clock. By analyzing the extent of the edits made to a given transcript, the authors were able to estimate to within an hour when that transcript was created.

  • The number of edits to a reporter transcript reflects how long ago it was made.
  • This timestamped RNA pinpoints transcription to within an hour.
  • The technique can record multiple transcriptional events within a cell.

The experiments took place in cell culture. Still, the authors believe the technique could be adapted for use in vivo by delivering the reporter gene with a virus. “We’re particularly interested in using timestamps to order the expression of immediate early genes between different neuronal populations. Other applications include tracking expression dynamics during differentiation or activation responses,” Chen wrote to Alzforum.

Florent Ginhoux at the Agency for Science, Technology and Research in Singapore, called the technology “mind-blowing.” “It will answer a lot of questions and open new directions [for research],” he wrote to Alzforum (full comment below). For example, adding a timestamp to cultured neurons or microglia could enable researchers to track the cells’ response to specific stimuli, he suggested.

Time Recorder. A tag added to messenger RNA includes three MS2 binding sites (keyhole shapes) amid adenosine residues (blue dots). The MS2 domains recruit a modified ADAR enzyme (yellow and blue), which gradually converts adenosines to inosines (red). [Courtesy of Rodriques et al., Nature Biotechnology.]

Previously, researchers attempting to chronicle the temporal aspect of transcription added a metabolic label to newly synthesized RNA (Herzog et al., 2017; Schofield et al., 2018; Erhard et al., 2019). However, this method records but a single moment in time; it cannot capture multiple transcriptional pulses.

To develop a better transcription timer, joint first authors Samuel Rodriques and Linlin Chen designed an RNA tag that situates three MS2 domains among multiple adenosine residues. MS2 domains bind a bioengineered version of the enzyme adenosine deaminase acting on RNA 2 catalytic domain (ADAR2cd). And therein lies the trick: ADAR2cd gradually changes adenosines to inosines over the course of hours. In other words, an RNA tagged with this sequence attracts ADAR2cd, and the percentage of inosines in the RNA’s timestamp allows researchers to calculate how long ago it was made.

Next, the authors attached this RNA tag to the 3' end of a fluorescent reporter gene under the control of a tetracycline-sensitive promoter. They transfected cultured HEK293T cells with this construct, and added doxycycline to the culture medium for one hour to switch on the gene. Then they inhibited transcription and waited a variable number of hours before lysing the cells and analyzing the timestamped transcripts. The inosine-to-adenosine ratio dated the transcripts to within about an hour of when they were made. The timestamp data was 86 percent accurate in time-sequencing 72 cells according to when their reporter gene had been switched on.

The technology can also record multiple separate transcription events. The authors transfected cells with two different timestamped reporter genes under the control of distinct promoters, inducing one after one hour and the other after five hours. By analyzing at least 200 transcripts, they were able to distinguish the two transcriptional events nine out of 10 times. Importantly, the timestamp technology worked in concert with single-cell RNA sequencing, pinpointing when the reporter gene was induced in each cell with 75 percent accuracy.

“RNA timestamps are, thus, the first technology that can record high-dimensional temporal information from individual cells on timescales relevant for understanding complex cellular transcriptional activity,” the authors noted. They believe the technology easily could be scaled up to time transcription from multiple different promoters.—Madolyn Bowman Rogers

Comments

  1. Mindblowing technology! It will answer lot of questions and open new directions. At this stage this is still an “in vitro” approach, hence restricted to cell culture. However, it could be used in iPSC cell lines that researchers can direct to become neurons or microglia, the brain-resident macrophages that more and more researchers are using to model AD.

    Such “time stamped” iPSC-derived microglia or neurons could be used to understand very precisely in a time-specific manner how microglia and/or neurons respond to any relevant stimulation.

    View all comments by Florent Ginhoux

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References

Paper Citations

  1. Herzog VA, Reichholf B, Neumann T, Rescheneder P, Bhat P, Burkard TR, Wlotzka W, von Haeseler A, Zuber J, Ameres SL. Thiol-linked alkylation of RNA to assess expression dynamics. Nat Methods. 2017 Dec;14(12):1198-1204. Epub 2017 Sep 25 PubMed.
  2. Schofield JA, Duffy EE, Kiefer L, Sullivan MC, Simon MD. TimeLapse-seq: adding a temporal dimension to RNA sequencing through nucleoside recoding. Nat Methods. 2018 Mar;15(3):221-225. Epub 2018 Jan 22 PubMed.
  3. Erhard F, Baptista MA, Krammer T, Hennig T, Lange M, Arampatzi P, Jürges CS, Theis FJ, Saliba AE, Dölken L. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature. 2019 Jul;571(7765):419-423. Epub 2019 Jul 10 PubMed.

Further Reading

Primary Papers

  1. Rodriques SG, Chen LM, Liu S, Zhong ED, Scherrer JR, Boyden ES, Chen F. RNA timestamps identify the age of single molecules in RNA sequencing. Nat Biotechnol. 2020 Oct 19; PubMed.

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