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RNA timestamps identify the age of single molecules in RNA sequencing


Current approaches to single-cell RNA sequencing (RNA-seq) provide only limited information about the dynamics of gene expression. Here we present RNA timestamps, a method for inferring the age of individual RNAs in RNA-seq data by exploiting RNA editing. To introduce timestamps, we tag RNA with a reporter motif consisting of multiple MS2 binding sites that recruit the adenosine deaminase ADAR2 fused to an MS2 capsid protein. ADAR2 binding to tagged RNA causes A-to-I edits to accumulate over time, allowing the age of the RNA to be inferred with hour-scale accuracy. By combining observations of multiple timestamped RNAs driven by the same promoter, we can determine when the promoter was active. We demonstrate that the system can infer the presence and timing of multiple past transcriptional events. Finally, we apply the method to cluster single cells according to the timing of past transcriptional activity. RNA timestamps will allow the incorporation of temporal information into RNA-seq workflows.

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Fig. 1: Encoding of temporal information through RNA edits.
Fig. 2: Timestamped RNAs can reveal temporal transcription programs.
Fig. 3: Identification of temporally separated transcriptional events.
Fig. 4: Timestamps can reveal transcriptional programs in single cells.

Data availability

Raw data were used in all figures that are not described in the captions as schematics. The data sets generated and analyzed during the current study are available on the Zenodo Archive, record 3897464. Raw sequencing data are available at the Sequence Read Archive under PRJNA658989.

Code availability

The code used to produce analysis and figures for the current study is available on the Zenodo Archive, record 3897464.


  1. 1.

    Muhar, M., Ameres, S. L. & Zuber, J. SLAM-seq defines direct gene-regulatory functions of the BRD4–MYC axis. Science 2793, 1–10 (2018).

    Google Scholar 

  2. 2.

    Herzog, V. A. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat. Methods 14, 1198–1204 (2017).

  3. 3.

    Schofield, J. A., Duffy, E. E., Kiefer, L., Sullivan, M. C. & Simon, M. D. TimeLapse-seq: adding a temporal dimension to RNA sequencing through nucleoside recoding. Nat. Methods 15, 221–225 (2018).

  4. 4.

    Erhard, F. et al. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571, 419–423 (2019).

  5. 5.

    La Manno, G. et al. RNA velocity of single cells. Nature 560, 484–498 (2018).

  6. 6.

    Fukuda, M. et al. Construction of a guide-RNA for site-directed RNA mutagenesis utilising intracellular A-To-I RNA editing. Sci. Rep. 7, 41478 (2017).

  7. 7.

    Montiel-Gonzalez, M. F., Vallecillo-Viejo, I., Yudowski, G. A. & Rosenthal, J. J. C. Correction of mutations within the cystic fibrosis transmembrane conductance regulator by site-directed RNA editing. Proc. Natl Acad. Sci. USA 110, 18285–18290 (2013).

    CAS  Article  Google Scholar 

  8. 8.

    Montiel-González, M. F., Vallecillo-Viejo, I. C. & Rosenthal, J. J. C. An efficient system for selectively altering genetic information within mRNAs. Nucleic Acids Res. 44, e157 (2016).

  9. 9.

    Wettengel, J., Reautschnig, P., Geisler, S., Kahle, P. J. & Stafforst, T. Harnessing human ADAR2 for RNA repair - recoding a PINK1 mutation rescues mitophagy. Nucleic Acids Res. 45, 2797–2808 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Cox, D. B. T., Gootenberg, J. S., Abudayyeh, O. O., Franklin, B. & Kellner, M. J. RNA editing with CRISPR–Cas13. Science 358, 1019–1027 (2017).

    CAS  Article  Google Scholar 

  11. 11.

    Matthews, M. M. et al. Structures of human ADAR2 bound to dsRNA reveal base-flipping mechanism and basis for site selectivity. Nat. Struct. Mol. Biol. 23, 426–433 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Kuttan, A. & Bass, B. L. Mechanistic insights into editing-site specificity of ADARs. Proc. Natl Acad. Sci. USA 109, 3295–3304 (2012).

    Article  Google Scholar 

  13. 13.

    Eifler, T., Pokharel, S. & Beal, P. A. RNA-seq analysis identifies a novel set of editing substrates for human ADAR2 present in Saccharomyces cerevisiae. Biochemistry 52, 7857–7869 (2013).

    CAS  Article  Google Scholar 

  14. 14.

    Bertrand, E. et al. Localization of ASH1 mRNA particles in living yeast. Mol. Cell 2, 437–445 (1998).

    CAS  Article  Google Scholar 

  15. 15.

    Piatkevich, K. D. et al. A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters. Nat. Chem. Biol. 14, 352–360 (2018).

  16. 16.

    Perry, R. P. & Kelley, D. E. Inhibition of RNA synthesis by actinomycin D: characteristic dose-response of different RNA species. J. Cell. Physiol. 76, 127–139 (1970).

    CAS  Article  Google Scholar 

  17. 17.

    Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    CAS  Article  Google Scholar 

  18. 18.

    Schwanhüusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    Article  Google Scholar 

  19. 19.

    Wang, X., Chen, X. & Yang, Y. Spatiotemporal control of gene expression by a light-switchable transgene system. Nat. Methods 9, 266–271 (2012).

    CAS  Article  Google Scholar 

  20. 20.

    Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    CAS  Article  Google Scholar 

  21. 21.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Perli, S. D. et al. Continuous genetic recording with self-targeting CRISPR–Cas in human cells. Science 353, 339–342 (2016).

    Article  Google Scholar 

  23. 23.

    Farzadfard, F. et al. Single-Nucleotide-Resolution Computing and Memory in Living Cells. Mol. Cell 75, 769–780.e4 (2019).

    CAS  Article  Google Scholar 

  24. 24.

    Kalhor, R. et al. Rapidly evolving homing CRISPR barcodes. Nat. Methods 14, 195–200 (2017).

    CAS  Article  Google Scholar 

  25. 25.

    Sheth, R. U., Yim, S. S., Wu, F. L. & Wang, H. H. Multiplex recording of cellular events over time on CRISPR biological tape. Science 358, 1457–1461 (2017).

  26. 26.

    Tang, W. & Liu, D. R. Rewritable multi-event analog recording in bacterial and mammalian cells. Science 360, eaap8992 (2018).

  27. 27.

    Chen, H. et al. Efficient, continuous mutagenesis in human cells using a pseudo-random DNA editor. Nat. Biotechnol. 38, 165–168 (2020).

    CAS  Article  Google Scholar 

  28. 28.

    Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2016).

    Article  Google Scholar 

  29. 29.

    Shipman, S. L. et al. Molecular recordings by directed CRISPR spacer acquisition. Science 353, aaf1175 (2016).

  30. 30.

    Zamft, B. M. et al. Measuring cation dependent DNA polymerase fidelity landscapes by deep sequencing. PLoS ONE 7, e43876 (2012).

  31. 31.

    Schmidt, F., Cherepkova, M. Y. & Platt, R. J. Transcriptional recording by CRISPR spacer acquisition from RNA. Nature 562, 380–385 (2018).

    CAS  Article  Google Scholar 

  32. 32.

    Farzadfard, F. & Lu, T. K. Genomically encoded analog memory with precise in vivo dna writing in living cell populations. Science 346, 1256272 (2014).

  33. 33.

    Tay, S. et al. Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing. Nature 466, 267–271 (2010).

    CAS  Article  Google Scholar 

  34. 34.

    Nandagopal, N. et al. Dynamic ligand discrimination in the notch signaling pathway. Cell 172, 869–880 (2018).

    CAS  Article  Google Scholar 

  35. 35.

    Rivera, V. M. et al. A humanized system for pharmacologic control of gene expression. Nat. Med. 2, 1028–1032 (1996).

    CAS  Article  Google Scholar 

  36. 36.

    Erhart, D. et al. Chemical development of intracellular protein heterodimerizers. Chem. Biol. 20, 549–557 (2013).

    CAS  Article  Google Scholar 

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We acknowledge N. Jakimo, A. T. Wassie, J. Gootenberg and O. Abuddayeh for helpful discussions. Plasmids containing ADAR2 mutants were generously provided by J. Gootenberg and O. Abuddayeh. Neuron culture was supplied by D. Park. We acknowledge Y. Lin and X. Sun for help with neuron induction experiments. Plasmids deposited on Addgene. F.C. acknowledges funding from 1DP5OD024583, the National Institutes of Health (NIH) Directorʼs Early Independence Award, the Paul G. Allen Frontiers Group, the Burroughs Wellcome Fund and the Schmidt Fellows Program at the Broad Institute. E.S.B. acknowledges funding by John Doerr, the Open Philanthropy Project, NIH 1R01MH114031, the HHMI-Simons Faculty Scholars Program, the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant numbers W911NF1510548, NIH 1RM1HG008525, NIH UF1NS107697, NIH 2R01DA029639, NIH 1R01MH103910, UF1NS107697, NIH Director’s Pioneer Award 1DP1NS087724 and the MIT Media Lab. E.S.B. also acknowledges L. Yang as a supporter of his lab. S.G.R. acknowledges funding through the Myhrvold and Havranek Family Charitable Fund Hertz Graduate Fellowship and the National Science Foundation Graduate Research Fellowship Program (award no. 1122374). J.S. acknowledges funding through the Hertz Graduate Fellowship. S.L. acknowledges funding through the Molecular Biophysics Training Grant, NIH/NIGMS T32 GM008313. E.D.Z. acknowledges funding through the National Science Foundation Graduate Research Fellowship Program (award no. 1122374) and through the Computational and Systems Biology training grant, T32 GM087237.

Author information




S.G.R., F.C. and E.S.B. conceived strategies for the design of the RNA timestamps. S.G.R., L.M.C. and J.S. conceived of and implemented the design of the reporter RNAs. S.G.R., L.M.C., F.C. and S.L. validated and characterized the timestamp system in cells. S.G.R. and E.D.Z. conceived of and implemented the gradient descent model. S.G.R. and F.C. analyzed the data. S.G.R., F.C. and E.S.B. wrote the manuscript.

Corresponding authors

Correspondence to Edward S. Boyden or Fei Chen.

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Supplementary Figs. 1–9 and Tables 1–3

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Rodriques, S.G., Chen, L.M., Liu, S. et al. RNA timestamps identify the age of single molecules in RNA sequencing. Nat Biotechnol 39, 320–325 (2021).

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