RNA timestamps identify the age of single molecules in RNA sequencing thumbnail

RNA timestamps identify the age of single molecules in RNA sequencing

Abstract

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.

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.

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Acknowledgements

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

Author notes

  1. These authors contributed equally: Samuel G. Rodriques, Linlin M. Chen.

  2. These authors jointly directed this work: Edward S. Boyden, Fei Chen.

Affiliations

  1. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Samuel G. Rodriques & Joseph R. Scherrer

  2. Department of Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Samuel G. Rodriques & Edward S. Boyden

  3. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Samuel G. Rodriques, Linlin M. Chen & Fei Chen

  4. Biophysics Program, Harvard University, Boston, MA, USA

    Sophia Liu

  5. Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sophia Liu

  6. Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ellen D. Zhong

  7. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Edward S. Boyden

  8. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Edward S. Boyden

  9. MIT McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    Edward S. Boyden

  10. Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    Edward S. Boyden

  11. Howard Hughes Medical Institute, Cambridge, MA, USA

    Edward S. Boyden

  12. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Fei Chen

Contributions

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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Competing interests

All authors are listed as inventors on a patent application for this technology.

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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 (2020). https://doi.org/10.1038/s41587-020-0704-z

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