Journal
NATURE BIOTECHNOLOGY
Volume 39, Issue 3, Pages 320-325Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41587-020-0704-z
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Categories
Funding
- Paul G. Allen Frontiers Group
- Burroughs Wellcome Fund
- Schmidt Fellows Program at the Broad Institute
- NIH [1R01MH114031]
- HHMI-Simons Faculty Scholars Program
- U.S. Army Research Office [W911NF1510548, NIH 1RM1HG008525, NIH UF1NS107697, NIH 2R01DA029639, NIH 1R01MH103910, UF1NS107697]
- NIH Director's Pioneer Award [1DP1NS087724]
- MIT Media Lab
- Myhrvold and Havranek Family Charitable Fund Hertz Graduate Fellowship
- National Science Foundation Graduate Research Fellowship Program [1122374]
- Hertz Graduate Fellowship
- Molecular Biophysics Training Grant [NIH/NIGMS T32 GM008313]
- Computational and Systems Biology training grant [T32 GM087237]
- Open Philanthropy Project
- U.S. Army Research Laboratory
- National Institutes of Health (NIH) Director's Early Independence Award
- [1DP5OD024583]
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The RNA timestamps method infers the age of individual RNAs in RNA-seq data using RNA editing, allowing determination of when the promoter was active and clustering single cells based on past transcriptional activity timing.
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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