RNA timestamps identify the age of single molecules in RNA sequencing
Name
nihms-1647079.pdf
Description
Accepted version
Size
1.3 MB
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Checksum (MD5)
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Author(s)
Rodriques, Samuel G.
Chen, Linlin M.
Liu, Sophia
Zhong, Ellen D.
Scherrer, Joseph R.
Boyden, Edward S.
Chen, Fei
Date Issued
October 2020
Journal
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Citation
Rodriques, Samuel G, Chen, Linlin M, Liu, Sophia, Zhong, Ellen D, Scherrer, Joseph R et al. 2021. "RNA timestamps identify the age of single molecules in RNA sequencing." Nature Biotechnology, 39 (3).
Version
Author's final manuscript
Abstract
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. 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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Koch Institute for Integrative Cancer Research at MIT
Howard Hughes Medical Institute
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1038/s41587-020-0704-z
