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Recording the age of RNA with deamination

View ORCID ProfileSamuel G. Rodriques, Linlin M. Chen, Sophia Liu, Ellen D. Zhong, Joseph R. Scherrer, View ORCID ProfileEdward S. Boyden, Fei Chen
doi: https://doi.org/10.1101/2020.02.08.939983
Samuel G. Rodriques
1Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139.
2MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139.
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  • ORCID record for Samuel G. Rodriques
Linlin M. Chen
3Broad Institute of Harvard and MIT, Cambridge, MA, 02142.
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Sophia Liu
4Biophysics Program, Harvard University, Boston, MA 02115.
5Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139.
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Ellen D. Zhong
6Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139.
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Joseph R. Scherrer
1Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139.
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Edward S. Boyden
2MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139.
7Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
8Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
9MIT McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
10Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
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  • For correspondence: chenf@broadinstitute.org esb@media.mit.edu
Fei Chen
3Broad Institute of Harvard and MIT, Cambridge, MA, 02142.
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  • For correspondence: chenf@broadinstitute.org esb@media.mit.edu
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Abstract

Transcriptional programs implemented by cells often consist of complex temporal features, but current approaches to single-cell RNA sequencing only provide limited information about the dynamics of gene expression. Here, we present RNA timestamps, a method for inferring the age of individual RNAs in a sequencing-based readout by leveraging RNA editing. Timestamped RNAs include a RNA reporter motif that accumulates A to I edits 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 are able to infer when in the past the promoter was active. We demonstrate that the system can infer the presence and timing of multiple past transcriptional events, with no prior knowledge. Finally, we apply this method to cluster single cells according to the times at which a particular transcriptional program was activated. RNA timestamps thus suggest a new approach for incorporating temporal information into RNA sequencing workflows.

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Posted February 10, 2020.
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Recording the age of RNA with deamination
Samuel G. Rodriques, Linlin M. Chen, Sophia Liu, Ellen D. Zhong, Joseph R. Scherrer, Edward S. Boyden, Fei Chen
bioRxiv 2020.02.08.939983; doi: https://doi.org/10.1101/2020.02.08.939983
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Recording the age of RNA with deamination
Samuel G. Rodriques, Linlin M. Chen, Sophia Liu, Ellen D. Zhong, Joseph R. Scherrer, Edward S. Boyden, Fei Chen
bioRxiv 2020.02.08.939983; doi: https://doi.org/10.1101/2020.02.08.939983

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