Quantitative mapping of the cellular small RNA landscape with AQRNA-seq
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nihms-1677898.pdf
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Accepted version
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2.56 MB
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Author(s) • • • • • • • • •
Hu, Jennifer F
Yim, Daniel
Ma, Duanduan
Huber, Sabrina M
Davis, Nick
Bacusmo, Jo Marie
Vermeulen, Sidney
Zhou, Jieliang
Begley, Thomas J
DeMott, Michael S
Date Issued
2021
Journal
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Citation
Hu, Jennifer F, Yim, Daniel, Ma, Duanduan, Huber, Sabrina M, Davis, Nick et al. 2021. "Quantitative mapping of the cellular small RNA landscape with AQRNA-seq." Nature Biotechnology, 39 (8).
Version
Author's final manuscript
Abstract
Current next-generation RNA-sequencing (RNA-seq) methods do not provide accurate quantification of small RNAs within a sample, due to sequence-dependent biases in capture, ligation and amplification during library preparation. We present a method, absolute quantification RNA-sequencing (AQRNA-seq), that minimizes biases and provides a direct, linear correlation between sequencing read count and copy number for all small RNAs in a sample. Library preparation and data processing were optimized and validated using a 963-member microRNA reference library, oligonucleotide standards of varying length, and RNA blots. Application of AQRNA-seq to a panel of human cancer cells revealed >800 detectable miRNAs that varied during cancer progression, while application to bacterial transfer RNA pools, with the challenges of secondary structure and abundant modifications, revealed 80-fold variation in tRNA isoacceptor levels, stress-induced site-specific tRNA fragmentation, quantitative modification maps, and evidence for stress-induced, tRNA-driven, codon-biased translation. AQRNA-seq thus provides a versatile means to quantitatively map the small RNA landscape in cells.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/S41587-021-00874-Y