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.
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All sequencing and proteomics data that support the findings of this study have been variously deposited in public databases: RNA-seq studies reported in Figs. 3–5, Supplementary Figs. 2, 4 and 8 and the proteomics studies reported in Fig. 4 and Supplementary Fig. 6 have been deposited in the NCBI Sequence Read Archive under BioProject ID PRJNA579244; miRNA and standards data shown in Fig. 2 have been deposited in the Gene Expression Omnibus (GEO) under accession no. GSE139936; and data for miRNA studies in HMEC cells shown in Fig. 6 have been deposited in GEO as accession no. GSE159434.
The software used in the studies presented here is publicly available as follows. Blast v.2.6.0 (nucleotide BLAST) available at https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download. Peakfit.m v.9.0 available at Tom O’Haver, MATLAB Central File Exchange: https://terpconnect.umd.edu/~toh/spectrum/. fgrep (Linux command) available at https://unix.stackexchange.com/questions/17949/what-is-the-difference-between-grep-egrep-and-fgrep. fastxtoolkit v.0.013 available at http://hannonlab.cshl.edu/fastx_toolkit/. Custom python scripts are available at GitHub https://github.com/dedonlab/ (https://github.com/dedonlab/aqrnaseq for prokaryotic process scripts and https://github.com/dedonlab/general_aqrnaseq for eukaryotic/general pipeline scripts).
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We thank members of D. Bartel’s laboratory (Whitehead Institute and MIT Department of Biology), especially A. Stefano and D. Bartel, for assistance with RNA blots. We thank P. Ivanov (Harvard Medical School) for sharing synthetic RNA standards. This work was supported by grants from the National Natural Science Foundation of China (no. 32070629 to B.C.), the US National Science Foundation (no. MCB-1412379 to V.C.-L.), the National Institute of Environmental Health Sciences (no. ES002109) and the National Research Foundation of Singapore through the Singapore-MIT Alliance for Research and Technology Antimicrobial Resistance IRG (P.C.D.). J.F.H. was supported by MIT Toxicology Training grant no. T32-ES007020, D.Y. by a postdoctoral fellowship from A*STAR, Singapore and S.M.H. by a postdoctoral fellowship from the Swiss National Science Foundation.
B.C., J.F.H., D.Y., S.M.H., M.S.D. and P.C.D. are co-inventors on two patents (PCT/US2019/013714, US 2019/0284624 A1) relating to the published work.
Peer review information Nature Biotechnology thanks James Hadfield and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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