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Quantitative mapping of the cellular small RNA landscape with AQRNA-seq

Nature Biotechnology volume 39, pages 978–988 (2021)Cite this article

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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.

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Fig. 1: Overview of AQRNA-seq.
Fig. 2: Quantitative validation of AQRNA-seq.
Fig. 3: Alignment plots of M. bovis BCG tRNAs.
Fig. 4: Starvation-induced changes in tRNA abundance correlate with changes in codon-biased translation in M. bovis BCG.
Fig. 5: Application of AQRNA-seq for quantitative mapping of the tRNA epitranscriptome.
Fig. 6: AQRNA-seq analysis of miRNAs in HMEC cancer cells.

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Data availability

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.

Code availability

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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Acknowledgements

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.

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Author notes

  1. Jennifer F. Hu

    Present address: Bristol Myers Squibb, Seattle, WA, USA

  2. Daniel Yim

    Present address: A*STAR Genome Institute of Singapore, Singapore, Singapore

  3. Sabrina M. Huber

    Present address: Laboratory of Toxicology, ETH Zürich, Zürich, Switzerland

  4. Nick Davis

    Present address: Theon Therapeutics, Cambridge, MA, USA

Authors and Affiliations

  1. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jennifer F. Hu

  2. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Daniel Yim, Sabrina M. Huber, Nick Davis, Sidney Vermeulen, Michael S. DeMott, Stuart S. Levine, Peter C. Dedon & Bo Cao

  3. BioMicro Center, Massachusetts Institute of Technology, Cambridge, MA, USA

    Duanduan Ma & Stuart S. Levine

  4. Department of Microbiology & Cell Science, University of Florida, Gainesville, FL, USA

    Jo Marie Bacusmo & Valérie de Crécy-Lagard

  5. KK Research Center, KK Women’s and ChildrenBristol Myers Squibb’s Hospital, Singapore, Singapore

    Jieliang Zhou

  6. The RNA Institute and Department of Biology, University at Albany, Albany, NY, USA

    Thomas J. Begley

  7. Center for Environmental Health Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Michael S. DeMott, Stuart S. Levine & Peter C. Dedon

  8. Singapore-MIT Alliance for Research and Technology Antimicrobial Resistance IRG, Singapore, Singapore

    Peter C. Dedon & Bo Cao

  9. College of Life Sciences, Qufu Normal University, Qufu, China

    Bo Cao

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Contributions

P.C.D., B.C. and J.F.H. conceived of AQRNA-seq, designed the experiments and wrote the first draft of the manuscript. P.C.D., J.F.H., B.C. and D.Y. developed the method and performed the sequencing experiments. J.F.H., B.C., D.M. and S.S.L. developed, implemented and interpreted the data-processing workflows and computational analyses. D.Y., S.V. and J.F.H. performed mycobacterial culturing and RNA isolation. T.J.B. analyzed proteomics data for codon usage patterns. J.M.B. performed E. coli culturing and RNA isolation. N.D. performed proteomics analyses. S.M.H. optimized experimental conditions and characterized demethylation efficiency by LC–MS. S.M.H. and J.F.H. performed RNA blot analyses. M.S.D. contributed reagents and analyzed miRNA data. J.Z. analyzed miRNA data. V.C.-L. supervised E. coli experiments and contributed insights and analysis. All authors participated in the writing of the manuscript.

Corresponding authors

Correspondence to Peter C. Dedon or Bo Cao.

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Competing interests

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.

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Hu, J.F., Yim, D., Ma, D. et al. Quantitative mapping of the cellular small RNA landscape with AQRNA-seq. Nat Biotechnol 39, 978–988 (2021). https://doi.org/10.1038/s41587-021-00874-y

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