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Small-seq for single-cell small-RNA sequencing

Nature Protocols volume 13, pages 2407–2424 (2018)Cite this article

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Abstract

Small RNAs participate in several cellular processes, including splicing, RNA modification, mRNA degradation, and translational arrest. Traditional methods for sequencing small RNAs require a large amount of cell material, limiting the possibilities for single-cell analyses. We describe Small-seq, a ligation-based method that enables the capture, sequencing, and molecular counting of small RNAs from individual mammalian cells. Here, we provide a detailed protocol for this approach that relies on standard reagents and instruments. The standard protocol captures a complex set of small RNAs, including microRNAs (miRNAs), fragments of tRNAs and small nucleolar RNAs (snoRNAs); however, miRNAs can be enriched through the addition of a size-selection step. Ready-to-sequence libraries can be generated in 2–3 d, starting from cell collection, with additional days needed to computationally map the sequence reads and calculate molecular counts.

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Fig. 1: Outline of the Small-seq library preparation protocol.
Fig. 2: Size selection by implementation of the crush and soak method.
Fig. 3: Expected results after Bioanalyzer profiling of the library pools.

References

  1. Morris, K. V. & Mattick, J. S. The rise of regulatory RNA. Nat. Rev. Genet. 15, 423–437 (2014).

    Article  CAS  Google Scholar 

  2. Vidigal, J. A. & Ventura, A. The biological functions of miRNAs: lessons from in vivo studies. Trends Cell Biol. 25, 137–147 (2015).

    Article  CAS  Google Scholar 

  3. Guo, H., Ingolia, N. T., Weissman, J. S. & Bartel, D. P. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466, 835–840 (2010).

    Article  CAS  Google Scholar 

  4. Wang, J., Chen, J. & Sen, S. MicroRNA as biomarkers and diagnostics. J. Cell. Physiol. 231, 25–30 (2016).

    Article  CAS  Google Scholar 

  5. Kirchner, S. & Ignatova, Z. Emerging roles of tRNA in adaptive translation, signalling dynamics and disease. Nat. Rev. Genet. 16, 98–112 (2014).

    Article  Google Scholar 

  6. Matera, A. G., Terns, R. M. & Terns, M. P. Non-coding RNAs: lessons from the small nuclear and small nucleolar RNAs. Nat. Rev. Mol. Cell Biol. 8, 209–220 (2007).

    Article  CAS  Google Scholar 

  7. Schimmel, P. The emerging complexity of the tRNA world: mammalian tRNAs beyond protein synthesis. Nat. Rev. Mol. Cell Biol. 19, 45–58 (2017).

    Article  Google Scholar 

  8. Dupuis-Sandoval, F., Poirier, M. & Scott, M. S. The emerging landscape of small nucleolar RNAs in cell biology. Wiley Interdiscip. Rev. RNA 6, 381–397 (2015).

    Article  CAS  Google Scholar 

  9. Tang, F. et al. 220-plex microRNA expression profile of a single cell. Nat. Protoc. 1, 1154–1159 (2006).

    Article  CAS  Google Scholar 

  10. White, A. K. et al. High-throughput microfluidic single-cell RT-qPCR. Proc. Natl. Acad. Sci. USA 108, 13999–14004 (2011).

    Article  CAS  Google Scholar 

  11. Faridani, O. R. et al. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34, 1264–1266 (2016).

    Article  CAS  Google Scholar 

  12. Landgraf, P. et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401–1414 (2007).

    Article  CAS  Google Scholar 

  13. Hafner, M. et al. RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries. RNA 17, 1697–1712 (2011).

    Article  CAS  Google Scholar 

  14. Munafó, D. B. & Robb, G. B. Optimization of enzymatic reaction conditions for generating representative pools of cDNA from small RNA. RNA 16, 2537–2552 (2010).

    Article  Google Scholar 

  15. Little, J. W. Lambda exonuclease. Gene Amplif. Anal. 2, 135–145 (1981).

    CAS  PubMed  Google Scholar 

  16. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  17. Burroughs, A. M. et al. A comprehensive survey of 3ʹ animal miRNA modification events and a possible role for 3’ adenylation in modulating miRNA targeting effectiveness. Genome Res. 20, 1398–1410 (2010).

    Article  CAS  Google Scholar 

  18. Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

    Article  CAS  Google Scholar 

  19. Parekh, S., Ziegenhain, C., Vieth, B., Enard, W. & Hellmann, I. zUMIs - a fast and flexible pipeline to process RNA sequencing data with UMIs. Gigascience 7, giy059 (2018).

  20. Kozomara, A. & Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014).

    Article  CAS  Google Scholar 

  21. Chan, P. P. & Lowe, T. M. GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 37, D93–D97 (2009).

    Article  CAS  Google Scholar 

  22. Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    Article  CAS  Google Scholar 

  23. Phelan, M. C. Basic techniques for mammalian cell tissue culture. Curr. Protoc. Cell Biol. 00, 1.1.1–1.1.10 (1998).

    Article  Google Scholar 

Download references

Acknowledgements

We thank B. Pannagel at the CMB FACS facility at Karolinska Institutet for performing the single-cell sorting. This research was supported by grants from the Swedish Research Council (grant no. 2017-01062 to R.S.) and the Bert L. & N. Kuggie Vallee Foundation (to R.S.).

Author information

Authors and Affiliations

  1. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden

    Michael Hagemann-Jensen, Ilgar Abdullayev & Rickard Sandberg

  2. Ludwig Institute for Cancer Research, Stockholm, Sweden

    Ilgar Abdullayev, Rickard Sandberg & Omid R Faridani

  3. Integrated Cardio Metabolic Centre (ICMC), Department of Medicine, Karolinska Institutet, Huddinge, Sweden

    Rickard Sandberg & Omid R Faridani

Authors
  1. Michael Hagemann-Jensen
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  2. Ilgar Abdullayev
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  3. Rickard Sandberg
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  4. Omid R Faridani
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Contributions

M.H.-J. developed and optimized the protocol, analyzed the data, and prepared the manuscript. I.A. constructed the bioinformatics pipeline, analyzed the data, and prepared the manuscript. R.S. supervised the development of the computational analyses and prepared the manuscript. O.R.F. conceived the study, developed the method, interpreted the results, and prepared the manuscript.

Corresponding author

Correspondence to Omid R Faridani.

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Competing interests

O.R.F. and R.S. have filed a patent application (PCT/US2017/037620) on the protocol for single-cell small-RNA sequencing. The remaining authors declare no competing interests.

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Related links

Key reference using this protocol

1. Faridani, O. R. et al. Nat. Biotechnol. 34, 1264–1266 (2016): https://doi.org/10.1038/nbt.3701

Integrated supplementary information

Supplementary Figure 1 Number of genes and molecules in a test example.

Number of captured small RNA–encoding genes (a) and small RNA molecules (b) in libraries containing the rRNA-masking oligonucleotide with HEK cells or without cells.

Supplementary Figure 2 Outline of the gating strategy implemented for sorting HEK293FT single cells.

Gates for selecting single cells are shown in plots for Side Scatter Area (SSC-A, logarithmic) against Forward Scatter Area (FSC-A) (a), Forward Scatter Width (FSC-W) against Forward Scatter Area (FSC-A) (b), and Side Scatter Width (SSC-W) against Side Scatter Area (SSC-A, logarithmic) (c). Propidium iodide was applied to stain for dead cells (d).

Supplementary Figure 3 Percentage of filtered and genome-mapped reads.

Libraries obtained from HEK cells or no cells (all with the rRNA-masking oligonucleotide) were generated and sequenced. Here, the percentages are shown for sequenced reads that are filtered (grey), cannot be mapped (green), are mapped to multiple loci (blue), and are uniquely mapped (purple).

Supplementary information

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Supplementary Figures 1–3 and Supplementary Table 1

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Hagemann-Jensen, M., Abdullayev, I., Sandberg, R. et al. Small-seq for single-cell small-RNA sequencing. Nat Protoc 13, 2407–2424 (2018). https://doi.org/10.1038/s41596-018-0049-y

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