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High-throughput full-length single-cell RNA-seq automation

Nature Protocols volume 16, pages 2886–2915 (2021)Cite this article

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Abstract

Existing protocols for full-length single-cell RNA sequencing produce libraries of high complexity (thousands of distinct genes) with outstanding sensitivity and specificity of transcript quantification. These full-length libraries have the advantage of allowing probing of transcript isoforms, are informative regarding single-nucleotide polymorphisms and allow assembly of the VDJ region of the T- and B-cell-receptor sequences. Since full-length protocols are mostly plate-based at present, they are also suited to profiling cell types where cell numbers are limiting, such as rare cell types during development. A disadvantage of these methods has been the scalability and cost of the experiments, which has limited their popularity as compared with droplet-based and nanowell approaches. Here, we describe an automated protocol for full-length single-cell RNA sequencing, including both an in-house automated Smart-seq2 protocol and a commercial kit–based workflow. The protocols take 3–5 d to complete, depending on the number of plates processed in a batch. We discuss these two protocols in terms of ease of use, equipment requirements, running time, cost per sample and sequencing quality. By benchmarking the lysis buffers, reverse transcription enzymes and their combinations, we have optimized the in-house automated protocol to dramatically reduce its cost. An automated setup can be adopted easily by a competent researcher with basic laboratory skills and no prior automation experience. These pipelines have been employed successfully for several research projects allied with the Human Cell Atlas initiative (www.humancellatlas.org).

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Fig. 1: Stepwise overview of plate-based single-cell RNA-seq methods.
Fig. 2: Comparison of enzymes and lysis buffers for the Zephyr 96-well method.

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Data availability

The raw sequencing data generated in this study have been deposited in ArrayExpress under the accession code E-MTAB-9345.

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Acknowledgements

This work was supported by the Wellcome Trust grant (206194) and ERC Consolidator Grant ThDEFINE, and H2020 FET MRG-GRAMMAR to SAT. ZM was supported by a Wellcome BioResource for a ‘Single Cell Gene Expression Atlas’ (WT 108437/Z/15/Z) and the Open Targets grant (OTAR2067). We thank X. Chen for expert technical assistance, and K. N. Natarajan for providing the mESC cell line. We thank the Sanger Flow Cytometry (especially B. L. Ng) and Sequencing facilities for cell sorting and sequencing, and R. Elmentaite, J. Park, C. Suo and F. A. Vieira Braga for proofreading the manuscript. Finally, we thank G. Bennett from PerkinElmer for his application support, and L. Apone, K. McKay, V. Panchapakesa and E. Dimalanta from NEB for their assistance in optimizing the library preparation process.

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Author notes

  1. These authors contributed equally: Lira Mamanova, Zhichao Miao.

Authors and Affiliations

  1. Wellcome Sanger Institute, Cambridge, UK

    Lira Mamanova, Zhichao Miao, Ayesha Jinat, Peter Ellis, Lesley Shirley & Sarah A. Teichmann

  2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK

    Zhichao Miao

  3. Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Sarah A. Teichmann

Authors
  1. Lira Mamanova
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Contributions

L.M. designed and supervised the project. Z.M. performed the bioinformatics analysis. L.M., A.J., P.E. and L.S. performed the experiments. L.M., Z.M., L.S. and S.A.T. wrote the manuscript. All authors reviewed and approved the manuscript. L.M. and Z.M. contributed equally to this paper.

Corresponding authors

Correspondence to Lira Mamanova or Sarah A. Teichmann.

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Competing interests

In the past 3 years, S.A.T. has consulted for Genentech and Roche, and is a member of Scientific Advisory Boards of Foresite Labs, Biogen and GlaxoSmithKline. The other authors declare no competing financial interests.

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Peer review information Nature Protocols thanks Wolfgang Enard and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Vieira Braga, F. A. et al. Nat. Med. 25, 1153–1163 (2019): https://doi.org/10.1038/s41591-019-0468-5

Vento-Tormo, R. et al. Nature 563, 347–353 (2018): https://doi.org/10.1038/s41586-018-0698-6

James, K. R. et al. Nat. Immun. 21, 343–353 (2020): https://doi.org/10.1038/s41590-020-0602-z

Extended data

Extended Data Fig. 1 The comparison of enzymes and lysis buffers in the Zephyr 96-well method (continued from Fig. 2).

a–h,This figure extends the comparison in Fig. 2 and shows the distributions of the percentage of reads mapped to exonic regions (a), the percentage of reads mapped to intronic regions (b), the percentage of reads mapped to intergenic regions (c), the total number of processed reads (d), the distribution of detection limits (e) and accuracy (f) as violin plots and jitter plots. (g) and (h) show the relative gene body coverage distributions for the SMT enzyme and Maxima enzyme, respectively. Appropriate institutional regulatory board permission was obtained for the animal experiments.

Extended Data Fig. 2 The assessment of NEBNext UltraTM II protocol on CD45+ cells and mESCs.

a, Schematic overview of the NEBNext UltraTM II scRNA-seq experiment of mESC and mouse splenocytes. Mouse spleen was dissociated into single cells, and the splenocytes were sorted by FACS after CD45 enrichment. Next, the dissociated splenocytes and the mES cells were treated with NEB cell lysis buffers and then with the reverse transcription enzyme. All the cells were converted into cDNA sequencing libraries by a conventional library construction protocol (fragmentation, end-repair, ligation). Finally, the cDNA libraries were sequenced on an Illumina HiSeq4000 platform. b, Detection limits (sensitivities) of single-cell RNA-seq were assessed by downsampling reads across two orders of magnitude (104 to 106 reads). c, Single-cell RNA-seq accuracies for mouse splenocytes were also measured across two orders of magnitude. The grey dotted lines in b and c indicate downsampled single cells at different read depths, while the red line indicates the limit for sequencing saturation. d–m, The distributions of the total number of mapped counts (d), the total number of genes (e), the percentages of rRNA mapped reads (f) and percentages of mitochondrial contents (g), detection limit (h), accuracy (i), percentage of reads mapped to the exonic regions (j), percentage of reads mapped to intronic regions (k), percentage of reads mapped to intergenic regions (l) are shown as violin plots and jitter plots. (m) shows the relative gene body coverage distributions in the two cell types. Appropriate institutional regulatory board permission was obtained for the animal experiments.

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Supplementary Figs. 1–7, Supplementary Tables 1–3, Supplementary Methods and Supplementary Protocol.

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Mamanova, L., Miao, Z., Jinat, A. et al. High-throughput full-length single-cell RNA-seq automation. Nat Protoc 16, 2886–2915 (2021). https://doi.org/10.1038/s41596-021-00523-3

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