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Strategies for Converting RNA to Amplifiable cDNA for Single-Cell RNA Sequencing Methods

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Single Molecule and Single Cell Sequencing

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1129))

Abstract

This review describes the features of molecular biology techniques for single-cell RNA sequencing (scRNA-seq), including methods developed in our laboratory. Existing scRNA-seq methods require the conversion of first-strand cDNA to amplifiable cDNA followed by whole-transcript amplification. There are three primary strategies for this conversion: poly-A tagging, template switching, and RNase H-DNA polymerase I-mediated second-strand cDNA synthesis for in vitro transcription. We discuss the merits and limitations of these strategies and describe our Reverse Transcription with Random Displacement Amplification technology that allows for direct first-strand cDNA amplification from RNA without the need for conversion to an amplifiable cDNA. We believe that this review provides all users of single-cell transcriptome technologies with an understanding of the relationship between the quantitative performance of various methods and their molecular features.

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References

  • Archer N, Walsh MD, Shahrezaei V, Hebenstreit D. Modeling enzyme processivity reveals that RNA-seq libraries are biased in characteristic and correctable ways. Cell Syst. 2016;3:467–479.e12.

    Article  CAS  Google Scholar 

  • Armour CD, Castle JC, Chen R, Babak T, Loerch P, Jackson S, et al. Digital transcriptome profiling using selective hexamer priming for cDNA synthesis. Nat Methods. 2009;6:647–9.

    Article  CAS  Google Scholar 

  • Avital G, Avraham R, Fan A, Hashimshony T, Hung DT, Yanai I. scDual-Seq: mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNA-sequencing. Genome Biol. 2017;18:19.

    Article  Google Scholar 

  • Belyavsky A, Vinogradova T, Rajewsky K. PCR-based cDNA library construction: general cDNA libraries at the level of a few cells. Nucleic Acids Res. 1989;17:2919–32.

    Article  CAS  Google Scholar 

  • Bose S, Wan Z, Carr A, Rizvi A, Vieira G, Pe’er D, et al. Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biol. 2015;16:120.

    Article  Google Scholar 

  • Brady G, Barbara M, Iscove NN. Representative in vitro cDNA amplification from individual hemopoietic cells and colonies. Methods Mol Cell Biol. 1990;2:17–25.. [referencex]

    CAS  Google Scholar 

  • Brady G, Billia F, Knox J, Hoang T, Kirsch IR, Voura EB, et al. Analysis of gene expression in a complex differentiation hierarchy by global amplification of cDNA from single cells. Curr Biol. 1995;5:909–22.

    Article  CAS  Google Scholar 

  • Dean FB, Hosono S, Fang L, Wu X, Faruqi AF, Bray-Ward P, et al. Comprehensive human genome amplification using multiple displacement amplification. Proc Natl Acad Sci U S A. 2002;99:5261–6.

    Article  CAS  Google Scholar 

  • Dulac C, Axel R. A novel family of genes encoding putative pheromone receptors in mammals. Cell. 1995;83:195–206.

    Article  CAS  Google Scholar 

  • Eberwine J, Yeh H, Miyashiro K, Cao Y, Nair S, Finnell R, et al. Analysis of gene expression in single live neurons. Proc Natl Acad Sci U S A. 1992;89:3010–4.

    Article  CAS  Google Scholar 

  • Eberwine J, Kacharmina JE, Andrews C, Miyashiro K, McIntosh T, Becker K, et al. mRNA expression analysis of tissue sections and single cells. J Neurosci. 2001;21:8310–4.

    Article  CAS  Google Scholar 

  • Fan X, Zhang X, Wu X, Guo H, Hu Y, Tang F, et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol. 2015;16:236.

    Article  Google Scholar 

  • Frohman MA, Dush MK, Martin GR. Rapid production of full-length cDNAs from rare transcripts: amplification using a single gene-specific oligonucleotide primer. Proc Natl Acad Sci U S A. 1988;85:8998–9002.

    Article  CAS  Google Scholar 

  • Gierahn TM, Wadsworth MH, Hughes TK, Bryson BD, Butler A, Satija R, et al. Seq-well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods. 2017;14:395–8.

    Article  CAS  Google Scholar 

  • Gubler U, Hoffman BJ. A simple and very efficient method for generating cDNA libraries. Gene. 1983;25:263–9.

    Article  CAS  Google Scholar 

  • Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, et al. Mapping the mouse cell atlas by microwell-seq. Cell. 2018;173:1307.

    Article  CAS  Google Scholar 

  • Hashimoto S, Tabuchi Y, Yurino H, Hirohashi Y, Deshimaru S, Asano T, et al. Comprehensive single-cell transcriptome analysis reveals heterogeneity in endometrioid adenocarcinoma tissues. Sci Rep. 2017;7:439.

    Article  Google Scholar 

  • Hashimshony T, Wagner F, Sher N, Yanai I. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2012;2:666–73.

    Article  CAS  Google Scholar 

  • Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y, Anavy L, et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-seq. Genome Biol. 2016;17:892.

    Article  Google Scholar 

  • Hayashi T, Sasagawa Y, Nikaido I. RIKEN. Method for nucleic acid amplification. In: Patent WO2016052619A1; 2016.

    Google Scholar 

  • Hayashi T, Ozaki H, Sasagawa Y, Umeda M, Danno H, Nikaido I. Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs. Nat Commun. 2018;9:1435.

    Article  Google Scholar 

  • Herman JS, Sagar, Grün D. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat Methods. 2018;15:379–86.

    Article  CAS  Google Scholar 

  • Hochgerner H, Lönnerberg P, Hodge R, Mikes J, Heskol A, Hubschle H, et al. STRT-seq-2i: dual-index 5′ single cell and nucleus RNA-seq on an addressable microwell array. Sci Rep. 2017;7:566.

    Article  Google Scholar 

  • Iscove NN, Barbara M, Gu M, Gibson M, Modi C, Winegarden N. Representation is faithfully preserved in global cDNA amplified exponentially from sub-picogram quantities of mRNA. Nat Biotechnol. 2002;20:940–3.

    Article  CAS  Google Scholar 

  • Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 2011;21:1160–7.

    Article  CAS  Google Scholar 

  • Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2013;11:163–6.

    Article  Google Scholar 

  • Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343:776–9.

    Article  CAS  Google Scholar 

  • Kamme F, Salunga R, Yu J, Tran D-T, Zhu J, Luo L, et al. Single-cell microarray analysis in Hippocampus CA1: demonstration and validation of cellular heterogeneity. J Neurosci. 2003;23:3607–15.

    Article  CAS  Google Scholar 

  • Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161:1187–201.

    Article  CAS  Google Scholar 

  • Kurimoto K. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 2006;34:e42.

    Article  Google Scholar 

  • Land H, Grez M, Hauser H, Lindenmaier W, Schütz G. 5′-Terminal sequences of eucaryotic mRNA can be cloned with high efficiency. Nucleic Acids Res. 1981;9:2251–66.

    Article  CAS  Google Scholar 

  • Lizardi PM, Huang X, Zhu Z, Bray-Ward P, Thomas DC, Ward DC. Mutation detection and single-molecule counting using isothermal rolling-circle amplification. Nat Genet. 1998;19:225–32.

    Article  CAS  Google Scholar 

  • Loh E, Elliott J, Cwirla S, Lanier L, Davis M. Polymerase chain reaction with single-sided specificity: analysis of T cell receptor delta chain. Science. 1989;243:217–20.

    Article  CAS  Google Scholar 

  • Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14.

    Article  CAS  Google Scholar 

  • Matsunaga H, Goto M, Arikawa K, Shirai M, Tsunoda H, Huang H, et al. A highly sensitive and accurate gene expression analysis by sequencing (“bead-seq”) for a single cell. Anal Biochem. 2015;471:9–16.

    Article  CAS  Google Scholar 

  • Matz M. Amplification of cDNA ends based on template-switching effect and step- out PCR. Nucleic Acids Res. 1999;27:1558–60.

    Article  CAS  Google Scholar 

  • Muraro MJ, Dharmadhikari G, Grün D, Groen N, Dielen T, Jansen E, et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 2016;3:385–394.e3.

    Article  CAS  Google Scholar 

  • Nakamura T, Yabuta Y, Okamoto I, Aramaki S, Yokobayashi S, Kurimoto K, et al. SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression. Nucleic Acids Res. 2015;43:e60.

    Article  Google Scholar 

  • Ohara O, Dorit RL, Gilbert W. One-sided polymerase chain reaction: the amplification of cDNA. Proc Natl Acad Sci U S A. 1989;86:5673–7.

    Article  CAS  Google Scholar 

  • Okayama H, Berg P. High-efficiency cloning of full-length cDNA. Mol Cell Biol. 1982;2:161–70.

    Article  CAS  Google Scholar 

  • Ozsolak F, Goren A, Gymrek M, Guttman M, Regev A, Bernstein BE, et al. Digital transcriptome profiling from attomole-level RNA samples. Genome Res. 2010;20:519–25.

    Article  CAS  Google Scholar 

  • Petalidis L. Global amplification of mRNA by template-switching PCR: linearity and application to microarray analysis. Nucleic Acids Res. 2003;31:142e–142.

    Article  Google Scholar 

  • Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096–8.

    Article  CAS  Google Scholar 

  • Ramsköld D, Luo S, Wang Y-C, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82.

    Article  Google Scholar 

  • Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, et al. The human cell atlas. eLife. 2017;6:503.

    Article  Google Scholar 

  • Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P, Yao Z, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018;360:176–82.

    Article  CAS  Google Scholar 

  • Roychoudhury R, Jay E, Wu R. Terminal labeling and addition of homopolymer tracts to duplex DNA fragments by terminal deoxynucleotidyl transferase. Nucleic Acids Res. 1976;3:863–78.

    Article  CAS  Google Scholar 

  • Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T, et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 2013;14:R31.

    Article  Google Scholar 

  • Sasagawa Y, Danno H, Takada H, Ebisawa M, Tanaka K, Hayashi T, et al. Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads. Genome Biol. 2018;19:14049.

    Article  Google Scholar 

  • Sheng K, Cao W, Niu Y, Deng Q, Zong C. Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nat Methods. 2017;14:267–70.

    Article  CAS  Google Scholar 

  • Soumillon M, Cacchiarelli D, Semrau S, van Oudenaarden A, Mikkelsen TS. Characterization of directed differentiation by high-throughput single-cell RNA-seq; 2014. https://doi.org/10.1101/003236.

    Book  Google Scholar 

  • Streets AM, Zhang X, Cao C, Pang Y, Wu X, Xiong L, et al. Microfluidic single-cell whole-transcriptome sequencing. Proc Natl Acad Sci U S A. 2014;111:7048–53.

    Article  CAS  Google Scholar 

  • Subkhankulova T, Livesey F. Comparative evaluation of linear and exponential amplification techniques for expression profiling at the single-cell level. Genome Biol. 2006;7:R18.

    Article  Google Scholar 

  • Takahashi H, Satoh T, Kanahara H, Kubota Y, Hirose T, Yamazaki H, et al. Development of a bench-top extra-cleanroom for DNA amplification. BioTechniques. 2016;61:42–6.

    Article  CAS  Google Scholar 

  • Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6:377–82.

    Article  CAS  Google Scholar 

  • Tang DTP, Plessy C, Salimullah M, Suzuki AM, Calligaris R, Gustincich S, et al. Suppression of artifacts and barcode bias in high-throughput transcriptome analyses utilizing template switching. Nucleic Acids Res. 2012;41:e44.

    Article  Google Scholar 

  • Tietjen I, Rihel JM, Cao Y, Koentges G, Zakhary L, Dulac C. Single-cell transcriptional analysis of neuronal progenitors. Neuron. 2003;38:161–75.

    Article  CAS  Google Scholar 

  • Van Gelder RN, Zastrow von ME, Yool A, Dement WC, Barchas JD, Eberwine JH. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci U S A. 1990;87:1663–7.

    Article  Google Scholar 

  • Wang Z, Gerstein M, Snyder M. RNA-seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63.

    Article  CAS  Google Scholar 

  • Wu AR, Neff NF, Kalisky T, Dalerba P, Treutlein B, Rothenberg ME, et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat Methods. 2013;11:41–6.

    Article  Google Scholar 

  • Yang L, Ma Z, Cao C, Zhang Y, Wu X, Lee R, et al. MR-seq: measuring a single cell’s transcriptome repeatedly by RNA-seq. Sci Bull. 2017;62:391–8.

    Article  Google Scholar 

  • Zajac P, Islam S, Hochgerner H, Lönnerberg P, Linnarsson S. Base preferences in non-templated nucleotide incorporation by MMLV-derived reverse transcriptases. Menéndez-Arias L, editor. PLoS One. 2013;8:e85270.

    Article  Google Scholar 

  • Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, Wako, Saitama, Japan

    Yohei Sasagawa, Tetsutaro Hayashi & Itoshi Nikaido

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  1. Yohei Sasagawa
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  2. Tetsutaro Hayashi
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  3. Itoshi Nikaido
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Correspondence to Itoshi Nikaido .

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Editors and Affiliations

  1. Department of Computational Biology and Medical Sciences, University of Tokyo, Chiba, Chiba, Japan

    Yutaka Suzuki

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Sasagawa, Y., Hayashi, T., Nikaido, I. (2019). Strategies for Converting RNA to Amplifiable cDNA for Single-Cell RNA Sequencing Methods. In: Suzuki, Y. (eds) Single Molecule and Single Cell Sequencing. Advances in Experimental Medicine and Biology, vol 1129. Springer, Singapore. https://doi.org/10.1007/978-981-13-6037-4_1

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