CpG Islands pp 257-283 | Cite as

High-Throughput Single-Cell RNA Sequencing and Data Analysis

  • Sagar
  • Josip Stefan Herman
  • John Andrew Pospisilik
  • Dominic Grün
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1766)

Abstract

Understanding biological systems at a single cell resolution may reveal several novel insights which remain masked by the conventional population-based techniques providing an average readout of the behavior of cells. Single-cell transcriptome sequencing holds the potential to identify novel cell types and characterize the cellular composition of any organ or tissue in health and disease. Here, we describe a customized high-throughput protocol for single-cell RNA-sequencing (scRNA-seq) combining flow cytometry and a nanoliter-scale robotic system. Since scRNA-seq requires amplification of a low amount of endogenous cellular RNA, leading to substantial technical noise in the dataset, downstream data filtering and analysis require special care. Therefore, we also briefly describe in-house state-of-the-art data analysis algorithms developed to identify cellular subpopulations including rare cell types as well as to derive lineage trees by ordering the identified subpopulations of cells along the inferred differentiation trajectories.

Key words

Single cell RNA sequencing High-throughput Single cell data analysis CEL-Seq2 Next-generation sequencing 

Notes

Acknowledgments

The authors would like to thank Thomas Boehm, Sebastian Hobitz, and Ulrike Bönisch for their help in developing the protocol.

References

  1. 1.
    Raj A, van den Bogaard P, Rifkin SA, van Oudenaarden A, Tyagi S (2008) Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods 5(10):877–879.  https://doi.org/10.1038/nmeth.1253. nmeth.1253 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Citri A, Pang ZP, Sudhof TC, Wernig M, Malenka RC (2011) Comprehensive qPCR profiling of gene expression in single neuronal cells. Nat Protoc 7(1):118–127.  https://doi.org/10.1038/nprot.2011.430. nprot.2011.430 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Luo L, Salunga RC, Guo H, Bittner A, Joy KC, Galindo JE, Xiao H, Rogers KE, Wan JS, Jackson MR, Erlander MG (1999) Gene expression profiles of laser-captured adjacent neuronal subtypes. Nat Med 5(1):117–122.  https://doi.org/10.1038/4806 CrossRefPubMedGoogle Scholar
  4. 4.
    Grun D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525(7568):251–255.  https://doi.org/10.1038/nature14966. nature14966 [pii]CrossRefPubMedGoogle Scholar
  5. 5.
    Tang F, Barbacioru C, Bao S, Lee C, Nordman E, Wang X, Lao K, Surani MA (2010) Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6(5):468–478.  https://doi.org/10.1016/j.stem.2010.03.015. S1934-5909(10)00114-1 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, Desai TJ, Krasnow MA, Quake SR (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509(7500):371–375.  https://doi.org/10.1038/nature13173. nature13173 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, Cahill DP, Nahed BV, Curry WT, Martuza RL, Louis DN, Rozenblatt-Rosen O, Suva ML, Regev A, Bernstein BE (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344(6190):1396–1401.  https://doi.org/10.1126/science.1254257. science.1254257 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Grun D, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A, Dharmadhikari G, van den Born M, van Es J, Jansen E, Clevers H, de Koning EJ, van Oudenaarden A (2016) De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19(2):266–277.  https://doi.org/10.1016/j.stem.2016.05.010. S1934-5909(16)30094-7 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Hashimshony T, Wagner F, Sher N, Yanai I (2012) CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep 2(3):666–673.  https://doi.org/10.1016/j.celrep.2012.08.003. S2211-1247(12)00228-8 [pii]CrossRefPubMedGoogle Scholar
  10. 10.
    Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, Linnarsson S (2011) Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21(7):1160–1167.  https://doi.org/10.1101/gr.110882.110.gr.110882.110[pii] CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, Lonnerberg P, Linnarsson S (2014) Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods 11(2):163–166.  https://doi.org/10.1038/nmeth.2772. nmeth.2772 [pii]CrossRefPubMedGoogle Scholar
  12. 12.
    Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, Peshkin L, Weitz DA, Kirschner MW (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161(5):1187–1201.  https://doi.org/10.1016/j.cell.2015.04.044. S0092-8674(15)00500-0 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM, Trombetta JJ, Weitz DA, Sanes JR, Shalek AK, Regev A, McCarroll SA (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161(5):1202–1214.  https://doi.org/10.1016/j.cell.2015.05.002. S0092-8674(15)00549-8 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10(11):1096–1098.  https://doi.org/10.1038/nmeth.2639. nmeth.2639 [pii]CrossRefPubMedGoogle Scholar
  15. 15.
    Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, Schroth GP, Sandberg R (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30(8):777–782. nbt.2282 [pii].  https://doi.org/10.1038/nbt.2282 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T, Ueda HR (2013) Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol 14(4):R31.  https://doi.org/10.1186/gb-2013-14-4-r31. gb-2013-14-4-r31 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y, Anavy L, Gennert D, Li S, Livak KJ, Rozenblatt-Rosen O, Dor Y, Regev A, Yanai I (2016) CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 17:77.  https://doi.org/10.1186/s13059-016-0938-8. 10.1186/s13059-016-0938-8 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Grun D, Kester L, van Oudenaarden A (2014) Validation of noise models for single-cell transcriptomics. Nat Methods 11(6):637–640.  https://doi.org/10.1038/nmeth.2930. nmeth.2930 [pii]CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sagar
    • 1
  • Josip Stefan Herman
    • 1
  • John Andrew Pospisilik
    • 1
  • Dominic Grün
    • 1
  1. 1.Max-Planck Institute of Immunobiology and EpigeneticsFreiburgGermany

Personalised recommendations