Advertisement

An Informative Approach to Single-Cell Sequencing Analysis

  • Yukie Kashima
  • Ayako SuzukiEmail author
  • Yutaka Suzuki
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1129)

Abstract

Recent advances in sequencing technologies enable us to obtain genome, epigenome and transcriptome data in individual cells. In this review, we describe various platforms for single-cell sequencing analysis across multiple layers. We mainly introduce an automated single-cell RNA-seq platform, the Chromium Single Cell 3′ RNA-seq system, and its technical features and compare it with other single-cell RNA-seq systems. We also describe computational methods for analyzing large, complex single-cell datasets. Due to the insufficient depth of single-cell RNA-seq data, resulting in a critical lack of transcriptome information for low-expressed genes, it is occasionally difficult to interpret the data as is. To overcome the analytical problems for such sparse datasets, there are many bioinformatics reports that provide informative approaches, including imputation, correction of batch effects, dimensional reduction and clustering.

Keywords

Single-cell sequencing scRNA-seq Chromium Imputation Computational approach 

References

  1. Alpert A, Moore LS, Dubovik T, Shen-Orr SS. Alignment of single-cell trajectories to compare cellular expression dynamics. Nat Methods. 2018;15:267–70.  https://doi.org/10.1038/nmeth.4628.CrossRefPubMedGoogle Scholar
  2. Angermueller C, Clark SJ, Lee HJ, et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods. 2016;13:229–32.  https://doi.org/10.1038/nmeth.3728.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Buenrostro JD, Giresi PG, Zaba LC, et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10:1213–8.  https://doi.org/10.1038/nmeth.2688.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Buenrostro JD, Wu B, Litzenburger UM, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523:486–90.  https://doi.org/10.1038/nature14590.CrossRefPubMedPubMedCentralGoogle Scholar
  5. Buenrostro JD, Corces MR, Lareau CA, et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell. 2018;173:1535–1548.e16.  https://doi.org/10.1016/j.cell.2018.03.074.CrossRefPubMedGoogle Scholar
  6. Butler A, Hoffman P, Smibert P, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411–20.  https://doi.org/10.1038/nbt.4096.CrossRefPubMedGoogle Scholar
  7. Cusanovich DA, Daza R, Adey A, et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015;348:910–4.  https://doi.org/10.1126/science.aab1601.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Datlinger P, Rendeiro AF, Schmidl C, et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods. 2017;14:297–301.  https://doi.org/10.1038/nmeth.4177.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Dey SS, Kester L, Spanjaard B, et al. Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol. 2015;33:285–9.  https://doi.org/10.1038/nbt.3129.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Dixit A, Parnas O, Li B, et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell. 2016;167:1853–1866.e17.  https://doi.org/10.1016/j.cell.2016.11.038.CrossRefPubMedPubMedCentralGoogle Scholar
  11. duVerle DA, Yotsukura S, Nomura S, et al. CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data. BMC Bioinformatics. 2016;17:363.  https://doi.org/10.1186/s12859-016-1175-6.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Fan J, Salathia N, Liu R, et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat Methods. 2016;13:241–4.  https://doi.org/10.1038/nmeth.3734.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Gao R, Davis A, McDonald TO, et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat Genet. 2016;48:1119–30.  https://doi.org/10.1038/ng.3641.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Guo H, Zhu P, Wu X, et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 2013;23:2126–35.  https://doi.org/10.1101/gr.161679.113.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Han X, Wang R, Zhou Y, et al. Mapping the mouse cell atlas by Microwell-Seq. Cell. 2018;172:1091–1097.e17.  https://doi.org/10.1016/j.cell.2018.02.001.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2:666–73.  https://doi.org/10.1016/j.celrep.2012.08.003.CrossRefGoogle Scholar
  17. Herring CA, Banerjee A, McKinley ET, et al. Unsupervised trajectory analysis of single-cell RNA-Seq and imaging data reveals alternative tuft cell origins in the g. Cell Syst. 2018;6:37–51.e9.  https://doi.org/10.1016/j.cels.2017.10.012.CrossRefPubMedGoogle Scholar
  18. Huang M, Wang J, Torre E, et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat Methods. 2018;15:539–42.  https://doi.org/10.1038/s41592-018-0033-z.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Jaitin DA, Weiner A, Yofe I, et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. Cell. 2016;167:1883–1896.e15.  https://doi.org/10.1016/j.cell.2016.11.039.CrossRefPubMedGoogle Scholar
  20. Kashima Y, Suzuki A, Liu Y, et al. Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response. Sci Rep. 2018;8:3482.  https://doi.org/10.1038/s41598-018-21161-y.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014;11:740–2.  https://doi.org/10.1038/nmeth.2967.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Kim C, Gao R, Sei E, et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell. 2018;173:879–893.e13.  https://doi.org/10.1016/j.cell.2018.03.041.CrossRefPubMedGoogle Scholar
  23. Kiselev VY, Kirschner K, Schaub MT, et al. SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017;14:483–6.  https://doi.org/10.1038/nmeth.4236.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161:1187–201.  https://doi.org/10.1016/j.cell.2015.04.044.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Lasken RS. Single-cell genomic sequencing using multiple displacement amplification. Curr Opin Microbiol. 2007;10:510–6.  https://doi.org/10.1016/j.mib.2007.08.005.CrossRefPubMedGoogle Scholar
  26. Li WV, Li JJ. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat Commun. 2018;9:997.  https://doi.org/10.1038/s41467-018-03405-7.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Li H, Courtois ET, Sengupta D, et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat Genet. 2017;49:708–18.  https://doi.org/10.1038/ng.3818.CrossRefPubMedGoogle Scholar
  28. Lummertz Da Rocha E, Rowe RG, Lundin V, et al. Reconstruction of complex single-cell trajectories using CellRouter. Nat Commun. 2018;9:892.  https://doi.org/10.1038/s41467-018-03214-y.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Macaulay IC, Haerty W, Kumar P, et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat Methods. 2015;12:519–22.  https://doi.org/10.1038/nmeth.3370.CrossRefPubMedGoogle Scholar
  30. Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14.  https://doi.org/10.1016/j.cell.2015.05.002.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Marco E, Karp RL, Guo G, et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc Natl Acad Sci U S A. 2014;111:E5643–50.  https://doi.org/10.1073/pnas.1408993111.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Matsunaga H, Goto M, Arikawa K, et al. A highly sensitive and accurate gene expression analysis by sequencing (“bead-seq”) for a single cell. Anal Biochem. 2015;471:9–16.  https://doi.org/10.1016/j.ab.2014.10.011.CrossRefGoogle Scholar
  33. Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472:90–5.  https://doi.org/10.1038/nature09807.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Peterson VM, Zhang KX, Kumar N, et al. Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol. 2017;35:936–9.  https://doi.org/10.1038/nbt.3973.CrossRefGoogle Scholar
  35. Picelli S, Björklund ÅK, Faridani OR, et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096–100.  https://doi.org/10.1038/nmeth.2639.CrossRefGoogle Scholar
  36. Pierson E, Yau C. ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 2015;16:241.  https://doi.org/10.1186/s13059-015-0805-z.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Qiu X, Mao Q, Tang Y, et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14:979–82.  https://doi.org/10.1038/nmeth.4402.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Ramsköld D, Luo S, Wang YC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82.  https://doi.org/10.1038/nbt.2282.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Risso D, Perraudeau F, Gribkova S, et al. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun. 2018;9:284.  https://doi.org/10.1038/s41467-017-02554-5.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Rotem A, Ram O, Shoresh N, et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol. 2015;33:1165–72.  https://doi.org/10.1038/nbt.3383.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Saelens W, Cannoodt R, et al. A comparison of single-cell trajectory inference methods: towards more accurate and robust tools. bioRxiv. 2018:276907.  https://doi.org/10.1101/276907.
  42. Sasagawa Y, Nikaido I, Hayashi T, et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA-Seq reveals non-genetic gene expression heterogeneity. Genome Biol. 2013;14:R31.  https://doi.org/10.1186/gb-2013-14-4-r31.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Satpathy AT, Saligrama N, Buenrostro JD, et al. Transcript-indexed ATAC-seq for precision immune profiling. Nat Med. 2018;24:580–90.  https://doi.org/10.1038/s41591-018-0008-8.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Savas P, Virassamy B, Ye C, et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med. 2018;24:986–93.  https://doi.org/10.1038/s41591-018-0078-7.CrossRefPubMedGoogle Scholar
  45. Smallwood SA, Lee HJ, Angermueller C, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods. 2014;11:817–20.  https://doi.org/10.1038/nmeth.3035.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Stoeckius M, Hafemeister C, Stephenson W, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14:865–8.  https://doi.org/10.1038/nmeth.4380.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Suzuki A, Matsushima K, Makinoshima H, et al. Single-cell analysis of lung adenocarcinoma cell lines reveals diverse expression patterns of individual cells invoked by a molecular target drug treatment. Genome Biol. 2015;16:66.  https://doi.org/10.1186/s13059-015-0636-y.CrossRefPubMedPubMedCentralGoogle Scholar
  48. Svensson V, Natarajan KN, Ly LH, et al. Power analysis of single-cell rnA-sequencing experiments. Nat Methods. 2017;14:381–7.  https://doi.org/10.1038/nmeth.4220.CrossRefPubMedPubMedCentralGoogle Scholar
  49. Telenius H, Carter NP, Bebb CE, et al. Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer. Genomics. 1992;13:718–25.  https://doi.org/10.1016/0888-7543(92)90147-K.CrossRefPubMedGoogle Scholar
  50. Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–6.  https://doi.org/10.1038/nbt.2859.CrossRefPubMedPubMedCentralGoogle Scholar
  51. van Dijk D, Sharma R, Nainys J, et al. Recovering gene interactions from single-cell data using data diffusion. Cell. 2018.  https://doi.org/10.1016/j.cell.2018.05.061.CrossRefGoogle Scholar
  52. Wang Y, Navin NE. Advances and applications of single-cell sequencing technologies. Mol Cell. 2015;58:598–609.CrossRefGoogle Scholar
  53. Wang Y, Waters J, Leung ML, et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature. 2014;512:155–60.  https://doi.org/10.1038/nature13600.CrossRefPubMedPubMedCentralGoogle Scholar
  54. Zheng C, Zheng L, Yoo JK, et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell. 2017a;169:1342–1356.e16.  https://doi.org/10.1016/j.cell.2017.05.035.CrossRefPubMedGoogle Scholar
  55. Zheng GXY, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017b;8:14049.  https://doi.org/10.1038/ncomms14049.CrossRefPubMedPubMedCentralGoogle Scholar
  56. Zong C, Lu S, Chapman AR, Xie XS. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science. 2012;338:1622–6.  https://doi.org/10.1126/science.1229164.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Graduate School of Frontier SciencesThe University of TokyoKashiwaJapan

Personalised recommendations