Advertisement

Single-Cell RNA Sequencing of Oligodendrocyte Lineage Cells from the Mouse Central Nervous System

  • Sueli Marques
  • David van Bruggen
  • Gonçalo Castelo-BrancoEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1936)

Abstract

Single-cell RNA sequencing has emerged as a powerful technique for the identification of distinct cell states/populations in complex tissues. We have recently used this technology to investigate heterogeneity of cells of the oligodendrocyte lineage in the mouse central nervous system. In this chapter, we describe methods to perform single-cell RNA sequencing on this glial cell lineage, and discuss experimental and computational approaches to explore the potential and to tackle hurdles associated with this technology.

Key words

Single-cell RNA sequencing Transcriptomics Oligodendrocyte Myelin Central nervous system 

Notes

Acknowledgments

We would like to thank Elisa Floriddia for proofreading and Amit Zeisel for comments. Work in G.C.-B.’s research group was supported by Swedish Research Council, European Union (FP7/Marie Curie Integration Grant EPIOPC, Horizon 2020 European Research Council Consolidator Grant EPIScOPE), European Committee for Treatment and Research in Multiple Sclerosis, Swedish Brain Foundation, Swedish Cancer Society, Ming Wai Lau Centre for Reparative Medicine, Petrus och Augusta Hedlunds Foundation and Karolinska Institutet.

References

  1. 1.
    Tang FC et al (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6:377–382. https://doi.org/10.1038/NMETH.1315CrossRefPubMedGoogle Scholar
  2. 2.
    Pollen AA et al (2014) Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol 32:1053–1058. https://doi.org/10.1038/nbt.2967CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Macosko EZ et al (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–1214. https://doi.org/10.1016/j.cell.2015.05.002CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Klein AM et al (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–1201. https://doi.org/10.1016/j.cell.2015.04.044CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Rosenberg AB et al (2018) Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:176–182. https://doi.org/10.1126/science.aam8999CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Zeisel A et al (2015) Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347:1138–1142. https://doi.org/10.1126/science.aaa1934CrossRefPubMedGoogle Scholar
  7. 7.
    Patel AP et al (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344:1396–1401. https://doi.org/10.1126/science.1254257CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Jaitin DA et al (2014) Massively parallel single-cell RNA-Seq for marker-free decomposition of tissues into cell types. Science 343:776–779. https://doi.org/10.1126/science.1247651CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Buettner F et al (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol 33:155–160. https://doi.org/10.1038/nbt.3102CrossRefPubMedGoogle Scholar
  10. 10.
    Yan LY et al (2013) Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 20:1131–1139. https://doi.org/10.1038/nsmb.2660CrossRefPubMedGoogle Scholar
  11. 11.
    La Manno G et al (2016) Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167:566–580.e19. https://doi.org/10.1016/j.cell.2016.09.027CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Telley L et al (2016) Sequential transcriptional waves direct the differentiation of newborn neurons in the mouse neocortex. Science 351:1443–1446. https://doi.org/10.1126/science.aad8361CrossRefPubMedGoogle Scholar
  13. 13.
    Poulin JF, Tasic B, Hjerling-Leffler J, Trimarchi JM, Awatramani R (2016) Disentangling neural cell diversity using single-cell transcriptomics. Nat Neurosci 19:1131–1141. https://doi.org/10.1038/nn.4366CrossRefPubMedGoogle Scholar
  14. 14.
    Marques S et al (2016) Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352:1326–1329. https://doi.org/10.1126/science.aaf6463CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Marques S, Vanichkina D, van Bruggen D, Floriddia EM, Munguba H, Väremo L, Giacomello S, Falcão AM, Meijer M, Samudyata S, Codeluppi S, Björklund AK, Linnarsson S, Hjerling-Leffler J, Taft R.J, Castelo-Branco G. (2018) Transcriptional convergence of oligodendrocyte lineage progenitors during development. Dev Cell 46:504–517.e7Google Scholar
  16. 16.
    Lassmann H (2012) The birth of oligodendrocytes in the anatomical and neuropathological literature: the seminal contribution of Pio del Rio-Hortega. 1921. Clin Neuropathol 31:435–436. https://doi.org/10.5414/NP301002CrossRefPubMedGoogle Scholar
  17. 17.
    Gill AS, Binder DK (2007) Wilder Penfield, Pio del Rio-Hortega, and the discovery of oligodendroglia. Neurosurgery 60:940–948. https://doi.org/10.1227/01.NEU.0000255448.97730.34 discussion 940–948CrossRefPubMedGoogle Scholar
  18. 18.
    Vinet J et al (2010) Subclasses of oligodendrocytes populate the mouse hippocampus. Eur J Neurosci 31:425–438. https://doi.org/10.1111/j.1460-9568.2010.07082.xCrossRefPubMedGoogle Scholar
  19. 19.
    Murtie JC, Macklin WB, Corfas G (2007) Morphometric analysis of oligodendrocytes in the adult mouse frontal cortex. J Neurosci Res 85:2080–2086. https://doi.org/10.1002/jnr.21339CrossRefGoogle Scholar
  20. 20.
    Bjartmar C, Hildebrand C, Loinder K (1994) Morphological heterogeneity of rat oligodendrocytes: electron microscopic studies on serial sections. Glia 11:235–244. https://doi.org/10.1002/glia.440110304CrossRefPubMedGoogle Scholar
  21. 21.
    Bakiri Y, Karadottir R, Cossell L, Attwell D (2011) Morphological and electrical properties of oligodendrocytes in the white matter of the corpus callosum and cerebellum. J Physiol 589:559–573. https://doi.org/10.1113/jphysiol.2010.201376CrossRefPubMedGoogle Scholar
  22. 22.
    Anderson ES, Bjartmar C, Westermark G, Hildebrand C (1999) Molecular heterogeneity of oligodendrocytes in chicken white matter. Glia 27:15–21CrossRefGoogle Scholar
  23. 23.
    Anderson ES, Bjartmar C, Hildebrand C (2000) Myelination of prospective large fibres in chicken ventral funiculus. J Neurocytol 29:755–764CrossRefGoogle Scholar
  24. 24.
    Kessaris N et al (2006) Competing waves of oligodendrocytes in the forebrain and postnatal elimination of an embryonic lineage. Nat Neurosci 9:173–179. https://doi.org/10.1038/nn1620CrossRefPubMedGoogle Scholar
  25. 25.
    Tripathi RB et al (2011) Dorsally and ventrally derived oligodendrocytes have similar electrical properties but myelinate preferred tracts. J Neurosci 31:6809–6819. https://doi.org/10.1523/JNEUROSCI.6474-10.2011CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Klinghoffer RA, Hamilton TG, Hoch R, Soriano P (2002) An allelic series at the PDGF alpha R locus indicates unequal contributions of distinct signaling pathways during development. Dev Cell 2:103–113. https://doi.org/10.1016/S1534-5807(01)00103-4CrossRefPubMedGoogle Scholar
  27. 27.
    Roesch K et al (2008) The transcriptome of retinal miller glial cells. J Comp Neurol 509:225–238. https://doi.org/10.1002/cne.21730CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Islam S et al (2014) Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods 11:163–166. https://doi.org/10.1038/nmeth.2772CrossRefPubMedGoogle Scholar
  29. 29.
    Islam S et al (2011) Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21:1160–1167. https://doi.org/10.1101/gr.110882.110CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Islam S et al (2012) Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat Protoc 7:813–828. https://doi.org/10.1038/nprot.2012.022CrossRefPubMedGoogle Scholar
  31. 31.
    Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16:133–145. https://doi.org/10.1038/nrg3833CrossRefPubMedGoogle Scholar
  32. 32.
    Tsafrir D et al (2005) Sorting points into neighborhoods (SPIN): data analysis and visualization by ordering distance matrices. Bioinformatics 21:2301–2308. https://doi.org/10.1093/bioinformatics/bti329CrossRefPubMedGoogle Scholar
  33. 33.
    Trapnell C et al (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32:381–U251. https://doi.org/10.1038/nbt.2859CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Magwene PM, Lizardi P, Kim J (2003) Reconstructing the temporal ordering of biological samples using microarray data. Bioinformatics 19:842–850. https://doi.org/10.1093/bioinformatics/btg081CrossRefPubMedGoogle Scholar
  35. 35.
    Nichterwitz S et al (2016) Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nat Commun 7:12139. https://doi.org/10.1038/ncomms12139CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Didar TF, Li K, Veres T, Tabrizian M (2013) Separation of rare oligodendrocyte progenitor cells from brain using a high-throughput multilayer thermoplastic-based microfluidic device. Biomaterials 34:5588–5593. https://doi.org/10.1016/j.biomaterials.2013.04.014CrossRefPubMedGoogle Scholar
  37. 37.
    Lun AT, Bach K, Marioni JC (2016) Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol 17:75. https://doi.org/10.1186/s13059-016-0947-7CrossRefPubMedGoogle Scholar
  38. 38.
    Rousseeuw PJ, Kaufman L (1990) Finding groups in data: an introduction to cluster analysis. John Wiley & Sons, Hoboken, NJGoogle Scholar
  39. 39.
    Hartigan JA, Wong MA (1979) Algorithm AS 136: A K-means clustering algorithm. J Roy Statist Soc Series C (Appl Statist) 28:100–108. https://doi.org/10.2307/2346830CrossRefGoogle Scholar
  40. 40.
    Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36:411–420. https://doi.org/10.1038/nbt.4096CrossRefPubMedGoogle Scholar
  41. 41.
    Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315:972–976. https://doi.org/10.1126/science.1136800CrossRefPubMedGoogle Scholar
  42. 42.
    Xu C, Su ZC (2015) Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31:1974–1980. https://doi.org/10.1093/bioinformatics/btv088CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Fan J et al (2016) Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat Methods 13:241–244. https://doi.org/10.1038/nmeth.3734CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Sueli Marques
    • 1
  • David van Bruggen
    • 1
  • Gonçalo Castelo-Branco
    • 1
    • 2
    Email author
  1. 1.Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and BiophysicsKarolinska InstitutetStockholmSweden
  2. 2.Ming Wai Lau Centre for Reparative Medicine, Stockholm NodeKarolinska InstitutetStockholmSweden

Personalised recommendations