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A Modified SMART-Seq Method for Single-Cell Transcriptomic Analysis of Embryoid Body Differentiation

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Embryonic Stem Cell Protocols

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2520))

Abstract

Embryoid bodies (EBs) are aggregate of cells that contain three embryonic germ layers. They can be formed by direct differentiation from pluripotent embryonic stem cells (ESCs), which serves as a useful model for understanding early embryo development. Due to the mixture of different cell types, it is necessary to investigate EBs at the single-cell level. Here, we describe a robust and straightforward method for single-cell gene expression profiling during mouse EB differentiation from mouse ESCs (mESCs). The protocol is modified from a widely used method in the SMART-seq family, which only requires standard molecular biology techniques and lab equipment. It allows for accurate 3′ counting of transcript at the single-cell level, which helps reveal cellular identities during EB formation. Combined with perturbation experiments, the method provides an opportunity for mechanistic studies of embryo development at the single-cell level.

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References

  1. Smith AG (2001) Embryo-derived stem cells: of mice and men. Cell Dev Biol 17:435–462. https://doi.org/10.1146/annurev.cellbio.17.1.435

    Article  CAS  Google Scholar 

  2. Evans MJ, Kaufman MH (1981) Establishment in culture of pluripotential cells from mouse embryos. Nature 292:154–156. https://doi.org/10.1038/292154a0

    Article  CAS  PubMed  Google Scholar 

  3. Martin GR (1981) Isolation of a pluripotent cell line from early mouse embryos cultured in medium conditioned by teratocarcinoma stem cells. Proc Natl Acad Sci U S A 78:7634–7638. https://doi.org/10.1073/pnas.78.12.7634

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ying Q-L, Wray J, Nichols J et al (2008) The ground state of embryonic stem cell self-renewal. Nature 453:519–523. https://doi.org/10.1038/nature06968

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Doetschman TC, Eistetter H, Katz M et al (1985) The in vitro development of blastocyst-derived embryonic stem cell lines: formation of visceral yolk sac, blood islands and myocardium. J Embryol Exp Morph 87:27–45. https://doi.org/10.1242/dev.87.1.27

    Article  CAS  PubMed  Google Scholar 

  6. Chen X, Teichmann SA, Meyer KB (2018) From tissues to cell types and back: single-cell gene expression analysis of tissue architecture. Annu Rev Biomed Data Sci 1:1–23. https://doi.org/10.1146/annurev-biodatasci-080917-013452

    Article  Google Scholar 

  7. Clark SJ, Lee HJ, Smallwood SA et al (2016) Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol 17:72. https://doi.org/10.1186/s13059-016-0944-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang Y, Navin NE (2015) Advances and applications of single-cell sequencing technologies. Mol Cell 58:598–609. https://doi.org/10.1016/j.molcel.2015.05.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Tang F, Barbacioru C, Wang Y et al (2009) mRNA-seq whole-transcriptome analysis of a single cell. Nat Methods 6:377–382. https://doi.org/10.1038/nmeth.1315

    Article  CAS  PubMed  Google Scholar 

  10. Tang F, Barbacioru C, Bao S et al (2010) Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-seq analysis. Cell Stem Cell 6:468–478. https://doi.org/10.1016/j.stem.2010.03.015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Macosko EZ, Basu A, Satija R 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.2015.05.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Klein AM, Mazutis L, Akartuna I 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.2015.04.044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gierahn TM, Wadsworth MH, Hughes TK et al (2017) Seq-well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 14:395–398. https://doi.org/10.1038/nmeth.4179

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Han X, Wang R, Zhou Y et al (2018) Mapping the mouse cell atlas by Microwell-seq. Cell 172:1091–1107.e17. https://doi.org/10.1016/j.cell.2018.02.001

    Article  CAS  PubMed  Google Scholar 

  15. Zheng GXY, Terry JM, Belgrader P et al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049. https://doi.org/10.1038/ncomms14049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Jaitin DA, Kenigsberg E, Keren-Shaul H 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.1247651

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Islam S, Kjällquist U, Moliner A 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.110882.110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ramsköld D, Luo S, Wang Y-C et al (2012) Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30:777–782. https://doi.org/10.1038/nbt.2282

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Picelli S, Björklund ÅK, Faridani OR et al (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10:1096–1098. https://doi.org/10.1038/nmeth.2639

    Article  CAS  PubMed  Google Scholar 

  20. Hagemann-Jensen M, Ziegenhain C, Chen P et al (2020) Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat Biotechnol 38:708–714. https://doi.org/10.1038/s41587-020-0497-0

    Article  CAS  PubMed  Google Scholar 

  21. Sasagawa Y, Danno H, Takada H et al (2018) Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads. Genome Biol 19:29. https://doi.org/10.1186/s13059-018-1407-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hashimshony T, Senderovich N, Avital G et al (2016) CEL-Seq2: sensitive highly-multiplexed single-cell RNA-seq. Genome Biol 17:77. https://doi.org/10.1186/s13059-016-0938-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bagnoli JW, Ziegenhain C, Janjic A et al (2018) Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat Commun 9:2937. https://doi.org/10.1038/s41467-018-05347-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cao J, Packer JS, Ramani V et al (2017) Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–667. https://doi.org/10.1126/science.aam8940

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rosenberg AB, Roco CM, Muscat RA et al (2018) Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:eaam8999. https://doi.org/10.1126/science.aam8999

    Article  CAS  Google Scholar 

  26. Scialdone A, Tanaka Y, Jawaid W et al (2016) Resolving early mesoderm diversification through single-cell expression profiling. Nature 535:289–293. https://doi.org/10.1038/nature18633

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mohammed H, Hernando-Herraez I, Savino A et al (2017) Single-cell landscape of transcriptional heterogeneity and cell fate decisions during mouse early gastrulation. Cell Rep 20:1215–1228. https://doi.org/10.1016/j.celrep.2017.07.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cao J, Spielmann M, Qiu X et al (2019) The single-cell transcriptional landscape of mammalian organogenesis. Nature 566:496–502. https://doi.org/10.1038/s41586-019-0969-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pijuan-Sala B, Griffiths JA, Guibentif C et al (2019) A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566:490–495. https://doi.org/10.1038/s41586-019-0933-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kolodziejczyk AA, Kim JK, Tsang JCH et al (2015) Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 17:471–485. https://doi.org/10.1016/j.stem.2015.09.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gao X, Nowak-Imialek M, Chen X et al (2019) Establishment of porcine and human expanded potential stem cells. Nat Cell Biol 21:687–699. https://doi.org/10.1038/s41556-019-0333-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Spangler A, Su EY, Craft AM, Cahan P (2018) A single cell transcriptional portrait of embryoid body differentiation and comparison to progenitors of the developing embryo. Stem Cell Res 31:201–215. https://doi.org/10.1016/j.scr.2018.07.022

    Article  PubMed  PubMed Central  Google Scholar 

  33. Kim IS, Wu J, Rahme GJ et al (2020) Parallel single-cell RNA-seq and genetic recording reveals lineage decisions in developing embryoid bodies. Cell Rep 33:108222. https://doi.org/10.1016/j.celrep.2020.108222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kaminow B, Yunusov D, Dobin A (2021) STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data. Biorxiv 2021.05.05.442755. https://doi.org/10.1101/2021.05.05.442755

  35. Satija R, Farrell JA, Gennert D et al (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33:495–502. https://doi.org/10.1038/nbt.3192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wolf FA, Angerer P, Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15. https://doi.org/10.1186/s13059-017-1382-0

    Article  PubMed  PubMed Central  Google Scholar 

  37. Becht E, McInnes L, Healy J et al (2019) Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol 37:38–44. https://doi.org/10.1038/nbt.4314

    Article  CAS  Google Scholar 

  38. Traag VA, Waltman L, van Eck NJ (2019) From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9:5233. https://doi.org/10.1038/s41598-019-41695-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Picelli S, Björklund ÅK, Reinius B et al (2014) Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res 24:2033–2040. https://doi.org/10.1101/gr.177881.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Melsted P, Booeshaghi AS, Liu L et al (2021) Modular, efficient and constant-memory single-cell RNA-seq preprocessing. Nat Biotechnol 39(7):1–6. https://doi.org/10.1038/s41587-021-00870-2

    Article  CAS  Google Scholar 

  41. Kent WJ, Sugnet CW, Furey TS et al (2002) The human genome browser at UCSC. Genome Res 12:996–1006. https://doi.org/10.1101/gr.229102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Frankish A, Diekhans M, Ferreira A-M et al (2018) GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 47(D1):D766–D773. https://doi.org/10.1093/nar/gky955

    Article  CAS  PubMed Central  Google Scholar 

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Acknowledgments

We thank all members from the Chen lab and the Zhang lab for the helpful comments on the manuscript. The work was supported by Shenzhen Science And Technology Innovation Committee (ZDSYS20200811144002008) and National Natural Science Foundation of China (31970812). The computational work was supported by Center for Computational Science and Engineering at Southern University of Science and Technology.

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Authors and Affiliations

  1. Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China

    Jianqun Zheng, Qiushi Xu, Wei Xu & Xi Chen

  2. Department of Physiology, School of Basic Medical Sciences, Binzhou Medical University, Yantai, China

    Wensheng Zhang

  3. Cam-Su Genomic Resource Center, Soochow University, Suzhou, China

    Ying Ye & Wensheng Zhang

Authors
  1. Jianqun Zheng
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  2. Ying Ye
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  3. Qiushi Xu
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  4. Wei Xu
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  5. Wensheng Zhang
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  6. Xi Chen
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Corresponding authors

Correspondence to Wensheng Zhang or Xi Chen .

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Editors and Affiliations

  1. Ottawa, ON, Canada

    Kursad Turksen

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Zheng, J., Ye, Y., Xu, Q., Xu, W., Zhang, W., Chen, X. (2021). A Modified SMART-Seq Method for Single-Cell Transcriptomic Analysis of Embryoid Body Differentiation. In: Turksen, K. (eds) Embryonic Stem Cell Protocols . Methods in Molecular Biology, vol 2520. Humana, New York, NY. https://doi.org/10.1007/7651_2021_435

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  • DOI: https://doi.org/10.1007/7651_2021_435

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2436-4

  • Online ISBN: 978-1-0716-2437-1

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