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Digital PCR pp 423–444Cite as

Very Low Abundance Single-Cell Transcript Quantification with 5-Plex ddPCRTM Assays

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1768))

Abstract

Gene expression studies have provided one of the most accessible windows for understanding the molecular basis of cell and tissue phenotypes and how these change in response to stimuli. Current PCR-based and next generation sequencing methods offer great versatility in allowing the focused study of the roles of small numbers of genes or comprehensive profiling of the entire transcriptome of a sample at one time. Marrying of these approaches to various cell sorting technologies has recently enabled the profiling of expression in single cells, thereby increasing the resolution and sensitivity and strengthening the inferences from observed expression levels and changes. This chapter presents a quick and efficient 1-day workflow for sorting single cells with a small laboratory cell-sorter followed by an ultrahigh sensitivity, multiplexed digital PCR method for quantitative tracking of changes in 5–10 genes per single cell.

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References

  1. Thomas PS (1980) Hybridization of denatured RNA and small DNA fragments transferred to nitrocellulose. Proc Natl Acad Sci U S A 77(9):5201–5205

    Article  CAS  Google Scholar 

  2. Karlin-Neumann GA, Sun L, Tobin EM (1988) Expression of light-harvesting chlorophyll a/b-protein genes is phytochrome-regulated in etiolated Arabidopsis thaliana seedlings. Plant Physiol 88:1323–1331

    Article  CAS  Google Scholar 

  3. Berk AJ, Sharp PA (1977) Sizing and mapping of early adenovirus mRNAs by gel electrophoresis of S1 endonuclease-digested hybrids. Cell 12(3):721–732

    Article  CAS  Google Scholar 

  4. Zinn K, DiMaio D, Maniatis T (1983) Identification of two distinct regulatory regions adjacent to the human beta-interferon gene. Cell 34(3):865–879

    Article  CAS  Google Scholar 

  5. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real- time quantitative PCR and the 2-ΔΔCT method. Methods 25:402–408. https://doi.org/10.1006/meth.2001.1262

    Article  CAS  PubMed  Google Scholar 

  6. Livak KJ, Wills QF, Tipping AJ et al (2012) Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells. Methods 59(1):71–79. https://doi.org/10.1016/j.ymeth.2012.10.004

    Article  CAS  PubMed  Google Scholar 

  7. Schena M, Shalon D, Davis RW et al (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–470

    Article  CAS  Google Scholar 

  8. DeRisi JL, Iyer VR, Brown PO (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278(5338):680–686

    Article  CAS  Google Scholar 

  9. Iyer VR, Eisen MB, Ross DT et al (1999) The transcriptional program in the response of human fibroblasts to serum. Science 283:83–87. https://doi.org/10.1126/science.283.5398.83

    Article  CAS  PubMed  Google Scholar 

  10. Lin Z, Fillmore GC, Um T-H et al (2003) Comparative microarray analysis of gene expression during activation of human peripheral blood T cells and leukemic Jurkat T cells. Lab Investig 83(6):765–776

    Article  CAS  Google Scholar 

  11. Canales RD, Luo Y, Willey JC et al (2006) Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol 24(9):1115–1122. https://doi.org/10.1038/nbt1236

    Article  CAS  PubMed  Google Scholar 

  12. Losick R (2015) A love affair with Bacillus subtilis. J Biol Chem 290(5):2529–2538. https://doi.org/10.1074/jbc.X114.634808

    Article  CAS  PubMed  Google Scholar 

  13. Sorlie T, Tibshirani R, Parker J et al (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100(14):8418–8423. https://doi.org/10.1073/pnas.0932692100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tobin EM, Silverthorne J (1985) Light regulation of gene expression in higher plants. Annu Rev Plant Physiol 36:569–593. https://doi.org/10.1146/annurev.pp.36.060185.003033

    Article  CAS  Google Scholar 

  15. Sanders R, Mason DJ, Foy CA et al (2013) Evaluation of digital PCR for absolute RNA quantification. PLoS One 8(9):e75296. https://doi.org/10.1371/journal.pone.0075296

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hindson CM, Chevillet JR, Briggs HA et al (2013) Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat Methods 10(10):1003–1005. https://doi.org/10.1038/nmeth.2633

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lemos DR, Babaeijandaghi F, Low M et al (2015) Nilotinib reduces muscle fibrosis in chronic muscle injury by promoting TNF-mediated apoptosis of fibro/adipogenic progenitors. Nat Medicine 21(7):786–794. https://doi.org/10.1038/nm.3869

    Article  CAS  Google Scholar 

  18. Nagalakshmi U, Wang Z, Waern K et al (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320(5881):1344–1349. https://doi.org/10.1126/science.1158441

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wilhelm BT, Marguerat S, Watt S et al (2008) Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453(7199):1239–1243. https://doi.org/10.1038/nature07002

    Article  CAS  PubMed  Google Scholar 

  20. Mortazavi A, Williams BA, McCue K et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628. https://doi.org/10.1038/nmeth.1226

    Article  CAS  PubMed  Google Scholar 

  21. Ståhlberg A, Bengtsson M (2010) Single-cell gene expression profiling using reverse transcription quantitative real-time PCR. Methods 50:282–288. https://doi.org/10.1016/j.ymeth.2010.01.002

    Article  CAS  PubMed  Google Scholar 

  22. 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 

  23. Shalek A, Satija R, Shuga J et al (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510(7505):363–369. https://doi.org/10.1038/nature13437

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 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 

  25. 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 

  26. Whale A, Huggett J, Tzonev S (2016) Fundamentals of multiplexing with digital PCR. Biomol Detect Quantif 10:15–23. https://doi.org/10.1016/j.bdq.2016.05.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Monzo HJ, Park TIH, Montgomery JM et al (2012) A method for generating high-yield enriched neuronal cultures from P19 embryonal carcinoma cells. J Neurosci Methods 204:87–103. https://doi.org/10.1016/j.jneumeth.2011.11.008

    Article  CAS  PubMed  Google Scholar 

  28. Zhong Q, Bhattacharya S, Kotsopoulos S et al (2011) Multiplex digital PCR: breaking the one target per color barrier of quantitative PCR. Lab Chip 11(13):2167–2174. https://doi.org/10.1039/c1lc20126c

    Article  CAS  PubMed  Google Scholar 

  29. Pender A, Garcia-Murillas I, Rana S et al (2015) Efficient genotyping of KRAS mutant non-small cell lung cancer using a multiplexed droplet digital PCR approach. PLoS One 10(9):e0139074. https://doi.org/10.1371/journal.pone.0139074

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hughesman CB, XJD L, Liu KYP et al (2016) A robust protocol for using multiplexed droplet digital PCR to quantify somatic copy number alterations in clinical tissue specimens. PLoS One 11(8):e0161274. https://doi.org/10.1371/journal.pone.0161274

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kinz E, Leiherer A, Lang AH et al (2015) Accurate quantitation of JAK2 V617F allele burden by array-based digital PCR. Int J Lab Hematol 37(2):217–224. https://doi.org/10.1111/ijlh.12269

    Article  CAS  PubMed  Google Scholar 

  32. Madic J, Zocevic A, Senlis V et al (2016) Three-color crystal digital PCR. Biomol Detect Quantif 10:34–46. https://doi.org/10.1016/j.bdq.2016.10.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We wish to thank our many colleagues who have assisted in the development of the instrumentation and reagents necessary to develop this protocol, especially Shenglong Wang who began earlier single-cell work from which this was developed. Special thanks also to Svilen Tzonev, Doug Hauge, Niels Klitgord, and Dimitri Skvortsov for assistance with the quantification analysis, and to Marcos Oquendo for assistance with the S3e cell sorter.

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Correspondence to George Karlin-Neumann .

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Karlin-Neumann, G., Zhang, B., Litterst, C. (2018). Very Low Abundance Single-Cell Transcript Quantification with 5-Plex ddPCRTM Assays. In: Karlin-Neumann, G., Bizouarn, F. (eds) Digital PCR. Methods in Molecular Biology, vol 1768. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7778-9_24

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  • DOI: https://doi.org/10.1007/978-1-4939-7778-9_24

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7776-5

  • Online ISBN: 978-1-4939-7778-9

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