Single-Cell PCR Profiling of Gene Expression in Hematopoiesis

  • José Teles
  • Tariq Enver
  • Cristina Pina
Part of the Methods in Molecular Biology book series (MIMB, volume 1185)


Single-cell analysis of gene expression offers the possibility of exploring cellular and molecular heterogeneity in stem and developmental cell systems, including cancer, to infer routes of cellular specification and their respective gene regulatory modules. PCR-based technologies, although limited to the analysis of a predefined set of genes, afford a cost-effective balance of throughput and biological information and have become a method of choice in stem cell laboratories. Here we describe an experimental and analytical protocol based on the Fluidigm microfluidics platform for the simultaneous expression analysis of 48 or 96 genes in multiples of 48 or 96 cells. We detail wet laboratory procedures and describe clustering, principal component analysis, correlation, and classification tools for the inference of cellular pathways and gene networks.

Key words

Single-cell quantitative RT-PCR Microfluidics Hierarchical clustering Principal component analysis Machine learning Random forests Logistic regression Correlation-based gene networks 



We are grateful to Elizabeth Kruse and Mattias Ohlsson for critical reading of this chapter. We acknowledge Swedish Foundation for Strategic Research for funding to José Teles and Leukaemia and Lymphoma Research and Cancer Research, UK, for Programme Grants to Tariq Enver.


  1. 1.
    Adolfsson J, Mansson R, Buza-Vidas N et al (2005) Identification of Flt3+ lympho-myeloid stem cells lacking erythro-megakaryocytic potential a revised road map for adult blood lineage commitment. Cell 121:295–306PubMedCrossRefGoogle Scholar
  2. 2.
    Kiel MJ, Yilmaz OH, Iwashita T et al (2005) SLAM family receptors distinguish hematopoietic stem and progenitor cells and reveal endothelial niches for stem cells. Cell 121:1109–1121PubMedCrossRefGoogle Scholar
  3. 3.
    Pronk CJ, Rossi DJ, Mansson R et al (2007) Elucidation of the phenotypic, functional, and molecular topography of a myeloerythroid progenitor cell hierarchy. Cell Stem Cell 1:428–442PubMedCrossRefGoogle Scholar
  4. 4.
    Karlsson G, Rorby E, Pina C et al (2013) The tetraspanin CD9 affords high-purity capture of all murine hematopoietic stem cells. Cell Rep 4:642–648PubMedCrossRefGoogle Scholar
  5. 5.
    Chambers I, Silva J, Colby D et al (2007) Nanog safeguards pluripotency and mediates germline development. Nature 450:1230–1234PubMedCrossRefGoogle Scholar
  6. 6.
    Hayashi K, Lopes SM, Tang F et al (2008) Dynamic equilibrium and heterogeneity of mouse pluripotent stem cells with distinct functional and epigenetic states. Cell Stem Cell 3:391–401PubMedCrossRefGoogle Scholar
  7. 7.
    Kalmar T, Lim C, Hayward P et al (2009) Regulated fluctuations in nanog expression mediate cell fate decisions in embryonic stem cells. PLoS Biol 7:e1000149PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Pina C, Fugazza C, Tipping AJ et al (2012) Inferring rules of lineage commitment in haematopoiesis. Nat Cell Biol 14:287–294PubMedCrossRefGoogle Scholar
  9. 9.
    Macarthur BD, Sevilla A, Lenz M et al (2012) Nanog-dependent feedback loops regulate murine embryonic stem cell heterogeneity. Nat Cell Biol 14:1139–1147PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Cheng T, Shen H, Giokas D et al (1996) Temporal mapping of gene expression levels during the differentiation of individual primary hematopoietic cells. Proc Natl Acad Sci U S A 93:13158–13163PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Hu M, Krause D, Greaves M et al (1997) Multilineage gene expression precedes commitment in the hemopoietic system. Gene Dev 11:774–785PubMedCrossRefGoogle Scholar
  12. 12.
    Miyamoto T, Iwasaki H, Reizis B et al (2002) Myeloid or lymphoid promiscuity as a critical step in hematopoietic lineage commitment. Dev Cell 3:137–147PubMedCrossRefGoogle Scholar
  13. 13.
    Ramos CA, Bowman TA, Boles NC et al (2006) Evidence for diversity in transcriptional profiles of single hematopoietic stem cells. PLoS Genet 2:e159PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Guo G, Luc S, Marco E et al (2013) Mapping cellular hierarchy by single-cell analysis of the cell surface repertoire. Cell Stem Cell 13(4):492–505PubMedCrossRefGoogle Scholar
  15. 15.
    Teles J, Pina C, Eden P et al (2013) Transcriptional regulation of lineage commitment – a stochastic model of cell fate decisions. PLoS Comput Biol 9:e1003197PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Moignard V, Macaulay IC, Swiers G et al (2013) Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis. Nat Cell Biol 15:363–372PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Sturn A, Quackenbush J, Trajanoski Z (2002) Genesis: cluster analysis of microarray data. Bioinformatics 18:207–208PubMedCrossRefGoogle Scholar
  18. 18.
    Williams G (2011) Data mining with rattle and R: the art of excavating data for knowledge discovery. Springer, New YorkCrossRefGoogle Scholar
  19. 19.
    Buganim Y, Faddah DA, Cheng AW et al (2012) Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. Cell 150:1209–1222PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Dalerba P, Kalisky T, Sahoo D et al (2011) Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat Biotechnol 29:1120–1127PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Guo G, Huss M, Tong GQ et al (2010) Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev Cell 18:675–685PubMedCrossRefGoogle Scholar
  22. 22.
    Goardon N, Marchi E, Atzberger A et al (2011) Coexistence of LMPP-like and GMP-like leukemia stem cells in acute myeloid leukemia. Cancer Cell 19:138–152PubMedCrossRefGoogle Scholar
  23. 23.
    Stahlberg A, Andersson D, Aurelius J et al (2011) Defining cell populations with single-cell gene expression profiling: correlations and identification of astrocyte subpopulations. Nucleic Acids Res 39:e24PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Stem Cell LaboratoryUniversity College London Cancer InstituteLondonUK
  2. 2.Computational Biology and Biological Physics, Department of Astronomy and Theoretical PhysicsLund UniversityLundSweden
  3. 3.Department of Haematology, NHS-Blood and TransplantUniversity of CambridgeCambridgeUK

Personalised recommendations