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Quantitative single-cell gene expression measurements of multiple genes in response to hypoxia treatment

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Abstract

Cell-to-cell heterogeneity in gene transcription plays a central role in a variety of vital cell processes. To quantify gene expression heterogeneity patterns among cells and to determine their biological significance, methods to measure gene expression levels at the single-cell level are highly needed. We report an experimental technique based on the DNA-intercalating fluorescent dye SYBR green for quantitative expression level analysis of up to ten selected genes in single mammalian cells. The method features a two-step procedure consisting of a step to isolate RNA from a single mammalian cell, synthesize cDNA from it, and a qPCR step. We applied the method to cell populations exposed to hypoxia, quantifying expression levels of seven different genes spanning a wide dynamic range of expression in randomly picked single cells. In the experiment, 72 single Barrett’s esophageal epithelial (CP-A) cells, 36 grown under normal physiological conditions (controls) and 36 exposed to hypoxia for 30 min, were randomly collected and used for measuring the expression levels of 28S rRNA, PRKAA1, GAPDH, Angptl4, MT3, PTGES, and VEGFA genes. The results demonstrate that the method is sensitive enough to measure alterations in gene expression at the single-cell level, clearly showing heterogeneity within a cell population. We present technical details of the method development and implementation, and experimental results obtained by use of the procedure. We expect the advantages of this technique will facilitate further developments and advances in the field of single-cell gene expression profiling on a nanotechnological scale, and eventually as a tool for future point-of-care medical applications.

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References

  1. Furusawa C, Kaneko K (2009) Chaotic expression dynamics implies pluripotency: when theory and experiment meet. Biol Direct 4:17–28

    Article  Google Scholar 

  2. Losick R, Desplan C (2008) Stochasticity and cell fate. Science 320:65–68

    Article  CAS  Google Scholar 

  3. Levsky JM, Shenoy SM, Pezo RC, Singer RH (2002) Single-cell gene expression profiling. Science 297:836–840

    Article  CAS  Google Scholar 

  4. Cohen AA, Geva-Zatorsky N, Eden E, Frenkel-Morgenstern M, Issaeva I, Sigal A, Milo R, Cohen-Saidon C, Liron Y, Kam Z, Cohen L, Danon T, Perzov N, Alon U (2008) Dynamic proteomics of individual cancer cells in response to a drug. Science 322:1511–1516

    Article  CAS  Google Scholar 

  5. Fraser D, Kaern M (2009) A chance at survival: gene expression noise and phenotypic diversification strategies. Mol Microbiol 71:1333–1340

    Article  CAS  Google Scholar 

  6. Elowitz MB, Levine AJ, Siggia ED, Swain PS (2002) Stochastic gene expression in a single cell. Science 297:1183–1186

    Article  CAS  Google Scholar 

  7. Diercks A, Kostner H, Ozinsky A (2009) Resolving cell population heterogeneity: real-time PCR for simultaneous multiplexed gene detection in multiple single-cell samples. PLoS ONE 4:e6326

    Article  Google Scholar 

  8. Taniguchi K, Kajiyama T, Kambara H (2009) Quantitative analysis of gene expression in a single cell by qPCR. Nat Methods 6:503–506

    Article  CAS  Google Scholar 

  9. Bengtsson M, Hemberg M, Rorsman P, Stahlberg A (2008) Quantification of mRNA in single cells and modelling of RT-qPCR induced noise. BMC Mol Biol 9:63–74

    Article  Google Scholar 

  10. Stahlberg A, Bengtsson M (2010) Single-cell gene expression profiling using reverse transcription quantitative real-time PCR. Methods 50:282–288

    Article  CAS  Google Scholar 

  11. Hartshorn C, Anshelevich A, Wangh LJ (2005) Rapid, single-tube method for quantitative preparation and analysis of RNA and DNA in samples as small as one cell. BMC Biotechnol 5:2–15

    Article  Google Scholar 

  12. Hartshorn C, Eckert JJ, Hartung O, Wangh LJ (2007) Single-cell duplex RT-LATE-PCR reveals Oct4 and Xist RNA gradients in 8-cell embryos. BMC Biotechnol 7:87–101

    Article  Google Scholar 

  13. Gong YA, Ogunniyi AO, Love JC (2010) Massively parallel detection of gene expression in single cells using subnanolitre wells. Lab Chip 10:2334–2337

    Article  CAS  Google Scholar 

  14. Joglekar MV, Wei C, Hardikar AA (2010) Quantitative estimation of multiple miRNAs and mRNAs from a single cell. Cold Spring Harb Protoc 2010: pdb prot5478

  15. Bertout JA, Patel SA, Simon MC (2008) The impact of O2 availability on human cancer. Nat Rev Cancer 8:967–975

    Article  CAS  Google Scholar 

  16. Lopez-Lazaro M (2007) Why do tumors metastasize? Cancer Biol Ther 6:141–144

    Article  CAS  Google Scholar 

  17. Mazumdar J, Dondeti V, Simon MC (2009) Hypoxia-inducible factors in stem cells and cancer. J Cell Mol Med 13:4319–4328

    Article  CAS  Google Scholar 

  18. Gao W, Zhang W, Meldrum DR (2011) Quantitative RT-qPCR analysis of gene expression in single bacterial cells. J Microbiol Meth 85:221–227

    Article  CAS  Google Scholar 

  19. Zhong H, Agani F, Baccala AA, Laughner E, Rioseco-Camacho N, Isaacs WB, Simons JW, Semenza GL (1998) Increased expression of hypoxia inducible factor-1 alpha in rat and human prostate cancer. Cancer Res 58:5280–5284

    CAS  Google Scholar 

  20. Yoshiji H, Gomez DE, Shibuya M, Thorgeirsson UP (1996) Expression of vascular endothelial growth factor, its receptor, and other angiogenic factors in human breast cancer. Cancer Res 56:2013–2016

    CAS  Google Scholar 

  21. Mu J, Birnbaum MJ (2001) A role for AMP-activated protein kinase in contraction and hypoxia regulated glucose transport in skeletal muscle. FASEB J 15:A1164–A1164

    Google Scholar 

  22. Arany Z, Huang LE, Eckner R, Bhattacharya S, Jiang C, Goldberg MA, Bunn HF, Livingston DM (1996) An essential role for p300/CBP in the cellular response to hypoxia. Proc Natl Acad Sci USA 93:12969–12973

    Article  CAS  Google Scholar 

  23. Wang B, Wood IS, Trayhurn P (2008) PCR arrays identify metallothionein-3 as a highly hypoxia-inducible gene in human adipocytes. Biochem Biophys Res Commun 368:88–93

    Article  CAS  Google Scholar 

  24. Murata M, Yudo K, Nakamura H, Chiba J, Okamoto K, Suematsu N, Nishioka K, Beppu M, Inoue K, Kato T, Masuko K (2009) Hypoxia upregulates the expression of angiopoietin-like-4 in human articular chondrocytes: role of angiopoietin-like-4 in the expression of matrix metalloproteinases and cartilage degradation. J Orthop Res 27:50–57

    Article  CAS  Google Scholar 

  25. Lee JJ, Natsuizaka M, Ohashi S, Wong GS, Takaoka M, Michaylira CZ, Budo D, Tobias JW, Kanai M, Shirakawa Y, Naomoto Y, Klein-Szanto AJ, Haase VH, Nakagawa H (2010) Hypoxia activates the cyclooxygenase-2-prostaglandin E synthase axis. Carcinogenesis 31:427–434

    Article  CAS  Google Scholar 

  26. Zhong H, Simons JW (1999) Direct comparison of GAPDH, beta-actin, cyclophilin, and 28S rRNA as internal standards for quantifying RNA levels under hypoxia. Biochem Biophys Res Commun 259:523–526

    Article  CAS  Google Scholar 

  27. Mayer MP, Bukau B (2005) Hsp70 chaperones: cellular functions and molecular mechanism. Cell Mol Life Sci 62:670–684

    Article  CAS  Google Scholar 

  28. Mu J, Brozinick JT, Valladares O, Bucan M, Birnbaum MJ (2001) A role for AMP-activated protein kinase in contraction- and hypoxia-regulated glucose transport in skeletal muscle. Mol Cell 7:1085–1094

    Article  CAS  Google Scholar 

  29. Tian YQ, Shumway BR, Meldrum DR (2010) A new cross-linkable oxygen sensor covalently bonded into poly(2-hydroxyethyl methacrylate)-co-polyacrylamide thin film for dissolved oxygen sensing. Chem Mater 22:2069–2078

    Article  CAS  Google Scholar 

  30. Anis YH, Holl MR, Meldrum DR (2010) Automated selection and placement of single cells using vision-based feedback control. IEEE Trans Autom Sci Eng 7:598–606

    Article  Google Scholar 

  31. Fleige S, Pfaffl MW (2006) RNA integrity and the effect on the real-time qRT-PCR performance. Mol Aspects Med 27:126–139

    Article  CAS  Google Scholar 

  32. Cazes A, Galaup A, Chomel C, Bignon M, Bréchot N, Le Jan S, Weber H, Corvol P, Muller L, Germain S, Monnot C (2006) Extracellular matrix-bound angiopoietin-like 4 inhibits endothelial cell adhesion, migration, and sprouting and alters actin cytoskeleton. Circ Res 99:1207–1215

    Article  CAS  Google Scholar 

  33. Galaup A, Cazes A, Le Jan S, Philippe J, Connault E, Le Coz E, Mekid H, Mir LM, Opolon P, Corvol P, Monnot C, Germain S (2006) Angiopoietin-like 4 prevents metastasis through inhibition of vascular permeability and tumor cell motility and invasiveness. Proc Natl Acad Sci USA 103:18721–18726

    Article  CAS  Google Scholar 

  34. Gentil C, Le Jan S, Philippe J, Leibowitch J, Sonigo P, Germain S, Piétri-Rouxel F (2006) Is oxygen a key factor in the lipodystrophy phenotype? Lipids Health Dis 5:27–38

    Article  Google Scholar 

  35. Gustavsson M, Mallard C, Vannucci SJ, Wilson MA, Johnston MV, Hagberg H (2007) Vascular response to hypoxic preconditioning in the immature brain. J Cereb Blood Flow Metab 27:928–938

    CAS  Google Scholar 

  36. Iyer NV, Kotch LE, Agani F, Leung SW, Laughner E, Wenger RH, Gassmann M, Gearhart JD, Lawler AM, Yu AY, Semenza GL (1998) Cellular and developmental control of O2 homeostasis by hypoxia-inducible factor 1 alpha. Genes Dev 12:149–162

    Article  CAS  Google Scholar 

  37. Liu YX, Cox SR, Morita T, Kourembanas S (1995) Hypoxia regulates vascular endothelial growth factor gene expression in endothelial cells identification of a 5' enhancer. Circ Res 77:638–643

    CAS  Google Scholar 

  38. Foldager CB, Munir S, Ulrik-Vinther M, Søballe K, Bünger C, Lind M (2009) Validation of suitable house keeping genes for hypoxia-cultured human chondrocytes. BMC Mol Biol 10:94–102

    Article  Google Scholar 

  39. Hu Z, Fan C, Livasy C, He X, Oh DS, Ewend MG, Carey LA, Subramanian S, West R, Ikpatt F, Olopade OI, van de Rijn M, Perou CM (2009) A compact VEGF signature associated with distant metastases and poor outcomes. BMC Med 7:9–23

    Article  Google Scholar 

  40. Kubista M, Andrade JM, Bengtsson M, Forootan A, Jonák J, Lind K, Sindelka R, Sjöback R, Sjögreen B, Strömbom L, Ståhlberg A, Zoric N (2006) The real-time polymerase chain reaction. Mol Aspects Med 27:95–125

    Article  CAS  Google Scholar 

  41. Golding I, Paulsson J, Zawilski SM, Cox EC (2005) Real-time kinetics of gene activity in individual bacteria. Cell 123:1025–1036

    Article  CAS  Google Scholar 

  42. Le TT, Harlepp S, Guet CC, Dittmar K, Emonet T, Pan T, Cluzel P (2005) Real-time RNA profiling within a single bacterium. Proc Natl Acad Sci USA 102:9160–9164

    Article  CAS  Google Scholar 

  43. Strovas TJ, Sauter LM, Guo X, Lidstrom ME (2007) Cell-to-cell heterogeneity in growth rate and gene expression in Methylobacterium extorquens AM1. J Bacteriol 189:7127–7133

    Article  CAS  Google Scholar 

  44. Le TT, Cheng JX (2009) Single-cell profiling reveals the origin of phenotypic variability in adipogenesis. PLoS ONE 4:e5189

    Article  Google Scholar 

  45. Valencia-Burton M, Shah A, Sutin J, Borogovac A, McCullough RM, Cantor CR, Meller A, Broude NE (2009) Spatiotemporal patterns and transcription kinetics of induced RNA in single bacterial cells. Proc Natl Acad Sci USA 106:16399–16404

    Article  CAS  Google Scholar 

  46. Lidstrom ME, Konopka MC (2010) The role of physiological heterogeneity in microbial population behavior. Nat Chem Biol 6:705–712

    Article  CAS  Google Scholar 

  47. Siegal-Gaskins D, Crosson S (2008) Tightly regulated and heritable division control in single bacterial cells. Biophys J 95:2063–2072

    Article  CAS  Google Scholar 

  48. Bengtsson M, Stahlberg A, Rorsman P, Kubista M (2005) Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res 15:1388–1392

    Article  CAS  Google Scholar 

  49. Kelly RT, Woolley AT (2005) Microfluidic systems for integrated, high-throughput DNA analysis. Anal Chem 77:96a–102a

    CAS  Google Scholar 

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Acknowledgment

This work was supported by a grant from the NIH National Human Genome Research Institute, Centers of Excellence in Genomic Sciences (Grant Number 5 P50 HG002360 to D.R.M.). We thank Patti Senechal-Willis for the assistance with cell culture. We thank Tong Fu for help with fluorescence-activated cell sorting.

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Authors and Affiliations

  1. Center for Biosignatures Discovery Automation, The Biodesign Institute, Arizona State University, P.O. Box 876501, Tempe, AZ, 85287–6501, USA

    Jia Zeng, Jiangxin Wang, Weimin Gao, Aida Mohammadreza, Laimonas Kelbauskas, Weiwen Zhang, Roger H. Johnson & Deirdre R. Meldrum

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  1. Jia Zeng
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  2. Jiangxin Wang
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  3. Weimin Gao
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  6. Weiwen Zhang
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  8. Deirdre R. Meldrum
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Correspondence to Laimonas Kelbauskas or Weiwen Zhang.

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Jia Zeng and Jiangxin Wang contributed equally to the paper.

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Zeng, J., Wang, J., Gao, W. et al. Quantitative single-cell gene expression measurements of multiple genes in response to hypoxia treatment. Anal Bioanal Chem 401, 3–13 (2011). https://doi.org/10.1007/s00216-011-5084-2

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  • DOI: https://doi.org/10.1007/s00216-011-5084-2

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