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Molecular Biotechnology

, Volume 54, Issue 2, pp 623–633 | Cite as

Determination and Validation of Reference Gene Stability for qPCR Analysis in Polysaccharide Hydrogel-Based 3D Chondrocytes and Mesenchymal Stem Cell Cultural Models

  • Wai Hon Chooi
  • Ruijie Zhou
  • Suan Siong Yeo
  • Feng Zhang
  • Dong-An WangEmail author
Research

Abstract

Gene expression study is widely used to obtain information of the cell activities and phenotypes. To quantify gene expression, measurement of the mRNA copy number is commonly done by quantitative RT-PCR (RT-qPCR). However, proper reference gene is needed for different tissues to normalize the expression level of different genes accurately. In this study, reference gene determination was done for three-dimensional (3D) artificial tissue constructs in hydrogel. Porcine synovium-derived mesenchymal stem cells (SMSCs) and rabbit chondrocytes were cultured in both alginate and agarose hydrogels to set up four different 3D culture systems to form the artificial tissue constructs. The gene expression levels of candidate genes were determined by RT-qPCR and then analyzed by geNorm, Bestkeeper, and Normfinder. For porcine SMSCs, PPIA, and TBP were selected for tissue in alginate scaffold whereas HPRT and TBP were selected for the agarose scaffold system. On the other hand, HPRT, PPIA, and RPL18 were the stable reference genes for rabbit chondrocytes in alginate scaffold while TBP, RPL5, and RPL18 were selected for rabbit chondrocytes in agarose scaffold. This study has further indicated that suitable reference genes are different for each tissue and study purpose. The reference genes are expressed in different stability when a scaffold of different material is used.

Keywords

Quantitative PCR Reference gene Hydrogel 3D culture Chondrocytes Mesenchymal stem cells Tissue engineering 

References

  1. 1.
    She, X., et al. (2009). Definition, conservation and epigenetics of housekeeping and tissue-enriched genes. BMC Genomics, 10, 269.CrossRefGoogle Scholar
  2. 2.
    Radonic, A., et al. (2005). Reference gene selection for quantitative real-time PCR analysis in virus infected cells: SARS corona virus, yellow fever virus, human herpesvirus-6, camelpox virus and cytomegalovirus infections. Virology Journal, 2, 7.CrossRefGoogle Scholar
  3. 3.
    Zhu, G. Z., et al. (2001). Fudenine, a C-terminal truncated rat homologue of mouse prominin, is blood glucose-regulated and can up-regulate the expression of GAPDH. Biochemical and Biophysical Research Communications, 281(4), 951–956.CrossRefGoogle Scholar
  4. 4.
    Selvey, S., et al. (2001). Beta-actin—An unsuitable internal control for RT-PCR. Molecular and Cellular Probes, 15(5), 307–311.CrossRefGoogle Scholar
  5. 5.
    Thellin, O., et al. (1999). Housekeeping genes as internal standards: Use and limits. Journal of Biotechnology, 75(2–3), 291–295.CrossRefGoogle Scholar
  6. 6.
    Kubista, M., et al. (2006). The real-time polymerase chain reaction. Molecular Aspects of Medicine, 27(2–3), 95–125.CrossRefGoogle Scholar
  7. 7.
    Bustin, S. A. (2000). Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. Journal of Molecular Endocrinology, 25(2), 169–193.CrossRefGoogle Scholar
  8. 8.
    Gibbs, P. J., et al. (2003). House keeping genes and gene expression analysis in transplant recipients: A note of caution. Transplant Immunology, 12(1), 89–97.CrossRefGoogle Scholar
  9. 9.
    Haller, F., et al. (2004). Equivalence test in quantitative reverse transcription polymerase chain reaction: Confirmation of reference genes suitable for normalization. Analytical Biochemistry, 335(1), 1–9.CrossRefGoogle Scholar
  10. 10.
    Bustin, S. A., et al. (2005). Quantitative real-time RT-PCR: A perspective. Journal of Molecular Endocrinology, 34(3), 597–601.CrossRefGoogle Scholar
  11. 11.
    Langer, R., & Vacanti, J. P. (1993). Tissue engineering. Science, 260(5110), 920–926.CrossRefGoogle Scholar
  12. 12.
    Chung, S., & King, M. W. (2011). Design concepts and strategies for tissue engineering scaffolds. Biotechnology and Applied Biochemistry, 58(6), 423–438.CrossRefGoogle Scholar
  13. 13.
    Bottaro, D. P., Liebmann-Vinson, A., & Heidaran, M. A. (2002). Molecular signaling in bioengineered tissue microenvironments. Annals of the New York Academy of Sciences, 961, 143–153.CrossRefGoogle Scholar
  14. 14.
    Harrison, K. (2007). Introduction to polymeric scaffolds for tissue engineering. In M. Jenkins (Ed.), Biomedical polymers (pp. 1–32). Cambridge: Woodhead Publishing Limited.CrossRefGoogle Scholar
  15. 15.
    Wang, C., et al. (2008). RNA extraction from polysaccharide-based cell-laden hydrogel scaffolds. Analytical Biochemistry, 380(2), 333–334.CrossRefGoogle Scholar
  16. 16.
    Huggett, J., et al. (2005). Real-time RT-PCR normalisation; strategies and considerations. Genes and Immunity, 6(4), 279–284.CrossRefGoogle Scholar
  17. 17.
    Bustin, S. (2002). Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): Trends and problems. Journal of Molecular Endocrinology, 29(1), 23–39.CrossRefGoogle Scholar
  18. 18.
    Langelaan, M.L., et al. (2010). Advanced maturation by electrical stimulation: Differences in response between C2C12 and primary muscle progenitor cells. Journal of Tissue Engineering and Regenerative Medicine.Google Scholar
  19. 19.
    Taylor, S. E., et al. (2009). Gene expression markers of tendon fibroblasts in normal and diseased tissue compared to monolayer and three dimensional culture systems. BMC Musculoskeletal Disorders, 10, 27.CrossRefGoogle Scholar
  20. 20.
    Suzuki, T., Higgins, P. J., & Crawford, D. R. (2000). Control selection for RNA quantitation. BioTechniques, 29(2), 332–337.Google Scholar
  21. 21.
    Schmittgen, T. D., & Zakrajsek, B. A. (2000). Effect of experimental treatment on housekeeping gene expression: Validation by real-time, quantitative RT-PCR. Journal of Biochemical and Biophysical Methods, 46(1–2), 69–81.CrossRefGoogle Scholar
  22. 22.
    Radonic, A., et al. (2004). Guideline to reference gene selection for quantitative real-time PCR. Biochemical and Biophysical Research Communications, 313(4), 856–862.CrossRefGoogle Scholar
  23. 23.
    Graven, K. K., & Farber, H. W. (1998). Endothelial cell hypoxic stress proteins. Journal of Laboratory and Clinical Medicine, 132(6), 456–463.CrossRefGoogle Scholar
  24. 24.
    Yao, L., et al. (2012). Selection of housekeeping genes for normalization of RT-PCR in hypoxic neural stem cells of rat in vitro. Molecular Biology Reports, 39(1), 569–576.CrossRefGoogle Scholar
  25. 25.
    Monaco, E., et al. (2010). Selection and reliability of internal reference genes for quantitative PCR verification of transcriptomics during the differentiation process of porcine adult mesenchymal stem cells. Stem Cell Research & Therapy, 1(1), 7.CrossRefGoogle Scholar
  26. 26.
    Fox, B. C., et al. (2010). Validation of reference gene stability for APAP hepatotoxicity studies in different in vitro systems and identification of novel potential toxicity biomarkers. Toxicology in Vitro, 24(7), 1962–1970.CrossRefGoogle Scholar
  27. 27.
    Foldager, C. B., et al. (2009). Validation of suitable house keeping genes for hypoxia-cultured human chondrocytes. BMC Molecular Biology, 10, 94.CrossRefGoogle Scholar
  28. 28.
    De Bari, C., et al. (2001). Multipotent mesenchymal stem cells from adult human synovial membrane. Arthritis and Rheumatism, 44(8), 1928–1942.CrossRefGoogle Scholar
  29. 29.
    Vandesompele, J., et al. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology, 3(7), RESEARCH0034.Google Scholar
  30. 30.
    Perez, R., Tupac-Yupanqui, I., & Dunner, S. (2008). Evaluation of suitable reference genes for gene expression studies in bovine muscular tissue. BMC Molecular Biology, 9, 79.CrossRefGoogle Scholar
  31. 31.
    Pfaffl, M. W., et al. (2004). Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotechnology Letters, 26(6), 509–515.CrossRefGoogle Scholar
  32. 32.
    Andersen, C. L., Jensen, J. L., & Orntoft, T. F. (2004). Normalization of real-time quantitative reverse transcription-PCR data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Research, 64(15), 5245–5250.CrossRefGoogle Scholar
  33. 33.
    Livak, K. J., & Schmittgen, T. D. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2(-delta delta C(T)) method. Methods, 25(4), 402–408.CrossRefGoogle Scholar
  34. 34.
    Vandesompele, J., Kubista, M., & Pfaffl, M. W. (2009). Reference gene validation software for improved normalization. In J. Logan, K. Edwards, & N. Saunders (Eds.), Real-time PCR: Current technology and applications (pp. 47–64). Norfolk, UK: Caister Academic Press.Google Scholar
  35. 35.
    Willems, E., et al. (2006). Selection of reference genes in mouse embryos and in differentiating human and mouse ES cells. International Journal of Developmental Biology, 50(7), 627–635.CrossRefGoogle Scholar
  36. 36.
    Awad, H. A., et al. (2004). Chondrogenic differentiation of adipose-derived adult stem cells in agarose, alginate, and gelatin scaffolds. Biomaterials, 25(16), 3211–3222.CrossRefGoogle Scholar
  37. 37.
    Diduch, D. R., et al. (2000). Marrow stromal cells embedded in alginate for repair of osteochondral defects. Arthroscopy, 16(6), 571–577.CrossRefGoogle Scholar
  38. 38.
    Mouw, J. K., et al. (2005). Variations in matrix composition and GAG fine structure among scaffolds for cartilage tissue engineering. Osteoarthritis Cartilage, 13(9), 828–836.CrossRefGoogle Scholar
  39. 39.
    Beekman, L., et al. (2011). Evaluation of suitable reference genes for gene expression studies in bronchoalveolar lavage cells from horses with inflammatory airway disease. BMC Molecular Biology, 12, 5.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Wai Hon Chooi
    • 1
  • Ruijie Zhou
    • 1
  • Suan Siong Yeo
    • 1
  • Feng Zhang
    • 1
  • Dong-An Wang
    • 1
    Email author
  1. 1.Division of Bioengineering, School of Chemical and Biomedical EngineeringNanyang Technological UniversitySingaporeRepublic of Singapore

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