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Optimizations for identifying reference genes in bone and cartilage bioengineering

Open access
Xiong, Fei, Cheng, Xiangyun, Zhang, Chao, Klar, Roland Manfred, He, Tao·2021·BMC Biotechnology
DOI 10.1186/s12896-021-00685-8PMC PMC7972220

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Abstract

Background: Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) remains one of the best-established techniques to assess gene expression patterns. However, appropriate reference gene(s) selection remains a critical and challenging subject in which inappropriate reference gene selction can distort results leading to false interpretations. To date, mixed opinions still exist in how to choose the most optimal reference gene sets in accodrance to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guideline. Therefore, the purpose of this study was to investigate which schemes were the most feasible for the identification of reference genes in a bone and cartilage bioengineering experimental setting. In this study, rat bone mesenchymal stem cells (rBMSCs), skeletal muscle tissue and adipose tissue were utilized, undergoing either chondrogenic or osteogenic induction, to investigate the optimal reference gene set identification scheme that would subsequently ensure stable and accurate interpretation of gene expression in bone and cartilage bioengineering. Results: The stability and pairwise variance of eight candidate reference genes were analyzed using geNorm. The V 0.15 - vs. V min -based normalization scheme in rBMSCs had no significant effect on the eventual normalization of target genes. In terms of the muscle tissue, the results of the correlation of NF values between the V 0.15 and V min schemes and the variance of target genes expression levels generated by these two schemes showed that different schemes do indeed have a significant effect on the eventual normalization of target genes. Three selection schemes were adopted in terms of the adipose tissue, including the three optimal reference genes (Opt 3 ), V 0.20 and V min schemes, and the analysis of NF values with eventual normalization of target genes showed that the different selection schemes also have a significant effect on the eventual normalization of target genes. Conclusions: Based on these results, the proposed cut-off value of Vn/n + 1 under 0.15, according to the geNorm algorithm, should be considered with caution. For cell only experiments, at least rBMSCs, a Vn/n + 1 under 0.15 is sufficient in RT-qPCR studies. However, when using certain tissue types such as skeletal muscle and adipose tissue the minimum Vn/n + 1 should be used instead as this provides a far superior mode of generating accurate gene expression results. We thus recommended that when the stability and variation of a candidate reference genes in a specific study is unclear the minimum Vn/n + 1 should always be used as this ensures the best and most accurate gene expression value is achieved during RT-qPCR assays.

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Fig. 1 V0.15- vs. Vmin-based normalization scheme for gene expression assay in rBMSCs undergoing osteogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.15 = 0.078, Vmin = 0.055; b The sequencing of eight candidate reference genes based on M value; c Scatter plots of NFs between V0.15 and Vmin-based scheme (r = Spearman rank correlation coefficient); d The comparison of target gene expression levels (CNRQ) using either the V0.15 or Vmin-based scheme. rBMSCs, rat bone marrow mesenchymal stem cells; NF, normalization factor; CNRQ, calibrated normalized relative quantity
Fig. 1 V0.15- vs. Vmin-based normalization scheme for gene expression assay in rBMSCs undergoing osteogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.15 = 0.078, Vmin = 0.055; b The sequencing of eight candidate reference genes based on M value; c Scatter plots of NFs between V0.15 and Vmin-based scheme (r = Spearman rank correlation coefficient); d The comparison of target gene expression levels (CNRQ) using either the V0.15 or Vmin-based scheme. rBMSCs, rat bone marrow mesenchymal stem cells; NF, normalization factor; CNRQ, calibrated normalized relative quantityCC BY · PubMed Central
Fig. 2 V0.15- vs. Vmin-based normalization scheme for gene expression assay in rBMSCs undergoing chondrogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.15 = 0.068, Vmin = 0.058; b The sequencing of eight candidate reference genes based on M value; c Scatter plots of NFs between V0.15 and Vmin-based scheme (r = Spearman rank correlation coefficient). b The comparison of target gene expression levels (CNRQ) using either the V0.15 or Vmin-based scheme. rBMSCs, rat bone marrow mesenchymal stem cells; NF, normalization factor; CNRQ, calibrated normalized relative quantity
Fig. 2 V0.15- vs. Vmin-based normalization scheme for gene expression assay in rBMSCs undergoing chondrogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.15 = 0.068, Vmin = 0.058; b The sequencing of eight candidate reference genes based on M value; c Scatter plots of NFs between V0.15 and Vmin-based scheme (r = Spearman rank correlation coefficient). b The comparison of target gene expression levels (CNRQ) using either the V0.15 or Vmin-based scheme. rBMSCs, rat bone marrow mesenchymal stem cells; NF, normalization factor; CNRQ, calibrated normalized relative quantityCC BY · PubMed Central
Fig. 3 V0.15- vs. Vmin-based normalization scheme for gene expression assay on muscle tissue undergoing osteogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.15 = 0.108, Vmin = 0.067; b The sequencing of eight candidate reference genes based on M value; c Scatter plots of NFs between V0.15 and Vmin-based scheme (r = Spearman rank correlation coefficient). d The comparison of target gene expression levels (CNRQ) using either the V0.15 or Vmin-based scheme. NF, normalization factor; CNRQ, calibrated normalized relative quantity
Fig. 3 V0.15- vs. Vmin-based normalization scheme for gene expression assay on muscle tissue undergoing osteogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.15 = 0.108, Vmin = 0.067; b The sequencing of eight candidate reference genes based on M value; c Scatter plots of NFs between V0.15 and Vmin-based scheme (r = Spearman rank correlation coefficient). d The comparison of target gene expression levels (CNRQ) using either the V0.15 or Vmin-based scheme. NF, normalization factor; CNRQ, calibrated normalized relative quantityCC BY · PubMed Central
Fig. 4 V0.15- vs. Vmin-based normalization scheme for gene expression assay on muscle tissue undergoing chondrogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.15 = 0.079, Vmin = 0.057; b The sequencing of eight candidate reference genes based on M value; c Scatter plots of NFs between V0.15 and Vmin-based scheme (r = Spearman rank correlation coefficient). d The comparison of target gene expression levels (CNRQ) using either the V0.15 or Vmin-based scheme. NF, normalization factor; CNRQ, calibrated normalized relative quantity
Fig. 4 V0.15- vs. Vmin-based normalization scheme for gene expression assay on muscle tissue undergoing chondrogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.15 = 0.079, Vmin = 0.057; b The sequencing of eight candidate reference genes based on M value; c Scatter plots of NFs between V0.15 and Vmin-based scheme (r = Spearman rank correlation coefficient). d The comparison of target gene expression levels (CNRQ) using either the V0.15 or Vmin-based scheme. NF, normalization factor; CNRQ, calibrated normalized relative quantityCC BY · PubMed Central
Fig. 5 V0.20/Opt3− vs. Vmin-based normalization schemes for gene expression assay on adipose tissue undergoing osteogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.20 = 0.194, V3/4 = 0.254, Vmin = 0.170; b The sequencing of eight candidate reference genes based on M value; c, e Scatter plots of NFs using three schemes (r = Spearman rank correlation coefficient). d, f The comparison of target gene expression levels (CNRQ) between V0.20/Opt3- and Vmin-based normalization schemes. NF, normalization factor; CNRQ, calibrated normalized relative quantity
Fig. 5 V0.20/Opt3− vs. Vmin-based normalization schemes for gene expression assay on adipose tissue undergoing osteogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.20 = 0.194, V3/4 = 0.254, Vmin = 0.170; b The sequencing of eight candidate reference genes based on M value; c, e Scatter plots of NFs using three schemes (r = Spearman rank correlation coefficient). d, f The comparison of target gene expression levels (CNRQ) between V0.20/Opt3- and Vmin-based normalization schemes. NF, normalization factor; CNRQ, calibrated normalized relative quantityCC BY · PubMed Central
Fig. 6 V0.20/Opt3− vs. Vmin-based normalization schemes for gene expression assay on adipose tissue undergoing chondrogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.20 = 0.181, V3/4 = 0.226, Vmin = 0.168; b The sequencing of eight candidate reference genes based on M value; c, e Selected scatterplots of normalization factors using three schemes (r = Spearman rank correlation coefficient). d, f The comparison of target gene expression levels (CNRQ) between V0.20/Opt3- and Vmin-based normalization schemes. NF, normalization factor; CNRQ, calibrated normalized relative quantity
Fig. 6 V0.20/Opt3− vs. Vmin-based normalization schemes for gene expression assay on adipose tissue undergoing chondrogenic differentiation. a Pairwise variation (Vn/n + 1) analysis between NFn and NFn + 1 to determine the optimal number of reference genes required for accurate normalization, V0.20 = 0.181, V3/4 = 0.226, Vmin = 0.168; b The sequencing of eight candidate reference genes based on M value; c, e Selected scatterplots of normalization factors using three schemes (r = Spearman rank correlation coefficient). d, f The comparison of target gene expression levels (CNRQ) between V0.20/Opt3- and Vmin-based normalization schemes. NF, normalization factor; CNRQ, calibrated normalized relative quantityCC BY · PubMed Central
Fig. 7 Schematic diagram on choosing the optimal normalization scheme for identifying proper reference genes. rBMSCs, rat bone marrow mesenchymal stem cells; RGs, reference genes; NF, normalization factor; CNRQ, calibrated normalized relative quantity
Fig. 7 Schematic diagram on choosing the optimal normalization scheme for identifying proper reference genes. rBMSCs, rat bone marrow mesenchymal stem cells; RGs, reference genes; NF, normalization factor; CNRQ, calibrated normalized relative quantityCC BY · PubMed Central

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Research works printed but not linked: 51

  1. Richardson SM, Hoyland JA, Mobasheri R, Csaki C, Shakibaei M, Mobasheri A. Mesenchymal stem cells in regenerative medicine: opportunities and challenges for articular cartilage and intervertebral disc tissue engineering. J Cell Physiol. 2010;222(1):23–32. https://doi.org/10.1002/jcp.21915. · DOI
  2. Bhosale AM, Kuiper JH, Johnson WE, Harrison PE, Richardson JB. Midterm to long-term longitudinal outcome of autologous chondrocyte implantation in the knee joint: a multilevel analysis. Am J Sports Med. 2009;37(Suppl 1):131S–8S. · DOI
  3. Richardson SM, Kalamegam G, Pushparaj PN, Matta C, Memic A, Khademhosseini A, Mobasheri R, Poletti FL, Hoyland JA, Mobasheri A. Mesenchymal stem cells in regenerative medicine: focus on articular cartilage and intervertebral disc regeneration. Methods. 2016;99:69–80. https://doi.org/10.1016/j.ymeth.2015.09.015. · DOI
  4. Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich J. The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends Biotechnol. 2019;37(7):761–74. https://doi.org/10.1016/j.tibtech.2018.12.002. · DOI
  5. Bustin SA. Why the need for qPCR publication guidelines?--the case for MIQE. Methods. 2010;50(4):217–26. https://doi.org/10.1016/j.ymeth.2009.12.006. · DOI
  6. Vitorino Carvalho A, Courousse N, Crochet S, Coustham V. Identification of Reference Genes for Quantitative Gene Expression Studies in Three Tissues of Japanese Quail. Genes (Basel). 2019;10(3):197. https://doi.org/10.3390/genes10030197. · DOI
  7. Fang Z, Cui X. Design and validation issues in RNA-seq experiments. Brief Bioinform. 2011;12(3):280–7. https://doi.org/10.1093/bib/bbr004. · DOI
  8. Git A, Dvinge H, Salmon-Divon M, Osborne M, Kutter C, Hadfield J, Bertone P, Caldas C. Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA. 2010;16(5):991–1006. https://doi.org/10.1261/rna.1947110. · DOI · Paper page
  9. Sanders R, Mason DJ, Foy CA, Huggett JF. Considerations for accurate gene expression measurement by reverse transcription quantitative PCR when analysing clinical samples. Anal Bioanal Chem. 2014;406(26):6471–83. https://doi.org/10.1007/s00216-014-7857-x. · DOI
  10. Sanders R, Bustin S, Huggett J, Mason D. Improving the standardization of mRNA measurement by RT-qPCR. Biomol Detect Quantif. 2018;15:13–7. https://doi.org/10.1016/j.bdq.2018.03.001. · DOI
  11. Zhang WX, Fan J, Ma J, Rao YS, Zhang L, Yan YE. Selection of Suitable Reference Genes for Quantitative Real-Time PCR Normalization in Three Types of Rat Adipose Tissue. Int J Mol Sci. 2016;17(6):968. https://doi.org/10.3390/ijms17060968. · DOI
  12. Murphy J, Bustin SA. Reliability of real-time reverse-transcription PCR in clinical diagnostics: gold standard or substandard? Expert Rev Mol Diagn. 2009;9(2):187–97. https://doi.org/10.1586/14737159.9.2.187. · DOI
  13. Andersen CL, Jensen JL, Orntoft TF. 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 Res. 2004;64(15):5245–50. https://doi.org/10.1158/0008-5472.CAN-04-0496. · DOI · Paper page
  14. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7):RESEARCH0034. · DOI · Paper page
  15. Pfaffl MW, Horgan GW, Dempfle L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002;30(9):e36–336. https://doi.org/10.1093/nar/30.9.e36. · DOI
  16. Guenin S, Mauriat M, Pelloux J, Van Wuytswinkel O, Bellini C, Gutierrez L. Normalization of qRT-PCR data: the necessity of adopting a systematic, experimental conditions-specific, validation of references. J Exp Bot. 2009;60(2):487–93. https://doi.org/10.1093/jxb/ern305. · DOI
  17. Farrokhi A, Eslaminejad MB, Nazarian H, Moradmand A, Samadian A, Akhlaghi A. Appropriate reference gene selection for real-time PCR data normalization during rat mesenchymal stem cell differentiation. Cell Mol Biol (Noisy-le-grand). 2012;58 Suppl:OL1660–70.
  18. Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, Schnall-Levin M, White J, Sanford EM, An P, Sun J, Juhn F, Brennan K, Iwanik K, Maillet A, Buell J, White E, Zhao M, Balasubramanian S, Terzic S, Richards T, Banning V, Garcia L, Mahoney K, Zwirko Z, Donahue A, Beltran H, Mosquera JM, Rubin MA, Dogan S, Hedvat CV, Berger MF, Pusztai L, Lechner M, Boshoff C, Jarosz M, Vietz C, Parker A, Miller VA, Ross JS, Curran J, Cronin MT, Stephens PJ, Lipson D, Yelensky R. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31(11):1023–31. https://doi.org/10.1038/nbt.2696. · DOI · Paper page
  19. D'Haene B, Vandesompele J, Hellemans J. Accurate and objective copy number profiling using real-time quantitative PCR. Methods. 2010;50(4):262–70. https://doi.org/10.1016/j.ymeth.2009.12.007. · DOI
  20. Pombo-Suarez M, Calaza M, Gomez-Reino JJ, Gonzalez A. Reference genes for normalization of gene expression studies in human osteoarthritic articular cartilage. BMC Mol Biol. 2008;9(1):17. https://doi.org/10.1186/1471-2199-9-17. · DOI
  21. Schulze F, Malhan D, El Khassawna T, Heiss C, Seckinger A, Hose D, Rosen-Wolff A. A tissue-based approach to selection of reference genes for quantitative real-time PCR in a sheep osteoporosis model. BMC Genomics. 2017;18(1):975. https://doi.org/10.1186/s12864-017-4356-4. · DOI
  22. He T, Huang Y, Chak JC, Klar RM. Recommendations for improving accuracy of gene expression data in bone and cartilage tissue engineering. Sci Rep. 2018;8(1):14874. https://doi.org/10.1038/s41598-018-33242-z. · DOI
  23. Okamura K, Inagaki Y, Matsui TK, Matsubayashi M, Komeda T, Ogawa M, Mori E, Tanaka Y. RT-qPCR analyses on the osteogenic differentiation from human iPS cells: an investigation of reference genes. Sci Rep. 2020;10(1):11748. https://doi.org/10.1038/s41598-020-68752-2. · DOI
  24. Ayers D, Clements DN, Salway F, Day PJ. Expression stability of commonly used reference genes in canine articular connective tissues. BMC Vet Res. 2007;3(1):7. https://doi.org/10.1186/1746-6148-3-7. · DOI
  25. Hosseini A, Sauerwein H, Mielenz M. Putative reference genes for gene expression studies in propionate and beta-hydroxybutyrate treated bovine adipose tissue explants. J Anim Physiol Anim Nutr (Berl). 2010;94(5):e178–84. https://doi.org/10.1111/j.1439-0396.2010.01002.x. · DOI
  26. An Y, Reimers K, Allmeling C, Liu J, Lazaridis A, Vogt PM. Validation of differential gene expression in muscle engineered from rat groin adipose tissue by quantitative real-time PCR. Biochem Biophys Res Commun. 2012;421(4):736–42. https://doi.org/10.1016/j.bbrc.2012.04.073. · DOI
  27. Lee WJ, Jeon RH, Jang SJ, Park JS, Lee SC, Baregundi Subbarao R, Lee SL, Park BW, King WA, Rho GJ. Selection of reference genes for quantitative gene expression in porcine Mesenchymal stem cells derived from various sources along with differentiation into multilineages. Stem Cells Int. 2015;2015:235192. · DOI
  28. Zhang G, Zhao M, Song C, Luo A, Bai J, Guo S. Characterization of reference genes for quantitative real-time PCR analysis in various tissues of Anoectochilus roxburghii. Mol Biol Rep. 2012;39(5):5905–12. https://doi.org/10.1007/s11033-011-1402-1. · DOI
  29. Perez R, Tupac-Yupanqui I, Dunner S. Evaluation of suitable reference genes for gene expression studies in bovine muscular tissue. BMC Mol Biol. 2008;9(1):79. https://doi.org/10.1186/1471-2199-9-79. · DOI
  30. He T, Hausdorf J, Chevalier Y, Klar RM. Trauma induced tissue survival in vitro with a muscle-biomaterial based osteogenic organoid system: a proof of concept study. BMC Biotechnol. 2020;20(1):8. https://doi.org/10.1186/s12896-020-0602-y. · DOI
  31. Huang Y, Seitz D, König F, Müller PE, Jansson V, Klar RM. Induction of articular Chondrogenesis by chitosan/hyaluronic-acid-based biomimetic matrices using human adipose-derived stem cells. Int J Mol Sci. 2019;20(18):4487. https://doi.org/10.3390/ijms20184487. · DOI
  32. Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GA, Zumla A. The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem. 2005;344(1):141–3. https://doi.org/10.1016/j.ab.2005.05.022. · DOI
  33. Eisenberg E, Levanon EY. Human housekeeping genes, revisited. Trends Genet. 2013;29(10):569–74. https://doi.org/10.1016/j.tig.2013.05.010. · DOI
  34. Goossens K, Van Poucke M, Van Soom A, Vandesompele J, Van Zeveren A, Peelman LJ. Selection of reference genes for quantitative real-time PCR in bovine preimplantation embryos. BMC Dev Biol. 2005;5(1):27. https://doi.org/10.1186/1471-213X-5-27. · DOI
  35. Smits K, Goossens K, Van Soom A, Govaere J, Hoogewijs M, Vanhaesebrouck E, Galli C, Colleoni S, Vandesompele J, Peelman L. Selection of reference genes for quantitative real-time PCR in equine in vivo and fresh and frozen-thawed in vitro blastocysts. BMC Res Notes. 2009;2(1):246. https://doi.org/10.1186/1756-0500-2-246. · DOI
  36. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper--excel-based tool using pair-wise correlations. Biotechnol Lett. 2004;26(6):509–15. https://doi.org/10.1023/B:BILE.0000019559.84305.47. · DOI · Paper page
  37. Tong Z, Gao Z, Wang F, Zhou J, Zhang Z. Selection of reliable reference genes for gene expression studies in peach using real-time PCR. BMC Mol Biol. 2009;10(1):71. https://doi.org/10.1186/1471-2199-10-71. · DOI
  38. Erkens T, Van Poucke M, Vandesompele J, Goossens K, Van Zeveren A, Peelman LJ. Development of a new set of reference genes for normalization of real-time RT-PCR data of porcine backfat and longissimus dorsi muscle, and evaluation with PPARGC1A. BMC Biotechnol. 2006;6(1):41. https://doi.org/10.1186/1472-6750-6-41. · DOI
  39. Ragni E, Vigano M, Rebulla P, Giordano R, Lazzari L. What is beyond a qRT-PCR study on mesenchymal stem cell differentiation properties: how to choose the most reliable housekeeping genes. J Cell Mol Med. 2013;17(1):168–80. https://doi.org/10.1111/j.1582-4934.2012.01660.x. · DOI
  40. Lu Y, Yuan M, Gao X, Kang T, Zhan S, Wan H, Li J. Identification and validation of reference genes for gene expression analysis using quantitative PCR in Spodoptera litura (Lepidoptera: Noctuidae). PLoS One. 2013;8(7):e68059. https://doi.org/10.1371/journal.pone.0068059. · DOI
  41. Ren B, Betz VM, Thirion C, Salomon M, Jansson V, Muller PE, Betz OB. Osteoinduction within BMP-2 transduced muscle tissue fragments with and without a fascia layer: implications for bone tissue engineering. Gene Ther. 2019;26(1–2):16–28. https://doi.org/10.1038/s41434-018-0047-2. · DOI
  42. Bach AD, Beier JP, Stern-Staeter J, Horch RE. Skeletal muscle tissue engineering. J Cell Mol Med. 2004;8(4):413–22. https://doi.org/10.1111/j.1582-4934.2004.tb00466.x. · DOI
  43. Clements DN, Vaughan-Thomas A, Peansukmanee S, Carter SD, Innes JF, Ollier WE, Clegg PD. Assessment of the use of RNA quality metrics for the screening of articular cartilage specimens from clinically normal dogs and dogs with osteoarthritis. Am J Vet Res. 2006;67(8):1438–44. https://doi.org/10.2460/ajvr.67.8.1438. · DOI
  44. Hunter MJ, Commerford SL. Pressure homogenization of mammalian tissues. Biochim Biophys Acta. 1961;47(3):580–6. https://doi.org/10.1016/0006-3002(61)90553-4. · DOI
  45. Wehrli BM, Huang W, De Crombrugghe B, Ayala AG, Czerniak B. Sox9, a master regulator of chondrogenesis, distinguishes mesenchymal chondrosarcoma from other small blue round cell tumors. Hum Pathol. 2003;34(3):263–9. https://doi.org/10.1053/hupa.2003.41. · DOI
  46. Estes BT, Guilak F. Three-dimensional culture systems to induce chondrogenesis of adipose-derived stem cells. Methods Mol Biol. 2011;702:201–17. https://doi.org/10.1007/978-1-61737-960-4_15. · DOI
  47. Yang Q, Peng J, Guo Q, Huang J, Zhang L, Yao J, Yang F, Wang S, Xu W, Wang A, Lu S. A cartilage ECM-derived 3-D porous acellular matrix scaffold for in vivo cartilage tissue engineering with PKH26-labeled chondrogenic bone marrow-derived mesenchymal stem cells. Biomaterials. 2008;29(15):2378–87. https://doi.org/10.1016/j.biomaterials.2008.01.037. · DOI
  48. Sottile V, Thomson A, McWhir J. In vitro osteogenic differentiation of human ES cells. Cloning Stem Cells. 2003;5(2):149–55. https://doi.org/10.1089/153623003322234759. · DOI
  49. Warrington JA, Nair A, Mahadevappa M, Tsyganskaya M. Comparison of human adult and fetal expression and identification of 535 housekeeping/maintenance genes. Physiol Genomics. 2000;2(3):143–7. https://doi.org/10.1152/physiolgenomics.2000.2.3.143. · DOI
  50. Dheda K, Huggett JF, Bustin SA, Johnson MA, Rook G, Zumla A. Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques. 2004;37(1):112–4, 116, 118-119. https://doi.org/10.2144/04371RR03. · DOI
  51. VanGuilder HD, Vrana KE, Freeman WM. Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques. 2008;44(5):619–26. https://doi.org/10.2144/000112776. · DOI · Paper page

Metadata

Authors
Xiong, F., Cheng, X., Zhang, C., Klar, R. M., & He, T.
Affiliations
  • Soochow University (CN)
  • Shanghai Jiao Tong University (CN)
  • LMU Klinikum (DE)
Corresponding author
  • Klar, Roland Manfred
  • He, Tao
ORCID
Venue
BMC Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2021-03-17
Volume
21
Issue
1
Pages
25-25
ISSN
1472-6750 (ISSN-L)
DOI
10.1186/s12896-021-00685-8
PMC
PMC7972220
OpenAlex
W3136910573
Cited by
12
References
2
Citations by year
2025: 2 · 2024: 2 · 2023: 2 · 2022: 3 · 2021: 3
FWCI
0.78
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Biotechnology (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
China Scholarship Council (201708140085)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Health Sciences › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
Keywords
reference genes, qRT-PCR normalization, geNorm, gene expression stability, bone mesenchymal stem cells, chondrogenic differentiation, osteogenic differentiation, skeletal muscle, adipose tissue, MIQE guidelines, candidate reference genes
MeSH terms
Animals, Bone and Bones, Cartilage, Osteogenesis, Reference Standards, Chondrogenesis, Gene Expression Profiling, Rats, Bioengineering, Mesenchymal Stem Cells, Real-Time Polymerase Chain Reaction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Optimizations for identifying reference genes in bone and cartilage bioengineering. https://app.cochat.ai/papers/10.1186/s12896-021-00685-8
The paper
Xiong, F., Cheng, X., Zhang, C., Klar, R. M., & He, T. (2021). Optimizations for identifying reference genes in bone and cartilage bioengineering. BMC Biotechnology. https://doi.org/10.1186/s12896-021-00685-8