Optimizations for identifying reference genes in bone and cartilage bioengineering

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 V0.15- vs. Vmin-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 V0.15 and Vmin 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 (Opt3), V0.20 and Vmin 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.

Validation of quantitative real-time PCR reference genes and spatial expression profiles of detoxication-related genes under pesticide induction in honey bee, Apis mellifera

“…V n /V n+1 < 0.15 is usually used as a cutoff value in geNorm pairwise variation analysis [ 51 , 61 , 62 ]; however, the V n /V n+1 value seems to be applied leniently. According to previous studies [ 51 , 66 ], V n /V n+1 < 0.2 was also suggested to be the cutoff value in pairwise variation analysis, and the lowest V n /V n+1 was widely applied to suggest the number of reference genes for target gene normalization regardless of the value [ 67 ], suggesting that the cutoff value of V n /V n+1 is not crucial. Based on the V n /V n+1 values in this study, geNorm pairwise variation analysis showed that combinations with different numbers of reference genes were suggested for target gene normalization in different conditions of honey bee samples, across body parts and pesticide treatments ( Fig 3 ).…”

Evaluation of candidate reference genes for quantitative real-time PCR normalization in blood from red deer developing antlers

“…5 , all pairwise variation values fell below a cut-off value of 0.15 in our study, indicating that the use of two reference genes would be sufficient. However, because all of the different algorithms revealed that B2M, ACTB and RPLP0 were the most stable genes, we selected all three as reference genes for normalization of our qRT-PCR data to increase the resolution and accuracy of results 37 , 47 because we could not exclude the possibility that these stable genes might participate in other biochemical pathways other than antler development.

Figure 5 Optimal number of reference genes for normalization in antler blood samples of both young and adult red deer.

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Circulating MicroRNAs as Non-invasive Biomarkers for Canine Cushing's Syndrome

“…We used geNorm software ( 23 ) to determine whether these miRNAs were indeed stably expressed in EDTA plasma samples ( n = 44; 6 healthy dogs and 19 dogs with PDH, pre-op and post-op), in serum samples ( n = 32; 6 healthy dogs and 26 dogs with csACT), and in the combined EDTA plasma/serum samples. The combination of miRNAs was interpreted as suitably stable when the Pairwise Variance (V-score) was lower than 0.15 (V 0.15 ) ( 23 , 27 ). However, although the V-score decreased when all 5 miRNAs were used compared to when 3 or 4 miRNAs were used, they did not reach the V 0.15 ( Figure 1A ).…”