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Relative quantification of mRNA: comparison of methods currently used for real-time PCR data analysis.

Cikos S, Bukovská A, Koppel J - BMC Mol. Biol. (2007)

Bottom Line: The use of individual amplification efficiencies in DART-PCR, LinRegPCR and Liu & Saint exponential methods significantly impaired the results.We also compared amplification efficiencies (E) and found that although the E values determined by different methods of analysis were not identical, all the methods were capable to identify two genes whose E values significantly differed from other genes.Our results show that all the tested methods can provide quantitative values reflecting the amounts of measured mRNA in samples, but they differ in their accuracy and reproducibility.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Physiology, Slovak Academy of Sciences, Soltésovej 4, 04001 Kosice, Slovakia. cikos@saske.sk

ABSTRACT

Background: Fluorescent data obtained from real-time PCR must be processed by some method of data analysis to obtain the relative quantity of target mRNA. The method chosen for data analysis can strongly influence results of the quantification.

Results: To compare the performance of six techniques which are currently used for analysing fluorescent data in real-time PCR relative quantification, we quantified four cytokine transcripts (IL-1beta, IL-6 TNF-alpha, and GM-CSF) in an in vivo model of colonic inflammation. Accuracy of the methods was tested by quantification on samples with known relative amounts of target mRNAs. Reproducibility of the methods was estimated by the determination of the intra-assay and inter-assay variability. Cytokine expression normalized to the expression of three reference genes (ACTB, HPRT, SDHA) was then determined using the six methods for data analysis. The best results were obtained with the relative standard curve method, comparative Ct method and with DART-PCR, LinRegPCR and Liu & Saint exponential methods when average amplification efficiency was used. The use of individual amplification efficiencies in DART-PCR, LinRegPCR and Liu & Saint exponential methods significantly impaired the results. The sigmoid curve-fitting (SCF) method produced medium performance; the results indicate that the use of appropriate type of fluorescence data and in some instances manual selection of the number of amplification cycles included in the analysis is necessary when the SCF method is applied. We also compared amplification efficiencies (E) and found that although the E values determined by different methods of analysis were not identical, all the methods were capable to identify two genes whose E values significantly differed from other genes.

Conclusion: Our results show that all the tested methods can provide quantitative values reflecting the amounts of measured mRNA in samples, but they differ in their accuracy and reproducibility. Selection of the appropriate method can also depend on the design of a particular experiment. The advantages and disadvantages of the methods in different applications are discussed.

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Coefficients of variation for normalized quantity of IL-1β in serial dilutions of the RT-PCR template. Quantities of IL-1β, HPRT, SDHA and ACTB mRNAs (the R0 values) were determined in each dilution of the RT-PCR template (six dilutions of total RNA) using all the tested methods of real-time PCR data analysis. The amount of IL-1β mRNA in each dilution was then divided by the relative amount of HPRT, ACTB, or by the normalization factor (NF, geometric mean of HPRT, SDHA and ACTB amounts) of the dilution. Arithmetical mean, standard deviation and coefficient of variation of the normalized IL-1β quantity for each type of normalization (HPRT, ACTB, NF) and each method of real-time PCR data analysis was then calculated. Coefficients of variation (CV) are shown: the first columns are CV values after normalization with the normalization factor, the second columns are CV values after normalization with ACTB, and the third columns are CV values after normalization with HPRT. Methods for real-time PCR data analysis: St. C., relative standard curve; COM, comparative Ct; SCF, sigmoid curve-fitting; DAR iE, DART-PCR with individual E values; DAR aE, DART-PCR with average E values; L&S iE, Liu & Saint-exp with individual E values, L&S aE, Liu & Saint-exp with average E values; LR iE, LinRegPCR (using individual E values); LR-Ct, LinRegPCR combined with Ct (using average E values)
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Figure 2: Coefficients of variation for normalized quantity of IL-1β in serial dilutions of the RT-PCR template. Quantities of IL-1β, HPRT, SDHA and ACTB mRNAs (the R0 values) were determined in each dilution of the RT-PCR template (six dilutions of total RNA) using all the tested methods of real-time PCR data analysis. The amount of IL-1β mRNA in each dilution was then divided by the relative amount of HPRT, ACTB, or by the normalization factor (NF, geometric mean of HPRT, SDHA and ACTB amounts) of the dilution. Arithmetical mean, standard deviation and coefficient of variation of the normalized IL-1β quantity for each type of normalization (HPRT, ACTB, NF) and each method of real-time PCR data analysis was then calculated. Coefficients of variation (CV) are shown: the first columns are CV values after normalization with the normalization factor, the second columns are CV values after normalization with ACTB, and the third columns are CV values after normalization with HPRT. Methods for real-time PCR data analysis: St. C., relative standard curve; COM, comparative Ct; SCF, sigmoid curve-fitting; DAR iE, DART-PCR with individual E values; DAR aE, DART-PCR with average E values; L&S iE, Liu & Saint-exp with individual E values, L&S aE, Liu & Saint-exp with average E values; LR iE, LinRegPCR (using individual E values); LR-Ct, LinRegPCR combined with Ct (using average E values)

Mentions: Since the determined quantity of cytokines and reference genes should reflect the template dilutions by the same manner, the same normalized quantity of cytokines should be determined in each dilution. Variability of the normalized quantity between the known template dilutions can then be used as a measure of the quantification accuracy – the lower the variability between dilutions the higher the accuracy of quantification. Figure 2 shows coefficients of variation (CV) for normalized quantity of IL-1β in six RT-PCR template dilutions (similar results were obtained for IL-6, TNF-α, and GM-CSF; data not shown). In most methods, similar CV values were found after normalization to the normalization factor, to the „low Ct“-gene or to the „high Ct“-gene (which is caused by similar expression stability of the three reference genes as shown in Table 3); differences detected by the three methods utilizing individual E values were not consistent nor among the three methods neither among the four measured cytokines. On the other hand, comparison of CV values between individual methods of analysis showed markedly higher coefficients of variation in the three methods utilizing individual E values (DART-PCR, Liu & Saint-exp and LinRegPCR with individual E) than in other methods (Fig. 2).


Relative quantification of mRNA: comparison of methods currently used for real-time PCR data analysis.

Cikos S, Bukovská A, Koppel J - BMC Mol. Biol. (2007)

Coefficients of variation for normalized quantity of IL-1β in serial dilutions of the RT-PCR template. Quantities of IL-1β, HPRT, SDHA and ACTB mRNAs (the R0 values) were determined in each dilution of the RT-PCR template (six dilutions of total RNA) using all the tested methods of real-time PCR data analysis. The amount of IL-1β mRNA in each dilution was then divided by the relative amount of HPRT, ACTB, or by the normalization factor (NF, geometric mean of HPRT, SDHA and ACTB amounts) of the dilution. Arithmetical mean, standard deviation and coefficient of variation of the normalized IL-1β quantity for each type of normalization (HPRT, ACTB, NF) and each method of real-time PCR data analysis was then calculated. Coefficients of variation (CV) are shown: the first columns are CV values after normalization with the normalization factor, the second columns are CV values after normalization with ACTB, and the third columns are CV values after normalization with HPRT. Methods for real-time PCR data analysis: St. C., relative standard curve; COM, comparative Ct; SCF, sigmoid curve-fitting; DAR iE, DART-PCR with individual E values; DAR aE, DART-PCR with average E values; L&S iE, Liu & Saint-exp with individual E values, L&S aE, Liu & Saint-exp with average E values; LR iE, LinRegPCR (using individual E values); LR-Ct, LinRegPCR combined with Ct (using average E values)
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
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Figure 2: Coefficients of variation for normalized quantity of IL-1β in serial dilutions of the RT-PCR template. Quantities of IL-1β, HPRT, SDHA and ACTB mRNAs (the R0 values) were determined in each dilution of the RT-PCR template (six dilutions of total RNA) using all the tested methods of real-time PCR data analysis. The amount of IL-1β mRNA in each dilution was then divided by the relative amount of HPRT, ACTB, or by the normalization factor (NF, geometric mean of HPRT, SDHA and ACTB amounts) of the dilution. Arithmetical mean, standard deviation and coefficient of variation of the normalized IL-1β quantity for each type of normalization (HPRT, ACTB, NF) and each method of real-time PCR data analysis was then calculated. Coefficients of variation (CV) are shown: the first columns are CV values after normalization with the normalization factor, the second columns are CV values after normalization with ACTB, and the third columns are CV values after normalization with HPRT. Methods for real-time PCR data analysis: St. C., relative standard curve; COM, comparative Ct; SCF, sigmoid curve-fitting; DAR iE, DART-PCR with individual E values; DAR aE, DART-PCR with average E values; L&S iE, Liu & Saint-exp with individual E values, L&S aE, Liu & Saint-exp with average E values; LR iE, LinRegPCR (using individual E values); LR-Ct, LinRegPCR combined with Ct (using average E values)
Mentions: Since the determined quantity of cytokines and reference genes should reflect the template dilutions by the same manner, the same normalized quantity of cytokines should be determined in each dilution. Variability of the normalized quantity between the known template dilutions can then be used as a measure of the quantification accuracy – the lower the variability between dilutions the higher the accuracy of quantification. Figure 2 shows coefficients of variation (CV) for normalized quantity of IL-1β in six RT-PCR template dilutions (similar results were obtained for IL-6, TNF-α, and GM-CSF; data not shown). In most methods, similar CV values were found after normalization to the normalization factor, to the „low Ct“-gene or to the „high Ct“-gene (which is caused by similar expression stability of the three reference genes as shown in Table 3); differences detected by the three methods utilizing individual E values were not consistent nor among the three methods neither among the four measured cytokines. On the other hand, comparison of CV values between individual methods of analysis showed markedly higher coefficients of variation in the three methods utilizing individual E values (DART-PCR, Liu & Saint-exp and LinRegPCR with individual E) than in other methods (Fig. 2).

Bottom Line: The use of individual amplification efficiencies in DART-PCR, LinRegPCR and Liu & Saint exponential methods significantly impaired the results.We also compared amplification efficiencies (E) and found that although the E values determined by different methods of analysis were not identical, all the methods were capable to identify two genes whose E values significantly differed from other genes.Our results show that all the tested methods can provide quantitative values reflecting the amounts of measured mRNA in samples, but they differ in their accuracy and reproducibility.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Physiology, Slovak Academy of Sciences, Soltésovej 4, 04001 Kosice, Slovakia. cikos@saske.sk

ABSTRACT

Background: Fluorescent data obtained from real-time PCR must be processed by some method of data analysis to obtain the relative quantity of target mRNA. The method chosen for data analysis can strongly influence results of the quantification.

Results: To compare the performance of six techniques which are currently used for analysing fluorescent data in real-time PCR relative quantification, we quantified four cytokine transcripts (IL-1beta, IL-6 TNF-alpha, and GM-CSF) in an in vivo model of colonic inflammation. Accuracy of the methods was tested by quantification on samples with known relative amounts of target mRNAs. Reproducibility of the methods was estimated by the determination of the intra-assay and inter-assay variability. Cytokine expression normalized to the expression of three reference genes (ACTB, HPRT, SDHA) was then determined using the six methods for data analysis. The best results were obtained with the relative standard curve method, comparative Ct method and with DART-PCR, LinRegPCR and Liu & Saint exponential methods when average amplification efficiency was used. The use of individual amplification efficiencies in DART-PCR, LinRegPCR and Liu & Saint exponential methods significantly impaired the results. The sigmoid curve-fitting (SCF) method produced medium performance; the results indicate that the use of appropriate type of fluorescence data and in some instances manual selection of the number of amplification cycles included in the analysis is necessary when the SCF method is applied. We also compared amplification efficiencies (E) and found that although the E values determined by different methods of analysis were not identical, all the methods were capable to identify two genes whose E values significantly differed from other genes.

Conclusion: Our results show that all the tested methods can provide quantitative values reflecting the amounts of measured mRNA in samples, but they differ in their accuracy and reproducibility. Selection of the appropriate method can also depend on the design of a particular experiment. The advantages and disadvantages of the methods in different applications are discussed.

Show MeSH