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Real-time relative qPCR without reference to control samples and estimation of run-specific PCR parameters from run-internal mini-standard curves.

Bernth Jensen JM, Petersen MS, Stegger M, Østergaard LJ, Møller BK - PLoS ONE (2010)

Bottom Line: In this study, we compared RIMS-based drqPCR with classical quantifications based on external standard curves and the "comparative Ct method".Compared with classical approaches, we found that RIMS-based drqPCR provided superior precision and comparable accuracy.Also, lab-to-lab comparability can be greatly simplified.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Immunology, University Hospital of Aarhus, Skejby, Denmark. jejensen@rm.dk

ABSTRACT

Background: Real-Time quantitative PCR is an important tool in research and clinical settings. Here, we describe two new approaches that broaden the scope of real-time quantitative PCR; namely, run-internal mini standard curves (RIMS) and direct real-time relative quantitative PCR (drqPCR). RIMS are an efficient alternative to traditional standard curves and provide both run-specific and target-specific estimates of PCR parameters. The drqPCR enables direct estimation of target ratios without reference to conventional control samples.

Methodology/principal findings: In this study, we compared RIMS-based drqPCR with classical quantifications based on external standard curves and the "comparative Ct method". Specifically, we used a raw real-time PCR dataset as the basis for more than two-and-a-half million simulated quantifications with various user-defined conditions. Compared with classical approaches, we found that RIMS-based drqPCR provided superior precision and comparable accuracy.

Conclusions/significance: The obviation of referencing to control samples is attractive whenever unpaired samples are quantified. This may be in clinical and research settings; for instance, studies on chimerism, TREC quantifications, copy number variations etc. Also, lab-to-lab comparability can be greatly simplified.

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Precision of RIMS-based single ratio drqPCR.The importance of C, RIMS sample replicate number (1: black, 2: dark grey, 3: light grey, or 4: white) for quantitative precision (SD (LOG (Ris,Norm))). The 8,694 sets of RIMS-derived parameters (α and β) of similar C and RIMS sample replicate number were paired for ERV1 and TUPLE1. Each of the 4,347 paired RIMS-parameter sets were used to calculate all possible run-internal (unicate) LOG (Ris) from the remaining individual Cqs not included in the specific RIMS pair. Each LOG (Ris) was normalized by subtracting the logarithmic transformed actual target ratio (determined from the sample's position in the serial dilutions). This provided a total of 2,500,848 LOG (Ris,Norm)s. These were sub grouped according to C and RIMS replicate number. The SD of subgroups is illustrated. The number of Ris,Norms in each subgroup is calculable as: (7−LOG (C))·(4!/(m!·(4−m)!))2·3·(28−m)2. A ten-fold increase of C as well as introduction of an additional RIMS sample replicate provided significantly (p<0.001) better precision with the exceptions indicated by arrows in the figure.
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pone-0011723-g002: Precision of RIMS-based single ratio drqPCR.The importance of C, RIMS sample replicate number (1: black, 2: dark grey, 3: light grey, or 4: white) for quantitative precision (SD (LOG (Ris,Norm))). The 8,694 sets of RIMS-derived parameters (α and β) of similar C and RIMS sample replicate number were paired for ERV1 and TUPLE1. Each of the 4,347 paired RIMS-parameter sets were used to calculate all possible run-internal (unicate) LOG (Ris) from the remaining individual Cqs not included in the specific RIMS pair. Each LOG (Ris) was normalized by subtracting the logarithmic transformed actual target ratio (determined from the sample's position in the serial dilutions). This provided a total of 2,500,848 LOG (Ris,Norm)s. These were sub grouped according to C and RIMS replicate number. The SD of subgroups is illustrated. The number of Ris,Norms in each subgroup is calculable as: (7−LOG (C))·(4!/(m!·(4−m)!))2·3·(28−m)2. A ten-fold increase of C as well as introduction of an additional RIMS sample replicate provided significantly (p<0.001) better precision with the exceptions indicated by arrows in the figure.

Mentions: Next, we investigated the quantitative precision and accuracy of our two approaches when used in combination. In total, 2,500,848 unicate quantifications were determined from the raw data of the six 28-sample data sets. The quantitative precision is summarized in Figure 2. Increases in C and RIMS-sample replicate numbers both generally conferred significant precision improvements. However, the effect of using four as opposed to three RIMS-sample replicates was insignificant. The accuracy was unaffected by C or RIMS-sample replicate number and ranged between 94% and 110% of the true target ratios. Double-ratio drqPCR as argued in the introduction, the double-ratio approach (Eq. I.4) can provide Ris-estimates if the control sample contains A and B in equal concentrations. Using Eq. R.1, LOG (Ris) can be estimated as:(R.2)A prerequisite is that αs and βs are constant between interest and control samples for each target.


Real-time relative qPCR without reference to control samples and estimation of run-specific PCR parameters from run-internal mini-standard curves.

Bernth Jensen JM, Petersen MS, Stegger M, Østergaard LJ, Møller BK - PLoS ONE (2010)

Precision of RIMS-based single ratio drqPCR.The importance of C, RIMS sample replicate number (1: black, 2: dark grey, 3: light grey, or 4: white) for quantitative precision (SD (LOG (Ris,Norm))). The 8,694 sets of RIMS-derived parameters (α and β) of similar C and RIMS sample replicate number were paired for ERV1 and TUPLE1. Each of the 4,347 paired RIMS-parameter sets were used to calculate all possible run-internal (unicate) LOG (Ris) from the remaining individual Cqs not included in the specific RIMS pair. Each LOG (Ris) was normalized by subtracting the logarithmic transformed actual target ratio (determined from the sample's position in the serial dilutions). This provided a total of 2,500,848 LOG (Ris,Norm)s. These were sub grouped according to C and RIMS replicate number. The SD of subgroups is illustrated. The number of Ris,Norms in each subgroup is calculable as: (7−LOG (C))·(4!/(m!·(4−m)!))2·3·(28−m)2. A ten-fold increase of C as well as introduction of an additional RIMS sample replicate provided significantly (p<0.001) better precision with the exceptions indicated by arrows in the figure.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2908630&req=5

pone-0011723-g002: Precision of RIMS-based single ratio drqPCR.The importance of C, RIMS sample replicate number (1: black, 2: dark grey, 3: light grey, or 4: white) for quantitative precision (SD (LOG (Ris,Norm))). The 8,694 sets of RIMS-derived parameters (α and β) of similar C and RIMS sample replicate number were paired for ERV1 and TUPLE1. Each of the 4,347 paired RIMS-parameter sets were used to calculate all possible run-internal (unicate) LOG (Ris) from the remaining individual Cqs not included in the specific RIMS pair. Each LOG (Ris) was normalized by subtracting the logarithmic transformed actual target ratio (determined from the sample's position in the serial dilutions). This provided a total of 2,500,848 LOG (Ris,Norm)s. These were sub grouped according to C and RIMS replicate number. The SD of subgroups is illustrated. The number of Ris,Norms in each subgroup is calculable as: (7−LOG (C))·(4!/(m!·(4−m)!))2·3·(28−m)2. A ten-fold increase of C as well as introduction of an additional RIMS sample replicate provided significantly (p<0.001) better precision with the exceptions indicated by arrows in the figure.
Mentions: Next, we investigated the quantitative precision and accuracy of our two approaches when used in combination. In total, 2,500,848 unicate quantifications were determined from the raw data of the six 28-sample data sets. The quantitative precision is summarized in Figure 2. Increases in C and RIMS-sample replicate numbers both generally conferred significant precision improvements. However, the effect of using four as opposed to three RIMS-sample replicates was insignificant. The accuracy was unaffected by C or RIMS-sample replicate number and ranged between 94% and 110% of the true target ratios. Double-ratio drqPCR as argued in the introduction, the double-ratio approach (Eq. I.4) can provide Ris-estimates if the control sample contains A and B in equal concentrations. Using Eq. R.1, LOG (Ris) can be estimated as:(R.2)A prerequisite is that αs and βs are constant between interest and control samples for each target.

Bottom Line: In this study, we compared RIMS-based drqPCR with classical quantifications based on external standard curves and the "comparative Ct method".Compared with classical approaches, we found that RIMS-based drqPCR provided superior precision and comparable accuracy.Also, lab-to-lab comparability can be greatly simplified.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Immunology, University Hospital of Aarhus, Skejby, Denmark. jejensen@rm.dk

ABSTRACT

Background: Real-Time quantitative PCR is an important tool in research and clinical settings. Here, we describe two new approaches that broaden the scope of real-time quantitative PCR; namely, run-internal mini standard curves (RIMS) and direct real-time relative quantitative PCR (drqPCR). RIMS are an efficient alternative to traditional standard curves and provide both run-specific and target-specific estimates of PCR parameters. The drqPCR enables direct estimation of target ratios without reference to conventional control samples.

Methodology/principal findings: In this study, we compared RIMS-based drqPCR with classical quantifications based on external standard curves and the "comparative Ct method". Specifically, we used a raw real-time PCR dataset as the basis for more than two-and-a-half million simulated quantifications with various user-defined conditions. Compared with classical approaches, we found that RIMS-based drqPCR provided superior precision and comparable accuracy.

Conclusions/significance: The obviation of referencing to control samples is attractive whenever unpaired samples are quantified. This may be in clinical and research settings; for instance, studies on chimerism, TREC quantifications, copy number variations etc. Also, lab-to-lab comparability can be greatly simplified.

Show MeSH
Related in: MedlinePlus