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Relative Quantification with the LightCycler® Carousel-Based System

Please note the LightCycler® 2.0 Instrument is no longer available for sale as of 18 December 2018. Contact your local sales representative for more information.


Read in this article:
Relative Quantification Module
Relative Quantification with External Standard
Relative Quantification, Calibrator Normalized, with Efficiency Correction
Relative Quantification, Calibrator Normalized, without Efficiency Correction (ΔΔct-method)

Relative Quantification Module

Relative Quantification analysis compares two ratios: the ratio of the target DNA sequence to a reference DNA sequence (e.g., a housekeeping gene) in an unknown sample, is compared with the ratio of the same two sequences in a standard sample (e.g., a cell line) called a calibrator. The calibrator contains typical proportions of the target and the reference sequences. The result of a Relative Quantification analysis is therefore expressed as a normalized ratio. You can perform Relative Quantification analysis as a single channel experiment “Relative Quantification-Monocolor” or a multichannel experiment “Relative Quantification-Dual Color”. If you perform a dual-color experiment, each pair of target and reference samples must be in the same capillary.

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Relative Quantification with External Standard

For gene expression quantification, usually an absolute value for the sample concentration is not relevant. Various traditional techniques (e.g., nothern blotting) express the target amount of an unknown sample relative to a reference which is usually the transcript of a housekeeping gene, which is assumed to be constitutively expressed.

The identical concept can be achieved with the LightCycler® Software 4.1/4.05 quantification methods: the target concentration in each sample is calculated relative to this non-regulated reference and the result is expressed as a target to reference ratio. Standard curves for the target and the reference are generated by serial dilutions of external standards with known copy number. The respective curves are used for quantification of the target and the housekeeping gene in each sample. For each unknown target, the result is the expressed a relative ratio of the target to the housekeeping gene, thus normalizing each sample. This method has the advantage, that it corrects for differences in quantity and quality of sample material, differences in cDNA synthesis efficiencies, etc. as described below.

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Relative Quantification, Calibrator Normalized, with Efficiency Correction

Relative quantification using efficiency correction can avoid the error generation and provides reliable and meaningful quantification data (see Table 1).

  Without efficiency correction Efficiency correction with linear fit function Efficiency correction with non-linear fit function
Adrenal Gland RNA      
40 ng 1.03 1.18 1.41
8 ng 2.21 1.79 1.01
1.6 ng 6.00 4.17 1.17
Mean value 3.08 2.38 1.21
Standard deviation 2.5967 1.5799 0.2173
Coefficient of variation 84.3% 66.4% 18.0%

Table 1: Results Tab of the Relative Quantification Analysis of a non–linear PCR, showing the impact of efficiency correction performed with a standard curve displayed with linear fit function versus non-linear fit function.


For the determination of efficiency, a statistically valid standard curve, which has to cover at least 3 - 5 orders of magnitude in the range of the samples to be analyzed, is used. The slope of a standard curve can be directly converted into efficiency by the mathematical correlation:

  A slope of -3.32 indicates a perfect reaction efficiency of 2.00, which means, the amount of PCR product doubles during each cycle. Generally, most amplification reactions do not reach this perfect efficiency, due to suboptimal reaction conditions, or inhibitory residuals from RNA preparation.


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Relative Quantification, Calibrator Normalized, without Efficiency Correction (ΔΔct-method)

This method assumes, that the efficiency of reference gene and target gene amplification is identical and is equal to 2.00. Yet, identical efficiency of two individual PCR reactions is highly unlikely, due to the multitude of influencing factors (e.g., quantity of target material, primers, fragment length, sequence etc.) The effect of differences in PCR efficiencies, when two different targets are compared, is accumulating during detection cycles.

Table 2 gives an impression for the expected systematical error caused by PCR efficiencies different from 2.00.

PCR efficiency (E) Detection Cycle (n)
  10 20 30
2.00 - - -
1.97 16% 35% 57%
1.95 29% 66% 113%
1.90 67% 179% 365%
1.80 187% 722% 2260%
1.70 408% 2480% 13,000%

Table 2: Error calculation: (2 n/E n –1) x 100.


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