ACBB Biotechnology Project

Calibration and Sample Calculation

M MD = E>< F N O = E>< Q = 6à67897:;< 8=>?;7 2" # #Once the C value of the calibrator is calculated, it is possible to calculate that of the otherTsamples:- Knowing that sample B has double the starting amount of sample A and that in eachamplification cycle the DNA amount is doubled, then 1 CYCLE LESS is needed for sample Bto hit its C value. C value of sample A is 6, then the C value of sample B is 5 (6 -1).àT T T- Knowing that sample C has double the starting amount of sample B and that in eachamplification cycle the DNA amount is doubled, then 1 CYCLE LESS is needed for sample Cto hit its C value. C value of sample B is 5, then the C value of sample C is 4 (5 -1).àT T TWhen diluting a sample 2-folds, its C will be 1 number higher than the C of the calibrator. ThisT Tmeans that if the C of calibrator is 24, the C of the sample diluted 2-fold will be 25 (24 + 1).T TWhen concentrating a sample 2-folds, its C will be 1 number lower of the C

of the calibrator.T TThis means that if the C of the calibrator is 24, the C of the sample concentrated 2-fold is 23 (24T T-1).FLAVIA GRECO ACBB BP 22/23 59CMB: NEUROBIOLOGICAL TRACK [Digitare qui] ACBB BP 22/23

EFFICIENCY

In order to have 100% efficiency when performing any PCR, especially real time PCR, it is important not to have primer dimers nor secondary structures, whilst having a short amplification product. Otherwise, the analysis may be flawed.

To evaluate the efficiency of the qPCR, a sample known to express the gene of interest is diluted 1-, 2-, 4-, 8-, 16-, 32-, 64-folds and its C evaluated each time. If there is a one-cycle shift (+1) inTthe C of each sample that has been diluted 2-folds, it means that the reaction has 100%Tefficiency.

To evaluate the efficiency of each designed primer, a similar process if followed, but the sample known to express the gene of interest is diluted 10-, 100-, 1000-folds, etc. Doing the base 2 logarithm of 10, 3.3 is obtained. Therefore, 3.3 is the

cycle shift (+3.32) in the C of each sample that has been diluted 10-folds.

Remember, increasing the dilution increased the C.

The two graphs above show a line each. This line is the so-called standard curve, which is given by plotting the log of the starting template quantity (or the dilution factor, for unknown quantities) against the C value obtained during the amplification of each dilution.

The R coefficient shown on the two graphs above is the so-called coefficient of determination. It shows how linear the data are, and this linearity gives a measure of whether the amplification efficiency is the same for different starting template copy numbers (R value must be >0.980).

The efficiency is calculated from the slope of the standard curve (in reality it is not a curve but a line on the graphs). If the efficiency of a qPCR in which samples have been diluted 10-folds each time is 100%, then the slope (indicated as the number before x in the equation of the line) of the standard curve.

The efficiency of the qPCR can be calculated using the formula:

E = 10^-m

where m is the slope. However, an efficiency between 90% and 105% is accepted.

The more general formula to calculate the efficiency of a qPCR is:

E = 10^-1*(m-1)

For example:

If the slope is -3.32, then the efficiency will be = 100%10^-1*(-3.32-1)

If the slope is -3.5, then the efficiency will be = 93%10^-1*(-3.5-1)

If the slope is -3.6, then the efficiency will be = 90%10^-1*(-3.6-1)

The C value varies with qPCR efficiency.

In the graph above, the blue standard curve has a slope of -3.3, indicating that the efficiency of the reaction is 100%. The green standard curve has a slope of -4 indicating that the reaction has an efficiency of 78%. When a high quantity (Y) is amplified under low efficiency conditions (green, 78%), its C value is detected earlier in comparison to its detection under high efficiency conditions.

efficacy conditionsT(blue, 100%). When a low quantity (X) is amplified under low efficiency conditions (green, 78%), its C is detected later in comparison to its detection under high efficiency conditions (blue, 100%).TSeveral variables can affect the efficiency of the qPCR, including a length (very short) of the amplicon, secondary structures, the primer design or contaminated reagents/template.

FLAVIA GRECO ACBB BP 22/23 61CMB: NEUROBIOLOGICAL TRACK [Digitare qui] ACBB BP 22/23

RELATIVE vs ABSOLUTE QUANTIFICATION

When calculating the relative quantification of a qPCR, one of the samples has to be set as a calibrator and the other samples quantified in relation to the calibrator. This means that the calibrator will be considered as 1 (ie. 1x standard).

Relative quantification is used to verify trends and compare different samples for the expression of one gene. The main goal with this type of quantification is to detect an increase or decrease in the expression of a gene among different samples.

graphs are the same, but their y-axis are different.

FLAVIA GRECO ACBB BP 22/23 62CMB: NEUROBIOLOGICAL TRACK [Digitare qui] ACBB BP 22/23

REFERENCE GENES

These are used as housekeeping genes.

It is possible to include an exogenous normalizer in the reaction mix, following RNA extraction.

This means that this is a normalizer of the retro-transcription, which verifies that all samples have been retro-transcribed with the same efficiency.

More commonly, endogenous normalizers are used. These are genes present in the samples that are known to not be affected by the treatment, nor to differ among the samples, as their expression is constant.

Nowadays, different software's are available to see if the selected endogenous normalizer might be affected by the treatment, and thus choose the appropriate one for the experiment.

The advantage of using a reference gene is that this method circumvents the need for accurate quantification and loading of the starting material. This is especially convenient.

when performing relative gene expression experiments where starting material is frequently limited. When comparing multiple samples, a qPCR must be performed for each one of them. One of the samples is chosen as the calibrator, and the expression of the target gene in all other samples is expressed as an increase or decrease relative to the calibrator. Usually, the untreated or baseline sample is chosen as the calibrator.

Different methods can be used to determine the expression level of the target gene in the test samples relative to the calibrator sample. The most used is the Livak method, also known as the ΔΔCT2 method.

FLAVIA GRECO ACBB BP 22/23 63CMB: NEUROBIOLOGICAL TRACK [Digitare qui] ACBB BP 22/23-ΔΔCT

LIVAK METHOD or 2 METHOD

This method assumes that both the target and the reference genes are amplified with efficiencies near 100% and within 5% of each other (i.e. very similar efficiencies).

–ΔΔCT

Before using the 2 method, it is essential to

verify the assumptions by determining the amplification efficiencies of the target and the reference genes. If the target and the reference genes have identical amplification efficiency, but the efficiency is not equal to 2, a modified –ΔΔCT version of the 2 method may be used by replacing the 2 in the equation by the actual –ΔΔCT amplification efficiency normalized expression ratio = E (E=efficiency).à

Step 1: Copy the C of the target gene and the C of the reference gene for calibrator and for the T samples.

Step 2: Normalize the C of the target gene to the C of the reference gene for calibrator and for the T T the samples by calculating the ΔC .

Usually, normalization is performed by dividing the amount of the sample by the amount of the housekeeping gene. In this case, for each sample and the calibrator, the C of the target gene T would be divided by the reference gene.

Since both C values are exponential numbers with the same base (ie. 2), ΔC

is given by the difference between the C_{target gene} and the C_{reference gene}: ∆C = C_{target gene} - C_{reference gene}

Step 3: Normalize the ∆C of the samples to the ∆C of the calibrator by calculating the ∆∆C. ∆∆C = ∆C_sample - ∆C_calibrator

Step 4: Calculate the normalized relative quantification by raising 2 to the power of -∆∆C. This means that 2 indicates the fold change in gene expression. %∆∆/-;>9=8E:_HG ]^32*'`^ a?8;7:V:I87:>; = bFLAVIA GRECO ACBB BP 22/23 64CMB: NEUROBIOLOGICAL TRACK [Digitare qui] ACBB BP 22/23-∆∆CT

Using 2 is useful to express the upregulation of a gene, whereas the -∆∆CT is better suited to express the downregulation of a gene. Both approaches are good, however it is important to specify in the figure legend what is being shown on the x-axis (ie ∆∆CT).

samples) and what is being shown on the y-axis (whether the 2 foldchange or the -ΔΔCT).-ΔΔCTWith 2 it is possible to appreciate the fold induction, but the downregulation is graphicallyunderestimated when compared to upregulation (instead of -2 and -4, the graph shows 0.5 and025).With -ΔΔC both upregulation and downregulation are shown with the same intensity.TThe drawback is that this method requires the availability of a known reference gene or geneswith constant expression in all samples being tested, genes whose ex