Normalization

Differences in amplification efficiency are not the only source of variability in a qPCR experiment. Several factors are responsible for noise in qPCR experiments e.g. differences in:

amount of template cDNA between wells
RNA integrity of samples
efficiency of enzymes used in the PCR or in the reverse transcription

Normalization will eliminate this noise as much as possible. In this way it is possible to make a distinction between genes that are really upregulated and genes with high expression levels in one group of samples simply because higher cDNA concentrations were used in these samples. In qPCR analysis, normalization is done based on housekeeping genes.

Housekeeping genes are measured in all samples along with the genes of interest. In theory, a housekeeping gene should have identical RQ values in all samples. In reality, noise generates variation in the expression levels of the housekeeping genes. This variation is a direct measure of the noise and is used to calculate a normalization factor for each sample. These normalization factors are used to adjust the RQ values of the genes of interest accordingly so that the variability is eliminated.

These adjusted RQ values are called Normalized Relative Quantities (NRQs). In qbase+ housekeeping genes are called reference genes. In our data set there are three reference genes: Stable, Non-regulated and Flexible. On the Normalization page we can define the normalization strategy we are going to use, appoint the reference genes and check their stability of expression.

How to specify the normalization strategy you want to use?

You can specify the normalization strategy you want to use on the Normalization method page:

Reference genes normalization is based on the RQ values of the housekeeping genes
Global mean normalization calculates normalization factors based on the RQ values of all genes instead of only using the reference genes. This strategy is recommended for experiments with more than 50 random genes. Random means that the genes are randomly distributed over all biological pathways.
Custom value normalization is used for specific study types. This strategy allows users to provide custom normalization factors such as for example the cell count.
None means that you choose to do no normalization at all. This option should only be used for single cell qPCR.

We have incorporated 3 housekeeping genes in our experiment so we select the Reference genes strategy.

How to appoint reference targets?

You have to indicate which targets should be used as reference genes since qbase+ treats all genes as targets of interest unless you explicitly mark them as reference genes on the Normalization method page:

We have measured 3 housekeeping genes: Stable, Flexible and Non-regulated so we tick the boxes in front of their names.

It’s not because you have appointed genes as reference genes that they necessarily are good reference genes. They should have stable expression values over all samples in your study. Fortunately, qbase+ checks the quality of the reference genes. For each appointed reference gene, qbase+ calculates two indicators of expression stability

M (geNorm expression stability value): calculated based on the pairwise variations of the reference genes.
CV (coefficient of variation): the ratio of the standard deviation of the NRQs of a reference gene over all samples to the mean NRQ of that reference gene.

It is considered that the higher these indicators the less stable the reference gene.

Are Flexible, Stable and Nonregulated good reference targets?

M and CV values of the appointed reference genes are automatically calculated by qbase+ and shown on the Normalization method page:

The default limits for M and CV were determined by checking M-values and CVs for established reference genes in a pilot experiment that was done by Biogazelle. Based on the results of this pilot experiment, the threshold for CV and M was set to 0.2 and 0.5 respectively. If a reference gene does not meet these criteria it is displayed in red. As you can see the M and CV values of all our reference exceed the limits and are displayed in red.

If the quality of the reference genes is not good enough, it is advised to remove the reference gene with the worst M and CV values and re-evaluate the remaining reference genes.

Which reference target are you going to remove?

Both the M-value and the CV are measures of variability. The higher these values the more variable the expression values are. So we will remove the gene with the highest M and CV.

You can remove a reference gene simply by unticking the box in front of its name.

Are the two remaining reference genes good references?

After removing Flexible as a reference gene the M and CV values of the two remaining reference genes decrease drastically to values that do meet the quality criteria. M and CV values that meet the criteria are displayed in green.

This exercise shows the importance of using a minimum of three reference genes. If one of the reference genes does not produce stable expression values as is the case for Flexible, you always have two remaining reference genes to do the normalization.

See how to select reference genes for your qPCR experiment.

So after normalization you have one NRQ value for each gene in each sample.

Click Next to go to the Scaling page.