Exercise 1: Reference Genes for Mouse Liver

We come back on the 8 candidate reference genes that we selected for mouse liver:

  • 4 commonly used reference genes: ACTB, TUBB4B, GAPDH and HPRT
  • 4 candidate reference genes with very stable medium expression levels selected based on expression data coming from more than 600 microarrays of mouse liver samples using Genevestigator: Gm16845, MUSK, OTOP3, EDN3

We have measured their expression in a represetative set of 16 of our mouse liver samples, each in triplicate. We will now analyze the stability of these candidate reference genes in our samples.

Creating a new Experiment

  • Create a new Experiment called GeNormMouse in Project1
  • Open qbase+ or, if the software is already open, click the Launch Wizard button.

You can find the details on how to create a new experiment in Creating a project and an experiment

Loading the data into qbase+

The data is stored in the RefGenes folder. It consists of 8 Excel files, one file for each candidate reference gene. If you are not working on a BITS laptop, download and unzip the folder.

  • Import the data. This files are in qBase format.
  • You can find the details on how to start the data import in Loading data into qbase+

Unlike the previous exercise, qbase+ does not allow you to do a quick import this time. In the Import Run window Manual import is selected: Make sure that Upload file to Biogazelle support for further analysis is NOT selected and click Next Make sure the correct File type is selected (qBase) and click Finish. This file contains the data of the geNorm pilot experiment. In the pilot experiment, 8 candidate reference genes were measured in 16 representative mouse liver samples.

Analyzing the geNorm pilot data

Specify the aim of the experiment.

In this experiment we want to select the ideal reference genes for our next experiments so we choose selection of reference genes (geNorm)

Check the quality of the replicates (use default parameter settings)

You can find the details on how to check the quality of the replicates in the Checking the quality of technical replicates and controls section of Analyzing gene expression data in qbase+

We haven’t included any positive or negative controls so you don’t need to show their details.

Select the Amplification efficiencies strategy you want to use

You can find the details on how to select the Amplification effciencies strategy in the Taking into account amplification efficiencies section of Analyzing gene expression data in qbase+

We haven’t included dilution series nor do we have data from previous qPCR experiments regarding the amplification efficiencies so we choose to use the same efficiency for all genes. It is of course better to include a dilution series for each gene to have an idea of the amplification efficiencies of each primer pair.

Convert all genes to Reference genes

You can convert all the genes simultaneously by selecting Use all targets as candidate reference genes

Click Finish.

Which genes are you going to use as reference targets in further experiments?

Upon clicking Finish, the geNorm window containing the analysis results is automatically opened. The geNorm window consists of three tabs. The tabs are located at the bottom of the window: geNorm M, geNorm V and Interpretation. The first tab, geNorm M, shows a ranking of candidate genes according to their stability, expressed in M values, from the most unstable genes at the left (highest M value) to the best reference genes at the right (lowest M value): The second tab, geNorm V, shows a bar chart that helps determining the optimal number of reference genes to be used in subsequent analyses:

The number of reference genes is a trade-off between practical considerations and accuracy. It is a waste of resources to quantify more genes than necessary if all candidate reference genes are relatively stably expressed and if normalization factors do not significantly change when more genes are included. However, Biogazelle recommends the minimal use of 3 reference genes and stepwise inclusion of more reference genes until the next gene has no significant contribution to the normalization factors. To determine the need of including more than 3 genes for normalization, pairwise variations Vn/n+1 are calculated between two sequential normalization factors. Simply stated: V is measure of the added value of adding a next reference gene to the analysis. A large variation means that the added gene has a significant effect and should be included. In normal experiments like the Gene expression experiment (see Analyzing gene expression data in qbase+), we only have 3 reference genes so we will see only 1 bar here. But in this geNorm pilot experiment, we analyzed 8 candidate reference genes, so we see 6 bars. All pairwise variations are very low, so even the inclusion of a third gene has no significant effect. Based on a preliminary experiment that was done by Biogazelle, 0.15 is taken as a cut-off value for V, below which the inclusion of an additional reference gene is not required. Normally this threshold is indicated by a green line on the geNorm V bar chart. However since all V-values fall below the threshold in this geNorm pilot experiment, you don’t see this line on the bar chart. So, these results mean that for all subsequent experiments on these samples, two reference genes, EDN3 and MUSK, would be sufficient. However, as stated before, Biogazelle recommends to always include at least three reference genes in case something goes wrong with one of the reference genes (so also include Gm16845). | These are artificial data. But when you read the paper by Hruz et al., 2011 you see that the genes that are selected by Genevestigator are often outperforming the commonly used reference genes.