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| Technical bulletin - geNorm |
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During the preparation of cDNA for real-time PCR analysis there is significant potential for small errors to accumulate. For example, differences in sample size, RNA extraction efficiency, pippetting accuracy and reverse transcription efficiency will all add variability to your samples. Thus, if you are going to perform relative gene expression between samples, it becomes essential to answer the question; “how much cDNA is really in these samples?”
Strategies to consider
When comparing one cDNA sample to another researchers have employed a number of strategies to try and “normalise” their data;
Standardising sample size. Its good practice to try and use similar sized samples (e.g. a similar number of cultured cells or similar sized biopsy) However this is insufficient as a normalisation strategy because it does not account for the cumulative errors that can occur in cDNA preparation.
Normalising to genomic DNA. This strategy can be effective but is impractical because most RNA preparation protocols deliberately eliminate the presence of DNA.
Normalising to total RNA. RNA quantitation using a Bioanalyzer (Agilent) is a useful but time consuming step. The analysis provide useful information about the quality of your RNA but again does not account for the cumulative errors that can occur in cDNA preparation.
Normalising to an artificial molecule. Spiking RNA with an artificial RNA molecule at a known concentration has been suggested as a method to normalise the errors that occur during cDNA preparation. However this approach does not provide normalisation for the actual concentration of sample cDNA
Normalising to a housekeeping gene. Normalising to a stably expressed housekeeping gene that is representative of the cDNA concentration in a sample is the most commonly used normalisation approach. The housekeeping gene is subject to the same errors in cDNA preparation as the gene of interest so makes an excellent normalising control. However careful and strategic selection if the most stably expressed housekeeping gene is essential. Random selection of a gene can add large unpredictable error to your analysis.
Housekeeping gene selection
Normalising real-time PCR data to the expression of a housekeeping gene is an excellent and practical strategy. However, housekeeping genes are not stably expressed in all scenarios. Use of a variably expressed housekeeping gene for normalisation will add large unpredictable errors to your analysis. It is essential therefore to establish which housekeeping genes are the most stably expressed in your particular experimental model.
geNormTM
geNormTM is a piece of software designed to establish the most stably expressed housekeeping genes for any particular model. Following measurement of a number of candidate housekeeping genes in 10 or so samples by real-time PCR the user inputs the data in to geNormTM. The software then carries out analysis of the relative ratio of expression of each housekeeping gene. Thus the candidate housekeeping genes are ranked in order of stability.
The software also provides useful information about the optimal number of housekeeping genes that need to be averaged in order to achieve the very best normalisation strategy. The geNormTM software is a proven application and the original article sighting the technique is currently ranked 4 in the all-time most viewed articles on BioMed Central, with over 500 citations in other peer reviewed articles.
The geNormTM kit
In collaboration with Gent University (Belgium), PrimerDesign can provide geNormTM kits for anyone working with human, rat or mouse samples. The kit comprises · 6 or 12 high quality housekeeping gene real time PCR assays (200 reactions) · The latest geNorm software. · An easy to use geNorm handbook containing protocols as well as a guide to aid analysis of the results obtained. The geNormTM kit provides the simplest, quickest and most affordable route to establishing the best normalisation strategy for your real-time PCR research.
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A real example
A researcher within the University of Southampton School of Medicine was studying the expression of 'target gene A' during mouse embryogenesis. He harvested mouse lungs from embryonic day 11 through to birth (E11-E19) and then on days 1 and 8 post birth (P1 and P8) and in adult mouse tissue (AM) He used real-time PCR to quantify the expression of 'gene A' over his time period of interest. Initially he normalised his data to 3 randomly selected genes. The data was very different when using different housekeeping genes
'gene A' mRNA expression relative to 18s Ribosomal mRNA Embryonic days 11-19. Postpartum days 1 and 8 and Adult mouse: 
'gene A' mRNA expression relative to bActin mRNA Embryonic days 11-19. Postpartum days 1 and 8 and Adult mouse:
'gene A' mRNA expression relative to GAPDH mRNA Embryonic days 11-19. Postpartum days 1 and 8 and Adult mouse:
These data highlighted the importance of selecting the best normalising control to achieve reliable quantitative real-time PCR data. In order to establish which housekeeping genes would make the best normalising control the researcher used a PrimerDesign geNormTM kit;
The geNorm kit ranks the candidate housekeeping genes in order of stability. From these data the researcher was able to establish that an average of ATP5A, GAPDH and CYC1 was the best normalising control.
Subsequently the researcher was able to normalise his data to an excellent and stable normalising control to discover the "real" result for his gene of interest;
The "real" answer! gene A' mRNA expression relative to an average of ATP5A, CYC1 and GAPDH. These data are currently being prepared for submission to a high quality journal:
The geNormTM kit provides the simplest, quickest and most affordable route to establishing the best normalisation strategy for your real-time PCR research.
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