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qPCR data analysis, beginner session •Anders Bergkvist, PhD, at LabClusterTour 2010
sigma-aldrich.com
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Application 1: Absolute Quantification
log(Conc)
Cq
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3x1010 target
copies 3 target
copies
Standard Curve and Assay Efficiency
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• Amplification plots:
• Baseline is horizontal
• Threshold is in LOG region of curve
• Curves are parallel
• y=mx+c, E=10-1/slope-1
• Slope = -1/log102 = -3.323 between -3.5 and -3.2
• RSqu > 0.98, should be 0.99
• Intercept on y axis gives a measure of sensitivity
Cq
log10 gene copies
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Standard Curve Quality, Residual Plot
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Absolute quantification
Cq
log10 gene copies
Measured
Cq
Variation in
measurement
Estimated
copy number Range of
estimation
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Application 2: Relative Quantification
Normal Treated
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RQ – Basic Premises 1/3
• One Gene (at a time)!
Normal Treated
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RQ – Basic Premises 2/3
• Samples from two different populations!
Normal Treated
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RQ – Basic Premises 3/3
• Unknown mechanisms contribute to
confounding variabilities!
Normal Treated
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Reference Gene Normalization Strategies
• Purpose of normalization
• A universally valid reference gene does not exist
• Optimum number of reference genes
• Comparing total RNA estimates to specific reference genes for
normalization
• NormFinder
• geNorm
• GenEx from MultiD Analyses AB
“Choosing a Normalization Strategy for …”, Bergkvist et al., GEN Vol.28, No.13 (2008)
http://genengnews.com/gen-articles/choosing-a-normalization-strategy-for-rt-pcr/2530/
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Fundamentals of gene expression
normalization
No treatment Treatment
GOI not regulated
GOI regulated
Stable reference
Unstable reference
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Fundamentals of gene expression
normalization
No treatment Treatment
GOI regulated
GOI not regulated
Unstable reference
Stable reference
2 cell
count
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Fundamentals of gene expression
normalization
No treatment No treatment
2 cell
volume
GOI mod by cell count
GOI mod by cell volume
RG prop to cell count
RG prop to cell volume
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Reference Gene Candidate Panel
Samples collected from a human
cell culture in two different
treatment groups.
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geNorm
• Calculates gene stability M-values
• Iterative process
• A rule of thumb judges M-value < 0.50 as stably expressed
• Assumes independent reference gene candidates
“qBase relative quantification framework and software for management and …”,
Hellemans et al., Genome Biology 2007, 8:R19
“Accurate normalization of real-time quantitative RT-PCR data by geometric …”,
Vandesompele et al., Genome Biology 2002, 3(7)
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NormFinder
• A process of analysis of group variances
• May specifically consider sample subgroups for variation
estimation
• The obtained measure is directly related to estimated
expression variation
• A rule of thumb judges SD < 0.20 as stably expressed
• Assumes independent reference gene candidates
“Normalization of Real-Time Quantitative Reverse Transcription-PCR Data …”,
Andersen, Jensen and Orntoft, CANCER RESEARCH 64, 5245–5250, August 1, 2004
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Reference Gene Candidate Whole Panel
geNorm NormFinder
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Reference Gene Candidate Profiles
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Reference Gene Candidate Good Ones
geNorm NormFinder
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Summary:
• Different types of data analyses
• PCR efficiency and standard curve quality
• Aim to reduce confounding variabilities
• Reference genes are supposed to be stable under exp. conditions
• Validate reference genes against panel of candidates
• For further information see seminars in the MIQE series or contact
• Request qPCR assay design through
www.sigma.com/designmyprobe
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