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

Oligotechserv@sial.com

• Request qPCR assay design through

www.sigma.com/designmyprobe

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