Different approaches on normalisation of gene expression RT-qPCR data Jan Hellemans PhD, Ghent University co-founder and CEO, Biogazelle Lo studio dell‟espressione genica in real -time PCR September 5, 2008 Siena, Italy
acknowledgement Jo Vandesompele (geNorm, qBase) Filip Pattyn Jasmien Hoebeeck Katleen De Preter Nurten Yigit Frank Speleman Geert Mortier
qPCR workflow Experiment design Sample prep Assay design qPCR reactions Cq values Data processing Statistical analysis & interpretations
qPCR workflow – data processing Experiment design Data processing absolute vs relative quantification Sample prep Assay design relative quantification Cq values to relative quantities unwanted technical variation qPCR reactions normalization inter-run calibration Cq values Data processing Statistical analysis & interpretations
absolute vs relative quantification “absolute” quantification how many copies or molecules (molarity) standard (dilution series) quantification relative to the standard “all quantification is relative” the accuracy of external standard quantification is entirely dependent on the accuracy of the standards log linear relationship between input and Ct + reproducibility precise and reproducible answer, but not necessarily an accurate answer exception: digital PCR relative quantification one sample relative to another one transcript relative to another (e.g. splice isoforms) Vandenbroucke et al., Nucleic Acids Research , 2001 comparative Cq method / delta-Cq method
relative quantification – Cq values to relative quantities fluorescence RQ = 2 Δ Cq RQ 1/3 = 2 4 = 16 RQ 2/3 = 2 2 = 4 RQ 3/3 = 2 0 = 1 1 2 3 threshold 4 2 PCR cycle Cq 1 Cq 2 Cq 3
relative quantification – variation differences in RQ due to different gene expression level
Data processing - relative quantification differences in RQ due to different gene expression level different total starting amount
relative quantification – variation differences in RQ due to different gene expression level different total starting amount run dependent differences
relative quantification – variation differences in RQ due to different gene expression level different total starting amount run dependent differences
relative quantification – variation technical variation avoid minimize correct Cq RQ NRQ CNRQ Normalization Inter-run calibration
relative quantification – Cq values to relative quantities Cq RQ NRQ CNRQ RQ = 2 Δ Cq Calculate gene specific amplification efficiency (E) from RQ = E Δ Cq • dilution series • fluorescence curve
relative quantification – amplification efficiencies n Q Q Cq Cq a a a 1 slope n 2 Q Q a a 1 1 slope E 10 n 2 Cq Cq a , measured a , predicted a 1 s e n 2 n 1 2 s Q Q x a n 1 a 1 calculate and propagate the error on E s e SE slope minimize SE(E) s ( n 1 ) x increase number of dilution points (n) E ln 10 SE slope SE E increase range of dilution 2 slope
relative quantification – amplification efficiencies increase number of points increase range of points 1-2 1&2 SE(E)=0.032 SE(E)=0.032
relative quantification – amplification efficiencies increase number of points increase range of points 1-2 1&2 SE(E)=0.032 SE(E)=0.032 1-3 1&3 SE(E)=0.013 SE(E)=0.018
relative quantification – amplification efficiencies increase number of points increase range of points 1-2 1&2 SE(E)=0.032 SE(E)=0.032 1-3 1&3 SE(E)=0.013 SE(E)=0.018 1-4 1&4 SE(E)=0.008 SE(E)=0.008
relative quantification – amplification efficiencies increase number of points increase range of points 1-2 1&2 SE(E)=0.032 SE(E)=0.032 1-3 1&3 SE(E)=0.013 SE(E)=0.018 1-4 1&4 SE(E)=0.008 SE(E)=0.008 1-5 1&5 SE(E)=0.005 SE(E)=0.004
relative quantification – amplification efficiencies increase number of points increase range of points 1-2 1&2 SE(E)=0.032 SE(E)=0.032 1-3 1&3 SE(E)=0.013 SE(E)=0.018 1-4 1&4 SE(E)=0.008 SE(E)=0.008 1-5 1&5 SE(E)=0.005 SE(E)=0.004 1-6 1&6 SE(E)=0.003 SE(E)=0.002
relative quantification – amplification efficiencies 101% 920%
relative quantification – normalization Cq RQ NRQ CNRQ minimize technical variation sample: size and type RNA extraction: quality and quantity RNA degradation cDNA synthesis correct for technical variation normalization
relative quantification – normalization Livak and Schmittgen (2001) 100% PCR efficiency Ct NRQ 2 1 reference gene Pfaffl (2001) Ct , goi adjusted PCR efficiency E goi NRQ 1 reference gene Ct , ref E ref qBase model (2007) adjusted PCR efficiency Ct , goi multiple reference genes E goi NRQ n Ct , ref E n i ref i i
relative quantification – inter-run calibration Cq RQ NRQ CNRQ different analysis settings
relative quantification – inter-run calibration Cq RQ NRQ CNRQ different analysis settings instrument, reagents and consumable variation
relative quantification – inter-run calibration Cq RQ NRQ CNRQ avoid inter-run variation use sample maximization minimize inter-run variation use the same instrument, reagents and consumables correct for inter-run variation inter-run calibration
relative quantification – inter-run calibration [3 GOI + 3 REF] x [11 samples + 1 NTC]
relative quantification – inter-run calibration sample maximization is to be preferred no increase in variation due to absence of inter-run variation suitable for retrospective studies and controlled experiments gene maximization introduces (under-estimated) inter-run variation applicable for prospective studies or large studies in which the number of samples do not fit on the plate anymore inter-run variation can be controlled and corrected for using inter-run calibrators (IRC) inter-run calibration possible on two levels: o Ct values o normalized relative quantities the more inter-run calibrators, the better simple vs. complex inter-run calibration specialised software is needed
relative quantification – inter-run calibration Cq RQ NRQ CNRQ correct for inter- run variation by including IRC‟s IRC: inter-run calibrator identical sample measured for the same gene in different runs + = NRQ low IRC run 1 CNRQ + = NRQ high IRC run 2
relative quantification - qBase
relative quantification – qBase & qBasePlus Hellemans et al., Genome Biology, 2007 http://www.qbaseplus.com
relative quantification - qBasePlus
qPCR workflow – data processing Experiment design Normalization why reference genes? Sample prep Assay design why multiple reference genes? geNorm effect of sample quality qPCR reactions normalization quality control Cq values Data processing Statistical analysis & interpretations
normalization 2 sources of variation in gene expression measurements gene-specific (biological) variation (true fold changes) non-specific (technical) variation o RNA extraction yield o RNA quantity & quality o reverse transcription efficiency o PCR efficiency (inhibitors) purpose of normalization is reduction of the technical/experimental variation
normalization
normalization – reference genes reference genes most popular captures most variation attention! reference genes (might) vary in expression until recently, non-validated reference genes were used (assuming stable expression) normalisation against 3 or more validated reference genes is considered as the most appropriate and universally applicable method which genes? how to do the calculations?
normalization – multiple reference genes framework for qPCR gene expression normalisation using the reference gene concept: quantified errors related to the use of a single reference gene (> 3 fold in 25% of the cases; > 6 fold in 10% of the cases) developed a robust algorithm for assessment of expression stability of candidate reference genes proposed the geometric mean of at least 3 reference genes for accurate and reliable normalisation Vandesompele et al., Genome Biology, 2002
normalization – multiple reference genes quantitative RT-PCR analysis of 10 reference genes (belonging to different functional and abundance classes) on 85 samples from 13 different human tissues 4 3 ACTB HMBS 2 HPRT1 TBP 1 UBC 0 A B C D E F G 15 fold difference between A and B if normalized by only one gene ( ACTB or HMBS)
normalization – geNorm pairwise variation V (between 2 genes) gene A gene B sample 1 a1 b1 log2(a1/b1) sample 2 a2 b2 log2(a2/b2) sample 3 a3 b3 log2(a3/b3) … … … … sample n an bn log2(an/bn) standard deviation = V gene stability measure M average pairwise variation V of a gene with all other genes
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