From reference genes to global mean normalization Jo Vandesompele professor, Ghent University co-founder and CEO, Biogazelle qpcr Symposium USA November 9, 2009 Millbrae, CA
outline what is normalization gold standard for mrna normalization global mean normalization and selection of stable small RNAs for microrna normalization
introduction to normalization 2 sources of variation in gene expression results biological variation (true fold changes) experimentally induced variation (noise and bias) purpose of normalization is reduction of the experimental variation input quantity: RNA quantity, cdna synthesis efficiency, input quality: RNA integrity, RNA purity, gold standard is the use of multiple stably expressed reference genes which genes? how many? how to do the calculations?
normalization: genorm solution 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
genorm software automated analysis ranking of candidate reference genes according to their stability determination of how many genes are required for reliable normalization http://medgen.ugent.be/genorm
genorm validation (I) cancer patients survival curve statistically more significant results log rank statistics NF4 0.003 NF1 0.006 0.021 0.023 0.056 Hoebeeck et al., Int J Cancer, 2006
genorm validation (II) mrna haploinsufficiency measurements accurate assessment of small expression differences patient / control 3 independent experiments 95% confidence intervals Hellemans et al., Nature Genetics, 2004
normalization using multiple stable reference genes genorm is the de facto standard for reference gene validation and normalization > 2,000 citations of our genorm technology > 10,000 genorm software downloads in 100 countries
global mean normalization when a large set of genes are measured, the average expression level reflects the input amount and could be used for normalization e.g. microarray based normalization o lowess, mean ratio, SAGE / NGS sequencing counts the set of genes must be unbiased and sufficiently large we make use of this principle to normalize microrna data from experiments in which we quantify a substantial number of mirnas (450 or 650) in a given sample
global mean normalization small-rna controls classic normalization strategy small nuclear RNAs, small nucleolar RNAs 18 available from Applied Biosystems global mean normalization method applied for microarray data universal: applicable for every mirna dataset many datapoints needed (megaplex vs. multiplex) mirnas/controls that resemble the mean minimal standard deviation when comparing mirna expression with mean ( genorm V value, standard deviation of log transformed ratios) compatible with multiplex assays need to determine mean
small RNA controls How stable is the global mean compared to controls? genorm analysis using controls and mean as input variables exclusion of potentially co-regulated controls HY3 7q36 RNU19 5q31.2 RNU24 9q34 RNU38B 1p34.1-p32 RNU43 22q13 RNU44 1q25.1 RNU48 6p21.32 RNU49 17p11.2 RNU58A 18q21 RNU58B 18q21 RNU66 1p22.1 RNU6B 10p13 U18 15q22 U47 1q25.1 U54 8q12 U75 1q25.1 Z30 17q12 RPL21 13q12.2
mirna expression datasets neuroblastoma tumour samples T-ALL samples EVI1 deregulated leukemias retinoblastoma tumour samples normal tissues normal bone marrow
expression stability T-ALL genorm ranking 1,8 1,6 1,4 1,2 1 0,8 0,6 0,4 0,2 0
genorm ranking neuroblastoma leukemia EVI1 overexpression bone marrow pool normal tissues
neuroblastoma removal of variation 120 100 80 60 40 not normalised stable controls mean mirnas 20 0 0 50 100 150 200 250 300
removal of variation T-ALL leukemia EVI1 overexpression bone marrow pool normal tissues
Fold enrichment CATGTG CATGTG CATGTG CACGTG CACGTG CATGTG CATGTG biological validation MYCN binds to the mir-17-92 promoter -5 kb CpG island mir-17-92 cluster +5 kb 12 11 10 9 8 7 6 5 4 3 2 1 0 A B C IMR5 WAC2 A B C Amplicon
biological validation choice of normalization strategy influences differential mirna expression 3,5 Mir-17-92 expression in neuroblastoma tumours 3 2,5 2 1,5 1 0,5 stable controls mean mirnas 0
biological validation choice of normalization strategy influences differential mirna expression 3,5 Mir-17-92 expression in neuroblastoma tumours 3 2,5 2 1,5 1 0,5 stable controls mean mirnas 0
biological validation choice of normalization strategy influences differential mirna expression 3,5 Mir-17-92 expression in neuroblastoma tumours 3 2,5 2 1,5 1 0,5 stable controls mean mirnas 0
fold change (MYCN amplified vs. MYCN single copy) balanced differential expression 3 2 controls mean 1 0-1 -2-3 -4 average FC controls = -0.404 average Fc mean = 0.050 average FC mirnas = 0.124-5 -6-7
correlation MYCN downregulated genes 2 normalization strategies stable mirna control normalisation mean normalisation
strategy also works for microarray data each sample is measured by RT-qPCR and microarray global mean normalization standardization per method hierarchical clustering samples cluster by sample (and NOT by method)
conclusions global mean normalization novel and powerful mirna normalization strategy maximal reduction of technical noise improved identification of differentially expressed genes balancing of differential expression universally applicable o global mean o multiple stable endogenous controls Mestdagh et al., Genome Biology, 2009
qbase PLUS normalization most powerful, flexible and user-friendly real-time PCR data-analysis software based on Ghent University s genorm and qbase technology state of the art normalization procedures o one or more classic reference genes o global mean normalization o expressed repeat normalization detection and correction of inter-run variation dedicated error propagation fully automated analysis; no manual interaction required booth 19 http://www.qbaseplus.com
conclusions proper normalization has a major impact on your results provides statistically more significant results enables accurate assessment of small expression differences gold standard for mrna gene expression analysis genorm evaluation of candidate reference genes geometric mean of multiple stably expressed reference genes global mean normalization and subsequent genorm based selection of reference genes that resemble the mean is a valid option when measuring a large and unbiased set of genes (e.g. all mirnas)
acknowledgments mirna Pieter Mestdagh (UGent) Frank Speleman (UGent) Applied Biosystems qbase PLUS Jan Hellemans (Biogazelle UGent) Stefaan Derveaux (Biogazelle UGent)
January 28-29, 2010 Ghent, Belgium www.advances-in-genomics.org