Post on 21-Mar-2020
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
0.003
0.0060.0210.0230.056
NF4
NF1
cancer patients survival curve
statistically more significant results
geNorm validation (I)
log rank statistics
Hoebeeck et al., Int J Cancer, 2006
mRNA haploinsufficiency measurements
accurate assessment of small expression differences
geNorm validation (II)
Hellemans et al., Nature Genetics, 2004
patient / control
3 independent experiments
95% confidence intervals
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
normalization using multiple stable reference genes
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
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80
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120
0 50 100 150 200 250 300
not normalised
stable controls
mean
miRNAs
neuroblastoma – removal of variation
biological validation
MYCN binds to the mir-17-92 promoter
CpG island
mir-17-92 cluster
+5 kb-5 kb
CA
TG
TG
CA
TG
TG
CA
TG
TG
CA
CG
TG
CA
CG
TG
CA
TG
TG
CA
TG
TG
A B C
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101112
A B C
Fold
en
rich
men
t
Amplicon
IMR5
WAC2
biological validation
choice of normalization strategy influences differential miRNA expression
Mir-17-92 expression in neuroblastoma tumours
0
0,5
1
1,5
2
2,5
3
3,5
stable controls
mean
miRNAs
biological validation
choice of normalization strategy influences differential miRNA expression
Mir-17-92 expression in neuroblastoma tumours
0
0,5
1
1,5
2
2,5
3
3,5
stable controls
mean
miRNAs
biological validation
choice of normalization strategy influences differential miRNA expression
Mir-17-92 expression in neuroblastoma tumours
0
0,5
1
1,5
2
2,5
3
3,5
stable controls
mean
miRNAs
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
fold
ch
an
ge
(M
YC
N a
mp
lifie
d v
s. M
YC
N s
ing
le c
op
y)
controls
mean
average FCcontrols = -0.404average Fcmean = 0.050average FCmiRNAs = 0.124
balanced differential expression
correlation MYCN downregulated genes – 2 normalization strategies
stable miRNA control normalisation
mean n
orm
alis
atio
n
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
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
qbasePLUS normalization
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
qbasePLUS
Jan Hellemans (Biogazelle – UGent)
Stefaan Derveaux (Biogazelle – UGent)