Transcript Quantitative PCR Bioinformatics & Gene DiscoveryWilhelm Johannsen Centre for Functional Genome Research.

Quantitative PCR
Bioinformatics & Gene
Discovery
2007
Wilhelm Johannsen Centre for Functional Genome Research
QPCR & Gene discovery in the
Post-genomic Era
•
The human genome is sequenced, then why go gene discovering?
•
Other genomes to work on !
•
Gaps in the human genome remain
•
Not all human genes have yet been identified
•
Not all human expressed sequences are mapped to the DNAgenome
•
Splice-variants or aberrant composite proteins
•
Novel functions or relations assigned to old proteins
•
Non-coding RNA
Wilhelm Johannsen Centre for Functional Genome Research
Overview
• What is PCR?
• Quantitation of gene
expression
• Methodology
• Experimental design
• Problems
• Applications at WJC
Wilhelm Johannsen Centre for Functional Genome Research
What is PCR?
•
•
•
A PCR (Polymerase Chain Reaction)
is a highly specific, enzymatic
process, where a well defined
DNA sequence is amplified
exponentially
The process use a simple nonisothermal enzymatic reaction
using primers nucleotides & a
thermostable DNA-polymerase
Ideally, after 40 cycles, one
starting copy of a gene would yield
240 copies of that DNA fragment,
i.e., ~1.1x1012 copies
Yields μg worth of DNA, plenty to
be able to sequence, clone and
visualize on an agarose gel
90
Extension
80
Temperature
•
Denaturation
100
70
60
50
Annealing
40
0,14
0,12
0,1
0,08
0,06
0,04
0,02
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
Cycle
Some graphics modified from Andy Vierstrate, http://users.ugent.be/~avierstr/principles/pcr.html
Wilhelm Johannsen Centre for Functional Genome Research
Quantitation of gene expression
• Quantitation of gene expression can
supply important biological information
about gene function and relationships
• Quantitation of gene expression may
discriminate between normal and
diseased states
• Always remember that high or low
gene expression not necessarily
indicate high/low protein levels
Wilhelm Johannsen Centre for Functional Genome Research
Quantitation of gene expresion
-- Immobilised Methodology-• Northern blotting
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–
–
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Gel-based
Relatively inexpensive equipment
Involves hybridisation steps
Time, sample & labor intensive
Few samples, target genes to be
handled simultaneously
Simple data calculations
• Micro-arrays
–
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–
–
–
–
Tiao, Hobler, et al.: JCI, 99, 163-168, 1999
Chip-based
Expensive equipment
Involves hybridisation steps
Technology time and labor intensive
Many samples, target genes to be
handled simultaneously
Extensive data calculations
http://www.well.ox.ac.uk/genomics/facilitites/Microarray/Welcome.shtml
Wilhelm Johannsen Centre for Functional Genome Research
Quantitation of gene expresion
--PCR Methodology-• Semi-quantitative PCR
–
–
–
–
–
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Gel-based
Inexpensive equipment
Involves hybridisation steps
Time, sample & labor conservative
Multiple samples but few target genes
simultaneously
Simple data calculations
Schulze, Hansen et al, Nature Genet. 1996
• Real-time PCR (QPCR)
–
–
–
–
–
–
Gel-free?
Expensive equipment
Involves hybridisation steps
Time, sample & labor conservative
Multiple samples but few target genes
simultaneously
Extensive data calculations
Wilhelm Johannsen Centre for Functional Genome Research
QPCR - why ?
•
•
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•
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•
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•
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Conservative (10-50 ng template)
Sensitive
Broad dynamic range
Rapid (1-2 hrs)
Relatively inexpensive (DKK 5-15/sample)
Multiple samples can be processed
simultaneously (1->96)
Possible multiplexing
Unambiguous results
Gradual expression differences can be
detected
Gel-free
Wilhelm Johannsen Centre for Functional Genome Research
What is QPCR?
•
Denaturation
100
PCR as usual
90
Optional Melting curve
generation
Temperature
•
Additional quantitation step
Quantitat
e
70
60
Annealing
50
40 0,14
0,12
Fluorescence
•
Extensio
n
80
Melting curve
Plateau
0,1
0,08
0,06
0,04
0,02
Exponential
Signal
noise
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
Cycle
Wilhelm Johannsen Centre for Functional Genome Research
Semi-quantitative endpoint PCR
vs. QPCR
0,14
Fluorescence
0,12
0,1
0,08
0,06
C(t)=11
C(t)=18.5
0,04
0,02
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
Cycle
Wilhelm Johannsen Centre for Functional Genome Research
Melting curves – circumvention of
’dirty’ reactions
100
90
Temperature
80
70
60
50
40
Wilhelm Johannsen Centre for Functional Genome Research
Pro’s & con’s
Endpoint analysis
•
•
•
•
Simple
Inexpensive
Gel-based system
’Yes/No’ quantitation
• Multiplexing possible
• Broad enzyme range
• Variable cycle number
QPCR
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•
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•
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•
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•
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Little more complex
Slightly More expensive
Gel-free system
Relative quantitation
Absolute quantitation
Multiplexing possible
Clean PCR ?
Limited enzyme range
Invariable cycle number
Wilhelm Johannsen Centre for Functional Genome Research
Overview
• What is PCR?
• Quantitation of gene
expression
• Methodology
• Experimental design
• Problems
• Applications at WJC
Wilhelm Johannsen Centre for Functional Genome Research
Chemistry 1
•
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SYBR green (quantitation, melting curve)
Taqman Assay (quantitation, genotyping, multiplex)
Hybridization probes (quantitation, genotyping)
Molecular beacons (quantitation, genotyping)
Scorpions (genotyping)
Light-Up probes (quantitation, genotyping)
Ampliflour universal detection system
(quantitation, multiplex)
• LUX fluorogenic primers (quantitation, multiplex)
• Universal Probe Library (quantitation)
Wilhelm Johannsen Centre for Functional Genome Research
Selected QPCR strategies
SYBR
Hyb. probes
Taqman
Lux
Wilhelm Johannsen Centre for Functional Genome Research
Chemistry 2
• Commercially available kits
•
•
•
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Variation in kit quality
Lower batch-to-batch variation
Limited range of thermostable polymerases
For ’difficult’ fragments kits may be a poor choice
• Do it yourself (DIY) kit
• Select your own polymerase
• Relatively simple to set-up
• Higher Batch-to-batch variation
Wilhelm Johannsen Centre for Functional Genome Research
Rapid DIY kit
0.5X
SYBR
1X
SYBR
2.5X
SYBR
5X
SYBR
Wilhelm Johannsen Centre for Functional Genome Research
Overview
• What is PCR?
• Quantitation of gene
expression
• Methodology
• Experimental design
• Problems
• Applications at WJC
Wilhelm Johannsen Centre for Functional Genome Research
Experimental design
• Search WWW for good ideas & help
• Always design the experiment before
actually doing it & equally important, stick
to it!!
• Decide how you want to calculate your
results
• Take the time to create spreadsheets that
you will use for the calculations!!
Wilhelm Johannsen Centre for Functional Genome Research
Wilhelm Johannsen Centre for Functional Genome Research
QPCR calculation strategies
• Serial dilution of ’known’
standards (standard curves)
• ∆c(t)
• ∆∆c(t)
• PCR efficiency
Wilhelm Johannsen Centre for Functional Genome Research
QPCR-at-a-glance
- WJC-NSGene SOP •
•
•
•
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RNA extraction/purchase
RNA quantitation
DNAse treatment
Test for DNA contamination
RNA quantitation
Reverse transcription
Prepare primers spanning intron (if
possible)
• QPCR gene of interest (GOI)
• QPCR house keeping gene (HKG)
• Calculation, quality control & normalisation
Wilhelm Johannsen Centre for Functional Genome Research
Software
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GeNorm (Freeware/shareware)
REST (Freeware/shareware)
qBase (Freeware/shareware)
Genex
qGene (Freeware/shareware)
SoFAR (Commercial)
Bestkeeper (Freeware/shareware)
LinReg PCR (Freeware/shareware)
Dart PCR
DATAN (Commercial)
Wilhelm Johannsen Centre for Functional Genome Research
Spreadsheets
• Use a standardized spreadsheet for calculations
– it pays off in the long run and saves you a lot
of aggravation!!
• Use somebody else’s spreadsheet
• Build your own spreadsheet around somebody
else’s basic work – it saves time!
• Create your own spreadsheet from scratch
Wilhelm Johannsen Centre for Functional Genome Research
WJC-NSgene Spreadsheet
• Bestkeeper normalisation (Pfaffl, MW. 2004)
• Multiple calculation strategies
•
•
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•
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Selective removal of:
Kinetic outliers (Bar, T. 2003)
Data points with aberrant melting curves
Data points with large sample variation
Data points outside standard curve
Wilhelm Johannsen Centre for Functional Genome Research
Overview
• What is PCR?
• Quantitation of gene
expression
• Methodology
• Experimental design
• Problems
• Applications at WJC
Wilhelm Johannsen Centre for Functional Genome Research
Selected QPCR problems
•
•
•
•
RNA quantity/quality
Quantitation of RNA
Reverse transcription
QPCR itself
• Standard curves
• Normalisation
Wilhelm Johannsen Centre for Functional Genome Research
Quantitation of RNA
• Spectrophotometric determination
• Advantages
– Cheap
– Fast
• Disadvantages
– Inaccurate
• Fluorimetric determination
• Advantages
– More accurate
– More sensitive
• Disadvantages
– More expensive
– Slower
Wilhelm Johannsen Centre for Functional Genome Research
RNA quality
• RNA quality - a key item for successful QPCR
• RT or PCR inhibitors may be carried over during
extraction of RNA
• Always store RNA at -80 C
• Wear gloves
• Assess RNA quality best as possible
• Agarose gels – rule of thumb: 2 bands; upper twice as
intensive as lower
• Chip (e.g. Agilent Bioanalyzer)
Wilhelm Johannsen Centre for Functional Genome Research
Reverse Transcription
• Reverse transcription as a major
cause for QPCR inconsistency:
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RNA extraction
RT time
Choice of Reverse transcriptase
Amount of RNA transcribed
Inhibition by Reverse Transcriptase
Potentially sequence dependent
Wilhelm Johannsen Centre for Functional Genome Research
Reverse transcription 1
- RT time 110
100
1003-90
1103-90
96
100
100
82
80
72
70
63
% expression
90
60
50
40
1003-50
1103-50
1203-50
1203-90
•
Same RNA
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3 RT-reactions
•
Same RT-mix
•
50 min RT, average of 3 genes
•
90 min RT, average of 3 genes
Wilhelm Johannsen Centre for Functional Genome Research
Reverse transcription 2
- RT variation 110
NSG3
B2M
G6PD
100
% expression
90
80
70
60
50
40
1003
•
•
•
1103
Same RNA
3 RT-reactions-3 different days
Different RT-mixes
Wilhelm Johannsen Centre for Functional Genome Research
1203
Reverse transcription 5
- Summary • Find optimal Time for RT reaction
• If possible use same RNA extraction method
• Prepare adequate amounts of cDNA to perform
all experiments simultaneously
• Only compare results from different RT
reactions with some scepticism
Wilhelm Johannsen Centre for Functional Genome Research
cDNA stability
• cDNA is remarkable stable when stored at
appropriate conditions (-20 C)
• No detectable degradation for > 12 months
with repeated thawing/freezing cycles
• Check cDNA panel occasionally to verify
quality
Wilhelm Johannsen Centre for Functional Genome Research
PCR itself as a problem
• The PCR reaction
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•
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Template concentration
Inhibitors
Optimization
Plastware
Inadequate thermocycler
• The operator
• Pipetting errors
• Setting up reactions
• Wrong PCR programs
Wilhelm Johannsen Centre for Functional Genome Research
Standard curves
• Serial dilutions of known sequences
used for ‘metering’ of unknown
concentrations
• Complexity much different from real
life!
• Simple to construct
• Clones
• Purified PCR products
• Dynamic range might be compromised
Wilhelm Johannsen Centre for Functional Genome Research
Dynamic range
1,E+00
1,E-02
1,E-04
1,E-06
1,E-08
y = 9,483e -0,6993x
1,E-10
R2 = 0,9991
1,E-12
0
10
20
30
40
Wilhelm Johannsen Centre for Functional Genome Research
50
Fuzzing ’bout dynamic range & target genes
1,E+00
1,E-02
1,E-04
1,E-06
1,E-08
y = 9,483e -0,6993x
1,E-10
R2 = 0,9991
1,E-12
0
10
20
30
40
Wilhelm Johannsen Centre for Functional Genome Research
50
Some ways to circumvent ‘short’
standard curves
•
Resuspend standard template in a suitable carrier (e.g., tRNA,
bacterial DNA, linear acrylamide), to increase complexity
•
Decrease reaction volume
•
Increase amount of template in PCR reaction
•
Change plastware, transparent  white plates increase signal
strength
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Prepare new primers
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Change enzyme/kit
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Further optimize PCR reaction (e.g., Magnesium etc.)
•
Despair……..
Wilhelm Johannsen Centre for Functional Genome Research
Standard curve 1
- Weirdo 41103
041103Dil
51103
cDNA data
261103 data
-1
-3
-5
-7
-9
-11
-13
y = -0,263x + 1,0546
R2 = 0,9863
-15
0
5
10
15
20
25
30
35
Wilhelm Johannsen Centre for Functional Genome Research
40
45
Standard curve 2
- Weirdo 41103
-1
041103Dil
51103
cDNA data
-3
261103 data
-5
-7
-9
-11
-13
y = -0,2472x + 0,9779
R2 = 0,9901
-15
0
5
10
15
20
25
30
35
Wilhelm Johannsen Centre for Functional Genome Research
40
45
Standard curve 3
- Weirdo 41103
-1
041103Dil
51103
cDNA data
-3
261103 data
-5
-7
-9
-11
-13
y = -0,2433x + 0,8723
R2 = 0,9858
-15
0
5
10
15
20
25
30
35
Wilhelm Johannsen Centre for Functional Genome Research
40
45
Standard curve
- Summary • Standard curves can be extended
and complexity restored by various
additives
• Be aware of potential PCR
inhibitors!
Wilhelm Johannsen Centre for Functional Genome Research
Selected QPCR problems
•
•
•
•
RNA quality
Quantitation of RNA
Reverse transcription
QPCR itself
• Standard curves
• Normalisation
Wilhelm Johannsen Centre for Functional Genome Research
Why normalise?
• Correct for differences in input template
• Initial RNA quantitation
• Pipetting errors
• Cdna synthesis
• ’Housekeeping’ genes used for this purpose
should be:
• Expressed ubiquitously
• Expressed at even levels in all tissues examined
• Good ’Housekeeping’ genes – do they exist?
Wilhelm Johannsen Centre for Functional Genome Research
Normalisation is a relative
problem
• Single or few related tissues
• Many Gene of interest (GOI)
• Need few HKGs
• Multiple tissues
• Many GOI
• Need many HKGs
Wilhelm Johannsen Centre for Functional Genome Research
WJC/NsGene cDNA panel
Adrenal gland
Bone marrow
Cerebellum
Adult brain
Heart
Kidney
Liver
Lung
Placenta
Prostate
Pancreas
Spinal cord
Salivary gland
Skeletal muscle
Spleen
Testis
Thymus
Thyroid
Trachea
Uterus
Colon
Small intestine
Fetal brain
Fetal liver
Corpus callosum
Amygdala
Caudate nucleus
Hippocampus
Thalamus
Pituitary gland
Wilhelm Johannsen Centre for Functional Genome Research
‘Semi-related’ tissues
5
B2M
ALAS1
PBGD
G6PD
4,5
4
3,5
3
2,5
2
1,5
1
0,5
0
Caudate
nucleus
Amygdala
Corpus
callosum
Hippocampus
Thalamus
Wilhelm Johannsen Centre for Functional Genome Research
Pituitary
Genorm’ed HKG factor
B2M
2,5
ALAS1
PBGD
Genorm
2
1,5
1
1,3
0,9
1,0
1,1
1,0
0,8
0,5
0
Caudate
nucleus
Amygdala
Corpus
callosum
Hippocampus
Thalamus
Wilhelm Johannsen Centre for Functional Genome Research
Pituitary
U
te
ru
uc
s
os
a
Sm
ll
in
al
in
li
g
nt
es
ti n
Sp
e
in
al
co
rd
Fe
ta
ll
iv
er
Fe
ta
lb
ra
in
Pa
nc
re
as
PBGD
/m
ALAS1
w
HPRT
Wilhelm Johannsen Centre for Functional Genome Research
ol
on
B2M
tis
Th
ym
Th
us
yr
oi
d
gl
an
d
Tr
ac
he
a
cox1a
Te
s
Lu
ng
Pl
ac
en
ta
Pr
o
st
Sa
at
liv
e
ar
Sk
y
gl
el
an
et
al
d
m
us
cl
e
Sp
le
en
Li
ve
r
co41a
C
ea
r
ra
in
t
ki
dn
ey
H
B
dr
en
al
gl
B
on
an
e
d
m
ar
ro
C
w
er
eb
el
lu
m
A
Multi-tissue HKG quagmire!
1000
ATP6A
G6PD
373 fold
difference
100
10
1
Re
tin
Br a
Do
Pu ai
rs
t n
al Su a m
ro b. en
ot
N
G igr
an a
Fe g li
o
t
Ce al l n
re i v
W be er
ho llu
le m
Fe Br
ta ain
l
Fe bra
ta in
ll
iv
e
He r
ar
Lu t
Pl n g
ac
Sa Pr enta
o
Sk l iva sta
e l ry te
et
al glan
m
us d
Sp cle
le
Th en
y
Tr mus
ac
h
Ut ea
er
Sm
u
a l Co s
lI
n t lon
es
St tin
e
o
Pa m ac
nc h
re
a
K
Sp id s
in ne
al y
co
Te rd
st
is
12
8
Bestkeeper
10
Panel 1
6 fold
difference
(fold)
Panel 2
9 fold
difference
6
4
2
0
Wilhelm Johannsen Centre for Functional Genome Research
QPCR-at-a-glance
- WJC/NSGene SOP •
•
•
•
•
•
•
•
•
RNA extraction/purchase
RNA quantitation
DNAse treatment
Test for DNA contamination
RNA quantitation
Reverse transcription
Prepare primers spanning intron (if possible)
QPCR GOI
QPCR HKG
• Run 10-12 different HKGs
• Use the Bestkeeper to select HKGs used
• Calculation, quality control & normalisation
• Use Bestkeeper values
Wilhelm Johannsen Centre for Functional Genome Research
Normalisation
- Summary • Huge variation in expression of HKGs
• Finding suitable HKGs can be troublesome
• For most purposes using a single HKG is
insufficient
• Using statistics and geometric averages
appear to be best solution for multiple
tissue expression analysis
Wilhelm Johannsen Centre for Functional Genome Research
Overview
• What is PCR?
• Quantitation of gene
expression
• Methodology
• Experimental design
• Problems
• Applications at WJC
Wilhelm Johannsen Centre for Functional Genome Research
What’s QPCR good for?
• Screening transfectant cell lines for best
’expressors’
• Verification of microarray data
• SiRNA studies
• ’What happens if?’ studies
• Multiple tissue expression studies
• Should be an integral part in gene discovery
• Potential in disease diagnostics
Wilhelm Johannsen Centre for Functional Genome Research
A Gene Hunting Strategy
• Identify novel entity
• Bioinformatics
• Wet biology
• Verify that gene is
expressed
• RT-PCR
• Assess Expression
profile
• QPCR
• Obtain full-length
cDNA
• Cloning
• PCR
• Express novel entity in
appropriate cell system
• Select best cell line(s)
• QPCR
• Characterize novel
entity further
• QPCR
Wilhelm Johannsen Centre for Functional Genome Research
Best transfectant
GOI standard curve
GAPDH standardcurve
1,E-02
1,E-02
1,E-04
1,E-06
1,E-06
1,E-08
1,E-08
y = 0,7906e -0,6716x
1,E-10
1,E-10
y = 1.5297e -0.6892x
R2 = 0.9993
0
5
10
R2 = 0,9987
Fold
expression (GAPDH)
1,E-12
1,E-12
Fold expression (Log)
Fluorescence
1,E-04
15
20
25
30
35
40
0
5
10
15
20
25
30
1,E+07
1,E+05
1,E+03
1,E+01
1,E-01
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35
Sample #
Wilhelm Johannsen Centre for Functional Genome Research
35
40
Microarray verification
• Dissected human Fetal tissues
• Microarray data evaluated
• Novel genes with increased
expression levels >43%, were
selected (~40 genes)
• 9 genes selected for primary
verification
• 4 known tissue specific genes were
used as controls to verify the
experimental setup
Wilhelm Johannsen Centre for Functional Genome Research
Controls
1800
742
184
181
3
10w-B
10w-D**
10w-C
10w-A
10w-A
9.5w-A
3
5
5
3
7w-B
8w-A
8w-A
8w-C
8w-C
8w-C
2
8w-D**
3
6w-B
5.5w-A
94
65
54
58
41
152
23
24
28
18
22
98
54
56
49
9
1
2
5
7
3
10w-B
10w-D**
10w-C
10w-A
10w-A
9.5w-A
8w-D**
8w-D**
8w-D**
8w-C
8w-C
8w-C
8w-A
8w-A
7w-B
6w-B
5.5w-B
5.5w-A
5.5w-A
3
5.5w-B
3
5w-A+B
5.5w-B
4
10w-B
10w-D**
10w-C
10w-A
10w-A
9.5w-A
8w-D**
8w-D**
8w-C
8w-D**
8w-C
5.5w-B
6w-B
7w-B
8w-A
8w-A
8w-C
1
7
3
2
2
2
2
12
3
6
7
9
39
5.5w-B
5.5w-A
5.5w-B
5.5w-A
12
16
2
5w-A+B
2
8
Wilhelm Johannsen Centre for Functional Genome Research
8w-D**
16
5.5w-B
5.5w-A
60,0
8w-D**
2
6
5.5w-B
2
5.5w-B
2
5w-A+B
342
147
20,0
1
1
10w-B
10w-D**
10w-C
10w-A
10w-A
9.5w-A
Control 4
Control 3
524
127
1
51
2
8w-D**
8w-D**
8w-C
8w-D**
3
8w-C
8w-C
2
4
8w-A
8w-A
2
7
7w-B
6w-B
5.5w-B
5.5w-B
5.5w-B
70,0
250
573
338
427
202
58
3
6
5.5w-A
5.5w-A
7
5w-A+B
30,0
100
10,0
50
5
3
80,0
300
40,0
150
90,0
350
50,0
200
100,0
400
949
661
800
1078
1885
600
1000
0,0
0
1311
1200
0
0
81
200
800
1400
400
600
1000
1600
1200
2000
200
400
Control 2
Control 1
Microarray verification
396
92
40
44
50
10w-B
10w-D**
10w-C
10w-A
84
74
34
40
4 10w-A
59
8w-D**
1 9.5w-A
8w-D**
8w-D**
8w-C
8w-C
6w-B
27
18
18
19
22
19
8
10
11
10w-B
10w-D**
10w-C
10w-A
10w-A
9.5w-A
8w-D**
8w-D**
8w-D**
8w-C
8w-C
8w-C
8w-A
8w-A
7w-B
5.5w-B
6w-B
5.5w-B
5
9
5.5w-B
8
9
5w-A+B
5.5w-A
5.5w-A
5.5w-B
5.5w-B
5.5w-B
6w-B
7w-B
8w-A
8w-A
8w-C
8w-C
8w-C
8w-D**
8w-D**
8w-D**
9.5w-A
10w-A
10w-A
10w-C
10w-D**
10w-B
5w-A+B
5.5w-A
5.5w-A
10
1
10
2
1
1
1
1
2
1
1
1
Wilhelm Johannsen Centre for Functional Genome Research
1816
90,0
301
411
212
8w-C
8w-A
10 8w-A
54
43
5.5w-B
7w-B
36
5.5w-B
108
34
5.5w-A
5.5w-B
17
5.5w-A
6
4
3
2
2
2
10
89
66
60
8
0
0
5w-A+B
14
90
13
80
12
4
4
3
20
106
10w-B
10w-D**
10w-C
10w-A
10w-A
9.5w-A
8w-D**
8w-D**
8w-D**
8w-C
8w-C
8w-C
8w-A
Gene 3
Gene 2
231
5
1
2
3
8w-A
7w-B
6w-B
70
10
1196
23
9
7
9
3
5.5w-B
5.5w-B
5.5w-A
5.5w-B
3
5.5w-A
7
6
5w-A+B
30
5
100
15
0
4
0,0
797
40,0
1810
100,0
1000
41
50,0
2000
94
65
24
28
18
20,0
54
58
60,0
1500
70,0
500
10,0
22
30,0
Gene 1
Control 4
80,0
Microarray verification
94
50
70,0
45
15
9
6
4
10w-B
13
11
12
12
10w-D**
10w-C
10w-A
10w-A
9.5w-A
5.5w-B
9
10
8w-D**
8w-D**
2
5.5w-B
8
7
119
129
4
79
4
3
3
3
3
10w-B
10w-D**
10w-C
10w-A
10w-A
9.5w-A
2
10w-A
10w-A
8w-C
8w-C
8w-D**
8w-D**
8w-D**
9.5w-A
1
10w-C
10w-D**
10w-B
5w-A+B
5.5w-A
5.5w-A
5.5w-B
5.5w-B
5.5w-B
6w-B
7w-B
8w-A
8w-A
8w-C
8w-C
8w-C
8w-D**
8w-D**
8w-D**
2
1
2
2
2
1
2
2
2
2
54
14
8
4
8w-C
8w-A
8w-A
7w-B
1
5.5w-A
5.5w-B
5.5w-B
0 5.5w-B
0 6w-B
1
7
4
9
11
15
13
5.5w-A
5w-A+B
Wilhelm Johannsen Centre for Functional Genome Research
8w-D**
8w-C
8w-C
5
8w-A
8w-A
7w-B
6w-B
2
5.5w-A
4
8w-C
6
4
2
1
5.5w-A
5.5w-B
1
5w-A+B
10w-D**
10w-C
Gene 6
Gene 5
25
24
9
9
6
1
10w-A
14
197
2
50
10w-B
5
10w-A
8w-D**
8w-D**
8w-D**
8w-C
8w-C
8w-C
8w-A
8w-A
4
75
31
41
23
9
9.5w-A
3
2
1
7
9
7w-B
6w-B
5.5w-B
6
100
8
125
30
40,0
47
55
80,0
53
90,0
65
54
58
24
28
3
5.5w-B
5.5w-B
5.5w-A
5.5w-A
11
20
0
22
18
7
3
6
5w-A+B
150
5
4
200
0
25
35
50,0
0
10
0,0
15
10,0
20
20,0
40
60,0
60
100,0
25
30,0
Gene 4
Control 4
175
Microarray verification
94
3
2
1
1
1
90,0
1
10w-B
10w-D**
10w-C
10w-A
10w-A
9.5w-A
8w-D**
8w-D**
8w-D**
8w-C
8w-C
8w-C
8w-A
8
7
7
5
4
3
10
10
3
10w-B
10w-D**
10w-A
10w-C
2
8w-C
8w-C
8w-D**
8w-D**
8w-D**
9.5w-A
10w-A
2
1
2
8w-A
8w-C
1
2
8w-A
7w-B
6w-B
5.5w-B
5.5w-B
2
5.5w-B
2
2
2
5w-A+B
5.5w-A
5.5w-A
5.5w-B
5.5w-B
5.5w-B
6w-B
7w-B
8w-A
8w-A
8w-C
8w-C
8w-C
8w-D**
8w-D**
8w-D**
9.5w-A
10w-A
10w-A
10w-C
10w-D**
10w-B
5w-A+B
5.5w-A
2
2
3
2
1
2
5.5w-A
1
1
1
2
2
3
1
2
6
6
7
4
4
1
1
2
Wilhelm Johannsen Centre for Functional Genome Research
3
2
2
2
27
14
4
12
10
8w-A
Gene 9
Gene 8
2
2
5.5w-B
7w-B
5.5w-B
6w-B
1
1
5.5w-B
1
1
5.5w-A
5.5w-A
5w-A+B
10w-B
10w-D**
10w-C
10w-A
10w-A
9.5w-A
8w-D**
8w-D**
8w-D**
8w-C
0
0
1
5
1
2
8w-C
8w-C
8w-A
15
2
1
9
7
3
8w-A
7w-B
6w-B
8
25
2
41
23
24
9
3
5.5w-B
5.5w-B
5.5w-A
5.5w-B
3
5.5w-A
7
6
10
30
2
40,0
3
3
65
50,0
4
100,0
0
5w-A+B
0,0
4
2
5
28
18
20,0
54
58
60,0
3
70,0
1
10,0
22
30,0
Gene 7
Control 4
80,0
20
6
Summary
•
Troublesome technique!
•
Need to define experiments!
•
Rapid & relatively inexpensive
Method
•
Invaluable in gene discovery
•
Smart tool for selection of best
‘expressors’
•
Fast, conservative & rapid tool for
verification of other expression
data
•
Valuable tool for assessment of
disease potential
The Persistence of memory. Salvador Dali, 1931.
The Museum of modern Art, NY
Wilhelm Johannsen Centre for Functional Genome Research
• Karen Friis Henriksen
• Niels Tommerup
• Claus Hansen
•
•
•
•
•
•
•
•
Jesper Roland Jørgensen
Jens Johansen
Lone Dagø
Philip Kusk
Mette Grønborg
Nikolaj Blom
Teit E. Johansen
Lars Wahlberg
Wilhelm Johannsen Centre for Functional Genome Research