Post on 08-Jan-2016
description
Wilhelm 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 DNA-genome
• 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 non-isothermal 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
40
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Cycle
Denaturation
Extension
Annealing
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
– 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– 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
Tiao, Hobler, et al.: JCI, 99, 163-168, 1999
Wilhelm Johannsen Centre for Functional Genome Research
Quantitation of gene expresion
--PCR Methodology--• Semi-quantitative PCR
– Gel-based– Inexpensive equipment– Involves hybridisation steps– Time, sample & labor conservative– Multiple samples but few target
genes simultaneously– Simple data calculations
• 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
Schulze, Hansen et al, Nature Genet. 1996
Wilhelm Johannsen Centre for Functional Genome Research
QPCR - why ?
• 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
0
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Cycle
Flu
ore
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ce
What is QPCR?• PCR as usual
• Additional quantitation step
• Optional Melting curve generation
40
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Tem
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re
Denaturation
Exponential
Extension
Quantitate
Melting curve
Signal noise
Plateau
Annealing
Wilhelm Johannsen Centre for Functional Genome Research
Semi-quantitative endpoint PCR
vs. QPCR
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Cycle
Flu
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C(t)=11 C(t)=18.5
Wilhelm Johannsen Centre for Functional Genome Research
Melting curves – circumvention of ’dirty’ reactions
40
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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• 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
• 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
Taqman
Hyb. probes
Lux
Wilhelm Johannsen Centre for Functional Genome Research
Chemistry 2
• Commercially available kits• 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 SYB
R
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 -• 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
• 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
• 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:
•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 -
72
63
82
100
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96
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1003-50 1103-50 1203-50 1003-90 1103-90 1203-90
% e
xp
res
sio
n
• Same RNA• 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
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1003 1103 1203
% e
xpre
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NSG3 B2M G6PD
Reverse transcription 2- RT variation -
• Same RNA• 3 RT-reactions-3 different days• Different RT-mixes
Wilhelm Johannsen Centre for Functional Genome Research
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• 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
y = 9,483e-0,6993x
R2 = 0,99911,E-12
1,E-10
1,E-08
1,E-06
1,E-04
1,E-02
1,E+00
0 10 20 30 40 50
Wilhelm Johannsen Centre for Functional Genome Research
y = 9,483e-0,6993x
R2 = 0,9991
1,E-12
1,E-10
1,E-08
1,E-06
1,E-04
1,E-02
1,E+00
0 10 20 30 40 50
Fuzzing ’bout dynamic range & target genes
Wilhelm Johannsen Centre for Functional Genome Research
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
• Prepare new primers
• Change enzyme/kit
• Further optimize PCR reaction (e.g., Magnesium etc.)
• Despair……..
Wilhelm Johannsen Centre for Functional Genome Research
y = -0,263x + 1,0546
R2 = 0,9863
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-9
-7
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-3
-1
0 5 10 15 20 25 30 35 40 45
41103
041103Dil
51103
cDNA data
261103 data
Standard curve 1- Weirdo -
Wilhelm Johannsen Centre for Functional Genome Research
Standard curve 2- Weirdo -
y = -0,2472x + 0,9779
R2 = 0,9901
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-13
-11
-9
-7
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-3
-1
0 5 10 15 20 25 30 35 40 45
41103
041103Dil
51103
cDNA data
261103 data
Wilhelm Johannsen Centre for Functional Genome Research
Standard curve 3- Weirdo -
y = -0,2433x + 0,8723
R2 = 0,9858
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-13
-11
-9
-7
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-1
0 5 10 15 20 25 30 35 40 45
41103
041103Dil
51103
cDNA data
261103 data
Wilhelm Johannsen Centre for Functional Genome Research
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 panelAdrenal gland Salivary glandBone marrow Skeletal
muscleCerebellum SpleenAdult brain TestisHeart ThymusKidney ThyroidLiver TracheaLung UterusPlacenta ColonProstate Small intestinePancreas Fetal brainSpinal cord Fetal liver
Corpus callosum
AmygdalaCaudate
nucleusHippocampusThalamusPituitary gland
Wilhelm Johannsen Centre for Functional Genome Research
‘Semi-related’ tissues
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5
Caudatenucleus
Amygdala Corpuscallosum
Hippocampus Thalamus Pituitary
B2M ALAS1 PBGD G6PD
Wilhelm Johannsen Centre for Functional Genome Research
0,80,9
1,0 1,0
1,31,1
0
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1
1,5
2
2,5
Caudatenucleus
Amygdala Corpuscallosum
Hippocampus Thalamus Pituitary
B2M ALAS1 PBGD Genorm
Genorm’ed HKG factor
Wilhelm Johannsen Centre for Functional Genome Research
Multi-tissue HKG quagmire!
1
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1000
Adren
al g
land
Bone
mar
row
Cereb
ellu
mBra
inHea
rt
kidn
eyLi
ver
Lung
Place
nta
Prost
ate
Saliv
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gland
Skele
tal m
uscle
Splee
n
Test
is
Thym
us
Thyr
oid
gland
Trac
hea
Uteru
s
Colon
w/m
ucos
al li
ning
Smal
l int
estin
e
Spina
l cor
d
Feta
l liv
er
Feta
l bra
in
Pancr
eas
co41a cox1a B2M HPRT ALAS1 PBGD ATP6A G6PD
373 fold differenc
e
Wilhelm Johannsen Centre for Functional Genome Research
0
2
4
6
8
10
12
Panel 1 Panel 2
Bestkeeper (fold)
9 fold differenc
e
6 fold differenc
e
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
Fold expression (GAPDH)
1,E-01
1,E+01
1,E+03
1,E+05
1,E+07
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Sample #
Fo
ld e
xpre
ssio
n (
Lo
g)
GAPDH standardcurve
y = 1.5297e-0.6892x
R2 = 0.9993
1,E-12
1,E-10
1,E-08
1,E-06
1,E-04
1,E-02
0 5 10 15 20 25 30 35 40
Flu
ore
sc
en
ce
GOI standard curve
y = 0,7906e-0,6716x
R2 = 0,99871,E-12
1,E-10
1,E-08
1,E-06
1,E-04
1,E-02
0 5 10 15 20 25 30 35 40
Wilhelm Johannsen Centre for Functional Genome Research
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
ControlsControl 1
3 7 5 6 3
81 58
202
7 2 4 2 3
1311
427
661
2
51 1
127
1885
338
0
200
400
600
800
1000
1200
1400
1600
1800
2000
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
Control 2
1 2 2 1 2 6
16 3 3 5 5 3
949
573 524
2
181
3
184
1078
742
0
200
400
600
800
1000
1200
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
Control 3
8 2 2
12
16
39
7
49
3 2 2 2 1
147
56
98
2
12 3
54
152
342
0
50
100
150
200
250
300
350
400
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
Control 44 6 3
7
18
22
3
28
9
3
24
7
2
58 54
65
1
9
5
23
94
41
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
90,0
100,05w
-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
Wilhelm Johannsen Centre for Functional Genome Research
Microarray verificationGene 1
10
6 66 17
89 34
36
43
10
8 54 10
23
1
21
2
18
10
79
7
11
96
1
59 4
30
1
18
16
39
6
41
1
0
500
1000
1500
2000
5w
-A+
B
5.5
w-A
5.5
w-A
5.5
w-B
5.5
w-B
5.5
w-B
6w
-B
7w
-B
8w
-A
8w
-A
8w
-C
8w
-C
8w
-C
8w
-D**
8w
-D**
8w
-D**
9.5
w-A
10
w-A
10
w-A
10
w-C
10
w-D
**
10
w-B
Gene 2
1 1 1
3
2
4
2
4
2
1 1 1
2
12
8
13
1
3
2
4
14
6
0
5
10
15
5w
-A+
B
5.5
w-A
5.5
w-A
5.5
w-B
5.5
w-B
5.5
w-B
6w
-B
7w
-B
8w
-A
8w
-A
8w
-C
8w
-C
8w
-C
8w
-D**
8w
-D**
8w
-D**
9.5
w-A
10
w-A
10
w-A
10
w-C
10
w-D
**
10
w-B
Gene 3
1
19
10 9
10 9
5
8
22
11
19
8
18
74
27
84
10
18
34
40
92
44
0
10
20
30
40
50
60
70
80
90
1005
w-A
+B
5.5
w-A
5.5
w-A
5.5
w-B
5.5
w-B
5.5
w-B
6w
-B
7w
-B
8w
-A
8w
-A
8w
-C
8w
-C
8w
-C
8w
-D**
8w
-D**
8w
-D**
9.5
w-A
10
w-A
10
w-A
10
w-C
10
w-D
**
10
w-B
Control 4
4 6 3
7
18
22
3
28
9
3
24
7
2
58 54
65
1
9
5
23
94
41
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
90,0
100,0
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
Wilhelm Johannsen Centre for Functional Genome Research
Microarray verification
Gene 6
2
2
3
1
2
3
1
2
4
2
3
3
2
13
8
9
2 2
4
7
12 1
1
0
2
4
6
8
10
12
145
w-A
+B
5.5
w-A
5.5
w-A
5.5
w-B
5.5
w-B
5.5
w-B
6w
-B
7w
-B
8w
-A
8w
-A
8w
-C
8w
-C
8w
-C
8w
-D**
8w
-D**
8w
-D**
9.5
w-A
10
w-A
10
w-A
10
w-C
10
w-D
**
10
w-B
Gene 4
1 1 1 2 2
6
2
9
6
4 4 5
9
31
24
53
4
9
6
15
47
25
0
5
10
15
20
25
30
35
40
45
50
55
60
5w
-A+
B
5.5
w-A
5.5
w-A
5.5
w-B
5.5
w-B
5.5
w-B
6w
-B
7w
-B
8w
-A
8w
-A
8w
-C
8w
-C
8w
-C
8w
-D**
8w
-D**
8w
-D**
9.5
w-A
10
w-A
10
w-A
10
w-C
10
w-D
**
10
w-B
Gene 5
11
20 1
3
7
1 0 0 1 4
15
9
4
11
12
9
79
8
1 2
14
54
11
9
19
7
0
25
50
75
100
125
150
175
200
5w
-A+
B
5.5
w-A
5.5
w-A
5.5
w-B
5.5
w-B
5.5
w-B
6w
-B
7w
-B
8w
-A
8w
-A
8w
-C
8w
-C
8w
-C
8w
-D**
8w
-D**
8w
-D**
9.5
w-A
10
w-A
10
w-A
10
w-C
10
w-D
**
10
w-B
Control 4
4 6 3
7
18
22
3
28
9
3
24
7
2
58 54
65
1
9
5
23
94
41
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
90,0
100,0
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
Wilhelm Johannsen Centre for Functional Genome Research
Microarray verification
Gene 8
1 2 1
4 4
7
2
6
3
2 2 2 1
12 1
0
14
1
2 2
6
27
10
0
5
10
15
20
25
30
5w
-A+
B
5.5
w-A
5.5
w-A
5.5
w-B
5.5
w-B
5.5
w-B
6w
-B
7w
-B
8w
-A
8w
-A
8w
-C
8w
-C
8w
-C
8w
-D**
8w
-D**
8w
-D**
9.5
w-A
10
w-A
10
w-A
10
w-C
10
w-D
**
10
w-B
Gene 9
1
1
2 2 2
2
1
3
3
1
2 2
1
7
5
8
2
2 2
4
7
3
0
2
4
6
8
105
w-A
+B
5.5
w-A
5.5
w-A
5.5
w-B
5.5
w-B
5.5
w-B
6w
-B
7w
-B
8w
-A
8w
-A
8w
-C
8w
-C
8w
-C
8w
-D**
8w
-D**
8w
-D**
9.5
w-A
10
w-A
10
w-A
10
w-C
10
w-D
**
10
w-B
Gene 7
1
2
2
1 1 1 1 1
2
2
1 1
1
3
2
3
2
1
2
2
3
3
0
1
2
3
4
5w
-A+
B
5.5
w-A
5.5
w-A
5.5
w-B
5.5
w-B
5.5
w-B
6w
-B
7w
-B
8w
-A
8w
-A
8w
-C
8w
-C
8w
-C
8w
-D**
8w
-D**
8w
-D**
9.5
w-A
10
w-A
10
w-A
10
w-C
10
w-D
**
10
w-B
Control 4
4 6 3
7
18
22
3
28
9
3
24
7
2
58 54
65
1
9
5
23
94
41
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
90,0
100,0
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
Wilhelm Johannsen Centre for Functional Genome Research
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