Quantitative PCR

Wilhelm Johannsen Centre for Functional Genome Research

Quantitative PCR

Bioinformatics & Gene Discovery

2007


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


Overview

• What is PCR?• Quantitation of gene

expression• Methodology• Experimental design• Problems• Applications at WJC


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

50

60

70

80

90

100

Tem

per

atu

re

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Cycle

Denaturation

Extension

Annealing

Some graphics modified from Andy Vierstrate, http://users.ugent.be/~avierstr/principles/pcr.html


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


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


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


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


0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Cycle

Flu

ore

scen

ce

What is QPCR?• PCR as usual

• Additional quantitation step

• Optional Melting curve generation

40

50

60

70

80

90

100

Tem

per

atu

re

Denaturation

Exponential

Extension

Quantitate

Melting curve

Signal noise

Plateau

Annealing


Semi-quantitative endpoint PCR

vs. QPCR

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Cycle

Flu

ore

sc

en

ce

C(t)=11 C(t)=18.5


Melting curves – circumvention of ’dirty’ reactions

40

50

60

70

80

90

100

Tem

per

atu

re


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


Overview




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)


Selected QPCR strategies

SYBR

Taqman

Hyb. probes

Lux


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


Rapid DIY kit

0.5X SYBR

1X SYBR

2.5X SYBR

5X SYB

R


Overview




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!!


QPCR calculation strategies

• Serial dilution of ’known’ standards (standard curves)

• ∆c(t)• ∆∆c(t)• PCR efficiency


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


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)


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


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


Overview




Selected QPCR problems

• RNA quantity/quality• Quantitation of RNA• Reverse transcription• QPCR itself

•Standard curves

• Normalisation


Quantitation of RNA

• Spectrophotometric determination

• Advantages– Cheap– Fast

• Disadvantages– Inaccurate

• Fluorimetric determination• Advantages

– More accurate– More sensitive

• Disadvantages– More expensive– Slower


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)


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


Reverse transcription 1- RT time -

72

63

82

100

100

96

40

50

60

70

80

90

100

110

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


40

50

60

70

80

90

100

110

1003 1103 1203

% e

xpre

ssio

n

NSG3 B2M G6PD

Reverse transcription 2- RT variation -

• Same RNA• 3 RT-reactions-3 different days• Different RT-mixes


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


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


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


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


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


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


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……..


y = -0,263x + 1,0546

R2 = 0,9863

-15

-13

-11

-9

-7

-5

-3

-1

0 5 10 15 20 25 30 35 40 45

41103

041103Dil

51103

cDNA data

261103 data

Standard curve 1- Weirdo -



y = -0,2472x + 0,9779

R2 = 0,9901

-15

-13

-11

-9

-7

-5

-3

-1

0 5 10 15 20 25 30 35 40 45

41103

041103Dil

51103

cDNA data

261103 data



y = -0,2433x + 0,8723

R2 = 0,9858

-15

-13

-11

-9

-7

-5

-3

-1

0 5 10 15 20 25 30 35 40 45

41103

041103Dil

51103

cDNA data

261103 data


Standard curve - Summary -

• Standard curves can be extended and complexity restored by various additives

• Be aware of potential PCR inhibitors!


Selected QPCR problems

• RNA quality• Quantitation of RNA• Reverse transcription• QPCR itself

•Standard curves

• Normalisation


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?


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


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


‘Semi-related’ tissues

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

Caudatenucleus

Amygdala Corpuscallosum

Hippocampus Thalamus Pituitary

B2M ALAS1 PBGD G6PD


0,80,9

1,0 1,0

1,31,1

0

0,5

1

1,5

2

2,5

Caudatenucleus

Amygdala Corpuscallosum

Hippocampus Thalamus Pituitary

B2M ALAS1 PBGD Genorm

Genorm’ed HKG factor


Multi-tissue HKG quagmire!

1

10

100

1000

Adren

al g

land

Bone

mar

row

Cereb

ellu

mBra

inHea

rt

kidn

eyLi

ver

Lung

Place

nta

Prost

ate

Saliv

ary

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


0

2

4

6

8

10

12

Panel 1 Panel 2

Bestkeeper (fold)

9 fold differenc

e

6 fold differenc

e


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


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


Overview




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


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


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


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


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


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



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



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


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


• 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

Quantitative PCR

Documents

Transcript of Quantitative PCR