quantification of mrna using real-time rt-pcr · • dr helen lacey and prof colin sibley...

Quantification of mRNA using Real-Time RT-PCR

Tania Nolan, Rebecca Hands and Stephen [email protected]

mailto:[email protected]

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Normalisation, Optimisation and Standardisation

1. Assay design and optimisation2. Template quality3. Normalisation considerations

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Using the standard curve for quality control

y=mx+c•Slope = -3.323•RSqu = 0.98Intercept on y gives a measure of sensitivity

Amplification plots:•Baseline is horizontal•Threshold is in LOG region of curve•Curves are parallel

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IL-15 AssayOne tube assay (RNA dilutions) -Specific primers

y = -2.9408x + 50.441

R2 = 0.9682

30.0

32.0

34.0

36.0

38.0

40.0

42.0

3.5 4.0 4.5 5.0 5.5 6.0

Slope = -2.9

y = -3.7168x + 47.12

R2 = 0.9255

25

27

29

31

33

35

3.5 4.0 4.5 5.0 5.5

Two tube assay (cDNA dilutions) -Random primers

Slope = -3.7

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IL-15 predicted structure

60ºC

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GAPDH 5’37ºC 60ºC

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Total RNA targetGAPDH specific primed dilution series

y = -3.456x + 44.285R2 = 0.9992

y = -3.2243x + 43.256R2 = 0.9995

y = -3.308x + 42.935R2 = 0.9945

0

5

10

15

20

25

30

35

40

0 2 4 6 8 10

FAMHexCy5Linear (FAM)Linear (Hex)Linear (Cy5)

5’

3’

Centre

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Optimisation can improve assay sensitivity

Mouse cDNA was amplified using PCR primers specific to Hepcadin 1 (Nemeth et al, 2004). All combinations of primer concentrations ranging between 50nM and 600nM were used (Nolan et al 2006; Nils Gerke, Eppendorf and Jens Stolte, EMBL Heidelberg qRT-PCR workshop, 2006).

Forward primer concentration

600F/400R 50F/400R

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Total RNA purification – agarose gel visualisation

RNA from FFPE tissue sections

Extracted from frozentissue sample

Extracted from FFPEtissue sample

From: Anna Antonacopoulou, Patras, Greece

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Treatment of samples affects data

Data source: Prof Stephen Bustin, London

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Degradation of tissue extracted total RNA

RIN 10.0 9.2 9.2 8.6 8.1 7.8 7.2 6.7 6.0 5.0 4.4 4.0

18 S

28 S

5 S

Data source: Michael Pfaffl and Simone Fleige, Freising

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Thought Experiment:Effect of RNA degradation (RIN) on Ct

40

Ct

20101

RIN

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Influence of total RNA quality on qRT-PCRIL-1: Crossing Point

14,0

16,0

18,0

20,0

22,0

24,0

26,0

28,0

30,0

32,0

34,0

0 1 2 3 4 5 6 7 8 9 10RNA Integrity Number

Cro

ssin

g P

oint

Reticulum (E) Lymph nodes (E) Lymph nodes (P) Colon (P) Lung (E)Corpus luteum (P) Caecum (P) Spleen (P) Abomasum (P)

Data source: Michael Pfaffl and Simone Fleige, Freising

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Poster 26

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Agilent 2100 Bioanalyzer analysis of 5 RNA samples

A C

Conc. 110 ng/ulRatio: 2.5RIN: 10

B

Conc. 110 ng/ulRatio: 2.5RIN: 10

Conc. 62 ng/ulRatio: 0.0RIN: 2.4

D


E


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5’ / 3’ integrity assay

5’ assay Centre assay 3’ assay

AA

FAM HEX CY5

• Perform RT using oligo dT• If RNA is intact detection of 5’, centre and 3’ should be equal• If RNA is degraded detection of 3’ > 5’

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GAPDH 5’/3’ Multiplex Assay – Intact RNA

GAPDH 3’

GAPDH 5’

A

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GAPDH 3’

GAPDH 5’

C

GAPDH 5’/3’ Multiplex Assay – Degraded RNA

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GAPDH 5’ 3’ Multiplex Assay - FFPE RNA

Seq Ct

5’ : 30.5

3’ : 20.2

Ce : 24.1

5’

C

3’

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Inhibitors are not created equal

GAPDH 3’

GAPDH 5’

D

Conc. 30 ng/ulRatio: 2.7RIN: 9.1 (125mM EDTA)

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SPUD: for detection of inhibitorsD


62.5mM EDTA

125mM EDTA

Samples A,B,C


E 62.5mM EDTAD 125mM EDTACt (SPUD + Water) = 24

Reaction 1

Ct (SPUD + sample) = 26

Reaction 2

SPUD

(Phenol from extraction reagent)

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Also; see poster 62 (Tanya Novak and Jim Huggett)for further developments and clinical applications

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Normalisation• Correct for different amounts of input target• Correct for RT differences • Express data relative to;

- total RNA- a stable reference gene or multiple genes- DNA- number of cells- cDNA- a relevant gene (SIR) (Stephen Bustin, London, UK)- Alu repeats (J.Vandesompele, Uni Ghent, Belgium)- a spiked target (Gilsbach, Weinstephan, Germany)

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Random primed RNA (2x) dilution series (QPCR NHE1)

2.5 5 0.5 0.250.05 0.025 0.005

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050

100150200250300350400450

2 3 4 5 6 7 8 9 10 11

AgilentRibogreenUV SpecNanodrop

Extraction and quantification of RNA

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RNA Quantification

RNA quantification

0

50

100

150

200

250

300

Nanod

ropNan

odrop

Ribogre

enRibo

green

Experi

onExp

erion

Bio Ana

lyser

Bio Ana

lyser

A260/A

280

A260/A

280

ABCDE

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Independent reverse transcription reactions relative to input RNA

(Medium/Low expressed gene, NHE1)

Gene 1 relative to input RNA

00.20.40.60.8

11.2

Cal

ibra

tor

H re

f RN

A

Cal

ibra

tor

H re

f RN

A

111 2 3 4 5 6 7 8 9 10

RT batch 2RT batch 1

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Reverse transcription reactions normalised to constant input RNA value (β actin)

B actin expression relative to input RNA

00.020.040.060.080.1

0.12

Cal

ibra

tor

Ref

RN

A

Cal

ibra

tor

Ref

RN

A

RT batch 1

1 2 3 4 5

RT batch 2

6 7 8 9 10

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Gene quantification is not reproducible between different RT reactions

0.1

1

10

1 2 3 4

GAPDH

0.1

1

10

1 2 3 4

βactin

0.1

1

10

100

1 2 3 4

NHE1

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Correcting for batch to batch variations

• Assumption: The gene quantity in the calibrator represents the RT reaction efficiency for that gene in that sample batch

• Definition: Gene quantity in calibrator is 100% (for each batch)

• Quantities of the gene in the sample are expressed relative to gene quantity in calibrator (processed in same batch)

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Gene specific priming RT and QPCR (10 fold dilutions, GAPDH)

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Comparing RT Priming Strategies

GAPDHConstant RNA input concentration

0 1 2 3 4 5 6 7 8 9 10

15

20

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Specific total RNArandom total RNAoligo totalr+o total

Colorectal cancer sample

C t

1 2 3 4 5 6 7 8 9 10

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20

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Specific mRNArandom mRNAoligo mRNA


C t

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0 1 2 3 4 5 6 7 8 9 1020

21

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random totalrandom mRNA


C t

Total RNA vs mRNA - IGF-I

0 1 2 3 4 5 6 7 8 9 1028

30

32

34

36

38

40

specific totalspecific mRNA


C t

0 1 2 3 4 5 6 7 8 9 1015171921

23252729313335

oligo-dT totaloligo-dT mRNA


C t

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Samples

IGF-I/GAPDH total RNA

10-6

10-5

10-4

10-3

10-2

10-1

100

SpecificRandom hexamersOligo (dT)15Mixed

IGF-I/GAPDH mRNA

10-7

10-6

10-5

10-4

10-3

10-2

10-1

Samples

SpecificRandom hexamersOligo (dT)15

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Summary:• Use best quality RNA/DNA possible• QC everythingWhen using RT and random/oligo dT primers:• Use same RNA quantity in each RT reaction • Minimise RT batches and correct for differencesWhen using RT and specific primers:• Design in open regions of transcript (mfold)Assistance throughout the process:

ask the qPCR team at www.designmyprobe.com

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Many Thanks to:• Prof Stephen Bustin (QMUL, London, UK)

• Dr Michael Pfaffl (Freising, Germany)• Dr Jo Vandesompele (University Ghent, Belgium)• Dr Reinhold Mueller and Gothami Padmabandu (Formerly

Stratagene, La Jolla, USA)• Dr Anna Antonacopoulou, Patras, Greece• Dr Helen Lacey and Prof Colin Sibley (Manchester University

Medical School, UK)

• Dr Natalie Simpson (Formerly Sigma-Genosys, UK)• Dr Steffen Mueller (Stratagene, Germany)• Tanya Novak and Dr Jim Huggett (UCL, UK)• Dr Vladimir Benes and the EMBL Team Heidelberg

www.designmyprobe.com

quantification of mrna using real-time rt-pcr · • dr helen lacey and prof colin sibley...

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