Reverse transcription-quantitative PCR (RT-qPCR): Reporting and minimizing the uncertainty in data accuracy.

Reverse transcription-quantitative PCR (RT-qPCR)
Reporting and minimizing the uncertainty in data accuracy
Ann Cuypers
Environmental Biology
Centre for Environmental Sciences
Hasselt University
Belgium

 Hallo
 Reverse transcription and quantitative (real-time) PCR
 Gene expression analysis
 Steady state mRNA levels
 Highly sensitive to technical variation
 Accuracy and precision depends on
 Minimizing technical errors
 Normalization to stably expressed reference genes
Remans et al. (2014) Plant Cell Commentary
2

Outline
 MIQE guidelines
 Selecting reference genes for RT-qPCR
 Reporting and minimizing the uncertainty in data accuracy
3

MIQE guidelines
 Uniform standard for reporting qPCR data
Bustin et al. (2010): Practical implementation of MIQE
 International consortium of academic scientists
4

MIQE guidelines checklist
Sample/Template details Checklist
Source If cancer, was biopsy screened for adjacent normal tissue?
Method of preservation Liquid N2/RNAlater/formalin
Storage time (if appropriate) If using samples >6 months old
Handling fresh/frozen/formalin
Extraction method TriZol/columns
RNA: DNA-free Intron-spanning primers/no RT control
Concentration Nanodrop/ribogreen/microfluidics
RNA: integrity Microfluidics/3':5' assay
Inhibition-free Method of testing
Assay optimisation/validation
Accession number RefSeq XX_1234567
Amplicon details exon location, amplicon size
Primer sequence even if previously published
Probe sequence* identify LNA or other substitutions
In silico BLAST/Primer-BLAST/m-fold
empirical primer concentration/annealing temperature
Priming conditions oligo-dT/random/combination/target-specific
PCR efficiency dilution curve
Linear dynamic range spanning unknown targets
Limits of detection LOD detection/accurte quantification
Intra-assay variation copy numbers not Cq
RT/PCR
Protocols detailed description, concentrations, volumes
Reagents supplier, Lot number
Duplicate RT DCq
NTC Cq & melt curves
NAC DCq beginning:end of qPCR
Positive control inter-run calibrators
Data analysis
Specialist software e.g., QBAsePlus
Statistical justification e.g., biological replicates
Transparent, validated
normalisation e.g., GeNorm summary
5

0.5
1
2
4
8
0 100 250 500
RelativeRBOHFexpression
Correct interpretation?
 Normalized data
 No further data available
7
-2
µM Zn

Outline
 MIQE guidelines
8

Selecting reference genes for RT-qPCR
 Golden standard
 Multiple reference genes
 Validated minimal expression variation
 Selection flowchart
 Select genes to validate
 Different sources
 Validate candidate reference genes
 Minimum 10 genes
 Using the same cDNA as for GOI measurements
 Apply evaluation algorithm (geNorm, Normfinder, GRAYNORM)
 Revalidation of chosen reference genes
 Related or repeated experiments
9

Selection flowchart
10
Remans et al (2014) Plant Cell Commentary
1. SELECT 2. VALIDATE
3. REVALIDATE

Outline
 MIQE guidelines
11

Uncertainty in Data Accuracy
 Origin?
 Quantification
Minimizing
A new algorithm for selecting reference genes: GrayNorm
Reporting
 Histogram
 Table
12

Uncertainty in data accuracy: origin
SAMPLE 1
Control
SAMPLE 2
Treated
Technical variation t1 t2SAMPLE-SPECIFIC
Gene of interest (GOI)
Reference gene (REF)
t1RQGOI
t1RQREF
Measurements:
t2RQGOI
t2RQREF
Normalization: t1/t1NRQGOI t2/t2NRQGOI
13

SAMPLE 1
Control
SAMPLE 2
Treated
Technical variation t1 = 1 t2 = 2SAMPLE-SPECIFIC
1RQGOI
1RQREF
Measurements:
2RQGOI
2RQREF
Normalization: 1NRQGOI 1NRQGOI
Reference genes correct for sample-specific technical variation
Example: RNA input for SAMPLE 1 = 1/2 RNA input for SAMPLE 2
14

SAMPLE 1
Control
SAMPLE 2
Treated
Technical variation t1 = 1 t2 = 2SAMPLE-SPECIFIC
Reference genes correct for sample-specific technical variation
ASSUMPTION: perfect reference genes
15
Example: RNA input for SAMPLE 1 = 1/2 RNA input for SAMPLE 2

SAMPLE 1
Control
SAMPLE 2
Treated
Biological variation
in reference gene
b1 b2SAMPLE-SPECIFIC
RQGOI
b1RQREF
Measurements:
RQGOI
b2RQREF
Normalization: 1/b1NRQGOI 1/b2NRQGOI
Imperfect reference genes
16

Example: REF expression in SAMPLE 1 = 1/2 REF expression in SAMPLE 2
SAMPLE 1
Control
SAMPLE 2
Treated
in reference gene
b1 = 1 b2 = 2SAMPLE-SPECIFIC
RQGOI
1RQREF
Measurements:
RQGOI
2RQREF
Normalization: 1/1NRQGOI 1/2NRQGOI
Reference gene-specific biological variation
is inversely imposed on GOI
17

SAMPLE 1
Control
SAMPLE 2
Treated
in reference gene
t1 t2
SAMPLE-SPECIFIC
t1RQGOI
t1b1RQREF
Measurements:
t2RQGOI
t2b2RQREF
Technical variation
b1 b2
18
Reference genes correct for technical variation,
but impose biological variation on GOI

SAMPLE 1
Control
SAMPLE 2
Treated
Reference gene (REF) t1b1RQREF t2b2RQREF
Technical and biological variation
RQREF of near perfect reference genes
RQREF of experiment with high technical quality
Two possible assumptions
19

Assumption 1: perfect reference genes – no BIOLOGICAL variation
SAMPLE 1
Control
SAMPLE 2
Treated
in reference gene
in reference gene
t1 t2
SAMPLE-SPECIFIC
t1RQGOI
t1b1RQREF
Measurements:
t2RQGOI
t2b2RQREF
Technical variation
b1 = b2 b2 = b1
Normalized data are accurate
20

Assumption 2: perfect technical experiment – no TECHNICAL variation
SAMPLE 1
Control
SAMPLE 2
Treated
in reference gene
in reference gene
t1 = t2 t2 = t1
SAMPLE-SPECIFIC
t1RQGOI
t1b1RQREF
Measurements:
t2RQGOI
t2b2RQREF
Technical variation
b1 b2
Non-normalized data are more accurate
21

Uncertainty in data accuracy: quantification
The “truth” lies between normalized and non-normalized data
 Normalized data: correction for technical variation
 Non-normalized data: no biological variation is imposed
 “Gene expression sensitivity” (GES) test
 Statistics on normalized data
 Statistics on non-normalized data
 BOTH SHOULD BE SIGNIFICANT
22

Uncertainty in data accuracy: minimize!
 Normalized data: correction for technical variation
 Non-normalized data: no biological variation is imposed
 Distance between normalized and non-normalized data
 Uncertainty
 Created by using the normalisation factor = 1/NF
 GrayNorm algorithm:
combination of reference genes with lowest deviation from 1 of
1/NF
23

0.5
1
2
4
8
0 100 250 500
Uncertainty in data accuracy
24
0.5
1
2
4
8
0 100 250 500
µM Zn
-2
µM Zn
-2
Normalized relative quantities Normalized relative quantities
Non-normalized relative quantities

Uncertainty in Data Accuracy
 Origin?
 Quantification
Minimizing
A new algorithm for selecting reference genes: GrayNorm
Reporting
 Histogram
 Table
25

Data representation
Histogram of normalized and non-normalized data
 Statistics on normalized data
 Statistics on non-normalized data (sensitivity analysis)
 Both should be significant!
26
0.5
1
2
4
8
0 100 250 500
µM Zn
-2
*
*
*
*
*

Data representation
Table of normalized and non-normalized data
 Supplement
 Provide “resolution” values: 1/NF per condition
27
Time (h) Genotype 1 Genotype 2
RESOLUTION
0
2
24
72
1.00 ± 0.14
0.46 ± 0.09
0.95 ± 0.11
1.26 ± 0.43
1.00 ± 0.08
0.79 ± 0.17
0.90 ± 0.27
0.65 ± 0.21
GOI
0
2
24
72
1.00 ± 0.09
0.34 ± 0.05
0.29 ± 0.04
0.45 ± 0.16
1.00 ± 0.09
0.44 ± 0.03
0.33 ± 0.09
0.68 ± 0.18

Reverse transcription-quantitative PCR (RT-qPCR): Reporting and minimizing the uncertainty in data accuracy.

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