Inverse regression for the determination of the cycle cut-off of a real-time PCR
method for the detection of bovine tissues in feedingstuffs
Viviane PLANCHON, Robert OGER
Aline MARIEN, Gilbert BERBEN, Olivier FUMIERE
Background
Concept of threshold cycle
In 1987 : bovine spongiform encephalopathy (BSE) epidemic. Most probable dissemination way of the disease =
feeding with meat and bone meals (MBM)
Concept of cut-off
Linear scale
Clear-cut
signals
In 2000 : TOTAL BAN on the use of processed animal proteins (PAPs) in feed decided by European Commission (EC)
in order to eradicate BSE in Europe (Council decision 2000/766/EC, Regulation 2001/999/EC, 2002/1774/EC)
Cut-off
Amplification : +
Progressive lift of the ban could be considered by the EC, if there is NO DANGER for the health or the policy for
eradication of BSE (TSE Road Map 2005)
No amplification : -
Need of reliable analytical methods and tools for the species-specific detection and the quantificationof
PAPs in the feedingstuffs : SAFEED-PAP European project.
Objective of the project : to develop and validate a suitable PCR kit for the species-specific detection
of bovine proteins in compound feed BUT the test is qualitative and requires a criterion to determine
if a result is positive or negative : CUT-OFF DERTERMINATION
Logarithmic
scale
•
Exponential amplification
phase
•
Real-Time PCR test
Ct ~ 22 cycles
Depending of the operator:
20 < Ct < 25
Real-time PCR = enzymatic reaction for the amplification of DNA
fragment with a fluorescent detection. The production of fluorescence
at each cycle depend on the concentration of targeted DNA in the
reaction.
Late signals
PCR Uniformity in Log Phase
•
PCR can sometimes give late signals that are not significant and such results have to be
considered as negative. To that purpose, a cut-off value must be determined.
It is generally expressed in terms of cycles to be compared to the Ct of an amplification curve.
This Ct is the threshold cycle which means the number of cycles required for that
amplification to reach a given fluorescence threshold. If the Ct of an amplification curve is
higher than the cut-off, the result is considered as negative; however the Ct is a relative
concept that is dependent on the thermocycler, the reagents used and the way to set the
fluorescence threshold.
At CRA-W, the cut-off was initially determined empirically to correspond to 40 cycles with the
specific PCR conditions and the way the fluorescence threshold is set.
Setting of the threshold in the middle of the
linear part in a logarithmic view
Objectives
96 replicates
To define a scientifically sound way to find out rapidly what
is the cut-off value of any other PCR platform (thermocycler,
reagent, laboratory environment)
Fluorescence
1.00E-01
4200
Threshold
8.00E-02
6.00E-02
4.00E-02
Ct = # cycles needed
to pass threshold
2.00E-02
420
20
22
24
26
28
30
To evaluate the repeatability and inter thermocycler
variability (intermediate precision) and reproducibility of the
cut-off estimated through an inter-laboratory study
-2.00E-02
Cycle Number
42
Statistical aspects of inverse regression and cut-off estimation
Application to cut-off estimation
Statistical aspects of inverse regression
Inverse regression
X-axis : independant variable
allow to predict Y
Y0
Xˆ 0 ?
3
Log (Number of copies)
and for a
specific value
Log (number of copies)
unknown
value
Yˆ  b0  b1 X
from
Y-axis : dependant variable
predicted by X
 The cut-off determination was solved statistically using calibrations
curves with plasmids carrying the PCR target and the application of
inverse regression (Draper and Smith,1998) between the logarithm
of the copy number and the Ct
 The cut-off value is defined as the upper value (XU) of the
confidence interval for the Ct values corresponding to 1 copy of the
target
 XU is specific of a PCR platform
2.5
2
1.5
2
Number of runs to evaluate XU ?
y = 10.914 -0.2774 x
0.5
28.00
39.92
0
29
32.00
known value
34.00
36.00
38.00
CP
Confidence intervals of an
estimated value X0
30
31
32
33
34
35
36
37
38
39
40
39.88
for 2, 4, 8, 10 and 16 runs
data are grouped
CP
Inverse regression for the determination
of the cut-off for Y0=0
2
39.86
39.84
39.82
16
8
4
10
4
8
0.12
12
16
~ 10 calibrations (run) needed
to set a robust platform cut-off
value
0.08
0.04
4
20
8
0
16
10
2
0.00
39.80
0
1
0.16
4
8
12
16
20
Nb runs
Nb runs
Number of copy levels and replications to evaluate XU ?
known
value
bias
variance
Initially, calibrations with 28 points : 7 levels and 4
replicates/level
Y(X0)=
Y0
Based on bias, variance and practical aspects
decision for calibrations with 9 points :
3 levels, 3 replicates/level
unknown
value
Precision of cut-off estimation and european inter-laboratory study
CRA-W Inter thermocycler precision
Mean
Repeatability
Repeatability
sr
r
r%
Mean
ABI7000
40.24
0.799
2.21
5.50
ABI7000
40.24
ABI7500
38.87
0.608
1.69
4.34
ABI7500
38.87
Design of the inter-laboratory study
LC480
40.09
0.375
1.04
2.59
LC480
40.09
1
PCR controls
80 copies
6 calibrations
PCR controls
0 copy
640 copies
160 copies
2
5 6
3
4
7 8
40 copies
R%
1.124
3.12
7.74
0.824
2.28
5.88
Results
Distribution of the cut-off values calculated
9 10 11 12
A
B
C
D
E
F
G
H
Percentages of blocks (of 3 replicates)
detected as positive
45
% 100
44
90
80
70
60
50
40
30
20
10
0
43
42
Cutoff
min
41
40
39
38
Blind samples : to be analysed in
triplicates : 5, 2, 1, 0.6, 0.4, 0.1
copy/PCR
Reproducibility
Intermediate sR
precision
R
6 plates with 6 calibrations on each plate
0.476
1.32 19 laboratories:
3.29 17 from the European Union, 1 from Japan, 1 from Australia
37
0
Lab 1 Lab 3 Lab 5 Lab 7 Lab 9 Lab 11 Lab 13 Lab 15 Lab 17 Lab 19 Lab 21 CRA-W
1
2
3
4
5
Number of copies
The cut-off of the platforms are distributed
on a wide range of Ct values
37.73 < cut-off < 43.68
A percentage of 95 % of blocks of triplicates detected
corresponds to a level between 1 and 2 copies of the
target
Conclusions
A scientifically sound way to find out rapidly what is the cut-off value of any other PCR platform (thermocycler, PCR reagent) has been determined based on inverse regression. Accuracy has
been evaluated through evaluation of trueness and precision during an inter-laboratory study.
The protocol designed for determination of the cut-off value is fit for purpose
References
Draper N. and Smith H. (1980) Applied regression analysis (Second edition). New York: John Wiley & Sons.
Fumière O., Dubois M., Baeten V., von Holst C., Berben G. (2006). Effective PCR detection of animal species in highly processed animal by-products and compound feeds. Analytical and Bioanalytical Chemistry, 385, 1045-1054.
Fumière O., Veys P., Boix A., von Holst C., Baeten V., Berben G. (2009). Methods of detection, species identification and quantification of processed animal proteins in feedingstuffs. Biotechnologie, Agronomie, Société et Environnement, 13(S), 59-70.
Prado M., Berben G., Fumière O., Van Duijn G., Mensinga-Kruize J., Reaney S., Boix A., von Holst C. (2007). Detection of Ruminant Meat and Bone Meals in Animal Feed by Real-Time Polymerase Chain Reaction: Result of an Interlaboratory Study. Journal of Agricultural and Food Chemistry, 55, 7495-7501.
Aknowledgments
EU Commission – DG Research and DG SANCO
Walloon Agricultural Research Center (CRA-W)
SAFEED-PAP : Detection of presence of species-specific
Agriculture and Natural Environment Department (D3) – Valorisation of Agricultural products (D4)
processed animal proteins in animal feed (2006-2009)
Agricultural systems, Territory and Information Technologies Unit (U11) – Authentification and traceability Unit (U16)
http://safeedpap.feedsafety.org/
Léon Lacroix Building - Rue de Liroux, 9 -– Henseval Building – Chaussée de Namur, 126 - B–5030 GEMBLOUX (Belgium)
CRL-AP : Community Reference Laboratory
Tel : + 32 (0)81 62.65.71 - Fax : + 32 (0)81 62.65.59 - Tel : + 32 (0)81 62.03.51 - Fax : + 32 (0)81 62.03.88
for the detection of animal proteins in feedingstuffs
planchon@cra.wallonie.be - fumiere@cra.wallonie.be - http://cra.wallonie.be
Animal Proteins (2006-2011) http://www.crl.cra.wallonie.be/
RL
41
0.20
1
39.90
30.00
X0 XU
0.5
Evolution of the mean and the variance of XU
1.5
1
10 copies/5 µl
1
2.5
Variance of X U
Y   0  1 X  
3
Mean of Xu
Classical linear regression
640 copies/5 µl
320 copies/5 µl
160 copies/5 µl
80 copies/5 µl
40 copies/5 µl
AGROSTAT 2010
February 23-26, 2010
Benevento, Italy
Walloon Agricultural Research Center
0.00E+00