JAM_5305_sm_FigS1-TableS1-S5andAppendixA-B

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Supporting Information
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Manuscript title:
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Effectiveness of qPCR permutations, internal controls and dilution as means for
minimizing the impact of inhibition while measuring Enterococcus in environmental waters
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Running Head: Enterococcus qPCR inhibition
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Authors:
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Yiping Caoa,*, John F. Griffitha, Samuel Dorevitchb, Stephen B. Weisberga
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a
Southern California Coastal Water Research Project, 3535 Harbor Blvd, Suite 110, Costa Mesa,
CA 92626
b
University of Illinois at Chicago School of Public Health; Division of Environmental and
Occupational Health Sciences; Institute for Environmental Science and Policy; 2121 W. Taylor
St, Chicago, IL 60612
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The Supporting Information contains five tables, one figure, and two appendices.
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Materials and Methods
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Direct determination of Enterococcus inhibition by serial dilution. Our findings utilized the
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definition of “true” Enterococcus target assay inhibition as ΔCt between neighboring 5-fold
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dilutions one cycle less than expected (i.e., 1.32 = log25 -1). This definition assumed a perfect
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amplification efficiency of 2, which is higher than that measured (>1.9) for the Enterococcus
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assays. An amplification efficiency of 1.9 only increases the expected ΔCt from a 5-fold dilution
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by 0.18 cycles. Moreover, the measured amplification efficiencies were not statistically different
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between the five qPCR methods or between simplex and duplex within each method, indicating
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the same efficiency can be used for all assays. Therefore, it was scientifically more conservative
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and logistically simpler to use the log25 as the expected ΔCt from a 5-fold dilution. We also
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assumed a natural variability of 0.5 cycles for qPCR replicates, allowing a total of 1 cycle
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deviation from the log25 expected ΔCt. This is slightly higher than that estimated by standard
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deviation, which ranged 0.14 to 0.42 cycles for the 22 standard curve runs during this study.
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However, when inhibition analysis was repeated using a more stringent rule (i.e., 1.82 = log25 -
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2x0.25 instead of 1.32 cycles), similar results regarding relative susceptibility to inhibition,
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performance of ICs and dilution were obtained (data not shown).
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Table S1. Thermal conditions for the five qPCR methods.
qPCR method
Initial holding
TaqRegular
TaqFast
TaqFastfast
TaqEnviron
ScorpionN
50oC, 120s; 95oC, 600s
same as in TaqRegular
95oC, 20s
same as in TaqRegular
95oC, 120s
Thermal cycling (# of
cycles)
95oC, 15s; 60oC, 60s (40)
95oC, 5s; 60oC, 30s (40)
95oC, 5s; 62oC, 43s (45)
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2
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Table S2. Primer and probe sequences. All sequences for the ScorpionN method are proprietary
and not listed here.
Assay
Primer and probe sequences (5'→3')
Enterococccus For, GAGAAATTCCAAACGAACTTG; rev,
CAGTGCTCTACCTCCATCATT; probe, FAMTGGTTCTCTCCGAAATAGCTTTAGGGCTA-TAMRA
Sketa
For, GGTTTCCGCAGCTGGG; rev*, CCGAGCCGTCCTGGTC;
probe, FAM-AGTCGCAGGCGGCCACCGT-TAMRA
UCP
Probe, VIC-CCTGCCGTCTCGTGCTCCTCA-TAMRA
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45
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* This is the Sketa22 reverse primer (as in EPA Method A, USEPA 2010), which eliminates the
two 3'-terminal bases of the former Sketa2 reverse primer (Haugland et al. 2005. Water Res 39,
559–568).
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3
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Results
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Table S3. Standard curves for all qPCR assays *
qPCRMethod Assay/Target
TaqRegular
Enterococcus
Enterococcus
Sketa
UCP
TaqFast
Enterococcus
Enterococcus
Sketa
UCP
TaqFastfast
Enterococcus
Enterococcus
Sketa
UCP
TaqEnviron
Enterococcus
Enterococcus
Sketa
UCP
†
ScorpionN
Enterococcus
Enterococcus
Enterococcus
Sketa
IAC
Duplex
no
UCP
UCP
no
UCP
no
UCP
no
no
IAC
SSC
-
Standard Curve Equation
Y = -3.47 X + 39.11
Y = -3.39 X + 38.8
Y = -3.44 X + 15.32
Y = -3.88 X + 39.05
Y = -3.58 X + 41.82
Y = -3.48 X + 42.38
Y = -3.44 X + 15.45
Y = -3.92 X + 39.47
Y = -3.6 X + 40.48
Y = -3.59 X + 40.36
Y = -3.37 X + 15.15
Y = -4.56 X + 43.33
Y = -3.37 X + 40.85
Y = -3.52 X + 43.22
Y = -3.37 X + 15.3
Y = -3.31 X + 38.99
Y = -3.43 X + 40.97
Y = -3.57 X + 41.74
Y = -3.52 X + 41.28
Y = -3.36 X + 25.46
Y = -3.4 X + 44.61
R2
1.00
1.00
1.00
0.99
1.00
0.99
1.00
0.98
1.00
1.00
1.00
0.98
0.99
0.98
1.00
0.98
1.00
0.99
1.00
1.00
0.99
E
1.94
1.97
1.95
1.81
1.90
1.94
1.95
1.80
1.90
1.90
1.98
1.66
1.98
1.92
1.98
1.99
1.96
1.91
1.92
1.99
1.97
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* Standard curves (8 point, triplicate) were run as 5-fold serial dilution from 1.57 x 10^6 cells
per filter for Enterococcus, 5-fold serial dilution from 1 ng (Taqman) or 10ng (ScorpionN) per
reaction for Sekta, 2-fold serial dilution from 1600 copies per reaction for UCP and IAC. Higher
concentrations were used for Sketa in ScorpionN than in Taqman assays because the former had
lower analytical sensitivity.
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†
Standard curves for SSC were not done because SSC was supplied premixed with primer and
probe and serial dilution of the DNA template therefore was not possible.
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Table S4. Internal control Ct values for uninhibited reference materials.
qPCRMethod
TaqRegular
TaqFast
TaqFastfast
TaqEnviron
ScorpionN
IC
Sketa
UCP
Sketa
UCP
Sketa
UCP
Sketa
UCP
Sketa
IAC
SSC
Average Ct ± standard
deviation (n) across all
plates
20.7 ± 1.0 (134 )
32.8 ± 1.2 (135 )
21.2 ± 0.6 (107 )
33.6 ± 0.8 (108 )
20.5 ± 1.1 (131 )
35.0 ± 1.2 (132 )
22.1 ± 0.7 (90 )
33.2 ± 0.6 (90 )
26.6 ± 1.1 (132 )
34.2 ± 1.9 (128 )
31.3 ± 1.4 (134 )
Average standard
deviation for triplicates
within each plate
0.06
0.39
0.06
0.47
0.06
0.50
0.09
0.27
0.09
0.44
0.27
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Table S5. Enterococcus concentration (calibrator cell equivalent per 100ml) by qPCR from
selected environmental water samples, based on qPCR screening results prior to inhibition
analysis.
Location
Chicago Area Waterways System
(Chicago, IL)
Site ID
BR
CP
LA
NA
SK
CO
EL
Inland lakes in the greater Chicago
BW
area (Busse Woods, Lake Arlington, LAR
Skokie Lagoon, Tampier Lake)
SL
TL
North branch dam (Chicago, IL)
NBD
Fox River (Suburbs of Chicago, IL) FR
Montrose Harbor (Chic ago, IL)
MH
Avalon Beach (Avalon, CA)
A
B
C
Newport Bay (Newport Beach, CA) BNB35
n
4
2
6
4
9
2
2
4
2
6
6
8
2
2
12
11
2
2
65
66
5
Min
1100
2100
4550
4000
3050
24000
2950
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11000
0
26
325
7100
0
0
1554
1728
22885
Max
18000
7900
31000
41000
15000
51000
6800
2500
46500
120000
290
13000
15450
76
4949
30041
3089
36279
Median
2218
5000
9275
22500
6200
37500
4875
1260
28750
32023
80
1455
11275
38
1721
3408
2409
29582
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Figure S1. Quantification of (A) ICA and SSC, (B) UCP, and (C) Sketa in presence of various
concentrations of Enterococcus. Error bars represent standard deviation.
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In order to examine the Enterococcus concentration range where IC quantification was
unaffected, IC assays were run in presence of Enterococcus at the same eight Enterococcus
concentrations as in the standard curves (i.e., 5-fold serial dilution from 1.57 x 106 to 20 cells per
filter). This range varied with IC and sometimes by qPCR methods.
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Specifically, IAC (panel A) experienced no or delayed amplification at the four highest
Enterococcus concentrations in the ScorpionN qPCR method. SSC (panel A) experienced
delayed amplification only at the highest Enterococcus concentration in the ScorpionN qPCR
method. UCP (panel B) experienced no or delayed amplification at the three highest
Enterococcus concentrations in all Taqman® qPCR methods except in TaqEnviron where UCP
exhibited normal amplification at all eight Enterococcus concentrations tested. Interestingly, for
Sketa (panel C) in all five qPCR methods, smaller Ct values for sketa (simplex assay) were
observed when Enterococcus DNA were present in the same tube at the two highest
concentrations, indicating potentially cross reaction of Sketa assay with Enterococcus genomic
DNA.
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90
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Appendix S1. An example of using simulation to assess impact of Enterococcus qPCR
inhibition in ambient water monitoring
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This example assesses the impact of inhibition using hypothetic data. Because it may not be
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practical to directly assess Enterococcus inhibition routinely through the serial dilution approach
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(as in the present study), one may survey the beach sites for frequency of inhibition detected by a
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given internal control. For example, if inhibition detected by Sketa (in ScorpionN) ranged from
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8 to 30% for certain beach sites, the Enterococcus inhibition could be estimated to vary from 10
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to 38% based on Bayes' theorum (see below).
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A simple standard formula for the Bayes' theorem is P(A|B)=P(B|A) x P(A) / P(B). P(A)
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is the unconditional probability of event A, and P(B) is the unconditional probability of
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event B. P(A|B) is the conditional probability of event A given B (i.e., the probability of
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event A occurs if B happened). P(B|A), similarly, is the conditional probability of event B
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given A.
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In this example, we have
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P(A) = Probability of inhibition declared by the internal control Sketa (threshold=3.0
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cycles) regardless if there is inhibition on the Enterococcus qPCR (ScorpionN).
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P(A)=0.08~0.3is assumed in this example
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P(B) = Probability of the Enterococcus ScorpionN qPCR being inhibited. P(B) is what
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we are estimating.
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P(A|B) = Conditional probability of Sketa correctly declaring inhibition if the
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Enterococcus qPCR is inhibited. P(A|B)=0.37/(0.37+0.13) (Table 3).
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P(B|A) = Conditional probability of the Enterococcus qPCR being inhibited if Sketa
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declares inhibition. P(B|A)=0.37/(0.02+0.37) (Table 3).
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Therefore, P(B)=P(B|A) x P(A) / P(A/B) = 0.10~0.38.
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Second, consider the worst case scenario (P(B) = 0.38), 40% Enterococcus target assay
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inhibition was assumed for ScorpionN and adjusted for other qPCR methods based on their
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relative susceptibility to inhibition (Table 2). Then, the extent of false negative due to
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Enterococcus qPCR inhibition not being captured by IC and the percentage of unusable data due
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to "detected" inhibition by ICs were estimated based on performance of ICs for each IC-qPCR
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method combination (Table 3).
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Then the impact of inhibition on Enterococcus qPCR enumeration can be assessed (Table S3).
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Table S3. Simulation to assess impact of inhibition on rapid recreational water monitoring using
Enterococcus qPCR methods in southern California beach sites.
% Target
inhibition
in ambient
water*
20%
% Target
inhibition
resolved by
1:5 dilution†
95%
TaqFast
26%
95%
TaqFastfast
60%
79%
TaqEnviron**
ScorpionN
4%
40%
100%
78%
qPCR
method
TaqRegular
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128
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131
132
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134
135
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IC
Sketa
UCP
Sketa
UCP
Sketa
UCP
n/a
Sketa
IAC
SSC
*
% Target assay
inhibition
captured by IC‡
% False
negative
26%
56%
27%
80%
28%
92%
n/a
74%
72%
39%
0.7%
0.4%
1.0%
0.3%
9.1%
1.0%
0
2.3%
2.5%
5.4%
§
% False
alarm
by IC‡
%
Unusabl
e data§
0%
15%
0%
40%
5%
38%
n/a
4%
24%
6%
0%
15%
0%
41%
8%
45%
0
10%
28%
8%
Enterococcus target assay inhibition in ambient water was estimated by Bayes' Theorem based
on the hypothetic % inhibition detected by ScorpionN-Sketa.
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Values obtained from Table 2. A 1:5 dilution of unpurified DNA extract was assumed prior to
qPCR.
‡
Values obtained from Table 3. A threshold of 3.0 cycles was used for all ICs for detecting
inhibition.
§
A false negative occurs when an inhibited sample is not flagged by the IC, leading to report of
an underestimated Enterococcus concentration. Data from a sample is deemed unusable if this
sample is declared inhibited by the IC used.
**
Further simulation was not applicable as target assay inhibition in TaqEnviron qPCR was fully
resolved after 1:5 dilution.
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Appendix S2. Standard Operating Procedure for a spiking-followed-by-dilution approach
to detect inhibition for qPCR assays
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Background:
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Because different assays are inhibited differently by different inhibitors, only assessment based
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on the target assay itself can truly reflect inhibition on the target assay. This standard operating
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procedure (SOP) is based upon the dilution approach: If the sample is inhibited, then the
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difference between Ct values (ΔCtdil) of the undiluted and diluted DNA extracts will be smaller
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than expected for samples not inhibited. A 5-fold dilution is used in this SOP. However, to
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ensure the diluted samples (when not inhibited) still have enough targets to produce precise Ct
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values, a spiking-followed-by-dilution approach is used for detection of inhibition. Reference
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material used as standards for quantification in the corresponding qPCR will be used to spike
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samples. Although the standard reference material could be different from the actual DNA
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targets in real samples, it is considered appropriate for detection of inhibition if it is used as the
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basis for quantification in the corresponding qPCR assay. An EXCEL spreadsheet that
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automates all calculations in this SOP is available upon request.
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Procedure:
1. Perform standard curve measurements as described in the corresponding qPCR SOP
to determine
1) Amplification efficiency (E): E will be calculated from slope estimated using the
standard curves using the formula E=10-1/slope.
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2) Spiking concentration: a spiking concentration (S28) equivalent to approximately a
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Ct of 28 is selected (based on standard curves).
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2. Prepare spiked-undiluted and spiked-diluted template (i.e., DNA extract from a sample)
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for all samples to be tested (see step 3). Also prepare no template reference: S28 and
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diluted S28 without any DNA extracts from samples.
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3. Perform qPCR in duplicate (this SOP) or triplicate as described in corresponding qPCR
SOP.
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For each batch of samples tested, run S28 and 1:5 diluted S28. This will serve as a
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reference to see if expected Ct values are obtained, for both quality control and
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determination of inhibition.
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For each sample, there will be 4 reactions with 2 reactions for each of the following.
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a) Spiked-undiluted: template spiked with standard DNA (final conc. of standard
DNA = S28)
b) Spiked-diluted: 1:5 dilution of the spiked template (i.e., 3(a))
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4. Assess inhibition based on Ct3(a) and ΔCtb-a= Ct3(b) -Ct3(a), i.e., the average Ct difference
between 3(b) and 3(a)
1) Calculate expected Ct difference between 3(a) and 3(b) when there is no
inhibition: ΔCtdil,exp = logE5
2) Define minimum Ct3(a) and acceptable ΔCtdil for uninhibited sample

Ct3(a) min = CtS28 - 1, i.e., in absence of inhibition, the spiked-undiluted
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sample should at a minimum produce a Ct as small as that produced by
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S28.
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
ΔCtdil =ΔCtdil,exp - 1 (ΔCtdil,exp = logE5).
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This above assumes 0.5 cycle natural variability for Ct values from
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replicate qPCR reactions. ΔCtdil outside this natural variability is
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deemed to have been caused by inhibition. Empirically determined
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standard deviation (SD) for a given qPCR assay can also be used in place
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of the 0.5 assumed natural variability, in which case ΔCtdil =ΔCtdil,exp- 2 x
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SD. Similarly, Ct3(a)min = CtS28 - 2 x SD.
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3) Determine if the DNA extract, when undiluted, is inhibitory for the target qPCR
assay being tested.
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
If Ct3(a) < Ct3(a) min, the undiluted extract from the sample is inhibitory.
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
If ΔCtb-a < ΔCtdil, the undiluted extract from the sample is inhibitory.
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
If ΔCtb-a ≥ ΔCtdil, the undiluted extract from the sample is not inhibitory (a
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ΔCtb-a much higher than logE5 may indicate a data error)
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