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Detection of rare drug resistance mutations by digital PCR in a human influenza A 1
virus model system and clinical samples 2
3
Alexandra S. Whalea#, Claire Bushella, Paul R. Grantb, Simon Cowenc, Ion Gutierrez-4
Aguirred, Denise M. O’Sullivana, Jana Želd, Mojca Milavecd, Carole A. Foya, Eleni 5
Nastoulib, Jeremy A. Garsone, Jim F. Huggetta,e. 6
7
Molecular and Cell Biology Team, LGC Ltd, Teddington, UKa, Virology Laboratory, 8
Clinical Microbiology and Virology, University College London Hospital NHS 9
Foundation Trust, London, UKb, Statistics Team, LGC Ltd, Teddington, UKc, National 10
Institute of Biology, Ljubljana, Sloveniad, Department of Infection, Division of 11
Infection and Immunity, University College London, London, UKe. 12
13
Running Title: Evaluation of digital PCR for SNP detection. 14
15
#Address correspondence to Alexandra S. Whale, [email protected] 16
17
JCM Accepted Manuscript Posted Online 9 December 2015J. Clin. Microbiol. doi:10.1128/JCM.02611-15Copyright © 2015 Whale et al.This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium,provided the original author and source are credited.
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ABSTRACT 18
Digital PCR (dPCR) is being increasingly used for the quantification of sequence 19
variations including single nucleotide polymorphisms (SNPs) due to its high accuracy 20
and precision in comparison with techniques such as quantitative PCR (qPCR) and 21
melt curve analysis. To develop and evaluate dPCR for SNP detection using DNA, RNA 22
and clinical samples, an influenza model of resistance to oseltamivir (Tamiflu) was 23
used. Firstly, this study was able to recognise and reduce off-target amplification in 24
dPCR quantification thereby enabling technical sensitivities down to 0.1% SNP 25
abundancy at a range of template concentrations; a 50-fold improvement on the 26
qPCR assay used routinely in the clinic. Secondly, a method was developed for 27
determining the false positive rate (background) signal. Finally, comparison of dPCR 28
with qPCR on clinical samples demonstrated the potential impact dPCR could have 29
on clinical research and patient management by earlier (trace) detection of rare drug 30
resistant sequence variants. Ultimately this could reduce the quantity of ineffective 31
drugs taken and facilitate early switching to alternative medication when available. 32
In the short term such methods could advance our understanding of microbial 33
dynamics and therapeutic responses in a range of infectious diseases such as HIV, 34
viral hepatitis and tuberculosis. Furthermore, the findings presented here are 35
directly relevant to other diagnostic areas such as the detection of rare SNPs in 36
malignancy, monitoring of graft rejection and foetal screening. 37
38
INTRODUCTION 39
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In 2001 the World Health Organisation (WHO) identified the threat of 40
antimicrobial resistance as requiring immediate action with the need for worldwide 41
cooperation (1). The development of new molecular methods for early diagnosis and 42
monitoring of drug resistance is one such action (2, 3). In this study, digital PCR 43
(dPCR) was employed to detect a clinically relevant SNP in a human influenza A 44
(H1N1) model involving resistance to the neuraminidase inhibitor oseltamivir 45
(Tamiflu). Resistance is acquired by a de novo SNP mutation (p.H275Y) encoded in 46
segment 6 of the viral genome (4) that changes the structure of the neuraminidase 47
protein such that oseltamivir is unable to bind (5). This resistance conveys no loss of 48
fitness to the virus thus permitting transmission between humans and enabling 49
resistance to spread (6). Between 1999 and 2002, oseltamivir resistance was present 50
at a background rate of 0.33% in influenza A N1 isolates (4). However, since 2007 the 51
spread of the resistant A (H1N1) virus increased and in 2008 the resistance rates 52
were estimated to be up to 70% in some European countries (6). Subsequently, the 53
WHO recommended vigilant monitoring for the emergence of oseltamivir resistance 54
(7, 8). Since the disappearance of the 2009 pandemic A (H1N1), the vast majority of 55
circulating viruses are sensitive to oseltamivir (~ 99 %) (9). 56
dPCR has been reported to enable rare SNP detection with technical sensitivities 57
(also referred to as fractional abundance) down to 0.001% of the wild type (WT) in 58
genomic DNA extracts (10, 11). To achieve this, dPCR subdivides a PCR into a large 59
number of partitions so that a proportion of them contain no template molecules 60
(12, 13). While this partitioning may increase the accuracy and precision of dPCR 61
over the more widely used quantitative PCR (qPCR) (14-17), it may also improve the 62
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sensitivity when measuring rare mutations within a high abundance WT background. 63
Rare SNP detection by dPCR is being used in an increasing range of clinical 64
applications including cancer stratification (18, 19), foetal screening (20, 21), 65
monitoring of organ transplant rejection (22) and detection of antimicrobial 66
resistance (23, 24). 67
However, in order for such methods to be effectively applied in research and 68
ultimately be translated into routine clinical analysis, validation of assay sensitivity is 69
essential along with additional considerations such as cost, speed and throughput. 70
To achieve a given sensitivity, a PCR assay must firstly have sufficient specificity to 71
allow confident discrimination between the SNP and WT molecules within a sample. 72
Secondly, a very small number of mutant molecules must be detectable in the 73
presence of a large excess of WT molecules. 74
In this study, we addressed the issue of technical sensitivity, for which we 75
evaluated the ability of dPCR to detect the H275Y SNP at abundancies down to 0.1% 76
of the WT, in a range of nucleic acid concentrations using an in vitro transcribed RNA 77
material. We then validated our dPCR method by quantifying the H275Y SNP in 78
clinical samples from patients who received oseltamivir treatment with comparison 79
to a qPCR method that is used routinely in the clinic that has a SNP detection 80
sensitivity of 5%. 81
82
MATERIALS AND METHODS 83
Unless otherwise stated experiments were performed in a single laboratory (LGC 84
Ltd, UK) and all kits and instruments were used following the manufacturer’s 85
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instructions. RNA/DNA LoBind micro-centrifuge tubes (Eppendorf) were used 86
throughout the study. 87
Neuraminidase constructs and production of synthetic RNA transcripts. 88
Segment 6 encompassing the full neuraminidase sequence for the human influenza A 89
virus pandemic 2009 (H1N1) strain, containing either the sensitive wild type 90
sequence (WT: p.H275) or the oseltamivir drug-resistance mutation (SNP: p.H275Y; C 91
to T transition), was cloned into the pEX-K plasmid. To generate biologically relevant 92
negative strand RNA, in vitro transcription (IVT) of linearised plasmids was 93
performed. IVTs were diluted to ~1 x 109 copies/µL in carrier (15 ng/μl RNA extracted 94
from human lung cells (Ambion™)) and stored in aliquots to reduce freeze-thawing 95
effects. Full details are given in the supplemental material (section 1 and Figure S1). 96
Clinical samples. Clinical samples were used in accordance with local ethical 97
guidelines. Residual material originating from throat swabs (TS), nasal swabs (NS), 98
combined throat and nasal swabs (CTNS) and endotracheal aspirates (ETA), as part of 99
routine diagnostic laboratory practice in the Department of Clinical Microbiology and 100
Virology at UCLH, were inactivated by mixing with an equal volume of Buffer AL 101
(QIAGEN) prior to storage at 4 °C for several weeks before transferring to -20 °C for 102
long term storage. A positive SNP control (POS) was extracted from a cell culture 103
(provided by R. Gunson, West of Scotland Specialist Virology Centre, Glasgow) 104
propagating a pandemic H1N1 virus encoding the SNP. All viral RNA samples were 105
extracted from 200 µL clinical sample/Buffer AL mix using the EZ1 Virus Mini Kit v2.0 106
on a BioRobot EZ1 and eluted in 90 µL AVE buffer (all QIAGEN). These extracts were 107
provided blind to LGC. 108
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Design of the H275Y genotyping assay. 733 pandemic influenza H1N1 109
neuraminidase gene sequences were downloaded from the NCBI influenza virus 110
sequence database (25). These were aligned and viewed in the multiple sequence 111
alignment program Clustal X (26). The location of the oseltamivir drug-resistance 112
mutation was identified. Primer express v2.0 software (Applied Biosystems™) was 113
used to design two TaqMan® MGB probes to the region containing the SNP; one 114
probe to target the wild type (WT) sequence and the other to target the resistant 115
(SNP) sequence. Universal primers were designed to flank the probe target sequence 116
to generate a 74 bp amplicon (Table S1). 117
Specificity to the pandemic influenza H1N1 serotype was determined by both in 118
silico alignments (http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html) and RT-119
qPCR. Pre-pandemic seasonal influenza A strains (H1N1 and H3N2), the avian 120
influenza A strain H5N1, Influenza B and other common respiratory viruses 121
(respiratory syncytial virus, parainfluenza viruses, human metapneumovirus, and 122
adenovirus) were analysed with the H275Y assay; none of these viruses were 123
detected demonstrating that the assay was specific to pandemic influenza A H1N1 124
virus only. Details of the RT-qPCR method are given in the relevant section below 125
and supplemental material (section 5). 126
Reverse transcription. All reverse transcription (RT) experiments were 127
performed in accordance with the dMIQE guidelines (Table S2) (27). The Maxima 128
cDNA synthesis kit (Thermo Scientific) was used according to the manufacturer’s 129
instructions with the addition of 200 nM of H275Y reverse primer to each 20 µL 130
reaction. For all experiments, triplicate RT reactions were performed with a single 131
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dPCR analysis per RT reaction. To monitor DNA contamination, reactions containing 132
1 x 105 IVT RNA copies per reaction with the reverse transcriptase omitted were 133
performed; in all cases no amplification occurred (Figure S2F). No template controls 134
(NTCs) were performed using carrier in place of template in every experiment; in all 135
cases no DNA was detected (Figure S2A). RT was perfumed with an initial incubation 136
at 25 °C for 10 minutes followed by RT at 50 °C for 15 minutes. The enzyme was 137
inactivated at 85 °C for 5 minutes. The cDNA was either stored at -80 °C or 138
immediately analysed by dPCR. 139
Digital PCR. dPCR experiments were implemented in accordance with the 140
dMIQE guidelines (Table S2) (27). dPCR experiments were performed using the 141
QX100/QX200™ Droplet Digital PCR System (Bio-Rad): 20 µl reactions containing 1 X 142
ddPCR™ Supermix for Probes with no dUTP (Bio-Rad), H275Y assay (Table S1; 143
LGC_dPCR) and 2 µl cDNA were pipetted into the sample well of a DG8 cartridge. 144
Droplet formation was performed as described previously (28). Thermocycling 145
conditions were 95 °C for 10 minutes, 40 cycles of 94 °C for 30 seconds and 60 °C for 146
1 minute, followed by 98 °C for 10 minutes and a 4 °C hold. The ramp rate for each 147
step was set to 2 °C/second. Droplets were read using the QX100/QX200™ Droplet 148
Reader and the data was analysed using QuantaSoft™ version 1.6.6.0320. Thresholds 149
were set to define the positive and negative droplets and the data was exported as a 150
.csv file. No template controls (NTCs) were performed using water in place of 151
template in every experiment; in all cases no amplification occurred (Figure S2A). 152
Optimisation of the H275Y assay for dPCR was performed at two laboratories; LGC, 153
UK and NIB, Slovenia (supplemental material, section 2). 154
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One-step reverse transcription quantitative PCR. All RT-qPCR experiments were 155
performed in accordance with the MIQE guidelines (Table S3) (29). For H1N1 typing 156
of the clinical samples at UCLH, the Superscript® III Platinum one step qRT-PCR kit 157
(Invitrogen™) was used with an N1 typing assay published previously (FluA H1N1 158
assay) (30). For the initial genotyping of the H275Y SNP in clinical samples at UCLH, 159
the Superscript® III Platinum one step qRT-PCR kit (Invitrogen™) was used with the 160
UCLH_H275Y assay. Comparison of RT-qPCR with dPCR on the clinical samples at LGC 161
was performed at LGC with the AgPath-ID™ One-step RT-PCR Reagents kit (Applied 162
Biosystems™). Cycling and data capture was performed using the 7500 Fast or Prism 163
7900HT Real Time PCR systems (both Applied Biosystems™). Analysis was performed 164
using the SDS software v2.4 (Applied Biosystems™) to calculate the quantification 165
cycle (Cq) values and the data was exported as a .csv file. Full protocol conditions 166
and experimental setups for all RT-qPCR methods are given in the supplemental 167
material (section 5). 168
Data Analysis. Exported .csv files from dPCR and qPCR experiments were 169
imported into MS Excel 2010 or R statistical programming environment 170
(http://www.r-project.org/) for statistical analysis. For dPCR experiments, the 171
average number of copies per partition (λ) with the associated confidence intervals 172
were calculated in MS Excel 2010 as described previously (27, 31, 32). The mean 173
values for template concentration and % SNP (fractional abundance = λSNP/(λSNP + 174
λWT)) were calculated in MS Excel 2010 along with the associated standard deviation, 175
coefficient of variance, regression analysis (R2), T tests and ANOVA. The RT efficiency 176
was defined as the % cDNA yield that was calculated as (λSNP/λexp), where λexp is the 177
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expected λ based on the concentration estimate using the Qubit® 2.0 fluorimeter 178
with the dsDNA HS Assay Kit (Invitrogen). Where necessary, data was transformed by 179
taking the natural logarithm as described previously (15). Figures were generated 180
either directly from the software programs or using Prism 6 (GraphPad). 181
For the calculations of the false positive rate (FPR), data was log transformed (λ 182
and % SNP) using the R statistical programming environment as described previously 183
(15). A standard analytical chemistry method based on the dispersion of samples 184
containing WT only molecules was employed to compute the detection limits (33). 185
The decision limits were calculated using two approaches: using the maximum 186
observed value, and an estimate based on Chebyshev's inequality (34). Full details 187
are given in the supplemental material (section 4). 188
189
RESULTS 190
Evaluation of the sensitivity of digital PCR in quantification of rare single 191
nucleotide polymorphisms. Initial experiments were performed to optimise and 192
evaluate the ability of the QX100/200™ dPCR system to detect the H275Y SNP using 193
the linearised plasmid DNA encoding either the WT or SNP sequence. The following 194
parameters were investigated to optimise the specificity of the H275Y assay: primer 195
and probe concentrations, MGB probe fluorophore dye swaps, uniplex and duplex 196
reactions and the annealing temperature (supplemental material, section 2 and 197
Figure S2). Duplex reactions containing 900 nM of each primer and 250 nM of each 198
probe (SNP probe conjugated with FAM and WT probe with VIC) with an annealing 199
temperature of 60 °C were selected for optimum specificity. 200
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To evaluate the sensitivity of the method, a range of SNP abundancies (between 201
100% and 0.1%) at different IVT RNA concentrations corresponding to different viral 202
titres were generated: [high] 1.2 x 105 copies/RT (expected cDNA λ ~ 5.1, assuming 203
100% conversion rate of RNA to cDNA), [medium] 4 x 104 copies/RT (λ ~ 1.7) and 204
[low] 2 x 104 copies/RT (λ ~ 0.85) (supplemental material, section 3, Table S4 and 205
Table S5). The range of these viral loads represented those obtained from 206
respiratory samples of patients with pandemic H1N1 influenza A (35). The Maxima 207
cDNA synthesis kit was selected due to the high conversion rate of RNA to cDNA (RT 208
efficiency) obtained following reverse transcription (Figure S3). Each % SNP at each 209
concentration was measured with triplicate RTs (and one dPCR for each RT) in three 210
independent experiments (9 data points per % SNP at each concentration). For 211
measurement of the false positive rate (FPR) of the SNP assay (commonly referred to 212
as the signal to noise ratio or ‘blank’), WT only (0% SNP) controls were also prepared 213
at the three concentrations with quadruplicate measurements made in each of the 214
three experiments. 215
Strong linearity between the expected (as estimated by Qubit® 2.0 fluorimeter) 216
and observed WT and SNP molecules per partition (λ) was measured for all 217
experimental replicates (slope = 1.028, y-intercept = – 0.076, R2 = 0.9962) with no 218
significant difference between the replicates (p > 0.65) (Figure S3A). The RT 219
efficiencies were normally distributed with an average of 88% (CV = 9%) and 76% (CV 220
= 23%) for the WT and SNP assays respectively (Figure S3B). 221
The smallest SNP abundancy that could be measured as significantly different 222
from the WT only controls across all three RNA concentrations was 0.5% (p < 0.036). 223
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For both the [high] and [medium] samples the SNP molecule could also be resolved 224
at 0.1% (p < 0.024 and p < 0.013 respectively) (Figure 1A). The CV for each SNP 225
abundancy increased with reduced sample concentration; measurements of the 226
[high] and [medium] sample had a CV of < 20% for the 100% to 0.5% SNP 227
abundancies while the 0.1% SNP had a CV or 17 and 21%, respectively. The [low] 228
sample had a CV > 29% for all SNP abundancies of 1% or less (Figure 1B). 229
Defining the detection capability of dPCR. The detection of SNP molecules in 230
the 0% SNP controls represents the FPR of the SNP assay and ranged from 0.06 to < 231
0.001% (average 0.012%) across the three different sample concentrations (Figure 232
1A). The observed variance associated with these controls was heteroscedastic with 233
a CV > 90% (Figure 1B). The limit of detection (LOD) of an assay is linked to the FPR 234
and its associated variance; the lower the FPR, the smaller the abundancy of the SNP 235
that can be measured as significantly different from the WT only control. Therefore, 236
it was hypothesised that the theoretical limit of detection for a given sample 237
concentration could be extrapolated from the experimental FPR data. 238
Using two methods: the maximum observed value and Chebyshev’s inequality 239
(34), the decision limit and the detection capability were calculated (Table 1 and 240
Figure S4). The detection capability, that defines the LOD in this context, was lower 241
than those tested experimentally for the [high] and [medium] samples (Table 1). 242
Chebyshev’s inequality, which generates more conservative estimates, defined the 243
limit of detection as 0.13% for the [low] sample (Table 1) and is consistent with the 244
inability to detect the 0.1% SNP abundancy in the [low] sample as significantly 245
different from the WT only controls. 246
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Identification of oseltamivir resistance mutants in patients with influenza A 247
H1N1. Following the 2009 influenza A H1N1 pandemic, the H1N1 type was detected 248
in patients undergoing oseltamivir treatment at UCLH with the one-step RT-qPCR 249
method using the FluA H1N1 assay (30). Patient samples were screened for 250
oseltamivir resistance using a one-step RT-qPCR method (UCLH_RT-qPCR) to identify 251
the presence of WT (oseltamivir sensitive) and/or SNP (oseltamivir resistant) 252
molecules (Table 2 and Figure 2). 253
Absolute quantification of the clinical samples by RT-qPCR was performed using 254
standard curves generated from the IVT RNA (LGC_RT-qPCR). The limit of detection 255
was defined as 75 RNA copies/reaction (equivalent viral load to 2.65 log10 copies/mL 256
extract) (Figure S6B) with a specificity for the WT and SNP probes of 90% and 87%, 257
respectively, regardless of sample concentration (Figure S6C). Due to the number of 258
samples that were quantified by dPCR only, comparison of LGC_RT-qPCR and dPCR 259
measurements identified a poor linear correlation. Analysis of the six samples that 260
were quantified by both dPCR and LGC_RT-qPCR, identified a good linear correlation; 261
R2 = 0.9223. The gradient of the slope (0.5429) was significantly different from 1 (p = 262
0.04) indicating a measurement bias (Figure 2A). The CV was on average lower for 263
the dPCR measurements compared with qPCR (13% and 23%, respectively). Spike-in 264
experiments confirmed the absence of inhibitors carried over from the sampling or 265
extraction process disrupting the qPCR or dPCR reactions (Figure S6D). 266
Fairly good concordance for detection of WT and SNP molecules was observed 267
between the UCLH_RT-qPCR, LGC_RT-qPCR and dPCR results (Table 2; light grey 268
shaded). Five samples had very low concentrations as defined by dPCR (samples 3.2, 269
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3.3, 3.4, 4.2 and 5.2) and were below the limit of sensitivity for qPCR. Furthermore, 270
only one of these five clinical samples was identified as positive for H1N1 by 271
UCLH_RT-qPCR (sample 3.2) with the detection of both WT and SNP sequences 272
(Table 2; dark grey shaded). WT sequences were detected in one sample (sample 273
4.1) by LGC_RT-qPCR at LGC while no sequences were detected by UCLH_RT-qPCR or 274
by dPCR. 275
Both WT and SNP molecules were identified in two clinical samples by LGC_RT-276
qPCR and dPCR (samples P1.3 and P1.4) (Figure 2B). SNP molecules were measured 277
in a further two clinical samples by LGC_RT-qPCR but not by dPCR (Figure 2B); the 278
proportion of SNP molecules as measured by LGC_RT-qPCR were 13.53% and 11.86% 279
for samples 1.1 and 5.1, respectively (Figure 2B; asterisk). As the limit of specificity of 280
the assay was defined as 87%, these samples were identified as potential false 281
positives for the presence of SNP molecule as the uncertainty in their quantification 282
was above the limit of the specificity for the SNP assay (Table 2). Absence of 283
amplification of either molecule in the experimental negative controls (NEG) 284
indicated that the SNP signal was unlikely to be caused by contaminating molecules 285
(Figure 2B). The limitations of the specificity of the LGC_RT-qPCR assay were further 286
highlighted in the analysis of the SNP only sample (POS), where the SNP abundancy 287
was measured as 95.82% (Figure 2B). 288
Five samples were taken from patient 1 over the course of a month during 289
treatment with oseltamivir (samples 1.1 to 1.5). Longitudinal analysis of the 290
concentration of the WT and SNP molecules revealed that the SNP molecule became 291
predominant by day 28. From days 28 to 29, the amount of SNP molecules quantified 292
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by both qPCR and dPCR increased nearly two-fold (Figure 2C). While the 293
concentration of the WT molecule stayed constant over the duration as measured by 294
dPCR, it significantly increased over 1.5-fold (p = 0.003) when measured by qPCR 295
(Figure 2D). 296
297
DISCUSSION 298
Using a human influenza A model system, we have developed a dPCR method 299
for rare drug resistance mutations by dPCR. We have investigated a number of 300
challenges associated with SNP detection, namely the specificity, sensitivity and the 301
false positive (FPR) of an assay. We then validated our method using clinical samples. 302
Our study has confirmed the ability to recognise and reduce off-target 303
amplification (mismatch binding of WT probe with the SNP sequence and vice versa), 304
thereby improving the specificity of the assay and so tackles one of the great 305
challenges of SNP detection. We have demonstrated the ability of dPCR to detect 306
rare SNPs down to 0.1% abundancy (Figure 1), although it should be acknowledged 307
that this was using material generated from IVTs in a background of human lung RNA 308
and so does not consider the effects more complex respiratory matrices may have on 309
the reaction. 310
While there is currently no clinical data demonstrating the necessity of detecting 311
SNPs down to 0.1% abundancy, by developing a method with increased levels of 312
sensitivity we provide the means by which future studies could determine an 313
appropriate cut-off for clinical use and by which the development of resistance can 314
be more accurately monitored during the treatment. Indeed, there is a need to 315
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consistently measure the 1% population abundance that is the current background 316
rate of the mutation (4, 6, 9) in order to follow the recommended guidelines by the 317
WHO (7, 8). The same assay (primers and probes) in qPCR has a cut-off at 318
approximately 5% abundancy due to the off-target amplification of the WT 319
molecules with the SNP probe (36) and therefore our study represents a 50-fold 320
increase in sensitivity by dPCR. This is consistent with the reported limits of other 321
qPCR assays for SNP detection (37, 38) and with the identification of false positive 322
results for two clinical samples (P1.1 and P5.1) that were identified as containing SNP 323
sequences by qPCR only. 324
The increased sensitivity of dPCR over qPCR was shown by the proportion of 325
clinical samples that were detectable by dPCR only (Figures 2A and 2B). A recent 326
study using a different assay that targets the same H275Y SNP observed comparable 327
differences in the sensitivity between qPCR and dPCR (24). While improved 328
quantification and precision of dPCR over qPCR has also been observed in other RNA 329
viral models (39, 40), studies using infectious agents with DNA genomes have shown 330
further improvement in the quantification and precision by both dPCR and qPCR (41, 331
42). This suggests that the RT step could have a greater effect on the sensitivity of 332
qPCR, although dPCR is not immune from variability in the RT efficiency (Figure S3B). 333
However, it is acknowledged that without a third method to arbitrate the 334
discrepancies between qPCR and dPCR, we cannot be certain that the dPCR result is 335
the correct result. 336
In addition to the off-target amplification, the greater specificity and sensitivity 337
afforded by dPCR, with respect to qPCR, is related to both the FPR and precision of 338
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the measurement. Here we developed a method to define the FPR that is associated 339
with the sample concentration. When sample concentration is not limiting and a 340
DNA template is used, it is possible to detect abundancies down to 0.001% using 341
dPCR as demonstrated in two genomic DNA cancer models (10, 11). While this is 342
impressive, there are a number of other factors that prevent sensitivities of this 343
magnitude from being routinely achieved in clinical samples. 344
The sample concentration limits are self-explanatory; to detect 0.001% 345
sequence abundance, substantially more than 100,000 sequences must be measured 346
(equates to a viral titre > 8 log10 copies/mL). However, a sample is influenced not 347
only by the sample concentration, but also by the quality of the sample, for example 348
sputum-containing samples can be difficulty to homogenise prior to extraction (43, 349
44). The type of respiratory samples used in this study involved RNA extracted from 350
TS, NS, CTNS and ETA, which could also impact on the results and may be a 351
contributing factor to the difference in performance between qPCR and dPCR. It has 352
previously been shown that inhibitors carried over from the extraction process in a 353
CMV model affect qPCR and dPCR to different extents (45). Furthermore, the RNA 354
yield obtained from extraction of RNA from influenza viruses from sputum can vary 355
from 20-100% depending on the extraction method used (43). These are important 356
areas for future investigation into the ability of dPCR to measure rare sequences in 357
RNA viruses. 358
Additional considerations include the sample concentration, as this is reduced, 359
the associated precision of dPCR is also reduced, although not to the same extent as 360
qPCR, and so smaller differences between samples could be harder to measure with 361
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confidence (46). While these factors may have implications for clinical samples such 362
as those used in this study, it is also of importance for the longitudinal monitoring of 363
patients whose viral loads are expected to reduce in response to treatment over 364
time. 365
This is clearly demonstrated by the analysis of longitudinal samples from a single 366
patient (Figures 2C and 2D); dPCR measured the WT molecules as remaining at a 367
constant level while there was an increase in the abundance of the SNP molecule 368
during treatment. In contrast, LGC_RT-qPCR measured an increase in both the WT 369
and SNP molecules and so implied that despite treatment, the overall viral load of 370
the patient was increasing. Recently, a similar longitudinal analysis of a patient 371
infected with oseltamivir resistant H1N1 Influenza A demonstrated that dPCR is less 372
prone to operator effects and has reduced inter-assay and intra-assay variability in 373
SNP quantification than qPCR (24). Together these findings show the potential 374
advantages of dPCR for monitoring and surveillance of antimicrobial drug resistance. 375
In this study, we demonstrated that the FPR determined the technical sensitivity 376
of the [low] sample rather than the precision of dPCR and therefore demonstrates 377
that the FPR is linked with the sample concentration. A separate study investigating 378
SNP mutations in a cfDNA model identified the FPR as being independent of sample 379
concentration (18), however, this study was investigating the genotype of cell free 380
DNA extracted from plasma and so the WT sequences were very low in 381
concentration indicating that the FPR can be difficult to define at very low 382
concentration samples. The FPR of an assay can be split into two sources; 383
experimental and technical. Experimental FPR can be attributed to laboratory 384
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contamination that was absent in this study (Figures S2, Table S5) and is reduced by 385
appropriate optimisation and good laboratory practice. 386
This leaves the technical sources contributing the FPR that are harder to define 387
but could include off-target amplification and enzymatic error, such as those that 388
arise from reverse transcriptases and/or polymerases (47). By defining the limit of 389
detection mathematically and relating it the concentration of the sample as 390
described, experimental constraints that limit the number of negative controls (for 391
example, different dPCR platforms have different maximum sample numbers per 392
experiment) could be overcome (46). This is in sharp contrast to other methods, such 393
as qPCR, that are limited by the quality of the calibration curve or next generation 394
sequencing that can be limited by technical error rates (48). 395
Finally, the robustness of dPCR allowed a qPCR assay that is used routinely in the 396
clinic to be transferred on to a dPCR platform with minimal optimisation. 397
Furthermore, the reproducibility of dPCR compared with qPCR (24) demonstrates 398
the potential of dPCR for clinical use. Various other methods such as CAST 399
(competitive allele-specific TaqMan) PCR (38, 49, 50), ARMS (amplification-refractory 400
mutation system) (51) and the use of LNA (locked nucleic acid) probes (52, 53) have 401
been developed to improve the 5-10 % SNP abundancy limit of qPCR. While these 402
methods are capable of detecting down to 0.1-1 % SNP, they require extensive 403
optimisation, including in some cases a total re-design of the assay. 404
Despite all these advantages, in the short term the non-reliance of dPCR on 405
calibration materials for quantification could be exploited as an alternative value 406
assignment method that is independent of mass or fluorescent measurements (54). 407
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Previous studies have shown that UV spectrometry methods can over quantify 408
nucleic acid abundance compared with dPCR (14, 16). Therefore the apparently 409
higher concentrations of the clinical samples as measured by qPCR compared with 410
dPCR (Figure 2A) could be attributed to the inherent limitations of the UV 411
quantification of the IVT RNA used for the calibration curve rather than bias within 412
the qPCR method itself. 413
The ability of dPCR to detect rare drug resistance SNP mutations could enable 414
earlier detection, facilitating earlier switching to alternative therapeutic agents and 415
resulting in an overall reduction in the amount of ineffective drugs used. This should 416
result in a reduction in both drug resistance and cost. Other infectious diseases such 417
as HIV, viral hepatitis and bacterial infections including tuberculosis and gonorrhoea 418
could also benefit from this approach to antimicrobial drug resistance monitoring. 419
Finally, the findings of this study should be applicable in a wide range of other 420
diagnostic areas including the monitoring of cell free nucleic acid in malignancy and 421
foetal screening. 422
423
FUNDING INFORMATION 424
The work described in this manuscript was partially funded by the UK National 425
Measurement System (NMS) and the European Metrology Research Programme 426
(EMRP) joint research project [HLT08] “Infect-Met” (http://infectmet.lgcgroup.com) 427
which is jointly funded by the EMRP participating countries within EURAMET and the 428
European Union. 429
430
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ACKNOWLEDGMENT 431
The authors would like to acknowledge George Karlin-Neumann and Svilen 432
Tzonev from Bio-Rad for extensive assistance in the development of this study and 433
Dr Rory Gunson of the West of Scotland Specialist Virology Centre in Glasgow who 434
kindly donated the cell culture propagating a pandemic H1N1 virus encoding the 435
H275Y mutation. 436
437
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626
FIGURE LEGENDS 627
Figure 1: Measurement of mutant SNPs by digital PCR is dependent on 628
template concentration and background signal. (A) Graph showing expected % SNP 629
of each sample (x-axis) against the observed % SNP abundance of the SNP molecule 630
(y-axis with log scale) for three concentrations. Each data point shows the mean and 631
95% confidence interval (CI) for the three experiments (performed on different days; 632
3 measurements), with the exception of the 0% SNP (WT only) control that was 633
performed in quadruplicate in all three experiments (12 measurements). The solid 634
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and dashed horizontal lines represent the mean and 95% CI of the [low] 635
concentration 0% SNP control. There was no significant (ns; p > 0.05) difference 636
between the 0.1% and 0% SNP for the [low] sample. All other SNP abundancies and 637
sample concentrations were significantly different (p < 0.05) from their respective 638
0% SNP controls. (B) Line graph showing coefficient of variance (precision) of the 639
measurements for the three sample concentrations. Horizontal dashed line indicates 640
a CV of 20%. 641
Figure 2: Identification of oseltamivir resistant mutants in patients with 642
Influenza A H1N1. (A) Linear regression for the 18 clinical samples. Data is plotted as 643
the log10 total RNA copies (WT and SNP) per mL of clinical sample extracts between 644
two-step dPCR and one-step RT-qPCR. The regression line (solid line) with its 645
associated 95% CI (dashed lines) is shown. (B) % SNP abundance measured in each 646
clinical sample (x-axis) by two-step LGC_dPCR (red) one-step LGC_RT-qPCR (blue). A 647
single mean value (horizontal dash in red or blue) at 0% indicates a sample in which 648
only WT sequences were measured. Error bars represent the 95% confidence 649
interval in the measurement of the % SNP. The solid and dashed horizontal lines 650
represent the limit of detection of the SNP assay by LGC_RT-qPCR. Two samples 651
were identified as false positives (*). (C and D) Longitudinal quantification of SNP (C) 652
and WT (D) sequences in patient 1 over one month by dPCR (red) and qPCR (blue). 653
Key: POS; SNP only control, NEG; extraction buffer only. 654
655
TABLES 656
Table 1. Calculation of the detection capability. 657
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Method Sample
name
dPCR
input
copies/rxn
Decision
Limit Detection Capability
Ln λ Ln λ Ln % SNP % SNP
Maximum
observed
value
[high] 120,000 -6.349 -5.965 -7.54 0.05%
[medium] 40,000 -8.078 -7.694 -8.11 0.03%
[low] 20,000 -7.645 -7.261 -6.96 0.10%
Chebyshev's
inequality α =
0.05
[high] 120,000 -5.784 -5.400 -6.96 0.09%
[medium] 40,000 -7.505 -7.121 -7.53 0.05%
[low] 20,000 -7.300 -6.916 -6.61 0.13%
For full details for calculation of these values, see text and supplementary materials 658
and methods. 659
660
Table 2: Analysis of clinical samples by qPCR and dPCR. 661
Patient
Code Day
Sample
Type
Flu A
H1N1
Result
UCLH_R
T-qPCR
Result
LGC_RT-qPCR LGC_dPCR
Total
viral
load
Result
Total
viral
load
Result
P1.1 0 TS POS Sen 4.35 FP 3.92 Sen
P1.2 26 TS n/d n/d n/d n/d
P1.3 28 CTNS POS Res 5.34 Res 4.53 Res
P1.4 29 CTNS POS Res 5.54 Res 4.96 Res
P1.5 32 TS LLP Res n/d n/d
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P2 0 TS POS Res n/d n/d
P3.1 0 CTNS POS Sen 3.49 Sen 3.51 Sen
P3.2 5 CTNS POS Res n/d 3.78 Sen
P3.3 11 NS n/d n/d n/d 3.36 Sen
P3.4 15 CTNS n/d n/d n/d 3.51 Sen
P4.1 0 ETA POS n/d 4.59 Sen n/d
P4.2 0 CTNS n/d n/d n/d 3.04 Sen
P4.3 5 CTNS n/d n/d n/d n/d
P5.1 0 ETA POS Sen 5.48 FP 4.56 Sen
P5.2 12 CTNS n/d n/d n/d 3.69 Sen
P6 0 CTNS LLP n/d n/d n/d
P7 0 CTNS LLP n/d n/d n/d
P8 0 TS POS Sen n/d 3.03 Sen
POS
POS Res 3.23 Res 3.61 Res
NEG n/d n/d n/d n/d
Total viral load is given as Log10 copies/mL extract. Key. Sample type: TS = Throat 662
Swab, NS = Nasal swab, CTNS = combined Throat and Nasal swab, ETA = 663
Endotracheal aspirate. POS = positive by PCR for Flu A H1N1, n/d = not 664
detected/below limit of detection; LLP = Low Level Positive, Res = resistant 665
sequences (SNP) detected in sample, Sen = only sensitive sequences (WT) detected; 666
FP = false positive, see text for details; Grey shaded = concordance between LGC and 667
UCLH results. 668
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