Screening Newborn Blood Spots for 22q11.2 Deletion Syndrome Using Multiplex Droplet Digital PCR (original) (raw)

Abstract

BACKGROUND

The diagnosis of 22q11 deletion syndrome (22q11DS) is often delayed or missed due to the wide spectrum of clinical involvement ranging from mild to severe, often life-threatening conditions. A delayed diagnosis can lead to life-long health issues that could be ameliorated with early intervention and treatment. Owing to the high impact of 22q11DS on public health, propositions have been made to include 22q11DS in newborn screening panels; however, the method of choice for detecting 22q11DS, fluorescent in situ hybridization, requires specialized equipment and is cumbersome for most laboratories to implement as part of their routine screening. We sought to develop a new genetic screen for 22q11DS that is rapid, cost-effective, and easily used by laboratories currently performing newborn screening.

METHODS

We evaluated the accuracy of multiplex droplet digital PCR (ddPCR) in the detection of copy number of 22q11DS by screening samples from 26 patients with 22q11DS blindly intermixed with 1096 blood spot cards from the general population (total n = 1122).

RESULTS

Multiplex ddPCR correctly identified all 22q11DS samples and distinguished between 1.5- and 3-Mb deletions, suggesting the approach is sensitive and specific for the detection of 22q11DS.

CONCLUSIONS

These data demonstrate the utility of multiplex ddPCR for large-scale population-based studies that screen for 22q11DS. The use of samples from blood spot cards suggests that this approach has promise for newborn screening of 22q11DS, and potentially for other microdeletion syndromes, for which early detection can positively impact clinical outcome for those affected.

22q11.2 deletion syndrome (MIM #611867) [also known as 22q11DS4 DiGeorge (MIM #188400), Velocardiofacial (MIM #192430), Shprintzen, and Conotruncal Anomaly Face] is considered to be the most common chromosomal deletion associated with birth defects in humans and is characterized by a wide range of clinical manifestations (1). A 3-Mb deletion in the 22q11 deletion region is the most common, occurring in 80% to 90% of individuals with a 22q11DS, and the second most common deletion is a nested 1.5-Mb deletion occurring in 10% to 20% of these individuals. Atypical deletions within the 22q region are present in a small percentage of individuals with a 22q11DS (2).

The syndrome is characterized by a complex display of subphenotypes, comprising heart defects, velopharyngeal inadequacies, recurrent infections, immunological anomalies, behavioral disorders, and learning disabilities (3). However, the ability to quickly and accurately identify the disease soon after birth would empower physicians to screen for structural deformities commonly associated with the syndrome, such as heart defects, which can often be surgically repaired. An early molecular diagnosis of 22q11DS would prompt reflex testing for other treatable conditions such as hypocalcemia, which if left unaddressed can lead to additional medical problems, including seizures, learning difficulties, and developmental delays. Thus, early identification of the deletion would allow for timely interventions and therapies that would undoubtedly improve the patient's prognosis. Because of the high impact on public health, propositions to include 22q11DS in newborn screening (NBS) panels have been suggested. However, although in January 2012 compelling arguments in favor of NBS for 22q11DS were presented to the US Secretary of Health's Committee on Heritable Disorders from several groups and foundations throughout the US, Canada, and the UK, the application for adding 22q11DS screening to the current federally mandated list of NBS was denied. The decision was primarily based on the lack of appropriate prevalence rates and pilot studies attesting the incidence of 22q11DS in the general population.

Numerous studies have proposed that the prevalence of 22q11DS ranges between 1:4000 and 1:9700 (4–7). These values are approximate and likely underestimate the actual prevalence of 22q11DS because this syndrome is associated with many different clinical findings that extend from mild to severe and life-threatening conditions; thus, diagnosis is often uncertain and therefore it may be delayed or missed (8). Additionally, owing to the high cost of large population testing in asymptomatic individuals, current studies estimated the prevalence rate in a specific clinical or geographical population, and thus the prevalence rate of 22q11DS in the general population is unmeasured (9).

The gold standard method of diagnosis is a fluorescent in situ hybridization (FISH) assay using the TUP-1 like enhancer of split protein/histon cell cycle regulation (TUPLE1/HIRA) probe for the histone cell cycle regulator (HIRA)5 gene (also known as TUPLE1). Although this approach is accurate, it is expensive and labor-intensive, requiring trained personnel and equipment, and has therefore been impractical for screening purposes. Multiple studies have attempted to develop a faster and more cost-effective screening methodology. PCR and MLPA (multiplex ligation dependent probe amplification) assays have been proposed; however, the cost and efficiency requirements to produce an effective screening program are still unmet by these methods (10–14).

In this report, we present the development and the applicability of a high-throughput, PCR-based assay, namely droplet digital PCR (ddPCR). ddPCR is a robust, reliable, accurate, and cost-effective test that allows absolute measures of nucleic acid concentrations and thus provides a highly accurate estimation of DNA copy number (CN) that is crucial for clinical diagnostics. We have recently demonstrated the applicability of this resource for accurate CN variation (CNV) analysis for the identification of individuals with 22q11DS using DNA isolated from whole blood obtained from individuals known to have 22q11DS (15). Here, we extend that work by reducing the number of reactions used for 22q11DS detection down from 8 duplex assays to a single 3-plex assay that incorporates 2 gene markers within the 22q11 region that can identify a deletion and also distinguish between the 1.5- and 3-Mb size deletions. We demonstrated the utility of this approach in a >1000-sample study showing that multiplex ddPCR is a highly sensitive and specific method for analyzing DNA extracted from blood spot cards, a necessary requirement for NBS programs.

Materials and Methods

HUMAN PARTICIPANTS

Anonymous blood spots were collected on blood spot cards (FTA or 903) and peripheral blood was collected in EDTA-containing tubes. A total of 1096 blood spot cards were obtained from anonymous individuals from the general population, and isolated DNA was randomized—in a blind fashion—with 26 blood spot card DNA samples from individuals with 22q11DS (total number of screened samples, 1122) recruited through the 22q deletion clinic at the UC Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute. Three positive (with 22q11DS) and negative controls (without 22q11DS) were also included in the study. All samples were collected under protocols approved by the UC Davis Institutional Review Board at the MIND Institute located in Sacramento, California.

DNA ISOLATION

Genomic DNA (gDNA) isolated from 2-mm punches, taken from FTA/903 blood spot cards (Whatman, Inc.), was used for each ddPCR reaction. Isolation of DNA from blood spot cards was performed as previously described (16). Briefly, for each sample the discs were incubated in 300 μL of Tissue Digest buffer (Qiagen Sciences) and 20 μL of Digest enzyme (Qiagen) at 56 °C for 1 h. DNA was isolated from the supernatant and eluted in Tris sodium EDTA buffer (10 mmol/L Tris-HCl, pH 8, 0.1 mmol/L Na2EDTA) using a QIAxtractor instrument following manufacturer's instruction (Qiagen) and stored at −20 °C. For the analytic experiments gDNA was isolated from peripheral blood mononuclear cells (PBMCs) using standard methods (Qiagen).

DROPLET DIGITAL PCR

Approximately 10–50 ng of gDNA per sample was dispensed into a single well on a 96-well plate already containing Droplet PCR Supermix (Bio-Rad Laboratories), 1.25 U of the restriction enzyme _Mse_I (New England Biolabs), 900 nmol/L primers, and 250 nmol/L probe in a final volume of 25 μL. Primer and probe sequences are as follow:

RPP30-FAM probe 5′-6FAM-CTGACCTGAAGGCTCT-MGB-3′, forward primer 5′-GATTTGGACCTGCGAGCG-3′, and reverse primer 5′-GGTTGGCCAGGCGCGAAG-3′; RPP30-VIC probe 5′-VIC-CTGACCTGAAGGCTCT-MGB-NFQ-3′, forward primer 5′-GATTTGGACCTGCGAGCG-3′ and reverse primer 5′-GGTTGGCCAGGCGCGAAG-3′; COMTFAM probe 5′-6FAM-ACTTCCTAGCACACGTGCGC-3BHQ_1–3′, forward primer 5′-GTGCTACTGGCTGACAACGTGAT-3′ and reverse primer 5′-GGAACGATTGGTAGTGTGTGCA-3′; and PI4KA-HEX probe 5′-5HEX-AGCTGAAGACCTCTTTGGCAGC-3BHQ_2–3′, forward primer 5′-ATGCTTGTGCGACGCAGAC-3′ and reverse primer 5′-CCTCAGCCATGTTGACTCAGC-3′.

Using an aluminum foil seal to prevent liquid loss, the plates were briefly vortex mixed and rapidly centrifuged. The generation of droplets was performed using 20 μL of the assay mix and 70 μL of droplet oil into the QX200 DG cartridge (Bio-Rad), then loaded into the QX200 Droplet Generator (Bio-Rad). After droplet generation was completed droplets were carefully transferred into a PCR plate (Eppendorf), and the plate was sealed with a pierceable foil seal at 180 °C for 5 s using the Bio-Rad PX1 Plate Sealer (Bio-Rad). PCR reactions were performed using the Applied Biosystems Gene Amplification PCR system 9700 using the following PCR conditions: 95 °C for 10 min followed by 40 cycles of denaturation at 94 °C for 30 s, annealing and extension at 59 °C for 1 min, and a final extension step at 98 °C for 10 min. Plates were stored at 12 °C until droplets were counted using the Bio-Rad QX200 Droplet Reader (Bio-Rad). An overview of the work flow process is diagramed in Fig. 1.

Work flow chart of ddPCR. gDNA, isolated from either blood spots or whole blood was mixed with _Mse_I restriction enzyme, with Droplet PCR Supermix, and fluorescent tagged probes.

Fig. 1.

DNA droplets were generated using the droplet generator and transferred in the PCR thermocycler. PCR plates were finally loaded onto the droplet reader for CNV detection analysis.

EVALUATION OF DNA CONCENTRATION: SERIAL DILUTIONS

A ddPCR test using a 2-fold serial DNA dilution (25, 12.5, 6.25, 3.1, and 1.6 ng) was performed to estimate the lower concentration (copies/μL) of gDNA isolated from blood spots necessary to generate accurate and reliable CN values. This evaluation was necessary because DNA fragmentation has been observed to increase over time with stored dried blood spot cards (17). Fragmentation can affect the efficiency of the PCR reaction due to reduced availability of amplicon regions spanning the gene of interest. A similar 2-fold serial dilution (60, 30, 15, 7.5, and 3.75 ng) was performed on gDNA extracted from PBMCs to illustrate differences due to the DNA quality.

EVALUATION OF BLOOD SPOT PUNCH SIZE

gDNA was isolated from 22q11DS and control cases using various punch sizes of dried blood in FTA/903 cards including 1 × 2–mm, 2 × 2–mm, 3 × 2–mm, 1 × 3–mm, and 2 × 3–mm card punches. The yield of gDNA varied from 1 ng/uL to 4 ng/μL for samples with 22q11DS and from 1.5 ng/uL to 5 ng/μL for control samples. Positive and negative droplets were generated in all cases, with the greater counts of positive droplets and acceptable quality and concentration of DNA isolated from 2 × 2–mm, 3 × 2–mm, and 2 × 3–mm discs.

DATA ANALYSIS

The QuantaSoft software (version 1.4.0.99) includes a freedraw tool that enables proper classification of the multiple clusters created when a multiplex assay is used. Because QuantaSoft is able to report the CN of only 1 target at a time, it is necessary to independently classify droplets for phosphatidylinositol 4-kinase (PI4KA) and catechol-O-methyltransferase (COMT) quantification. For example, to determine the CN of COMT (Fig. 2A), all _COMT_-positive droplets are classified as channel 1 positive and all _RPP30_-positive droplets are classified as channel 2 positive (the influence of PI4KA on cluster formation is ignored). A similar approach is followed for determining the CN of PI4KA (Fig. 2B). Despite PI4KA appearing in channel 2 (HEX), the freedraw tool in the software is used to classify PI4KA droplets as being channel 1 (FAM) positive and the RPP30 droplets as channel 2 (HEX) positive. To calculate the CN of a target, the software divides the concentration of the target (PI4KA or COMT) by the concentration of the reference (RPP30), multiplied by 2, for the number of reference gene copies per cell.

Clustering of droplets in ddPCR analysis.

Fig. 2.

Droplet clusters selected in gray indicate negative droplets (containing no target or reference genes), in green indicate droplets positive for the reference gene RPP30, in blue indicate droplets positive for the target gene (COMT or PI4KA), and in brown indicate droplets positive for both reference and target genes. (A), Selection of clusters from channel 1 identifies positive droplets for the COMT gene. (B), Selection of clusters from channel 2 identifies positive droplets for the PI4KA gene.

Results

MULTIPLEX ddPCR

The main principle of ddPCR is that a single sample is partitioned into uniform-size droplets that are subsequently analyzed by standard PCR and the reading of the fluorescence of each droplet after PCR is recorded as positive or negative fluorescence depending on the presence or the absence of an amplification product. The use of specific target genes results in a fluorescent signal derived from the droplets that contain the DNA target (amplification product), whereas no signal will be detected from the negative ones, which are the ones that do not contain the target DNA amplicon. One of the main advantages of ddPCR is the generation of thousands of droplets per sample. ddPCR does not require replicates because it directly counts the number of target molecules rather than relying on a standard curve generated by reference standards or endogenous controls.

We previously designed a multiplex ddPCR test to assay genes within the 22q11 deletion region (15). Two genes were selected as markers for 1.5-Mb (PI4KA) and 3.0-Mb deletions (COMT). Individuals with a 1.5-Mb deletion in 22q11 have only 1 copy of COMT, whereas those with a larger 3-Mb deletion have 1 copy of both COMT and PI4KA (Fig. 3). Typical developing controls have 2 copies of both COMT and PI4KA genes. Briefly, to multiplex using a set of 3 Taqman probe–based assays, probes specific for the 2 CN variable genes were labeled with the fluorophores HEX and FAM, respectively (PI4KA-HEX and COMT-FAM). The reference gene RPP30, a 50% FAM- and 50% VIC-labeled probe, was added to the same well. This 50/50 probe mixture positions of ribonuclease P/MRP 30kDa subunit (RPP30) containing droplets in the center of a 2D fluorescent plot, in which they can be clearly distinguished from the droplets containing only PI4KA or COMT (Fig. 2, A and B). At sufficiently high DNA loads targets can colocalize in droplets, causing the appearance of as many as 8 discrete clusters, which can be easily classified for proper quantification and CN determination using the 2 clustering strategies shown in Fig. 2, A and B.

Diagram of chromosome 22 spanning the deleted region in 22q11DS.

Fig. 3.

The common 3-Mb and nested 1.5-Mb deletion regions and the genes COMT and PI4KA (in bold), which were utilized as markers for 3.0-Mb and 1.5-Mb deletions in the study, are shown. Also represented within the chromosome are the low-copy regions A–D, the 2 DNA markers D22S181 and D22S926, and the genes PRODH [proline dehydrogenase (oxidase) 1], HIRA, COMT, ZNF74, PI4KA, and VPREB1.

ANALYTICAL STUDIES

In preparation for the analysis of DNA extracted from blood spot cards, several analytical experiments were performed to optimize a ddPCR multiplex test for the detection of 22q11DS. Because the mean length of DNA fragments recovered from dried blood spots decreases with storage time (17–19), we determined the extent of damage to the DNA and therefore the integrity of the nucleic acids purified from blood spot samples by designing 2 assays of different lengths (amplicons 100-bp and 60-bp long). DNA was extracted from blood spots collected between 2009 and 2013, and the concentration was measured by ultraviolet spectroscopy. As a comparison, DNA was also isolated from whole blood samples. Both the 100-bp and 60-bp assays measured similar amounts of amplifiable DNA (copies/μL) when analyzing the gDNA purified from whole blood, suggesting there was no bias in quantification between the 2 assays. In contrast, for gDNA purified from the blood spots, a greater number of copies per microliter were obtained with the 60-bp than with the 100-bp assay (data not shown). These data reaffirm that blood spot DNA is likely fragmented and suggest that shorter assay size enables a higher yield of specific target amplicons to be amplified.

To identify 22q11DS samples from control samples by ddPCR, sufficient DNA is required for high-precision CN estimation. To this end, we tested different amounts of extracted DNA from individuals with 22q11DS and controls (using both samples from PBMCs and from blood spot cards) for COMT and PI4KA CN determination (Fig. 4; also see Fig. 1, a and b, in the Data Supplement that accompanies the online version of this report at http://www.clinchem.org/content/vol61/issue1). Using gDNA extracted from whole blood as a benchmark, we found that the lowest DNA load tested (3.7 ng) allowed for excellent CN discrimination between control and 22q11DS individuals (Fig. 4A; also see online Supplementary Fig. 1a). In contrast, when looking at similar loads of gDNA isolated from blood spots, we found that at least 12.5 ng of DNA, was required to obtain sufficient amount of positive droplets that allow reliable determination of the CN (Fig. 4B and online Supplementary Fig. 1b).

Optimization of ddPCR.

Fig. 4.

Line plots showing the effect of various DNA concentrations (x axis) on the number of copies/μL (y axis, top panel A and B) and on CNV of PI4KA (y axis, bottom panel A and B). Data are shown in closed circles for control individuals and in open circles for individuals with 22q11DS. (A), gDNA was isolated from PBMCs. B) gDNA was isolated from blood spot cards. (C), Line plots show copies/μL (top panel) and CNV estimates (bottom panel) from gDNA isolated from blood spot cards using 1 × 2–mm, 2 × 2–mm, 3 × 2–mm, 1 × 3–mm, and 2 × 3–mm punches.

Next, we sought to determine what blood punch size(s) provided sufficient gDNA for the ddPCR assay. Using dried blood spots from 22q11DS and healthy control cases, gDNA was isolated from the following punch sizes: 1 × 2–mm, 2 × 2–mm, 3 × 2–mm, 1 × 3–mm, and 2 × 3–mm punches (Fig. 4C). DNA extraction from 2 × 2–mm, 3 × 2–mm, and 2 × 3–mm punches yielded above 3 copies/μL of amplifiable DNA, whereas extractions from 1 × 2–mm and 1 × 3–mm punches did not meet our criteria for acceptable levels of recovered amplifiable genomic DNA.

SCREENING DNA ISOLATED FROM BLOOD SPOT CARDS IN MULTIPLEX

Using our multiplex ddPCR approach, we have screened a total of 1122 blood spots, including 26 samples from individuals with 22q11DS randomly distributed among twelve 96-well plates. The location of the 22q11DS samples within the plates was unknown to the operator. Analysis of the CN was determined by the number of copies of our target genes, COMT or PI4KA, relative to the number of copies for our reference gene, RPP30. Samples were scored for CN if they contained a minimum concentration of 3 copies/μL. The range of concentrations observed varied between 5 and 265 copies/μL with a mean (SD) of 20.2 (0.495) for COMT and 21.53 (0.523) for PI4KA among the samples with 2 copies and between 3 and 201 copies/μL with a mean (SD) of 14.5 (4.62) for COMT and 29.5 (10.231) for PI4KA among samples with 1 copy (Table 1).

CNV estimates by ddPCR.

a

TD, typical developing controls.

a

TD, typical developing controls.

Our screening identified 26 samples that were 22q11DS from among the pool of 1122 anonymous samples from the general population, whether they were collected on FTA or 903 cards. In 1 case a 22q11DS sample was not identified as having a deletion; however, the concentration of this sample was below 3 copies/μL and only samples that reached concentrations above 3 copies/μL were included in the study because we estimated that lower concentrations lead to misinterpretation of the CNV due to subsampling error. Among samples scored as having 2 copies, a range of CNV values spanned from 1.2 to 4.0, with the mean values at 1.8 (0.01) for COMT and 1.7 (0.01) for PI4KA. All samples scored as having 1 copy and confirmed to be 22q11DS showed CNV values of <1, ranging from 0.5 to 0.9, with the mean CNV of 0.8 (0.03) for both COMT and PI4KA (Fig. 5 and Table 1).

Diagram of CNV values by Quantasoft.

Fig. 5.

CNV values for 40 samples showing the presence of an individual with a 1.5-Mb deletion (red circle) and of 3 individuals with a 3-Mb deletion (arrows). SE bars of 95% CI are shown for each sample.

Discussion

It is estimated that as many as 1 in every 4000–9700 babies are born with 22q11DS and their diagnosis generally does not occur until later in life when the clinical phenotype has manifested and patients are in need of medical intervention. The predicted prevalence of 22q11DS has been estimated using FISH on the basis of the retrospective analysis of birth records (4, 5, 7); however, many individuals do not present with obvious anomalies at birth. Indeed, 30%–35% of individuals with 22q11DS do not present signs of congenital heart disease, or overt cleft palates, and their diagnosis is often delayed or overlooked. Children with 22q11DS experience a host of medical difficulties in early life but with an identified cause, premonitory surveillance and the prevention of some of the medical and neurodevelopmental sequelae is possible. This active approach can reduce societal costs, family stress and trauma, and isolation for the child in response to treatable illness or medical intervention. Early life stress, arising from undetected but early-occurring cognitive impairment, can also exacerbate childhood and even adult psychopathologies for which this population is at an exceptionally high risk (20–23).

Because 22q11DS is a developmental disorder with clinical findings that are not apparent until later in life, data collected from birth records likely underestimate the population prevalence. Due to the extreme high cost foreseen for large-population testing in asymptomatic individuals, the latter studies, reviewed in (24), estimated prevalence in specific clinical or geographical populations. In this regard, higher prevalence estimates (1:1600–2000) are observed in more developed countries, in which most children survive surgery for congenital heart disease (8, 24). However, the true incidence and prevalence of this syndrome will be found only through population-based screening, which has yet to be performed. Specifically, there has been no systematic screen for the frequency of 22q11DS to determine an unbiased prevalence directly from dried blood spots in a large sample size. This in part is due to the lack of a rapid, high-throughput, and reliable methodology capable of providing accurate and reliable estimates of CNV.

The use of digital PCR for CNV analysis has emerged in recent years as a robust and reliable methodology. We have recently demonstrated the efficiency and accuracy of using ddPCR CNV analysis for the identification of 22q11DS from PBMCs of individuals with 22q11DS (15). In this study, we have further examined the utility of ddPCR as a tool for 22q11DS screening of gDNA samples obtained from blood spot cards. Our study correctly identified 26 22q11DS cases among a pool of 1122 samples and demonstrated 100% accuracy when sufficient concentrations of DNA were used. Using serial dilutions we determined the DNA concentration range necessary for accurate scoring, therefore providing a positive correlation between DNA concentration and CNV accuracy. As little as 12.5 ng of DNA proved sufficient for obtaining a reliable count of positive droplets with concentrations >3 copies/μL, which we determined is the minimum concentration that allows for accurate determination of CNs and a correct diagnosis.

The use of the 2 genes, COMT and PI4KA, harbored within the deleted region also allowed the distinction between the 1.5-Mb and the 3.0-Mb deletion. On the basis of our current methodology using a 96-well plate format, up to 192 samples could be easily processed in a single day on a single instrument. The cost of reagents to isolate DNA from the blood spot cards and for the ddPCR is approximately 5–5–5–6 per reaction, making this methodology suitable for large population screening by collecting and using a small amount of DNA from blood spots, and it should lend itself to automation. To our knowledge the ddPCR application has never been successfully demonstrated or applied to the screening of 22q11DS, from the general population or to high-risk screening. Additionally, another advantage of the ddPCR is that it could be applied to the detection of CNV of target loci throughout the genome (including, i.e., those on chromosomes 15, 16, 17, and 21), which when altered, could lead to serious medical conditions.

In conclusion, whether from whole blood or blood spot cards, ddPCR provides absolute measurements of nucleic acid concentrations and highly accurate DNA CN estimates, which are crucial for clinical diagnostics. The development of this method, which uses DNA extracted from dried blood spot cards, also will enable large-scale population screens to more precisely measure the prevalence of this disease.

4 Nonstandard abbreviations

• 22q11.2DS
  22q11.2 deletion syndrome
• NBS
• FISH
  fluorescent in-situ hybridization
• TUPLE1/HIRA
  TUP-1 like enhancer of split protein/histon cell cycle regulation
• ddPCR
• CN
• CNV
• MIND
  Medical Investigation of Neurodevelopmental Disorders
• gDNA
• qPCR
• PBMC
  peripheral blood mononuclear cell.

5 Human genes

• HIRA
  histone cell cycle (also known as TUPLE1)
• PI4KA
  phosphatidylinositol 4-kinase (also known as PIK4CA)
• COMT
  catechol-O-methyltransferase
• RPP30
  ribonuclease P/MRP 30kDa subunit
• ZNF74
• VPREB1
  pre-B lymphocyte 1 PRODH, proline dehydrogenase (oxidase) 1.

Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: D. Maar, Bio-Rad Laboratories.

Consultant or Advisory Role: D. Maar, Bio-Rad Laboratories; J. Regan, Bio-Rad Laboratories.

Stock Ownership: D. Maar, Bio-Rad Laboratories; J. Regan, Bio-Rad Laboratories.

Honoraria: None declared.

Research Funding: F. Tassone, Gift Award.

Expert Testimony: None declared.

Patents: J. Regan, patent number PCT/US12/24573.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or preparation or approval of manuscript.

Acknowledgments

We would like to thank Dr. Emanual Maverakis for making the QX200 Droplet Digital PCR System available to us. This work is dedicated to the memory of Matteo.

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Author notes

†

Dalyir Pretto and Dianna Maar contributed equally to the work, and both should be considered as first authors.

© 2015 The American Association for Clinical Chemistry