Digital PCR as an effective tool for GMO quantification in complex matrices

Košir, Alexandra Bogožalec; Demšar, Tina; Štebih, Dejan; Žel, Jana; Milavec, Mojca

doi:10.1016/j.foodchem.2019.05.029

Cited by 66 publications

(36 citation statements)

References 10 publications

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“…Because the CV is a measure of variance, a scale parameter, we plotted the CVs on a log-scale and plotted concentration on a log-scale and observed a linear relationship between data points (Figure 3). Despite different numbers of replicates used to estimate CV values, we also observed similar linear regressions fitted to the log 10 -transformed CV values and log 10 -transformed copies per ddPCR well (Figure 4) in imprecision profiles published by others (30, 31). In Figure 4, we plotted linear regressions derived from multiple assays (including both simplex and duplex assays) from each referenced study.…”

Section: Discussionsupporting

confidence: 81%

“…CV values plotted against copies per ddPCR well on log 10 -log 10 axes for data obtained from this study (Zhu et al), Bogožalec Košir et al 2019 (30), and Milošević et al 2018 (31). We fitted linear regressions to the log 10 transformed CV values and log 10 transformed copies per ddPCR well.…”

Section: Discussionmentioning

confidence: 81%

“…To avoid such erroneous estimations and to better understand how values of the CV change as a function of the level of concentration, we sought to estimate the true values of the CV by using a relatively large number of replicates ( n = 46). Although the CV/RSD of the target concentration is widely used as an imprecision goal when determining the ddPCR aLLoQ, there is no consensus on the number of replicates required, with 5 to 20 replicates being commonly used (30–32). In the absence of agreement on the number of replicates, we used many replicates in combination with cumulative moving plots to assess our estimation of the mean positive droplets per well and the concentration CV (Figures S2 and S3).…”

Section: Discussionmentioning

confidence: 99%

“…We chose to set our aLLoQ to the level of concentration that would result in a coefficient of variation (CV, as calculated by the standard deviation of concentrations divided by the mean of concentrations) of 25% (30, 31). While aLLoQs are often set using “less-than or equal to” a CV target (for example, ≤ 25%), the difficulty in finding the level of concentration that results in a specific CV results in potentially setting aLLoQs by satisfying the less than condition (< 25%) rather than the equal to (= 25%) condition.…”

Section: Discussionmentioning

confidence: 99%

“…We chose to define the analytical lower limit of quantification as the minimum level of analyte in a sample that will result in a coefficient of variation (CV) of 25%. The target CV of 25% or less is a commonly used aLLoQ goal for ddPCR platforms (30, 31). The CV, also referred to as the relative standard deviation (RSD), is defined as the ratio of the standard deviation, σ, to the mean, μ.…”

Section: Methodsmentioning

confidence: 99%

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A Novel Droplet Digital PCR Human mtDNA Assay for Global Fecal Source Tracking

Zhu

Suttner

Konstantinidis

et al. 2019

Preprint

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229 / 250 words max.) 6 Human mitochondrial DNA (mtDNA) provides a promising target for microbial source tracking 7 because it is unique to humans and universal across human individuals. We developed a droplet 8 digital PCR (ddPCR) assay, hCYTB484, targeting the cytochrome b gene of the human mtDNA 9 and compared the performance of the hCYTB484 assay with a widely used assay targeting 10 human-associated Bacteroides, the HF183/BacR287 assay. We also defined and validated the 11 analytical limit of detection and analytical lower limit of quantification; these analytical limits 12 determine the concentration levels above which samples are declared to be positive and 13 quantifiable for the target, respectively. We found both assays to be highly specific (95%) 14 against cow and pig feces; however, the hCYTB484 was more sensitive when tested against 15 individual human feces from US, Bangladesh, and Mozambique (100% versus a mean of 51% 16 across the 3 countries). To further compare the performance of the two assays, we utilized a 17 receiver operating characteristic curve, showing that the hCYTB484 marker was widely 18 distributed across human feces from the 3 different geographical regions tested and in higher 19 quantities than the HF183/BacR287 marker. The higher performance of the hCYTB484 marker 20 in individual feces is a desirable characteristic in the detection of fecal pollution from sources to 21 which fewer individuals contribute, such as non-sewered types of sanitation that serve most of 22 Earth's population and carry the highest risk of exposure to fecal-oral pathogens. 23 Importance (142/150 words) 24 The usefulness of an ideal human-specific, culture-and library-independent marker to the 25 microbial source tracking field is reflected by the numerous efforts to develop such markers; 26 however, thus far, microbial-based markers of this type have exhibited variable source-27 specificity across geographies and variable sensitivity across scales of fecal waste. Most of the 28 3 world's population is served by non-sewered forms of sanitation that also carry the highest risk 29 of exposure to fecal-oral pathogens. This reality underscores the need for markers of human fecal 30 contamination that have high sensitivity in fecal pollution sources to which fewer individuals 31 contribute to, such as fecal sludges found in pit latrines. We show that human mtDNA-based 32 methods can be highly source-specific and highly sensitive in smaller scales of fecal pollution, 33 providing a useful addition to the microbial source tracking toolbox by complementing the 34 variable performance of microbial-based markers. 35 4 Word Count 3854/6000 (should not exceed approximately 6,000 words, exclusive of methods, 36 references, figure legends, tables, and supplemental material) 37 Introduction 38Within the microbial source tracking (MST) field, the development of an ideal human-specific, 39 culture-and library-independent marker has been a goal for many research efforts for a variety of 40 reasons-nota...

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Novel Droplet Digital PCR Human mtDNA Assay for Global Fecal Source Tracking

Zhu

Suttner

Konstantinidis

et al. 2019

Preprint

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The future of microbiome analysis: Biosensor methods for big data collection and clinical diagnostics

Akarapipad

Yoon

2020

Med Devices & Sens

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Worldwide interest in the human microbiome is rapidly expanding, as the microbiome is a potential key to understanding human health and improving diagnostic and treatment strategies. A wide array of microorganisms, including bacteria, archaea, unicellular/multicellular eukaryotes (i.e. fungi and helminths) and viruses, colonize on the human body. Their activity and interactions with each other impact their host (Rowan-Nash, Korry, Mylonakis, & Belenky, 2019). Although sometimes used interchangeably, the word 'microbiota' refers to the collective community of microorganisms residing in a particular environment (such as a region of the body), whereas the word 'microbiome' refers to the entirety of this community's genetic information (Allaband et al., 2019; Kuczynski et al., 2012). The different regions of the human body contain various combinations of microbes that could provide information not only about infectious diseases at specific sites, but also about the potential health conditions of a person (Mimee et al., 2018). It is remarkable to consider that, according to recently revised estimates, the human body houses approximately as many bacteria (the most abundant member of the microbiota) as human cells, with bacteria comprising 0.3% of total body mass (Sender, Fuchs, & Milo, 2016). According to the

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