Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 374

The DOI matches its Crossref record.

Tolerance of Droplet-Digital PCR vs Real-Time Quantitative PCR to Inhibitory Substances

Open access
Dingle, Tanis C, Sedlak, Ruth Hall, Cook, Linda, Jerome, Keith R·2013·Clinical Chemistry

Abstract

To the Editor: Real-time quantitative PCR (qPCR)1 is a rapid and sensitive method that forms the foundation for many clinical diagnostic tests. Droplet digital PCR (ddPCR) shares these qualities with qPCR, but owing to reaction partitioning, ddPCR is proposed to exhibit increased tolerance to interfering substances, making it an attractive alternative to qPCR for diagnostic applications (1, 2). The data to support this phenomenon and its mechanism, however, are currently lacking in the literature (3). Herein, we describe a series of experiments to compare the inhibition tolerance of laboratory-developed CMV qPCR and ddPCR (Bio-Rad Laboratories, QX-100) assays by introducing a panel of clinically relevant inhibitors (SDS, EDTA, and heparin) directly into the PCR reactions (4). Differences in the resulting inhibition curves and the half-maximal inhibitory concentrations (IC50) were then assessed. The laboratory-developed CMV qPCR is a double primer/probe Taqman assay that amplifies and detects the genes UL123 (IE)2 (enhances activation by IE2; interacts with basal transcriptional machinery and cellular transcription factor; disrupts ND10; involved in gene regulation [Human herpesvirus 5]) and UL55 (gB) (type 1 membrane protein; possible membrane fusogen; binds cell surface heparan sulphate; involved in cell entry; involved in cell-to-cell spread [Human herpesvirus 5]) with primers and probes previously described (5). The ddPCR assay uses the same primers and probes, with the dyes HEX replacing 6-FAM on the gB probe and BHQ-1 replacing TAMRA on both probes (Sigma-Aldrich). SDS, EDTA, and heparin were serially diluted and added directly to CMV (AD169 whole virus; Advanced Biotechnologies) qPCR and ddPCR reactions. On average 14 143 (439) droplets were analyzed per well of any given ddPCR reaction. The mean concentration of the CMV template in uninhibited samples of the ddPCR was 9.27 copies/μL (in a 20-μL reaction) over 3 biological replicates. Droplets in partially inhibited samples show fluorescent units ranging between those of positive and negative droplets and can be easily visualized in 1-dimensional amplitude plots (Fig. 1, wells 4–7). In a typical uninhibited sample, a tight threshold (Fig. 1, orange line) is placed near the cluster of positive droplets and is constant for a specific target. However, in the case of partial inhibition, a broad threshold is applied to the ddPCR QX100 analysis near the negative droplets to appropriately incorporate all partially inhibited droplets (Fig. 1, blue line). (Left panel), Droplet plot of the CMV IE ddPCR assay for 12 wells containing a dilution series of heparin. Each well is demarcated by a yellow dashed line. The heparin concentration decreases from well 1 to well 12. Gray droplets are negative for CMV. Blue droplets are positive for CMV. The orange line represents the typical thresholding level for noninhibited samples (wells 10–12). The blue line represents the broad threshold applied to inhibited samples for accurate quantification in the presence of inhibitory concentrations of heparin. (Right panel), Inhibition curves corresponding to the threshold applied to the data analysis of the ddPCR are shown. The inhibition curve for the CMV qPCR is also indicated. Using this strategy, we calculated log IC50 values from the resulting inhibition curves. Greater than a half log increase in IC50 was observed for both the IE and gB targets of ddPCR over qPCR for both SDS (absolute log difference in IC50 qPCR vs ddPCR IE, 0.554, and vs ddPCR gB, 0.628) and heparin (absolute log difference in IC50 qPCR vs ddPCR IE, 0.655, and vs ddPCR gB, 0.855). The probability of difference between the data sets for ddPCR and qPCR was >99.99% for both inhibitors and both ddPCR targets, indicating that ddPCR tolerated the presence of these inhibitors better than qPCR. However, this difference was not noted when we compared ddPCR and qPCR in the presence of EDTA for both ddPCR targets (log difference in IC50 qPCR vs ddPCR IE, 0.116, and vs ddPCR gB, 0.0198), possibly owing to different inhibition mechanisms. EDTA is a calcium chelator, whereas SDS and heparin both act on DNA polymerase. The ddPCR CMV assay is more tolerant to SDS and heparin than the qPCR assay, indicating that reaction partitioning through digitization may reduce susceptibility to these traditional PCR inhibitors. The data suggest that individual microreactions mitigate the impact of inhibitors on PCR amplification by retaining discernible positive signals even when moderate PCR inhibition is occurring in a droplet. Since PCR reactions are not partitioned in qPCR, amplification is dependent on the concentration of inhibitor in the entire reaction, leading to an increased number of amplification cycles required to reach a signal above a given threshold. In turn, this will result in inaccurate quantification of template in the original sample. In ddPCR, on the other hand, quantification is dictated by the Poisson distribu

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1373/clinchem.2013.211045

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 374

Metadata

Authors
Dingle, T. C., Sedlak, R. H., Cook, L., & Jerome, K. R.
Venue
Clinical Chemistry
Published
2013-11-01
Volume
59
Issue
11
Pages
1670-1672
ISSN
  • 0009-9147 (ISSN-L)
  • 1530-8561
OpenAlex
W2106058008
Cited by
374
References
0
FWCI
7.93
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Tolerance of Droplet-Digital PCR vs Real-Time Quantitative PCR to Inhibitory Substances. https://app.cochat.ai/papers/10.1373/clinchem.2013.211045
The paper
Dingle, T. C., Sedlak, R. H., Cook, L., & Jerome, K. R. (2013). Tolerance of Droplet-Digital PCR vs Real-Time Quantitative PCR to Inhibitory Substances. Clinical Chemistry. https://doi.org/10.1373/clinchem.2013.211045

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.