Using an anticipated concentration of the undiluted sample i. The estimated LLOQ was 2. A LLOD95 was estimated by fitting a logistic regression model with detection results. Each dot indicates an actual detection rate at each dilution. The dashed horizontal line indicates an estimated LLOQ of 2.
However, the difference was not deemed significant due to small sample sizes. The sensitivities for detecting DENV2 Thus, the quantification results from the two protocols conducted in the same laboratory were expected to be similar. To compare quantification results, the experiments were done with dengue specimens used in the sensitivity analyses.
Serotype-specific analyses also showed strong correlations of quantification results Figure S2. The moderate negative correlations in Bland—Altman plots were also observed in all four serotypes. The diagonal line represents a hypothetical trend where results by the two methods are identical.
The solid dots represent measured counts within the limit of quantification. The horizontal blue line shows the mean difference between the two methods bias. A linear regression line and a confidence area show an association between measured counts and bias. Quantification results from those three laboratories were subsequently compared based on regression lines and Bland—Altman plots Figure 5 and Figure 6. Linear regression analyses for interlaboratory variations.
The diagonal line blue dashed line represents a hypothetical trend where results from the two laboratories are identical. Bland—Altman plots for interlaboratory variations. The horizontal black dashed lines represent agreement lines. The horizontal blue lines show mean differences between the two methods bias.
The horizontal red lines show agreement limits. We demonstrated these variations by performing qRT-PCR with identical sets of clinical specimens in two different laboratories using two different protocols Figure S4.
This experiment imitated an actual situation in which each laboratory had developed its own in-house qRT-PCR protocols as adopting a protocol from another laboratory is complicated and often impractical. Since it was not possible to extract the effect of the assay errors from the effect of the difference in protocols on the interlaboratory variations, the RT-ddPCR protocol reported here was not primarily intended to reduce the assay errors compared to qRT-PCR.
Instead, the protocol was designed to be reproducible in other laboratories with no or limited experience in quantifying virus RNA. Although Abachin et al.
The sequences of primers and probes were checked against all DENV genomes reported in GenBank to ensure that viruses collected in or earlier were detected. In addition, the primers and probes were tested against the extracted viral RNA from cultures of other flaviviruses including Japanese encephalitis virus, yellow fever virus, and zika virus using conventional RT-PCR and qRT-PCR yielding no false-positive results Songjaeng, Thiemmeca and Avirutnan et al.
Initially, the standard protocol recommended by the ddPCR manufacturer was tested. DTT was found to disturb real-time PCR by inducing quenching of the passive reference signal resulting in an overestimation of DNA concentrations [ 28 ]. Thus, we decided to use anticipated concentrations as proposed by Persson et al.
We found that RT-ddPCR tended to report concentrations lower than those expected from dilution factors as previously reported [ 30 ].
The inefficiency of the reverse transcription might be one of the possible reasons [ 31 ]. We found that reporting quantification as absolute copies resulted in large variances which prohibited LLOQ determination. In addition, viral load is often reported and statistically analyzed as logcopies due to its wide range and skewed distribution. In this study, we defined LLOQ with relatively strict criteria [ 27 ]. One possible explanation is that the partitioning of reactions in RT-ddPCR increases the template concentration in each droplet resulting in higher amplification efficiency and tolerance to potential inhibitors in the sample [ 34 ].
The superior detection and quantification performance of RT-ddPCR over qRT-PCR with environmental and clinical samples containing low target concentrations was also reported in previous studies on rotavirus [ 13 ], pepper mild mottle virus [ 35 ], pepino mosaic virus [ 36 ], and severe acute respiratory syndrome coronavirus 2 [ 37 ].
RT-ddPCR actually reported 3. Therefore, the detection result could not be reported with confidence. We also performed serotype-specific analyses for sensitivity. However, the difference was not deemed statistically significant due to small serotype-specific sample sizes. We calculated the sample size for this study to confidently measure specificity and serotype-specific sensitivity of RT-ddPCR.
Thus, the sample size was not considered for comparing serotype-specific sensitivities between the two methods. Therefore, quantification results should be highly reproducible, and interlaboratory variations should be small.
We found that some specimens had quantification results exceeding ULOQ and were not included in interlaboratory variation analyses. Repeated measurements of these specimens were not conducted due to limited quantities of samples. A few studies reported incomplete genome or defective particles of DENV in clinical samples [ 40 , 41 ]. Currently, we are optimizing the multiplex RT-ddPCR protocol to accurately quantify defective genomes in clinical specimens.
Although several studies linked DENV levels in plasma to disease severity [ 39 , 42 , 43 , 44 , 45 ], some studies reported conflicting results [ 46 , 47 , 48 , 49 ]. We analyzed the correlation of DENV levels and disease severity from the cross-sectional samples described in Section 3.
However, the kinetics of DENV levels from longitudinal sample collections should be analyzed to verify the correlation between viral loads and disease severity as reported by Avirutnan et al. RT-ddPCR can also help accelerate multi-center drug or vaccine trials by eliminating the need for transferring specimens to a single central laboratory.
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