key: cord-0866565-07wpb710
authors: Kinloch, Natalie N.; Ritchie, Gordon; Dong, Winnie; Cobarrubias, Kyle D.; Sudderuddin, Hanwei; Lawson, Tanya; Matic, Nancy; Montaner, Julio S.G.; Leung, Victor; Romney, Marc G.; Lowe, Christopher F.; Brumme, Chanson J.; Brumme, Zabrina L.
title: SARS-CoV-2 RNA quantification using droplet digital RT-PCR
date: 2021-05-29
journal: J Mol Diagn
DOI: 10.1016/j.jmoldx.2021.04.014
sha: 04fb308f455ff5fe8b9a16d7d1139199bb58d738
doc_id: 866565
cord_uid: 07wpb710

Quantitative viral load assays have transformed our understanding of viral diseases. They hold similar potential to advance COVID-19 control and prevention, but SARS-CoV-2 viral load tests are not yet widely available. SARS-CoV-2 molecular diagnostic tests, which typically employ real-time reverse transcriptase-polymerase chain reaction (RT-PCR), yield semi-quantitative results only. Droplet digital RT-PCR (RT-ddPCR) offers an attractive platform for SARS-CoV-2 RNA quantification. We evaluated eight primer/probe sets originally developed for real-time RT-PCR-based SARS-CoV-2 diagnostic tests for use in RT-ddPCR, and identified three (Charité-Berlin E-Sarbeco and Pasteur Institute IP2 and IP4) as the most efficient, precise and sensitive for RT-ddPCR-based SARS-CoV-2 RNA quantification. For example, E-Sarbeco primer/probe set analytical efficiency was approximately 83%, while assay precision, measured as coefficient of variation, was approximately 2% at 1000 input copies/reaction. Lower limits of quantification and detection for this primer/probe set were 18.6 and 4.4 input SARS-CoV-2 RNA copies/reaction, respectively. SARS-CoV-2 RNA viral loads in a convenience panel of 48 COVID-19-positive diagnostic specimens spanned a 6.2log(10) range, confirming substantial viral load variation in vivo. We further calibrated RT-ddPCR-derived SARS-CoV-2 E gene copy numbers against cycle threshold (C(t)) values from a commercial real-time RT-PCR diagnostic platform. This log-linear relationship can be used to mathematically-derive SARS-CoV-2 RNA copy numbers from C(t) values, allowing the wealth of available diagnostic test data to be harnessed to address foundational questions in SARS-CoV-2 biology.

Quantitative viral load assays have revolutionized our ability to manage viral diseases 1-6 .

While not yet widely available for SARS-CoV-2, quantitative assays could advance our understanding of COVID-19 biology and inform infection prevention and control measures 7, 8 .

Most SARS-CoV-2 molecular diagnostic assays however, which use real-time reverse transcriptase PCR (RT-PCR) to detect one or more SARS-CoV-2 genomic targets using sequence-specific primers coupled with a fluorescent probe, are only semi-quantitative. These tests produce cycle threshold (C t ) values as readouts, which represent the PCR cycle where the sample began to produce fluorescent signal above background. While each C t value decrement corresponds to a roughly two-fold higher viral load (due to the exponential nature of PCR amplification), C t values cannot be directly interpreted as SARS-CoV-2 viral loads without calibration to a quantitative standard 9, 10 . Rather, C t values are interpreted as positive, indeterminate or negative based on assay-specific cutoffs and evolving clinical guidelines. Due to differences in nucleic acid extraction method, viral target and other parameters, C t values are also not directly comparable across assays or technology platforms.

Reverse transcriptase droplet digital PCR (RT-ddPCR) offers an attractive platform for SARS-CoV-2 RNA quantification 11, 12 . Like real-time RT-PCR, ddPCR employs target-specific primers coupled with a fluorescent probe, making it relatively straightforward to adapt assays. In ddPCR however, each reaction is fractionated into 20,000 nanolitre-sized droplets prior to massively parallel PCR amplification. At end-point, each droplet is categorized as positive (target present) or negative (target absent), allowing for absolute target quantification using Poisson statistics. This sensitive and versatile technology has been used for mutation detection and copy number determination in the human genome 13 , target verification following genome J o u r n a l P r e -p r o o f editing 14 , and copy number quantification for viral pathogens [15] [16] [17] [18] [19] [20] [21] . Several real-time RT-PCR SARS-CoV-2-specific primer/probe sets have been used in RT-ddPCR ( 11, 12, 22, 23 and manufacturer protocol) with results achieving high sensitivity in some reports 12, 22, [24] [25] [26] , but few studies have rigorously evaluated SARS-CoV-2-specific primer/probe set performance in RT-ddPCR using RNA as a template. Furthermore, no studies to our knowledge have calibrated SARS-CoV-2 viral loads to diagnostic test C t values. Here, we evaluate eight SARS-CoV-2specific primer/probe sets originally developed for real-time RT-PCR 27 , for use in RT-ddPCR.

We also derive a linear equation relating RT-ddPCR-derived SARS-CoV-2 viral loads and realtime RT-PCR-derived C t values for a commercial diagnostic assay, the LightMix Modular SARS-CoV (COVID19) E-gene assay, allowing conversion of existing COVID-19 diagnostic results to viral loads.

Eight SARS-CoV-2-specific primer/probe sets developed for real-time RT-PCR COVID-19 diagnostic assays 27 were assessed for use in RT-ddPCR (Table 1) . These included the Charité-Berlin E gene ('E-Sarbeco') set 28 IDT]), as recommended for ddPCR (manufacturer protocol).

RT-ddPCR assays were evaluated using commercial synthetic SARS-CoV-2 RNA standards comprising six non-overlapping 5,000 base fragments of equal quantities encoding the Wuhan-Hu-1 SARS-CoV-2 genome (Control 2, Genbank, https://www.ncbi.nlm.nih.gov/genbank/ accession number MN908947.3; Twist Biosciences, San Francisco, CA; supplied at approximately 1 million copies/fragment/μl). To avoid degradation, RNA standards were stored at -80°C and thawed only once, immediately before use, to perform the analytical efficiency, precision, analytical sensitivity and dynamic range analyses described herein. Moreover, to mimic nucleic acid composition of a real biological specimen, all assays employing these standards were supplemented with a consistent, physiologically relevant amount of nucleic acid extracted from pooled remnant SARS-CoV-2-negative nasopharyngeal swabs (Supplementary Figure 1) . Briefly, 1mL aliquots of pooled viral transport medium were extracted on the NucliSens EasyMag (BioMerieux, Marcy-l'Etoile, France), eluted in 60μl and re-pooled. The resulting material contained DNA from on average 2,200 human cells/μl (as quantified using human RPP30 DNA copy numbers by ddPCR as described in 33 ) and 4,400 human RNAse P copies/μl extract (as quantified by RT-ddPCR as described in 34 ), concentrations that are in line with human DNA and RNA levels recovered on nasopharyngeal swabs 33, 34 .

RT-ddPCR reactions were performed by combining relevant SARS-CoV-2 RNA template with target-specific primers and probe (900nM and 250nM, respectively, Integrated J o u r n a l P r e -p r o o f DNA Technologies, Coralville, IA; Table 1 ), One-Step RT-ddPCR Advanced Kit for Probes Supermix, Reverse Transcriptase and DTT (300nM) (all from BioRad, Hercules, CA), XhoI restriction enzyme (New England Biolabs, Ipswich, MA), background nucleic acid (for reactions employing synthetic RNA template only, see above) and nuclease free water. Droplets were generated using an Automated Droplet Generator (BioRad) and cycled under primer/probe setspecific conditions (see below and Figure 1 ). Analysis was performed on a QX200 Droplet Reader (BioRad) using QuantaSoft software (BioRad, version 1.7.4) where replicate wells were merged prior to analysis.

For each primer/probe set, acceptable thermal cycling temperature ranges for reverse transcription (RT) and PCR annealing/extension were determined by modifying the manufacturer-recommended default conditions, which are 42-50°C for 1 hour (for reverse transcription); 95°C for 10 minutes; 40 cycles of (94°C for 30 seconds followed by 50-63°C for 1 minute); 98°C for 10 minutes and 4°C infinite hold. To determine acceptable temperature ranges for reverse transcription, a thermal gradient from 42-51.5°C was performed while fixing the annealing/extension step at 52°C. Using the optimized reverse transcription temperature, a thermal gradient from 50-63°C was then performed to identify acceptable annealing/extension temperature ranges. Temperatures that produced insufficient separation of positive from negative droplets or non-specific amplification were deemed unacceptable, as were those that produced consecutive 95% confidence intervals of copy number estimates outside those of the maximal point-estimate.

J o u r n a l P r e -p r o o f

The analytical efficiency of each primer/probe set to quantify SARS-CoV-2 RNA by RT-ddPCR was determined using synthetic SARS-CoV-2 RNA standards at 1000 and 100 input copies. A minimum of three (maximum four) technical replicates were performed at each concentration, where results were expressed as the point estimate with 95% Total Poisson Confidence Interval derived from merged replicates. As such, primer/probe sets that yield nonoverlapping 95% Total Poisson Confidence Intervals around the point estimate can be considered significantly different from one another. Analytical efficiency was calculated by dividing the measured SARS-CoV-2 copy number by the expected input copy number, and multiplying by 100. Precision was expressed as the coefficient of variation (CV), expressed as a percentage, across technical replicates.

The linear dynamic range (LDR) of each primer/probe set of interest was determined across a serial 1:2 dilution series from 114,286 to 1.2 SARS-CoV-2 RNA copies/reaction. This range of concentrations was chosen as it crosses the entire range of recommended input copies for a ddPCR reaction seeking to quantify the target of interest (see manufacturer protocol).

Reactions were performed in duplicate. The upper and lower limits of quantification of (ULOQ and LLOQ, respectively) were defined as the upper and lower boundaries of the concentration range over which the relationship between measured and input SARS-CoV-2 RNA copies was linear. This was determined by iteratively restricting the range of concentrations included in the linear regression of measured versus input SARS-CoV-2 RNA copies to identify that which maximized the coefficient of determination (R 2 ) value and minimized the residuals.

J o u r n a l P r e -p r o o f Assay analytical sensitivity, defined as the Lower Limit of Detection (LLOD), was determined for primer/probe sets of interest by serially diluting synthetic SARS-CoV-2 RNA standards to between 47.6 and 0.74 SARS-CoV-2 RNA copies/reaction. Between 6 and 18 technical replicates were performed for each dilution and results were analyzed using probit regression. The LLOD, determined through interpolation of the probit curve, was defined as the concentration of input SARS-CoV-2 RNA in a reaction where the probability of detection was 95%.

Optimized RT-ddPCR assays were applied to a convenience sample of 48 consecutive remnant SARS-CoV-2-positive diagnostic nasopharyngeal swab specimens that were originally submitted to the St. Paul's Hospital Virology Laboratory in Vancouver, Canada for diagnostic testing using the Roche cobas SARS-CoV-2 assay. For these samples, total nucleic acids were re-extracted from 250μl remnant media using the BioMerieux NucliSens EasyMag and eluted in 50μl. Eluates were aliquoted and frozen at -80°C prior to single use. SARS-CoV-2 copy numbers were quantified by RT-ddPCR as described above. As our main goal was to characterize the relationship between C t values and SARS-CoV-2 RNA levels without confounding by extraction platform, quantity of input material or SARS-CoV-2 genomic target, we re-tested these extracts using a commercial real-time RT-PCR SARS-CoV-2 diagnostic assay that uses the E-Sarbeco primer/probe set: the LightMix 2019-nCoV real-time RT-PCR assay E-gene target (Tib-Molbiol, Berlin, Germany), implemented on LightCycler 480 (Roche Diagnostics, Basel, Switzerland) 35 .

Finally, to be responsive to a recent recommendation that SARS-CoV-2 viral loads be reported in terms of SARS-CoV-2 RNA copies per human cell equivalents 9 , we measured human cells/μl J o u r n a l P r e -p r o o f extract by ddPCR as previously described 33 and additionally reported results as SARS-CoV-2 RNA copies/1,000 human cells.

SARS-CoV-2 RNA copy numbers were expressed as point estimates with 95% Total Poisson Confidence Intervals derived from merged replicates, calculated using QuantaSoft, (version 1.7.4, Bio-Rad). Assay precision was reported as the coefficient of variation (CV), expressed as a percentage, across technical replicates. Assay Linear Dynamic Range was determined by identifying the range of concentrations that maximized the coefficient of determination (R 2 ) and minimized the residuals in the relationship between measured and input SARS-CoV-2 RNA copies. Assay Lower Limit of Detection (LLOD) was determined by probit regression. Correlation between SARS-CoV-2 RNA gene copies measured using different primer/probe sets was determined by Spearman's rho (ρ). Where it was appropriate to measure concordance, we calculated Lin's concordance correlation coefficient (ρc). The relationship between SARS-CoV-2 viral load as measured by RT-ddPCR and diagnostic test C t value was evaluated using linear regression. Statistical analyses were performed using GraphPad Prism 

This study was approved by the Providence Health Care/University of British Columbia and Simon Fraser University Research Ethics Boards under protocol H20-01055.

J o u r n a l P r e -p r o o f Eight primer/probe sets originally developed for SARS-CoV-2 diagnostic testing by real time RT-PCR were evaluated for use in RT-ddPCR (Table 1) . As these primer/probe sets vary in sequence, amplicon length and SARS-CoV-2 genomic target, we first determined the acceptable temperature ranges for reverse transcription (RT) and PCR annealing/extension. Most primer/probe sets were tolerant to a wide temperature range, and background signal was essentially zero at all temperatures tested ( Figure 1) . The E-Sarbeco primer/probe set for example produced consistent amplitude profiles, copy number estimates and essentially zero background at annealing/extension temperatures ranging from 50-63°C ( Figure 1A and data not shown). The HKU-ORF primer/probe performed acceptably over a 50-60.5°C annealing/extension range, but positive and negative droplet separation was insufficient at higher temperatures ( Figure 1B) . Acceptable temperature ranges for each primer/probe set are shown in Table 2 . All subsequent experiments were performed at RT 42.7°C and annealing/extension 50.9°C except those for HKU-ORF and US-CDC-N1, which were performed at RT 45.7°C and annealing/extension 55.1°C, based on initial, more subjective assessments of RT-ddPCR plot quality.

We next evaluated the analytical efficiency of SARS-CoV-2 RNA quantification for each primer/probe set, calculated as the percentage of input viral RNA copies detected by the assay.

We also evaluated precision, calculated as the dispersion of measured copies around the mean (coefficient of variation, CV). Analytical efficiency and precision were evaluated at 1000 and 100 SARS-CoV-2 RNA target input copies. At 1000 input copies, primer/probe set analytical efficiency ranged from 83% (E-Sarbeco) to 15% (US-CDC-N1) (Figure 2A ). At 100 copies, the analytical efficiency hierarchy was identical, with values ranging from 74% (E-Sarbeco) to 12% J o u r n a l P r e -p r o o f (US-CDC-N1). Of all primer/probe sets evaluated, the E-Sarbeco, IP2 and IP4 sets had the highest analytical efficiencies by a substantial margin. At 1000 and 100 target copies, E-Sarbeco analytical efficiency was 83% (95% Total Poisson Confidence Interval [CI]: 79-87%) and 74% (95% CI: 63-84%), respectively; IP2, analytical efficiency was 70% (95% CI: 67-73%) and 55% (95% CI: 46-64%), respectively; and IP4 analytical efficiency was 69% (95% CI: 66-72%) and 59% (95% CI: 50-69%), respectively. In contrast, analytical efficiency of the China-ORF primer/probe set was only 46% and 39% at 1000 and 100 input copies, respectively, and the analytical efficiencies of the remaining sets were less than 30% regardless of input copy number.

Furthermore, while measurement precision generally decreased at the lower template concentration 36 , the E-Sarbeco, IP2 and IP4 primer/probe sets were nevertheless among the most precise, with coefficients of variation (CV) of less than 5% at 1,000 input copies and less than 15% at 100 input copies ( Figure 2B ). Combined analytical efficiency and precision data confirmed E-Sarbeco, IP2 and IP4 as the best-performing primer/probe sets in RT-ddPCR ( Figures 2C and 2D ), so these were moved forward for further characterization.

As IP2 and IP4 were originally designed for duplexing in real-time RT-PCR 29 , we evaluated them in duplex for RT-ddPCR. Duplexing however decreased analytical efficiency, from 70% to 52% (at 1000 input copies) and 55% to 37% (at 100 input copies) for IP2, and from 69% to 49% (at 1000 input copies) and 59% to 38% (at 100 input copies) for IP4 (Supplemental Figure 2A ). Duplexing also decreased precision (Supplemental Figure 2B ). For IP2, CV increased from 5% to 11% when duplexing at 1000 input copies, and from 15% to 25% when duplexing at 100 input copies. For IP4, CV increased from 4% to 7% (1000 input copies) and J o u r n a l P r e -p r o o f from 14% to 21% (100 input copies) with duplexing. Duplexing of these reactions is therefore not recommended in RT-ddPCR, and all IP2 and IP4 assays were performed as single reactions.

Droplet digital PCR can achieve absolute target copy number quantification without a standard curve. To investigate the linear dynamic range (LDR) of quantification of the E-Sarbeco, IP2 and IP4 assays, we set up 18 two-fold serial dilutions of synthetic SARS-CoV-2 RNA beginning at 114,286 copies/reaction (this copy number is obtained when 120,000 copies are added to a 21μl reaction, of which 20μl is used for droplet generation) and ending with 2.32 copies/reaction. This input copy number range crosses nearly the entire manufacturerrecommended template input range for ddPCR reactions seeking to quantify the target of interest, which is 1-100,000 copies/reaction (see manufacturer protocol).

The LDR of each assay was determined by iteratively restricting the range of concentrations included in the linear regression of measured versus input SARS-CoV-2 RNA copies to identify the range that maximized the R 2 value and minimized the residuals. For E-Sarbeco, the regression spanning 18.6-114,286 input SARS-CoV-2 RNA copies per reaction, an approximately 6,100-fold concentration range, yielded an R 2 value of 0.9995 ( Figure 3A, left) .

Restricting the linear regression to this range also minimized the residuals of all included data points to ±0.065log 10 copies/reaction ( Figure 3A, right) . The IP2 assay, while less efficient than E-Sarbeco, had the same estimated LDR of 18.6-114,286 input copies/reaction ( Figure 3B, left) .

This produced an R 2 value of 0.9995 and residuals within ±0.065log 10 copies/reaction across the LDR ( Figure 3B, right) . The LDR of IP4 was estimated as 37.2-114,286 input copies/reaction, an approximately 3,000-fold range, which yielded an R 2 = 0.9975 and produced residuals within ±0.11log 10 copies/reaction across this range ( Figure 3C ). For all three assays, 114,286 input J o u r n a l P r e -p r o o f copies/reaction should be considered a conservative estimate of the upper limit of quantification, as saturation of the RT-ddPCR reaction or loss of linearity was still not achieved at this concentration.

We next determined the lower limit of detection (LLOD) of the E-Sarbeco, IP2 and IP4

RT-ddPCR assays (Figure 4) . Probit regression analysis applied to serial dilutions of synthetic SARS-CoV-2 RNA standards revealed the E-Sarbeco RT-ddPCR assay to be the most analytically sensitive of the three, which is consistent with it also having the highest analytical efficiency. Specifically, the estimated LLOD of the E-Sarbeco assay was 4.4 (95% Confidence Interval [CI]: 2.4-5.7) SARS-CoV-2 RNA copies/reaction ( Figure 4A ). The estimated LLOD of the IP2 assay was 7.8 (95% CI: 4.4-10.3) SARS-CoV-2 RNA copies/reaction ( Figure 4B ), while that of IP4 was 12.6 (95% CI: 6.9-16.5) SARS-CoV-2 RNA copies per reaction ( Figure 4C ).

using the E-Sarbeco, IP2 and IP4 primer/probe sets (note that samples with original diagnostic test C t values <19 required RNA extracts to be diluted up to 1:200 prior to quantification to ensure that input copies measurements fell within each assay's LDR). The results revealed that SARS-CoV-2 RNA in these biological samples varied over a 6.2 log 10 range ( Figure 5A ).

Average copy numbers measured using the E-Sarbeco assay (which targets the E gene) were higher than those using the IP2 and IP4 assays (which target ORF1a and ORF1b, respectively) ( Figure 5A ). This is consistent with assay analytical efficiency ( Figure 2 ) and in vivo coronavirus RNA expression patterns, where transcripts covering the 3' end of the genome are more abundant than those covering the 5' end [37] [38] [39] [40] . Specifically, the median E gene copy number was J o u r n a l P r e -p r o o f 5.1 (IQR 3.9-5.7) log 10 copies/μl extract compared to a median of 4.9 (IQR 3.9-5.5) log 10 copies/μl extract for the IP2 target, and a median of 4.9 (IQR 3.9-5.6) log 10 copies/μl extract for the IP4 target. SARS-CoV-2 E gene, IP2 and IP4 copy numbers in biological samples correlated strongly with one another (Spearman's ρ>0.99; p<0.0001 for all pairwise analyses; Figure 5BCD ). Consistent with comparable ORF1a and ORF1b RNA transcript levels in vivo 37, 38, 40 , IP2 and IP4 copy numbers were also highly concordant (Lin's concordance correlation coefficient, ρc=0.9996 [95% CI: 0.9993-0.9998]) ( Figure 5D ). Based on a recent recommendation 9 , we also report our results in terms of SARS-CoV-2 RNA copies per human cell equivalents: results for E-Sarbeco spanned an 7-fold range from 1.05 to 7.3 log 10 SARS-CoV-2 RNA copies/1,000 human cells, with IP2 and IP4 log 10 copy numbers lower, as expected (Supplemental Figure 3A) . The Spearman's correlation between absolute and human cellnormalized viral loads was strong (p < 0.0001; Supplementary Figure 3B ), which is consistent with the assumption that the amount of biological material collected by nasopharyngeal swabs is relatively consistent.

Finally, we characterized the relationship between C t values produced by a commercial COVID-19 diagnostic platform and SARS-CoV-2 RNA copy numbers. We selected the Figure 4) . The relationship between C t value and absolute SARS-CoV-2 E gene copies can thus be given by log 10 SARS-CoV-2 E gene copies equivalent = -0.3038C t +11.7 ( Figure 6 ). That is, a C t value of 20 corresponds to 453,942 (i.e. 5.66 log 10 ) SARS-CoV-2 RNA copies, while a C t value of 30 corresponds to 416 (i.e. 2.62 log 10 ) viral copies. This equation also predicts that the C t values corresponding to the LLOQ and LLOD of the E-Sarbeco RT-ddPCR assays are 34.8 and 36.84, respectively. When measured SARS-CoV-2 RNA copy numbers are expressed as human cell-normalized viral loads, the relationship with Ct value is given by log 10 SARS-CoV-2 E gene copies/1,000 human cells = -0.3041C t + 10.8 (Supplemental Figure 5 ). An extract that yielded a C t value of 20 therefore is estimated to have contained 48,978 (i.e. 4.69 log 10 ) SARS-CoV-2 RNA copies/1,000 human cells, while one with C t value of 30 is estimated to have contained 45 (i.e. 1.66 log 10 ) copies/1,000 human cells

While real-time and droplet digital RT-PCR both employ target-specific primers coupled with fluorescence-based amplicon detection, there are key differences in reaction chemistry (e.g.

RT-ddPCR reagents must be compatible with water-in-oil droplet partitioning) and probe chemistry (e.g. while real-time RT-PCR uses fluorescent quenchers, ddPCR typically uses dark quenchers). As a result, assays developed for one platform may not always translate seamlessly to the other. For example, ddPCR probes should ideally not have a Guanine at their 5' end because this quenches the fluorescence signal even following hydrolysis (see manufacturer protocol), but the HKU-N probe has a G at its 5' end (Table 1) .

It is perhaps therefore not surprising that the overall performance of the eight primer/probe sets in RT-ddPCR did not exactly mirror that in real-time RT-PCR 41, 42 .

Nevertheless, E-Sarbeco, IP2 and IP4, which represented the most efficient and precise primer/probe sets for SARS-CoV-2 RNA quantification by RT-ddPCR are also among the most efficient in real-time RT-PCR 41, 42 . Our results also confirm previous reports of the E-Sarbeco primer/probe set performing well in RT-ddPCR 11, 23 . Other primer/probe sets however, notably US CDC-N1, HKU-ORF and China-ORF, did not perform as well in our RT-ddPCR assay compared to a previous report 11 . One key difference is that, while we used sequence-specific reverse transcription (with the reverse primer) in a one-step RT-ddPCR reaction, the previous study featured an independent reverse transcription reaction primed with random hexamers and oligo dT − which can yield higher efficiency than sequence-specific priming 36, 43-45 − to generate cDNA for input into a ddPCR reaction. To our knowledge, ours is the first study to evaluate IP2 or IP4 primer/probe sets in RT-ddPCR.

The analytical sensitivities of the RT-ddPCR assays reported here are nevertheless comparable to existing estimates. The limit of detection of the BioRad SARS-CoV-2 ddPCR Kit, for example, is estimated at 150 copies/mL (manufacturer protocol), which is comparable to our E-Sarbeco RT-ddPCR assay (estimated at 75.8 copies/mL assuming 100% extraction efficiency).

Similarly, the LLODs of the TargetingOne (Beijing, China) COVID-19 digital PCR detection kit 24 and a multiplex assay that included the E-Sarbeco primer/probe set 23 were reported at 10 copies/test and 5 copies/reaction, respectively, both comparable to the LLOD determined here.

While a number of studies have reported that RT-ddPCR can detect SARS-CoV-2 RNA in low viral load clinical samples with higher sensitivity than real-time RT-PCR 12, 21, 22, 24-26 , our study was not designed to evaluate this. Our estimated LLOD of 4.4 copies/reaction by RT-ddPCR using the E-Sarbeco primer/probe set ( Figure 4 ) is in fact comparable to the LLOD reported by the manufacturers for many real-time RT-PCR-based COVID-19 diagnostic assays 46 , though it is important to note that these lower limits are theoretical. In practice, various factors impact assay efficiency, most notably the presence of PCR inhibitors in biological samples that are not removed by the extraction process 47 , as well as the efficiency of the extraction process itself. In theory, the sample partitioning and end-point measurement used in ddPCR should make this technology more robust to small quantities of inhibitors than real-time PCR technologies that rely on initial detection of fluorescent signal above background ( 48-51 and manufacturer protocol), though all platforms will be affected by nucleic acid extraction efficiency.

The ability to quantify SARS-CoV-2 viral loads in biological samples can advance our understanding of COVID-19 biology, and RT-ddPCR offers an attractive platform 7, 8 . Our observation that, in a small convenience sample, both absolute and human cell-normalized 9 SARS-CoV-2 loads spanned more than a 6 log 10 range confirms an enormous viral load range in vivo 52 and suggests that some of the high viral load samples measured here were from individuals with early and progressive infection 24, [53] [54] [55] RNA copy number means that existing diagnostic test results can be converted to viral loads without re-testing samples. While calibration of viral load measurements against all real-time RT-PCR platforms is beyond our scope, this is achievable and in some cases data may already be available 24 .

Some limitations merit mention. We only tested eight commonly-used SARS-CoV-2specific primer/probe sets, and others may exist that adapt well to RT-ddPCR. Our assay performance estimates should be considered approximate, as the manufacturer-reported concentration of the synthetic SARS-CoV-2 RNA standards used in our study may vary by up to 20% error (Twist Bioscience, personal communication). Moreover, we solely evaluated a onestep RT-ddPCR protocol, and therefore assay performance estimates will likely differ from protocols that feature independent cDNA generation followed by ddPCR. We could not precisely define the upper boundary of the linear dynamic range of the E-Sarbeco, IP2 and IP4 RT-ddPCR assays as linearity was maintained at the maximum input of 114,286 target copies/reaction, which already exceeds the manufacturer's estimated upper range of quantification in a ddPCR reaction (manufacturer protocol). Our convenience panel of 48 SARS-CoV-2-positive diagnostic specimens also likely did not capture the full range of biological variation in viral loads, though data from larger cohorts 52 suggests that it was reasonably comprehensive. We also acknowledge that there is measurement uncertainty with real-time RT-PCR C t values that may subtly affect the linear relationship between C t value and RT-ddPCR-derived SARS-CoV-2 viral load described here. Importantly, however, measuring quantitative viral loads enables an objective evaluation of the RT-PCR C t value cutoffs used to distinguish "positive", "indeterminate" and "negative" resultsthresholds that can vary across assays and laboratories. Finally, our estimates of assay performance may not completely reflect those of the entire diagnostic process, as the nucleic acid extraction step introduces additional inefficiencies.

In conclusion, primer/probe sets used in real-time RT-PCR-based COVID-19 diagnostic tests can be migrated to RT-ddPCR to achieve SARS-CoV-2 RNA quantification with varying analytical efficiency, precision and sensitivity. Of the primer/probe sets tested, the E-Sarbeco, IP2 and IP4 sets performed best, where LLOQ and LLOD estimates for the E-Sarbeco assay Precision of each primer/probe set, defined as the coefficient of variation (expressed as a percentage, CV%) of measured copies, is shown for reactions containing 1,000 and 100 input copies of synthetic SARS-CoV-2 RNA. (C). Plotting precision versus analytical efficiency at 1,000 input SARS-CoV-2 RNA copies identifies E-Sarbeco, IP2 and IP4 primer/probe sets as having analytical efficiencies >50% and CV (%) <15% (white background). All other primer/probe sets had analytical efficiencies <50% and in many cases CV (%) >15% (grey background). (D). Same as C, but for 100 input SARS-CoV-2 RNA copies. reaction where the probability of detection in the assay was 95%, was interpolated from the probit curve and is shown as a colored dashed line (B). Same as A, but for the IP2 primer/probe set (C). Same as A, but for the IP4 primer/probe set. 

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Shedding light: The importance of reverse transcription efficiency standards in data interpretation. Biomolecular detection and quantification

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High-Resolution Analysis of Coronavirus Gene Expression by RNA Sequencing and Ribosome Profiling

Introduction to Modern Virology

Characterisation of the transcriptome and proteome of SARS-CoV-2 reveals a cell passage induced in-frame deletion of the furin-like cleavage site from the spike glycoprotein

Analytical sensitivity and efficiency comparisons of SARS-CoV-2 RT-qPCR primer-probe sets

Performance Assessment of SARS-CoV-2 PCR Assays Developed by WHO Referral Laboratories

Quantitative real-time RT-PCR--a perspective

Evaluation of digital PCR for absolute RNA quantification

Detection limits of several commercial reverse transcriptase enzymes: impact on the low-and high-abundance transcript levels assessed by quantitative RT-PCR

In vitro diagnostics of coronavirus disease 2019: Technologies and application

Controlled RNA contamination and degradation and its impact on qPCR gene expression in S. epidermidis biofilms

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An international comparability study on quantification of mRNA gene expression ratios: CCQM-P103.1. Biomolecular detection and quantification

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Table 1. SARS-CoV-2 primer/probe sets assessed for use in RT-ddPCR

Integrated DNA Technologies); 3IABkFQ= 3' Iowa Black Black Hole Quencher (Integrated DNA Technologies)

Forward 5'-ACAGGTACGTTAATAGTTAATAGCGT-3' 26

Reverse 5'-ATATTGCAGCAGTACGCACACA-3' 26

Pasteur Institute IP2 ORF1a Forward. 5'-ATGAGCTTAGTCCTGTTG-3' 12

Reverse 5'-CTCCCTTTGTTGTGTTGT-3' 12

Reverse 5'-CTGGTCAAGGTTAATATAGG-3' 14

ORF ORF1a Forward 5'-CCCTGTGGGTTTTACACTTAA-3' 13

Reverse 5'-ACGATTGTGCATCAGCTGA-3' 13

Reverse 5'-CAGACATTTTGCTCTCAAGCTG-3' 28

Reverse. 5'-AACRCGCTTAACAAAGCACTC-3' 18

Reverse 5'-CGAAGGTGTGACTTCCATG-3' 29

Reverse 5'-TCTGGTTACTGCCAGTTGAATCTG-3' 28