Development of a droplet digital PCR assay to detect and quantify BYDV-MAV and BYDV-PAS in their barley host and aphid vectors

Abstract

Barley yellow dwarf viruses (BYDVs) belong to a complex of several species, all vectored by aphids. Due to the abundance of Sitobion avenae and Rhopalosiphum padi, BYDV-MAV and BYDV-PAS are among the prevalent species in Irish crops. Several BYDV detection methods, such as immunosorbent assays and PCR-based diagnostic tests, are available and routinely used. However, there are opportunities to develop improved assays to capture viral load information from different sample matrices. Here, we successfully developed a droplet digital PCR assay to detect and quantify BYDV-MAV and BYDV-PAS in both aphid and barley samples. The high specificity shown by this assay allows us to differentiate the two species from each other within a wide dynamic range. This assay will provide a better overview of the process underlying BYDV infection and transmission from the early stage of infection to the appearance of the symptoms.

Main article text

Introduction

Yellow dwarf viruses (YDVs) are a major threat for cereal production (Mc Namara et al., 2020; Aradottir & Crespo-Herrera, 2021; Miller & Lozier, 2022), causing yellowing of leaves and stunted growth in a wide range of Poaceae. In crops, this causes yield reductions of up to 80% (Dewar et al., 2016; Mc Namara et al., 2020), leading to significant economic losses (Choudhury et al., 2019).

Yellow dwarf viruses include the cereal YDVs from the genus Polerovirus and the barley yellow dwarf viruses (BYDVs) from the genus Luteovirus. These genera are now classified into two different families, the Tombusviridae and the Solemoviridae, respectively (Miller & Lozier, 2022). In this study, we focus on BYDVs from the Polerovirus genus. To date, several different BYDV species have been identified and characterised for cereals, including BYDV-PAV, BYDV-MAV, BYDV-PAS, BYDV-kerII, BYDV-kerIII, BYDV-GAV and BYDV-SGV (Miller & Lozier, 2022), all of which are transmitted by aphids, with 25 species known to vector BYDV alone (Mc Namara et al., 2020; Van den Eynde et al., 2020).

In Ireland, the grain aphid (Sitobion avenae, Fabricius, 1794) and the bird cherry-oat aphid (Rhopalosiphum padi, Linneaus, 1758) are considered the most prolific vectors of BYDVs. For this reason, they are the main targets of insect monitoring programs in the tillage sector. Sitobion avenae has been shown to be the main vector of BYDV-MAV but also transmits BYDV-PAV and BYDV-PAS efficiently (Jarošová et al., 2013; Van den Eynde et al., 2020). Rhopalosiphum padi is a more efficient vector of BYDV-PAV and BYDV-PAS when compared to BYDV-MAV (Jarošová et al., 2013; Van den Eynde et al., 2020). Recently, BYDV-PAS was reported in Ireland (Byrne et al., 2024) and is considered a more severe variant of BYDV-PAV in barley (Chay et al., 1996). This species was also reported in high proportions in central Europe, especially Poland and Czeck Republic, where BYDV-PAV was thought to be the most important, if not the only BYDV species (Jarošová et al., 2013; Trzmiel, 2020). Barley YDV-PAS occurrences were also documented in the United States (Ohio, Kansas) (Laney et al., 2018; Hodge et al., 2020).

In support of integrated pest management approaches, we need robust tools to monitor aphid-borne viruses. Ideally, a rapid diagnostic tool would allow us to compare infection rates in migrating and in field aphids to anticipate potential transmission waves. In addition, extending the diagnostic assessment to host plants will enable the identification of possible viral reservoirs in crops. Such assays would also provide a means to measure viral loads and enable the evaluation of new plant varieties with potential resistance genes.

To date, only a few diagnostic methods have been developed and rolled out for routine BYDV monitoring. These programs typically involve serological tests, such as enzyme-linked immunosorbent assay (ELISA) (Lister & Rochow, 1979), which are known to have technical and sensitivity limitations (Fabre et al., 2003). Multiplex reverse transcription polymerase chain reaction (RT-PCR) assays for BYDVs have also been developed (Malmstrom & Shu, 2004; Deb & Anderson, 2008) and are useful for end-point detection of multiple BYDV species. More recently, the development of methods such as recombinase polymerase amplification (RPA) (Kim et al., 2020, 2022) and high-throughput sequencing (Sõmera et al., 2021) has resulted in improved sensitivity in viral detection and more precise BYDV identification in addition to the discovery of new YDVs.

In addition to identifying the species and strain of virus present, the capacity to accurately determine viral load would provide a better overview of the process underlying BYDV infection and transmission from the early stage of infection to the appearance of the symptoms. Although quantification of viral load has been typically carried out using real-time quantitative PCR (qPCR), new methods based on droplet digital PCR (ddPCR) are now being applied. The ddPCR is an end-point PCR in which small volumes of reaction mix are loaded into a reaction chamber and split into thousands of nanolitre droplets that serve as sub-reaction units. The distribution of the target sequences inside the chamber is considered to follow a Poisson distribution, which allows us to calculate the initial amount of target sequence (Lievens et al., 2016).

Digital drop PCR-based approaches have several advantages over qPCR including (i) more accurate quantification due to absolute quantification instead of calculating the relationship between measured fluorescence and estimated mass of genetic material (Hindson et al., 2013; Coudray-Meunier et al., 2015; Lievens et al., 2016; Persson et al., 2018; Pandey et al., 2020), eliminating the need for the development of standard curves and (ii) reduced susceptibility to PCR inhibitors (Coudray-Meunier et al., 2015).

In this study, we designed a ddPCR assay that will enable us to screen both barley plants and aphids for two of the main BYDV species found in Ireland (BYDV-MAV and BYDV-PAS). This will support studies on virus epidemiology and support screening of plant material during evaluation and development of improved varieties.

Material and methods

Design of primer–probe and gene fragments for assay development

Primers and probes used in the assay were designed using the Primer3 (Rozen & Skaletsky, 2000) program, implemented in Geneious Prime (V2022.0.2, Biomatters, New Zealand). Sequence data for BYDVs generated as part of a sequencing survey of symptomatic barley crops (Byrne et al., 2024) were used to aid primer–probe design. Primer–probe sets were designed individually for BYDV-MAV and BYDV-PAS, targeting different genomic regions. The regions were chosen to maximise (i) sequence conservation within species and (ii) sequence divergence between BYDV-MAV and BYDV-PAS (Table 1). Alignments of BYDV-PAS, BYDV-MAV and BYDV-PAV for the target regions including the primers and probes are presented in Supplementary Figure S1. Primer–probe sets were also designed to target glyceraldehyde-3-phosphate dehydrogenase in both Hordeum vulgare (XM_045099798) (Mascher et al., 2017) and S. avenae (g17439.t1) (Byrne et al., 2022) (Table 1).

Table 1:

Primer–probe sets used to target BYDV-MAV and BYDV-PAS along with internal GAPDH for use in virus detection in both aphid and barley samples

Name	Channel – fluorophore names	Amplicon size (bp)	Function	Sequence (5′ to 3′) with modifications
IRE-MAV-3342	Blue – FAM	110	F_primer	GCCCAACAGATTGACGGGAA
			R_primer	GTGATGATGAATTGCCCGGC
			Probe	/6-FAM/TCAGGGAGA/ZEN/GCACGGTGAACCA/IABkFQ/
IRE-PAS-0809	Green – HEX	110	F_primer	TGAAAAGGTCGAGACTGGCC
			R_primer	CTGTGTCCCTAATGGAGCGC
			Probe	/HEX/TGGTGCAAG/ZEN/GGGAGGAAACCA/IABkFQ/
GAPDH-HV	Red – Cy5	110	F_primer	GGAGTCCACCGGTGTTTTCA
			R_primer	AGACAAACATGGGAGCGTCC
			Probe	/Cy5/AGGACAAGG/TAO/CTGCAGCTCACA/IAbRQSp/
GAPDH-SA	Red – Cy5	106	F_primer	GGCGAAGTTTCTGTTGATGG
			R_primer	CAGCACCAGCAGATCCCC
			Probe	/Cy5/GGTGTTCTC/TAO/TGAACGCGACCCA/IAbRQSp/

Modifications are shown for each probe. The Iowa Black quenchers used were IABkFq (Iowa Black^® FQ that has a broad absorbance spectra ranging from 420 to 620 nm with peak absorbance at 531 nm) and IAbRQSp (Iowa Black^® RQ that has a broad absorbance spectra ranging from 500 to 700 nm with peak absorbance at 656 nm). The internal quenchers used were ZEN and TAO.

BPDV, barley yellow dwarf virus; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; SA, Sitobion avenae; HV, hordeum vulgare.

Probes were modified to include reporter dyes and quenchers supplied by Integrated DNA Technologies (IDT, IA, USA). We opted for a double-quenched probes assay including the dye at the 5′ extremity, and both an internal quencher (either ZEN™ or TAO™) and a 3′ quencher (either IABkFq Iowa Black^® FQ or IAbRQSp Iowa Black^® RQ). This was to lower the background fluorescence and increase the signal to noise ratio in comparison to single quenched probes (Hirotsu et al., 2020).

In the case of both primer–probe assays, we designed gene fragments consisting of sequences of 300 bp encompassing the amplified region. Three gene fragments were designed for each primer/probe assay, targeting the regions in (i) BYDV-PAS, (ii) BYDV-PAV and (iii) BYDV-MAV (Supplementary Material_IRE-Gene_Fragments_ddPCR, Supplementary Table S1). These represent the three species most commonly found in Irish spring and winter barley (Byrne et al., 2024). Gene fragments were also designed on sequences surrounding the internal controls. The purpose of the gene fragments was to support assay development and confirm the specificity of probes. The synthesis of primers, probes and gene fragments was carried out by IDT (IA, USA). Primers/probe sets were delivered as “PrimeTime qPCR Assay tubes”. The dried primers/probes were resuspended following IDT’s instructions in ultrapure water to obtain a “10×” stock.

Plant and insect samples

In addition to the gene fragments synthesised for each target, we also established positive and negative control materials for each target sample matrix. A collection of aphid colonies is maintained in incubators at Oak Park, at 20°C 16/8 h day–night cycle, on spring barley plants (variety: Planet). Each colony is isolated from the others with insect cages. The collection comprises BYDV-PAS, BYDV-MAV and virus-free colonies of S. avenae and R. padi. The specific BYDV species (PAS or MAV) infecting each colony is confirmed using Illumina RNA sequencing.

Two samples of 10 adult aphids were collected for each category, S. avenae BYDV-MAV, S. avenae virus-free and R. padi BYDV-PAS. The S. avenae colonies were established in May 2021, using single aphids collected in County Laois, Ireland. The R. padi colony was established in February 2022 using a single individual. The samples were homogenised with buffer RTL (QIAGEN, Hilden, Germany) in 1.5 mL tubes, using individual mini pestles. Total RNA was then extracted using an RNeasy Mini Kit (QIAGEN), following the manufacturer’s protocol.

Two barley samples were taken from BYDV-MAV, and BYDV-PAS colonies, along with barley samples from plants germinated and grown in insect-free cages. Each sample was composed of six leaves (including symptomatic leaves in the case of virus-infected colonies). Leaf material was immediately flash frozen in liquid nitrogen and stored at −80°C until RNA extraction. Material was homogenised in liquid nitrogen using a pre-cooled mortar and pestle. Fifty milligrams of ground material was used for extraction with an RNeasy Plant kit (QIAGEN), with on column DNase I digestion according to manufacturer’s instructions. After the final clean-up step, the total RNA was eluted from the column in 50 μL of ddH₂O.

cDNA synthesis

cDNA synthesis was performed using the same method on all plant and aphid materials. First, 1 μL of random hexamer was mixed with 9.5 μL of ddH₂O and 2 μL of sample RNA. After 5 min of incubation at 65°C followed by a brief centrifugation, the reaction tubes were stored on ice. Four microlitres of reaction buffer (RevertAid), 0.5 μL of RiboLock RNase, 2 μL of dNTP mix (10 mM each) and 1 μL of RevertAid reverse transcriptase were then added to the mix.

The final volume was 20 μL for each sample. The final reaction mixes were incubated at 25°C for 10 min, followed by 60 min at 42°C and 10 min at 70°C.

A 1:10 dilution of the cDNA was performed on all the samples directly after cDNA synthesis and samples were stored at −20°C until further processing. After a first run of the samples on the ddPCR, a further dilution of 1:30 of the cDNA was performed to avoid saturating the loading chambers.

Droplet digital PCR assay

PrimeTime Mini qPCR Assays for each target were supplied as a mixture of 1 nmol of each primer and 0.5 nmol of probe (Integrated DNA Technologies, IA, USA). They were reconstituted in molecular biology grade nuclease-free water following the manufacturer’s recommendations to a final concentration of “10×”.

For each sample, the reaction mix was composed of 1 μL of naica^® multiplex PCR MIX Buffer A (5×), 0.2 μL of naica^® multiplex PCR MIX Buffer B (5×), 0.5 μL of IRE-MAV-3342 (10×), 0.5 μL of IRE-PAS-0809 (10×), 0.5 μL of GAPDH-SA (10×) or GAPDH-HV (10×), depending on the sample matrix, 1.3 μL of molecular biology nuclease-free water and 1 μL of template cDNA. The final reaction volume of 5 μL per sample was then pipetted into each chamber of a Ruby chip (Stilla Technologies, Villejuif, France).

The PCR and data acquisition were carried out with the Stilla Technologies’ naica^® system for Crystal Digital PCR™ (Stilla Technologies, Villejuif, France). We applied the following condition in the Geode automated droplet generator and thermocycler: 95°C for 10 min followed by 40 cycles of 15 s at 95°C and 1 min at 60°C. At the end of the reaction, we used the Prism3 multi-colour fluorescence imager to scan the chips.

Initial runs utilised the gene fragments for each target to establish compensation matrix to account for potential fluorescence spillover. This involved running separate reactions with templates for each of the four targets (two viruses, and the GAPDH internal control for aphid and barley), alongside no-template controls. The compensation matrix was saved and applied to correct for fluorescence spillover in all subsequent runs.

Assay limit of blanc (LoB) and limit of detection (LoD)

Limit of blancs and LoDs were calculated for BYDV-MAV and BYDV-PAS under a confidence level of 95% for each sample matrix separately (Table 2).

Table 2:

Values of LoB and LoD calculated for each sample matrix

Host	BYDV	LoB (cp/μL)	LoD (cp/μL)
Barely	MAV	0.2995	2.280
	PAS	0.5995	2.861
Aphid	MAV	0.7470	1.159
	PAS	0.3350	1.834

BPDV, barley yellow dwarf virus; cp, copy number; LoB, limit of blanc; LoD, limit of detection.

The LoB defines the limit of concentration, in probe sequence copy (cp) number, below which a sample is considered negative. We calculated the LoB using a non-parametric approach by testing 31 replicates of a negative barley sample and 32 replicates of a S. avenae-negative sample. To do so, we ranked the concentrations measured in cp/μL for each replicate in the ascending order. Each value was then assigned a score corresponding to its rank (for example rank 1 to rank 31 for barley). A number denoted by X, corresponding to the rank position of the confidence level, was then calculated using the following equation:

$$X\hspace{0.17em}=\hspace{0.17em}0.5+(N\ast P_{\text{LoB}}),$$

where N = number of samples and P _LoB = confidence level, here 0.95. The LoB was then obtained using the following equation:

$$\text{LoB}\hspace{0.17em}=C1\hspace{0.17em}+\hspace{0.17em}Y\ast (C2–C1),$$

where C1 corresponds to the concentration for rank X1 (rank below X) and C2 to the concentration for rank X2 (rank above X). Y is given by the number 0. Z where Z corresponds to the digits after the decimal point of X. Note, if Y = 0, then LoB = C1.

The LoD is another concentration threshold designed as the lowest amount of analyte likely to produce a true positive result (Linnet & Kondratovich, 2004). It was calculated using the concentration measures of 30 low-level (LL) samples for each target. We targeted the concentration of the LL samples to be between one and five times the LoB in order to obtain 95% of the actual LL measures above the LoB, assuming a Gaussian distribution (Linnet & Kondratovich, 2004). The adequate dilution factor was estimated by testing a range of serial dilution of the synthesised gene fragments (Figure 1). The 30 LL samples were composed of six replicates of five independent dilutions of the gene fragments. The LL samples were composed of a mix of the IRE-MAV-3342 and the IRE-PAS-0809 gene fragments on which serial dilutions were performed until the targeted concentration was reached. Ten microlitres of these gene fragments were then mixed with 10 μL of a negative control sample in order to calculate the LoD of targets in a typical sample background for each sample matrix.

Figure 1.

Linear range of the assay. Correlation between the measured concentrations (ddPCR) and the expected concentrations, calculated given the dilution range and the initial concentration of the gene fragment stocks. BYDV, barley yellow dwarf virus; ddPCR, droplet digital PCR.

For each group of the LL sample (LL1–LL5) we calculated the standard deviation of the measured concentrations (cp/μL) SD _i . We tested the homogeneity of variances between LL samples using a Bartlett’s homoscedasticity test. One outlier replicate for BYDV-MAV (barley) was removed prior to LoD calculation as on inspection the droplets with fluorescent signal passing threshold were all aggregated in a small cluster on the chip.

The LoD was calculated as follows:

$$\text{LoD}=\text{LoB}+\text{Cp}\ast {\text{SD}}_L$$

SD _L is the standard deviation estimated among all the LL samples:

$${\text{SD}}_L=\hspace{0.17em}\sqrt{\left({\displaystyle {\sum }_{i=1}^J(n_i-1){\text{SD}}_i{2}^{}}\right)/\left({\displaystyle {\sum }_{i=1}^J(n_i-1)}\right)},$$

where SD _i is the standard deviation of the i ^th LL samples group, n_i is the number of replicates in the i ^th group, and J is the number of LL sample groups, here 5. The values of SD _i calculated for each channel are presented in Supplementary Table S2.

C_p corresponds to the coefficient for which 95% of the possible LL values will be taking into account.

$$C_p=\frac{Z_{1-\beta }}{1-\left(\frac{1}{4(f)}\right)},$$

where Z _{1− β} is defined as the critical value of the area under a Gaussian curve for the x ^th percentile. For the 95^th percentile, this value is 1.645 (Linnet & Kondratovich, 2004). It has to be reported to the mean estimated standard deviation that can be approximated by $1-\left(\frac{1}{4(f)}\right)$ , where f represents the sum of the degrees of freedom of each LL sample: n_i −1 (Linnet & Kondratovich, 2004).

Assay specificity

In the first instance, the specificity of the assay was assessed by testing each primer–probe set on gene fragments from each species and the results were compared to expectations as described in Supplementary Table S1.

Specificity was further confirmed on BYDV-PAS and BYDV-MAV-positive aphid and barley samples, alongside non-viruliferous samples for both sample matrices. BYDV-PAS and MAV samples mixed together were also tested in order to simulate a coinfection.

Evaluation of diagnostic and quantitative performance

Serial dilutions of the gene fragments were carried out to verify the linearity of the assay (Figure 1). To do so, we measured a serial dilution of a mixture of the IRE-MAV-3342 and IRE-PAS-0809 fragments. Each step of the dilution was mixed with the same volume of negative barley cDNA to simulate a typical sample background. For each target, we plotted the ddPCR results at different concentrations against the expected concentration in cp/μL (Figure 1).

Measures obtained for saturated chambers were discarded as the values obtained were outside the linear range. We then fitted a linear model for each target to the plotted values using the “lm” function in R.

Knowing the initial concentration of the gene fragments stocks, measured using a Qubit fluorometer (Thermo Fisher Scientific, US) (with a broad range assay), and the dilution factors, we were able to assess the linearity of the assay’s quantification by converting the ng/μL to cp/μL:

$$\hspace{0.17em}{N}_{\text{cp}}=\hspace{0.17em}\frac{M_{\text{ng}}\ast (6.022\ast {10}^{23})}{L_{\text{bp}}\ast \hspace{0.17em}{10}^9\ast 660},$$

where N _cp is the cp number, M _ng is the mass of DNA expressed in nanograms and L _bp is the length of the molecule, equal to 300 for all gene fragments synthesised here. The calculation uses Avogadro’s number, 6.022 × 10²³ molecules/mol, and the average mass of a base pair, 660 Da, which we can multiply by 10⁹ in order to express the result in nanograms. This can be simplified as

$$N_{\text{cp}}=\hspace{0.17em}3.041\ast {10}^9{M}_{\text{ng}}.$$

Results

ddPCR assay development

Our initial focus was to determine if the primers could efficiently amplify the target regions of the gene fragments and then amplify targets in real barley and aphid control samples. Only samples producing more than 10,000 droplets passed the quality control. When less droplets were generated, the samples were repeated. In addition, when we observed saturation of positive droplets in the chambers, the results were discarded, and the samples were diluted prior to repeating the analysis. This was necessary as a balance of positive and negative droplets is required to meet the underlying assay assumptions. Our results showed that we were able to detect both virus targets and internal controls using gene fragments as templates (Figure 2). When real sample matrices were tested (barley and aphid), we were also able to successfully detect targets and internal controls (Figure 2).

Figure 2.

Left: fluorescence signals observed for each synthesised gene fragment tested individually as template with the IRE-MAV-3342, IRE-PAS-0809 and GAPDH-SA probes. Right: fluorescence signals observed when aphid’s cDNA was used as the template. Signals for the last column were obtained from a mix of R. padi infected with BYDV-PAS and S. avenae infected with BYDV-MAV. The same tests were performed with the barley samples, using the IRE-MAV-3342, IRE-PAS-0809 and GAPDH-HV probes; these results are presented in Supplementary Figure S2. BYDV, barley yellow dwarf virus.

Among the 31 samples used for the calculation of the LoB for the barley matrix, only 2 presented positive droplets in the FAM channel and 7 in the HEX channel. In aphid samples, 5 out of 32 showed positive droplets for the FAM channel and 2 for the HEX channel. The small number of false positives was considered as biological noise and included in the LoB determination according to recommendations (Stilla Technologies, 2024).

The LoD values were different by one copy with respect to the sample matrix (Table 2). This difference in the sensitivity of the assay can be explained by the fact that the standard deviations of aphid LL samples were on average lower than those of the barley samples (Supplementary Table S2). However, this variation did not seem to affect the performances of the assay as the LoD was higher for BYDV-PAS than for BYDV-MAV in both sample matrices. This corresponds to the trend observed in Figure 1 where the concentration of BYDV-PAS was above the concentration of BYDV-MAV along the linear range of the assay.

Assay specificity

To test the specificity of the BYDV-MAV and BYDV-PAS probe/primers sets, we carried out the assay with gene fragments synthesised for both regions, for three BYDV species (BYDV-PAS, BYDV-MAV and BYDV-PAV) (Supplementary material IRE-Gene_Fragments_ddPCR, Supplementary Table S1), used as the template.

We observed that the probes specifically annealed to their targets alone (Figure 2; results in barley matrix shown in Supplementary Figure S2). This confirmed the specificity of the probes and the ability of the assay to distinguish between BYDV-PAS and BYDV-MAV.

Quantitative performances

The initial concentration of the reconstituted gene fragments was measured using a Qubit fluorimeter at 11.4 and 15.4 ng/μL for IRE-MAV-3342 and IRE-PAS-0809, respectively. These measures explained the differences of concentration between BYDV-MAV and BYDV-PAV observed in Figure 1 and in the LoD (Table 2). The expected concentrations were obtained by converting these measures to cp/μL. We observed a linear relationship between these expected concentrations and the measured concentration (Figure 1). The range of this linear relationship comprised between the LoD, around 3 cp/μL (Table 2) and 10⁴ cp/μL.

Discussion

Developing a ddPCR assay presented several advantages over qPCR approaches. First, it has been shown that ddPCR can produce more reliable data than real-time PCR (Hindson et al., 2013; Coudray-Meunier et al., 2015; Mehle et al., 2018; Pandey et al., 2020; Wang et al., 2023), mainly due to the fundamental difference in the quantification method. In qPCR, the quantification is inferred by comparing the fluorescence intensity of different standards of known concentration which makes the quality of the results dependent on the quality of the standards.

On the other hand, the absolute quantification enabled with ddPCR made the design of the assay more efficient. To produce this assay, we did not need a standard curve for quantification, which allows for greater reproducibility across laboratories and the generation of consistent results over long periods of time. Many ddPCR assays have been developed in order to detect and quantify viruses in a wide range of plants of economic interests (Mehle et al., 2018; Pandey et al., 2020; Lee et al., 2021; Vargas-Hernández et al., 2022; Kim et al., 2023; Lee et al., 2023). In many cases these studies usually conclude that ddPCR outperforms qPCR (Mehle et al., 2018; Pandey et al., 2020; Lee et al., 2021; Kim et al., 2023; Lee et al., 2023), regardless of the sample matrix or the virus targeted.

Given that both viral targets have the same amplicon size, the direct comparison of viral loads between BYDV-MAV and BYDV-PAS is enabled by this assay. Comparison of sample concentrations when primers and probes are designed on different genomes or even different parts of the same genome would be difficult with qPCR. It would require both amplifications to have the same efficiency as quantification is measured during the amplification. This represents an advantage of end-point measurement compared to real-time measurement (Cankar et al., 2006; Mehle et al., 2018).

The linear range of the assay, presented in Figure 1, indicates that it can quantify lower viral loads than the qPCR assay developed by Fabre et al. (2003). This qPCR assay was already 10 to 100 times more sensitive than standard RT-PCR assays and ELISA. In Kim et al. (2020), the authors determined that their RPA assays were 100 times more sensitive than standard RT-PCR assays (see also Kim et al., 2022). Under these conditions, we can estimate that the range of quantification of our ddPCR assay is comparable to these RPA assays. This enables the possibility to detect viral infections in the early stages, for example, shortly after inoculation by the aphid.

Once BYDV has been detected, it is necessary to identify to species level. Given the observed specificity of the probes that we used (Figure 2, Supplementary Table S1 and Supplementary Figure S2), we can confirm that this assay can identify BYDV-MAV and BYDV-PAS with a high level of confidence. These two species are not as often monitored as BYDV-PAV (Figueira et al., 1997; Balaji et al., 2003; Fabre et al., 2003; Jarošová et al., 2013; Svanella-Dumas et al., 2013; Adhikari et al., 2020), which is the predominant strain in the UK and continental Europe. This assay is well adapted to an Irish agricultural context where BYDV-MAV is predominantly reported in higher abundance and frequency when compared to BYDV-PAV (Kennedy & Connery, 2012; Byrne et al., 2024).

An application of this new ddPCR assay involves the provision of high-quality diagnostics to support BYDV monitoring. This may include virus identification in complex insect samples from suction-tower traps or using eRNA approaches on collection solutions. This can be used as part of monitoring programmes to study the epidemiology of aphid-borne viruses, including monitoring that can feed into decision support tools. It will also help us to better understand the mechanisms of virus transmission and propagation in the plant, with the possibility to quantify how the infection develops temporally. Viral load information will also support the assessment of tolerant and susceptible crops. Although it is clear that ddPCR assays have great potential for virus surveillance and diagnostics, their widespread application is currently limited by the requirement to access specialised equipment. However, until such equipment becomes more readily available, assays such as the ones developed here can at least be transferred to qPCR systems to ensure continued application.

In conclusion, this study set out to develop and validate a robust ddPCR assay to quantitatively detect BYDV-MAV and BYDV-PAS in both their barley host and aphid vectors. Our results demonstrate that the ddPCR approach developed here is able to detect and quantify both virus species in both sample matrices with high specificity. Inclusion of internal controls enables us to verify the RNA extraction and cDNA synthesis. In particular, this will support studies on virus epidemiology and support screening of plant material during evaluation and development of improved varieties.

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Supplementary material

Supplementary Figure S1.

Primers/probe sets aligned on their target region and the corresponding regions in non-target species genomes. (A) The IRE-PAS-0809 set was aligned on a BYDV-PAS genome and confronted to sequences from BYDV-MAV and BYDV-PAV genomes. (B) The IRE-MAV-3342 set was aligned on a BYDV-MAV genome and confronted to sequences from BYDV-PAS and BYDV-PAV genomes. The genomes used here are three isolates from BYDV sampled in County Kilkenny, Ireland. They are available in GeneBank; the accession numbers are mentioned in the sequence names, in the figure. BYDV, barley yellow dwarf virus.

Supplementary Figure S2.

Left: fluorescence signals observed for all the synthesised gene fragments used individually as templates, tested with the IRE-MAV-3342, IRE-PAS-0809 and GAPDH-HV probes. Right: fluorescence signals obtained with barley sample’s cDNA.

Supplementary Table S1:

Expected specificity of the probes on the synthesised gene fragments

Fragment name	IRE-MAV-3342	IRE-PAS-0809	IRE-GAPDH-HV	IRE-GAPDH-SA
IRE-3342-MAV-gbloc	✓	✗	✗	✗
IRE-3342-PAV-gbloc	✗	✗	✗	✗
IRE-3342-PAS-gbloc	✗	✗	✗	✗
IRE-0809-MAV-gbloc	✗	✗	✗	✗
IRE-0809-PAV-gbloc	✗	✗	✗	✗
IRE-0809-PAS-gbloc	✗	✓	✗	✗
IRE-GAPDH-HV	✗	✗	✓	✗
IRE-GAPDH-SA	✗	✗	✗	✓

Supplementary Table S2:

Standard deviations (SDi) of each LL sample used to calculate the LoDs for the FAM and the HEX fluorescent channels and for each sample matrix

LL sample	Replicates	IRE-MAV-3342 (FAM) Concentration (cp/μL) Standard deviation	IRE-PAS-0809 (HEX) Concentration (cp/μL) Standard deviation
Aphid LL1	6	0.399	0.826
Aphid LL2	6	0.406	1.028
Aphid LL3	6	0.653	0.609
Aphid LL4	6	0.606	1.288
Aphid LL5	6	0.379	0.547
Barley LL1	5	0.898	1.826
Barley LL2	6	0.591	0.543
Barley LL3	6	0.359	1.422
Barley LL4	6	0.423	1.282
Barley LL5	6	1.185	1.402

LL, low level; LoD, limit of detection.