Optimising droplet digital PCR analysis approaches for detection and quantification of bacteria: a case study of fire blight and potato brown rot

Here we report on the first assessment of droplet digital PCR (ddPCR) for detection and absolute quantification of two quarantine plant pathogenic bacteria that infect many species of the Rosaceae and Solanaceae families: Erwinia amylovora and Ralstonia solanacearum. An open-source R script was written for the ddPCR data analysis. Analysis of a set of samples with known health status aided the assessment and selection of different threshold settings (QuantaSoft analysis, definetherain pipeline and manual threshold), which led to optimal diagnostic specificity. The interpretation of the E. amylovora ddPCR was straightforward, and the analysis approach had little influence on the final results and the concentrations determined. The sensitivity and linear range were similar to those for real-time PCR (qPCR), for the analysis of both bacterial suspensions and plant material, making ddPCR a viable choice when both detection and quantification are desired. With the R. solanacearum ddPCR, the use of a high global threshold was necessary to exclude false-positive reactions that are sometimes observed in healthy plant material. ddPCR significantly improved the analytical sensitivity over that of qPCR, and improved the detection of low concentrations of R. solanacearum in potato tuber samples. Accurate and rapid absolute quantification of both of these bacteria in pure culture was achieved by direct ddPCR. Our data confirm the suitability of these ddPCR assays for routine detection and quantification of plant pathogens and for preparation of defined in-house reference materials with known target concentrations.