An Improved Technique for Sorting Developmental Stages and Assessing Egg Viability of Globodera pallida using High-Throughput Complex Object Parametric Analyzer and Sorter

Potato Cyst Nematode Egg Viability Assessment and Preparasitic Juvenile Screening Using a Large Particle Flow Cytometer and Sorter

Potato cyst nematode (PCN) cysts consist of heterogenous populations of eggs, juveniles, and eggshells that make manual sorting of individual life stages cumbersome. The number of viable PCN eggs is a major determinant of crop damage. An accurate high-throughput PCN egg viability assay is useful for developing effective management and eradication plans. In this study, we present a method for rapid and precise enumeration and sorting of PCN eggs and juveniles, along with an egg viability assessment by staining eggs with the fluorescent stain, acridine orange, and sorting with the Complex Object Parametric Analyzer and Sorter (COPAS) system, a large particle flow cytometer. Both size sorting and fluorescent sorting capabilities of the COPAS were explored. By using the COPAS, sorting efficiency for eggs and preparasitic second-stage juveniles (J2s) was 97.6 and 97.2%, respectively, with 99% recovery at a flow rate of 15 events/s. Purity of sorted live and dead eggs was 95.5 and 94.1%, respectively. Sorting of J2s by size indicated that 15 to 16.4% of Globodera ellingtonae or G. pallida had an average body length of 436.1 ± 3.4 µm compared with an average size of 512.9 ± 4.4 µm for the majority of the J2 population for both species. A red autofluorescing J2 population was also identified through sorting. Sorting of eggs by flow cytometry did not significantly affect hatching (55.1 ± 1.2 and 53.9 ± 1.6%, respectively, for sorted or nonsorted eggs) or juvenile motility (91.3 ± 1.0 or 90.1 ± 1.1%, respectively), thus confirming that the method does not impair the biological activity of the nematode.

New methods of removing debris and high-throughput counting of cyst nematode eggs extracted from field soil

The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging pathogen of soybeans in the United States. To assess the severity of nematode infestations in the field, SCN egg population densities are determined. Cysts (dead females) of the nematode must be extracted from soil samples and then ground to extract the eggs within. Sucrose centrifugation commonly is used to separate debris from suspensions of extracted nematode eggs. We present a method using OptiPrep as a density gradient medium with improved separation and recovery of extracted eggs compared to the sucrose centrifugation technique. Also, computerized methods were developed to automate the identification and counting of nematode eggs from the processed samples. In one approach, a high-resolution scanner was used to take static images of extracted eggs and debris on filter papers, and a deep learning network was trained to identify and count the eggs among the debris. In the second approach, a lensless imaging setup was developed using off-the-shelf components, and the processed egg samples were passed through a microfluidic flow chip made from double-sided adhesive tape. Holographic videos were recorded of the passing eggs and debris, and the videos were reconstructed and processed by custom software program to obtain egg counts. The performance of the software programs for egg counting was characterized with SCN-infested soil collected from two farms, and the results using these methods were compared with those obtained through manual counting.