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

TL;DR

This paper introduces new methods for extracting and automating high-throughput counting of cyst nematode eggs from soil samples, improving accuracy and efficiency over traditional techniques.

Contribution

It presents a novel OptiPrep density gradient method and two automated imaging approaches, including deep learning and holographic video analysis, for nematode egg detection and counting.

Findings

OptiPrep improves egg recovery compared to sucrose centrifugation

Deep learning accurately identifies eggs in static images

Holographic imaging enables flow-based egg counting

Abstract

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.