A High-Throughput Automated Technique for Counting Females of Heterodera glycines using a Fluorescence-Based Imaging System.
The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging pathogen of soybean. Methods to phenotype soybean varieties for resistance to SCN are currently very laborious and time consuming. Streamlining a portion of this phenotyping process could increase productivity and accuracy. Here we report an automated method to count SCN females using a fluorescence-based imaging system that is well suited to high-throughput SCN phenotyping methods used in greenhouse screening. For optimal automated imaging, females were washed from roots at 30 days post-inoculation into small Petri dishes. Using a Kodak Image Station 4000MM Pro, the Petri dishes were scanned using excitation and emission wavelengths of 470 nm and 535 nm, respectively. Fluorescent images were captured and analyzed with Carestream Molecular Imaging Software for automated counting. We demonstrate that the automated fluorescent-based imaging system is just as accurate (r(2) ≥ 0.95) and more efficient (>50% faster) than manual counting under a microscope. This method can greatly improve the consistency and turnaround of data while reducing the time and labor commitment associated with SCN female counting.
Division of Plant Sciences and Bond Life Sciences Center, University of Missouri-Columbia, Columbia, MO 65211, USA.
Sabrina
S
Brown
Gregory
G
Yeckel
Robert
R
Heinz
Kerry
K
Clark
Dave
D
Sleper
Melissa G
MG
Mitchum