Fully-Automated High-Throughput NMR System for Screening of Haploid Kernels of Maize (Corn) by Measurement of Oil Content

Abstract

One of the modern crop breeding techniques uses doubled haploid plants that contain an identical pair of chromosomes in order to accelerate the breeding process. Rapid haploid identification method is critical for large-scale selections of double haploids. The conventional methods based on the color of the endosperm and embryo seeds are slow, manual and prone to error. On the other hand, there exists a significant difference between diploid and haploid seeds generated by high oil inducer, which makes it possible to use oil content to identify the haploid. This paper describes a fully-automated high-throughput NMR screening system for maize haploid kernel identification. The system is comprised of a sampler unit to select a single kernel to feed for measurement of NMR and weight, and a kernel sorter to distribute the kernel according to the measurement result. Tests of the system show a consistent accuracy of 94% with an average screening time of 4 seconds per kernel. Field test result is described and the directions for future improvement are discussed.

Novel technologies in doubled haploid line development

“…For rapid discrimination of haploid progeny from diploid seed, Liu et al . () proposed a high‐throughput system based on kernel oil content by pollination with high oil inducer lines (Melchinger et al ., ; Wang et al ., ).…”

“…HvCENH3 has translation activity and can be loaded properly to the centromeres of H. vulgare, but whether HbCENH3 has translation activity is unknown. The H. bulbosum centromere has no activity during anaphase leading to chromosome elimination and H. vulgare haploid embryo development (Watts et al, 2016). Centromere inactivity attributes to centromeric loss of CENH3 protein instead of uniparental transcription inactivation of CENH3 genes (Sanei et al, 2011).…”

“…At present, haploid identification largely relies on dominant marker gene R1-nj (purple scutellum and aleurone). For rapid discrimination of haploid progeny from diploid seed, proposed a highthroughput system based on kernel oil content by pollination with high oil inducer lines Wang et al, 2016).…”

Advancing the science of wheat quality evaluation using nuclear magnetic resonance (NMR) and ultrasound‐based techniques

“…Figueroa et al (2012) also demonstrated the capability of their stress relaxation technique to discriminate wheat kernels from good and poor breadmaking quality wheat classes. Promising results from small deformation stress relaxation measurements (1% strain) on rice kernels, (Wang et al, 2016, would encourage the development of kernel stress relaxation technique for wheat protein quality evaluation. The authors found that the relaxation time of wheat kernels was significantly affected by alleles of good and poor quality, indicating the potential of this parameter to evaluate wheat protein quality based on single kernel stress relaxation measurements.…”

“…Most MRI studies on grains and seeds have been carried out at high-field range (1.5-3 Tesla) which makes this technique a costly means of wheat protein content and quality evaluation (Kirtil & Oztop, 2016;Patel, Khan, & Kar, 2015;Van As & Van Duynhoven, 2013). Hence, low-field (magnet field < 0.5 Tesla) proton NMR (LF-NMR) has been considered as the most widely used type of NMR spectroscopy in screening seeds and grains based on their different contents of water and fat (Jia, Wang, Li, & Liu, 2017;Krishnan, Singh, Verma, Joshi, & Singh, 2014;Moore, Richards, & Rebetzke, 2015;Niu, Li, Chen, & Xu, 2014;Todt, Guthausen, Burk, Schmalbein, & Kamlowski, 2006;Wang et al, 2016Wang et al, , 2017. Hence, low-field (magnet field < 0.5 Tesla) proton NMR (LF-NMR) has been considered as the most widely used type of NMR spectroscopy in screening seeds and grains based on their different contents of water and fat (Jia, Wang, Li, & Liu, 2017;Krishnan, Singh, Verma, Joshi, & Singh, 2014;Moore, Richards, & Rebetzke, 2015;Niu, Li, Chen, & Xu, 2014;Todt, Guthausen, Burk, Schmalbein, & Kamlowski, 2006;Wang et al, 2016Wang et al, , 2017.…”