GrainQuest - using Artifical Intelligence and high resolution multimodal imaging to dissect the developmental and genetic basis of seed composition

Composition (protein, lipid, carbohydrate, etc) determines quality and end-use so these traits are important in breeding. Thus, protein content is important for barley and wheat. High oil oat lines may lead to novel food products such as non-dairy yogurts. However, grains arise from a double fertilization event that produces a composite structure with three genetically distinct tissues or compartments - the maternal pericarp, the embryo and the endosperm. Their relative sizes and compositions contribute different constituents to overall grain composition.We have developed state-of-the-art imaging methods (microCT scanning and multi/hyperspectral imaging at Aber and Laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) at QUB) that provide spatially resolved information on the different grain tissues. Our barley study demonstrated that enlarged embryos could explain high protein lines (Cook et al 2017) but this required painstaking manual analyses. To remove this bottleneck, a collaboration with Computer Science developed AI routines to quickly extract and measure features that can reveal the developmental basis of compositional variation. Understanding the genetic and developmental "components" of compositional variation is important, not only intellectually, but because it can accelerate breeding progress.Building on these technical advances, this project will use grain from IBERS' experimental breeding populations projects, where whole-grain composition has already been measured using traditional methods. The availability of these extensive and variable datasets will enable the student to focus on the central hypothesis that the relative sizes and compositions of different grain compartments do vary between genotypes, affecting overall composition. The specific objectives are:Obj1. Machine learning toolsTo develop robust deep learning protocols, a set of characterized test grains will be used to train neural networks to recognize and quantify features that may be related to compositional variation. We have recently used uCT scanning to measure floral organ volume in cereals (Adamski et al 2021) and recently used deep learning (DL) to undertake tasks that humans find almost impossible (i.e. measuring embryo-endosperm relative volumes). DL will be extended to quantify a wider variety of traits including hyperspectral indicies as proxies for biochemical composition (Feng et al., 2017), while retaining positional information within the spike (wheat) or panicle (oats).Obj2. Co-registration across imaging modalities To validate the outputs from AI, composition must be directly measured in the different compartments. Therefore, intact grains will be CT scanned and imaged using a hyperspectral camera before transferring to QUB for LA-REIMS, an emerging technique for spatial analysis of endogenous complex lipids and metabolites at a high level of chemical identification. LA-REIMS results will be spatially co-registered with the other imaging modalities, directly testing the predicted composition.Obj3. Verification of concept To formally address whether variation in these novel traits is associated with genetic variation (and therefore be useful to breeders), the student will use grain from genetically unstructured (mapping) populations that have been previously created by crossing selected pairs of oats with distinctive grain composition. The student will build quantitative models to account for the genotypic contribution to variation in features contributing to yield and compositional quality and compare to models produced using conventional whole grain data.Obj3 will provide a rigorous test of the hypothesis in a meaningful genetic context.