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

GrainQuest - 使用人工智能和高分辨率多模态成像来剖析种子成分的发育和遗传基础

• 批准号: 2879608
• 金额: --
• 依托单位: Aberystwyth University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2879608
• 关键词: GrainQuest; using; Artifical; Intelligence; resolution

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.

组成（蛋白质，脂质，碳水化合物等）决定了质量和最终用途，因此这些性状在育种中很重要。因此，蛋白质含量对大麦和小麦很重要。高油燕麦系列可能会导致新的食品，如非乳制品酸奶。然而，谷粒是由双受精事件产生的，双受精事件产生了具有三个遗传上不同的组织或隔室的复合结构-母体果皮、胚和胚乳。它们的相对尺寸和组成对整体谷物组成贡献了不同的成分。我们开发了最先进的成像方法（Aber的microCT扫描和多/高光谱成像以及QUB的激光辅助快速蒸发电离质谱法（LA-REIMS），提供不同谷物组织的空间分辨信息。我们的大麦研究表明，扩大的胚可以解释高蛋白系（Cook et al 2017），但这需要艰苦的手动分析。为了消除这一瓶颈，与计算机科学合作开发了人工智能程序，以快速提取和测量可以揭示成分变化的发展基础的特征。了解组成变异的遗传和发育“成分”不仅在智力上很重要，而且因为它可以加速育种进程。在这些技术进步的基础上，该项目将使用IBERS实验育种种群项目中的谷物，其中全谷物成分已经使用传统方法测量。这些广泛和可变的数据集的可用性将使学生能够专注于中心假设，即不同谷物隔室的相对大小和组成确实因基因型而异，影响整体组成。具体目标是：目标1。机器学习工具为了开发强大的深度学习协议，将使用一组特征化的测试颗粒来训练神经网络，以识别和量化可能与成分变化相关的特征。我们最近使用uCT扫描来测量谷物中的花器官体积（Adamski et al 2021），最近使用深度学习（DL）来完成人类几乎不可能完成的任务（即测量胚胎-胚乳相对体积）。DL将被扩展以量化更广泛的性状，包括作为生化组成的代理的高光谱指标（Feng et al.，2017），同时保留穗（小麦）或穗（燕麦）内的位置信息。为了验证AI的输出，必须直接测量不同隔室中的成分。因此，在转移到QUB进行LA-REIMS之前，将使用高光谱相机对完整颗粒进行CT扫描和成像，LA-REIMS是一种新兴的技术，用于在高水平化学鉴定下对内源性复合脂质和代谢物进行空间分析。LA-REIMS结果将与其他成像模式进行空间配准，直接测试预测的成分。为了正式解决这些新性状的变异是否与遗传变异相关（因此对育种者有用），学生将使用来自遗传非结构化（映射）群体的谷物，这些群体是通过将选择的燕麦对与独特的谷物成分杂交而创建的。学生将建立定量模型来解释基因型对产量和成分质量特征变异的贡献，并与使用传统全谷物数据建立的模型进行比较。目标3将在有意义的遗传背景下对假设进行严格检验。