Seeing the light: automatically identifying key anatomical changes in light sheet microscopy images of plant roots

看到光：自动识别植物根部光片显微镜图像中的关键解剖变化

• 批准号: BB/N018575/1
• 负责人: Andrew French
• 金额: $ 56.31万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN018575%2F1
• 关键词: Seeing; light; automatically; identifying; key

To meet an increasing demand on food production, there is more need now than ever before to understand and improve the efficiency of crop growth and yield. Here, we consider the anatomical changes of roots under low phosphate levels that give rise to important architectural changes in the root system. The study of these anatomical changes in features such as cell divisions over time is only possible to due to recent technological advances in imaging and expertise in developing analysis software. For the first time, we aim to find the origins of these anatomical changes.Phosphorus is one of the key macronutrients (alongside nitrogen and potassium) required for healthy plant growth, and is a widely used constituent of fertilizer used in commercial crop production. Phosphorus is a limited strategic resource: it is derived from a finite natural supply. Understanding phosphorus use and its effects on growth in plants is therefore of key importance to Global Food Security. Phosphorus mobility in the soil is limited by slow diffusion, and so areas of low phosphate concentration are created around root system. Much work has been carried out examining the overall architecture of root systems under differing conditions; however, much less is understood about the anatomical changes, and how they develop. There are only a few instances where root anatomical and architectural traits have been combined in a systematic way to select for plants with enhanced nutrient acquisition, but when this has been done the improvements have been staggering . For example, one study in Mozambique selected common bean varieties with shallow root angle (to enhance topsoil exploration) and enhanced root hair growth (to increase contact with the soil), this led to an almost 300% increase in plant biomass on low phosphorus soils, twice that expected from the additive benefits of these traits in isolation. This shows a great importance in understanding anatomical traits and understanding how they respond to low nutrient environments. There are several reasons for our lack of knowledge in adaptive anatomical responses in roots. First, the equipment has not been available to image the plants growing over the time periods these anatomical changes take place. Second, the ability to alter the growth conditions (such as nutrient levels) around the root whilst being imaged has not been possible. Third, discovering subtle anatomical changes in the huge datasets that would be generated is a very challenging manual task. Our work will allow us to overcome these challenges, allowing study of anatomical changes in low phosphorus media dynamically. In this proposal, we use a cutting-edge microscope, a light sheet fluoresence microscope (or LSFM) to image cellular anatomy as plants grow. A key challenge with LSFM time series data is the huge volume of data produced. This requires new analysis methods to permit us to gain biological insight. One such problem is identifying formative divisions that give rise to anatomical patterns in 3D datasets of roots resolved over time. This proposal seeks to automatically identify particular anatomical changes in big datasets. With LSFM it is possible to acquire gigabytes of data per minute. Time course experiments can easily generate tens of gigabytes of data. This turns visualisation and analysis into a bottleneck. We propose a solution. We will use machine learning approaches to allow new software to identify regions of interest within these datasets. We will build visualisation tools which will use the results of these approaches to allow biologists to navigate the data in meaningful ways, rather than blindly moving through the whole dataset. We will use these tools to investigate how root anatomy is altered in plants grown in low nutrient environments.

为了满足日益增长的粮食生产需求，现在比以往任何时候都更需要了解和提高作物生长和产量的效率。在这里，我们认为，在低磷水平下，引起重要的建筑变化的根系的解剖变化。研究这些解剖学特征的变化，如随着时间的推移细胞分裂，是唯一可能的，因为最近的成像技术进步和开发分析软件的专业知识。磷是植物健康生长所需的关键常量营养素之一（与氮和钾一起），也是经济作物生产中广泛使用的肥料成分。磷是一种有限的战略资源：它来自有限的自然供应。因此，了解磷的使用及其对植物生长的影响对全球粮食安全至关重要。磷在土壤中的流动性受到缓慢扩散的限制，因此在根系周围产生了低磷浓度的区域。人们已经做了大量的工作来研究不同条件下根系的整体结构;然而，对解剖学变化以及它们如何发展的了解要少得多。只有少数情况下，根的解剖和结构特征已被结合在一个系统的方式来选择植物与增强营养物质的获取，但当这已经完成的改进一直是惊人的。例如，在莫桑比克进行的一项研究选择了具有浅根角（以加强表土勘探）和增强根毛生长（以增加与土壤的接触）的普通豆类品种，这导致低磷土壤上的植物生物量增加近300%，是这些性状单独产生的附加效益预期的两倍。这表明了理解解剖特征和理解它们如何对低营养环境作出反应的重要性。有几个原因，我们缺乏知识的适应性解剖反应的根。首先，设备还没有可用于对这些解剖学变化发生的时间段内生长的植物进行成像。其次，在成像的同时改变根部周围的生长条件（例如营养水平）的能力是不可能的。第三，在将要生成的巨大数据集中发现细微的解剖变化是一项非常具有挑战性的手动任务。我们的工作将使我们能够克服这些挑战，允许在低磷介质中动态地研究解剖学变化。在这个提议中，我们使用一种尖端的显微镜，一种光片荧光显微镜（或LSFM），随着植物的生长对细胞解剖进行成像。LSFM时间序列数据的一个关键挑战是产生的数据量巨大。这需要新的分析方法，使我们能够获得生物学的洞察力。一个这样的问题是识别随着时间的推移在根的3D数据集中产生解剖模式的形成分区。该提案旨在自动识别大数据集中的特定解剖学变化。使用LSFM，可以每分钟获取千兆字节的数据。时间过程实验可以很容易地生成数十千兆字节的数据。这使得可视化和分析成为一个瓶颈。我们提出一个解决方案。我们将使用机器学习方法来允许新软件识别这些数据集中的感兴趣区域。我们将构建可视化工具，这些工具将使用这些方法的结果，让生物学家以有意义的方式浏览数据，而不是盲目地浏览整个数据集。我们将使用这些工具来研究在低营养环境中生长的植物的根解剖结构是如何改变的。

项目成果

Cellular Patterning of Arabidopsis Roots Under Low Phosphate Conditions.

• DOI: 10.3389/fpls.2018.00735
• 发表时间: 2018
• 期刊: Frontiers in plant science
• 影响因子: 5.6
• 作者: Janes G;von Wangenheim D;Cowling S;Kerr I;Band L;French AP;Bishopp A
• 通讯作者: Bishopp A

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

• DOI: 10.1093/gigascience/gix083
• 发表时间: 2017-10-01
• 期刊: GigaScience
• 影响因子: 9.2
• 作者: Pound MP;Atkinson JA;Townsend AJ;Wilson MH;Griffiths M;Jackson AS;Bulat A;Tzimiropoulos G;Wells DM;Murchie EH;Pridmore TP;French AP
• 通讯作者: French AP