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

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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.