Plant Phenotyping and Phenomics for Plant Breeding

As a consequence of the global climate change, both the reduction on yield potential and the available surface area of cultivated species will compromise the production of food needed for a constant growing population. There is consensus about the significant gap between world food consumption projected for the coming decades and the expected crop yield-improvements, which are estimated to be insufficient to meet the demand. The complexity of this scenario will challenge breeders to develop cultivars that are better adapted to adverse environmental conditions, therefore incorporating a new set of morpho-physiological and physico-chemical traits; a large number of these traits have been found to be linked to heat and drought tolerance. Currently, the only reasonable way to satisfy all these demands is through acquisition of high-dimensional phenotypic data (high-throughput phenotyping), allowing researchers with a holistic comprehension of plant responses, or ‘Phenomics’. Phenomics is still under development. This Research Topic aims to be a contribution to the progress of methodologies and analysis to help understand the performance of a genotype in a given environment

Analyzing colony dynamics and visualizing cell diversity in spatiotemporal experiments

Hattab G. Analyzing colony dynamics and visualizing cell diversity in spatiotemporal experiments. Bielefeld: Universität Bielefeld; 2018.Bioimaging technologies enable the description of the life cycle of organisms at the microscopic scale, for example bacterial cells. In the particular case of time lapse imaging, the coupling of experimental setups and marker protocols results in the acquisition of biological changes in spatiotemporal experiments. Such experiments are devised to obtain a time-lapse image data, which I refer to as biomovies. Understanding how a cell behaves at every time point is crucial. In fact, this motivated all cell studies in the literature, which are single cell oriented. For the present biomovies, the task is to identify similarly fluorescing subpopulations across space and time. My interest lies in isogenic bacterial populations of *Sinorhizobium meliloti*. The biomovies’ particularity is a dynamic range of high values for a set of different properties (e.g. cell density, cell count, etc), herein, leading to a bottleneck. State of the art methods cannot address such a task, which is partly due to their inability to handle highly dense populations and their adaptability to different experimental setups. In particular, they fall short either at the segmentation step (to delineate individual cells and extract their abstraction, e.g. cell centroid) or at the tracking step (to follow identified cells in each frame). To gain insight into bacterial growth at the population level, I claim that one does not really need to know the fate of each single cell. In the context of this thesis, I present a series of pipelines and algorithms. First, preprocessing pipelines to reduce noise and enhance the object-to-background contrast. Second, an adaptive algorithm to correct spatial shift in the images (i.e. registration) and of each biomovie. Third and last, a modular algorithm that constructs coherent patch lineages by employing two adapted data abstractions, the particle and the patch, that are essential to solving the aforementioned bottleneck and are defined as follows: A particle is an intuitive geometric abstraction that results from considering whether the neighborhood around a pixel falls within a cell by checking for signal characteristics such as signal intensity, edge orientation, fluorescence signals, or texture. A patch is the aggregation of spatially contiguous particle trajectories that feature similar fluorescence patterns. The methodology that creates coherent patch lineages is automatic and modular. By integrating aspects of object recognition and spatiotemporal changes, it lays down the foundation for investigating colony growth. All of the aforementioned pipelines represent a new methodological contribution to the field of lineage analysis and colony growth. I evaluate the proposed pipelines and algorithms on simulated and biological data, respectively. In turn this enabled me to validate the algorithms, interpret changes in the colony growth and differences among conditions of an experiment. In particular, I found that in a same condition, two isogenic bacterial colonies grew differently when faced with the same stress. The methods pioneered herein provide a key step to investigating colony growth

SIMULTANEOUS CELL TRACKING AND IMAGE ALIGNMENT IN 3D CLSM IMAGERY OF GROWING ARABIDOPSIS THALIANA SEPALS

In this research we propose a combined cell matching and image alignment method for tracking cells based on their nuclear locations in 3D fluorescent Confocal Laser Scanning Microscopy (CLSM) image sequences. We then apply it to study the cell division pattern in the developing sepal of the small plant Arabidopsis thaliana. The method is based on geometric hashing and inherits its invariance to rotation, translation and scale. The method consists of three steps. In the first step the centroids of nuclei are detected using a previously developed cell detection algorithm, reducing the CLSM volumes to 3D point clouds, wherein every point represents a nuclear centroid with an associated confidence level. In the second step centroids between images are matched in two phases. First geometric hashing is used to find an initial set of centroid matches, then using the initial matches a dense matching is obtained through a novel iterative point matching algorithm. In the last step centroid matches are used to estimate transformations and register all input images to a common frame. Our algorithm has successfully aligned 12 volumes encompassing 72 hours data set and matched 258 nuclear lifelines. 1

Simultaneous cell tracking and image alignment in 3D CLSM imagery of growing arabidopsis thaliana sepals

In this research we propose a combined cell matching and image alignment method for tracking cells based on their nuclear locations in 3D fluorescent Confocal Laser Scanning Microscopy (CLSM) image sequences. We then apply it to study the cell division pattern in the developing sepal of the small plant Arabidopsis thaliana. The method is based on geometric hashing and inherits its invariance to rotation, translation and scale. The method consists of three steps. In the first step the centroids of nuclei are detected using a previously developed cell detection algorithm, reducing the CLSM volumes to 3D point clouds, wherein every point represents a nuclear centroid with an associated confidence level. In the second step centroids between images are matched in two phases. First geometric hashing is used to find an initial set of centroid matches, then using the initial matches a dense matching is obtained through a novel iterative point matching algorithm. In the last step centroid matches are used to estimate transformations and register all input images to a common frame. Our algorithm has successfully aligned 12 volumes encompassing 72 hours data set and matched 258 nuclear lifelines