RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

© The Author(s) 2019. Published by Oxford University Press. BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever

Combining LC-MS/MS and genetic analysis to unravel plant hormone metabolism in Arabidopsis thaliana

Plants synthesize a myriad of metabolites, far more than those produced by most other organisms. Plant hormones are a unique set of compounds, with distinctive metabolism and properties that affect physiological processes during development and growth. Their underlying genetic variation will have attracted much research attention by virtue of junctions of NGS technologies and metabolomics. The aim of this thesis was to study natural variation of plant hormones in Arabidopsis. To obtain reliable quantitative data of levels of hormone using LC-ESI-MS, I first developed a set of methodological processes related to extraction and purification. A set of Arabidopsis ecotypes was chosen, and studied to earn various aspects of natural variation with traits of mature roots. Based on results of these works, my study has moved to classical linkage mapping analysis to find genetic loci responsible for levels of a few sets of hormone in roots and leaves of Arabidopsis. Chapter 2 describes the problem of matrix effects caused by impurities in semi-purified extracts, on the accuracy of data derived from LC-ESI-mass spectrometry. Matrix effects may result in both ion suppression and enhancement, and severely affects quantitative data of hormone levels. Without proper ways to minimize matrix effects, hormone data would be unreliable, and would not allow accurate QTL mapping. I validated a few key-points that are critical for determining the levels of a wide range of plant hormones in Arabidopsis extracts based on a one-step solid phase extraction (SPE) method. For the simplified purification of Arabidopsis extracts, a commercially available HLB sorbent was chosen to recover a wide range of chemically diverse series of hormone analytes. Dilution using a much smaller starting sample (e.g., 2.5 mg) reduced the matrix effects considerably but additional measures were required for most of the analytes. Flushing the HLB-SPE column with acidic methanol was more effective to reduce matrix effect than acetonitrile based eluent. At the end, I proposed a series of steps and procedures to optimize the protocol for hormone analysis in LC-ESI-MS. Chapter 3 describes natural variation of hormone levels found in 13 Arabidopsis ecotypes. Variations of hormone level among the accessions were remarkably small, viz., less than three-fold difference between extremes. For CKs, relatively larger variations were found for ribosides and glucosides, as compared to the free bases. Root phenotypic traits of these accessions were also measured, using a new parameter (mature root unit) for complex root systems, and correlation analyses were done between hormone data and mature root traits. For root phenotyping, length-related traits—lateral root length and total root length—showed larger variations than lateral root number-related ones. Antagonistic interactions between hormones (IAA and trans-zeatin) were detected for root weight. These findings provide enough basis to warrant a quantitative genetic analysis in plant hormone metabolism and crucial information for the choice of a proper segregating population. Chapter 4 reports diverse QTLs that are responsible for hormone levels of CKs, SA and JA in roots of Arabidopsis Ler×Cvi RIL population. QTL analysis of two sub-populations, viz., vegetative and flowering plants revealed that many of the QTLs were development-specific, suggesting that the transition to flowering has a profound effect on hormone metabolism. Using near-isogenic lines, several significant QTLs were confirmed; three co-localized QTL regions were responsible for determining several CK metabolites. Using a knock-out plant, a functional role of zeatin N-glucosyltransferase gene (UGT76C2) underlying a large-effect QTL for levels of tZ N-glucosides and tZRMP was evaluated in CK metabolism. Pleotropic effects of this gene were found for levels of CK in both roots and leaves, but significant changes of morphological traits were observed only in roots. This suggests that CK N- glucosides play an important role in root development. I also advocated the possibility of genetic regulation of concentration ratio between tZ7G and tZ9G based on a newly observed QTL of the trait. As a further step of QTL analysis after Chapter 4, the study was extended to leaves. Chapter 5 describes the analysis of 5 groups of hormones in rosette leaves of the same RIL population. QTL analysis showed a multitude of significant loci for levels of IAA, ABAs and CKs. Also for leaves, development-specific QTLs were detected in two sub-populations, vegetative and flowering lines. QTLs for ratios between hormone metabolites belonging to the same group but also to different groups were found and some of them partly co-localized with those of single compounds, implying that QTLs for single hormones may also affect the balance between hormones. The detection of QTLs for ratios between structurally unrelated hormones (e.g. auxin : ABA) and further fine mapping may help unravelling genetic elements underlying hormone interactions in the regulation of plant development and stress responses. Finally, in Chapter 6, several issues arising from the separate experiments are taken into consideration. The main significance of this PhD thesis is the experimental confirmation that finding QTLs for hormone metabolism is feasible and worth being extended to other populations of Arabidopsis and to crop plants. I anticipate that in the near future metabolomics study towards natural variation of plant hormones will be part of interesting theme in quantitative genetics. It will provide us to gain a better understanding of the complexity of molecular mechanism underlying hormone metabolism in plants

Translational regulation contributes to the elevated CO2 response in two Solanum species.

Understanding the impact of elevated CO2 (eCO2 ) in global agriculture is important given climate change projections. Breeding climate-resilient crops depends on genetic variation within naturally varying populations. The effect of genetic variation in response to eCO2 is poorly understood, especially in crop species. We describe the different ways in which Solanum lycopersicum and its wild relative S. pennellii respond to eCO2 , from cell anatomy, to the transcriptome, and metabolome. We further validate the importance of translational regulation as a potential mechanism for plants to adaptively respond to rising levels of atmospheric CO2

Watching plants grow:A position paper on computer vision and Arabidopsis thaliana

The authors present a comprehensive overview of image processing and analysis work done to support research into the model flowering plant Arabidopsis thaliana. Beside the plant's importance in biological research, using image analysis to obtain experimental measurements of it is an interesting vision problem in its own right, involving the segmentation and analysis of sequences of images of objects whose shape varies between individual specimens and also changes over time. While useful measurements can be obtained by segmenting a whole plant from the background, they suggest that the increased range and precision of measurements made available by leaf‐level segmentation makes this a problem well worth solving. A variety of approaches have been tried by biologists as well as computer vision researchers. This is an interdisciplinary area and the computer vision community has an important contribution to make. They suggest that there is a need for publicly available datasets with ground truth annotations to enable the evaluation of new approaches and to support the building of training data for modern data‐driven computer vision approaches, which are those most likely to result in the kind of fully automated systems that will be of use to biologists

Arabidopsis phenotyping through geometric morphometrics

Background: Recently, great technical progress has been achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it possible to extract shape and size parameters for genetic, physiological, and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of the platform and segmentation software used are still lacking, and shape descriptions still rely on ad hoc or even contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis, and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations among groups and measure them in shape distance units. Results: Here, a particular scheme of landmark placement on Arabidopsis rosette images is proposed to study shape variation in viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown, and reproducibility issues are assessed. Conclusions: Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.Instituto de BiotecnologíaFil: Manacorda, Carlos Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; ArgentinaFil: Asurmendi, Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Imaging as a tool to study leaf development in Arabidopsis thaliana

In contrast to humans and animals, the body plan of a plant is not completely defined within the embryonic stages. Organ formation continues throughout plant development and this iterative and modular process is continuously controlled by environmental cues such as light, gravity, temperature, humidity and chemicals. In most plant species, the above-ground plant body is dominated by leaves, the organs specialized in photosynthesis. This process converts carbon dioxide into organic components utilizing energy from sunlight; making leaves the energy production site and the growth engine of plants. In addition, in many cases the majority of a plant’s biomass consists of leaves, also making them important organs for the production of food, feed and bio-energy. The final leaf size is determined by the total number of cells and the average cell size that result from cell division and cell expansion, respectively. During leaf development of dicotyledonous species, a cell proliferation phase, characterized by actively dividing cells, is followed by a cell expansion phase, characterized by cell growth and differentiation. After expansion, cells mature and the final leaf size is reached. At the proliferation-to-expansion phase transition, cell division ceases along a longitudinal gradient from leaf tip to base. In this thesis, we set out to gain further insight in these developmental processes affecting leaf size, assisted by the use of imaging technology and automated image analysis. For these studies we used the model species Arabidopsis thaliana, focusing primarily on the epidermis of the developing leaves as divisions there are strictly anticlinal. Moreover this layer is thought to be the main tissue layer controlling leaf growth. As a first step, we developed different image analysis tools to allow for a better and more efficient analysis of the leaf developmental process. In the first place we developed an online framework, designated Leaf Image Analysis Interface (LIMANI), in which venation patterns are automatically segmented and measured on dark-field images. Image segmentation may be manually corrected through use of an interactive interface, allowing supervision and rectification steps in the automated image analysis pipeline and ensuring high-fidelity analysis. We subsequently used this framework to study vascular differentiation during leaf development and to analyze the venation pattern in transgenic lines with contrasting cellular and leaf size traits. A major conclusion from this work was that, as vascular differentiation occurs relatively late in development, the influence of a fully functional and differentiated venation pattern on final leaf size is rather limited. Furthermore, we describe a proof-of-concept to automate the kinematic analysis of leaf growth based on DIC pictures, by a sophisticated image processing chain and a data analysis pipeline. Next, we also developed imaging scripts to extract complete seedlings grown on soil and on Petri dishes and integrated those into three phenotyping platforms which monitor plant growth. Finally, we investigated the potential of emerging imaging technologies, particularly X-ray computed tomography, for future applications in plant growth analysis. The newly developed kinematic analysis tools allowed us to show that the transcription factors, SHORT-ROOT (SHR) and SCARECROW (SCR), next to their specific roles in cortex/endodermis differentiation and stem cell maintenance in the root, primarily function as general regulators of cell proliferation in leaves. The analysis of leaf growth revealed how these proteins affect the cellular growth dynamics and formed the basis to unravel the molecular mechanism controlling this. It turned out that they promote leaf growth mainly by the down-regulation of cell cycle inhibitors, known to restrain the activity of the transcription factor, E2Fa, stimulating S-phase progression. Although the dynamics of cell division and cell expansion processes can be analyzed rigorously by the leaf growth kinematics, knowledge of cell cycle duration, cell expansion, and their interaction at the individual cell level is still poorly understood, not only because of technical obstacles to study these phenomena, but also because the processes are intimately intertwined, shown by the fact that a reduced cell proliferation is often compensated by an increase in cell size and vice versa. A mathematical model fitted to detailed cellular measurements retrieved by automated image analysis of microscopic drawings of the leaf epidermis, revealed that average cell cycle duration remains constant throughout leaf development. Surprisingly, no evidence for a maximum cell size threshold for cell division of pavement cells was found in this analysis. We could estimate the division and expansion parameters of pavement and guard cell populations within the growing leaf separately and the model predicted that neighboring cells of different sizes within the epidermis expand at distinctly different relative rates. We could finally verify this by direct observations using live imaging. The mathematical model helped us to gain a better and more detailed insight into the processes that define leaf growth. But the transition from cell proliferation to cell expansion was a developmental time point that was still not characterized in detail. Differences in the timing of this transition strongly affects the number of cells formed and therefore potentially also serves as a control point determining mature leaf size. Several genes have been identified that alter leaf size by affecting the transition from primary to secondary morphogenesis. We characterized the progression of the transition on the morphological and molecular level using transcriptome analysis and imaging algorithms to visualize and quantify the size and shape of pavement cells along the proximal-distal axis of the leaf during transition. Both analyses showed that the transition from cell proliferation to expansion was established and abolished abruptly. Furthermore, the establishment of the cell cycle arrest front occurs simultaneously with the onset of photomorphogenesis. We provide evidence that retrograde signaling from chloroplasts can affect the onset of transition, revealing a previously unknown level of regulatory complexity during the transition from primary to secondary morphogenesis

Arabidopsis phenotyping through geometric morphometrics

Background: Recently, great technical progress has been achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it possible to extract shape and size parameters for genetic, physiological, and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of the platform and segmentation software used are still lacking, and shape descriptions still rely on ad hoc or even contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis, and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations among groups and measure them in shape distance units. Results: Here, a particular scheme of landmark placement on Arabidopsis rosette images is proposed to study shape variation in viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown, and reproducibility issues are assessed. Conclusions: Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.Fil: Manacorda, Carlos Augusto. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; ArgentinaFil: Asurmendi, Sebastian. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin