A new method for efficiently converting electron backscatter diffraction data obtained using serial sectioning by focused ion beam of a polycrystalline thin film into a computational, three-dimensional (3D) structure is presented. The reported data processing method results in a more accurate representation of the grain surfaces, reduced computer memory usage, and improved processing speed compared to traditional voxel methods. The grain structure of a polycrystalline absorption layer from a high-efficiency Cu(In,Ga)Se2 solar cell (19.5%) is reconstructed in 3D and the grain size and surface distribution is investigated. The grain size distribution is found to be best fitted by a log-normal distribution. We further find that the grain size is determined by the [Ga]/([Ga] + [In]) ratio in vertical direction, which was measured by glow discharge optical emission spectroscopy. Finally, the 3D model derived from the structural information is applied in optoelectronic simulations, revealing insights into the effects of grain boundary recombination on the open-circuit voltage of the solar cell. An accurate 3D structure like the one obtained with our method is a prerequisite for a detailed understanding of mechanical properties and for advanced optical and electronic simulations of polycrystalline thin films.

Leaf epidermis pavement cells form highly complex shapes with interlocking lobes and necks at their anticlinal walls. The microtubule cytoskeleton plays essential roles in pavement cell morphogenesis, in particular at necks. Vice versa, shape generates stress patterns that regulate microtubule organization. Genetic or pharmacological perturbations that affect pavement cell shape often affect microtubule organization. Pavement cell shape and microtubule organization are therefore closely interconnected. Here, we present commonly used approaches for the quantitative analysis of pavement cell shape characteristics and of microtubule organization. In combination with ablation experiments, these methods can be applied to investigate how different genotypes (or treatments) affect the organization and stress responsiveness of the microtubule cytoskeleton.

Roots and their temporal development play an important role in plant research. Over the decades image-based monitoring of root growth has become a key methodology in this research field. The growing amount of image data is often tackled with automatic image analysis approaches. In particular convolutional neural networks (CNNs) recently gained increasing interest for root segmentation. This segmentation of roots is usually only the first step of an analysis pipeline and needs to be supplemented by topological reconstruction of the complete root system architecture.In this paper we present a comprehensive study of different CNN architectures, loss functions and parameter settings for root image segmentation. In addition, we show how main and lateral roots can be identified based on the skeletons of segmented root components as a first step towards topological reconstruction of root system architecture. We present quantitative and qualitative results on data released in the course of the CVPPA Arabidopsis Root Segmentation Challenge 2021.

Leaf epidermis pavement cells develop complex jigsaw puzzle-like shapes in many plant species, including the model plant Arabidopsis thaliana. Due to their complex morphology, pavement cells have become a popular model system to study shape formation and coordination of growth in the context of mechanically coupled cells at the tissue level. To facilitate robust assessment and analysis of pavement cell shape characteristics in a high-throughput fashion, we have developed PaCeQuant and a collection of supplemental tools. The ImageJ-based MiToBo plugin PaCeQuant supports fully automatic segmentation of cell contours from microscopy images and the extraction of 28 shape features for each detected cell. These features now also include the Largest Empty Circle criterion as a proxy for mechanical stress. In addition, PaCeQuant provides a set of eight features for individual lobes, including the categorization as type I and type II lobes at two- and three-cell junctions, respectively. The segmentation and feature extraction results of PaCeQuant depend on the quality of input images. To allow for corrections in case of local segmentation errors, the LabelImageEditor is provided for user-friendly manual postprocessing of segmentation results. For statistical analysis and visualization, PaCeQuant is supplemented with the R package PaCeQuantAna, which provides statistical analysis functions and supports the generation of publication-ready plots in ready-to-use R workflows. In addition, we recently released the FeatureColorMapper tool which overlays feature values over cell regions for user-friendly visual exploration of selected features in a set of analyzed cells.

The microtubule cytoskeleton plays important roles in cell morphogenesis. To investigate the mechanisms of cytoskeletal organization, for example, during growth or development, in genetic studies, or in response to environmental stimuli, image analysis tools for quantitative assessment are needed. Here, we present a method for texture measure-based quantification and comparative analysis of global microtubule cytoskeleton patterns and subsequent visualization of output data. In contrast to other approaches that focus on the extraction of individual cytoskeletal fibers and analysis of their orientation relative to the growth axis, CytoskeletonAnalyzer2D quantifies cytoskeletal organization based on the analysis of local binary patterns. CytoskeletonAnalyzer2D thus is particularly well suited to study cytoskeletal organization in cells where individual fibers are difficult to extract or which lack a clearly defined growth axis, such as leaf epidermal pavement cells. The tool is available as ImageJ plugin and can be combined with publicly available software and tools, such as R and Cytoscape, to visualize similarity networks of cytoskeletal patterns.

Plant microtubules form a highly dynamic intracellular network with important roles for regulating cell division, cell proliferation, and cell morphology. Their organization and dynamics are co-ordinated by various microtubule-associated proteins (MAPs) that integrate environmental and developmental stimuli to fine-tune and adjust cytoskeletal arrays. IQ67 DOMAIN (IQD) proteins recently emerged as a class of plant-specific MAPs with largely unknown functions. Here, using a reverse genetics approach, we characterize Arabidopsis IQD5 in terms of its expression domains, subcellular localization, and biological functions. We show that IQD5 is expressed mostly in vegetative tissues, where it localizes to cortical microtubule arrays. Our phenotypic analysis of iqd5 loss-of-function lines reveals functions of IQD5 in pavement cell (PC) shape morphogenesis. Histochemical analysis of cell wall composition further suggests reduced rates of cellulose deposition in anticlinal cell walls, which correlate with reduced anisotropic expansion. Lastly, we demonstrate IQD5-dependent recruitment of calmodulin calcium sensors to cortical microtubule arrays and provide first evidence for important roles for calcium in regulation of PC morphogenesis. Our work identifies IQD5 as a novel player in PC shape regulation and, for the first time, links calcium signaling to developmental processes that regulate anisotropic growth in PCs.

Minirhizotrons are commonly used to study root turnover which is essential for understanding ecosystem carbon and nutrient cycling. Yet, extracting data from minirhizotron images requires extensive annotation effort. Existing annotation tools often lack flexibility and provide only a subset of the required functionality. To facilitate efficient root annotation in minirhizotrons, we present the user-friendly open source tool rhizoTrak. rhizoTrak builds on TrakEM2 and is publicly available as Fiji plugin. It uses treelines to represent branching structures in roots and assigns customizable status labels per root segment. rhizoTrak offers configuration options for visualization and various functions for root annotation mostly accessible via keyboard shortcuts. rhizoTrak allows time-series data import and particularly supports easy handling and annotation of time-series images. This is facilitated via explicit temporal links (connectors) between roots which are automatically generated when copying annotations from one image to the next. rhizoTrak includes automatic consistency checks and guided procedures for resolving inconsistencies. It facilitates easy data exchange with other software by supporting open data formats. rhizoTrak covers the full range of functions required for user-friendly and efficient annotation of time-series images. Its flexibility and open source nature will foster efficient data acquisition procedures in root studies using minirhizotrons.

Plastids are involved in many fundamental biochemical pathways in plants. They can produce tubular membrane out-folds from their surface. These so-called stromules have initially been described over a century ago, but their functional role is still elusive. To identify cellular processes or genetic elements underlying stromule formation screens of large populations of mutant plants or plants under different treatments are carried out and stromule frequencies are extracted. Due to a lack of automatic methods, however, this quantification is usually done manually rendering this step a main bottleneck in stromule research. Here, we present a new approach for quantification of stromule frequencies. Plastids are extracted from microscope images using local wavelet analysis over multiple scales combined with statistical hypothesis testing to resolve competing detections from different scales. Subsequently, for each plastid region evidence for the existence of stromules in its vicinity is investigated applying ridge detection techniques and geometric criteria. Experimental results prove that our approach is suitable to properly identify stromules. Even in microscopy images with a high noise level and distracting signals extracted stromule counts are comparable to those of biological experts.

The structure of the microtubule cytoskeleton provides valuable information related to morphogenesis of cells. The cytoskeleton organizes into diverse patterns that vary in cells of different types and tissues, but also within a single tissue. To assess differences in cytoskeleton organization methods are needed that quantify cytoskeleton patterns within a complete cell and which are suitable for large data sets. A major bottleneck in most approaches, however, is a lack of techniques for automatic extraction of cell contours. Here, we present a semi-automatic pipeline for cell segmentation and quantification of microtubule organization. Automatic methods are applied to extract major parts of the contours and a handy image editor is provided to manually add missing information efficiently. Experimental results prove that our approach yields high-quality contour data with minimal user intervention and serves a suitable basis for subsequent quantitative studies.

Morphological analysis of cell shapes requires segmentation of cell contours from input images and subsequent extraction of meaningful shape descriptors that provide the basis for qualitative and quantitative assessment of shape characteristics. Here, we describe the publicly available ImageJ plugin PaCeQuant and its associated R package PaCeQuantAna, which provides a pipeline for fully automatic segmentation, feature extraction, statistical analysis, and graphical visualization of cell shape properties. PaCeQuant is specifically well suited for analysis of jigsaw puzzle-like leaf epidermis pavement cells from 2D input images and supports the quantification of global, contour-based, skeleton-based, and pavement cell-specific shape descriptors.

Calcium (Ca2+) signaling and dynamic reorganization of the cytoskeleton are essential processes for the coordination and control of plant cell shape and cell growth. Calmodulin (CaM) and closely related calmodulin-like (CML) polypeptides are principal sensors of Ca2+ signals. CaM/CMLs decode and relay information encrypted by the second messenger via differential interactions with a wide spectrum of targets to modulate their diverse biochemical activities. The plant-specific IQ67 DOMAIN (IQD) family emerged as possibly the largest class of CaM-interacting proteins with undefined molecular functions and biological roles. Here, we show that the 33 members of the IQD family in Arabidopsis (Arabidopsis thaliana) differentially localize, using green fluorescent protein (GFP)-tagged proteins, to multiple and distinct subcellular sites, including microtubule (MT) arrays, plasma membrane subdomains, and nuclear compartments. Intriguingly, the various IQD-specific localization patterns coincide with the subcellular patterns of IQD-dependent recruitment of CaM, suggesting that the diverse IQD members sequester Ca2+-CaM signaling modules to specific subcellular sites for precise regulation of Ca2+-dependent processes. Because MT localization is a hallmark of most IQD family members, we quantitatively analyzed GFP-labeled MT arrays in Nicotiana benthamiana cells transiently expressing GFP-IQD fusions and observed IQD-specific MT patterns, which point to a role of IQDs in MT organization and dynamics. Indeed, stable overexpression of select IQD proteins in Arabidopsis altered cellular MT orientation, cell shape, and organ morphology. Because IQDs share biochemical properties with scaffold proteins, we propose that IQD families provide an assortment of platform proteins for integrating CaM-dependent Ca2+ signaling at multiple cellular sites to regulate cell function, shape, and growth.

Pavement cells (PCs) are the most frequently occurring cell type in the leaf epidermis and play important roles in leaf growth and function. In many plant species, PCs form highly complex jigsaw-puzzle-shaped cells with interlocking lobes. Understanding of their development is of high interest for plant science research because of their importance for leaf growth and hence for plant fitness and crop yield. Studies of PC development, however, are limited, because robust methods are lacking that enable automatic segmentation and quantification of PC shape parameters suitable to reflect their cellular complexity. Here, we present our new ImageJ-based tool, PaCeQuant, which provides a fully automatic image analysis workflow for PC shape quantification. PaCeQuant automatically detects cell boundaries of PCs from confocal input images and enables manual correction of automatic segmentation results or direct import of manually segmented cells. PaCeQuant simultaneously extracts 27 shape features that include global, contour-based, skeleton-based, and PC-specific object descriptors. In addition, we included a method for classification and analysis of lobes at two-cell junctions and three-cell junctions, respectively. We provide an R script for graphical visualization and statistical analysis. We validated PaCeQuant by extensive comparative analysis to manual segmentation and existing quantification tools and demonstrated its usability to analyze PC shape characteristics during development and between different genotypes. PaCeQuant thus provides a platform for robust, efficient, and reproducible quantitative analysis of PC shape characteristics that can easily be applied to study PC development in large data sets.

Data analysis procedures can often be modeled as a set of manipulation operations applied to input data and resulting in transformed intermediate and result data. The Java library Alida is providing an advanced development framework to support programmers in developing data analysis applications adhering to such a scheme. The main intention of Alida is to foster re-usability by offering well-defined, unified, modular APIs and execution procedures for operators, and to ease development by releasing developers from tedious tasks. Alida features automatic generation of handy graphical and command line user interfaces, a built-in graphical editor for workflow design, and an automatic documentation of analysis pipelines. Alida is available from its project webpage http://www.informatik.uni-halle.de/alida, on Github and via our Maven server.

MiToBo is a toolbox and Java library for solving basic as well as advanced image processing and analysis tasks. It features a rich collection of fundamental, intermediate and high-level image processing operators and algorithms as well as a couple of sophisticated tools for specific biological and biomedical applications. These tools include operators for elucidating cellular morphology and locomotion as well as operators for the characterization of certain intracellular particles and structures. MiToBo builds upon and integrates into the widely-used image analysis software packages ImageJ and Fiji [11, 10], and all of its operators can easily be run in ImageJ and Fiji via a generic operator runner plugin. Alternatively MiToBo operators can directly be run from command line, and using its functionality as a library for developing own applications is also supported. Thanks to the Alida library [8] forming the base of MiToBo all operators share unified APIs fostering reusability, and graphical as well as command line user interfaces for operators are automatically generated. MiToBo is available from its website http://www.informatik.uni-halle.de/mitobo, on Github, via an Apache Archiva Maven repository server, and it can easily be activated in Fiji via its own update site.

Solving image analysis problems is not restricted to the pure delineation of algorithms suitable to tackle the task at hand. Rather these also need to be made available to the users promptly and equipped with handy user interfaces to foster progress in the intended field of application. Alida is a software framework to advance the integrated development of algorithms and appropriate user interfaces. It automatically generates user interfaces for implemented algorithms, offers an automatic documentation of analysis procedures, and ships with a graphical editor for designing complex workflows. Alida's Java implementation is licensed under GPL 3.0 and publicly available at http://www.informatik.uni-halle.de/alida.

During cellular stress, protein synthesis is severely reduced and bulk mRNA is recruited to stress granules (SGs). Previously, we showed that the SG-recruited IGF2 mRNA-binding protein 1 (IGF2BP1) interferes with target mRNA degradation during cellular stress. Whether this requires the formation of SGs remained elusive. Here, we demonstrate that the sustained inhibition of visible SGs requires the concomitant knockdown of TIA1, TIAR and G3BP1. FRAP and photo-conversion studies, however, indicate that these proteins only transiently associate with SGs. This suggests that instead of forming a rigid scaffold for mRNP recruitment, TIA proteins and G3BP1 promote SG-formation by constantly replenishing mRNPs. In contrast, RNA-binding proteins like IGF2BP1 or HUR, which are dispensable for SG-assembly, are stably associated with SGs and the IGF2BP1/HUR-G3BP1 association is increased during stress. The depletion of IGF2BP1 enhances the degradation of target mRNAs irrespective of inhibiting SG-formation, whereas the turnover of bulk mRNA remains unaffected when SG-formation is impaired. Together these findings indicate that the stabilization of mRNAs during cellular stress is facilitated by the formation of stable mRNPs, which are recruited to SGs by TIA proteins and/or G3BP1. Importantly, however, the aggregation of mRNPs to visible SGs is dispensable for preventing mRNA degradation.

The analysis of F-actin organization in cells is a key topic in many fields of biomedical research. While standard protocols for imaging immunostained actin are well established, assessment of the resulting microscopy images is most of the time still performed manually and with a high degree of subjectivity. In this paper, we present a new approach for the analysis of actin structures in microscopy images and the quantification of differences and similarities in actin organization between cells. Compared to existing methods, our approach does not require any previous knowledge about the cells or structures to be analyzed. It works in an unsupervised fashion, combining statistical texture measures and clustering techniques. By this, our method yields large flexibility and allows for application in a wide range of experimental scenarios, and also to heterogeneous cell populations. Experimental evaluation on sample data proves that our method yields meaningful results for biomedical investigations.

The function of complex networks in the nervous system relies on the proper formation of neuronal contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is essential to establish unbiased automated tools allowing the correlation of neurite morphology and the subcellular distribution of molecules by quantitative means. We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with protein profiles determined by indirect immunostaining of primary neurons. The prominent feature of our approach is the ability to extract subcellular distributions of distinct biomolecules along neurites. To extract the complete areas of neurons, required for this analysis, we employ active contours with a new distance based energy. For locating the structural parts of neurons and various morphological parameters we adopt a wavelet based approach. The presented approach is able to extract distinctive profiles of several proteins and reports detailed morphology measurements on neurites. We compare the detected neurons from NeuronAnalyzer2D with those obtained by NeuriteTracer and Vaa3D-Neuron, two popular tools for automatic neurite tracing. The distinctive profiles extracted for several proteins, for example, of the mRNA binding protein ZBP1, and a comparative evaluation of the neuron segmentation results proves the high quality of the quantitative data and proves its practical utility for biomedical analyses.

Solving image analysis problems not only requires the development of suitable sets of algorithms to produce desired result data, but also demands for suitable user interfaces (UIs) to foster use in practice. Here we present our library Alida which aims to promote the deployment of UIs by featuring their automatic generation from algorithm code. Alida supports command line and graphical UIs (GUIs), and ships with a graphical editor for designing more complex workflows. Enforcing only a small set of rules to obey Alida significantly reduces implementation overhead for developers and allows for focusing on algorithm rather than UI design. The suitability of Alida’s concept for real-life applications is shown by the library MiToBo for biomedical image analysis implemented based on Alida.