An automated quantitative image analysis tool for the identification of microtubule patterns in plants

Automatic extraction of actin networks in plants

The actin cytoskeleton is essential in eukaryotes, not least in the plant kingdom where it plays key roles in cell expansion, cell division, environmental responses and pathogen defence. Yet, the precise structure-function relationships of properties of the actin network in plants are still to be unravelled, including details of how the network configuration depends upon cell type, tissue type and developmental stage. Part of the problem lies in the difficulty of extracting high-quality, quantitative measures of actin network features from microscopy data. To address this problem, we have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level. Using this algorithm, we then studied a number of cases in Arabidopsis thaliana, including several different tissues, a variety of actin-affected mutants, and cells responding to powdery mildew. In many cases we found statistically-significant differences in actin network properties. In addition to these results, our algorithm is designed to be easily adaptable to other tissues, mutants and plants, and so will be a valuable asset for the study and future biological engineering of the actin cytoskeleton in globally-important crops.

ILEE: Algorithms and toolbox for unguided and accurate quantitative analysis of cytoskeletal images

The eukaryotic cytoskeleton plays essential roles in cell signaling and trafficking, broadly associated with immunity and diseases in humans and plants. To date, most studies describing cytoskeleton dynamics and function rely on qualitative/quantitative analyses of cytoskeletal images. While state-of-the-art, these approaches face general challenges: the diversity among filaments causes considerable inaccuracy, and the widely adopted image projection leads to bias and information loss. To solve these issues, we developed the Implicit Laplacian of Enhanced Edge (ILEE), an unguided, high-performance approach for 2D/3D-based quantification of cytoskeletal status and organization. Using ILEE, we constructed a Python library to enable automated cytoskeletal image analysis, providing biologically interpretable indices measuring the density, bundling, segmentation, branching, and directionality of the cytoskeleton. Our data demonstrated that ILEE resolves the defects of traditional approaches, enables the detection of novel cytoskeletal features, and yields data with superior accuracy, stability, and robustness. The ILEE toolbox is available for public use through PyPI and Google Colab.

Super-resolution imaging and quantitative analysis of microtubule arrays in model neurons show that epothilone D increases the density but decreases the length and straightness of microtubules in axon-like processes

Microtubules are essential for the development of neurons and the regulation of their structural plasticity. Microtubules also provide the structural basis for the long-distance transport of cargo. Various factors influence the organization and dynamics of neuronal microtubules, and disturbance of microtubule regulation is thought to play a central role in neurodegenerative diseases. However, imaging and quantitative assessment of the microtubule organization in the densely packed neuronal processes is challenging. The development of super-resolution techniques combined with the use of nanobodies offers new possibilities to visualize microtubules in neurites in high resolution. In combination with recently developed computational analysis tools, this allows automated quantification of neuronal microtubule organization with high precision. Here we have implemented three-dimensional DNA-PAINT (Point Accumulation in Nanoscale Topography), a single-molecule localization microscopy (SMLM) technique, which allows us to acquire 3D arrays of the microtubule lattice in axons of model neurons (neuronally differentiated PC12 cells) and dendrites of primary neurons. For the quantitative analysis of the microtubule organization, we used the open-source software package SMLM image filament extractor (SIFNE). We found that treatment with nanomolar concentrations of the microtubule-targeting drug epothilone D (EpoD) increased microtubule density in axon-like processes of model neurons and shifted the microtubule length distribution to shorter ones, with a mean microtubule length of 2.39 µm (without EpoD) and 1.98 µm (with EpoD). We also observed a significant decrease in microtubule straightness after EpoD treatment. The changes in microtubule density were consistent with live-cell imaging measurements of ensemble microtubule dynamics using a previously established Fluorescence Decay After Photoactivation (FDAP) assay. For comparison, we determined the organization of the microtubule array in dendrites of primary hippocampal neurons. We observed that dendritic microtubules have a very similar length distribution and straightness compared to microtubules in axon-like processes of a neuronal cell line. Our data show that super-resolution imaging of microtubules followed by algorithm-based image analysis represents a powerful tool to quantitatively assess changes in microtubule organization in neuronal processes, useful to determine the effect of microtubule-modulating conditions. We also provide evidence that the approach is robust and can be applied to neuronal cell lines or primary neurons, both after incorporation of labeled tubulin and by anti-tubulin antibody staining.

Emotional Intervention and Education System Construction for Rural Children Based on Semantic Analysis

Objective

Under the background of the policy of caring for the healthy growth of left-behind children, the purpose of selecting the topic is to study some common negative emotional problems of left-behind children in rural areas, focusing on the guidance of negative emotions of left-behind children in rural areas. In emotional problems, we analyze and find out the reasons for these negative emotions through observation and research.

Method

In this paper, a platform for acquiring emotional semantic data of scene images in an open behavioral experimental environment is designed, which breaks the limitations of time and place, and thus acquires a large amount of emotional semantic data of scene images and then uses principal component analysis to evaluate the validity of the data analysis. Psychological testing was used to measure parent-child affinity, adversity beliefs, and positive/negative emotion scales, respectively, to examine children whose parents went out, children whose fathers went out, and non-left-behind children. The characteristics of parent-child affinity, adversity beliefs, and positive/negative emotions in three types of children were examined, and the direct predictive effects of parent-child affinity and adversity beliefs on the positive/negative emotions of the three types of children were examined. Results/Discussion. Adversity beliefs played a partial mediating role between children's parent-child bonding and positive emotions. The predictive effect of adversity beliefs on children's emotional adaptation differs by emotional type. The main effects of the left-behind category were significant for both positive and negative emotions. The gender main effect of negative emotion was significant, and the negative emotion level of girls was significantly higher than that of boys. The main effect of the left-behind category of adversity beliefs was significant, and the adversity belief levels of children whose parents went out to rural areas were significantly lower than those of children whose fathers went out and non-left-behind children. The negative emotions generated by left-behind children in rural areas are channeled, and to a certain extent, they are improved and alleviated. Through the emotional counseling and improvement of the rural left-behind children in the research site in the article, the service objects can have better emotions, promote mental health, make them happy and grow up healthily, and also provide a certain theory for the establishment of the local left-behind children care system.

Breakdancing Movement Based on Image Recognition Promotes Preschool Children's Executive Function and Intervention Plan

With the continuous development of science and technology, people can apply more and more technology to the cultivation of children's abilities. In the process of cultivating children's ability, the most fancy is the study of executive function, and this is the research topic of this article. In the past, training methods such as music, mindfulness, and exercise have been used in the study of children's executive abilities to promote the development of preschool children's executive functions. While various approaches have had some effect, researchers have been exploring more comprehensive approaches to effective training. This article is aimed at studying how to use image recognition technology to conduct an intervention analysis of breakdancing in promoting the executive function of preschool children. For this reason, this paper proposes image recognition technology based on deep learning neural network and conducts research, analysis, and improvement on related technologies obtained from deep learning. This makes it more suitable for the research topic of this article and design-related experiments and analysis to explore its related performance. The experimental results in this paper show that the improved image recognition technology has improved accuracy by 31.2%. And the performance of its algorithm is also improved by 21%, which can be very effective in monitoring preschool children during breakdancing.

Quantitative mapping of keratin networks in 3D

Mechanobiology requires precise quantitative information on processes taking place in specific 3D microenvironments. Connecting the abundance of microscopical, molecular, biochemical, and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps needed for biophysical modeling is a particular challenge for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. To date, useful tools are available for the segmentation and modeling of actin filaments and microtubules but comprehensive tools for the mapping of intermediate filament organization are still lacking. In this work, we describe a workflow to model and examine the complete 3D arrangement of the keratin intermediate filament cytoskeleton in canine, murine, and human epithelial cells both, in vitro and in vivo. Numerical models are derived from confocal airyscan high-resolution 3D imaging of fluorescence-tagged keratin filaments. They are interrogated and annotated at different length scales using different modes of visualization including immersive virtual reality. In this way, information is provided on network organization at the subcellular level including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature, and orientation. We show that the comparison of these parameters helps to identify, in quantitative terms, similarities and differences of keratin network organization in epithelial cell types defining subcellular domains, notably basal, apical, lateral, and perinuclear systems. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.

Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review

Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods. Aim of Review: This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects. Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes.

Recent developments and potential of robotics in plant eco-phenotyping

Automated acquisition of plant eco-phenotypic information can serve as a decision-making basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for plant breeders and plant physiologists. Because the microscopic components and macroscopic morphology of plants will be affected by the ecological environment, research on plant eco-phenotyping is more meaningful than the study of single-plant phenotyping. To achieve high-throughput acquisition of phenotyping information, the combination of high-precision sensors and intelligent robotic platforms have become an emerging research focus. Robotic platforms and automated systems are the important carriers of phenotyping monitoring sensors that enable large-scale screening. Through the diverse design and flexible systems, an efficient operation can be achieved across a range of experimental and field platforms. The combination of robot technology and plant phenotyping monitoring tools provides the data to inform novel artificial intelligence (AI) approaches that will provide steppingstones for new research breakthroughs. Therefore, this article introduces robotics and eco-phenotyping and examines research significant to this novel domain of plant eco-phenotyping. Given the monitoring scenarios of phenotyping information at different scales, the used intelligent robot technology, efficient automation platform, and advanced sensor equipment are summarized in detail. We further discuss the challenges posed to current research as well as the future developmental trends in the application of robot technology and plant eco-phenotyping. These include the use of collected data for AI applications and high-bandwidth data transfer, and large well-structured (meta) data storage approaches in plant sciences and agriculture.

Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review

Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.

CytoSeg 2.0: automated extraction of actin filaments

MOTIVATION

Actin filaments (AFs) are dynamic structures that substantially change their organization over time. The dynamic behavior and the relatively low signal-to-noise ratio during live-cell imaging have rendered the quantification of the actin organization a difficult task.

RESULTS

We developed an automated image-based framework that extracts AFs from fluorescence microscopy images and represents them as networks, which are automatically analyzed to identify and compare biologically relevant features. Although the source code is freely available, we have now implemented the framework into a graphical user interface that can be installed as a Fiji plugin, thus enabling easy access by the research community.

AVAILABILITY AND IMPLEMENTATION

CytoSeg 2.0 is open-source software under the GPL and is available on Github: https://github.com/jnowak90/CytoSeg2.0.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The use of quantitative imaging to investigate regulators of membrane trafficking in Arabidopsis stomatal closure

Expansion of gene families facilitates robustness and evolvability of biological processes but impedes functional genetic dissection of signalling pathways. To address this, quantitative analysis of single cell responses can help characterize the redundancy within gene families. We developed high-throughput quantitative imaging of stomatal closure, a response of plant guard cells, and performed a reverse genetic screen in a group of Arabidopsis mutants to five stimuli. Focussing on the intersection between guard cell signalling and the endomembrane system, we identified eight clusters based on the mutant stomatal responses. Mutants generally affected in stomatal closure were mostly in genes encoding SNARE and SCAMP membrane regulators. By contrast, mutants in RAB5 GTPase genes played specific roles in stomatal closure to microbial but not drought stress. Together with timed quantitative imaging of endosomes revealing sequential patterns in FLS2 trafficking, our imaging pipeline can resolve non-redundant functions of the RAB5 GTPase gene family. Finally, we provide a valuable image-based tool to dissect guard cell responses and outline a genetic framework of stomatal closure.

Quantitative and Comparative Analysis of Global Patterns of (Microtubule) Cytoskeleton Organization with CytoskeletonAnalyzer2D

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.