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Background Motor and non-motor behavior analyses are increasingly utilizied in drug discovery and screening, detection of neurobehavioral disorders and chemical toxicology. The emergence of computational approaches has helped to develop different tools to analyse complex behaviors. Analysis of locomotor behavior helps in understanding the motor neuron disorders like Parkinson’s Disease. Although many animal models are available to study the locomotion, adult zebrafish has emerged as a simple and efficient model to study this behavior. An inexpensive and easily customizable tool is required to replace the licensed and expensive set-up to analyse the locomotor behavior. New method In this study we have optimized the ImageJ plugin wrMTrck to analyse motor and non-motor behaviors in adult zebrafish. We have generated a macro to simplify the preprocessing and tracking. Subsequently, we have developed a data analysis sheet to analyse various behavioral end points. Results We have successfully developed an inexpensive video acquisition set-up and optimized wrMTrck for adult zebrafish. In order to demonstrate the efficacy of this method, adult zebrafish were injected with MPTP and motor and non-motor behaviors were analysed. Expectedly, MPTP injected fish showed decrease in dopamine level and dat expression level, which subsequently led to locomotor behavioral defects as well as anxiety, a non-motor symptom of PD. Comparison with existing method(s) Further, the obtained results were validated by another ImageJ macro developed by Pelkowski et al. (2011) and we observed identical trajectories. Conclusions The usefulness of popular ImageJ plugin wrMTrck and this extended protocol will be helpful to quantify motor and non-motor behavioral parameters.

When confined, cells have recently been shown to undergo a phenotypic switch to what has been termed, fast amoeboid (leader bleb-based) migration. However, as this is a nascent area of research, few tools are available for the rapid analysis of cell behavior. Here, we demonstrate that a novel Fiji/ImageJ-based plugin, Analyze_Blebs, can be used to quickly obtain cell migration parameters and morphometrics from time lapse images. As validation, we show that Analyze_Blebs can detect significant differences in cell migration and morphometrics, such as the largest bleb size, upon introducing different live markers of F-actin, including F-tractin and LifeAct tagged with green and red fluorescent proteins. We also demonstrate, using flow cytometry, that live markers increase total levels of F-actin. Furthermore, that F-tractin increases cell stiffness, which was found to correlate with a decrease in migration, thus reaffirming the importance of cell mechanics as a determinant of Leader Bleb-Based Migration (LBBM).Highlight summaryA new plugin, Analyze_Blebs, enables the rapid analysis of cell migration and morphometrics of fast amoeboid cells. Morphometrics combined with cell stiffness are found to be predictive of confined, fast amoeboid (leader bleb-based), migration.

Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing of multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, thereby identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber regeneration differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.

AnnotatorJ combines single-cell identification with deep learning (DL) and manual annotation. Cellular analysis quality depends on accurate and reliable detection and segmentation of cells so that the subsequent steps of analyses, for example, expression measurements, may be carried out precisely and without bias. DL has recently become a popular way of segmenting cells, performing unimaginably better than conventional methods. However, such DL applications may be trained on a large amount of annotated data to be able to match the highest expectations. High-quality annotations are unfortunately expensive as they require field experts to create them, and often cannot be shared outside the lab due to medical regulations. We propose AnnotatorJ, an ImageJ plugin for the semiautomatic annotation of cells (or generally, objects of interest) on (not only) microscopy images in 2D that helps find the true contour of individual objects by applying U-Net-based presegmentation. The manual labor of hand annotating cells can be significantly accelerated by using our tool. Thus, it enables users to create such datasets that could potentially increase the accuracy of state-of-the-art solutions, DL or otherwise, when used as training data.

Morphometry characterization is an important procedure in describing neuronal cultures and identifying phenotypic differences. This task usually requires labor-intensive measurements and the classification of numerous neurites from large numbers of neurons in culture. To automate these measurements, we wrote AutoNeuriteJ, an imageJ/Fiji plugin that measures and classifies neurites from a very large number of neurons. We showed that AutoNeuriteJ is able to detect variations of neuritic growth induced by several compounds known to affect the neuronal growth. In these experiments measurement of more than 5000 mouse neurons per conditions was obtained within a few hours. Moreover, by analyzing mouse neurons deficient for the microtubule associated protein 6 (MAP6) and wild type neurons we illustrate that AutoNeuriteJ is capable to detect subtle phenotypic difference in axonal length. Overall the use of AutoNeuriteJ will provide rapid, unbiased and accurate measurement of neuron morphologies.

Author(s): Love, Natalie L Rossington; Park, Isaac W; Mazer, Susan J | Abstract: PremiseHerbarium specimens have been used to detect climate-induced shifts in flowering time by using the day of year of collection (DOY) as a proxy for first or peak flowering date. Variation among herbarium sheets in their phenological status, however, undermines the assumption that DOY accurately represents any particular phenophase. Ignoring this variation can reduce the explanatory power of pheno-climatic models (PCMs) designed to predict the effects of climate on flowering date.MethodsHere we present a protocol for the phenological scoring of imaged herbarium specimens using an ImageJ plugin, and we introduce a quantitative metric of a specimen's phenological status, the phenological index (PI), which we use in PCMs to control for phenological variation among specimens of Streptanthus tortuosus (Brassicaceeae) when testing for the effects of climate on DOY. We demonstrate that including PI as an independent variable improves model fit.ResultsIncluding PI in PCMs increased the model R 2 relative to PCMs that excluded PI; regression coefficients for climatic parameters, however, remained constant.DiscussionOur protocol provides a simple, quantitative phenological metric for any observed plant. Including PI in PCMs increases R 2 and enables predictions of the DOY of any phenophase under any specified climatic conditions.

An efficient and low-cost method of examining larval movement in Drosophila melanogaster is needed to study how mutations and/or alterations in the muscular, neural, and olfactory systems affect locomotor behavior. Here, we describe the implementation of wrMTrck, a freely available ImageJ plugin originally developed for examining multiple behavioral parameters in the nematode C. elegans. Our optimized method is rapid, reproducible and does not require automated microscope setups or the purchase of proprietary software. To demonstrate the utility of this method, we analyzed the velocity and crawling paths of two Drosophila mutants that affect muscle structure and/or function. Additionally, we show that this approach is useful for tracking the behavior of adult insects, including Tribolium castaneum and Drosophila melanogaster.

Microscopic images are widely used in plant biology as an essential source of information on morphometric characteristics of the cells and the topological characteristics of cellular tissue pattern due to modern computer vision algorithms. High-resolution 3D confocal images allow extracting quantitative characteristics describing the cell structure of leaf epidermis. For some issues in the study of cereal leaves development, it is required to apply the staining techniques with fluorescent dyes and to scan rather large fragments consisting of several frames. We aimed to develop a tool for processing multi-frame multi-channel 3D images obtained from confocal laser scanning microscopy and taking into account the peculiarities of the cereal leaves staining. We elaborated an ImageJ-plugin LSM-W2 that allows extracting data on Leaf Surface Morphology from Laser Scanning Microscopy images. The plugin is a crucial link in a workflow for obtaining data on structural properties of leaf epidermis and morphological properties of epidermal cells. It allows converting large lsm-files (laser scanning microscopy) into segmented 2D/3D images or tables with data on cells and/or nuclei sizes. In the article, we also represent some case studies showing the plugin application for solving biological tasks. Namely the plugin is applied in the following cases: defining parameters of jigsaw-puzzle pattern for maize leaf epidermal cells, analysis of the pavement cells morphological parameters for the mature wheat leaf grown under control and water deficit conditions, initiation of cell longitudinal rows, and detection of guard mother cells emergence at the initial stages of the stomatal morphogenesis in the growth zone of a wheat leaf. The proposed plugin is efficient for high-throughput analysis of cellular architecture for cereal leaf epidermis. The workflow implies using inexpensive and rapid sample preparation and does not require the applying of transgenesis and reporter genetic structures expanding the range of species and varieties to study. Obtained characteristics of the cell structure and patterns further could act as a basis for the development and verification for spatial models of plant tissues formation mechanisms accounting for structural features of cereal leaves. The implementation of this workflow is available as an ImageJ plugin distributed as a part of the Fiji project (FijiisjustImageJ: https://fiji.sc/ ). The plugin is freely available at https://imagej.net/LSM_Worker , https://github.com/JmanJ/LSM_Worker and http://pixie.bionet.nsc.ru/LSM_WORKER/ .

Controlling relative daughter cell size is key during cytokinesis. Uncontrolled size asymmetries can lead to aneuploidy and division failure. At the same time, precisely regulated size asymmetries are of crucial importance in many divisions during embryonic development. Therefore, being able to monitor daughter cell size is important in cytokinesis studies. However, freely available tools allowing to effectively measure the size of daughter cells in three dimensions during cytokinesis are missing. Here, we describe an open-access plugin for ImageJ or Fiji based on an active contour surface representation of the cells. Our method provides a user-friendly and accurate way to monitor the size of the two daughter cells throughout cytokinesis.

Setting an open-access computer assisted sperm analysis (CASA) may benefit the evaluation of motility in mammalian sperm, especially when economic constraints do not allow the use of a commercial system. There have been successful attempts to develop such a device in Zebra fish sperm and the system has been used in very few studies on mammalian spermatozoa. Against this background, the present study aimed at developing an open-access CASA system for mammalian sperm using the horse as a model and based upon the Image J software previously established for Zebra fish sperm. Along with determining the sperm progressive motility and other kinetic parameters (such as amplitude of lateral head displacement), the “results” window was adjusted to simplify subsequent statistical analyses. The path window was enriched with colored sperm trajectories on the basis of the subpopulation they belong to and a number that allowed the sperm track to be associated to the sperm motility data shown in the “results” window. Data obtained from the novel plugin (named as CASA_bgm) were compared with those of the commercial CASA Hamilton-Thorn IVOS Vers.12, through Bland Altman’s plots. While the percentage of total and progressive motile sperm, VCL, VAP, VSL, LIN and STR and ALH were in agreement with those obtained with the commercial system, BCF significantly differed between the two systems probably due to their settings. Interestingly, a positive and significant correlation between the percentages of total motile sperm evaluated through CASA_bgm and those showing high mitochondrial membrane potential evaluated by JC-1 staining was found. In conclusion, CASA_bgm ImageJ plugin could be useful and reliable for stallion sperm motility analysis and it is our aim to apply this system to other mammalian species.

We have developed a publicly available tool, AxonJ, which quantifies the axons in optic nerve sections of rodents stained with paraphenylenediamine (PPD). In this study, we compare AxonJ’s performance to human experts on 100x and 40x images of optic nerve sections obtained from multiple strains of mice, including mice with defects relevant to glaucoma. AxonJ produced reliable axon counts with high sensitivity of 0.959 and high precision of 0.907, high repeatability of 0.95 when compared to a gold-standard of manual assessments and high correlation of 0.882 to the glaucoma damage staging of a previously published dataset. AxonJ allows analyses that are quantitative, consistent, fully-automated, parameter-free, and rapid on whole optic nerve sections at 40x. As a freely available ImageJ plugin that requires no highly specialized equipment to utilize, AxonJ represents a powerful new community resource augmenting studies of the optic nerve using mice.

ND is an image processing plugin that can be used to calculate the average size and distance between particles and their closest neighbors in many-particle systems. It is written in Java and implemented in ImageJ, the open source Java-based software developed by National Institute of Health (NIH). ND is particularly useful in analysis of porous synthetic and natural constructs (known as scaffolds) that are commonly used in the field of regenerative medicine and tissue engineering. Architecture of these scaffolds including pore size and density significantly affects the behavior and fate of cells cultured on them. ND adds to the built-in functionalities of ImageJ to provide a fast and user-friendly method to better characterize the porosity of these scaffolds.

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the primary photosynthetic organ, and their size and shape vary developmentally and environmentally within a plant. For these reasons studies on leaf morphology require measurement of multiple parameters from numerous leaves, which is best done by semi-automated phenomics tools5,6. Canopy shade is an important environmental cue that affects plant architecture and life history; the suite of responses is collectively called the shade avoidance syndrome (SAS)7. Among SAS responses, shade induced leaf petiole elongation and changes in blade area are particularly useful as indices8. To date, leaf shape programs (e.g. SHAPE9, LAMINA10, LeafAnalyzer11, LEAFPROCESSOR12) can measure leaf outlines and categorize leaf shapes, but can not output petiole length. Lack of large-scale measurement systems of leaf petioles has inhibited phenomics approaches to SAS research. In this paper, we describe a newly developed ImageJ plugin, called LeafJ, which can rapidly measure petiole length and leaf blade parameters of the model plant Arabidopsis thaliana. For the occasional leaf that required manual correction of the petiole/leaf blade boundary we used a touch-screen tablet. Further, leaf cell shape and leaf cell numbers are important determinants of leaf size13. Separate from LeafJ we also present a protocol for using a touch-screen tablet for measuring cell shape, area, and size. Our leaf trait measurement system is not limited to shade-avoidance research and will accelerate leaf phenotyping of many mutants and screening plants by leaf phenotyping.

Exocytosis is an important process of membrane traffic and functions such as secretion of proteins, turnover of plasma membrane and presenting receptors to plasmas membrane. However, there are few tools to observe the dynamic of vesicles and detect exocytosis automatically. PTrackII was developed to track the secretory granules with different size and motility. According to the dynamics of all tracked vesicles, exocytosis can also be recorded by PTrackII. But it is still time consuming to manually identify exocytotic events from the great amount of tracking results generated by PTrackII. This paper introduces a novel tool of ImageJ plugin based on PTrackII to automatically detect exocytosis, and this plugin can run on different platform and operating system with very high execution speed. The system can detect exocytotic events based on the differences of intensity, and list only exocytosis details instead of lots of unnecessary information. It can also trace the suddenly appeared exocytosis process. Moreover, this plugin can be used on images with different kinds of qualities and tracked objects with various dynamic morphological properties, by setting of suitable parameters. This paper illustrates a time-saving and flexible tool to help the researchers save time in exocytosis only.

Branch knots and other traces of lateral axes, known as rameal traces, have generally been disregarded within architectural studies based on external observations and their analysis limited to tedious manual dissection methods. Based on non-destructive methods including X-ray CT scanning (XRCTS) and on the ontogenic knowledge progressively accumulated on Quercus petraea, this paper presents (1) the tracking methodology of all rameal traces with XRCTS and the "Gourmands" plugin, and software created downstream, notably the 3D visualisation software "Bil3D"; (2) preliminary results obtained on two Quercus petraea 50 cm-long logs; and (3) potential applications in the fields of biology and ecology. Of approximately 45 sequences of linked rameal traces (composing rameal sequences) per metre, half were directly connected to the pith as horizontal traces of primary epicormic buds, while the other half were connected to a branch by an oblique knot. Horizontal epicormic knots essentially emerged within the tree from the most intensive thinning treatment and led to additional knots in the clear wood. Secondary epicormic shoots may emerge from branch and epicormic knots, leading to a vertical spread of epicormic sites for the former and to more bud clusters for the latter. Engulfment of lateral axes can be quantified. Assumptions on the origin of between-tree variability are proposed. Our methodology opens the way to an exhaustive description of rameal traces. It is relevant for ontogenic and wood quality assessment at the trunk level. It may also be very useful for characterising the potentiality of reiteration and for repairing shoot damage and vegetative regeneration at the crown, collar and even root system level.

In vitro assays to measure neuronal growth are a fundamental tool used by many neurobiologists studying neuronal development and regeneration. The quantification of these assays requires accurate measurements of neurite length and neuronal cell numbers in neuronal cultures. Generally, these measurements are obtained through labor-intensive manual or semi-manual tracing of images. To automate these measurements, we have written NeuriteTracer, a neurite tracing plugin for the freely available image-processing program ImageJ. The plugin analyzes fluorescence microscopy images of neurites and nuclei of dissociated cultured neurons. Given user-defined thresholds, the plugin counts neuronal nuclei, and traces and measures neurite length. We find that NeuriteTracer accurately measures neurite outgrowth from cerebellar, DRG and hippocampal neurons. Values obtained by NeuriteTracer correlate strongly with those obtained by semi-manual tracing with NeuronJ and by using a sophisticated analysis package, MetaXpress. We reveal the utility of NeuriteTracer by demonstrating its ability to detect the neurite outgrowth promoting capacity of the rho kinase inhibitor Y-27632. Our plugin is an attractive alternative to existing tracing tools because it is fully automated and ready for use within a freely accessible imaging program.

The clonogenic or colony formation assay is a widely used method to study the number and size of cancer cell colonies that remain after irradiation or cytotoxic agent administration and serves as a measure for the anti-proliferative effect of these treatments. Alternatively, this assay is used to quantitate the transforming potential of cancer associated genes and chemical agents. Therefore, there is a need for a simplified and standardized analysis of colony formation assays for both routine laboratory use and for parallelized automated analysis. Here we describe the freely available ImageJ-plugin “ColonyArea”, which is optimized for rapid and quantitative analysis of focus formation assays conducted in 6- to 24-well dishes. ColonyArea processes image data of multi-well dishes, by separating, concentrically cropping and background correcting well images individually, before colony formation is quantitated. Instead of counting the number of colonies, ColonyArea determines the percentage of area covered by crystal violet stained cell colonies, also taking the intensity of the staining and therefore cell density into account. We demonstrate that these parameters alone or in combination allow for robust quantification of IC50 values of the cytotoxic effect of two staurosporines, UCN-01 and staurosporine (STS) on human glioblastoma cells (T98G). The relation between the potencies of the two compounds compared very well with that obtained from an absorbance based method to quantify colony growth and to published data. The ColonyArea ImageJ plugin provides a simple and efficient analysis routine to quantitate assay data of one of the most commonly used cellular assays. The bundle is freely available for download as supporting information. We expect that ColonyArea will be of broad utility for cancer biologists, as well as clinical radiation scientists.

The ability to accurately and reliably quantify viral infection is essential to basic and translational virology research. Here, we describe a simple and robust automated method for using fluorescence microscopy to estimate the proportion of virally infected cells in a monolayer. We provide details of the automated analysis workflow along with a freely available open-source ImageJ plugin, Infection Counter, for performing image quantification. Using hepatitis C virus (HCV) as an example, we have experimentally verified our method, demonstrating that it is equivalent, if not better, than the established focus-forming assay. Finally, we used Infection Counter to assess the anti-HCV activity of SMBz-CsA, a non-immunosuppressive cyclosporine analogue.

Morphometry characterization is an important procedure in describing neuronal cultures and identifying phenotypic differences. This task usually requires labor-intensive measurements and the classification of numerous neurites from large numbers of neurons in culture. To automate these measurements, we wrote AutoNeuriteJ, an imageJ/Fiji plugin that measures and classifies neurites from a very large number of neurons. We showed that AutoNeuriteJ is able to detect variations of neuritic growth induced by several compounds known to affect the neuronal growth. In these experiments measurement of more than 5000 mouse neurons per conditions was obtained within a few hours. Moreover, by analyzing mouse neurons deficient for the microtubule associated protein 6 (MAP6) and wild type neurons we illustrate that AutoNeuriteJ is capable to detect subtle phenotypic difference in axonal length. Overall the use of AutoNeuriteJ will provide rapid, unbiased and accurate measurement of neuron morphologies.