For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.
(1) Background: The detection of DNA double-strand breaks in vitro using the phosphorylated histone biomarker (γH2AX) is an increasingly popular method of measuring in vitro genotoxicity, as it is sensitive, specific and suitable for high-throughput analysis. The γH2AX response is either detected by flow cytometry or microscopy, the latter being more accessible. However, authors sparsely publish details, data, and workflows from overall fluorescence intensity quantification, which hinders the reproducibility. (2) Methods: We used valinomycin as a model genotoxin, two cell lines (HeLa and CHO-K1) and a commercial kit for γH2AX immunofluorescence detection. Bioimage analysis was performed using the open-source software ImageJ. Mean fluorescent values were measured using segmented nuclei from the DAPI channel and the results were expressed as the area-scaled relative fold change in γH2AX fluorescence over the control. Cytotoxicity is expressed as the relative area of the nuclei. We present the workflows, data, and scripts on GitHub. (3) Results: The outputs obtained by an introduced method are in accordance with expected results, i.e., valinomycin was genotoxic and cytotoxic to both cell lines used after 24 h of incubation. (4) Conclusions: The overall fluorescence intensity of γH2AX obtained from bioimage analysis appears to be a promising alternative to flow cytometry. Workflow, data, and script sharing are crucial for further improvement of the bioimage analysis methods.
- Keywords
- ImageJ, bioimage analysis, genotoxicity, high-throughput, in vitro testing,
- MeSH
- Biomarkers analysis MeSH
- HeLa Cells MeSH
- Humans MeSH
- Microscopy * MeSH
- Pilot Projects MeSH
- DNA Damage * MeSH
- Reproducibility of Results MeSH
- Valinomycin toxicity MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Biomarkers MeSH
- Valinomycin MeSH
Software based analyses of immunohistochemical staining are designed for obtaining quantitative, reproducible, and objective data. However, often times only a certain type of positive cells or structures need to be quantified thus whole image analysis cannot be performed. Such an example is Hofbauer placental cells, which show positivity of some antigens together with trophoblast, but only Hofbauer cells represent the regions of interest (ROIs). Two independent observers evaluated the immunohistochemical staining intensity of Hofbauer cells in placenta samples stained for cytoplasmic antigens by ImageJ, QuPath and light microscopy. Thus, the precise manual determination of ROIs, i.e. Hofbauer cells, was necessary. We detected low inter-observer variability in staining intensity. Almost perfect agreement between observers was reached for ImageJ and QuPath whilst substantial agreement was reached for light microscopy evaluation. As for the comparison of ImageJ, QuPath and light microscopy, the agreement of all three methods (identical immunohistochemical intensity) was achieved for 38.1% samples. The almost perfect agreement of staining intensities was reached between ImageJ and QuPath, and moderate agreement for comparison of the light microscopy to both software. Software analyses are much more time-consuming, thus their utilization is at least questionable to evaluate ROIs with selection.
- Keywords
- image analysis, immunohistochemical staining evaluation, inter-observer variability,
- Publication type
- Journal Article MeSH
UNLABELLED: ThunderSTORM is an open-source, interactive and modular plug-in for ImageJ designed for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy methods such as photo-activated localization microscopy and stochastic optical reconstruction microscopy. ThunderSTORM offers an extensive collection of processing and post-processing methods so that users can easily adapt the process of analysis to their data. ThunderSTORM also offers a set of tools for creation of simulated data and quantitative performance evaluation of localization algorithms using Monte Carlo simulations. AVAILABILITY AND IMPLEMENTATION: ThunderSTORM and the online documentation are both freely accessible at https://code.google.com/p/thunder-storm/.
Extracellular vesicle (EV) research increasingly demands for quantitative characterisation at the single vesicle level to address heterogeneity and complexity of EV subpopulations. Emerging, commercialised technologies for single EV analysis based on, for example, imaging flow cytometry or imaging after capture on chips generally require dedicated instrumentation and proprietary software not readily accessible to every lab. This limits their implementation for routine EV characterisation in the rapidly growing EV field. We and others have shown that single vesicles can be detected as light diffraction limited fluorescent spots using standard confocal and widefield fluorescence microscopes. Advancing this simple strategy into a process for routine EV quantitation, we developed 'EVAnalyzer', an ImageJ/Fiji (Fiji is just ImageJ) plugin for automated, quantitative single vesicle analysis from imaging data. Using EVAnalyzer, we established a robust protocol for capture, (immuno-)labelling and fluorescent imaging of EVs. To exemplify the application scope, the process was optimised and systematically tested for (i) quantification of EV subpopulations, (ii) validation of EV labelling reagents, (iii) in situ determination of antibody specificity, sensitivity and species cross-reactivity for EV markers and (iv) optimisation of genetic EV engineering. Additionally, we show that the process can be applied to synthetic nanoparticles, allowing to determine siRNA encapsulation efficiencies of lipid-based nanoparticles (LNPs) and protein loading of SiO2 nanoparticles. EVAnalyzer further provides a pipeline for automated quantification of cell uptake at the single cell-single vesicle level, thereby enabling high content EV cell uptake assays and plate-based screens. Notably, the entire procedure from sample preparation to the final data output is entirely based on standard reagents, materials, laboratory equipment and open access software. In summary, we show that EVAnalyzer enables rigorous characterisation of EVs with generally accessible tools. Since we further provide the plugin as open-source code, we expect EVAnalyzer to not only be a resource of immediate impact, but an open innovation platform for the EV and nanoparticle research communities.
- Keywords
- EV immunolabelling, cell uptake, exosomes, extracellular vesicles, lipid nanoparticles, liposomes, open innovation, silica nanoparticles, single particle imaging, single vesicle imaging,
- MeSH
- Biomarkers metabolism MeSH
- Diagnostic Imaging MeSH
- Extracellular Vesicles * metabolism MeSH
- Silicon Dioxide * metabolism MeSH
- Flow Cytometry methods MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Biomarkers MeSH
- Silicon Dioxide * MeSH
Fluorescence microscopy images of biological samples contain valuable information but require rigorous analysis for accurate and reliable determination of changes in protein localization, fluorescence intensity, and morphology of the studied objects. Traditionally, cells for microscopy are immobilized using chemicals, which can introduce stress. Analysis often focuses only on colocalization and involves manual segmentation and measurement, which are time-consuming and can introduce bias. Our new workflow addresses these issues by gently immobilizing cells using a small agarose block on a microscope coverslip. This approach is suitable for cell-walled cells (yeast, fungi, plants, bacteria), facilitates their live imaging under conditions close to their natural environment and enables the addition of chemicals during time-lapse experiments. The primary focus of the protocol is on the presented analysis workflow, which is applicable to virtually any cell type-we describe cell segmentation using the Cellpose software followed by automated analysis of a multitude of parameters using custom-written Fiji (ImageJ) macros. The results can be easily processed using the provided R markdown scripts or available graphing software. Our method facilitates unbiased batch analysis of large datasets, improving the efficiency and accuracy of fluorescence microscopy research. The reported sample preparation protocol and Fiji macros were used in our recent publications: Microbiol Spectr (2022), DOI: 10.1128/spectrum.01961-22; Microbiol Spectr (2022), DOI: 10.1128/spectrum.02489-22; J Cell Sci (2023), DOI: 10.1242/jcs.260554.
- Keywords
- Cellpose, Fiji, ImageJ, automated, blinded, cells, data analysis, microscopy, unbiased, yeast,
- Publication type
- Journal Article MeSH
- Review MeSH
The cortical microtubule and actin meshworks play a central role in the shaping of plant cells. Transgenic plants expressing fluorescent protein markers specifically tagging the two main cytoskeletal systems are available, allowing noninvasive in vivo studies. Advanced microscopy techniques, in particular confocal laser scanning microscopy (CLSM), spinning disk confocal microscopy (SDCM), and variable angle epifluorescence microscopy (VAEM), can be nowadays used for imaging the cortical cytoskeleton of living cells with unprecedented spatial and temporal resolution. With the aid of free computing tools based on the publicly available ImageJ software package, quantitative information can be extracted from microscopic images and video sequences, providing insight into both architecture and dynamics of the cortical cytoskeleton.
- Keywords
- Actin, CLSM, Fluorescent proteins, Image analysis, ImageJ, Microtubules, SDCM, VAEM,
- MeSH
- Arabidopsis ultrastructure MeSH
- Cytoskeleton ultrastructure MeSH
- Microscopy, Fluorescence methods MeSH
- Microscopy, Confocal methods MeSH
- Microtubules ultrastructure MeSH
- Image Processing, Computer-Assisted methods MeSH
- Plant Cells ultrastructure MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Derivation of periosteal and endosteal contours taken from transversal long bone cross-sections limits the accuracy of calculated biomechanical properties. Although several techniques are available for deriving both contours, the effect of these techniques on accuracy of calculated cross-sectional properties in non-adults is unknown. We examine a sample of 86 non-adult femora from birth to 12 years of age to estimate the effect of error in deriving periosteal and endosteal contours on cross-sectional properties. Midshaft cross-sections were taken from microCT scans and contours were derived using manual, fully automatic, spline, and ellipse techniques. Agreement between techniques was assessed against manually traced periosteal and endosteal contours using percent prediction error (%PE), reduced major axis analysis, and limits of agreement. The %PEs were highest in the medullary area and lowest in the total area. Mean %PEs were sufficiently below the 5% level of acceptable error, except for medullary areas, but individual values can greatly exceed this 5% boundary given the high standard deviation of %PE means and wide minimum-maximum range of %PEs. Automatic processing produces greater errors than does combination with manual, spline, and ellipse processing. Although periosteal contour is estimated with stronger agreement compared with endosteal contour, error in deriving periosteal contour has a substantially greater effect on calculated section moduli than does error in deriving endosteal contours. We observed no size effect on the resulting bias. Nevertheless, cross-sectional properties in a younger age category may be estimated with greater error compared with in an older age category. We conclude that non-adult midshaft cross-sectional properties can be derived from microCT scans of femoral diaphyses with mean error of < 5% and that derivation of endosteal contour can be simplified by the ellipse technique because fully automatic derivation of endosteal contour may increase the resulting error, especially in small samples.
- Keywords
- EPmacroJ, ImageJ, biomechanics, femora, microCT,
- Publication type
- Journal Article MeSH
Segmentation is one of the most important steps in microscopy image analysis. Unfortunately, most of the methods use fluorescence images for this task, which is not suitable for analysis that requires a knowledge of area occupied by cells and an experimental design that does not allow necessary labeling. In this protocol, we present a simple method, based on edge detection and morphological operations, that separates total area occupied by cells from the background using only brightfield channel image. The resulting segmented picture can be further used as a mask for fluorescence quantification and other analyses. The whole procedure is carried out in open source software Fiji.
- Keywords
- Fiji, ImageJ, brightfield segmentation, cells, image analysis, microscopy,
- Publication type
- Journal Article MeSH
BACKGROUND: Archaeobotanists and palaeoecologists extensively use geometric morphometrics to identify plant opal phytoliths. Particularly when applied to assemblages of phytoliths from concentrations retrieved from closed contexts, morphometric data from archaeological phytoliths compared with similar data from reference material may allow taxonomic attribution. Observer variation is one aspect of phytolith morphometry that has received little attention but may be an important source of error, and hence cause of potential misidentification of plant remains. SCOPE: To investigate inter- and intra-observer variation in phytolith morphometry, eight researchers (observers) from different laboratories measured 50 samples each from three phytolith morphotypes, Bilobate, Bulliform flabellate and Elongate dendritic, three times, under the auspices of the International Committee for Phytolith Morphometrics (ICPM). METHODS: Data for 17 size and shape variables were collected for each phytolith by manually digitising a phytolith outline (mask) from a photograph, followed by measurement of the mask with open-source morphometric software. KEY RESULTS: Inter-observer variation ranged from 0 to 23% difference from the mean of all observers. Intra-observer variation ranged from 0 to 9% difference from the mean of individual observers per week. Inter- and intra-observer variation was generally higher among inexperienced researchers. CONCLUSIONS: Scaling errors were a major cause of variation and occurred more with less experienced researchers, which is likely related to familiarity with data collection. The results indicate that inter- and intra-observer variation can be substantially reduced by providing clear instructions for and training with the equipment, photo capturing, software, data collection and data cleaning. In this paper, the ICPM provides recommendations to minimise variation.Advances in automatic data collection may eventually reduce inter- and intra-observer variation, but until this is common practice, the ICPM recommends that phytolith morphometric analyses adhere to standardised guidelines to assure that measured phytolith variables are accurate, consistent and comparable between different researchers and laboratories.
- Keywords
- Geometric morphometry, ImageJ, archaeobotany, botanical microremains, methodology, phytolith analysis, taxonomic identification,
- Publication type
- Journal Article MeSH
