Current challenges in open-source bioimage informatics

Denoising Autoencoder Trained on Simulation-Derived Structures for Noise Reduction in Chromatin Scanning Transmission Electron Microscopy

Scanning transmission electron microscopy tomography with ChromEM staining (ChromSTEM), has allowed for the three-dimensional study of genome organization. By leveraging convolutional neural networks and molecular dynamics simulations, we have developed a denoising autoencoder (DAE) capable of postprocessing experimental ChromSTEM images to provide nucleosome-level resolution. Our DAE is trained on synthetic images generated from simulations of the chromatin fiber using the 1-cylinder per nucleosome (1CPN) model of chromatin. We find that our DAE is capable of removing noise commonly found in high-angle annular dark field (HAADF) STEM experiments and is able to learn structural features driven by the physics of chromatin folding. The DAE outperforms other well-known denoising algorithms without degradation of structural features and permits the resolution of α-tetrahedron tetranucleosome motifs that induce local chromatin compaction and mediate DNA accessibility. Notably, we find no evidence for the 30 nm fiber, which has been suggested to serve as the higher-order structure of the chromatin fiber. This approach provides high-resolution STEM images that allow for the resolution of single nucleosomes and organized domains within chromatin dense regions comprising of folding motifs that modulate the accessibility of DNA to external biological machinery.

Motivation for using data-driven algorithms in research: A review of machine learning solutions for image analysis of micrographs in neuroscience

Machine learning is a powerful tool that is increasingly being used in many research areas, including neuroscience. The recent development of new algorithms and network architectures, especially in the field of deep learning, has made machine learning models more reliable and accurate and useful for the biomedical research sector. By minimizing the effort necessary to extract valuable features from datasets, they can be used to find trends in data automatically and make predictions about future data, thereby improving the reproducibility and efficiency of research. One application is the automatic evaluation of micrograph images, which is of great value in neuroscience research. While the development of novel models has enabled numerous new research applications, the barrier to use these new algorithms has also decreased by the integration of deep learning models into known applications such as microscopy image viewers. For researchers unfamiliar with machine learning algorithms, the steep learning curve can hinder the successful implementation of these methods into their workflows. This review explores the use of machine learning in neuroscience, including its potential applications and limitations, and provides some guidance on how to select a fitting framework to use in real-life research projects.

Replication Protein A Enhances Kinetics of Uracil DNA Glycosylase on ssDNA and Across DNA Junctions: Explored with a DNA Repair Complex Produced with SpyCatcher/SpyTag Ligation

DNA repair proteins participate in extensive protein-protein interactions that promote the formation of DNA repair complexes. To understand how complex formation affects protein function during base excision repair, we used SpyCatcher/SpyTag ligation to produce a covalent complex between human uracil DNA glycosylase (UNG2) and replication protein A (RPA). Our covalent "RPA-Spy-UNG2" complex could identify and excise uracil bases in duplex areas next to ssDNA-dsDNA junctions slightly faster than the wild-type proteins, but this was highly dependent on DNA structure, as the turnover of the RPA-Spy-UNG2 complex slowed at DNA junctions where RPA tightly engaged long ssDNA sections. Conversely, the enzymes preferred uracil sites in ssDNA where RPA strongly enhanced uracil excision by UNG2 regardless of ssDNA length. Finally, RPA was found to promote UNG2 excision of two uracil sites positioned across a ssDNA-dsDNA junction, and dissociation of UNG2 from RPA enhanced this process. Our approach of ligating together RPA and UNG2 to reveal how complex formation affects enzyme function could be applied to examine other assemblies of DNA repair proteins.

Deep Low-Shot Learning for Biological Image Classification and Visualization From Limited Training Samples

Predictive modeling is useful but very challenging in biological image analysis due to the high cost of obtaining and labeling training data. For example, in the study of gene interaction and regulation in Drosophila embryogenesis, the analysis is most biologically meaningful when in situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. However, labeling training data with precise stages is very time-consuming even for developmental biologists. Thus, a critical challenge is how to build accurate computational models for precise developmental stage classification from limited training samples. In addition, identification and visualization of developmental landmarks are required to enable biologists to interpret prediction results and calibrate models. To address these challenges, we propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images. Specifically, to enable accurate model training on limited training samples, we formulate the task as a deep low-shot learning problem and develop a novel two-step learning approach, including data-level learning and feature-level learning. We use a deep residual network as our base model and achieve improved performance in the precise stage prediction task of ISH images. Furthermore, the deep model can be interpreted by computing saliency maps, which consists of pixel-wise contributions of an image to its prediction result. In our task, saliency maps are used to assist the identification and visualization of developmental landmarks. Our experimental results show that the proposed model can not only make accurate predictions but also yield biologically meaningful interpretations. We anticipate our methods to be easily generalizable to other biological image classification tasks with small training datasets. Our open-source code is available at https://github.com/divelab/lsl-fly.

Prediction of Contaminated Areas Using Ultraviolet Fluorescence Markers for Medical Simulation: A Mobile Phone Application Approach

The use of ultraviolet fluorescence markers in medical simulations has become popular in recent years, especially during the COVID-19 pandemic. Healthcare workers use ultraviolet fluorescence markers to replace pathogens or secretions, and then calculate the regions of contamination. Health providers can use bioimage processing software to calculate the area and quantity of fluorescent dyes. However, traditional image processing software has its limitations and lacks real-time capabilities, making it more suitable for laboratory use than for clinical settings. In this study, mobile phones were used to measure areas contaminated during medical treatment. During the research process, a mobile phone camera was used to photograph the contaminated regions at an orthogonal angle. The fluorescence marker-contaminated area and photographed image area were proportionally related. The areas of contaminated regions can be calculated using this relationship. We used Android Studio software to write a mobile application to convert photos and recreate the true contaminated area. In this application, color photographs are converted into grayscale, and then into black and white binary photographs using binarization. After this process, the fluorescence-contaminated area is calculated easily. The results of our study showed that within a limited distance (50-100 cm) and with controlled ambient light, the error in the calculated contamination area was 6%. This study provides a low-cost, easy, and ready-to-use tool for healthcare workers to estimate the area of fluorescent dye regions during medical simulations. This tool can promote medical education and training on infectious disease preparation.

Synaptonemal & CO analyzer: A tool for synaptonemal complex and crossover analysis in immunofluorescence images

During the formation of ova and sperm, homologous chromosomes get physically attached through the synaptonemal complex and exchange DNA at crossover sites by a process known as meiotic recombination. Chromosomes that do not recombine or have anomalous crossover distributions often separate poorly during the subsequent cell division and end up in abnormal numbers in ova or sperm, which can lead to miscarriage or developmental defects. Crossover numbers and distribution along the synaptonemal complex can be visualized by immunofluorescent microscopy. However, manual analysis of large numbers of cells is very time-consuming and a major bottleneck for recombination studies. Some image analysis tools have been created to overcome this situation, but they are not readily available, do not provide synaptonemal complex data, or do not tackle common experimental difficulties, such as overlapping chromosomes. To overcome these limitations, we have created and validated an open-source ImageJ macro routine that facilitates and speeds up the crossover and synaptonemal complex analyses in mouse chromosome spreads, as well as in other vertebrate species. It is free, easy to use and fulfills the recommendations for enhancing rigor and reproducibility in biomedical studies.

Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy

We present an automated and deep-learning-based workflow to quantitatively analyze the spatiotemporal development of mammary epithelial organoids in two-dimensional time-lapse (2D+t) sequences acquired using a brightfield microscope at high resolution. It involves a convolutional neural network (U-Net), purposely trained using computer-generated bioimage data created by a conditional generative adversarial network (pix2pixHD), to infer semantic segmentation, adaptive morphological filtering to identify organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking procedure to reliably cherry-pick the organoid instances of interest in time. By validating it using real 2D+t sequences of mouse mammary epithelial organoids of morphologically different phenotypes, we clearly demonstrate that the workflow achieves reliable segmentation and tracking performance, providing a reproducible and laborless alternative to manual analyses of the acquired bioimage data.

HeartCV: a tool for transferrable, automated measurement of heart rate and heart rate variability in transparent animals

Heart function is a key component of whole-organismal physiology. Bioimaging is commonly, but not exclusively, used for quantifying heart function in transparent individuals, including early developmental stages of aquatic animals, many of which are transparent. However, a central limitation of many imaging-related methods is the lack of transferability between species, life-history stages and experimental approaches. Furthermore, locating the heart in mobile individuals remains challenging. Here, we present HeartCV: an open-source Python package for automated measurement of heart rate and heart rate variability that integrates automated localization and is transferrable across a wide range of species. We demonstrate the efficacy of HeartCV by comparing its outputs with measurements made manually for a number of very different species with contrasting heart morphologies. Lastly, we demonstrate the applicability of the software to different experimental approaches and to different dataset types, such as those corresponding to longitudinal studies.

Applications of Neural Networks in Biomedical Data Analysis

Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields.

Is jaw fractal dimension a reliable biomarker for osteoporosis screening? A systematic review and meta-analysis of diagnostic test accuracy studies

OBJECTIVES

To summarize the evidence on the feasibility of maxillomandibular imaging exams-related fractal dimension (FD) in screening patients with osteoporosis.

METHODS

This registered systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy statement. High sensitivity search strategies were developed for six primary databases and grey literature. Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) items evaluated the risk of bias, and the GRADE approach assessed the evidence certainty.

RESULTS

From 1034 records initially identified through database searching, four studies were included (total sample of 747 patients [osteoporosis, 136; control group, 611]). The meta-analysis showed that the overall sensitivity and specificity of the FD were 86.17 and 72.68%, respectively. In general, all studies showed low RoB and applicability concern. The certainty of the evidence was very low to moderate.

CONCLUSIONS

This systematic review showed that the jaw-related FD presented sensitivity and specificity values higher than 70%, and its sensitivity in osteoporosis screening was a better parameter than specificity.

Harnessing non-destructive 3D pathology

High-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens offer the promise of modernizing traditional histology workflows and delivering improvements in diagnostic performance. Advanced optical methods now enable the interrogation of orders of magnitude more tissue than previously possible, where volumetric imaging allows for enhanced quantitative analyses of cell distributions and tissue structures that are prognostic and predictive. Non-destructive imaging processes can simplify laboratory workflows, potentially reducing costs, and can ensure that samples are available for subsequent molecular assays. However, the large size of the feature-rich datasets that they generate poses challenges for data management and computer-aided analysis. In this Perspective, we provide an overview of the imaging technologies that enable 3D pathology, and the computational tools-machine learning, in particular-for image processing and interpretation. We also discuss the integration of various other diagnostic modalities with 3D pathology, along with the challenges and opportunities for clinical adoption and regulatory approval.

The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis

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.

Integration of the ImageJ Ecosystem in the KNIME Analytics Platform

Open-source software tools are often used for analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a global community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life-cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.

CytoPacq: a web-interface for simulating multi-dimensional cell imaging

MOTIVATION

Objective assessment of bioimage analysis methods is an essential step towards understanding their robustness and parameter sensitivity, calling for the availability of heterogeneous bioimage datasets accompanied by their reference annotations. Because manual annotations are known to be arduous, highly subjective and barely reproducible, numerous simulators have emerged over past decades, generating synthetic bioimage datasets complemented with inherent reference annotations. However, the installation and configuration of these tools generally constitutes a barrier to their widespread use.

RESULTS

We present a modern, modular web-interface, CytoPacq, to facilitate the generation of synthetic benchmark datasets relevant for multi-dimensional cell imaging. CytoPacq poses a user-friendly graphical interface with contextual tooltips and currently allows a comfortable access to various cell simulation systems of fluorescence microscopy, which have already been recognized and used by the scientific community, in a straightforward and self-contained form.

AVAILABILITY AND IMPLEMENTATION

CytoPacq is a publicly available online service running at https://cbia.fi.muni.cz/simulator. More information about it as well as examples of generated bioimage datasets are available directly through the web-interface.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ImagePy: an open-source, Python-based and platform-independent software package for bioimage analysis

Summary

This note presents the design of a scalable software package named ImagePy for analysing biological images. Our contribution is concentrated on facilitating extensibility and interoperability of the software through decoupling the data model from the user interface. Especially with assistance from the Python ecosystem, this software framework makes modern computer algorithms easier to be applied in bioimage analysis.

Availability and implementation

ImagePy is free and open source software, with documentation and code available at https://github.com/Image-Py/imagepy under the BSD license. It has been tested on the Windows, Mac and Linux operating systems.

Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures

Methods for measuring the properties of individual cells within their native 3D environment will enable a deeper understanding of embryonic development, tissue regeneration, and tumorigenesis. However, current methods for segmenting nuclei in 3D tissues are not designed for situations in which nuclei are densely packed, nonspherical, or heterogeneous in shape, size, or texture, all of which are true of many embryonic and adult tissue types as well as in many cases for cells differentiating in culture. Here, we overcome this bottleneck by devising a novel method based on labelling the nuclear envelope (NE) and automatically distinguishing individual nuclei using a tree-structured ridge-tracing method followed by shape ranking according to a trained classifier. The method is fast and makes it possible to process images that are larger than the computer's memory. We consistently obtain accurate segmentation rates of >90%, even for challenging images such as mid-gestation embryos or 3D cultures. We provide a 3D editor and inspector for the manual curation of the segmentation results as well as a program to assess the accuracy of the segmentation. We have also generated a live reporter of the NE that can be used to track live cells in 3 dimensions over time. We use this to monitor the history of cell interactions and occurrences of neighbour exchange within cultures of pluripotent cells during differentiation. We provide these tools in an open-access user-friendly format.

Quantitative bioimage analytics enables measurement of targeted cellular stress response induced by celastrol-loaded nanoparticles

The cellular stress response, which provides protection against proteotoxic stresses, is characterized by the activation of heat shock factor 1 and the formation of nuclear stress bodies (nSBs). In this study, we developed a computerized method to quantify the formation and size distribution of nSBs, as stress response induction is of interest in cancer research, neurodegenerative diseases, and in other pathophysiological processes. We employed an advanced bioimaging and analytics workflow to enable quantitative detailed subcellular analysis of cell populations even down to single-cell level. This type of detailed analysis requires automated single cell analysis to allow for detection of both size and distribution of nSBs. For specific induction of nSB we used mesoporous silica nanoparticles (MSNs) loaded with celastrol, a plant-derived triterpene with the ability to activate the stress response. To enable specific targeting, we employed folic acid functionalized nanoparticles, which yields targeting to folate receptor expressing cancer cells. In this way, we could assess the ability to quantitatively detect directed and spatio-temporal nSB induction using 2D and 3D confocal imaging. Our results demonstrate successful implementation of an imaging and analytics workflow based on a freely available, general-purpose software platform, BioImageXD, also compatible with other imaging modalities due to full 3D/4D and high-throughput batch processing support. The developed quantitative imaging analytics workflow opens possibilities for detailed stress response examination in cell populations, with significant potential in the analysis of targeted drug delivery systems related to cell stress and other cytoprotective cellular processes.

FiloGen: A Model-Based Generator of Synthetic 3-D Time-Lapse Sequences of Single Motile Cells With Growing and Branching Filopodia

The existence of diverse image datasets accompanied by reference annotations is a crucial prerequisite for an objective benchmarking of bioimage analysis methods. Nevertheless, such a prerequisite is hard to satisfy for time lapse, multidimensional fluorescence microscopy image data, manual annotations of which are laborious and often impracticable. In this paper, we present a simulation system capable of generating 3-D time-lapse sequences of single motile cells with filopodial protrusions of user-controlled structural and temporal attributes, such as the number, thickness, length, level of branching, and lifetime of filopodia, accompanied by inherently generated reference annotations. The proposed simulation system involves three globally synchronized modules, each being responsible for a separate task: the evolution of filopodia on a molecular level, linear elastic deformation of the entire cell with filopodia, and the synthesis of realistic, time-coherent cell texture. Its flexibility is demonstrated by generating multiple synthetic 3-D time-lapse sequences of single lung cancer cells of two different phenotypes, qualitatively and quantitatively resembling their real counterparts acquired using a confocal fluorescence microscope.

Sharing and reusing cell image data

The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery. We are now at the dawn of this revolution: infrastructure is being developed for data standardization, deposition, sharing, and analysis; some journals and funding agencies mandate data deposition; data journals publish high-content microscopy data sets; quantification becomes standard in scientific publications; new analytic tools are being developed and dispatched to the community; and huge data sets are being generated by individual labs and philanthropic initiatives. In this Perspective, I reflect on sharing and reusing cell image data and the opportunities that will come along with it.

Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat

BACKGROUND

Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.

RESULTS

Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way.

CONCLUSIONS

Leaf-GP is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

Quantification of Multicellular Organization, Junction Integrity, and Substrate Features in Collective Cell Migration

Quantitative analysis of multicellular organization, cell-cell junction integrity, and substrate properties is essential to understand the mechanisms underlying collective cell migration. However, spatially and temporally defining these properties is difficult within collectively migrating cell groups due to challenges in accurate cell segmentation within the monolayer. In this paper, we present Matlab®-based algorithms to spatially quantify multicellular organization (migration distance, interface roughness, and cell alignment, area, and morphology), cell-cell junction integrity, and substrate features in confocal microscopy images of two-dimensional collectively migrating endothelial monolayers. We used novel techniques, including measuring the migrating front roughness using a parametric curve formulation, automatically binning cells to obtain data as a function of distance from the migrating front, using iterative morphological closings to fully define cell boundaries, quantifying β-catenin localization as a measure of cell-cell junction integrity, and skeletonizing fibronectin to determine fiber length and orientation. These algorithms are widely accessible, as they use common fluorescent markers and Matlab® functions, and provide high-throughput critical feature quantification within collectively migrating cell groups. These image analysis algorithms can help standardize feature quantification among different experimental techniques, cell types, and research groups studying collective cell migration.

Quantitating the cell: turning images into numbers with ImageJ

Modern biological research particularly in the fields of developmental and cell biology has been transformed by the rapid evolution of the light microscope. The light microscope, long a mainstay of the experimental biologist, is now used for a wide array of biological experimental scenarios and sample types. Much of the great developments in advanced biological imaging have been driven by the digital imaging revolution with powerful processors and algorithms. In particular, this combination of advanced imaging and computational analysis has resulted in the drive of the modern biologist to not only visually inspect dynamic phenomena, but to quantify the involved processes. This need to quantitate images has become a major thrust within the bioimaging community and requires extensible and accessible image processing routines with corresponding intuitive software packages. Novel algorithms both made specifically for light microscopy or adapted from other fields, such as astronomy, are available to biologists, but often in a form that is inaccessible for a number of reasons ranging from data input issues, usability and training concerns, and accessibility and output limitations. The biological community has responded to this need by developing open source software packages that are freely available and provide access to image processing routines. One of the most prominent is the open-source image package ImageJ. In this review, we give an overview of prominent imaging processing approaches in ImageJ that we think are of particular interest for biological imaging and that illustrate the functionality of ImageJ and other open source image analysis software. WIREs Dev Biol 2017, 6:e260. doi: 10.1002/wdev.260 For further resources related to this article, please visit the WIREs website.

iSBatch: a batch-processing platform for data analysis and exploration of live-cell single-molecule microscopy images and other hierarchical datasets

Recent technical advances have made it possible to visualize single molecules inside live cells. Microscopes with single-molecule sensitivity enable the imaging of low-abundance proteins, allowing for a quantitative characterization of molecular properties. Such data sets contain information on a wide spectrum of important molecular properties, with different aspects highlighted in different imaging strategies. The time-lapsed acquisition of images provides information on protein dynamics over long time scales, giving insight into expression dynamics and localization properties. Rapid burst imaging reveals properties of individual molecules in real-time, informing on their diffusion characteristics, binding dynamics and stoichiometries within complexes. This richness of information, however, adds significant complexity to analysis protocols. In general, large datasets of images must be collected and processed in order to produce statistically robust results and identify rare events. More importantly, as live-cell single-molecule measurements remain on the cutting edge of imaging, few protocols for analysis have been established and thus analysis strategies often need to be explored for each individual scenario. Existing analysis packages are geared towards either single-cell imaging data or in vitro single-molecule data and typically operate with highly specific algorithms developed for particular situations. Our tool, iSBatch, instead allows users to exploit the inherent flexibility of the popular open-source package ImageJ, providing a hierarchical framework in which existing plugins or custom macros may be executed over entire datasets or portions thereof. This strategy affords users freedom to explore new analysis protocols within large imaging datasets, while maintaining hierarchical relationships between experiments, samples, fields of view, cells, and individual molecules.

Developing Epithelia: What the Eye Cannot Grasp

In this issue of Developmental Cell, Heller et al. (2016) introduce EpiTools, a new open-source image analysis toolkit that provides user-friendly graphical interfaces to perform automatic cell-based measurements from fluorescence microscopy time-lapse images of growing epithelia.

Non-canonical features of the Golgi apparatus in bipolar epithelial neural stem cells

Apical radial glia (aRG), the stem cells in developing neocortex, are unique bipolar epithelial cells, extending an apical process to the ventricle and a basal process to the basal lamina. Here, we report novel features of the Golgi apparatus, a central organelle for cell polarity, in mouse aRGs. The Golgi was confined to the apical process but not associated with apical centrosome(s). In contrast, in aRG-derived, delaminating basal progenitors that lose apical polarity, the Golgi became pericentrosomal. The aRG Golgi underwent evolutionarily conserved, accordion-like compression and extension concomitant with cell cycle-dependent nuclear migration. Importantly, in line with endoplasmic reticulum but not Golgi being present in the aRG basal process, its plasma membrane contained glycans lacking Golgi processing, consistent with direct ER-to-cell surface membrane traffic. Our study reveals hitherto unknown complexity of neural stem cell polarity, differential Golgi contribution to their specific architecture, and fundamental Golgi re-organization upon cell fate change.

Reconstruction and Visualization of Coordinated 3D Cell Migration Based on Optical Flow

Animal development is marked by the repeated reorganization of cells and cell populations, which ultimately determine form and shape of the growing organism. One of the central questions in developmental biology is to understand precisely how cells reorganize, as well as how and to what extent this reorganization is coordinated. While modern microscopes can record video data for every cell during animal development in 3D+t, analyzing these videos remains a major challenge: reconstruction of comprehensive cell tracks turned out to be very demanding especially with decreasing data quality and increasing cell densities. In this paper, we present an analysis pipeline for coordinated cellular motions in developing embryos based on the optical flow of a series of 3D images. We use numerical integration to reconstruct cellular long-term motions in the optical flow of the video, we take care of data validation, and we derive a LIC-based, dense flow visualization for the resulting pathlines. This approach allows us to handle low video quality such as noisy data or poorly separated cells, and it allows the biologists to get a comprehensive understanding of their data by capturing dynamic growth processes in stills. We validate our methods using three videos of growing fruit fly embryos.

Use of ImageJ to recover information from individual cells in a G protein-coupled receptor assay

Live-cell assays used in GPCR research often rely on fluorescence techniques that generate large amounts of raw image data. Consequently, the capacity to accurately and timely extract useful information from image and video data has become more and more important. Image J is an open-source program that provides powerful tools with a simple interface designed to fit the needs of image analysis of most researchers. In this chapter, Image J routines to extract information from individual cells in a calcium GPCR assay are described. In these routines, individual cells in the same image/video data can be separated using either a progressive threshold or a local threshold method. Both methods can be optimized to either a maximum number of selection or maximum area selected resulting in conceptually distinct selections.

Blobs and curves: object-based colocalisation for plant cells

Blobs and curves occur everywhere in plant bioimaging: from signals of fluorescence-labelled proteins, through cytoskeletal structures, nuclei staining and cell extensions such as root hairs. Here we look at the problem of colocalisation of blobs with blobs (protein-protein colocalisation) and blobs with curves (organelle-cytoskeleton colocalisation). This article demonstrates a clear quantitative alternative to pixel-based colocalisation methods and, using object-based methods, can quantify not only the level of colocalisation but also the distance between objects. Included in this report are computational algorithms, biological experiments and guidance for those looking to increase their use of computationally-based and quantified analysis of bioimages.

SOAX: a software for quantification of 3D biopolymer networks

Filamentous biopolymer networks in cells and tissues are routinely imaged by confocal microscopy. Image analysis methods enable quantitative study of the properties of these curvilinear networks. However, software tools to quantify the geometry and topology of these often dense 3D networks and to localize network junctions are scarce. To fill this gap, we developed a new software tool called “SOAX”, which can accurately extract the centerlines of 3D biopolymer networks and identify network junctions using Stretching Open Active Contours (SOACs). It provides an open-source, user-friendly platform for network centerline extraction, 2D/3D visualization, manual editing and quantitative analysis. We propose a method to quantify the performance of SOAX, which helps determine the optimal extraction parameter values. We quantify several different types of biopolymer networks to demonstrate SOAX's potential to help answer key questions in cell biology and biophysics from a quantitative viewpoint.

Low-cost motility tracking system (LOCOMOTIS) for time-lapse microscopy applications and cell visualisation

Direct visualisation of cells for the purpose of studying their motility has typically required expensive microscopy equipment. However, recent advances in digital sensors mean that it is now possible to image cells for a fraction of the price of a standard microscope. Along with low-cost imaging there has also been a large increase in the availability of high quality, open-source analysis programs. In this study we describe the development and performance of an expandable cell motility system employing inexpensive, commercially available digital USB microscopes to image various cell types using time-lapse and perform tracking assays in proof-of-concept experiments. With this system we were able to measure and record three separate assays simultaneously on one personal computer using identical microscopes, and obtained tracking results comparable in quality to those from other studies that used standard, more expensive, equipment. The microscopes used in our system were capable of a maximum magnification of 413.6×. Although resolution was lower than that of a standard inverted microscope we found this difference to be indistinguishable at the magnification chosen for cell tracking experiments (206.8×). In preliminary cell culture experiments using our system, velocities (mean µm/min ± SE) of 0.81 ± 0.01 (Biomphalaria glabrata hemocytes on uncoated plates), 1.17 ± 0.004 (MDA-MB-231 breast cancer cells), 1.24 ± 0.006 (SC5 mouse Sertoli cells) and 2.21 ± 0.01 (B. glabrata hemocytes on Poly-L-Lysine coated plates), were measured and are consistent with previous reports. We believe that this system, coupled with open-source analysis software, demonstrates that higher throughput time-lapse imaging of cells for the purpose of studying motility can be an affordable option for all researchers.

Anima: modular workflow system for comprehensive image data analysis

Modern microscopes produce vast amounts of image data, and computational methods are needed to analyze and interpret these data. Furthermore, a single image analysis project may require tens or hundreds of analysis steps starting from data import and pre-processing to segmentation and statistical analysis; and ending with visualization and reporting. To manage such large-scale image data analysis projects, we present here a modular workflow system called Anima. Anima is designed for comprehensive and efficient image data analysis development, and it contains several features that are crucial in high-throughput image data analysis: programing language independence, batch processing, easily customized data processing, interoperability with other software via application programing interfaces, and advanced multivariate statistical analysis. The utility of Anima is shown with two case studies focusing on testing different algorithms developed in different imaging platforms and an automated prediction of alive/dead C. elegans worms by integrating several analysis environments. Anima is a fully open source and available with documentation at www.anduril.org/anima.

Bioimage Informatics in the context of Drosophila research

Modern biological research relies heavily on microscopic imaging. The advanced genetic toolkit of Drosophila makes it possible to label molecular and cellular components with unprecedented level of specificity necessitating the application of the most sophisticated imaging technologies. Imaging in Drosophila spans all scales from single molecules to the entire populations of adult organisms, from electron microscopy to live imaging of developmental processes. As the imaging approaches become more complex and ambitious, there is an increasing need for quantitative, computer-mediated image processing and analysis to make sense of the imagery. Bioimage Informatics is an emerging research field that covers all aspects of biological image analysis from data handling, through processing, to quantitative measurements, analysis and data presentation. Some of the most advanced, large scale projects, combining cutting edge imaging with complex bioimage informatics pipelines, are realized in the Drosophila research community. In this review, we discuss the current research in biological image analysis specifically relevant to the type of systems level image datasets that are uniquely available for the Drosophila model system. We focus on how state-of-the-art computer vision algorithms are impacting the ability of Drosophila researchers to analyze biological systems in space and time. We pay particular attention to how these algorithmic advances from computer science are made usable to practicing biologists through open source platforms and how biologists can themselves participate in their further development.

Open-source solutions for SPIMage processing

Light sheet microscopy is an emerging technique allowing comprehensive visualization of dynamic biological processes, at high spatial and temporal resolution without significant damage to the sample by the imaging process itself. It thus lends itself to time-lapse observation of fluorescently labeled molecular markers over long periods of time in a living specimen. In combination with sample rotation light sheet microscopy and in particular its selective plane illumination microscopy (SPIM) flavor, enables imaging of relatively large specimens, such as embryos of animal model organisms, in their entirety. The benefits of SPIM multiview imaging come to the cost of image data postprocessing necessary to deliver the final output that can be analyzed. Here, we provide a set of practical recipes that walk biologists through the complex processes of SPIM data registration, fusion, deconvolution, and time-lapse registration using publicly available open-source tools. We explain, in plain language, the basic principles behind SPIM image-processing algorithms that should enable users to make informed decisions during parameter tuning of the various processing steps applied to their own datasets. Importantly, the protocols presented here are applicable equally to processing of multiview SPIM data from the commercial Zeiss Lightsheet Z.1 microscope and from the open-access SPIM platforms such as OpenSPIM.

It's all about the pentiums: The use, manipulation, and storage of digital microscopy imaging data for the biological sciences

Digital microscopy has revolutionized quantitative imaging, with binary-encoded computer files serving to capture and store imaging data sets for analysis. With the ever-present use of computers to generate and store imaging data, it becomes increasingly important to understand how these files are created, and how they can be both used and mis-used. This is a particularly important task for the biologist who may have limited background in computer science. Here we discuss some of the basic aspects of digital data storage and use, including file types, storage media, and the choice between commercial and open-source software. Often, open-source software is written by a user or group of users, and then distributed to the scientific community at large. These can be important tools, but there are some hidden costs to this freeware that we will discuss. We will also compare open-source software to commercial imaging software, which is often written for use by non-computer scientists.

Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression

MOTIVATION

Drosophila melanogaster is a major model organism for investigating the function and interconnection of animal genes in the earliest stages of embryogenesis. Today, images capturing Drosophila gene expression patterns are being produced at a higher throughput than ever before. The analysis of spatial patterns of gene expression is most biologically meaningful when images from a similar time point during development are compared. Thus, the critical first step is to determine the developmental stage of an embryo. This information is also needed to observe and analyze expression changes over developmental time. Currently, developmental stages (time) of embryos in images capturing spatial expression pattern are annotated manually, which is time- and labor-intensive. Embryos are often designated into stage ranges, making the information on developmental time course. This makes downstream analyses inefficient and biological interpretations of similarities and differences in spatial expression patterns challenging, particularly when using automated tools for analyzing expression patterns of large number of images.

RESULTS

Here, we present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In an analysis of 3724 images, the new approach shows high accuracy in predicting the developmental stage correctly (79%). In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores for all images containing expression patterns of the same gene enable a direct way to view expression changes over developmental time for any gene. We show that the genomewide-expression-maps generated using images from embryos in refined stages illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.

AVAILABILITY AND IMPLEMENTATION

The software package is availablefor download at: http://www.public.asu.edu/*jye02/Software/Fly-Project/.

Probing cellular processes by long-term live imaging--historic problems and current solutions

Living organisms, tissues, cells and molecules are highly dynamic. The importance of their continuous and long-term observation has been recognized for over a century but has been limited by technological hurdles. Improvements in imaging technologies, genetics, protein engineering and data analysis have more recently allowed us to answer long-standing questions in biology using quantitative continuous long-term imaging. This requires a multidisciplinary collaboration between scientists of various backgrounds: biologists asking relevant questions, imaging specialists and engineers developing hardware, and informaticians and mathematicians developing software for data acquisition, analysis and computational modeling. Despite recent improvements, there are still obstacles to be addressed before this technology can achieve its full potential. This Commentary aims at providing an overview of currently available technologies for quantitative continuous long-term single-cell imaging, their limitations and what is required to bring this field to the next level. We provide an historical perspective on the development of this technology and discuss key issues in time-lapse imaging: keeping cells alive, using labels, reporters and biosensors, and hardware and software requirements. We highlight crucial and often non-obvious problems for researchers venturing into the field and hope to inspire experts in the field and from related disciplines to contribute to future solutions.

Towards comprehensive cell lineage reconstructions in complex organisms using light-sheet microscopy

Understanding the development of complex multicellular organisms as a function of the underlying cell behavior is one of the most fundamental goals of developmental biology. The ability to quantitatively follow cell dynamics in entire developing embryos is an indispensable step towards such a system-level understanding. In recent years, light-sheet fluorescence microscopy has emerged as a particularly promising strategy for recording the in vivo data required to realize this goal. Using light-sheet fluorescence microscopy, entire complex organisms can be rapidly imaged in three dimensions at sub-cellular resolution, achieving high temporal sampling and excellent signal-to-noise ratio without damaging the living specimen or bleaching fluorescent markers. The resulting datasets allow following individual cells in vertebrate and higher invertebrate embryos over up to several days of development. However, the complexity and size of these multi-terabyte recordings typically preclude comprehensive manual analyses. Thus, new computational approaches are required to automatically segment cell morphologies, accurately track cell identities and systematically analyze cell behavior throughout embryonic development. We review current efforts in light-sheet microscopy and bioimage informatics towards this goal, and argue that comprehensive cell lineage reconstructions are finally within reach for many key model organisms, including fruit fly, zebrafish and mouse.

Light microscopy applications in systems biology: opportunities and challenges

Biological systems present multiple scales of complexity, ranging from molecules to entire populations. Light microscopy is one of the least invasive techniques used to access information from various biological scales in living cells. The combination of molecular biology and imaging provides a bottom-up tool for direct insight into how molecular processes work on a cellular scale. However, imaging can also be used as a top-down approach to study the behavior of a system without detailed prior knowledge about its underlying molecular mechanisms. In this review, we highlight the recent developments on microscopy-based systems analyses and discuss the complementary opportunities and different challenges with high-content screening and high-throughput imaging. Furthermore, we provide a comprehensive overview of the available platforms that can be used for image analysis, which enable community-driven efforts in the development of image-based systems biology.

Label-free segmentation of Co-cultured cells on a nanotopographical gradient

The function and fate of cells is influenced by many different factors, one of which is surface topography of the support culture substrate. Systematic studies of nanotopography and cell response have typically been limited to single cell types and a small set of topographical variations. Here, we show a radical expansion of experimental throughput using automated detection, measurement, and classification of co-cultured cells on a nanopillar array where feature height changes continuously from planar to 250 nm over 9 mm. Individual cells are identified and characterized by more than 200 descriptors, which are used to construct a set of rules for label-free segmentation into individual cell types. Using this approach we can achieve label-free segmentation with 84% confidence across large image data sets and suggest optimized surface parameters for nanostructuring of implant devices such as vascular stents.

Biological imaging software tools

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.