HIV RGB: Automated Single-Cell Analysis of HIV-1 Rev-Dependent RNA Nuclear Export and Translation Using Image Processing in KNIME.

Single-cell imaging has emerged as a powerful means to study viral replication dynamics and identify sites of virus−host interactions. Multivariate aspects of viral replication cycles yield challenges inherent to handling large, complex imaging datasets. Herein, we describe the design and implementation of an automated, imaging-based strategy, "Human Immunodeficiency Virus Red-Green-Blue" (HIV RGB), for deriving comprehensive single-cell measurements of HIV-1 unspliced (US) RNA nuclear export, translation, and bulk changes to viral RNA and protein (HIV-1 Rev and Gag) subcellular distribution over time. Differentially tagged fluorescent viral RNA and protein species are recorded using multicolor long-term (>24 h) time-lapse video microscopy, followed by image processing using a new open-source computational imaging workflow dubbed "Nuclear Ring Segmentation Analysis and Tracking" (NR-SAT) based on ImageJ plugins that have been integrated into the Konstanz Information Miner (KNIME) analytics platform. We describe a typical HIV RGB experimental setup, detail the image acquisition and NR-SAT workflow accompanied by a step-by-step tutorial, and demonstrate a use case wherein we test the effects of perturbing subcellular localization of the Rev protein, which is essential for viral US RNA nuclear export, on the kinetics of HIV-1 late-stage gene regulation. Collectively, HIV RGB represents a powerful platform for single-cell studies of HIV-1 post-transcriptional RNA regulation. Moreover, we discuss how similar NR-SAT-based design principles and open-source tools might be readily adapted to study a broad range of dynamic viral or cellular processes.

ImageJ and CellProfiler: Complements in Open-Source Bioimage Analysis.

ImageJ and CellProfiler have long been leading open-source platforms in the field of bioimage analysis. ImageJ's traditional strength is in single-image processing and investigation, while CellProfiler is designed for building large-scale, modular analysis pipelines. Although many image analysis problems can be well solved with one or the other, using these two platforms together in a single workflow can be powerful. Here, we share two pipelines demonstrating mechanisms for productively and conveniently integrating ImageJ and CellProfiler for (1) studying cell morphology and migration via tracking, and (2) advanced stitching techniques for handling large, tiled image sets to improve segmentation. No single platform can provide all the key and most efficient functionality needed for all studies. While both programs can be and are often used separately, these pipelines demonstrate the benefits of using them together for image analysis workflows. ImageJ and CellProfiler are both committed to interoperability between their platforms, with ongoing development to improve how both are leveraged from the other. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Studying cell morphology and cell migration in time-lapse datasets using TrackMate (Fiji) and CellProfiler Basic Protocol 2: Creating whole plate montages to easily assess adaptability of segmentation parameters.

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.

Ellipsoid Zone Defects in Retinal Vein Occlusion Correlates With Visual Acuity Prognosis: SCORE2 Report 14.

Purpose: To evaluate the association between ellipsoid zone (EZ) on spectral domain optical coherence tomography (SD-OCT) and visual acuity letter score (VALS) in participants with retinal vein occlusion in the Study of Comparative Treatments for Retinal Vein Occlusion 2. Methods: SD-OCT scans of 362 participants were qualitatively assessed at baseline and months 1, 6, 12, and 24 for EZ status as normal, patchy, or absent. The thickness of EZ layer in the central subfield was also obtained using machine learning. Results: EZ assessments were not possible at baseline due to signal blockage in >75% of eyes. At month 1, EZ was normal in 37.6%, patchy in 48.1%, and absent in 14.3%. EZ was measurable in 48.7% with a mean area of 0.07 ± 0.16 mm2. Mean VALS was better in eyes without an EZ defect compared to eyes with an EZ defect (P < 0.0001 at all visits). EZ defect at month 1 was associated with poorer VALS at all follow-up visits (P < 0.0001). Conclusions: Both qualitative and quantitative assessments of EZ status strongly correlated with VALS. Absence of EZ was associated with poorer VALS at both corresponding and future visits, with larger areas of EZ loss associated with worse VALS. Translational Relevance: Assessment of EZ can be used to identify patients with potentially poor response in eyes with retinal vein occlusion.

A semi-automated machine-learning based workflow for ellipsoid zone analysis in eyes with macular edema: SCORE2 pilot study.

BACKGROUND AND OBJECTIVE: To develop a semi-automated, machine-learning based workflow to evaluate the ellipsoid zone (EZ) assessed by spectral domain optical coherence tomography (SD-OCT) in eyes with macular edema secondary to central retinal or hemi-retinal vein occlusion in SCORE2 treated with anti-vascular endothelial growth factor agents. METHODS: SD-OCT macular volume scans of a randomly selected subset of 75 SCORE2 study eyes were converted to the Digital Imaging and Communications in Medicine (DICOM) format, and the EZ layer was segmented using nonproprietary software. Segmented layer coordinates were exported and used to generate en face EZ thickness maps. Within the central subfield, the area of EZ defect was measured using manual and semi-automated approaches via a customized workflow in the open-source data analytics platform, Konstanz Information Miner (KNIME). RESULTS: A total of 184 volume scans from 74 study eyes were analyzed. The mean±SD area of EZ defect was similar between manual (0.19±0.22 mm2) and semi-automated measurements (0.19±0.21 mm2, p = 0.93; intra-class correlation coefficient = 0.90; average bias = 0.01, 95% confidence interval of limits of agreement -0.18-0.20). CONCLUSIONS: A customized workflow generated via an open-source data analytics platform that applied machine-learning methods demonstrated reliable measurements of EZ area defect from en face thickness maps. The result of our semi-automated approach were comparable to manual measurements.

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.