Search

Human immunodeficiency virus type 1 (HIV-1) genome diversification is a key determinant of viral evolution and the pathogenesis of HIV/AIDS. Antiretroviral therapy is non-curative, and in the context of monitoring the latent reservoir, precision tools are needed to detect and enumerate HIV-1 genomes as well as to assess their heterogeneity, replication potential, and predict responses to therapy. Current sequencing-based methodologies are often unable to confirm intact genomes and most cell-based reporters provide limited information pertaining to viral fitness. In this study, we describe dual reporter sensor cells (DRSCs), an imaging-based reporter system designed to detect HIV-1 infection and measure several independent attributes of the virus in a single-cell high-content assay. We show that the DRSC assay can be used to measure infection, viral gene activation kinetics, and quantify viral circumvention of host antiviral responses. Using the DRSCs, we confirmed markedly different functional heterogeneity for vif alleles derived from diverse HIV-1 strains and subtypes affecting both rates of APOBEC3G degradation and the cell cycle. Furthermore, the assay allowed for the delineation of virus co-receptor preference (X4- vs R5-tropism) and visualization of virion assembly. Overall, our study illustrates proof-of-principle for a multivariate imaging-based cell-based system capable of detecting HIV-1 and studying viral genetic variability with greater data richness relative to prior available modalities., Importance: Human immunodeficiency virus type 1 (HIV-1) is highly heterogeneous and constantly mutating. These changes drive immune evasion and can cause treatment efforts to fail. Here, we describe the "dual reporter sensor cell" (DRSC) assay; a novel imaging-based approach that allows for the detection of HIV-1 infection coupled with a multivariate definition of several independent phenotypic aspects of viral genome activity in a single integrated assay. We validate the DRSC system by studying lab-adapted and patient isolate-derived versions of the viral Vif accessory protein, confirming marked differences in the capacity of diverse vif alleles to mediate downregulation of antiviral APOBEC3G proteins and dysregulate the cell cycle.

Virion Infectivity Factor (Vif) of the Human Immunodeficiency Virus type 1 (HIV-1) targets and degrades cellular APOBEC3 proteins, key regulators of intrinsic and innate antiretroviral immune responses, thereby facilitating HIV-1 infection. While Vif's role in degrading APOBEC3G is well-studied, Vif is also known to cause cell cycle arrest, but the detailed nature of Vif's effects on the cell cycle has yet to be delineated. In this study, we employed high-temporal single-cell live imaging and super-resolution microscopy to monitor individual cells during Vif-induced cell cycle arrest. Our findings reveal that Vif does not affect the G2/M boundary as previously thought. Instead, Vif triggers a unique and robust pseudo-metaphase arrest, distinct from the mild prometaphase arrest induced by Vpr. During this arrest, chromosomes align properly and form the metaphase plate, but later lose alignment, resulting in polar chromosomes. Notably, Vif, unlike Vpr, significantly reduces the levels of both Protein Phosphatase 1 (PP1) and 2A (PP2A) at kinetochores, which regulate chromosome-microtubule interactions. These results unveil a novel role for Vif in kinetochore regulation that governs the spatial organization of chromosomes during mitosis., Competing Interests: Competing Financial Interests The authors declare no further conflict of interests.

Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules: a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.

Importance: Unlike humans, mice are unable to support HIV-1 infection. This is due, in part, to a constellation of defined minor, species-specific differences in conserved host proteins needed for viral gene expression. Here, we used precision CRISPR/Cas9 gene editing to engineer a "mousified" version of one such host protein, cyclin T1 (CCNT1), in human T cells. CCNT1 is essential for efficient HIV-1 transcription, making it an intriguing target for gene-based inactivation of virus replication. We show that isogenic cell lines engineered to encode CCNT1 bearing a single mouse-informed amino acid change (tyrosine in place of cysteine at position 261) exhibit potent, durable, and broad-spectrum resistance to HIV-1 and other pathogenic lentiviruses, and with no discernible impact on host cell biology. These results provide proof of concept for targeting CCNT1 in the context of one or more functional HIV-1 cure strategies., Competing Interests: The authors declare no conflict of interest.

Technological advancements in biology and microscopy have empowered a transition from bioimaging as an observational method to a quantitative one. However, as biologists are adopting quantitative bioimaging and these experiments become more complex, researchers need additional expertise to carry out this work in a rigorous and reproducible manner. This Essay provides a navigational guide for experimental biologists to aid understanding of quantitative bioimaging from sample preparation through to image acquisition, image analysis, and data interpretation. We discuss the interconnectedness of these steps, and for each, we provide general recommendations, key questions to consider, and links to high-quality open-access resources for further learning. This synthesis of information will empower biologists to plan and execute rigorous quantitative bioimaging experiments efficiently., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Senft et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Hepatitis B virus (HBV) capsid assembly is traditionally thought to occur predominantly in the cytoplasm, where the virus gains access to the virion egress pathway. To better define sites of HBV capsid assembly, we carried out single cell imaging of HBV Core protein (Cp) subcellular trafficking over time under conditions supporting genome packaging and reverse transcription in Huh7 hepatocellular carcinoma cells. Time-course analyses including live cell imaging of fluorescently tagged Cp derivatives showed Cp to accumulate in the nucleus at early time points (~24 h), followed by a marked re-distribution to the cytoplasm at 48 to 72 h. Nucleus-associated Cp was confirmed to be capsid and/or high-order assemblages using a novel dual label immunofluorescence strategy. Nuclear-to-cytoplasmic re-localization of Cp occurred predominantly during nuclear envelope breakdown in conjunction with cell division, followed by strong cytoplasmic retention of Cp. Blocking cell division resulted in strong nuclear entrapment of high-order assemblages. A Cp mutant, Cp-V124W, predicted to exhibit enhanced assembly kinetics, also first trafficked to the nucleus to accumulate at nucleoli, consistent with the hypothesis that Cp's transit to the nucleus is a strong and constitutive process. Taken together, these results provide support for the nucleus as an early-stage site of HBV capsid assembly, and provide the first dynamic evidence of cytoplasmic retention after cell division as a mechanism underpinning capsid nucleus-to-cytoplasm relocalization. IMPORTANCE Hepatitis B virus (HBV) is an enveloped, reverse-transcribing DNA virus that is a major cause of liver disease and hepatocellular carcinoma. Subcellular trafficking events underpinning HBV capsid assembly and virion egress remain poorly characterized. Here, we developed a combination of fixed and long-term (>24 h) live cell imaging technologies to study the single cell trafficking dynamics of the HBV Core Protein (Cp). We demonstrate that Cp first accumulates in the nucleus, and forms high-order structures consistent with capsids, with the predominant route of nuclear egress being relocalization to the cytoplasm during cell division in conjunction with nuclear membrane breakdown. Single cell video microscopy demonstrated unequivocally that Cp's localization to the nucleus is constitutive. This study represents a pioneering application of live cell imaging to study HBV subcellular transport, and demonstrates links between HBV Cp and the cell cycle.

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 provides a framework for image processing across scientific domains while being fully open source. Over the years ImageJ has been substantially extended to support novel applications in scientific imaging as they emerge, particularly in the area of biological microscopy, with functionality made more accessible via the Fiji distribution of ImageJ. Within this software ecosystem, work has been done to extend the accessibility of ImageJ to utilize scripting, macros, and plugins in a variety of programming scenarios, e.g., from Groovy and Python and in Jupyter notebooks and cloud computing. We provide five protocols that demonstrate the extensibility of ImageJ for various workflows in image processing. We focus first on Fluorescence Lifetime Imaging Microscopy (FLIM) data, since this requires significant processing to provide quantitative insights into the microenvironments of cells. Second, we show how ImageJ can now be utilized for common image processing techniques, specifically image deconvolution and inversion, while highlighting the new, built-in features of ImageJ-particularly its capacity to run completely headless and the Ops matching feature that selects the optimal algorithm for a given function and data input, thereby enabling processing speedup. Collectively, these protocols can be used as a basis for automating biological image processing workflows. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Using PyImageJ for FLIM data processing Alternate Protocol: Groovy FLIMJ in Jupyter Notebooks Basic Protocol 2: Using ImageJ Ops for image deconvolution Support Protocol 1: Using ImageJ Ops matching feature for image inversion Support Protocol 2: Headless ImageJ deconvolution., (© 2021 Wiley Periodicals LLC.)

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., Competing Interests: 5Conflict of Interest CD, SH and MRB have a financial interest in KNIME GmbH, the company developing and supporting KNIME Analytics Platform. All other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Cells derived from mice and other rodents exhibit profound blocks to HIV-1 virion production, reflecting species-specific incompatibilities between viral Tat and Rev proteins and essential host factors cyclin T1 (CCNT1) and exportin-1 (XPO1, also known as CRM1), respectively. To determine if mouse cell blocks other than CCNT1 and XPO1 affect HIV's postintegration stages, we studied HIV-1 NL4-3 gene expression in mouse NIH 3T3 cells modified to constitutively express HIV-1-compatible versions of CCNT1 and XPO1 (3T3.CX cells). 3T3.CX cells supported both Rev-independent and Rev-dependent viral gene expression and produced relatively robust levels of virus particles, confirming that CCNT1 and XPO1 represent the predominant blocks to these stages. Unexpectedly, however, 3T3.CX cells were remarkably resistant to virus-induced cytopathic effects observed in human cell lines, which we mapped to the viral protein Vif and its apparent species-specific capacity to induce G 2 /M cell cycle arrest. Vif was able to mediate rapid degradation of human APOBEC3G and the PPP2R5D regulatory B56 subunit of the PP2A phosphatase holoenzyme in mouse cells, thus demonstrating that Vif NL4-3 's modulation of the cell cycle can be functionally uncoupled from some of its other defined roles in CUL5-dependent protein degradation. Vif was also unable to induce G 2 /M cell cycle arrest in other nonhuman cell types, including cells derived from nonhuman primates, leading us to propose that one or more human-specific cofactors underpin Vif's ability to modulate the cell cycle. IMPORTANCE Cells derived from mice and other rodents exhibit profound blocks to HIV-1 replication, thus hindering the development of a low-cost small-animal model for studying HIV/AIDS. Here, we engineered otherwise-nonpermissive mouse cells to express HIV-1-compatible versions of two species-specific host dependency factors, cyclin T1 (CCNT1) and exportin-1 (XPO1) (3T3.CX cells). We show that 3T3.CX cells rescue HIV-1 particle production but, unexpectedly, are completely resistant to virus-induced cytopathic effects. We mapped these effects to the viral accessory protein Vif, which induces a prolonged G 2 /M cell cycle arrest followed by apoptosis in human cells. Combined, our results indicate that one or more additional human-specific cofactors govern HIV-1's capacity to modulate the cell cycle, with potential relevance to viral pathogenesis in people and existing animal models., (Copyright © 2018 American Society for Microbiology.)