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Strambio-De-Castillia, C.

Publications and source records attributed to Strambio-De-Castillia, C..

9 recordsLinked to original sources

Accurate and robust 3D genome feature discovery from multiplexed DNA FISH

Multiplexed DNA FISH (also known as FISH Omics) is a powerful imaging technology to map three-dimensional (3D) genome organization in thousands of single cells at kilobase resolution. However, existing computational approaches to identify 3D genome features, such as A/B compartments, topologically associating domains (TADs) and chromatin loops, have been mainly adapted from Hi-C and do not account for the distinct measurement errors across the three XYZ physical axes. Here we present ArcFISH (accurate and robust chromatin analysis from FISH data), which explicitly models axis-specific measurement errors in a unified statistical framework to detect multi-level 3D genome features. Comprehensive evaluations using multiple simulated as well as experimental datasets show that ArcFISH reliably identifies chromatin loops under heterogeneous measurement errors, detects TAD boundaries highly enriched for active histone marks H3K4me3 and H3K36me3, and requires as few as 30 chromatin traces to faithfully assign A/B compartments. Applying ArcFISH to multiplexed DNA FISH data from mouse embryonic stem cells and mouse excitatory neurons reveals cell-type-specific chromatin loops and TADs, overlapping with differentially expressed genes. ArcFISH is freely available at https://hyuyu104.github.io/ArcFISH.

genomics↗

SnapFISH: a computational pipeline to identify chromatin loops from multiplexed DNA FISH data

Multiplexed DNA fluorescence in situ hybridization (FISH) imaging technologies have been developed to map the folding of chromatin fibers at tens of nanometer and tens of kilobase resolution in single cells. However, computational methods to reliably identify chromatin loops from such imaging datasets are still lacking. Here we present a Single-Nucleus Analysis Pipeline for multiplexed DNA FISH (SnapFISH), to process the multiplexed DNA FISH data and identify chromatin loops. SnapFISH can identify known chromatin loops from mouse embryonic stem cells with high sensitivity and accuracy. In addition, SnapFISH obtained comparable results of chromatin loops across datasets generated from diverse imaging technologies.

bioinformatics↗

MethodsJ2: A Software Tool to Improve Microscopy Methods Reporting

Proper reporting of metadata is essential to reproduce microscopy experiments, interpret results and share images. Experimental scientists can report details about sample preparation and imaging conditions while imaging scientists have the expertise required to collect and report the image acquisition, hardware and software metadata information. MethodsJ2 is an ImageJ/Fiji based software tool that gathers metadata and automatically generates text for the methods section of publications.

bioinformatics↗

Micro-Meta App: an interactive software tool to facilitate the collection of microscopy metadata based on community-driven specifications

For the information content of microscopy images to be appropriately interpreted, reproduced, and meet FAIR (Findable Accessible Interoperable and Reusable) principles, they should be accompanied by detailed descriptions of microscope hardware, image acquisition settings, image pixel and dimensional structure, and instrument performance. Nonetheless, the thorough documentation of imaging experiments is significantly impaired by the lack of community-sanctioned easy-to-use software tools to facilitate the extraction and collection of relevant microscopy metadata. Here we present Micro-Meta App, an intuitive open-source software designed to tackle these issues that was developed in the context of nascent global bioimaging community organizations, including BioImaging North America (BINA) and QUAlity Assessment and REProducibility in Light Microscopy (QUAREP-LiMi), whose goal is to improve reproducibility, data quality and sharing value for imaging experiments. The App provides a user-friendly interface for building comprehensive descriptions of the conditions utilized to produce individual microscopy datasets as specified by the recently proposed 4DN-BINA-OME tiered-system of Microscopy Metadata model. To achieve this goal the App provides a visual guide for a microscope-user to: 1) interactively build diagrammatic representations of hardware configurations of given microscopes that can be easily reused and shared with colleagues needing to document similar instruments. 2) Automatically extracts relevant metadata from image files and facilitates the collection of missing image acquisition settings and calibration metrics associated with a given experiment. 3) Output all collected Microscopy Metadata to interoperable files that can be used for documenting imaging experiments and shared with the community. In addition to significantly lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training users that have limited knowledge of the intricacies of light microscopy experiments. To ensure wide-adoption by microscope-users with different needs Micro-Meta App closely interoperates with MethodsJ2 and OMERO.mde, two complementary tools described in parallel manuscripts.

bioinformatics↗

Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model

1 -Digital light microscopy provides powerful tools for quantitatively probing the real-time dynamics of subcellular structures. While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be properly interpreted (quality), reproduced (reproducibility), and used to extract reliable information and scientific knowledge which can be shared for further analysis (value). Keeping notes on microscopy experiments and quality control procedures ought to be straightforward, as the microscope is a machine whose components are defined and the performance measurable. Nevertheless, to this date, no universally adopted community-driven specifications exist that delineate the required information about the microscope hardware and acquisition settings (i.e., microscopy "data provenance" metadata) and the minimally accepted calibration metrics (i.e., microscopy quality control metadata) that should be automatically recorded by both commercial microscope manufacturers and customized microscope developers. In the absence of agreed guidelines, it is inherently difficult for scientists to create comprehensive records of imaging experiments and ensure the quality of resulting image data or for manufacturers to incorporate standardized reporting and performance metrics. To add to the confusion, microscopy experiments vary greatly in aim and complexity, ranging from purely descriptive work to complex, quantitative and even sub-resolution studies that require more detailed reporting and quality control measures. To solve this problem, the 4D Nucleome Initiative (4DN) (1, 2) Imaging Standards Working Group (IWG), working in conjunction with the BioImaging North America (BINA) Quality Control and Data Management Working Group (QC-DM-WG) (3), here propose light Microscopy Metadata specifications that scale with experimental intent and with the complexity of the instrumentation and analytical requirements. They consist of a revision of the Core of the Open Microscopy Environment (OME) Data Model, which forms the basis for the widely adopted Bio-Formats library (4-6), accompanied by a suite of three extensions, each with three tiers, allowing the classification of imaging experiments into levels of increasing imaging and analytical complexity (7, 8). Hence these specifications not only provide an OME-based comprehensive set of metadata elements that should be recorded, but they also specify which subset of the full list should be recorded for a given experimental tier. In order to evaluate the extent of community interest, an extensive outreach effort was conducted to present the proposed metadata specifications to members of several core-facilities and international bioimaging initiatives including the European Light Microscopy Initiative (ELMI), Global BioImaging (GBI), and European Molecular Biology Laboratory (EMBL) - European Bioinformatics Institute (EBI). Consequently, close ties were established between our endeavour and the undertakings of the recently established QUAlity Assessment and REProducibility for Instruments and Images in Light Microscopy global community initiative (9). As a result this flexible 4DN-BINA-OME (NBO namespace) framework (7, 8) represents a turning point towards achieving community-driven Microscopy Metadata standards that will increase data fidelity, improve repeatability and reproducibility, ease future analysis and facilitate the verifiable comparison of different datasets, experimental setups, and assays, and it demonstrates the method for future extensions. Such universally accepted microscopy standards would serve a similar purpose as the Encode guidelines successfully adopted by the genomic community (10, 11). The intention of this proposal is therefore to encourage participation, critiques and contributions from the entire imaging community and all stakeholders, including research and imaging scientists, facility personnel, instrument manufacturers, software developers, standards organizations, scientific publishers, and funders.

bioinformatics↗

The DHODH Inhibitor PTC299 Arrests SARS-CoV-2 Replication and Suppresses Induction of Inflammatory Cytokines

The coronavirus disease 2019 (COVID-19) pandemic has created an urgent need for therapeutics that inhibit the SARS-CoV-2 virus and suppress the fulminant inflammation characteristic of advanced illness. Here, we describe the anti-COVID-19 potential of PTC299, an orally available compound that is a potent inhibitor of dihydroorotate dehydrogenase (DHODH), the rate-limiting enzyme of the de novo pyrimidine biosynthesis pathway. In tissue culture, PTC299 manifests robust, dose-dependent, and DHODH-dependent inhibition of SARS CoV-2 replication (EC50 range, 2.0 to 31.6 nM) with a selectivity index >3,800. PTC299 also blocked replication of other RNA viruses, including Ebola virus. Consistent with known DHODH requirements for immunomodulatory cytokine production, PTC299 inhibited the production of interleukin (IL)-6, IL-17A (also called IL-17), IL-17F, and vascular endothelial growth factor (VEGF) in tissue culture models. The combination of anti-SARS-CoV-2 activity, cytokine inhibitory activity, and previously established favorable pharmacokinetic and human safety profiles render PTC299 a promising therapeutic for COVID-19.

immunology↗

Cyclophilin A protects HIV-1 from restriction by human TRIM5α

The capsid (CA) protein lattice of HIV-1 and other retroviruses encases viral genomic RNA and regulates steps that are essential to retroviral invasion of target cells, including reverse transcription, nuclear trafficking, and integration of viral cDNA into host chromosomal DNA1. Cyclophilin A (CypA), the first cellular protein reported to bind HIV-1 CA2, has interacted with invading lentiviruses related to HIV-1 for millions of years3-7. Disruption of the CA-CypA interaction decreases HIV-1 infectivity in human cells8-12, but stimulates infectivity in non-human primate cells13-15. Genetic and biochemical data suggest that CypA interaction with CA protects HIV-1 from a restriction factor in human cells16-20. Discovery of the CA-specific restriction factor TRIM521, and of TRIM5-CypA fusion genes that were independently generated at least four times in phylogeny4,5,15,22-25, pointed to human TRIM5 as the CypA-sensitive restriction factor. However, significant HIV-1 restriction by human TRIM521, let alone inhibition of such activity by CypA26, has not been detected. Here, exploiting reverse genetic tools optimized for primary human CD4+ T cells, macrophages, and dendritic cells, we demonstrate that disruption of the CA-CypA interaction renders HIV-1 susceptible to restriction by human TRIM5, with the block occurring before reverse transcription. Identical findings were obtained with single-cycle vectors or with replication-competent HIV-1, including sexually-transmitted clones from sub-Saharan Africa. Endogenous TRIM5 was observed to associate with virion cores as they entered the macrophage cytoplasm, but only when the CA-CypA interaction was disrupted. These experiments resolve the long-standing mystery of the role of CypA in HIV-1 replication by demonstrating that this ubiquitous cellular protein shields HIV-1 from previously inapparent, but potent inhibition, imposed by human TRIM5. Hopefully this reinvigorates development of CypA-inhibitors for treatment of HIV-1 and other CypA-dependent pathogens27-30.

microbiology↗

An algorithm-centric Monte Carlo method to empiricallyquantify motion type estimation uncertainty in single-particle tracking

Quantitative analysis of microscopy images is ideally suited for understanding the functional biological correlates of individual molecular species identified by one of the several available \"omics\" techniques. Due to advances in fluorescent labeling, microscopy engineering and image processing, it is now possible to routinely observe and quantitatively analyze at high temporal and spatial resolution the real-time behavior of thousands of individual cellular structures as they perform their functional task inside living systems. Despite the central role of microscopic imaging in modern biology, unbiased inference, valid interpretation, scientific reproducibility and results dissemination are hampered by the still prevalent need for subjective interpretation of image data and by the limited attention given to the quantitative assessment and reporting of the error associated with each measurement or calculation, and on its effect on downstream analysis steps (i.e., error propagation). One of the mainstays of bioimage analysis is represented by single-particle tracking (SPT)1-5, which coupled with the mathematical analysis of trajectories and with the interpretative modelling of motion modalities, is of key importance for the quantitative understanding of the heterogeneous intracellular dynamic behavior of fluorescently-labeled individual cellular structures, vesicles, virions and single-molecules. Despite substantial advances, the evaluation of analytical error propagation through SPT and motion analysis pipelines is absent from most available tools 6. This severely hinders the critical evaluation, comparison, reproducibility and integration of results emerging from different laboratories, at different times, under different experimental conditions and using different model systems. Here we describe a novel, algorithmic-centric, Monte Carlo method to assess the effect of experimental parameters such as signal to noise ratio (SNR), particle detection error, trajectory length, and the diffusivity characteristics of the moving particle on the uncertainty associated with motion type classification The method is easily extensible to a wide variety of SPT algorithms, is made widely available via its implementation in our Open Microscopy Environment inteGrated Analysis (OMEGA) software tool for the management and analysis of tracking data 7, and forms an integral part of our Minimum Information About Particle Tracking Experiments (MIAPTE) data model 8.

bioinformatics↗

OMEGA: a software tool for the management, analysis, and dissemination of intracellular trafficking data that incorporates motion type classification and quality control

MOTIVATIONParticle tracking coupled with time-lapse microscopy is critical for understanding the dynamics of intracellular processes of clinical importance. Spurred on by advances in the spatiotemporal resolution of microscopy and automated computational methods, this field is increasingly amenable to multi-dimensional high-throughput data collection schemes (Snijder et al., 2012). Typically, complex particle tracking datasets generated by individual laboratories are produced with incompatible methodologies that preclude comparison to each other. There is therefore an unmet need for data management systems that facilitate data standardization, meta-analysis, and structured data dissemination. The integration of analysis, visualization, and quality control capabilities into such systems would eliminate the need for manual transfer of data to diverse downstream analysis tools. At the same time, it would lay the foundation for shared trajectory data, particle tracking, and motion analysis standards. RESULTSHere, we present Open Microscopy Environment inteGrated Analysis (OMEGA), a cross-platform data management, analysis, and visualization system, for particle tracking data, with particular emphasis on results from viral and vesicular trafficking experiments. OMEGA provides intuitive graphical interfaces to implement integrated particle tracking and motion analysis workflows while providing easy to use facilities to automatically keep track of error propagation, harvest data provenance and ensure the persistence of analysis results and metadata. Specifically, OMEGA: 1) imports image data and metadata from data management tools such as the Open Microscopy Environment Remote Objects (OMERO; Allan et al., 2012); 2) tracks intracellular particles movement; 3) facilitates parameter optimization and trajectory results inspection and validation; 4) performs downstream trajectory analysis and motion type classification; 5) estimates the uncertainty propagating through the motion analysis pipeline; and, 6) facilitates storage and dissemination of analysis results, and analysis definition metadata, on the basis of our newly proposed FAIRsharing.org complainant Minimum Information About Particle Tracking Experiments (MIAPTE; Rigano and Strambio-De-Castillia, 2016; 2017) guidelines in combination with the OME-XML data model (Goldberg et al., 2005). In so doing, OMEGA maintains a persistent link between raw image data, intermediate analysis steps, the overall analysis output, and all necessary metadata to repeat the analysis process and reproduce its results. Availability and implementationOMEGA is a cross-platform, open-source software developed in Java. Source code and cross-platform binaries are freely available on GitHub at https://github.com/OmegaProject/Omega (doi: 10.5281/zenodo.2535523), under the GNU General Public License v.3. Contactcaterina.strambio@umassmed.edu and alex.rigano@umassmed.edu Supplementary informationSupplementary Material is available at BioRxiv.org

bioinformatics↗