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Mateos Langerak, J.

Publications and source records attributed to Mateos Langerak, J..

2 recordsLinked to original sources

Monitoring microscope performance in an imaging facility using OMERO-metrics.

Microscopes are essential tools for discoveries on a scale invisible to the unaided human eye. The development of immuno-fluorescence followed by molecular biology techniques and fluorescent fusion proteins have revolutionised the use of optical microscopy in bioscience. The quality of the data produced is dependent upon the sample, its preparation and the instrument used. However, instruments can degrade over time without easily visible changes to the produced images and, in turn, negatively impacts results. By testing instruments and doing comparisons between results over time and between different instruments, problems can be highlighted and corrective action can be taken. Using small fluorescent beads the point spread function (PSF) of the microscope can be recorded and the image resolution measured. Beads were prepared in a concentration matched to the field of view size and dried onto coverslips and mounted on slides. The beads were then imaged as 3D Z-stacks of sufficient size to fully enclose the PSF of the system. This data was uploaded to OMERO and processed using OMERO-metrics, an OMERO plugin developed for this purpose. This paper summarizes the development of workflows and protocols to enable this process, presents the results obtained and demonstrates the detection of significant instrument issues.

biophysics↗

Easing OMERO adoption with ezomero

Many research laboratories need to manage, process, and analyze the increasingly large volumes and complexity of data being produced by state-of-the-art bioimaging platforms. OMERO is a popular open-source client-server application that provides a unified interface for managing and working with bioimages and their associated measurements and metadata. Integrating OMERO into analysis pipelines, such as those developed around the scientific Python ecosystem, will thus be a common pattern across the field of bioimaging. While OMERO has a powerful Python API, it provides minimal abstraction from the underlying OMERO object model and associated methods, which represent more complexity than most users are interested in for the context of an analysis script. We introduce ezomero, which was designed to provide a convenience layer on top of existing OMERO APIs and return data types that are either Python primitive or commonly used in scientific Python. Ezomero has minimal dependencies in addition to the OMERO Python library itself and is installable directly from PyPI. Here, we provide an overview of ezomero as well as several vignettes to illustrate how it can be used to accelerate discovery.

bioinformatics↗