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Uniyal, P.

Publications and source records attributed to Uniyal, P..

2 recordsLinked to original sources

Unsupervised 3D classification and characterisation of osteocyte and chondrocyte lacunae in the murine osteochondral unit

In the murine osteochondral unit, morphology of chondrocyte lacunae (CCL) within calcified articular cartilage and osteocyte lacunae (OCL) within the underlying subchondral bone reflects local biomechanical conditions that are altered in disease, making reliable discrimination of both lacunar populations important for studying osteochondral physiology and pathology. Histology is generally considered the gold standard for identifying CCL and OCL. However, it is inherently limited to two-dimensional assessment and a relatively small tissue section, restricting comprehensive characterisation of three-dimensional lacunar structure. Synchrotron-radiation micro-computed tomography (SR-microCT) enables volumetric quantification of lacunae; where classification is typically based on the position of each lacuna relative to the cement line separating calcified cartilage from bone, requiring manual delineation of this convoluted interface. Such approaches are labour-intensive, operator-dependent, and rely on access to specialised synchrotron infrastructure that is not widely available. To address these limitations, we present a fully automated, annotation-free pipeline for classifying OCL and CCL in the mouse osteochondral unit based on desktop micro-computed tomography (microCT), independent of cement line identification. The proposed unsupervised workflow requires neither tissue separation nor labelled training data, enabling objective and scalable discrimination of lacunar populations directly from volumetric image data. Seven morphometric and positional features per lacuna were extracted from SR-microCT and desktop microCT datasets and analysed using Uniform Manifold Approximation and Projection (UMAP). Validation of the pipeline outcome on SR-microCT against manual classification across specimens yielded very-high agreement, with Gwet's AC1, the Matthews correlation coefficient, and balanced accuracy each approaching 0.99, in both healthy and post-traumatic osteoarthritis specimens. Such a workflow can be readily adapted for the investigation of osteochondral interface in various other species.

bioengineering↗

Associations on the Fly, a new feature aiming to facilitate exploration of the Open Targets Platform evidence

MotivationThe Open Targets Platform (https://platform.opentargets.org) is a unique, comprehensive, open-source resource supporting systematic identification and prioritisation of targets for drug discovery. The Platform combines, harmonises and integrates data from >20 diverse sources to provide target-disease associations, covering evidence derived from genetic associations, somatic mutations, known drugs, differential expression, animal models, pathways and systems biology. An in-house target identification scoring framework weighs the evidence from each data source and type, contributing to an overall score for each of the 7.8M target-disease associations. However, the previous infrastructure did not allow user-led dynamic adjustments in the contribution of different evidence types for target prioritisation, a limitation frequently raised by our user community. Furthermore, the previous Platform user interface did not support navigation and exploration of the underlying target-disease evidence on the same page, occasionally making the user journey counterintuitive. ResultsHere, we describe "Associations on the Fly" (AOTF), a new Platform feature - developed as part of a wider product refactoring project - to enable formulation of more flexible and impactful therapeutic hypotheses through dynamic adjustment of the weight of contributing evidence from each source, altering the prioritisation of targets. Availability and implementationAll Open Targets code is available as open source: [https://github.com/opentargets]. This tool was implemented using React v18 and its code is accessible here: [https://github.com/opentargets/ot-ui-apps]. The tools described in the paper are accessible through the Open Targets Platform web interface [https://platform.opentargets.org/] and GraphQL API (https://platform-docs.opentargets.org/data-access/graphql-api). Data is available for download here: [https://platform.opentargets.org/downloads] and from the EMBL-EBI FTP: [https://ftp.ebi.ac.uk/pub/databases/opentargets/platform/]. Supplementary informationAdditional information on this tool can be found on the Platform documentation pages [https://platform-docs.opentargets.org/web-interface, https://platform-docs.opentargets.org/web-interface/associations-on-the-fly, https://platform-docs.opentargets.org/target-prioritisation] and training video [https://youtu.be/2A9bksboAag]. ContactAnnalisa Buniello, EMBL-EBI, buniello@ebi.ac.uk

bioinformatics↗