Community-driven fair data management and reproducibility for the whole image-data life cycle

Poster Nr. 1571 presented at the  2022 ASCB and EMBO Cell Biology Meeting held on December 3-7, 2022 in  Washington, DC, USA https://www.ascb.org/cellbio2022/  Abstract Biomedical advances crucially depend on the generation of high-quality Findable, Accessible, Interoperable, and Reproducible (FAIR; 10.1038/sdata.2016.18) datasets. This, in turn, requires the seamless integration of community-specified image documentation practices within the Research Data Management (RDM) processing pipelines required to ensure the execution, tracking, and documentation of the entire life cycle of data from sample preparation to publication (i.e., data provenance). This is important for microscopy, where data interpretation is crucially dependent on easy-to-use RDM software enabling the capture and reporting of knowledge that is collectively termed Image Metadata, and that consists of three key aspects: i) biological context (i.e., organism, growth conditions, sample-type); ii) image acquisition (i.e., microscope hardware/settings/quality-control); and iii) image processing (i.e., software, analysis steps). To illustrate these points, this presentation will first introduce recently published 4DN-BINA-OME-QUAREP community-driven Image Metadata specifications developed in the context of international bioimaging initiatives (10.1038/s41592-021-01327-9) and how they can be applied to typical light microscopy experiments. This will be followed by a deep dive into the importance of incorporating robust microscopy quality assessment and reporting procedures in the life cycle of light-microscopy data to ensure rigor, reproducibility, and reusability. The discussion will identify key stages in the pathway that includes image data acquisition, management, analysis, and dissemination and provide OMERO-based concrete and practical examples of how open-source tools and protocols developed by an international consortium of community initiatives led by QUality Assessment and REProducibility in Light Microscopy (QUAREP-LiMi), are being utilized in close collaboration with Canada BioImaging, at McGill University and UMass Medical School to capture and report the necessary quality-control metrics and metadata to support the reproducibility and reusability of image-based datasets. Finally, the presentation will also introduce the Micro-Meta App and MethodsJ2 software tools that allow researchers to collect detailed microscope hardware and acquisition settings metadata and generates draft methods text for publication.</p

Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications.

Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, have demonstrated that: (i) more than 50% of all antibodies failed in one or more applications, (ii) yet, ~50-75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and (iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts