Managing the image-data life cycle for the real world: connecting the dots from sample preparation to image acquisition, analysis, and publication

Session

QUAREP-LiMi: An International Collaboration for Microscope Quality Control

Authors

Dr. Caterina Strambio-De-Castillia (2), Dr. Judith Lacoste (1)

Affiliations

1. MIA Cellavie
2. University of Massachusetts Chan Medical School

Keywords

Metadata, Research Data Management, Reproducibility, Quality-Control, Microscopy

Abstract text

Rigorous biological science crucially depends on the generation of high-quality datasets in which all relevant information (i.e., metadata) about a microscopy experiment is reported using FAIR (Findable Accessible Interoperable Reusable) principles [1]. However, significant advances in spatiotemporal resolution have led to ever-expanding microscopy datasets which, without agreed-upon community guidelines, are challenging to quantitatively analyze (including AI-assisted strategies), reproduce, and re-use [2–4]. 

To overcome this hurdle, it is essential to integrate community-specified image documentation, and quality-control guidelines within easy-to-use Research Data Management (RDM) pipelines to support the streamlined execution, tracking, and documentation of the full life-cycle of image data from sample preparation to publication (i.e., data provenance).  This is particularly relevant for microscopy, where data interpretation is crucially dependent on knowledge of three key aspects: 1) experimental context (i.e., organism, growth conditions, sample type, environment, and staining protocol); 2) image acquisition (i.e., microscope hardware/settings/quality-control); and 3) image processing (i.e., software, processing steps).  This knowledge is collectively termed Image Metadata, and its capture and reporting with easy-to-use RDM software are essential to enable biomedical researchers to generate FAIR data. Funding agencies now require that researchers submit detailed research data management (RDM) plans for all data generated thanks to publicly funded research projects [5]. However, it is often difficult for microscope users to decide what information about their microscope is essential. Users often feel overwhelmed by the amount of technical information required to meet FAIR reporting and sharing standards for image data. In addition, individual principal investigators, postdoctoral fellows, and graduate students often have no training or education in managing research data. 

To meet this tremendous challenge, bioimaging organizations such as the Royal Microscopy Society (https://www.rms.org.uk), BioImaging UK [6], BioImaging North America (https://www.bioimagingnorthamerica.org/qc-dm-wg/), German BioImaging (https://gerbi-gmb.de/) and Global BioImaging (https://globalbioimaging.org) have recently joined forces with individual imaging scientists from both academia and industry to establish the QUality Assessment and REProducibility in Light Microscopy (QUAREP-LiMi; https://www.quarep.org) initiative. QUAREP-LiMi is a forum for building broad consensus on quality control, reporting, reproducibility, and sharing value for microscopy experiments. As such, the QUAREP-LiMi Metadata Working Group (WG7; https://quarep.org/working-groups/wg-7-metadata/) has adopted the recently published the 4DN-BINA-OME Microscopy Metadata specifications [7] extending the OME model [8], and it is now in the process of revising it to include the input from microscope manufacturers and facilitate its dissemination across the community [9,10].

Despite these advances, the broader impact of this work crucially depends on how easily biological scientists working at the bench will be able to adopt the proposed specifications and incorporate them into their everyday work regardless of imaging expertise. This means that the RDM processes community members utilize to annotate, upload, and process imaging datasets must strongly emphasize ease of use, be streamlined (and, when possible, automated), and provide clear added value for research scientists.

To meet these goals, the Micro-Meta App [11] was developed and integrated into the 4DN-Data Portal [12,13] in close collaboration with the 4DN Data Coordination and Integration Center (DCIC; https://data.4dnucleome.org/help/about/about-dcic). Micro-Meta App is an intuitive, highly interoperable, open-source software tool designed to facilitate the collection of relevant microscopy metadata as specified by the 4DN-BINA-OME Microscopy Metadata specifications [11]. In addition, to substantially lower the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes. Furthermore, MethodsJ2 and napari methods were developed as complementary tools for the automated generation of Material and Methods text for scientific publications[14]. 

More recently, a collaboration between imaging scientists at Canada BioImaging [15] and at other institutions, including UMass Medical School and The Rockefeller University, was launched to develop OMERO-based [16–18] Image Data Resources for image data import, metadata annotation [19], storage, processing, visualization, figure generation, and sharing  [15].

To illustrate these concepts, the talk will provide concrete examples to describe how the RDM tools described above can be utilized to capture the experimental, sample preparation, and image acquisition phases of a typical microscopy experiment and manage image data both during data production and after publication. In addition, examples will be provided to illustrate publicly available frameworks to develop Data Management and Sharing Plans for grant writing [5]. Following the presentation, ample time will be left for a Q&A session in which attendants will be encouraged to share questions and examples they have encountered in their day-to-day work.

References

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