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Imaging technologies are used throughout the life and biomedical sciences to understand mechanisms in biology and diagnosis and therapy in animal and human medicine. We present criteria for globally applicable guidelines for open image data tools and resources for the rapidly developing fields of biological and biomedical imaging.

A general approach for the sequential labeling of fusion proteins of O6-alkylguanine- DNA alkyltransferase (AGT) with different fluorophores in mammalian cells is presented. AGT fusion proteins with different localizations in the cell can be labeled specifically with different fluorophores, and the fluorescence labeling can be used for applications such as multicolor analysis of dynamic processes and fluorescence resonance energy transfer measurements. The facile access to a variety of different AGT substrates as well as the specificity of the labeling reaction should make the approach an important tool to study protein function in live cells.

Bioimaging has now entered the era of big data with faster-than-ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis, and management practices, which are currently hampering the full potential of image data being realized. Here, we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimaging data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.

Characterizing the movement, interactions, and chemical microenvironment of a protein inside the living cell is crucial to a detailed understanding of its function. Most strategies aimed at realizing this objective are based on genetically fusing the protein of interest to a reporter protein that monitors changes in the environment of the coupled protein. Examples include fusions with fluorescent proteins, the yeast two-hybrid system, and split ubiquitin 1 , 2 , 3 . However, these techniques have various limitations, and considerable effort is being devoted to specific labeling of proteins in vivo with small synthetic molecules capable of probing and modulating their function. These approaches are currently based on the noncovalent binding of a small molecule to a protein, the formation of stable complexes between biarsenical compounds and peptides containing cysteines, or the use of biotin acceptor domains 4 , 5 , 6 , 7 , 8 , 9 , 10 . Here we describe a general method for the covalent labeling of fusion proteins in vivo that complements existing methods for noncovalent labeling of proteins and that may open up new ways of studying proteins in living cells.

Multimodal bioimaging is a broad term used to describe experimental workflows that employ two or more different imaging modalities. Such approaches have been in use across life science domains for several years but these remain relatively limited in scope, in part due to the complexity of undertaking these types of analysis. Expanding these workflows to encompass diverse, emerging technology holds potential to revolutionize our understanding of spatial biology. In this perspective we reflect on the instrument and workflows in current use, emerging areas to consider and our experience of the barriers to broader adoption and progress. We propose several enabling solutions across the different challenge areas, emerging opportunities for consideration and highlight some of the key community activities to help move the field forward.

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ideal world scenario for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

In April 2020, the QUality Assessment and REProducibility for Instruments and Images in Light Microscopy (QUAREP-LiMi) initiative was formed. This initiative comprises imaging scientists from academia and industry who share a common interest in achieving a better understanding of the performance and limitations of microscopes and improved quality control (QC) in light microscopy. The ultimate goal of the QUAREP-LiMi initiative is to establish a set of common QC standards, guidelines, metadata models, and tools, including detailed protocols, with the ultimate aim of improving reproducible advances in scientific research. This White Paper 1) summarizes the major obstacles identified in the field that motivated the launch of the QUAREP-LiMi initiative; 2) identifies the urgent need to address these obstacles in a grassroots manner, through a community of stakeholders including, researchers, imaging scientists, bioimage analysts, bioimage informatics developers, corporate partners, funding agencies, standards organizations, scientific publishers, and observers of such; 3) outlines the current actions of the QUAREP-LiMi initiative, and 4) proposes future steps that can be taken to improve the dissemination and acceptance of the proposed guidelines to manage QC. To summarize, the principal goal of the QUAREP-LiMi initiative is to improve the overall quality and reproducibility of light microscope image data by introducing broadly accepted standard practices and accurately captured image data metrics.

Biological and biomedical imaging datasets record the constitution, architecture and dynamics of living organisms across several orders of magnitude of space and time. Imaging technologies are now used throughout the life and biomedical sciences to achieve discovery and understanding of biological mechanisms in the basic sciences as well as assessment, diagnosis and therapeutic intervention in clinical trials and animal and human medicine. The universal application and use of imaging raises an important question and opportunity: what is the value and ultimate destination of biological and medical imaging data? In the last few years, several informatics and data science technologies have matured sufficiently so that routine publication of these datasets is now possible. Participants in Global BioImaging from 15 countries and all populated continents have agreed on the need for recommendations and guidelines for the establishment of image data repositories and the formats they use for delivering data to the global scientific community. This white paper presents a shared, global view of criteria for these common, globally applicable guidelines and provisional proposals for open tools and resources that are available now and can provide a foundation for future development.