Tissue-Like 3D Standard and Protocols for Microscope Quality Management

Strategies for selecting and managing equipment in a light microscopy facility

Light microscopy facilities vary in the number of imaging systems and the scope of technologies they support. Each facility must craft an identity through the selection of equipment and development of staff in order to serve the needs of its local research environment. The process of crafting a light microscopy facility can be compared to curation of an art exhibition: great care should be given to the selection and placement of each object in order to make a coherent statement. Lay Description: Light microscopy facilities vary in the number of imaging systems and the scope of technologies they support. Each facility must develop an identity through the selection of equipment and development of staff in order to serve the needs of its local research environment. The process of crafting a light microscopy facility can be compared to curation of an art exhibition: great care should be given to the selection and placement of each object in order to make a coherent statement.

Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.