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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. 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 everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location., Comment: This manuscript is published with a closely related companion entitled, Enabling Global Image Data Sharing in the Life Sciences, which can be found at the following link, arXiv:2401.13023 [q-bio.OT]

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., Comment: 17 pages, 3 figures, shortened abstract, Co-Lead Authors: Glyn Nelson and Ulrike Boehm, Corresponding author: Roland Nitschke

The performance of a confocal imaging system may be no better than a general-purpose widefield system if it is not properly maintained or quality controlled. The publication of ISO 21073, 'Confocal microscopes- Optical data of fluorescence confocal microscopes for biological imaging', set a standard for the minimal Quality Control (QC) that should be performed for Confocal Microscopes. Here we describe methodology for performing the QC requirements to satisfy ISO 21073, as well as suggesting other QC methods that should be performed to obtain a minimum level of information about the microscope system., Comment: When drawing up this document, it became apparent that further work was required to determine best methodology and further minimal QC tests, as well as extend to other common imaging modalities. As a consequence, QUAREP-LiMi (https://quarep.org) was established to produce a more definitive and expansive QC Methodology manual for light microscopy to supersede this document

The application of microscopy in biomedical research has come a long way since Antonie van Leeuwenhoek discovered unicellular organisms. Countless innovations have positioned light microscopy as a cornerstone of modern biology and a method of choice for connecting omics datasets to their biological and clinical correlates. Still, regardless of how convincing published imaging data looks, it does not always convey meaningful information about the conditions in which it was acquired, processed, and analyzed. Adequate record-keeping, reporting, and quality control are therefore essential to ensure experimental rigor and data fidelity, allow experiments to be reproducibly repeated, and promote the proper evaluation, interpretation, comparison, and re-use. To this end, microscopy images should be accompanied by complete descriptions detailing experimental procedures, biological samples, microscope hardware specifications, image acquisition parameters, and image analysis procedures, as well as metrics accounting for instrument performance and calibration. However, universal, community-accepted Microscopy Metadata standards and reporting specifications that would result in Findable Accessible Interoperable and Reproducible (FAIR) microscopy data have not yet been established. To understand this shortcoming and to propose a way forward, here we provide an overview of the nature of microscopy metadata and its importance for fostering data quality, reproducibility, scientific rigor, and sharing value in light microscopy. The proposal for tiered Microscopy Metadata Specifications that extend the OME Data Model put forth by the 4D Nucleome Initiative and by Bioimaging North America [1-3] as well as a suite of three complementary and interoperable tools are being developed to facilitate the process of image data documentation and are presented in related manuscripts [4-6].

Human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) have promising clinical applications which often rely on clonally-homogeneous cell populations. To achieve this, cross-contamination and merger of colonies should be avoided. This motivates us to experimentally study and quantitatively model the growth of hESC colonies. The colony population is unexpectedly found to be multi-modal. We associate these sub-populations with different numbers of founding cells, and predict their occurrence by considering the role of cell-cell interactions and cell behaviour on randomly seeded cells. We develop a multi-population stochastic exponential model for the colony population which captures our experimental observations, and apply this to calculate the timescales for colony merges and over which colony size no longer predicts the number of founding cells. These results can be used to achieve the best outcome for homogeneous colony growth from different cell seeding densities.