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Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata Specifications that extend the OME Data Model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

As part of the Reproducibility Project: Cancer Biology we published a Registered Report (Fiering et al., 2015) that described how we intended to replicate selected experiments from the paper ‘Biomechanical remodeling of the microenvironment by stromal caveolin-1 favors tumor invasion and metastasis’ (Goetz et al., 2011). Here we report the results. Primary mouse embryonic fibroblasts (pMEFs) expressing caveolin 1 (Cav1WT) demonstrated increased extracellular matrix remodeling in vitro compared to Cav1 deficient (Cav1KO) pMEFs, similar to the original study (Goetz et al., 2011). In vivo, we found higher levels of intratumoral stroma remodeling, determined by fibronectin fiber orientation, in tumors from cancer cells co-injected with Cav1WT pMEFs compared to cancer cells only or cancer cells plus Cav1KO pMEFs, which were in the same direction as the original study (Supplemental Figure S7C; Goetz et al., 2011), but not statistically significant. Primary tumor growth was similar between conditions, like the original study (Supplemental Figure S7Ca; Goetz et al., 2011). We found metastatic burden was similar between Cav1WT and Cav1KO pMEFs, while the original study found increased metastases with Cav1WT (Figure 7C; Goetz et al., 2011); however, the duration of our in vivo experiments (45 days) were much shorter than in the study by Goetz et al. (2011) (75 days). This makes it difficult to interpret the difference between the studies as it is possible that the cells required more time to manifest the difference between treatments observed by Goetz et al. We also found a statistically significant negative correlation of intratumoral remodeling with metastatic burden, while the original study found a statistically significant positive correlation (Figure 7Cd; Goetz et al., 2011), but again there were differences between the studies in terms of the duration of the metastasis studies and the imaging approaches that could have impacted the outcomes. Finally, we report meta-analyses for each result.

The Reproducibility Project: Cancer Biology seeks to address growing concerns about reproducibility in scientific research by conducting replicating selected results from a number of high-profile papers in the field of cancer biology. The papers, which were published between 2010 and 2012 were selected on the basis of citations and Altimetric scores (Errington et al., 2014 ). This Registered report describes the proposed replication plan of key experiments from ‘Biomechanical remodeling of the microenvironment by stromal caveolin-1 favors tumor invasion and metastasis’ by Goetz and colleagues, published in Cell in 2011 (Goetz et al., 2011 ). The key experiments being replicated are those reported in Figures 7C (a-d), Supplemental Figure S2A, and Supplemental Figure S7C (a-c) (Goetz et al., 2011 ). In these experiments, which are a subset of all the experiments reported in the original publication, Goetz and colleagues show in a subcutaneous xenograft model that stromal caveolin-1 remodels the intratumoral microenvironment, which is correlated with increased metastasis formation. The Reproducibility Project: Cancer Biology is a collaboration between the Center for Open Science and Science Exchange and the results of the replications will be published in eLife.

For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.Micro-Meta App is an intuitive, highly interoperable, open-source software tool designed to facilitate the extraction and collection of relevant microscopy metadata as specified by recent community guidelines.

The induction of interferon (IFN) genes by viruses or double-stranded RNA (dsRNA) requires the assembly of a complex set of transcription factors on responsive DNA elements of IFN gene promoters. One of the factors necessary for regulating IFN-β gene transcription is nuclear factor NF-κB, the activation of which is triggered by dsRNA. It has previously been suggested that the dsRNA-activated p68 protein kinase (PKR) may act as an inducer-receptor, transducing the signal from dsRNA to NF-κB through phosphorylation of the inhibitor IκB. We present direct evidence that PKR can phosphorylate IκB-α (MAD-3) and activate NF-κB DNA binding activity in vitro. We further show that dsRNA induces an unusual phosphorylated form of IκB-α. The expression of a transdominant mutant PKR is able to perturb the dsRNA-mediated signaling pathway in vivo, suggesting a role for this kinase in IFN-β gene induction.

While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be properly interpreted (quality), reproduced (reproducibility), and used to extract reliable information and scientific knowledge which can be shared for further analysis (value). In the absence of agreed guidelines, it is inherently difficult for scientists to create comprehensive records of imaging experiments and ensure the quality of resulting image data or for manufacturers to incorporate standardized reporting and performance metrics. To solve this problem, the 4D Nucleome (4DN) Initiative and BioImaging North America (BINA) here propose light Microscopy Metadata specifications that scale with experimental intent and with the complexity of the instrumentation and analytical requirements. They consist of a set of three extensions of the Open Microscopy Environment (OME) Data Model, and because of their tiered nature they clearly specify which provenance and quality control metadata should be recorded for a given experiment. This endeavor is closely aligned with the undertakings of the recently established QUAlity Assessment and REProducibility in Light Microscopy (QUAREP-LiMi; quarep.org) global community initiative. As a result, the ensuing flexible 4DN-BINA-OME (NBO) framework represents a turning point towards increasing data fidelity, improving repeatability and reproducibility, easing future analysis, and facilitating the verifiable comparison of different datasets, experimental setups, and assays. The intention of this proposal is to encourage participation, critiques, and contributions from all imaging community stakeholders, including research and imaging scientists, facility personnel, instrument manufacturers, software developers, standards organizations, scientific publishers, and funders. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://zenodo.org/record/4710731 * https://zenodo.org/record/4711229 * https://zenodo.org/record/4711426

Biological imaging, combined with molecular insights into genes and proteins, holds immense promise for deepening our understanding of complex cellular systems and accelerating the development of predictive, personalized therapies for human health. To fully realize this potential at scale-and harness the power of AI/ML to extract novel biological insights and therapeutic interventions-it is necessary to transition from siloed datasets to globally shared, rigorously annotated, and computationally ready image data. This demands systematic harmonization of multidimensional bioimaging data, where interoperable formats, and standardized context-rich annotation, quality controls, and analytical pipelines transform scattered observations into a coherent knowledge base ripe for computational mining. Only this machine-actionable aggregation can provide the substrate for AI/ML to extract mechanistic insights into fundamental biological mechanisms, novel diagnostic biomarkers and intervention targets. Enabling seamless image data sharing in the life sciences requires addressing two key areas. The first is outlined in an accompanying publication, , which focuses on the publicly available repositories needed to share digital array data . This White Paper details a comprehensive set of requirements for integrated image data and metadata management - from acquisition through dissemination - ensuring the contextual information necessary for assessing quality, interpreting scientific validity, and enabling meaningful reuse remains intrinsically linked to the data throughout its lifecycle. Critically, it recognizes that generating harmonized, well-annotated publicly available corpora of FAIR bioimage data requires these datasets to be \"FAIR-from-the-start\" - an objective that can only be achieved by enabling experimental scientists to manage, organize, and analyze their data according to community standards from the very first experiment. Building on recent progress made by the bioimaging field towards establishing shared practices for bioimaging Quality Control (QC) and metadata capture, we present actionable recommendations to advance these efforts through embedding researcher-friendly integrated software infrastructure directly into pre-publication workflows, thus transforming disorganized data capture into structured, shareable resources ready for aggregation and reuse. Our ultimate goal is to expand the use of streamlined tools and practices thus transforming how researchers capture, annotate, analyze and eventually publish bioimaging data thus laying the foundation for a new era of data-driven discovery.