France-BioImaging

Vascular calcification (VC) is common in type 2 diabetes, and is associated with cardiovascular complications. Recent preclinical data suggest that metformin inhibits VC both in vitro and in animal models. However, metformin’s effects in patients with diabetic VC have not previously been characterized. The present study investigated the association between metformin use and lower-limb arterial calcification in patients with type 2 diabetes and high cardiovascular risk. The DIACART cross-sectional cohort study included 198 patients with type 2 diabetes but without severe chronic kidney disease. Below-the-knee calcification scores were assessed by computed tomography and supplemented by colour duplex ultrasonography. Data on anti-diabetic drugs were carefully collected from the patients’ medical records and during patient interviews. Biochemical and clinical data were studied as potential confounding factors. Metformin-treated patients had a significantly lower calcification score than metformin-free patients (mean ± standard deviation: 2033 ± 4514 and 4684 ± 9291, respectively; p = 0.01). A univariate analysis showed that metformin was associated with a significantly lower prevalence of severe below-the-knee arterial calcification (p = 0.02). VC was not significantly associated with the use of other antidiabetic drugs, including sulfonylureas, insulin, gliptin, and glucagon like peptide-1 analogues. A multivariate logistic regression analysis indicated that the association between metformin use and calcification score (odds ratio [95% confidence interval] = 0.33 [0.11–0.98]; p = 0.045) was independent of age, gender, tobacco use, renal function, previous cardiovascular disease, diabetes duration, neuropathy, retinopathy, HbA 1c levels, and inflammation. In patients with type 2 diabetes, metformin use was independently associated with a lower below-the-knee arterial calcification score. This association may contribute to metformin’s well-known vascular protective effect. Further prospective investigations of metformin’s potential ability to inhibit VC in patients with and without type 2 diabetes are now needed to confirm these results.

Expansion microscopy (ExM) has revolutionized biological imaging by physically enlarging samples, surpassing the light diffraction limit, and enabling nanoscale visualization using standard microscopes. While extensively employed across a wide range of biological samples, its application to plant tissues is sparse. In this work, we present ROOT-ExM, an expansion method suited for stiff and intricate multicellular plant tissues, focusing on the primary root of Arabidopsis (Arabidopsis thaliana). ROOT-ExM achieves isotropic expansion with a 4-fold increase in resolution, enabling super-resolution microscopy comparable to stimulated emission depletion (STED) microscopy. Labeling is achieved through immunolocalization, compartment-specific dyes, and native fluorescence preservation, while N-hydroxysuccinimide ester-dye conjugates reveal the ultrastructural context of cells alongside specific labeling. We successfully applied ROOT-ExM to image various organelles and subcellular compartments, including the Golgi apparatus, the endoplasmic reticulum, the cytoskeleton, and tiny wall-embedded structures such as plasmodesmata. Combination of ROOT-ExM with STED enabled reaching an unprecedented resolution of plasmodesmata by light microscopy. When combined with lattice light sheet microscopy, ROOT-ExM enabled 3D quantitative analysis of nanoscale cellular processes, such as the size quantification of vesicles near the cell plate during cell division. Achieving super-resolution fluorescence imaging in plant biology remains a formidable challenge. Our findings underscore that ROOT-ExM provides a remarkable, cost-effective solution to this challenge, paving the way for valuable insights into plant subcellular architecture.

Understanding tumor heterogeneity by examining how cells are organized in tissues is essential to better decipher the biological processes involved in cancer and to improve therapeutic management. Here, we present a protocol for a multiplex immunofluorescence (mIF) technique that enables the detection of an array of protein markers and allows immunophenotyping on a single section of human hepatocellular carcinoma. We describe the procedures for antibody labeling, mIF image acquisition using Cell DIVE, and preliminary analysis using QuPath software.

Recent advances in light microscopy have transformed the scale and complexity of biological imaging data, creating an urgent need for sophisticated computational pipelines capable of extracting meaningful biological insights. However, the current software ecosystem for bio-image analysis remains highly fragmented, with researchers needing to integrate tools written in disparate programming languages and architectural paradigms. This fragmentation gives rise to 'dependency hell' and creates significant technical barriers for life scientists. We present the novel and flexible architecture of BioImageIT, a lightweight open-source workflow management system designed to bridge the gap between advanced computational tools and end-user bio-image analysts. Built upon Python, BioImageIT has evolved into a dual interface architecture: a node-based visual programming GUI alongside a comprehensive Python Application Programming Interface (API). The system features the Wetlands environment management system for automatic dependency resolution, and adopts pandas DataFrames as the universal data structure for inter-node communication. BioImageIT enforces adherence to FAIR principles (Findable, Accessible, Interoperable, Reusable) throughout the analysis lifecycle, automatically capturing comprehensive metadata for every processing step. The architecture abstracts the underlying computational infrastructure, laying the groundwork for seamless scaling from local workstations to high-performance computing (HPC) clusters-a capability currently under active development.

Modern biological microscopy routinely generates large and complex image datasets, including multidimensional, multimodal, and time-resolved acquisitions. While imaging technologies have rapidly evolved, data management infrastructures within microscopy facilities often remain fragmented, relying on heterogeneous local solutions that are difficult to maintain, scale, and integrate with High-Performance Computing (HPC) centers and public data repositories. To address these issues, France BioImaging (FBI), the French national infrastructure for biological imaging, has developed FBI.DATA and the associated BioImage Cloud platform. This initiative aims to provide a coordinated national infrastructure connecting microscopy facilities, centralized storage resources, HPC environments, and public bioimaging archives through interoperable and scalable workflows.The proposed architecture combines open-source technologies including OMERO for image management, iRODS for distributed data orchestration, Authentik for federated authentication, and emerging standards such as OME-Zarr and REMBI metadata recommendations. The infrastructure is designed to support the complete imaging data lifecycle, from acquisition and transfer to visualization, analysis, sharing, and long-term archiving. Beyond the technical implementation, this work presents the organizational and governance strategies required to deploy a shared national infrastructure across distributed imaging facilities. We discuss the challenges associated with interoperability, metadata standardization, sustainability, and user adoption, as well as the perspectives opened by tighter integration between imaging data and large-scale computing resources for future AI-driven bioimage analysis workflows.

International audience

We present the Light My Cells Database, a large-scale open-access collection comprising 2,574 acquisition sets and 56,984 microscopy 2D images designed to support the development of machine learning models for fluorescence prediction from transmitted light images. The dataset aggregates data from 30 independent studies conducted across 8 national imaging centers and captures a wide diversity of biological samples, imaging modalities, and acquisition systems. Each transmitted light image - recorded in bright-field, phase contrast, or differential interference contrast -is paired with at least one fluorescence image labeling key subcellular structures: nucleus, mitochondria, tubulin, or actin. All images are standardized in OME-TIFF format and annotated with rich metadata following REMBI guidelines. A dedicated preprocessing pipeline ensures dimensional harmonization, best-focus plane selection, and consistent file naming. The database reflects the variability encountered in real-life microscopy experiments, making it suited for training and benchmarking generalizable deep learning models. It is accessible via the BioImage Archive and supports a range of downstream applications, including in silico labeling, segmentation, and cell profiling from label-free imaging.

Drug resistance limits the achievement of persistent cures for the treatment of melanoma, despite the efficacy of targeted therapies. This study explored how transcriptional regulation governs metabolic adaptations that underlie resistance. Our analysis of the metabolic profiles revealed a distinct shift in resistant melanoma cells-from glycolytic metabolism in BRAFi-sensitive cells to oxidative phosphorylation (OXPHOS) dependence. This transition was accompanied by a reprogramming of transcriptional networks, marked by the downregulation of MITF transcription factor and a pronounced upregulation and activation of the Aryl hydrocarbon Receptor (AhR). AhR emerged as a key regulator of this resistant phenotype, contributing to the metabolic switch that enhances mitochondrial function, elevates reactive oxygen species (ROS) production, and drives lipid peroxidation. This reprogramming sensitizes resistant cells to ferroptosis, a regulated cell death driven by iron-dependent lipid peroxidation. Importantly, pharmacological activation or stabilization of AhR exacerbated this susceptibility, while its inhibition mitigated ferroptotic responses-highlighting AhR not only as a mediator of resistance-associated metabolic rewiring but also as a potential therapeutic target. Collectively, these findings position AhR as a central node linking metabolic plasticity to ferroptosis vulnerability, offering a novel axis for therapeutic intervention in drug-resistant melanoma.

RATIONALE: Three-dimensional (3D) organoid models, such as alveolospheres, are unique tools for investigating the mechanisms underlying emphysema. However, high inter-organoid heterogeneity hampers consistent results in emphysema research and drug testing. OBJECTIVES: To develop a tunable 3D alveolosphere derived from human primary type II alveolar epithelial cells (AEC2) for modeling alterations linked to cigarette smoke exposure. METHODS: AEC2 (HTII-280+) were isolated from 52 lung samples from both COPD and non-COPD patients, then cultured in 3D, comparing Matrigel to preformed photopolymerized hydrogel microwells of adjustable size and stiffness. Topological and phenotypic characterization were performed on days (D)1, 7, and 14. Lamellar bodies (LBs) were quantified using artificial intelligence (AI) analysis of transmission electron microscopy (TEM) serial block-face images. Chronic exposure to 1% or 5% cigarette smoke extract (CSE) was performed for 5 consecutive days. RESULTS: Compared to Matrigel-based spheroid cultures, alveolospheres generated in microwells display reduced heterogeneity in size. Such alveolospheres were maintained in culture for 14 days and exhibited central lumen formation from D7 to D14. Across different hydrogel stiffness, a stiffness of 5 kPa was found to best support long-term organoid maintenance. The presence of tight junctions (TEM, ZO-1 immunostaining) suggested an auto-organization. AEC1 markers (P2XR4, PDPN) increased from D1 to D14 while AEC2 markers (ABCA3, SFTPA, SFTPC) persisted over time, in qPCR. TEM indicated surfactant synthesis, and AI-driven LB quantification revealed a decrease in LB-containing cells over time. CSE exposure resulted in cell death, architectural disorganization, oxidative stress, and inflammation. Similarly, alveolospheres derived from COPD patients showed increased expression of inflammatory and cell death markers. CONCLUSION: This standardized and adjustable 3D alveolosphere model, derived from human primary AEC2, successfully reproduces key native alveolar features. Exposure to CSE provides a relevant platform for studying responses to cigarette smoke exposure.

This is a minimal dataset to test [omero-quay](https://gitlab.in2p3.fr/fbi-data/omero-quay) See omero-quay documentation and tests for usage

International audience