The expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to reactive microscopy and computing fields requiring efficiency.

The functionality of photoreceptors, rods, and cones is highly dependent on their outer segments (POS), a cellular compartment containing highly organized membranous structures that generate biochemical signals from incident light. While POS formation and degeneration are qualitatively assessed on microscopy images, reliable methodology for quantitative analyses is still limited. Here, we developed methods to quantify POS (QuaPOS) maturation and quality on retinal sections using automated image analyses. POS formation was examined during the development and in adulthood of wild-type mice via light microscopy (LM) and transmission electron microscopy (TEM). To quantify the number, size, shape, and fluorescence intensity of POS, retinal cryosections were immunostained for the cone POS marker S-opsin. Fluorescence images were used to train the robust classifier QuaPOS-LM based on supervised machine learning for automated image segmentation. Characteristic features of segmentation results were extracted to quantify the maturation of cone POS. Subsequently, this quantification method was applied to characterize POS degeneration in “cone photoreceptor function loss 1” mice. TEM images were used to establish the ultrastructural quantification method QuaPOS-TEM for the alignment of POS membranes. Images were analyzed using a custom-written MATLAB code to extract the orientation of membranes from the image gradient and their alignment (coherency). This analysis was used to quantify the POS morphology of wild-type and two inherited retinal degeneration (“retinal degeneration 19” and “rhodopsin knock-out”) mouse lines. Both automated analysis technologies provided robust characterization and quantification of POS based on LM or TEM images. Automated image segmentation by the classifier QuaPOS-LM and analysis of the orientation of membrane stacks by QuaPOS-TEM using fluorescent or TEM images allowed quantitative evaluation of POS formation and quality. The assessments showed an increase in POS number, volume, and membrane coherency during wild-type postnatal development, while a decrease in all three observables was detected in different retinal degeneration mouse models. All the code used for the presented analysis is open source, including example datasets to reproduce the findings. Hence, the QuaPOS quantification methods are useful for in-depth characterization of POS on retinal sections in developmental studies, for disease modeling, or after therapeutic interventions affecting photoreceptors.

Recent advances in microscopy imaging and image analysis motivate more and more institutes worldwide to establish dedicated core-facilities for bioimage analysis. To maximise the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential competing interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.

Elucidating organismal developmental processes requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by reconstructions of cellular trajectories. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate-mapping experiments. To demonstrate the versatility of our multimodal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of neuro-mesodermal progenitors (NMPs) and the origins of a joint kidney-hemangioblast progenitor population.

Interdisciplinary communities, such as the life-sciences, have a strong need for efficient knowledge-transfer. In our community, computer scientists, bioimage analysts and biologists frequently come together to train each other in quantitative microscopy bioimage data analysis. For these trainings, re-usable high-quality training materials can be key. We advocate for publishing training materials according to the FAIR principles: Materials must be findable, openly accessible, stored in interoperable file formats, and most importantly made reusable by attaching open-access licenses. We are convinced that the path towards FAIR training materials leads us to more advanced and higher quality training, facilitating the advance of BioImage Analysis as a whole.

The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using two-dimensional images of GI wholemount preparations. GAT is developed in Fiji, has a user-friendly interface, and offers rapid and accurate segmentation via custom deep learning (DL)-based cell segmentation models developed using StarDist, as well as a ganglia segmentation model in deepImageJ. We apply proximal neighbor- based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput, allowing rapid unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples.

The coordination of cell proliferation, migration, and differentiation is crucial for organogenesis in many tissues, including the CNS and other organs that arise from pseudostratified epithelia (PSE). PSE feature densely packed elongated epithelial cells, with nuclei positioned along the apicobasal cell axis in a cell cycle-dependent manner. PSE serve as organ precursors in diverse developmental contexts across evolution. While the role of nuclear movements in PSE has been extensively studied, less is known about whether and how their nuclear packing arrangements and changes of packing state influence tissue morphogenesis. To address this, we analyzed nuclear shapes, sizes and neighborhood statistics by segmenting nuclei in 3D and over development in zebrafish retinal neuroepithelia (RNE). We find that in PSE nuclei exhibit orientational, nematic-like order but remain positionally disordered. This pattern is conserved in other, less packed, neuroepithelia, like the hindbrain, suggesting that nematic-like order is a hallmark of pseudostratification. Our analysis during retinal development also revealed that nuclear packing density increases, approaching theoretical packing limits for disordered monodisperse ellipsoids at stages when the tissue transitions to a laminated neuronal structure. As neurogenesis progresses, nuclear shapes are remodeled, enabling the RNE to shift to a crystalline, ordered structure, while maintaining orientational alignment. Failure to initiate neurogenesis results in severe tissue deformations due to increased buckling instability. Our results thus show an instance where nuclear shape and nuclear positioning and their changes are essential for proper retinal morphogenesis, a phenomenon most likely also found in other tissue arrangements. ### Competing Interest Statement The authors have declared no competing interest.

In the computational age, life-scientists often have to write Python code to solve bio-image analysis (BIA) problems. Many of them have not been formally trained in programming though. Code-generation, or coding assistance in general, with Large Language Models (LLMs) can have a clear impact on BIA. To the best of our knowledge, the quality of the generated code in this domain has not been studied. We present a quantitative benchmark to estimate the capability of LLMs to generate code for solving common BIA tasks. Our benchmark currently consists of 57 human-written prompts with corresponding reference solutions in Python, and unit-tests to evaluate functional correctness of potential solutions. We demonstrate our benchmark here and compare 18 state-of-the-art LLMs. To ensure that we will cover most of our community needs we also outline mid- and long-term strategies to maintain and extend the benchmark by the BIA open-source community. This work should support users in deciding for an LLM and also guide LLM developers in improving the capabilities of LLMs in the BIA domain. ### Competing Interest Statement The authors have declared no competing interest.

Bioimage analysis (BIA), a crucial discipline in biological research, overcomes the limitations of subjective analysis in microscopy through the creation and application of quantitative and reproducible methods. The establishment of dedicated BIA support within academic institutions is vital to improving research quality and efficiency and can significantly advance scientific discovery. However, a lack of training resources, limited career paths and insufficient recognition of the contributions made by bioimage analysts prevent the full realization of this potential. This Perspective - the result of the recent The Company of Biologists Workshop 'Effectively Communicating Bioimage Analysis', which aimed to summarize the global BIA landscape, categorize obstacles and offer possible solutions - proposes strategies to bring about a cultural shift towards recognizing the value of BIA by standardizing tools, improving training and encouraging formal credit for contributions. We also advocate for increased funding, standardized practices and enhanced collaboration, and we conclude with a call to action for all stakeholders to join efforts in advancing BIA.

Tooth classes are an innovation that has contributed to the evolutionary success of mammals. However, our understanding of the mechanisms by which tooth classes diversified remain limited. We use the evolutionary radiation of noctilionoid bats to show how the tooth developmental program evolved during the adaptation to new diet types. Combining morphological, developmental and mathematical modeling approaches, we demonstrate that tooth classes develop through independent developmental cascades that deviate from classical models. We show that the diversification of tooth number and size is driven by jaw growth rate modulation, explaining the rapid gain/loss of teeth in this clade. Finally, we mathematically model the successive appearance of tooth buds, supporting the hypothesis that growth acts as a key driver of the evolution of tooth number and size. Our work reveal how growth, by tinkering with reaction/diffusion processes, drives the diversification of tooth classes and other repeated structure during adaptive radiations.

Comprehending cellular changes of radiation-induced brain injury is crucial to prevent and treat the pathology. We provide a unique open dataset of proton-irradiated mouse brains consisting of medical imaging, radiation dose simulations, and large-scale microscopy images, all registered into a common coordinate system. This allows dose-dependent analyses on single-cell level.

To overcome the challenges posed by large and complex microscopy datasets, we have developed NanoPyx, an adaptive bioimage analysis framework designed for high-speed processing. At the core of NanoPyx is the Liquid Engine, an agent-based machine-learning system that predicts acceleration strategies for image analysis tasks. Unlike traditional single-algorithm methods, the Liquid Engine generates multiple CPU and GPU code variations using a meta-programming system, creating a competitive environment where different algorithms are benchmarked against each other to achieve optimal performance under the user”s computational environment. In initial experiments focusing on super-resolution analysis methods, the Liquid Engine demonstrated an over 10-fold computational speed improvement by accurately predicting the ideal scenarios to switch between algorithmic implementations. NanoPyx is accessible to users through a Python library, code-free Jupyter notebooks, and a napari plugin, making it suitable for individuals regardless of their coding proficiency. Furthermore, the optimisation principles embodied by the Liquid Engine have broader implications, extending their applicability to various high-performance computing fields.

Bio image analysis has recently become one keystone of biological research but biologists tend to get lost in a plethora of available software and the way to adjust available tools to their own image analysis problem. We present BIII, BioImage Informatic Index (www.biii.eu), the result of the first large community effort to bridge the communities of algorithm and software developers, bioimage analysts and biologists, under the form of a web-based knowledge database crowdsourced by these communities. Software tools (> 1300), image databases for benchmarking (>20) and training materials (>70) for bio image analysis are referenced and curated following standards constructed by the community and then reaching a broader audience. Software tools are organized as full protocol of analysis (workflow), specific brick (component) to construct a workflow, or software platform or library (collection). They are described using Edam Bio Imaging, which is iteratively defined using this website. All entries are exposed following FAIR principles and accessible for other usage.

Light microscopy enables researchers to observe cellular mechanisms with high spatial and temporal resolution. However, the increasing complexity of current imaging technologies, coupled with financial constraints of potential users, hampers the general accessibility and potential reach of cutting-edge microscopy. Open microscopy can address this issue by making well-designed and well-documented hardware and software solutions openly available to a broad audience. In this Comment, we provide a definition of open microscopy and present recent projects in the field. We discuss current and future challenges of open microscopy and their implications for funders, policymakers, researchers and scientists. We believe that open microscopy requires a holistic approach. Sample preparation, designing and building of hardware components, writing software, data acquisition and data interpretation must go hand in hand to enable interdisciplinary and reproducible science to the benefit of society.

With an increase in subject knowledge expertise required to solve specific biological questions, experts from different fields need to collaborate to address increasingly complex issues. To successfully collaborate, everyone involved in the collaboration must take steps to “meet in the middle.” We thus present a guide on truly cross-disciplinary work using bioimage analysis as a showcase, where it is required that the expertise of biologists, microscopists, data analysts, clinicians, engineers, and physicists meet. We discuss considerations and best practices from the perspective of both users and technology developers, while offering suggestions for working together productively and how this can be supported by institutes and funders. Although this guide uses bioimage analysis as an example, the guiding principles of these perspectives are widely applicable to other cross-disciplinary work.

We present LABKIT, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. LABKIT is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as well as a memory efficient and fast implementation of the random forest based pixel classification algorithm as the foundation of our software. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. LABKIT is easy to install on virtually all laptops and workstations. Additionally, LABKIT is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in LABKIT via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Finally, LABKIT comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use LABKIT in a number of practical real-world use-cases.

Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualising information derived from microscopy imaging data of biological samples. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software that we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the platform that best suits the user's needs, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.

NEUBIAS, the European Network of Bioimage Analysts, was created in 2016 with the goal of improving the communication and the knowledge transfer among the various stakeholders involved in the acquisition, processing and analysis of biological image data, and to promote the establishment and recognition of the profession of Bioimage Analyst. One of the most successful initiatives of the NEUBIAS programme was its series of 15 training schools, which trained over 400 new Bioimage Analysts, coming from over 40 countries. Here we outline the rationale behind the innovative three-level program of the schools, the curriculum, the trainer recruitment and turnover strategy, the outcomes for the community and the career path of analysts, including some success stories. We discuss the future of the materials created during this programme and some of the new initiatives emanating from the community of NEUBIAS-trained analysts, such as the NEUBIAS Academy. Overall, we elaborate on how this training programme played a key role in collectively leveraging Bioimaging and Life Science research by bringing the latest innovations into structured, frequent and intensive training activities, and on why we believe this should become a model to further develop in Life Sciences.

BACKGROUND AND AIMS:NAFLD is initiated by steatosis and can progress through fibrosis and cirrhosis to HCC. The RNA binding protein human antigen R (HuR) controls RNAs at the posttranscriptional level; hepatocyte HuR has been implicated in the regulation of diet-induced hepatic steatosis. The present study aimed to understand the role of hepatocyte HuR in NAFLD development and progression to fibrosis and HCC.APPROACH AND RESULTS:Hepatocyte-specific, HuR-deficient mice and control HuR-sufficient mice were fed either a normal diet or an NAFLD-inducing diet. Hepatic lipid accumulation, inflammation, fibrosis, and HCC development were studied by histology, flow cytometry, quantitative PCR, and RNA sequencing. The liver lipidome was characterized by lipidomics analysis, and the HuR-RNA interactions in the liver were mapped by RNA immunoprecipitation sequencing. Hepatocyte-specific, HuR-deficient mice displayed spontaneous hepatic steatosis and fibrosis predisposition compared to control HuR-sufficient mice. On an NAFLD-inducing diet, hepatocyte-specific HuR deficiency resulted in exacerbated inflammation, fibrosis, and HCC-like tumor development. A multi-omic approach, including lipidomics, transcriptomics, and RNA immunoprecipitation sequencing revealed that HuR orchestrates a protective network of hepatic-metabolic and lipid homeostasis-maintaining pathways. Consistently, HuR-deficient livers accumulated, already at steady state, a triglyceride signature resembling that of NAFLD livers. Moreover, up-regulation of secreted phosphoprotein 1 expression mediated, at least partially, fibrosis development in hepatocyte-specific HuR deficiency on an NAFLD-inducing diet, as shown by experiments using antibody blockade of osteopontin.CONCLUSIONS:HuR is a gatekeeper of liver homeostasis, preventing NAFLD-related fibrosis and HCC, suggesting that the HuR-dependent network could be exploited therapeutically.