Journal of Microscopy

Fluorescence microscopy is a fundamental tool in the life sciences, but the availability of sophisticated equipment required to yield high-quality, quantitative data is a major bottleneck in data production in many laboratories worldwide. This problem has long been recognized and the abundancy of low-cost electronics and the simplification of fabrication through 3D-printing have led to the emergence of open-source scientific hardware as a research field. Cost effective fluorescence microscopes can be assembled from cheaply mass-produced components, but lag behind commercial solutions in image quality. On the other hand, blueprints of sophisticated microscopes such as light-sheet or super-resolution systems, custom-assembled from high quality parts, are available, but require a high level of expertise from the user. Here we combine the UC2 microscopy toolbox with high-quality components and integrated electronics and software to assemble an automated high-resolution fluorescence microscope. Using this microscope, we demonstrate high resolution fluorescence imaging for fixed and live samples. When operated inside an incubator, long-term live-cell imaging over several days was possible. Our microscope reaches single molecule sensitivity, and we performed single particle tracking and SMLM super-resolution microscopy experiments in cells. Our setup costs a fraction of its commercially available counterparts but still provides a maximum of capabilities and image quality. We thus provide a proof of concept that high quality scientific data can be generated by lay users with a low-budget system and open-source software. Our system can be used for routine imaging in laboratories that do not have the means to acquire commercial systems and through its affordability can serve as teaching material to students.

Visual proteomics (VP) aims to allow researchers to visualise, measure and analyse proteins in the context of cell and tissue structure in health and disease. VP is becoming a reality through technological advances across several domains, including in situ structural biology and correlative light and electron microscopy (CLEM). However, widespread adoption remains limited due to the complexity and cost of the various VP approaches reported to date. Here we present the VP-CLEM-Kit, a disruptive cost-effective pipeline for super resolution volume CLEM (SR-vCLEM) that can be implemented with minimal advanced electron microscopy expertise and equipment, making it accessible to light microscopy facilities and research labs. SR-vCLEM is based on in-resin fluorescence (IRF), where fluorophores are preserved through processing into resin. The easyIRF protocol reported here reduces the requirement for complex costly sample preparation equipment and toxic chemicals compared to standard IRF protocols. easyIRF blocks are cut into ultrathin sections that are imaged using tomoSTORM, a new modular and cost-effective openFrame-based light microscope controlled by the open-source software package Micro-Manager that provides serial single molecule localisation microscopy in array tomography format. Sections are then post-stained and imaged using a tabletop scanning electron microscope controlled by open-source SBEMimage software to run in array tomography format. We demonstrate the potential of the VP-CLEM-Kit by imaging organelle reporters in human cell lines and stem cell derived neurons, fluorescently labelled protein in neurons, and immunolabelled cells in human kidney biopsy tissue from transplant patients. The VP-CLEM-Kit delivers a [~]5-fold improvement in resolution at a [~]7-fold lower cost, and thus provides new technical capability as well as a blueprint for more equitable access to advanced imaging workflows.

Three-dimensional structured illumination microscopy (3D-SIM) doubles the resolution of fluorescence imaging in all directions and increases the image contrast. However, 3D-SIM has not been widely applied to imaging deep in thick tissues due to its sensitivity to sample-induced aberrations, making the method difficult to apply beyond 10 {micro}m in depth. Furthermore, 3D-SIM has not been available in an upright configuration, limiting its use for live imaging while manipulating the specimen, for example with electrophysiology. Here, we have overcome these barriers by developing a novel upright 3D-SIM system (termed Deep3DSIM) that incorporates adaptive optics for aberration correction and remote focusing, reducing artefacts, improving contrast, restoring resolution, and removing the need to move the specimen or the objective lens in volume imaging. These advantages are equally applicable to inverted 3D-SIM systems. We demonstrate high-quality 3D-SIM imaging in various samples, including an example of imaging 130 {micro}m into Drosophila brain.

Proper reporting of metadata is essential to reproduce microscopy experiments, interpret results and share images. Experimental scientists can report details about sample preparation and imaging conditions while imaging scientists have the expertise required to collect and report the image acquisition, hardware and software metadata information. MethodsJ2 is an ImageJ/Fiji based software tool that gathers metadata and automatically generates text for the methods section of publications.

The mother machine is a microfluidic device for high-throughput time-lapse imaging of microbes. Here, we present MM3, a complete and modular image analysis pipeline. MM3 turns raw mother machine images, both phase contrast and fluorescence, into a data structure containing cells with their measured features. MM3 employs machine learning and non-learning algorithms, and is implemented in Python. MM3 is easy to run as a command line tool with the occasional graphical user interface on a PC or Mac. A typical mother machine experiment can be analyzed within one day. It has been extensively tested, is well documented and publicly available via Github.

To improve access to advanced optical microscopy in educational and resource-limited settings we have developed the eduWOSM (educational Warwick Open Source Microscope), an open hardware platform for transmitted-light and epifluorescence imaging in up to 4 colours, including single molecule imaging. EduWOSMs are robust, bright, compact, portable and ultra-stable. They are controlled entirely by open source hardware and software, with an option for remote control from a webpage. Here we describe the core eduWOSM technology and benchmark its performance using 3 example projects, single fluorophore tracking of tubulin heterodimers within gliding microtubules, 4D (deconvolution) imaging/tracking of chromosome motions in dividing human cells, and automated single particle tracking in vitro and in live cells with classification into subdiffusive, diffusive and superdiffusive motion.

Live-cell imaging (LCI) provides researchers the opportunity to understand biological phenomena at a temporal resolution and is achieved using dedicated imaging systems. These studies enable insight into dynamic phenotypic changes occurring in cells, which may otherwise be missed when studying fixed samples. Access to advanced microscopy is disproportionately available to researchers in high-income countries, whereas researchers in low-to middle-income countries (LMICs) are severely underrepresented in the adoption of such technologies. A major barrier to the dissemination of advanced microscopy centres around economic inequalities, with the cost of high-end imaging systems often being prohibitively expensive. Recognition of such disparities has motivated the wider microscopy community to manufacture frugal microscopes that are accessible to researchers in resource-constrained settings. The OpenFlexure Microscope (OFM) is an open source, customisable, 3D-printed microscope suitable for medical research and field-diagnostics. We have made adaptations to the OFM to enable its use for live-cell imaging in humid tissue culture incubators. By moving major electronic components outside of the microscope, we remove the risk of corrosion of the Raspberry Pi and Sangaboard used to operate the instrument. We tested four common 3D-printing polymer materials for increased thermal robustness and found ASA is the best plastic to print the main body of the microscope, offering both durability and image stability in 24- to 48-hour time course experiments. We have also created an optional 3D-printable weighted-hammock system to reduce external vibration artefacts during image acquisition. Critically, electronic modifications included custom extension cables from the motors and camera to the Raspberry Pi and Sangaboard, and the inclusion of 22 ohm ({Omega}) resistors to reduce the current to the stepper motors, preventing detrimental temperature increases inside sealed incubators during prolonged powering of the instrument. To remove dependence on WiFi connections for setting up timelapse experiments, we generated a simple application with a graphical user interface (GUI) that can be installed locally on a Raspberry Pi and is specifically designed for setting up timelapse experiments without extensive computational knowledge or experience. We validated our LCI-OFM adaptations with a 48-hour treatment of MDA-MB-231 breast cancer cells with the chemotherapeutic drug docetaxel, showcasing how the modified microscope can seamlessly feed into established bioimaging pipelines and generate biologically meaningful results. For researchers in LMICs, this adapted LCI-OFM provides new opportunities to study locally-relevant health challenges with timelapse microscopy, enabling deeper insight into biological dynamics and supporting the generation of preliminary data critical for securing grant funding and access to more advanced imaging systems in purpose-built regional imaging hubs.

Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.

Conventional two-dimensional (2D) histology relies upon destructive sample preparation and stereological estimation, frequently leading to sampling bias and loss of critical spatial context required for understanding renal structure relationships. Here, we detail a novel pipeline for high-resolution 3D histology of ex vivo murine kidneys using X-ray micro-computed tomography (micro-CT) at the high flux of a synchrotron light source, the architecture of the nephron and associated microvasculature necessitates three-dimensional (3D) analysis to accurately characterize its complexity. Soft-tissue contrast was optimized through an established phosphotungstic acid (PTA) staining protocol, enabling robust mapping of macro and microstructures via absorption contrast. Multi-scale imaging was performed, providing whole-organ context at resolutions around 3 m and achieving sub-micron detail (down to 400 nm) in targeted regions of interest (ROI) of the renal cortex. Utilizing machine learning segmentation pipelines optimized for large volumetric datasets, we extracted crucial 3D quantitative morphometric data. The results presented herein demonstrate accuracy and morphological insight achievable through synchrotron-based 3D imaging, establishing a robust method for quantitative preclinical research.

Smart microscopy is transforming life sciences by automating experimental imaging workflows and enabling real-time adaptation based on feedback from images and other data streams. This shift increases throughput, improves reproducibility, and expands the functional capabilities of microscopes. However, the current landscape is highly fragmented. Academic researchers often develop custom solutions for specific scientific needs, while industry offerings are typically proprietary and tied to specific hardware. This diversity, while fostering innovation, also creates major challenges in interoperability, reproducibility, and standardization, which slows progress and adaption. This article presents a collaborative effort between academic and industry leaders to survey the current state of smart microscopy, highlight representative implementations, and identify common technical and organizational barriers. We propose a framework for greater interoperability based on shared standards, modular software design, and community-driven development. Our goal is to support collaboration across the field and lay the groundwork for a more connected, reusable, and accessible smart microscopy ecosystem. We conclude with a call to action for researchers, hardware developers, and institutions to join in building an open, interoperable foundation that will unlock the full potential of smart microscopy in life science research.

One of the greatest challenges when generating large and/or high-resolution three-dimensional (3D) volume electron microscopy (vEM) datasets is long acquisition times. We developed a method that leverages Artificial Intelligence (AI) algorithms to increase acquisition throughput for 3D datasets by creating an AI-derived mask for the targeted region of interest, referred to as Adaptive Scanning. This allowed for specific structures to be imaged at high resolution, with the surrounding area captured at lower resolution, without artificially generating or enhancing any raw data. We demonstrate that this dynamic-resolution scanning approach significantly reduced volume acquisition time across a diverse array of organisms and tissues, including brains, parasites, cultured cells, and plants. This multi-resolution strategy has the potential to enhance Focused Ion Beam Scanning Electron Microscopy and other forms of vEM, by increasing time savings by up to 2-fold or more, enabling routine generation of multiple and/or larger vEM datasets, more efficiently and cost-effectively, allowing more data collection for increase statistical power for comparative studies.

Cell segmentation is crucial to the field of cell biology, as the accurate extraction of cell morphology, migration, and ultimately behavior from time-lapse live cell imagery are of paramount importance to elucidate and understand basic cellular processes. Here, we introduce a novel segmentation approach centered around optical flow and show that it achieves robust segmentation by validating it on multiple cell types, phenotypes, optical modalities, and in-vitro environments without the need of labels. By leveraging cell movement in time-lapse imagery as a means to distinguish cells from their background and augmenting the output with machine vision operations, our algorithm reduces the number of adjustable parameters needed for optimization to two. The code is packaged within a MATLAB executable file, offering an accessible means for general cell segmentation typically unavailable in most cell biology laboratories.

For the information content of microscopy images to be appropriately interpreted, reproduced, and meet FAIR (Findable Accessible Interoperable and Reusable) principles, they should be accompanied by detailed descriptions of microscope hardware, image acquisition settings, image pixel and dimensional structure, and instrument performance. Nonetheless, the thorough documentation of imaging experiments is significantly impaired by the lack of community-sanctioned easy-to-use software tools to facilitate the extraction and collection of relevant microscopy metadata. Here we present Micro-Meta App, an intuitive open-source software designed to tackle these issues that was developed in the context of nascent global bioimaging community organizations, including BioImaging North America (BINA) and QUAlity Assessment and REProducibility in Light Microscopy (QUAREP-LiMi), whose goal is to improve reproducibility, data quality and sharing value for imaging experiments. The App provides a user-friendly interface for building comprehensive descriptions of the conditions utilized to produce individual microscopy datasets as specified by the recently proposed 4DN-BINA-OME tiered-system of Microscopy Metadata model. To achieve this goal the App provides a visual guide for a microscope-user to: 1) interactively build diagrammatic representations of hardware configurations of given microscopes that can be easily reused and shared with colleagues needing to document similar instruments. 2) Automatically extracts relevant metadata from image files and facilitates the collection of missing image acquisition settings and calibration metrics associated with a given experiment. 3) Output all collected Microscopy Metadata to interoperable files that can be used for documenting imaging experiments and shared with the community. In addition to significantly lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training users that have limited knowledge of the intricacies of light microscopy experiments. To ensure wide-adoption by microscope-users with different needs Micro-Meta App closely interoperates with MethodsJ2 and OMERO.mde, two complementary tools described in parallel manuscripts.

Super-resolution microscopy overcomes the diffraction limit of light to achieve higher spatial resolutions than are typically available when using light microscopy techniques. However, these methods are usually restricted to imaging a very small field of view (FOV). Here, we have applied one of these super-resolution techniques, Super-Resolution Radial Fluctuations (SRRF) in conjunction with the Mesolens, which has the unusual combination of a low-magnification and high numerical aperture, to obtain super-resolved images over a FOV of 4.4 mm x 3.0 mm. We assessed the accuracy of these SRRF images through error maps calculated using a secondary analysis method, Super-resolution Quantitative Image Rating and Reporting of Error Locations (SQUIRREL). We demonstrate it is possible to achieve images with a resolution of 446.3 {+/-} 10.9 nm, providing a [~]1.6-fold improvement in spatial resolution over a uniquely large field, with consistent structural agreement between raw data and SRRF processed images. MotivationCurrent super-resolution imaging techniques allow for a greater understanding of cellular structures however they are often complex or only have the ability to image a few cells at once. This small field of view may not represent the behaviour across the entire sample and the manual selection of which restricted ROI to use may introduce bias. Currently, this is often circumvented by stitching and tiling methods which stitch many small ROI together, however this can result in artefacts across an image which poses an issue when analysing data. To combat this, we have used the Mesolens alongside Super-Resolution Radial Fluctuations analysis, to obtain super-resolved images over a field of view of 4.4 mm x 3.0 mm with minimal error.

Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM), that uses population-wide cell characterization to enable data-driven high-fidelity imaging of relevant phenotypes. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As proof-of-concept, we apply DDM with plugins for improved high-content screening and live adaptive microscopy. DDM also allows for easy correlative imaging in other systems with a plugin that uses the spatial relationship of the sample population for automated registration. We believe DDM will be a valuable approach for reducing human bias, increasing reproducibility, and placing singlecell characteristics in the context of the sample population when interpreting microscopy data, leading to an overall increase in data fidelity.

Quantitative fluorescence microscopy is more reproducible when instrument performance is measured and incorporated into the analysis. We show that routinely monitored quality-control (QC) metrics like the system resolution and inter-channel co-registration are determinant variables that can be used to normalize common image readouts and thereby separate instrument-induced variation from genuine biological changes. For intra-channel morphometry, a Gaussian approximation of the fluorescence imaging process yields analytical factors that predict how geometric measurements (length, separation distance, area, volume, etc.) inflate and scale with resolution blur due to optical misalignments or natural optical quality variations. We validate this behavior by deliberately perturbing the system resolution and by exploiting the natural resolution differences in three nominally equivalent objective lenses configured to image the exact same synapses in cultured hippocampal neurons, where structural differences subtle by eye nonetheless produced statistically significant shifts in measured synaptic puncta volumes. For dual-channel colocalization (overlap) measurements, we normalize the inter-channel co-registration QC metric by the measured point-spread function (PSF) resolutions (rather than theoretical limits associated with the objective lens) and demonstrate how fluorescent pre/postsynaptic cleft protein overlap signals decay in a predictable, exponential fashion as the PSF-normalized registration error increases, with the decay rate depending on the imaged object relative to the PSF size ratio. Mapped field-of-view gradients in channel registration also explain feature orientation flips/rotations and overlap loss without any underlying biological change. Finally, we outline a simple QC-aware microscope normalization workflow where each image measurement dataset is paired with its session PSFs and local co-registration error to remove instrument bias and optionally re-project the results to a declared reference PSF without altering the raw images. This approach improves image measurement accuracy and cross-instrument comparability of experiments and reframes light microscope QC from a passive certification of instrument health into a practical normalization that links the acquisition state to quantitative outcomes, thus ensuring the reliability and interpretability of morphometric and colocalization data in fluorescence microscopy.

Correlative Light and Electron Microscopy (CLEM) integrates the molecular specificity of light microscopy (LM) with the ultrastructural detail of electron microscopy (EM), enabling comprehensive spatial analysis of biological samples. Despite growing demand, processing 3D CLEM datasets remains challenging, specifically for service provision in facilities, due to their multimodal nature and the lack of unified approaches. Typical steps include EM slice alignment, LM-EM registration, segmentation, and 3D visualization. We present a modular, end-to-end pipeline that consolidates existing and newly developed tools into a coherent workflow for 3D CLEM analysis and allows railroading the approach. Designed as interoperable modules accessible through a user-friendly interface, the pipeline is fully open-source and scales from standard workstations to high-performance computing environments to address the need for analysis of growing datasets. While some steps still require manual input, individual components can be automated to increase throughput and reproducibility. Together, this integrated solution lowers technical barriers and supports broader adoption of 3D CLEM methodologies.

Correlative light and electron microscopy is a scientific method that encompasses several technologies and workflows. One of the pioneering workflows consisted in vitrifying by high pressure freezing a sample, shortly after live observation1. Despite its extraordinary potential, it did not turn into a routine technique for practical reasons. We redesigned the entire tool set, from the sample carrier to the high-pressure freezing machine, to standardize and democratise the technique. In our manuscript, we present all the technological developments that lead to a routine workflow for live to HPF CLEM. We demonstrate our ability to track rapidly moving endosomes live and retrieve them at the electron microscopy level, in three dimensions, with high confidence.

Machine learning algorithms hold the promise of greatly improving live cell image analysis by way of (1) analyzing far more imagery than can be achieved by more traditional manual approaches and (2) by eliminating the subjective nature of researchers and diagnosticians selecting the cells or cell features to be included in the analyzed data set. Currently, however, even the most sophisticated model based or machine learning algorithms require user supervision, meaning the subjectivity problem is not removed but rather incorporated into the algorithms initial training steps and then repeatedly applied to the imagery. To address this roadblock, we have developed a self-supervised machine learning algorithm that recursively trains itself directly from the live cell imagery data, thus providing objective segmentation and quantification. The approach incorporates an optical flow algorithm component to self-label cell and background pixels for training, followed by the extraction of additional feature vectors for the automated generation of a cell/background classification model. Because it is self-trained, the software has no user-adjustable parameters and does not require curated training imagery. The algorithm was applied to automatically segment cells from their background for a variety of cell types and five commonly used imaging modalities - fluorescence, phase contrast, differential interference contrast (DIC), transmitted light and interference reflection microscopy (IRM). The approach is broadly applicable in that it enables completely automated cell segmentation for long-term live cell phenotyping applications, regardless of the input imagerys optical modality, magnification or cell type.

Structured illumination microscopy (SIM) is widely used for fast, long-term, live-cell super-resolution imaging. However, SIM images can contain substantial artifacts if the sample does not conform to the underlying assumptions of the reconstruction algorithm. Here we describe a simple, easy to implement, process that can be combined with any reconstruction algorithm to alleviate many common SIM reconstruction artifacts and briefly discuss possible extensions.