Soft X-ray tomography (SXT) enables native-contrast three-dimensional (3D) imaging of fully hydrated, cryogenically preserved biological samples, revealing ultrastructural details without the need for staining, embedding, or sectioning. Traditionally available only at synchrotron facilities, recent advances in laser-driven plasma sources have led to the development of compact soft X-ray microscopes. Achieving a resolution of 54 nm full-pitch and tomogram acquisition times of 30 min to two hours, we validate the system across a range of biologically relevant contexts, including protists, yeast, and mammalian cells containing polymeric and inorganic nanoparticles. These use cases establish the robustness of the laboratory based system for studying cell architecture, organelle interactions, and nanoparticle trafficking. By showing that a compact SXT system can achieve reliable high-resolution imaging across various cell types, this study highlights a major step toward making correlative cryogenic X-ray imaging broadly accessible in laboratory settings. Future developments will aim at enhanced throughput, deeper integration with correlative imaging modalities, and extension to more complex specimen types, including tissue.

Understanding the intracellular fate of nanoparticles (NPs) is essential for advancing nanomedicine, particularly in targeted drug delivery for cancer therapy. Here, we present a complementary cryogenic microscopy workflow across scales to investigate the uptake and subcellular localization of zirconyl-containing inorganic-organic hybrid nanoparticles (IOH-NPs) in murine breast cancer cells. Our approach integrates cryogenic fluorescence microscopy (cryo-FM), cryo-focused ion beam scanning electron microscopy (cryo-FIBSEM), and cryo-soft X-ray tomography (cryo-SXT), enabling molecular specificity, high-resolution imaging, and volumetric ultrastructural analysis in near-native cellular states. We demonstrate that the cryogenic workflow provides enough contrast and resolution across all modalities for quantifying the IOH-NP uptake: NPs are internalized within 2 h of incubation and progressively accumulate in endolysosomes over time, as confirmed by fluorescence labeling and SXT. Quantitative analysis reveals a marked increase in endolysosomal accumulation of IOH-NPs from 2 to 24 h. Our findings help to establish multimodal cryogenic microscopy as a powerful tool for nanoscale imaging and quantitative analysis of NP uptake within close-to-native cells, offering new insights into NP trafficking and cellular responses relevant to nanomedicine development.

Correlated Multimodal Imaging (CMI) gathers information about the same specimen with two or more modalities that–combined–create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows one to reach beyond what is possible with a single modality and describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, and organisms in health and disease by untangling their molecular mechanisms within their native environment. The field of CMI has grown substantially over the last decade and previously unanswerable biological questions have been solved by applying novel CMI workflows. To disseminate these workflows and comprehensively share the scattered knowledge present within the CMI community, an initiative was started to bring together imaging, image analysis, and biomedical scientists and work towards an open community that promotes and disseminates the field of CMI. This community project was funded for the last 4 years by an EU COST Action called COMULIS (COrrelated MUltimodal imaging in the LIfe Sciences). In this review we share some of the showcases and lessons learnt from the action. We also briefly look ahead at how we anticipate building on this initial initiative.

Multimodal bioimaging is a broad term used to describe experimental workflows that employ two or more different imaging modalities. Such approaches have been in use across life science domains for several years but these remain relatively limited in scope, in part due to the complexity of undertaking these types of analysis. Expanding these workflows to encompass diverse, emerging technology holds potential to revolutionize our understanding of spatial biology. In this perspective we reflect on the instrument and workflows in current use, emerging areas to consider and our experience of the barriers to broader adoption and progress. We propose several enabling solutions across the different challenge areas, emerging opportunities for consideration and highlight some of the key community activities to help move the field forward.

Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re-assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results. To speed-up the process, from cell organelle detection to quantification across electron microscopy modalities, we propose a deep-learning based approach for fast automatic outline segmentation (FAMOUS), that involves organelle detection combined with image morphology, and 3D meshing to automatically segment, visualize and quantify cell organelles within volume electron microscopy datasets. From start to finish, FAMOUS provides full segmentation results within a week on previously unseen datasets. FAMOUS was showcased on a HeLa cell dataset acquired using a focused ion beam scanning electron microscope, and on yeast cells acquired by transmission electron tomography. RESEARCH HIGHLIGHTS: Introducing a rapid, multimodal machine-learning workflow for the automatic segmentation of 3D cell organelles. Successfully applied to a variety of volume electron microscopy datasets and cell lines. Outperforming manual segmentation methods in time and accuracy. Enabling high-throughput quantitative cell biology.

The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.

Translational control is an essential process for the cell to adapt to varying physiological or environmental conditions. To survive adverse conditions such as low nutrient levels, translation can be shut down almost entirely by inhibiting ribosomal function. Here we investigated eukaryotic hibernating ribosomes from the microsporidian parasite Spraguea lophii in situ by a combination of electron cryo-tomography and single-particle electron cryo-microscopy. We show that microsporidian spores contain hibernating ribosomes that are locked in a dimeric (100S) state, which is formed by a unique dimerization mechanism involving the beak region. The ribosomes within the dimer are fully assembled, suggesting that they are ready to be activated once the host cell is invaded. This study provides structural evidence for dimerization acting as a mechanism for ribosomal hibernation in microsporidia, and therefore demonstrates that eukaryotes utilize this mechanism in translational control.

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper-quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.

AbstractReliable data are the base of all scientific analyses, interpretations and conclusions. Evaluating data in a smart way speeds up the process of interpretation and conclusion and highlights where, when and how additionally acquired data in the field will support knowledge gain. An extended SMART monitoring concept is introduced which includes SMART sensors, DataFlows, MetaData and Sampling approaches and tools. In the course of the Digital Earth project, the meaning of SMART monitoring has significantly evolved. It stands for a combination of hard- and software tools enhancing the traditional monitoring approach where a SMART monitoring DataFlow is processed and analyzed sequentially on the way from the sensor to a repository into an integrated analysis approach. The measured values itself, its metadata, and the status of the sensor, and additional auxiliary data can be made available in real time and analyzed to enhance the sensor output concerning accuracy and precision. Although several parts of the four tools are known, technically feasible and sometimes applied in Earth science studies, there is a large discrepancy between knowledge and our derived ambitions and what is feasible and commonly done in the reality and in the field.

Translational control is an essential process for the cell to adapt to varying physiological or environmental conditions. To survive adverse conditions such as low nutrient levels, translation can be shut down almost entirely by inhibiting ribosomal function. Here we investigated eukaryotic hibernating ribosomes from the microsporidian parasite Spraguea lophii in situ by a combination of cryo-electron tomography (cryoET) and single particle cryoEM. We show that microsporidian spores contain ribosomes primed for host cell invasion and thus shed new light on the infection mechanism of this important pathogen. Prior to host infection, virtually all ribosomes are locked in the 100 S dimeric state, which appears to be formed by a unique dimerization mechanism that is distinct from its bacterial counterparts. Within the dimer, the hibernation factor MDF1 is bound within the E site, locking the L1 stalk in a closed conformation, and thus preventing the translation of mRNAs to polypeptides.

Few would have thought that when Porter and colleagues used light microscopy to target their cell of interest to be analyzed in the electron microscope in the 1940s, that Correlative Imaging would develop into the thriving field it is today. Even though the first use of Correlative Light Electron Microscopy (CLEM) was established in the 1940s, it is only since the year 2000 that there has been a real surge in the application of CLEM technology. The power of CLEM is recognized in the scientific community as evidenced by the growing number of publications and dedicated sessions at scientific meetings. The field is also broadening, incorporating a multitude of other techniques including preclinical research and diagnostics, and slowly but surely the overarching field of Correlative Multimodality Imaging (CMI) is taking its place as an established technique and a research area in its own right. In this chapter, we will look at the initiatives that are being developed within the scientific world to build a coherent CMI community, with a particular emphasis on the developments in Europe. To achieve this aim, the community will need to design mechanisms for the interdisciplinary exchange of knowledge and benefits, set up training schemes, and develop standards for CMI technology and its data.

High-resolution episcopic microscopy (HREM) is a three-dimensional (3D) episcopic imaging modality based on the acquisition of two-dimensional (2D) images from the cut surface of a block of tissue embedded in resin. Such images, acquired serially through the entire length/depth of the tissue block, are aligned and stacked for 3D reconstruction. HREM has proven to be specifically advantageous when integrated in correlative multimodal imaging (CMI) pipelines. CMI creates a composite and zoomable view of exactly the same specimen and region of interest by (sequentially) correlating two or more modalities. CMI combines complementary modalities to gain holistic structural, functional, and chemical information of the entire sample and place molecular details into their overall spatiotemporal multiscale context. HREM has an advantage over in vivo 3D imaging techniques on account of better histomorphologic resolution while simultaneously providing volume data. HREM also has certain advantages over ex vivo light microscopy modalities. The latter can provide better cellular resolution but usually covers a limited area or volume of tissue, with limited 3D structural context. HREM has predominantly filled a niche in the phenotyping of embryos and characterisation of anatomic developmental abnormalities in various species. Under the umbrella of CMI, when combined with histopathology in a mutually complementary manner, HREM could find wider application in additional nonclinical and translational areas. HREM, being a modified histology technique, could also be incorporated into specialised preclinical pathology workflows. This review will highlight HREM as a versatile imaging platform in CMI approaches and present its benefits and limitations.

In this proof-of-principle study, we established and implemented a cross-modality imaging (CMI) pipeline to characterize and compare bisphosphonate (BIS)-treated jawbones of Sprague-Dawley rats after tooth extraction after physical therapies (photobiomodulation and extracorporeal shockwave therapy (PBMT and ESWT)). We showcase the feasibility of such a CMI approach and its compatibility across imaging modalities to probe the same region of interest (ROI) of the same jawbone. Jawbones were imaged in toto in 3D using micro-Computed Tomography to identify ROIs for subsequent sequential 2D analysis using well-established technologies such as Atomic Force Microscopy and Scanning Electron Microscopy, and recent imaging approaches in biomedical settings, such as micro-X-Ray Fluorescence Spectroscopy. By combining these four modalities, multiscale information on the morphology, topography, mechanical stiffness (Young's modulus), and calcium, zinc and phosphorus concentrations of the bone was collected. Based on the CMI pipeline, we characterized and compared the jawbones of a previously published clinically relevant rat model of BIS-related osteonecrosis of the jawbone (BRONJ) before and after treatment with BISs, PBMT and ESWT. While we did not find that physical therapies altered the mechanical and elemental jawbone parameters with significance (probably due to the small sample size of only up to 5 samples per group), both ESWT and PBMT reduced pore thicknesses and bone-to-enamel distances significantly compared to the controls. Although focused on BIS-treated jawbones, the established CMI platform can be beneficial in the study of bone-related diseases in general (such as osteoarthritis or -porosis) to acquire complementary hallmarks and better characterize disease status and alleviation potentials.

Tumor vasculature and angiogenesis play a crucial role in tumor progression. Their visualization is therefore of utmost importance to the community. In this proof-of-principle study, we have established a novel cross-modality imaging (CMI) pipeline to characterize exactly the same murine tumors across scales and penetration depths, using orthotopic models of melanoma cancer. This allowed the acquisition of a comprehensive set of vascular parameters for a single tumor. The workflow visualizes capillaries at different length scales, puts them into the context of the overall tumor vessel network and allows quantification and comparison of vessel densities and morphologies by different modalities. The workflow adds information about hypoxia and blood flow rates. The CMI approach includes well-established technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and ultrasound (US), and modalities that are recent entrants into preclinical discovery such as optical coherence tomography (OCT) and high-resolution episcopic microscopy (HREM). This novel CMI platform establishes the feasibility of combining these technologies using an extensive image processing pipeline. Despite the challenges pertaining to the integration of microscopic and macroscopic data across spatial resolutions, we also established an open-source pipeline for the semi-automated co-registration of the diverse multiscale datasets, which enables truly correlative vascular imaging. Although focused on tumor vasculature, our CMI platform can be used to tackle a multitude of research questions in cancer biology.

In imaging, penetration depth comes at the expense of lateral resolution, which restricts the scope of 3D in-vivo imaging of small animals at micrometer resolution. Bioimaging will need to expand beyond correlative light and electron microscopy (CLEM) approaches to combine insights about in-vivo dynamics in a physiologically relevant 3D environment with ex-vivo information at micrometer resolution (or beyond) within the spatial, structural and biochemical contexts. Our report demonstrates the immense potential for biomedical discovery and diagnosis made available by bridging preclinical in-vivo imaging with ex-vivo biological microscopy to zoom in from the whole organism to individual structures and by adding localized spectroscopic information to structural and functional information. We showcase the use of two novel imaging pipelines to zoom into mural lesions (occlusions/hyperplasia and microcalcifications) in murine vasculature in a truly correlative manner, that is using exactly the same animal for all integrated imaging modalities. This correlated multimodality imaging (CMI) approach includes well-established technologies such as Positron Emission Tomography (microPET), Autoradiography, Magnetic Resonance Imaging (microMRI) and Computed Tomography (microCT), and imaging approaches that are more novel in the biomedical setting, such as X-Ray Fluorescence Spectroscopy (microXRF) and High Resolution Episcopic Microscopy (HREM). Although the current pipelines are focused on mural lesions, they would also be beneficial in preclinical and clinical investigations of vascular diseases in general.