Whole-body integration of gene expression and single-cell morphology

Volume electron microscopy for genetically and molecularly defined neural circuits

The brain networks responsible for adaptive behavioral changes are based on the physical connections between neurons. Light and electron microscopy have long been used to study neural projections and the physical connections between neurons. Volume electron microscopy has recently expanded its scale of analysis due to methodological advances, resulting in complete wiring maps of neurites in a large volume of brain tissues and even entire nervous systems in a growing number of species. However, structural approaches frequently suffer from inherent limitations in which elements in images are identified solely by morphological criteria. Recently, an increasing number of tools and technologies have been developed to characterize cells and cellular components in the context of molecules and gene expression. These advancements include newly developed probes for visualization in electron microscopic images as well as correlative integration methods for the same elements across multiple microscopic modalities. Such approaches advance our understanding of interactions between specific neurons and circuits and may help to elucidate novel aspects of the basal ganglia network involving dopamine neurons. These advancements are expected to reveal mechanisms for processing adaptive changes in specific neural circuits that modulate brain functions.

A computational pipeline for spatial mechano-transcriptomics

Advances in spatial profiling technologies are providing insights into how molecular programs are influenced by local signaling and environmental cues. However, cell fate specification and tissue patterning involve the interplay of biochemical and mechanical feedback. Here we develop a computational framework that enables the joint statistical analysis of transcriptional and mechanical signals in the context of spatial transcriptomics. To illustrate the application and utility of the approach, we use spatial transcriptomics data from the developing mouse embryo to infer the forces acting on individual cells, and use these results to identify mechanical, morphometric and gene expression signatures that are predictive of tissue compartment boundaries. In addition, we use geoadditive structural equation modeling to identify gene modules that predict the mechanical behavior of cells in an unbiased manner. This computational framework is easily generalized to other spatial profiling contexts, providing a generic scheme for exploring the interplay of biomolecular and mechanical cues in tissues.

Segment Anything for Microscopy

Accurate segmentation of objects in microscopy images remains a bottleneck for many researchers despite the number of tools developed for this purpose. Here, we present Segment Anything for Microscopy (μSAM), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything, a vision foundation model for image segmentation. We extend it by fine-tuning generalist models for light and electron microscopy that clearly improve segmentation quality for a wide range of imaging conditions. We also implement interactive and automatic segmentation in a napari plugin that can speed up diverse segmentation tasks and provides a unified solution for microscopy annotation across different microscopy modalities. Our work constitutes the application of vision foundation models in microscopy, laying the groundwork for solving image analysis tasks in this domain with a small set of powerful deep learning models.

Single-cell Technology in Stem Cell Research

Single-cell technology (SCT), which enables the examination of the fundamental units comprising biological organs, tissues, and cells, has emerged as a powerful tool, particularly in the field of biology, with a profound impact on stem cell research. This innovative technology opens new pathways for acquiring cell-specific data and gaining insights into the molecular pathways governing organ function and biology. SCT is not only frequently used to explore rare and diverse cell types, including stem cells, but it also unveils the intricacies of cellular diversity and dynamics. This perspective, crucial for advancing stem cell research, facilitates non-invasive analyses of molecular dynamics and cellular functions over time. Despite numerous investigations into potential stem cell therapies for genetic disorders, degenerative conditions, and severe injuries, the number of approved stem cell-based treatments remains limited. This limitation is attributed to the various heterogeneities present among stem cell sources, hindering their widespread clinical utilization. Furthermore, stem cell research is intimately connected with cutting-edge technologies, such as microfluidic organoids, CRISPR technology, and cell/tissue engineering. Each strategy developed to overcome the constraints of stem cell research has the potential to significantly impact advanced stem cell therapies. Drawing on the advantages and progress achieved through SCT-based approaches, this study aims to provide an overview of the advancements and concepts associated with the utilization of SCT in stem cell research and its related fields.

Alk1/Endoglin signaling restricts vein cell size increases in response to hemodynamic cues

Hemodynamic cues are thought to control blood vessel hierarchy through a shear stress set point, where flow increases lead to blood vessel diameter expansion, while decreases in blood flow cause blood vessel narrowing. Aberrations in blood vessel diameter control can cause congenital arteriovenous malformations (AVMs). We show in zebrafish embryos that while arteries behave according to the shear stress set point model, veins do not. This behavior is dependent on distinct arterial and venous endothelial cell (EC) shapes and sizes. We show that arterial ECs enlarge more strongly when experiencing higher flow, as compared to vein cells. Through the generation of chimeric embryos, we discover that this behavior of vein cells depends on the bone morphogenetic protein (BMP) pathway components Endoglin and Alk1. Endoglin (eng) or alk1 (acvrl1) mutant vein cells enlarge when in normal hemodynamic environments, while we do not observe a phenotype in either acvrl1 or eng mutant ECs in arteries. We further show that an increase in vein diameters initiates AVMs in eng mutants, secondarily leading to higher flow to arteries. These enlarge in response to higher flow through increasing arterial EC sizes, fueling the AVM. This study thus reveals a mechanism through which BMP signaling limits vein EC size increases in response to flow and provides a framework for our understanding of how a small number of mutant vein cells via flow-mediated secondary effects on wildtype arterial ECs can precipitate larger AVMs in disease conditions, such as hereditary hemorrhagic telangiectasia (HHT).

VI-VS: calibrated identification of feature dependencies in single-cell multiomics

Unveiling functional relationships between various molecular cell phenotypes from data using machine learning models is a key promise of multiomics. Existing methods either use flexible but hard-to-interpret models or simpler, misspecified models. VI-VS (Variational Inference for Variable Selection) balances flexibility and interpretability to identify relevant feature relationships in multiomic data. It uses deep generative models to identify conditionally dependent features, with false discovery rate control. VI-VS is available as an open-source Python package, providing a robust solution to identify features more likely representing genuine causal relationships.

Expanding the field of view - a simple approach for interactive visualisation of electron microscopy data

The unparalleled resolving power of electron microscopy is both a blessing and a curse. At 30,000× magnification, 1 µm corresponds to 3 cm in the image and the field of view is only a few micrometres or less, resulting in an inevitable reduction in the spatial data available in an image. Consequently, the gain in resolution is at the cost of loss of the contextual 'reference space', which is crucial for understanding the embedded structures of interest. This problem is particularly pronounced in immunoelectron microscopy, where the detection of a gold particle is crucial for the localisation of specific molecules. The common solution of presenting high-magnification and overview images side by side often insufficiently represents the cellular environment. To address these limitations, we propose here an interactive visualization strategy inspired by digital maps and GPS modules which enables seamless transitions between different magnifications by dynamically linking virtual low magnification overview images with primary high-resolution data. By enabling dynamic browsing, it offers the potential for a deeper understanding of cellular landscapes leading to more comprehensive analysis of the primary ultrastructural data.

Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues

The geometric shape and arrangement of individual cells play a role in shaping organ functions. However, analyzing multicellular features and exploring their connectomes in centimeter-scale plant organs remain challenging. Here, we established a set of frameworks named Large-Volume Fully Automated Cell Reconstruction (LVACR), enabling the exploration of three-dimensional (3D) cytological features and cellular connectivity in plant tissues. Through benchmark testing, our framework demonstrated superior efficiency in cell segmentation and aggregation, successfully addressing the inherent challenges posed by light sheet fluorescence microscopy (LSFM) imaging. Using LVACR, we successfully established a cell atlas of different plant tissues. Cellular morphology analysis revealed differences of cell clusters and shapes in between different poplar (P. simonii Carr. and P. canadensis Moench.) seeds, whereas topological analysis revealed that they maintained conserved cellular connectivity. Furthermore, LVACR spatiotemporally demonstrated an initial burst of cell proliferation, accompanied by morphological transformations at an early stage in developing the shoot apical meristem. During subsequent development, cell differentiation produced anisotropic features, thereby resulting in various cell shapes. Overall, our findings provided valuable insights into the precise spatial arrangement and cellular behavior of multicellular organisms, thus enhancing our understanding of the complex processes underlying plant growth and differentiation.

Microalgal biofilm induces larval settlement in the model marine worm Platynereis dumerilii

A free-swimming larval stage features in many marine invertebrate life cycles. To transition to a seafloor-dwelling juvenile stage, larvae need to settle out of the plankton, guided by specific environmental cues that lead them to an ideal habitat for their future life on the seafloor. Although the marine annelid Platynereis dumerilii has been cultured in research laboratories since the 1950s and has a free-swimming larval stage, specific environmental cues that induce settlement in this nereid worm are yet to be identified. Here, we demonstrate that microalgal biofilm is a key settlement cue for P. dumerilii larvae, inducing earlier onset of settlement and enhancing subsequent juvenile growth as a primary food source. We tested the settlement response of P. dumerilii to 40 different strains of microalgae, predominantly diatom species, finding that P. dumerilii have species-specific preferences in their choice of settlement substrate. The most effective diatom species for inducing P. dumerilii larval settlement were benthic pennate species including Grammatophora marina, Achnanthes brevipes and Nitzschia ovalis. The identification of specific environmental cues for P. dumerilii settlement enables a link between its ecology and the sensory and nervous system signalling that regulates larval behaviour and development. Incorporation of diatoms into P. dumerilii culture practices will improve the husbandry of this marine invertebrate model.

Serial Lift-Out: sampling the molecular anatomy of whole organisms

Cryo-focused ion beam milling of frozen-hydrated cells and subsequent cryo-electron tomography (cryo-ET) has enabled the structural elucidation of macromolecular complexes directly inside cells. Application of the technique to multicellular organisms and tissues, however, is still limited by sample preparation. While high-pressure freezing enables the vitrification of thicker samples, it prolongs subsequent preparation due to increased thinning times and the need for extraction procedures. Additionally, thinning removes large portions of the specimen, restricting the imageable volume to the thickness of the final lamella, typically <300 nm. Here we introduce Serial Lift-Out, an enhanced lift-out technique that increases throughput and obtainable contextual information by preparing multiple sections from single transfers. We apply Serial Lift-Out to Caenorhabditis elegans L1 larvae, yielding a cryo-ET dataset sampling the worm's anterior-posterior axis, and resolve its ribosome structure to 7 Å and a subregion of the 11-protofilament microtubule to 13 Å, illustrating how Serial Lift-Out enables the study of multicellular molecular anatomy.

A single-cell atlas of the miracidium larva of Schistosoma mansoni reveals cell types, developmental pathways, and tissue architecture

Schistosoma mansoni is a parasitic flatworm that causes the major neglected tropical disease schistosomiasis. The miracidium is the first larval stage of the life cycle. It swims and infects a freshwater snail, transforms into a mother sporocyst, where its stem cells generate daughter sporocysts that give rise to human-infective cercariae larvae. To understand the miracidium at cellular and molecular levels, we created a whole-body atlas of its ~365 cells. Single-cell RNA sequencing identified 19 transcriptionally distinct cell clusters. In situ hybridisation of tissue-specific genes revealed that 93% of the cells in the larva are somatic (57% neural, 19% muscle, 13% epidermal or tegument, 2% parenchyma, and 2% protonephridia) and 7% are stem. Whereas neurons represent the most diverse somatic cell types, trajectory analysis of the two main stem cell populations indicates that one of them is the origin of the tegument lineage and the other likely contains pluripotent cells. Furthermore, unlike the somatic cells, each of these stem populations shows sex-biased transcriptional signatures suggesting a cell-type-specific gene dosage compensation for sex chromosome-linked loci. The miracidium represents a simple developmental stage with which to gain a fundamental understanding of the molecular biology and spatial architecture of schistosome cells.

Made to measure: An introduction to quantifying microscopy data in the life sciences

Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement 'good' is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments.

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.

Annelid adult cell type diversity and their pluripotent cellular origins

Many annelids can regenerate missing body parts or reproduce asexually, generating all cell types in adult stages. However, the putative adult stem cell populations involved in these processes, and the diversity of cell types generated by them, are still unknown. To address this, we recover 75,218 single cell transcriptomes of the highly regenerative and asexually-reproducing annelid Pristina leidyi. Our results uncover a rich cell type diversity including annelid specific types as well as novel types. Moreover, we characterise transcription factors and gene networks that are expressed specifically in these populations. Finally, we uncover a broadly abundant cluster of putative stem cells with a pluripotent signature. This population expresses well-known stem cell markers such as vasa, piwi and nanos homologues, but also shows heterogeneous expression of differentiated cell markers and their transcription factors. We find conserved expression of pluripotency regulators, including multiple chromatin remodelling and epigenetic factors, in piwi+ cells. Finally, lineage reconstruction analyses reveal computational differentiation trajectories from piwi+ cells to diverse adult types. Our data reveal the cell type diversity of adult annelids by single cell transcriptomics and suggest that a piwi+ cell population with a pluripotent stem cell signature is associated with adult cell type differentiation.

Cephalopod-omics: Emerging Fields and Technologies in Cephalopod Biology

Few animal groups can claim the level of wonder that cephalopods instill in the minds of researchers and the general public. Much of cephalopod biology, however, remains unexplored: the largest invertebrate brain, difficult husbandry conditions, and complex (meta-)genomes, among many other things, have hindered progress in addressing key questions. However, recent technological advancements in sequencing, imaging, and genetic manipulation have opened new avenues for exploring the biology of these extraordinary animals. The cephalopod molecular biology community is thus experiencing a large influx of researchers, emerging from different fields, accelerating the pace of research in this clade. In the first post-pandemic event at the Cephalopod International Advisory Council (CIAC) conference in April 2022, over 40 participants from all over the world met and discussed key challenges and perspectives for current cephalopod molecular biology and evolution. Our particular focus was on the fields of comparative and regulatory genomics, gene manipulation, single-cell transcriptomics, metagenomics, and microbial interactions. This article is a result of this joint effort, summarizing the latest insights from these emerging fields, their bottlenecks, and potential solutions. The article highlights the interdisciplinary nature of the cephalopod-omics community and provides an emphasis on continuous consolidation of efforts and collaboration in this rapidly evolving field.

Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning

Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. However, the bottleneck in the analysis is the delineation of the cellular structures to enable quantitative measurements and analysis. We mitigated this limitation using deep learning to segment cells and subcellular ultrastructure in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. The ultrastructures, such as nuclei, nucleoli, mitochondria, endosomes, and lysosomes, are relatively better defined than their surroundings and can be segmented with high accuracy using a neural network trained with sparse manual labels. Cell segmentation, on the other hand, is much more challenging due to the lack of clear boundaries separating cells in the tissue. We adopted a multi-pronged approach combining detection, boundary propagation, and tracking for cell segmentation. Specifically, a neural network was employed to detect the intracellular space; optical flow was used to propagate cell boundaries across the z-stack from the nearest ground truth image in order to facilitate the separation of individual cells; finally, the filopodium-like protrusions were tracked to the main cells by calculating the intersection over union measure for all regions detected in consecutive images along z-stack and connecting regions with maximum overlap. The proposed cell segmentation methodology resulted in an average Dice score of 0.93. For nuclei, nucleoli, and mitochondria, the segmentation achieved Dice scores of 0.99, 0.98, and 0.86, respectively. The segmentation of FIB-SEM images will enable interpretative rendering and provide quantitative image features to be associated with relevant clinical variables.

Spiralian genomics and the evolution of animal genome architecture

Recent developments in sequencing technologies have greatly improved our knowledge of phylogenetic relationships and genomic architectures throughout the tree of life. Spiralia, a diverse clade within Protostomia, is essential for understanding the evolutionary history of parasitism, gene conversion, nervous systems and animal body plans. In this review, we focus on the current hypotheses of spiralian phylogeny and investigate the impact of long-read sequencing on the quality of genome assemblies. We examine chromosome-level assemblies to highlight key genomic features that have driven spiralian evolution, including karyotype, synteny and the Hox gene organization. In addition, we show how chromosome rearrangement has influenced spiralian genomic structures. Although spiralian genomes have undergone substantial changes, they exhibit both conserved and lineage-specific features. We recommend increasing sequencing efforts and expanding functional genomics research to deepen insights into spiralian biology.

Benchmarking strategies for cross-species integration of single-cell RNA sequencing data

The growing number of available single-cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. Here, we benchmark 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examine the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also develop a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieve a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs is beneficial. SAMap outperforms when integrating whole-body atlases between species with challenging gene homology annotation. We provide our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.

Minimal resin embedding of SBF-SEM samples reduces charging and facilitates finding a surface-linked region of interest

BACKGROUND

For decoding the mechanism of how cells and organs function information on their ultrastructure is essential. High-resolution 3D imaging has revolutionized morphology. Serial block face scanning electron microscopy (SBF-SEM) offers non-laborious, automated imaging in 3D of up to ~ 1 mm3 large biological objects at nanometer-scale resolution. For many samples there are obstacles. Quality imaging is often hampered by charging effects, which originate in the nonconductive resin used for embedding. Especially, if the imaged region of interest (ROI) includes the surface of the sample and neighbours the empty resin, which insulates the object. This extra resin also obscures the sample's morphology, thus making navigation to the ROI difficult.

RESULTS

Using the example of small arthropods and a fish roe we describe a workflow to prepare samples for SBF-SEM using the minimal resin (MR) embedding method. We show that for imaging of surface structures this simple approach conveniently tackles and solves both of the two major problems-charging and ROI localization-that complicate imaging of SBF-SEM samples embedded in an excess of overlying resin. As the surface ROI is not masked by the resin, samples can be precisely trimmed before they are placed into the imaging chamber. The initial approaching step is fast and easy. No extra trimming inside the microscope is necessary. Importantly, charging is absent or greatly reduced meaning that imaging can be accomplished under good vacuum conditions, typically at the optimal high vacuum. This leads to better resolution, better signal to noise ratio, and faster image acquisition.

CONCLUSIONS

In MR embedded samples charging is minimized and ROI easily targeted. MR embedding does not require any special equipment or skills. It saves effort, microscope time and eventually leads to high quality data. Studies on surface-linked ROIs, or any samples normally surrounded by the excess of resin, would benefit from adopting the technique.

Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury

Understanding the complexity of cellular function within a tissue necessitates the combination of multiple phenotypic readouts. Here, we developed a method that links spatially-resolved gene expression of single cells with their ultrastructural morphology by integrating multiplexed error-robust fluorescence in situ hybridization (MERFISH) and large area volume electron microscopy (EM) on adjacent tissue sections. Using this method, we characterized in situ ultrastructural and transcriptional responses of glial cells and infiltrating T-cells after demyelinating brain injury in male mice. We identified a population of lipid-loaded "foamy" microglia located in the center of remyelinating lesion, as well as rare interferon-responsive microglia, oligodendrocytes, and astrocytes that co-localized with T-cells. We validated our findings using immunocytochemistry and lipid staining-coupled single-cell RNA sequencing. Finally, by integrating these datasets, we detected correlations between full-transcriptome gene expression and ultrastructural features of microglia. Our results offer an integrative view of the spatial, ultrastructural, and transcriptional reorganization of single cells after demyelinating brain injury.

Phylogenetic tracing of midbrain-specific regulatory sequences suggests single origin of eubilaterian brains

Conserved cis-regulatory elements (CREs) control Engrailed-, Pax2-, and dachshund-related gene expression networks directing the formation and function of corresponding midbrain circuits in arthropods and vertebrates. Polarized outgroup analyses of 31 sequenced metazoan genomes representing all animal clades reveal the emergence of Pax2- and dachshund-related CRE-like sequences in anthozoan Cnidaria. The full complement, including Engrailed-related CRE-like sequences, is only detectable in spiralians, ecdysozoans, and chordates that have a brain; they exhibit comparable genomic locations and extensive nucleotide identities that reveal the presence of a conserved core domain, all of which are absent in non-neural genes and, together, distinguish them from randomly assembled sequences. Their presence concurs with a genetic boundary separating the rostral from caudal nervous systems, demonstrated for the metameric brains of annelids, arthropods, and chordates and the asegmental cycloneuralian and urochordate brain. These findings suggest that gene regulatory networks for midbrain circuit formation evolved within the lineage that led to the common ancestor of protostomes and deuterostomes.

High-resolution line-scan Brillouin microscopy for live imaging of mechanical properties during embryo development

Brillouin microscopy can assess mechanical properties of biological samples in a three-dimensional (3D), all-optical and hence non-contact fashion, but its weak signals often lead to long imaging times and require an illumination dosage harmful for living organisms. Here, we present a high-resolution line-scanning Brillouin microscope for multiplexed and hence fast 3D imaging of dynamic biological processes with low phototoxicity. The improved background suppression and resolution, in combination with fluorescence light-sheet imaging, enables the visualization of the mechanical properties of cells and tissues over space and time in living organism models such as fruit flies, ascidians and mouse embryos.

Multiplexed neuropeptide mapping in ant brains integrating microtomography and three-dimensional mass spectrometry imaging

Neuropeptides are important regulators of animal physiology and behavior. Hitherto the gold standard for the localization of neuropeptides have been immunohistochemical methods that require the synthesis of antibody panels, while another limiting factor has been the brain's opacity for subsequent in situ light or fluorescence microscopy. To address these limitations, we explored the integration of high-resolution mass spectrometry imaging (MSI) with microtomography for a multiplexed mapping of neuropeptides in two evolutionary distant ant species, Atta sexdens and Lasius niger. For analyzing the spatial distribution of chemically diverse peptide molecules across the brain in each species, the acquisition of serial mass spectrometry images was essential. As a result, we have comparatively mapped the three-dimensional (3D) distributions of eight conserved neuropeptides throughout the brain microanatomy. We demonstrate that integrating the 3D MSI data into high-resolution anatomy models can be critical for studying organs with high plasticity such as brains of social insects. Several peptides, like the tachykinin-related peptides (TK) 1 and 4, were widely distributed in many brain areas of both ant species, whereas others, for instance myosuppressin, were restricted to specific regions only. Also, we detected differences at the species level; many peptides were identified in the optic lobe of L. niger, but only one peptide (ITG-like) was found in this region in A. sexdens. Building upon MS imaging studies on neuropeptides in invertebrate model systems, our approach leverages correlative MSI and computed microtomography for investigating fundamental neurobiological processes by visualizing the unbiased 3D neurochemistry in its complex anatomic environment.

Annelid adult cell type diversity and their pluripotent cellular origins

Annelids are a broadly distributed, highly diverse, economically and environmentally important group of animals. Most species can regenerate missing body parts, and many are able to reproduce asexually. Therefore, many annelids can generate all adult cell types in adult stages. However, the putative adult stem cell populations involved in these processes, as well as the diversity of adult cell types generated by them, are still unknown. Here, we recover 75,218 single cell transcriptomes of Pristina leidyi, a highly regenerative and asexually-reproducing freshwater annelid. We characterise all major annelid adult cell types, and validate many of our observations by HCR in situ hybridisation. Our results uncover complex patterns of regionally expressed genes in the annelid gut, as well as neuronal, muscle and epidermal specific genes. We also characterise annelid-specific cell types such as the chaetal sacs and globin+ cells, and novel cell types of enigmatic affinity, including a vigilin+ cell type, a lumbrokinase+ cell type, and a diverse set of metabolic cells. Moreover, we characterise transcription factors and gene networks that are expressed specifically in these populations. Finally, we uncover a broadly abundant cluster of putative stem cells with a pluripotent signature. This population expresses well-known stem cell markers such as vasa, piwi and nanos homologues, but also shows heterogeneous expression of differentiated cell markers and their transcription factors. In these piwi+ cells, we also find conserved expression of pluripotency regulators, including multiple chromatin remodelling and epigenetic factors. Finally, lineage reconstruction analyses reveal the existence of differentiation trajectories from piwi+ cells to diverse adult types. Our data reveal the cell type diversity of adult annelids for the first time and serve as a resource for studying annelid cell types and their evolution. On the other hand, our characterisation of a piwi+ cell population with a pluripotent stem cell signature will serve as a platform for the study of annelid stem cells and their role in regeneration.

Ten-year distant-recurrence risk prediction in breast cancer by CanAssist Breast (CAB) in Dutch sub-cohort of the randomized TEAM trial

BACKGROUND

Hormone receptor (HR)-positive, HER2/neu-negative breast cancers have a sustained risk of recurrence up to 20 years from diagnosis. TEAM (Tamoxifen, Exemestane Adjuvant Multinational) is a large, multi-country, phase III trial that randomized 9776 women for the use of hormonal therapy. Of these 2754 were Dutch patients. The current study aims for the first time to correlate the ten-year clinical outcomes with predictions by CanAssist Breast (CAB)-a prognostic test developed in South East Asia, on a Dutch sub-cohort that participated in the TEAM. The total Dutch TEAM cohort and the current Dutch sub-cohort were almost similar with respect to patient age and tumor anatomical features.

METHODS

Of the 2754 patients from the Netherlands, which are part of the original TEAM trial, 592 patients' samples were available with Leiden University Medical Center (LUMC). The risk stratification of CAB was correlated with outcomes of patients using logistic regression approaches entailing Kaplan-Meier survival curves, univariate and multivariate cox-regression hazards model. We used hazard ratios (HRs), the cumulative incidence of distant metastasis/death due to breast cancer (DM), and distant recurrence-free interval (DRFi) for assessment.

RESULTS

Out of 433 patients finally included, the majority, 68.4% had lymph node-positive disease, while only a minority received chemotherapy (20.8%) in addition to endocrine therapy. CAB stratified 67.5% of the total cohort as low-risk [DM = 11.5% (95% CI, 7.6-15.2)] and 32.5% as high-risk [DM = 30.2% (95% CI, 21.9-37.6)] with an HR of 2.90 (95% CI, 1.75-4.80; P < 0.001) at ten years. CAB risk score was an independent prognostic factor in the consideration of clinical parameters in multivariate analysis. At ten years, CAB high-risk had the worst DRFi of 69.8%, CAB low-risk in the exemestane monotherapy arm had the best DRFi of 92.7% [vs CAB high-risk, HR, 0.21 (95% CI, 0.11-0.43), P < 0.001], and CAB low-risk in the sequential arm had a DRFi of 84.2% [vs CAB high-risk, HR, 0.48 (95% CI, 0.28-0.82), P = 0.009].

CONCLUSIONS

Cost-effective CAB is a statistically robust prognostic and predictive tool for ten-year DM for postmenopausal women with HR+/HER2-, early breast cancer. CAB low-risk patients who received exemestane monotherapy had an excellent ten-year DRFi.

Public archiving of volume EM data

Volume electron microscopy (vEM) techniques produce scientifically important datasets which are time and resource intensive to generate (Peddie et al., 2022). Public archival of such datasets, usually described in the literature, provides many benefits to the data depositors, to those making use of research results based on the datasets, and to the vEM community at large, both now and in the future. In this chapter we discuss these benefits, explain how EMBL-EBI's image data services support archival of both vEM and correlative imaging data, and discuss how future developments will unlock more value from these vEM datasets.

Mitochondria morphometry in 3D datasets obtained from mouse brains with serial block-face scanning electron microscopy

The dysfunction of mitochondria is linked with many diseases. In the nervous system, evidence of their implication in neurodegenerative disease is growing. Mitochondria health is assessed by their impact on cellular metabolism but alterations in their morphologies and locations in the cells can also be markers of dysfunctions. Light microscopy techniques allow us to look at mitochondria in vivo in cells or tissue. But in the case of the nervous system, in order to assess the precise location of mitochondria in different cell types and neuronal compartments (cell bodies, dendrites or axons), electron microscopy is required. While the percentage of volume occupied by mitochondria can be assessed on 2D images, alterations in length, branching, and interactions with other organelles require three-dimensional (3D) segmentation of mitochondria in volumes imaged at ultrastructural level. Nowadays three-dimensional volume electron microscopy (vEM) imaging techniques such as serial block face scanning electron microscopy (SBF-SEM) enable us to image 3D volumes of tissue at ultrastructural level and can be done routinely. Segmentation of all the neuropil is also successfully achieved at a large scale in the nervous system. Here, we show a workflow based on open access resources, which allows us to image, segment, and analyze mitochondria in 3D volumes of regions of interest in the mouse brain. Taking advantage of recent developments, e.g., pre-trained models for mitochondria, we speed up the reconstruction and analysis. We also critically assess the impact on the results of the different reconstruction methods chosen and the level of manual corrections invested.

Multiscale microscopy to decipher plant cell structure and dynamics

New imaging methodologies with high contrast and molecular specificity allow researchers to analyze dynamic processes in plant cells at multiple scales, from single protein and RNA molecules to organelles and cells, to whole organs and tissues. These techniques produce informative images and quantitative data on molecular dynamics to address questions that cannot be answered by conventional biochemical assays. Here, we review selected microscopy techniques, focusing on their basic principles and applications in plant science, discussing the pros and cons of each technique, and introducing methods for quantitative analysis. This review thus provides guidance for plant scientists in selecting the most appropriate techniques to decipher structures and dynamic processes at different levels, from protein dynamics to morphogenesis.

Serial block-face scanning electron microscopy of Schmidtea mediterranea

The flatworm planarian, Schmidtea mediterranea (Smed) is a master at regenerating and rebuilding whole animals from fragments. A full understanding of Smed's regenerative capabilities requires a high-resolution characterization of organs, tissues, and the adult stem cells necessary for regeneration in their native environment. Here, we describe a serial block face scanning electron microscopy (SBF-SEM) protocol, optimized for Smed specifically, for visualizing the ultrastructure of membranes and condensed chromosomes in this model organism.

Remodeling of the Platynereis Musculature during Sexual Maturation

BACKGROUND

The external transformations associated with sexual maturation in Platynereis dumerilii (Audouin and Milne Edwards) are well studied, whereas the internal changes along the body axis have not been systematically analyzed. Therefore, we examined muscle morphology in body regions located anterior or posterior to the prospective atokous/epitokous border to generate a structural basis for internal transformations.

RESULTS

All dorsal and ventral longitudinal muscles were significantly reduced in size and density after sexual maturation and strongly atrophied, with the greatest decrease in the anterior segments of females. Despite the general reduction in size throughout the longitudinal muscles, we found a specific degradation mechanism for the posterior segments, which were characterized by the formation of secondary bundle-like fibrous structures. In addition, we observed a profound remodeling of the transversal muscles in the posterior segments of both sexes, apparently resulting in excessive thickening of these muscles. Accordingly, the entire transversal muscle complex was severely swollen and ultrastructurally characterized by a greatly increased number of mitochondria. As a possible trigger for this remodeling, we discovered an enormous number of small, blind-ending blood vessels that completely penetrated the longitudinal and transversal muscles in posterior segments. In addition, both the number of visceral muscles as well as their coelothelial covering were reduced during sexual maturation.

CONCLUSIONS

We hypothesize that a possible reason for the secondary bundling of the longitudinal fibers, as well as the difference in size of the posterior transversal muscles, could be the high degree of posterior vascularization. The different degree of muscle remodeling thus depends on segmental affiliation and reflects the tasks in the motility of the different body regions after maturation. The strongest atrophy was found in the anterior segments, while signs of redifferentiation were encountered in posterior segments, supported by the vigorous growth of vessels supplying the transformed epitokous parapodia and associated muscles, which allows rapid swimming during swarming and gamete release.

MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy

Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes are now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid Platynereis dumerilii, it yields a visually consistent grouping of cells supported by specific gene expression profiles. Integration of features across spatial neighbours can retrieve tissues and organs, revealing, for example, a detailed organisation of the animal foregut. We envision that the unbiased nature of the proposed morphological descriptors will enable rapid exploration of very different biological questions in large EM volumes, greatly increasing the impact of these invaluable, but costly resources.

A high-throughput electron tomography workflow reveals over-elongated centrioles in relapsed/refractory multiple myeloma

Electron microscopy is the gold standard to characterize centrosomal ultrastructure. However, production of significant morphometrical data is highly limited by acquisition time. We therefore developed a generalizable, semi-automated high-throughput electron tomography strategy to study centrosome aberrations in sparse patient-derived cancer cells at nanoscale. As proof of principle, we present electron tomography data on 455 centrioles of CD138pos plasma cells from one patient with relapsed/refractory multiple myeloma and CD138neg bone marrow mononuclear cells from three healthy donors as a control. Plasma cells from the myeloma patient displayed 122 over-elongated centrioles (48.8%). Particularly mother centrioles also harbored gross structural abnormalities, including fragmentation and disturbed microtubule cylinder formation, while control centrioles were phenotypically unremarkable. These data demonstrate the feasibility of our scalable high-throughput electron tomography strategy to study structural centrosome aberrations in primary tumor cells. Moreover, our electron tomography workflow and data provide a resource for the characterization of cell organelles beyond centrosomes.

Crosshair, semi-automated targeting for electron microscopy with a motorised ultramicrotome

Volume electron microscopy (EM) is a time-consuming process - often requiring weeks or months of continuous acquisition for large samples. In order to compare the ultrastructure of a number of individuals or conditions, acquisition times must therefore be reduced. For resin-embedded samples, one solution is to selectively target smaller regions of interest by trimming with an ultramicrotome. This is a difficult and labour-intensive process, requiring manual positioning of the diamond knife and sample, and much time and training to master. Here, we have developed a semi-automated workflow for targeting with a modified ultramicrotome. We adapted two recent commercial systems to add motors for each rotational axis (and also each translational axis for one system), allowing precise and automated movement. We also developed a user-friendly software to convert X-ray images of resin-embedded samples into angles and cutting depths for the ultramicrotome. This is provided as an open-source Fiji plugin called Crosshair. This workflow is demonstrated by targeting regions of interest in a series of Platynereis dumerilii samples.

How innovations in methodology offer new prospects for volume electron microscopy

Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server-based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.

Cellular diversity and developmental hierarchy in the planarian nervous system

Our ability to dissect cell type diversity, development, and plasticity in the nervous system has been transformed by the recent surge of massive sequencing studies at the single-cell level. A large body of this work has focused primarily on organisms with nervous systems established early in development. Using planarian flatworms in which neurons are constantly respecified, replenished, and regenerated, we analyze several existing single-cell transcriptomic datasets and observe features in neuron identity, differentiation, maturation, and function that may provide the planarian nervous system with high levels of adaptability required to respond to various cues including injury. This analysis allows us to place many prior observations made by functional characterizations in a general framework and provide additional hypothesis and predictions to test in future investigations.

A Hitchhiker's Guide through the Bio-image Analysis Software Universe

Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualizing 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.

Volume electron microscopy

Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.

Cross-modality synthesis of EM time series and live fluorescence imaging

Analyses across imaging modalities allow the integration of complementary spatiotemporal information about brain development, structure, and function. However, systematic atlasing across modalities is limited by challenges to effective image alignment. We combine highly spatially resolved electron microscopy (EM) and highly temporally resolved time-lapse fluorescence microscopy (FM) to examine the emergence of a complex nervous system in Caenorhabditis elegans embryogenesis. We generate an EM time series at four classic developmental stages and create a landmark-based co-optimization algorithm for cross-modality image alignment, which handles developmental heterochrony among datasets to achieve accurate single-cell level alignment. Synthesis based on the EM series and time-lapse FM series carrying different cell-specific markers reveals critical dynamic behaviors across scales of identifiable individual cells in the emergence of the primary neuropil, the nerve ring, as well as a major sensory organ, the amphid. Our study paves the way for systematic cross-modality data synthesis in C. elegans and demonstrates a powerful approach that may be applied broadly.

Electron microscopy of cardiac 3D nanodynamics: form, function, future

The 3D nanostructure of the heart, its dynamic deformation during cycles of contraction and relaxation, and the effects of this deformation on cell function remain largely uncharted territory. Over the past decade, the first inroads have been made towards 3D reconstruction of heart cells, with a native resolution of around 1 nm³, and of individual molecules relevant to heart function at a near-atomic scale. These advances have provided access to a new generation of data and have driven the development of increasingly smart, artificial intelligence-based, deep-learning image-analysis algorithms. By high-pressure freezing of cardiomyocytes with millisecond accuracy after initiation of an action potential, pseudodynamic snapshots of contraction-induced deformation of intracellular organelles can now be captured. In combination with functional studies, such as fluorescence imaging, exciting insights into cardiac autoregulatory processes at nano-to-micro scales are starting to emerge. In this Review, we discuss the progress in this fascinating new field to highlight the fundamental scientific insight that has emerged, based on technological breakthroughs in biological sample preparation, 3D imaging and data analysis; to illustrate the potential clinical relevance of understanding 3D cardiac nanodynamics; and to predict further progress that we can reasonably expect to see over the next 10 years.

Open Frontiers in Neural Cell Type Investigations; Lessons From Caenorhabditis elegans and Beyond, Toward a Multimodal Integration

Nervous system cells, the building blocks of circuits, have been studied with ever-progressing resolution, yet neural circuits appear still resistant to schemes of reductionist classification. Due to their sheer numbers, complexity and diversity, their systematic study requires concrete classifications that can serve reduced dimensionality, reproducibility, and information integration. Conventional hierarchical schemes transformed through the history of neuroscience by prioritizing criteria of morphology, (electro)physiological activity, molecular content, and circuit function, influenced by prevailing methodologies of the time. Since the molecular biology revolution and the recent advents in transcriptomics, molecular profiling gains ground toward the classification of neurons and glial cell types. Yet, transcriptomics entails technical challenges and more importantly uncovers unforeseen spatiotemporal heterogeneity, in complex and simpler nervous systems. Cells change states dynamically in space and time, in response to stimuli or throughout their developmental trajectory. Mapping cell type and state heterogeneity uncovers uncharted terrains in neurons and especially in glial cell biology, that remains understudied in many aspects. Examining neurons and glial cells from the perspectives of molecular neuroscience, physiology, development and evolution highlights the advantage of multifaceted classification schemes. Among the amalgam of models contributing to neuroscience research, Caenorhabditis elegans combines nervous system anatomy, lineage, connectivity and molecular content, all mapped at single-cell resolution, and can provide valuable insights for the workflow and challenges of the multimodal integration of cell type features. This review reflects on concepts and practices of neuron and glial cells classification and how research, in C. elegans and beyond, guides nervous system experimentation through integrated multidimensional schemes. It highlights underlying principles, emerging themes, and open frontiers in the study of nervous system development, regulatory logic and evolution. It proposes unified platforms to allow integrated annotation of large-scale datasets, gene-function studies, published or unpublished findings and community feedback. Neuroscience is moving fast toward interdisciplinary, high-throughput approaches for combined mapping of the morphology, physiology, connectivity, molecular function, and the integration of information in multifaceted schemes. A closer look in mapped neural circuits and understudied terrains offers insights for the best implementation of these approaches.

The conserved core of the nereid brain: Circular CNS, apical nervous system and lhx6-arx-dlx neurons

When bilaterian animals first emerged, an enhanced perception of the Precambrian environment was key to their stunning success. This occurred through the acquisition of an anterior brain, as found in most extant bilaterians. What were the core circuits of the first brain, and how do they relate to today's diversity? With two landmark resources - the full connectome and a multimodal cellular atlas combining gene expression and ultrastructure - the young worm of the marine annelid Platynereis dumerilii takes center stage in comparative bilaterian neuroanatomy. The new data suggest a composite structure of the ancestral bilaterian brain, with the anterior end of a circular CNS fused to a sensory-neurosecretory apical system, and with lhx6-arx-dlx chemosensory circuits giving rise to associative centers in the descending bilaterian lineages.

OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies

The rapid pace of innovation in biological imaging and the diversity of its applications have prevented the establishment of a community-agreed standardized data format. We propose that complementing established open formats such as OME-TIFF and HDF5 with a next-generation file format such as Zarr will satisfy the majority of use cases in bioimaging. Critically, a common metadata format used in all these vessels can deliver truly findable, accessible, interoperable and reusable bioimaging data.

Profiling cellular diversity in sponges informs animal cell type and nervous system evolution

[Figure: see text].

Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology

Ultrastructural analysis of discrete neurobiological structures by volume scanning electron microscopy (SEM) often constitutes a "needle-in-the-haystack" problem and therefore relies on sophisticated search strategies. The appropriate SEM approach for a given relocation task not only depends on the desired final image quality but also on the complexity and required accuracy of the screening process. Block-face SEM techniques like Focused Ion Beam or serial block-face SEM are "one-shot" imaging runs by nature and, thus, require precise relocation prior to acquisition. In contrast, "multi-shot" approaches conserve the sectioned tissue through the collection of serial sections onto solid support and allow reimaging. These tissue libraries generated by Array Tomography or Automated Tape Collecting Ultramicrotomy can be screened at low resolution to target high resolution SEM. This is particularly useful if a structure of interest is rare or has been predetermined by correlated light microscopy, which can assign molecular, dynamic and functional information to an ultrastructure. As such approaches require bridging mm to nm scales, they rely on tissue trimming at different stages of sample processing. Relocation is facilitated by endogenous or exogenous landmarks that are visible by several imaging modalities, combined with appropriate registration strategies that allow overlaying images of various sources. Here, we discuss the opportunities of using multi-shot serial sectioning SEM approaches, as well as suitable trimming and registration techniques, to slim down the high-resolution imaging volume to the actual structure of interest and hence facilitate ambitious targeted volume SEM projects.

3D-cardiomics: A spatial transcriptional atlas of the mammalian heart

Understanding the spatial gene expression and regulation in the heart is key to uncovering its developmental and physiological processes, during homeostasis and disease. Numerous techniques exist to gain gene expression and regulation information in organs such as the heart, but few utilize intuitive true-to-life three-dimensional representations to analyze and visualise results. Here we combined transcriptomics with 3D-modelling to interrogate spatial gene expression in the mammalian heart. For this, we microdissected and sequenced transcriptome-wide 18 anatomical sections of the adult mouse heart. Our study has unveiled known and novel genes that display complex spatial expression in the heart sub-compartments. We have also created 3D-cardiomics, an interface for spatial transcriptome analysis and visualization that allows the easy exploration of these data in a 3D model of the heart. 3D-cardiomics is accessible from http://3d-cardiomics.erc.monash.edu/.

The Nereid on the rise: Platynereis as a model system

The Nereid Platynereis dumerilii (Audouin and Milne Edwards (Annales des Sciences Naturelles 1:195-269, 1833) is a marine annelid that belongs to the Nereididae, a family of errant polychaete worms. The Nereid shows a pelago-benthic life cycle: as a general characteristic for the superphylum of Lophotrochozoa/Spiralia, it has spirally cleaving embryos developing into swimming trochophore larvae. The larvae then metamorphose into benthic worms living in self-spun tubes on macroalgae. Platynereis is used as a model for genetics, regeneration, reproduction biology, development, evolution, chronobiology, neurobiology, ecology, ecotoxicology, and most recently also for connectomics and single-cell genomics. Research on the Nereid started with studies on eye development and spiralian embryogenesis in the nineteenth and early twentieth centuries. Transitioning into the molecular era, Platynereis research focused on posterior growth and regeneration, neuroendocrinology, circadian and lunar cycles, fertilization, and oocyte maturation. Other work covered segmentation, photoreceptors and other sensory cells, nephridia, and population dynamics. Most recently, the unique advantages of the Nereid young worm for whole-body volume electron microscopy and single-cell sequencing became apparent, enabling the tracing of all neurons in its rope-ladder-like central nervous system, and the construction of multimodal cellular atlases. Here, we provide an overview of current topics and methodologies for P. dumerilii, with the aim of stimulating further interest into our unique model and expanding the active and vibrant Platynereis community.

Deep learning for bioimage analysis in developmental biology

Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

Correlative light and volume electron microscopy (vCLEM): How community participation can advance developing technologies

Correlative light and electron microscopy is a valuable tool to image samples across resolution scales and link data on structure and function. While studies using this technique have been available since the 1960s, recent developments have enabled applying these workflows to large volumes of cells and tissues. Much of the development in this area has been facilitated through the collaborative efforts of microscopists and commercial companies to bring the methods, hardware and image processing technologies needed into laboratories and core imaging facilities. This is a prime example of how what was once a niche area can be brought into the mainstream of microscopy by the efforts of imaging pioneers who push the boundaries of possibility.