An open-access volume electron microscopy atlas of whole cells and tissues

Organization, dynamics and mechanoregulation of integrin-mediated cell-ECM adhesions

The ability of animal cells to sense, adhere to and remodel their local extracellular matrix (ECM) is central to control of cell shape, mechanical responsiveness, motility and signalling, and hence to development, tissue formation, wound healing and the immune response. Cell-ECM interactions occur at various specialized, multi-protein adhesion complexes that serve to physically link the ECM to the cytoskeleton and the intracellular signalling apparatus. This occurs predominantly via clustered transmembrane receptors of the integrin family. Here we review how the interplay of mechanical forces, biochemical signalling and molecular self-organization determines the composition, organization, mechanosensitivity and dynamics of these adhesions. Progress in the identification of core multi-protein modules within the adhesions and characterization of rearrangements of their components in response to force, together with advanced imaging approaches, has improved understanding of adhesion maturation and turnover and the relationships between adhesion structures and functions. Perturbations of adhesion contribute to a broad range of diseases and to age-related dysfunction, thus an improved understanding of their molecular nature may facilitate therapeutic intervention in these conditions.

Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset

Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train models on images that provide limited cellular contexts, precluding generality. To address this, we amassed a highly heterogeneous ∼1.5 × 10⁶ image 2D unlabeled cellular EM dataset and segmented ∼135,000 mitochondrial instances therein. MitoNet, a model trained on these resources, performs well on challenging benchmarks and on previously unseen volume EM datasets containing tens of thousands of mitochondria. We release a Python package and napari plugin, empanada, to rapidly run inference, visualize, and proofread instance segmentations. A record of this paper's transparent peer review process is included in the supplemental information.

CLEMSite, a software for automated phenotypic screens using light microscopy and FIB-SEM

In recent years, Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has emerged as a flexible method that enables semi-automated volume ultrastructural imaging. We present a toolset for adherent cells that enables tracking and finding cells, previously identified in light microscopy (LM), in the FIB-SEM, along with the automatic acquisition of high-resolution volume datasets. We detect the underlying grid pattern in both modalities (LM and EM), to identify common reference points. A combination of computer vision techniques enables complete automation of the workflow. This includes setting the coincidence point of both ion and electron beams, automated evaluation of the image quality and constantly tracking the sample position with the microscope's field of view reducing or even eliminating operator supervision. We show the ability to target the regions of interest in EM within 5 µm accuracy while iterating between different targets and implementing unattended data acquisition. Our results demonstrate that executing volume acquisition in multiple locations autonomously is possible in EM.

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.

Short-pulsed micro-magnetic stimulation of the vagus nerve

Vagus nerve stimulation (VNS) is commonly used to treat drug-resistant epilepsy and depression. The therapeutic effect of VNS depends on stimulating the afferent vagal fibers. However, the vagus is a mixed nerve containing afferent and efferent fibers, and the stimulation of cardiac efferent fibers during VNS may produce a rare but severe risk of bradyarrhythmia. This side effect is challenging to mitigate since VNS, via electrical stimulation technology used in clinical practice, requires unique electrode design and pulse optimization for selective stimulation of only the afferent fibers. Here we describe a method of VNS using micro-magnetic stimulation (µMS), which may be an alternative technique to induce a focal stimulation, enabling a selective fiber stimulation. Micro-coils were implanted into the cervical vagus nerve in adult male Wistar rats. For comparison, the physiological responses were recorded continuously before, during, and after stimulation with arterial blood pressure (ABP), respiration rate (RR), and heart rate (HR). The electrical VNS caused a decrease in ABP, RR, and HR, whereas µM-VNS only caused a transient reduction in RR. The absence of an HR modulation indicated that µM-VNS might provide an alternative technology to VNS with fewer heart-related side effects, such as bradyarrhythmia. Numerical electromagnetic simulations helped estimate the optimal coil orientation with respect to the nerve to provide information on the electric field’s spatial distribution and strength. Furthermore, a transmission emission microscope provided very high-resolution images of the cervical vagus nerve in rats, which identified two different populations of nerve fibers categorized as large and small myelinated fibers.

Islet primary cilia motility controls insulin secretion

Primary cilia are specialized cell-surface organelles that mediate sensory perception and, in contrast to motile cilia and flagella, are thought to lack motility function. Here, we show that primary cilia in human and mouse pancreatic islets exhibit movement that is required for glucose-dependent insulin secretion. Islet primary cilia contain motor proteins conserved from those found in classic motile cilia, and their three-dimensional motion is dynein-driven and dependent on adenosine 5'-triphosphate and glucose metabolism. Inhibition of cilia motion blocks beta cell calcium influx and insulin secretion. Human beta cells have enriched ciliary gene expression, and motile cilia genes are altered in type 2 diabetes. Our findings redefine primary cilia as dynamic structures having both sensory and motile function and establish that pancreatic islet cilia movement plays a regulatory role in insulin secretion.

Seeing Is Perceiving (Believing)

Our perception of distinct structures in brain cells and understanding of their function has been revised and updated overtime. Past approaches combined with current powerful technologies provide a more complete picture of the brain's organization, from how the neurons connect with each other to finer details of every corner inside the neurons.

Reciprocal regulation of actin filaments and cellular metabolism

For cells to adhere, migrate and proliferate, remodeling of the actin cytoskeleton is required. This process consumes a large amount of ATP while having an intimate connection with cellular metabolism. Signaling pathways that regulate energy homeostasis can also affect actin dynamics, whereas a variety of actin binding proteins directly or indirectly interact with the anabolic and catabolic regulators in cells. Here, we discuss the inter-regulation between actin filaments and cellular metabolism, reviewing recent discoveries on key metabolic enzymes that respond to actin remodeling as well as historical findings on metabolic stress-induced cytoskeletal reorganization. We also address emerging techniques that would benefit the study of cytoskeletal dynamics and cellular metabolism in high spatial-temporal resolution.

Electron microscopy of cellular ultrastructure in three dimensions

Electron microscopy in three dimensions (3D) of cells and tissues can be essential for understanding the ultrastructural aspects of biological processes. The quest for 3D information reveals challenges at many stages of the workflow, from sample preparation, to imaging, data analysis and segmentation. Here, we outline several available methods, including volume SEM imaging, cryo-TEM and cryo-STEM tomography, each one occupying a different domain in the basic tradeoff between field-of-view and resolution. We discuss the considerations for choosing a suitable method depending on research needs and highlight recent developments that are essential for making 3D volume imaging of cells and tissues a standard tool for cellular and structural biologists.

Computational methods for ultrastructural analysis of synaptic complexes

Electron microscopy (EM) provided fundamental insights about the ultrastructure of neuronal synapses. The large amount of information present in the contemporary EM datasets precludes a thorough assessment by visual inspection alone, thus requiring computational methods for the analysis of the data. Here, I review image processing software methods ranging from membrane tracing in large volume datasets to high resolution structures of synaptic complexes. Particular attention is payed to molecular level analysis provided by recent cryo-electron microscopy and tomography methods.

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.

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.

Powering morphogenesis: multiscale challenges at the interface of cell adhesion and the cytoskeleton

Among the defining features of the animal kingdom is the ability of cells to change shape and move. This underlies embryonic and postembryonic development, tissue homeostasis, regeneration, and wound healing. Cell shape change and motility require linkage of the cell's force-generating machinery to the plasma membrane at cell-cell and cell-extracellular matrix junctions. Connections of the actomyosin cytoskeleton to cell-cell adherens junctions need to be both resilient and dynamic, preventing tissue disruption during the dramatic events of embryonic morphogenesis. In the past decade, new insights radically altered the earlier simple paradigm that suggested simple linear linkage via the cadherin-catenin complex as the molecular mechanism of junction-cytoskeleton interaction. In this Perspective we provide a brief overview of our current state of knowledge and then focus on selected examples highlighting what we view as the major unanswered questions in our field and the approaches that offer exciting new insights at multiple scales from atomic structure to tissue mechanics.

Insights Into the Biogenesis and Emerging Functions of Lipid Droplets From Unbiased Molecular Profiling Approaches

Lipid droplets (LDs) are spherical, single sheet phospholipid-bound organelles that store neutral lipids in all eukaryotes and some prokaryotes. Initially conceived as relatively inert depots for energy and lipid precursors, these highly dynamic structures play active roles in homeostatic functions beyond metabolism, such as proteostasis and protein turnover, innate immunity and defense. A major share of the knowledge behind this paradigm shift has been enabled by the use of systematic molecular profiling approaches, capable of revealing and describing these non-intuitive systems-level relationships. Here, we discuss these advances and some of the challenges they entail, and highlight standing questions in the field.

The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?

Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

ESCRT-mediated membrane repair protects tumor-derived cells against T cell attack

Cytotoxic T lymphocytes (CTLs) and natural killer cells kill virus-infected and tumor cells through the polarized release of perforin and granzymes. Perforin is a pore-forming toxin that creates a lesion in the plasma membrane of the target cell through which granzymes enter the cytosol and initiate apoptosis. Endosomal sorting complexes required for transport (ESCRT) proteins are involved in the repair of small membrane wounds. We found that ESCRT proteins were precisely recruited in target cells to sites of CTL engagement immediately after perforin release. Inhibition of ESCRT machinery in cancer-derived cells enhanced their susceptibility to CTL-mediated killing. Thus, repair of perforin pores by ESCRT machinery limits granzyme entry into the cytosol, potentially enabling target cells to resist cytolytic attack.

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.

SuRVoS 2: Accelerating Annotation and Segmentation for Large Volumetric Bioimage Workflows Across Modalities and Scales

As sample preparation and imaging techniques have expanded and improved to include a variety of options for larger sized and numbers of samples, the bottleneck in volumetric imaging is now data analysis. Annotation and segmentation are both common, yet difficult, data analysis tasks which are required to bring meaning to the volumetric data. The SuRVoS application has been updated and redesigned to provide access to both manual and machine learning-based segmentation and annotation techniques, including support for crowd sourced data. Combining adjacent, similar voxels (supervoxels) provides a mechanism for speeding up segmentation both in the painting of annotation and by training a segmentation model on a small amount of annotation. The support for layers allows multiple datasets to be viewed and annotated together which, for example, enables the use of correlative data (e.g. crowd-sourced annotations or secondary imaging techniques) to guide segmentation. The ability to work with larger data on high-performance servers with GPUs has been added through a client-server architecture and the Pytorch-based image processing and segmentation server is flexible and extensible, and allows the implementation of deep learning-based segmentation modules. The client side has been built around Napari allowing integration of SuRVoS into an ecosystem for open-source image analysis while the server side has been built with cloud computing and extensibility through plugins in mind. Together these improvements to SuRVoS provide a platform for accelerating the annotation and segmentation of volumetric and correlative imaging data across modalities and scales.

Nuclear Fragility in Radiation-Induced Senescence: Blebs and Tubes Visualized by 3D Electron Microscopy

Irreparable DNA damage following ionizing radiation (IR) triggers prolonged DNA damage response and induces premature senescence. Cellular senescence is a permanent state of cell-cycle arrest characterized by chromatin restructuring, altered nuclear morphology and acquisition of secretory phenotype, which contributes to senescence-related inflammation. However, the mechanistic connections for radiation-induced DNA damage that trigger these senescence-associated hallmarks are poorly understood. In our in vitro model of radiation-induced senescence, mass spectrometry-based proteomics was combined with high-resolution imaging techniques to investigate the interrelations between altered chromatin compaction, nuclear envelope destabilization and nucleo-cytoplasmic chromatin blebbing. Our findings confirm the general pathophysiology of the senescence-response, with disruption of nuclear lamin organization leading to extensive chromatin restructuring and destabilization of the nuclear membrane with release of chromatin fragments into the cytosol, thereby activating cGAS-STING-dependent interferon signaling. By serial block-face scanning electron microscopy (SBF-SEM) whole-cell datasets were acquired to investigate the morphological organization of senescent fibroblasts. High-resolution 3-dimensional (3D) reconstruction of the complex nuclear shape allows us to precisely visualize the segregation of nuclear blebs from the main nucleus and their fusion with lysosomes. By multi-view 3D electron microscopy, we identified nanotubular channels formed in lamin-perturbed nuclei of senescent fibroblasts; the potential role of these nucleo-cytoplasmic nanotubes for expulsion of damaged chromatin has to be examined.

Cilia Action in Islets: Lessons From Mouse Models

Primary cilia as a signaling organelle have garnered recent attention as a regulator of pancreatic islet function. These rod-like sensors exist on all major islet endocrine cell types and transduce a variety of external cues, while dysregulation of cilia function contributes to the development of diabetes. The complex role of islet primary cilia has been examined using genetic deletion targeting various components of cilia. In this review, we summarize experimental models for the study of islet cilia and current understanding of mechanisms of cilia regulation of islet hormone secretion. Consensus from these studies shows that pancreatic cilia perturbation can cause both endocrine and exocrine defects that are relevant to human disease. We discuss future research directions that would further elucidate cilia action in distinct groups of islet cells, including paracrine and juxtacrine regulation, GPCR signaling, and endocrine-exocrine crosstalk.

Whole-cell organelle segmentation in volume electron microscopy

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes-ranging from endoplasmic reticulum to microtubules to ribosomes-in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM)¹. We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, 'OpenOrganelle', to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.