Visualizing subcellular rearrangements in intact β cells using soft x-ray tomography

Soft X-ray tomography analysis of mitochondria dynamics in Saccharomyces cerevisiae

BACKGROUND

Mitochondria are highly dynamic organelles that constantly undergo processes of fission and fusion. The changes in mitochondrial dynamics shape the organellar morphology and influence cellular activity regulation. Soft X-ray tomography (SXT) allows for three-dimensional imaging of cellular structures while they remain in their natural, hydrated state, which omits the need for cell fixation and sectioning. Synchrotron facilities globally primarily use flat grids as sample carriers for SXT analysis, focusing on adherent cells. To investigate mitochondrial morphology and structure in hydrated yeast cells using SXT, it is necessary to establish a method that employs the flat grid system for examining cells in suspension.

RESULTS

We developed a procedure to adhere suspended yeast cells to a flat grid for SXT analysis. Using this protocol, we obtained images of wild-type yeast cells, strains with mitochondrial dynamics defects, and mutant cells possessing distinctive mitochondria. The SXT images align well with the results from fluorescent microscopy. Optimized organellar visualization was achieved by constructing three-dimensional models of entire yeast cells.

CONCLUSIONS

In this study, we characterized the mitochondrial network in yeast cells using SXT. The optimized sample preparation procedure was effective for suspended cells like yeast, utilizing a flat grid system to analyze mitochondrial structure through SXT. The findings corresponded with the mitochondrial morphology observed under fluorescence microscopy, both in regular and disrupted dynamic equilibrium. With the acquired image of unique mitochondria in Δhap2 cells, our results revealed that intricate details of organelles, such as mitochondria and vacuoles in yeast cells, can be characterized using SXT. Therefore, this optimized system supports the expanded application of SXT for studying organellar structure and morphology in suspended cells.

Decoding Insulin Secretory Granule Maturation Using Genetically Encoded pH Sensors

Insulin is a peptide hormone secreted from pancreatic beta cells to regulate blood glucose homeostasis. Maturation of active insulin occurs within insulin secretory granules (ISG) by acidification of the lumen and enzymatic cleavage of insulin before secretion. This process is dysregulated in diabetes, and many questions remain on how the cell controls insulin maturation. We address this gap in knowledge by designing two genetically encoded fluorescence pH sensors and a fluorescence lifetime imaging and analysis pipeline to monitor the pH of individual secretory ISGs within live cells at higher resolution and precision than previously possible. We observed different subpopulations of ISGs based on their pH and subcellular localization. Signals regulating metabolism vs membrane depolarization mobilize different subpopulations of ISGs for secretion, and we confirm that maturation signals acidify ISGs. We conclude that different signaling networks uniquely impact ISG mobilization and secretion. Future applications of these tools will be useful for exploring how these processes are dysregulated in diabetes and provide new paths for developing more effective treatments.

Insulator-based dielectrophoresis-assisted separation of insulin secretory vesicles

Organelle heterogeneity and inter-organelle contacts within a single cell contribute to the limited sensitivity of current organelle separation techniques, thus hindering organelle subpopulation characterization. Here, we use direct current insulator-based dielectrophoresis (DC-iDEP) as an unbiased separation method and demonstrate its capability by identifying distinct distribution patterns of insulin vesicles from INS-1E insulinoma cells. A multiple voltage DC-iDEP strategy with increased range and sensitivity has been applied, and a differentiation factor (ratio of electrokinetic to dielectrophoretic mobility) has been used to characterize features of insulin vesicle distribution patterns. We observed a significant difference in the distribution pattern of insulin vesicles isolated from glucose-stimulated cells relative to unstimulated cells, in accordance with maturation of vesicles upon glucose stimulation. We interpret the difference in distribution pattern to be indicative of high-resolution separation of vesicle subpopulations. DC-iDEP provides a path for future characterization of subtle biochemical differences of organelle subpopulations within any biological system.

Three-Dimensional Label-Free Observing of the Self-Assembled Nanoparticles inside a Single Cell at Nanoscale Resolution

Understanding the intracellular behavior of nanoparticles (NPs) plays a key role in optimizing the self-assembly performance of nanomedicine. However, conducting the 3D, label-free, quantitative observation of self-assembled NPs within intact single cells remains a substantial challenge in complicated intracellular environments. Here, we propose a deep learning combined synchrotron radiation hard X-ray nanotomography approach to visualize the self-assembled ultrasmall iron oxide (USIO) NPs in a single cell. The method allows us to explore comprehensive information on NPs, such as their distribution, morphology, location, and interaction with cell organelles, and provides quantitative analysis of the heterogeneous size and morphologies of USIO NPs under diverse conditions. This label-free, in situ method provides a tool for precise characterization of intracellular self-assembled NPs to improve the evaluation and design of a bioresponsive nanomedicine.

Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.

Nitrogen-fixing organelle in a marine alga

Symbiotic interactions were key to the evolution of chloroplast and mitochondria organelles, which mediate carbon and energy metabolism in eukaryotes. Biological nitrogen fixation, the reduction of abundant atmospheric nitrogen gas (N2) to biologically available ammonia, is a key metabolic process performed exclusively by prokaryotes. Candidatus Atelocyanobacterium thalassa, or UCYN-A, is a metabolically streamlined N2-fixing cyanobacterium previously reported to be an endosymbiont of a marine unicellular alga. Here we show that UCYN-A has been tightly integrated into algal cell architecture and organellar division and that it imports proteins encoded by the algal genome. These are characteristics of organelles and show that UCYN-A has evolved beyond endosymbiosis and functions as an early evolutionary stage N2-fixing organelle, or "nitroplast."

Clickable X-ray Nanoprobes for Nanoscopic Bioimaging of Cellular Structures

Synchrotron-based X-ray microscopy (XRM) has garnered widespread attention from researchers due to its high spatial resolution and excellent energy (element) resolution. Existing molecular probes suitable for XRM include immune probes and genetic labeling probes, enabling the precise imaging of various biological targets within cells. However, immune labeling techniques are prone to cross-interference between antigens and antibodies. Genetic labeling technologies have limited systems that allow express markers independently, and moreover, genetically encoded labels based on catalytic polymerization lack a fixed morphology. When applied to cell imaging, this can result in reduced localization accuracy due to the diffusion of labels within the cells. Therefore, both techniques face challenges in simultaneously labeling multiple biotargets within cells and achieving high-precision imaging. In this work, we applied the click reaction and developed a third category of imaging probes suitable for XRM, termed clickable X-ray nanoprobes (Click-XRN). Click-XRN consists of two components: an X-ray-sensitive multicolor imaging module and a particle-size-controllable morphology module. Efficient identification of intra- and extracellular biotargets is achieved through click reactions between the probe and biomolecules. Click-XRN possesses a controllable particle size, and its loading of various metal ions provides distinctive signals for imaging under XRM. Based on this, we optimized the imaging energy of Click-XRN with different particle sizes, enabling single-color and two-color imaging of the cell membrane, cell nucleus, and mitochondria with nanoscale spatial nanometers. Our work provides a potent molecular tool for investigating cellular activities through XRM.

In situ label-free X-ray imaging for visualizing the localization of nanomedicines and subcellular architecture in intact single cells

Understanding the intracellular behaviors of nanomedicines and morphology variation of subcellular architecture impacted by nanomaterial-biology (nano-bio) interactions could help guide the safe-by-design, manufacturing and evaluation of nanomedicines for clinical translation. The in situ and label-free analysis of nano-bio interactions in intact single cells at nanoscale remains challenging. We developed an approach based on X-ray microscopy to directly visualize the 2D or 3D intracellular distribution without labeling at nanometer resolution and analyze the chemical transformation of nanomedicines in situ. Here, we describe an optimized workflow for cell sample preparation, beamline selection, data acquisition and analysis. With several model bionanomaterials as examples, we analyze the localization of nanomedicines in various primary blood cells, macrophages, dendritic cells, monocytes and cancer cells, as well as the morphology of some organelles with soft and hard X-rays. Our protocol has been successfully implemented at three beamline facilities: 4W1A of Beijing Synchrotron Radiation Facility, BL08U1A of Shanghai Synchrotron Radiation Facility and BL07W of the National Synchrotron Radiation Laboratory. This protocol can be completed in ~2-5 d, depending on the cell types, their incubation times with nanomaterials and the selected X-ray beamline. The protocol enables the in situ analysis of the varieties of metal-containing nanomaterials, visualization of intracellular endocytosis, distribution and excretion and corresponding subcellular morphological variation influenced by nanomedicines in cell lines or primary cells by using this universal and robust platform. The results facilitate the understanding of the true principle and mechanism underlying the nano-bio interaction.

Advanced Imaging Techniques for the Characterization of Subcellular Organelle Structure in Pancreatic Islet β Cells

Type 2 diabetes (T2D) affects more than 32.3 million individuals in the United States, creating an economic burden of nearly $966 billion in 2021. T2D results from a combination of insulin resistance and inadequate insulin secretion from the pancreatic β cell. However, genetic and physiologic data indicate that defects in β cell function are the chief determinant of whether an individual with insulin resistance will progress to a diagnosis of T2D. The subcellular organelles of the insulin secretory pathway, including the endoplasmic reticulum, Golgi apparatus, and secretory granules, play a critical role in maintaining the heavy biosynthetic burden of insulin production, processing, and secretion. In addition, the mitochondria enable the process of insulin release by integrating the metabolism of nutrients into energy output. Advanced imaging techniques are needed to determine how changes in the structure and composition of these organelles contribute to the loss of insulin secretory capacity in the β cell during T2D. Several microscopy techniques, including electron microscopy, fluorescence microscopy, and soft X-ray tomography, have been utilized to investigate the structure-function relationship within the β cell. In this overview article, we will detail the methodology, strengths, and weaknesses of each approach. © 2024 American Physiological Society. Compr Physiol 14:5243-5267, 2024.

Simultaneous multimaterial operando tomography of electrochemical devices

The performance of electrochemical energy devices, such as fuel cells and batteries, is dictated by intricate physiochemical processes within. To better understand and rationally engineer these processes, we need robust operando characterization tools that detect and distinguish multiple interacting components/interfaces in high contrast. Here, we uniquely combine dual-modality tomography (simultaneous neutron and x-ray tomography) and advanced image processing (iterative reconstruction and metal artifact reduction) for high-contrast multimaterial imaging, with signal and contrast enhancements of up to 10 and 48 times, respectively, compared to conventional single-modality imaging. Targeted development and application of these methods to electrochemical devices allow us to resolve operando distributions of six interacting fuel cell components (including void space) with the highest reported pairwise contrast for simultaneous yet decoupled spatiotemporal characterization of component morphology and hydration. Such high-contrast tomography ushers in key gold standards for operando electrochemical characterization, with broader applicability to numerous multimaterial systems.

Soft X-ray Microscopy in Cell Biology: Current Status, Contributions and Prospects

The recent advances achieved in microscopy technology have led to a significant breakthrough in biological research. Super-resolution fluorescent microscopy now allows us to visualize subcellular structures down to the pin-pointing of the single molecules in them, while modern electron microscopy has opened new possibilities in the study of protein complexes in their native, intracellular environment at near-atomic resolution. Nonetheless, both fluorescent and electron microscopy have remained beset by their principal shortcomings: the reliance on labeling procedures and severe sample volume limitations, respectively. Soft X-ray microscopy is a candidate method that can compensate for the shortcomings of both technologies by making possible observation of the entirety of the cellular interior without chemical fixation and labeling with an isotropic resolution of 40-70 nm. This will thus bridge the resolution gap between light and electron microscopy (although this gap is being narrowed, it still exists) and resolve the issue of compatibility with the former, and possibly in the near future, the latter methods. This review aims to assess the current state of soft X-ray microscopy and its impact on our understanding of the subcellular organization. It also attempts to look into the future of X-ray microscopy, particularly as relates to its seamless integration into the cell biology toolkit.

Loss of ZNF148 enhances insulin secretion in human pancreatic β cells

Insulin secretion from pancreatic β cells is essential to the maintenance of glucose homeostasis. Defects in this process result in diabetes. Identifying genetic regulators that impair insulin secretion is crucial for the identification of novel therapeutic targets. Here, we show that reduction of ZNF148 in human islets, and its deletion in stem cell-derived β cells (SC-β cells), enhances insulin secretion. Transcriptomics of ZNF148-deficient SC-β cells identifies increased expression of annexin and S100 genes whose proteins form tetrameric complexes involved in regulation of insulin vesicle trafficking and exocytosis. ZNF148 in SC-β cells prevents translocation of annexin A2 from the nucleus to its functional place at the cell membrane via direct repression of S100A16 expression. These findings point to ZNF148 as a regulator of annexin-S100 complexes in human β cells and suggest that suppression of ZNF148 may provide a novel therapeutic strategy to enhance insulin secretion.

Soft X-ray tomograms provide a structural basis for whole-cell modeling

Developing in silico models that accurately reflect a whole, functional cell is an ongoing challenge in biology. Current efforts bring together mathematical models, probabilistic models, visual representations, and data to create a multi-scale description of cellular processes. A realistic whole-cell model requires imaging data since it provides spatial constraints and other critical cellular characteristics that are still impossible to obtain by calculation alone. This review introduces Soft X-ray Tomography (SXT) as a powerful imaging technique to visualize and quantify the mesoscopic (~25 nm spatial scale) organelle landscape in whole cells. SXT generates three-dimensional reconstructions of cellular ultrastructure and provides a measured structural framework for whole-cell modeling. Combining SXT with data from disparate technologies at varying spatial resolutions provides further biochemical details and constraints for modeling cellular mechanisms. We conclude, based on the results discussed here, that SXT provides a foundational dataset for a broad spectrum of whole-cell modeling experiments.

An intensity-based post-processing tool for 3D instance segmentation of organelles in soft X-ray tomograms

Investigating the 3D structures and rearrangements of organelles within a single cell is critical for better characterizing cellular function. Imaging approaches such as soft X-ray tomography have been widely applied to reveal a complex subcellular organization involving multiple inter-organelle interactions. However, 3D segmentation of organelle instances has been challenging despite its importance in organelle characterization. Here we propose an intensity-based post-processing tool to identify and separate organelle instances. Our tool separates sphere-like (insulin vesicle) and columnar-shaped organelle instances (mitochondrion) based on the intensity of raw tomograms, semantic segmentation masks, and organelle morphology. We validate our tool using synthetic tomograms of organelles and experimental tomograms of pancreatic β-cells to separate insulin vesicle and mitochondria instances. As compared to the commonly used connected regions labeling, watershed, and watershed + Gaussian filter methods, our tool results in improved accuracy in identifying organelles in the synthetic tomograms and an improved description of organelle structures in β-cell tomograms. In addition, under different experimental treatment conditions, significant changes in volumes and intensities of both insulin vesicle and mitochondrion are observed in our instance results, revealing their potential roles in maintaining normal β-cell function. Our tool is expected to be applicable for improving the instance segmentation of other images obtained from different cell types using multiple imaging modalities.

Integrative modeling of the cell

A whole-cell model represents certain aspects of the cell structure and/or function. Due to the high complexity of the cell, an integrative modeling approach is often taken to utilize all available information including experimental data, prior knowledge and prior models. In this review, we summarize an emerging workflow of whole-cell modeling into five steps: (i) gather information; (ii) represent the modeled system into modules; (iii) translate input information into scoring function; (iv) sample the whole-cell model; (v) validate and interpret the model. In particular, we propose the integrative modeling of the cell by combining available (whole-cell) models to maximize the accuracy, precision, and completeness. In addition, we list quantitative predictions of various aspects of cell biology from existing whole-cell models. Moreover, we discuss the remaining challenges and future directions, and highlight the opportunity to establish an integrative spatiotemporal multi-scale whole-cell model based on a community approach.

Advanced Light Source Analytical Techniques for Exploring the Biological Behavior and Fate of Nanomedicines

Exploration of the biological behavior and fate of nanoparticles, as affected by the nanomaterial-biology (nano-bio) interaction, has become progressively critical for guiding the rational design and optimization of nanomedicines to minimize adverse effects, support clinical translation, and aid in evaluation by regulatory agencies. Because of the complexity of the biological environment and the dynamic variations in the bioactivity of nanomedicines, in-situ, label-free analysis of the transport and transformation of nanomedicines has remained a challenge. Recent improvements in optics, detectors, and light sources have allowed the expansion of advanced light source (ALS) analytical technologies to dig into the underexplored behavior and fate of nanomedicines in vivo. It is increasingly important to further develop ALS-based analytical technologies with higher spatial and temporal resolution, multimodal data fusion, and intelligent prediction abilities to fully unlock the potential of nanomedicines. In this Outlook, we focus on several selected ALS analytical technologies, including imaging and spectroscopy, and provide an overview of the emerging opportunities for their applications in the exploration of the biological behavior and fate of nanomedicines. We also discuss the challenges and limitations faced by current approaches and tools and the expectations for the future development of advanced light sources and technologies. Improved ALS imaging and spectroscopy techniques will accelerate a profound understanding of the biological behavior of new nanomedicines. Such advancements are expected to inspire new insights into nanomedicine research and promote the development of ALS capabilities and methods more suitable for nanomedicine evaluation with the goal of clinical translation.

Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy

Soft X-ray microscopy is a powerful technique for imaging cells with nanometer resolution in their native state without chemical fixation, staining, or sectioning. The studies performed in several laboratories have demonstrated the potential of applying this technique for imaging the internal structures of intact cells. However, it is currently used mainly on synchrotrons with restricted access. Moreover, the operation of these instruments and the associated sample-preparation protocols require interdisciplinary and highly specialized personnel, limiting their wide application in practice. This is why soft X-ray microscopy is not commonly used in biological laboratories as an imaging tool. Thus, a laboratory-based and user-friendly soft X-ray contact microscope would facilitate the work of biologists. A compact, desk-top laboratory setup for soft X-ray contact microscopy (SXCM) based on a laser-plasma soft X-ray source, which can be used in any biological laboratory, together with several applications for biological imaging, are described. Moreover, the perspectives of the correlation of SXCM with other super-resolution imaging techniques based on the current literature are discussed.

Auto-segmentation and time-dependent systematic analysis of mesoscale cellular structure in β-cells during insulin secretion

The mesoscale description of the subcellular organization informs about cellular mechanisms in disease state. However, applications of soft X-ray tomography (SXT), an important approach for characterizing organelle organization, are limited by labor-intensive manual segmentation. Here we report a pipeline for automated segmentation and systematic analysis of SXT tomograms. Our approach combines semantic and first-applied instance segmentation to produce separate organelle masks with high Dice and Recall indexes, followed by analysis of organelle localization based on the radial distribution function. We demonstrated this technique by investigating the organization of INS-1E pancreatic β-cell organization under different treatments at multiple time points. Consistent with a previous analysis of a similar dataset, our results revealed the impact of glucose stimulation on the localization and molecular density of insulin vesicles and mitochondria. This pipeline can be extended to SXT tomograms of any cell type to shed light on the subcellular rearrangements under different drug treatments.

Soft X-ray tomography to map and quantify organelle interactions at the mesoscale

Inter-organelle interactions are a vital part of normal cellular function; however, these have proven difficult to quantify due to the range of scales encountered in cell biology and the throughput limitations of traditional imaging approaches. Here, we demonstrate that soft X-ray tomography (SXT) can be used to rapidly map ultrastructural reorganization and inter-organelle interactions in intact cells. SXT takes advantage of the naturally occurring, differential X-ray absorption of the carbon-rich compounds in each organelle. Specifically, we use SXT to map the spatiotemporal evolution of insulin vesicles and their co-localization and interaction with mitochondria in pancreatic β cells during insulin secretion and in response to different stimuli. We quantify changes in the morphology, biochemical composition, and relative position of mitochondria and insulin vesicles. These findings highlight the importance of a comprehensive and unbiased mapping at the mesoscale to characterize cell reorganization that would be difficult to detect with other existing methodologies.

Integrative structural modelling and visualisation of a cellular organelle

Models of insulin secretory vesicles from pancreatic beta cells have been created using the cellPACK suite of tools to research, curate, construct and visualise the current state of knowledge. The model integrates experimental information from proteomics, structural biology, cryoelectron microscopy and X-ray tomography, and is used to generate models of mature and immature vesicles. A new method was developed to generate a confidence score that reconciles inconsistencies between three available proteomes using expert annotations of cellular localisation. The models are used to simulate soft X-ray tomograms, allowing quantification of features that are observed in experimental tomograms, and in turn, allowing interpretation of X-ray tomograms at the molecular level.

The use of soft X-ray tomography to explore mitochondrial structure and function

Background

Mitochondria are cellular organelles responsible for energy production, and dysregulation of the mitochondrial network is associated with many disease states. To fully characterize the mitochondrial network's structure and function, a three-dimensional whole cell mapping technique is required.

Scope of review

This review highlights the use of soft X-ray tomography (SXT) as a relatively high-throughput approach to quantify mitochondrial structure and function under multiple cellular conditions.

Major conclusions

The use of SXT opens the door for mapping cellular rearrangements during critical processes such as insulin secretion, stem cell differentiation, or disease progression. SXT provides unique information such as biochemical compositions or molecular densities of organelles and allows for unbiased, label-free imaging of intact whole cells. Mapping mitochondria in the context of the near-native cellular environment will reveal more information regarding mitochondrial network functions within the cell.

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Soft X-ray tomography (SXT) generates 3D organelle maps of intact cells.

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3D maps reveal the positions of mitochondria and their molecular densities.

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SXT can be used to quantify and compare organelle contacts between conditions.

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SXT is unbiased imaging that identifies the contents of subcellular neighborhoods.

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SXT provides an exciting path for exploring metabolic dysfunction.

Reagents and models for detecting endogenous GLP1R and GIPR

Glucagon-like peptide-1 receptor (GLP1R) agonists target the GLP1R, whereas dual GLP1R/ gastric inhibitory polypeptide receptor (GIPR) agonists target both the GLP1R and GIPR. Despite the importance of these drug classes for the treatment of diabetes and obesity, still very little is known about the localization of GLP1R and GIPR themselves. Complicating matters is the low abundance of GLP1R and GIPR mRNA/protein, as well as a lack of specific and validated reagents for their detection. Without knowing where GLP1R and GIPR are located, it is difficult to propose mechanisms of action in the various target organs, and whether this is indirect or direct. In the current review, we will explain the steps needed to properly validate reagents for endogenous GLP1R/GIPR detection, describe the available approaches to visualize GLP1R/GIPR, and provide an update on the state-of-art. The overall aim is to provide a reference resource for researchers interested in GLP1R and GIPR signaling.

Using soft X-ray tomography for rapid whole-cell quantitative imaging of SARS-CoV-2-infected cells

High-resolution and rapid imaging of host cell ultrastructure can generate insights toward viral disease mechanism, for example for a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Here, we employ full-rotation soft X-ray tomography (SXT) to examine organelle remodeling induced by SARS-CoV-2 at the whole-cell level with high spatial resolution and throughput. Most of the current SXT systems suffer from a restricted field of view due to use of flat sample supports and artifacts due to missing data. In this approach using cylindrical sample holders, a full-rotation tomogram of human lung epithelial cells is performed in less than 10 min. We demonstrate the potential of SXT imaging by visualizing aggregates of SARS-CoV-2 virions and virus-induced intracellular alterations. This rapid whole-cell imaging approach allows us to visualize the spatiotemporal changes of cellular organelles upon viral infection in a quantitative manner.

Bayesian metamodeling of complex biological systems across varying representations

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.

Visualizing insulin vesicle neighborhoods in β cells by cryo-electron tomography

Subcellular neighborhoods, comprising specific ratios of organelles and proteins, serve a multitude of biological functions and are of particular importance in secretory cells. However, the role of subcellular neighborhoods in insulin vesicle maturation is poorly understood. Here, we present single-cell multiple distinct tomogram acquisitions of β cells for in situ visualization of distinct subcellular neighborhoods that are involved in the insulin vesicle secretory pathway. We propose that these neighborhoods play an essential role in the specific function of cellular material. In the regions where we observed insulin vesicles, a measurable increase in both the fraction of cellular volume occupied by vesicles and the average size (diameter) of the vesicles was apparent as sampling moved from the area near the nucleus toward the plasma membrane. These findings describe the important role of the nanometer-scale organization of subcellular neighborhoods on insulin vesicle maturation.