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Actin mediates insulin secretion in pancreatic β-cells through remodeling. Hampered by limited resolution, previous studies have offered an ambiguous depiction as depolymerization and repolymerization. We report the in situ structure of actin remodeling in INS-1E β-cells during glucose-stimulated insulin secretion at nanoscale resolution. After remodeling, the actin filament network at the cell periphery exhibits three marked differences: 12% of actin filaments reorient quasi-orthogonally to the ventral membrane; the filament network mainly remains as cell-stabilizing bundles but partially reconfigures into a less compact arrangement; actin filaments anchored to the ventral membrane reorganize from a netlike to a blooming architecture. Furthermore, the density of actin filaments and microtubules around insulin secretory granules decreases, while actin filaments and microtubules become more densely packed. The actin filament network after remodeling potentially precedes the transport and release of insulin secretory granules. These findings advance our understanding of actin remodeling and its role in glucose-stimulated insulin secretion.

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.

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.

This article reports on the findings of a study that explored the benefits and challenges for women of working at an Australian regional university in early 2020 before the COVID-19 pandemic. It examines whether living and working at a regional university with dispersed campuses presented particular challenges for women and whether it had an impact on their career progression. Twenty-one women supplied written responses to a list of questions provided by the researchers. The main finding was that women enjoyed working in teams and preferred flexibility, autonomy and positive teamwork environments. To address challenges identified in the study about working across dispersed campuses and the limitations of virtual communication, particularly in the current pandemic, the article investigates the feasibility of a blended approach to teamwork using the concept of a third space.

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.

BackgroundMitochondria 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 reviewThis 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 conclusionsThe 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.

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.

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.

Cryo-electron tomography provides the opportunity for unsupervised discovery of endogenous complexes in situ. This process usually requires particle picking, clustering and alignment of subtomograms to produce an average structure of the complex. When applied to heterogeneous samples, template-free clustering and alignment of subtomograms can potentially lead to the discovery of structures for unknown endogenous complexes. However, such methods require scoring functions to measure and accurately rank the quality of aligned subtomogram clusters, which can be compromised by contaminations from misclassified complexes and alignment errors. Here, we provide the first study to assess the effectiveness of more than 15 scoring functions for evaluating the quality of subtomogram clusters, which differ in the amount of structural misalignments and contaminations due to misclassified complexes. We assessed both experimental and simulated subtomograms as ground truth data sets. Our analysis showed that the robustness of scoring functions varies largely. Most scores were sensitive to the signal-to-noise ratio of subtomograms and often required Gaussian filtering as preprocessing for improved performance. Two scoring functions, Spectral SNR-based Fourier Shell Correlation and Pearson Correlation in the Fourier domain with missing wedge correction, showed a robust ranking of subtomogram clusters without any preprocessing and irrespective of SNR levels of subtomograms. Of these two scoring functions, Spectral SNR-based Fourier Shell Correlation was fastest to compute and is a better choice for handling large numbers of subtomograms. Our results provide a guidance for choosing an accurate scoring function for template-free approaches to detect complexes from heterogeneous samples.

Type 2 diabetes is characterized by β and α cell dysfunction. We used phasor-FLIM (Fluorescence Lifetime Imaging Microscopy) to monitor oxidative phosphorylation and glycolysis in living islet cells before and after glucose stimulation. In healthy cells, glucose enhanced oxidative phosphorylation in β cells and suppressed oxidative phosphorylation in α cells. In Type 2 diabetes, glucose increased glycolysis in β cells, and only partially suppressed oxidative phosphorylation in α cells. FLIM uncovers key perturbations in glucose induced metabolism in living islet cells and provides a sensitive tool for drug discovery in diabetes.

Characterizing relationships between cell structures and functions requires mesoscale mapping of intact cells showing subcellular rearrangements following stimulation; however, current approaches are limited in this regard. Here, we report a unique application of soft x-ray tomography to generate three-dimensional reconstructions of whole pancreatic β cells at different time points following glucose-stimulated insulin secretion. Reconstructions following stimulation showed distinct insulin vesicle distribution patterns reflective of altered vesicle pool sizes as they travel through the secretory pathway. Our results show that glucose stimulation caused rapid changes in biochemical composition and/or density of insulin packing, increased mitochondrial volume, and closer proximity of insulin vesicles to mitochondria. Costimulation with exendin-4 (a glucagon-like peptide-1 receptor agonist) prolonged these effects and increased insulin packaging efficiency and vesicle maturation. This study provides unique perspectives on the coordinated structural reorganization and interactions of organelles that dictate cell responses.

Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.

Objectives: To examine changes in multiple myeloma incidence and mortality rates during 1982–2018, and to estimate its incidence, mortality, and prevalence for 2019–2043. Study design: Population‐based statistical modelling study; analysis of and projections based on Australian Institute of Health and Welfare multiple myeloma incidence, mortality, and survival data. Setting: Australia, 1982–2018 (historical data) and projections to 2043. Main outcome measures: Changes in multiple myeloma incidence and mortality rates, 1982–2018, determined by joinpoint regression analysis (age‐standardised to 2021 Australian population); projection of rates to 2043 based on age–period–cohort models; estimated 5‐ and 30‐year prevalence of multiple myeloma (modified counting method). Results: The incidence of multiple myeloma increased during 1982–2018 (eg, annual percentage change [APC], 2006–2018, 1.9%; 95% confidence interval [CI], 1.7–2.2%), but the mortality rate declined during 1990–2018 (APC, –0.4%; 95% CI, –0.5% to –0.2%). The age‐standardised incidence rate was projected to increase by 14.9% during 2018–2043, from 8.7 in 2018 to 10.0 (95% CI, 9.4–10.7) new cases per 100 000 population in 2043; the mortality rate was projected to decline by 27.5%, from 4.0 to 2.9 (95% CI, 2.6–3.3) deaths per 100 000 population. The annual number of people newly diagnosed with multiple myeloma was estimated to increase by 89.2%, from 2120 in 2018 to 4012 in 2043; the number of deaths from multiple myeloma was projected to increase by 31.7%, from 979 to 1289. The number of people living with multiple myeloma up to 30 years after initial diagnosis was projected to increase by 163%, from 10 288 in 2018 to 27 093 in 2043, including 13 019 people (48.1%) diagnosed during the preceding five years. Conclusion: Although the decline in the mortality rate was projected to continue, the projected increases in the incidence and prevalence of multiple myeloma in Australia over the next 25 years indicate that investment in prevention and early detection research, and planning for prolonged treatment and care, are needed. [ABSTRACT FROM AUTHOR]

The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.

BackgroundDespite advances in cancer diagnosis and treatment have significantly improved survival rates, patients post-treatment-related health needs are often not adequately addressed by current health services. The aim of the Women's Wellness after Cancer Program (WWACP), which is a digitised multimodal lifestyle intervention, is to enhance health-related quality of life in women previously treated for blood, breast and gynaecological cancers.MethodsA single-blinded, multi-centre randomized controlled trial recruited a total of 351 women within 24 months of completion of chemotherapy (primary or adjuvant) and/or radiotherapy. Women were randomly assigned to either usual care or intervention using computer-generated permuted-block randomisation. The intervention comprises an evidence-based interactive iBook and journal, web interface, and virtual health consultations by an experienced cancer nurse trained in the delivery of the WWACP. The 12 week intervention focuses on evidence-based health education and health promotion after a cancer diagnosis. Components are drawn from the American Cancer Research Institute and the World Cancer Research Fund Guidelines (2010), incorporating promotion of physical activity, good diet, smoking cessation, reduction of alcohol intake, plus strategies for sleep and stress management. The program is based on Bandura's social cognitive theoretical framework. The primary outcome is health-related quality of life, as measured by the Functional Assessment of Cancer Therapy-General (FACT-G). Secondary outcomes are menopausal symptoms as assessed by Greene Climacteric Scale; physical activity elicited with the Physical Activity Questionnaire Short Form (IPAQ-SF); sleep measured by the Pittsburgh Sleep Quality Index; habitual dietary intake monitored with the Food Frequency Questionnaire (FFQ); alcohol intake and tobacco use measured by the Australian Health Survey and anthropometric measures including height, weight and waist-to-hip ratio. All participants were assessed with these measures at baseline (at the start of the intervention), 12 weeks (at completion of the intervention), and 24 weeks (to determine the level of sustained behaviour change). Further, a simultaneous cost-effectiveness evaluation will consider if the WWACP provides value for money and will be reported separately.DiscussionWomen treated for blood, breast and gynaecological cancers demonstrate increasingly good survival rates. However, they experience residual health problems that are potentially modifiable through behavioural lifestyle interventions such as the WWACP.Trial registrationThe protocol for this study was registered with the Australian and New Zealand Clinical Trials Registry, Trial ID: ACTRN12614000800628 , July 28, 2014.