Biology subjects

White, K. L.

Publications and source records attributed to White, K. L..

10 recordsLinked to original sources

bioRxiv2026

Morphotype-Resolved 3D Morphometry Reveals a Structure-Density-Location Coupling in Mitochondrial Networks

Mitochondrial architecture plays a critical role in cellular function, yet how organelle structure, metabolic density, and subcellular position are jointly remodeled across whole cells remains poorly understood. We applied quantitative 3D soft X-ray tomography to analyze intact INS-1E cells in a native, cryo-hydrated state. By integrating morphometric profiling with voxel-level linear absorption coefficients (LAC) and contour-based radial mapping, we tracked the structural, biochemical, and spatial remodeling of fragmented, intermediate, and interconnected mitochondrial morphotypes under high glucose and Exendin-4 stimulation. High glucose induces morphotype-specific hypertrophy, fission, and perinuclear redistribution of low-density fragments. Co-stimulation with Exendin-4 stabilizes interconnected networks and increases metabolic density at the cell periphery. Morphotype-resolved analysis uncovers a structure-density-location coupling in which mitochondrial shape, macromolecular packing, and radial position shift in concert. These results offer a quantitative framework and high-resolution spatial constraints for whole-cell modeling of organelle dynamics. HighlightsO_LIMitochondrial morphotypes occupy distinct regions during metabolic stimulation C_LIO_LIHigh glucose drives early mitochondrial swelling, fission, and spatial dispersion C_LIO_LIExendin-4 prevents structural collapse and preserves dense interconnected networks C_LIO_LIMorphotype-resolved analysis uncovers a structure-density-location coupling in mitochondrial networks. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/712811v2_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1d495ecorg.highwire.dtl.DTLVardef@1bee80dorg.highwire.dtl.DTLVardef@74df9dorg.highwire.dtl.DTLVardef@104db6d_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2026

Integrated biophysical and spatial remodeling during insulin secretory granule maturation at the mitochondrial network

Effective insulin secretion and blood glucose homeostasis depend on the multistep maturation of insulin secretory granules (ISGs), a process that includes lumen acidification, enzymatic insulin processing, and biophysical remodeling of the granule. An under studied aspect of ISG maturation is the role of inter-organelle contacts in organelle remodeling. While a correlation between ISG-mitochondria contacts and ISG maturation has been observed, many questions remain on how this interaction may impact maturation (1-5). We sought to address this gap in knowledge by using multi-scale imaging approaches (fluorescent microscopy, soft X-ray tomography, and cryo-electron tomography) to examine how the biophysical properties and spatial organization of ISGs change around the mitochondrial network. Our data suggests that ISGs in proximity to mitochondria exhibit lower pH, higher biomolecular density, and smaller vesicle diameter. Time-resolved imaging using a SNAP tag labelling system also shows that as ISGs age, their proximity to the mitochondria network is increased between 3-6 hours after biosynthesis, suggesting that ISG-mitochondria association is dynamically spatiotemporally regulated in pancreatic {beta}-cells. These data suggest that mitochondrial proximity contributes to the maturation and remodeling of ISGs in pancreatic beta cells.

cell biology↗

bioRxiv2025

Dielectrophoresis Reveals Stimulus-Induced Remodeling of Insulin Granule Subpopulations

The pancreatic {beta}-cell contains several functional subpopulations of insulin secretory granules (ISGs). These subpopulations vary in maturity, age, and secretory capacity. Differences in protein and lipid composition of ISGs are correlated with disease but require further study to understand how ISG remodeling regulates normal biology. Due to limitations in traditional separation methods, the extent of these subpopulations, any overlap between them, and how they are affected by insulinotropic signals have not been determined. In this work, we adapted direct current insulator-based dielectrophoresis (DC-iDEP) to separate ISGs isolated from INS-1E cells, an immortalized rat insulinoma cell line model, according to their electrokinetic mobility ratio (EKMr). We were able to separate ISG subpopulations from unstimulated cells to determine a baseline distribution and identify characteristic profiles for immature, young, and old ISGs. We then analyzed distributions of subpopulations from cells stimulated with insulin secretion signals known to induce biophysical remodeling and maturation. We found significant changes in each subpopulation studied in response to stimulation, consistent with the increases in maturation, crystallization, and changes in size reported in the literature. This work provides new insights into how the cell controls ISG remodeling and may drive future development of more effective therapies. SIGNIFICANCEUnderstanding insulin secretory granule (ISG) heterogeneity and the functional role of subpopulations is a critical step towards unraveling the mechanisms of insulin secretion. We adapted direct current insulator-based dielectrophoresis (DC-iDEP) to resolve immature, young, and old ISG subpopulations based on their biophysical properties. We also explored the biophysical remodeling of ISGs under insulinotropic stimuli to further probe how the cell controls remodeling and secretion. Our work provides a new framework for quantifying granule heterogeneity by linking biophysical features to functional subtypes and assessing how environmental stimuli remodel ISGs. This methodology establishes a broadly applicable platform to interrogate organelle and vesicle diversity in complex biological systems.

biophysics↗

bioRxiv2025

MitoXRNet: Deep Learning-enabled 3D Segmentation of Mitochondria and Nucleus in Soft X-ray Tomograms

Mitochondrial morphology is crucial for cellular function, but large-scale analysis is limited by challenges in high-resolution imaging and segmentation. MitoXRNet, a compact 3D deep-learning model, efficiently segments mitochondria and nuclei from Soft X-ray Tomography data using multi-axis slicing, Sobel-based boundary enhancement, and combined BCE-Robust Dice loss. With 1.4M parameters, it achieves a Dice score of 73.8% on INS-1E cells, outperforming existing models. Automated analysis indicated that glucose induced larger mitochondria and higher matrix density, and that GIP and GKA induced smaller and denser mitochondria, highlighting previously unreported {beta}-cell mitochondrial remodeling. MitoXRNet allows for scalable profiling of organelle-level morpho-biophysical data. HighlightsO_LIA data-efficient method for 3D segmentation of mitochondria and nucleus from Soft X-ray tomograms. C_LIO_LIIncorporates domain-specific Sobel filter-based preprocessing to improve segmentation accuracy and quality under imperfect or noisy labels. C_LIO_LIEnables rapid and automated analysis of mitochondrial morphology, facilitating quantitative assessment of pharmacological effects on cellular ultrastructure. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/682919v2_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@8600f6org.highwire.dtl.DTLVardef@1a9230eorg.highwire.dtl.DTLVardef@13c6f09org.highwire.dtl.DTLVardef@9dc9f1_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

Classifying Biophysical Subpopulations of Insulin Secretory Granules using Quantitative Whole Cell Structure Analysis

Pancreatic {beta}-cells contain insulin secretory granules (ISGs), organelles where proinsulin is converted into insulin. As ISGs mature, they undergo extensive biophysical remodeling, producing a spectrum of subpopulations with heterogeneous molecular and spatial characteristics. However, systematic methods to define ISG subpopulations remain underdeveloped. To address this gap in knowledge, we employed soft x-ray tomography (SXT), which can quantitatively measure the biochemical density of ISGs within whole {beta}-cells. Using unsupervised clustering, we classified subpopulations based on molecular density, size, and spatial positioning. Across different insulin secretory stimuli, we observed shifts towards mature and releasable subtypes, demonstrating that exogenous signals can dynamically remodel ISG subpopulation distributions. We extended this methodology to primary {beta}-cells characterized using volume electron microscopy (vEM). Integrating subpopulations from SXT and vEM uncovered insights inaccessible by a single method in isolation. This strategy establishes a framework for defining therapeutic approaches aimed at enriching physiologically beneficial ISG subpopulations.

biophysics↗

bioRxiv2025

Secretory stimuli distinctly regulate insulin secretory granule maturation through structural remodeling

Insulin secretory granule (ISG) maturation is a crucial aspect of insulin secretion and glucose homeostasis. The regulation of this maturation remains poorly understood, especially how secretory stimuli affect ISG maturity and subcellular localization. In this study, we used soft X-tomography (SXT) to quantitatively map ISG morphology, density, and location in single INS-1E and mouse pancreatic {beta}-cells under the effect of various secretory stimuli. We found that the activation of glucokinase (GK), gastric inhibitory polypeptide receptor (GIPR), glucagon-like peptide-1 receptor (GLP-1R), and G-protein coupled receptor 40 (GPR40) promote ISG maturation. Each stimulus induces unique structural remodeling in ISGs, by altering size and density, depending on the specific signaling cascades activated. These distinct ISG subpopulations mobilize and redistribute in the cell altering overall cellular structural organization. Our results provide insight into how current diabetes and obesity therapies impact ISG maturation and may inform the development of future treatments that target maturation specifically.

biophysics↗

bioRxiv2024

A Versatile Light Field Microscopy Platform for Multi-purpose Dynamic Volumetric Bio-imaging

Light field microscopy (LFM) has emerged in recent years as a unique solution for fast, scan-free volumetric imaging of dynamic biological samples. This is achieved by using a microlens array in the detection path to record both the lateral and angular information of the light fields coming from the sample, capturing a 3-dimensional (3D) volume in a single 2-dimensional (2D) snapshot. In post-acquisition, the 3D sample volume is computationally reconstructed from the recorded 2D images, thus enabling unprecedented 3D capture speed, not limited by the typical constraint of physically scanning the focal plane over the sample volume. Up to date, most published LFM imaging setups have been specialized single-purpose platforms, optimized for a narrow performance window in field of view and resolution, thus hampering widespread adoption of LFM for biomedical research. Here, we present a versatile LFM platform for fast 3D imaging across multiple scales, enabling applications from cell to system-level biology on the same imaging setup. Our multiscale LFM is built as an add-on module to a conventional commercially available wide field microscope, and the various imaging applications, with different ranges of field of view and resolution, are achieved by simply switching between the standard microscope objectives available on the wide field microscope. Importantly, we provide an open-source end-to-end software package for calculating the system performance parameters, processing the experimentally measured point spread function, and light field 3D image reconstruction. We demonstrate the performance of our multiscale LFM platform through imaging the whole-brain activity map of seizures in larval zebrafish, calcium dynamics in ex vivo mouse pancreatic islets, and subcellular protein dynamics in cultured cells.

bioengineering↗

bioRxiv2021

iDEP-assisted isolation of insulin secretory vesicles

Organelle heterogeneity and inter-organelle associations 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 pancreatic {beta}-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 functional maturation of vesicles upon glucose stimulation, and interpret this to be indicative of high-resolution separation of vesicle subpopulation. DC-iDEP provides a path for future characterization of subtle biochemical differences of organelle subpopulations within any biological system.

biophysics↗

bioRxiv2021

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 (i) converted to a standardized statistical representation relying on Probabilistic Graphical Models, (ii) coupled by modeling their mutual relations with the physical world, and (iii) 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 {beta}-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 {beta}-Cell Consortium. Significance StatementCells are the basic units of life, yet their architecture and function remain to be fully characterized. This work describes Bayesian metamodeling, a modeling approach that divides-and-conquers a large problem of modeling numerous aspects of the cell into computing a number of smaller models of different types, followed by assembling these models into a complete map of the cell. Metamodeling enables a facile collaboration of multiple research groups and communities, thus maximizing the sharing of expertise, resources, data, and models. A proof-of-principle is provided by a model of glucose-stimulated insulin secretion produced by the Pancreatic {beta}-Cell Consortium.

cell biology↗

bioRxiv2020

Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography

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 useful scoring functions to measure the quality of aligned subtomogram clusters, which can be compromised by contaminations from misclassified complexes and alignment errors. To our knowledge, a comprehensive survey to assess the effectiveness of scoring functions for ranking the quality of subtomogram clusters does not exist yet. Here, we provide such a study and assess a total of 15 scoring functions for evaluating the quality of the 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 shows that the robustness of scoring functions varies largely. Most scores are sensitive to the signal-to-noise ratio of subtomograms and often require 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, show a robust ranking of subtomogram clusters even 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 a scoring function for template-free approaches to detect complexes from heterogeneous samples.

bioinformatics↗