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Cited by 3Pancreatic function and diabetesCellular transport and secretionAdvanced X-ray Imaging Techniques

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Classifying biophysical subpopulations of insulin secretory granules using quantitative whole-cell structure analysis

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Chang, Kevin, Deshmukh, Aneesh, Verma, Riva, Loconte, Valentina, White, Kate L.·2026·Structure
DOI 10.1016/j.str.2026.03.005PMC PMC12458229

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Abstract

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 toward 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.

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Figure 1. SXT imaging and calculation of ISG parameters. (A) SXT full-rotation projection images, which are combined to create a 3D image stack. (B) The LAC values in the SXT data correspond to biochemical content and are necessary for quantifying ISG maturities. (C) Blender rendering of a segmented INS-1E cell. ISG, nucleus, and mitochondria masks are shown. (D) Zoomed in image of an SXT orthoslice and LAC histogram of a selected ISG. Analysis at this resolution enables the creation of individual granule LAC statistics. (E) ISGs overlaid on the EDT of the plasma membrane. ISGs are displayed in white and PM displayed as a dashed white line. (F) Mitochondria mask inverse EDT with overlaid organelles. ISGs are again displayed in white, with PM displayed as a dashed white line. Mitochondria are colored black.
Figure 1. SXT imaging and calculation of ISG parameters. (A) SXT full-rotation projection images, which are combined to create a 3D image stack. (B) The LAC values in the SXT data correspond to biochemical content and are necessary for quantifying ISG maturities. (C) Blender rendering of a segmented INS-1E cell. ISG, nucleus, and mitochondria masks are shown. (D) Zoomed in image of an SXT orthoslice and LAC histogram of a selected ISG. Analysis at this resolution enables the creation of individual granule LAC statistics. (E) ISGs overlaid on the EDT of the plasma membrane. ISGs are displayed in white and PM displayed as a dashed white line. (F) Mitochondria mask inverse EDT with overlaid organelles. ISGs are again displayed in white, with PM displayed as a dashed white line. Mitochondria are colored black.CC BY-NC-ND · PubMed Central
Figure 2. Visualizing ISG subpopulations in INS-1E cells. (A) UMAP diagram displays a dimensional reduction of individual ISGs from our dataset. ISGs are colored by their subpopulation identity assignment. (B) ISG Subpopulation profile plot. Summarized parameters are calculated using the median Z-score. Subtitles indicate the percentage of subpopulations out of the total number of ISGs. The subpopulations are numbered according to their LAC Mean, while parameters are listed from top to bottom in order of importance to subpopulation identity. (C) Gallery of ISGs from the five subpopulations within SXT projection images. PM displayed as a dashed white line.
Figure 2. Visualizing ISG subpopulations in INS-1E cells. (A) UMAP diagram displays a dimensional reduction of individual ISGs from our dataset. ISGs are colored by their subpopulation identity assignment. (B) ISG Subpopulation profile plot. Summarized parameters are calculated using the median Z-score. Subtitles indicate the percentage of subpopulations out of the total number of ISGs. The subpopulations are numbered according to their LAC Mean, while parameters are listed from top to bottom in order of importance to subpopulation identity. (C) Gallery of ISGs from the five subpopulations within SXT projection images. PM displayed as a dashed white line.CC BY-NC-ND · PubMed Central
Figure 3. ISG Subpopulation shifts associated with insulin secretory stimuli. (A) Percentage of ISG subpopulations within each stimuli condition. Each condition subpanel shows percentages normalized to the total number of ISGs in that condition, summing to 100%. (B) Blender renderings of representative cells in each stimulation condition. Every cell contains a mask of the nucleus, ISGs, and PM. ISGs are colored according to subpopulation identity. GLM: Glimepiride, GKA50: glucokinase activator 50, TAK: TAK-875, GIP: Gastric Inhibitory Polypeptide, Ex-4: Exendin-4.
Figure 3. ISG Subpopulation shifts associated with insulin secretory stimuli. (A) Percentage of ISG subpopulations within each stimuli condition. Each condition subpanel shows percentages normalized to the total number of ISGs in that condition, summing to 100%. (B) Blender renderings of representative cells in each stimulation condition. Every cell contains a mask of the nucleus, ISGs, and PM. ISGs are colored according to subpopulation identity. GLM: Glimepiride, GKA50: glucokinase activator 50, TAK: TAK-875, GIP: Gastric Inhibitory Polypeptide, Ex-4: Exendin-4.CC BY-NC-ND · PubMed Central
Figure 4. Classifying variants of docked granules. (A) INS-1E cell rendering, with docked and undocked ISGs shown in different colors. Docked ISGs were about 6% of all total ISGs in our dataset. (B) SXT projection image closeup and the same area with the ≤ 50 nm distance from PM region colorized. The ISGs are colored white, while the docked region is colored yellow. Top center ISG is docked at the PM. (C) Profile plot of docked ISG classes. Most docked granules have low LAC. Clusters are organized numerically from lowest to highest LAC. (D) Docked classes organized by condition. These percentages correspond to percent of total overall docked ISGs, since the number of docked ISGs per condition is important to visualize in this analysis.
Figure 4. Classifying variants of docked granules. (A) INS-1E cell rendering, with docked and undocked ISGs shown in different colors. Docked ISGs were about 6% of all total ISGs in our dataset. (B) SXT projection image closeup and the same area with the ≤ 50 nm distance from PM region colorized. The ISGs are colored white, while the docked region is colored yellow. Top center ISG is docked at the PM. (C) Profile plot of docked ISG classes. Most docked granules have low LAC. Clusters are organized numerically from lowest to highest LAC. (D) Docked classes organized by condition. These percentages correspond to percent of total overall docked ISGs, since the number of docked ISGs per condition is important to visualize in this analysis.CC BY-NC-ND · PubMed Central
Figure 5. Interpreting Subpopulations in Electron Microscopy data (A) 3D rendering of a β-cell from the publicly available Müller et al. dataset,30 custom-rendered in Blender. Additional organelles like the Golgi apparatus and microtubules can be visualized and quantified using this method. (B) Profile plot of EM ISG Subpopulations. Subpopulations listed in order of distance from Golgi and parameters ranked by importance. (C) EM Subpopulation organized by condition. Total percentage in each subpanel adds up to 100%.
Figure 5. Interpreting Subpopulations in Electron Microscopy data (A) 3D rendering of a β-cell from the publicly available Müller et al. dataset,30 custom-rendered in Blender. Additional organelles like the Golgi apparatus and microtubules can be visualized and quantified using this method. (B) Profile plot of EM ISG Subpopulations. Subpopulations listed in order of distance from Golgi and parameters ranked by importance. (C) EM Subpopulation organized by condition. Total percentage in each subpanel adds up to 100%.CC BY-NC-ND · PubMed Central

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Research works printed but not linked: 69

  1. Macdonald, 2005, Glucose-sensing mechanisms in pancreatic β-cells, Philos. Trans. R. Soc. B, 360, 2211, 10.1098/rstb.2005.1762 · DOI
  2. Omar-Hmeadi, 2021, Insulin granule biogenesis and exocytosis, Cell. Mol. Life Sci., 78, 1957, 10.1007/s00018-020-03688-4 · DOI
  3. Germanos, 2021, Inside the Insulin Secretory Granule, Metabolites, 11, 515, 10.3390/metabo11080515 · DOI
  4. Lin, 2024, Decoding Insulin Secretory Granule Maturation Using Genetically Encoded pH Sensors, ACS Sens., 9, 6032, 10.1021/acssensors.4c01885 · DOI
  5. Yau, 2020, A fluorescent timer reporter enables sorting of insulin secretory granules by age, J. Biol. Chem., 295, 8901, 10.1074/jbc.RA120.012432 · DOI
  6. Hoboth, 2015, Aged insulin granules display reduced microtubule-dependent mobility and are disposed within actin-positive multigranular bodies, Proc. Natl. Acad. Sci. USA, 112, 667, 10.1073/pnas.1409542112 · DOI
  7. Neukam, 2024, Purification of time-resolved insulin granules reveals proteomic and lipidomic changes during granule aging, Cell Rep., 43, 10.1016/j.celrep.2024.113836 · DOI
  8. Kasai, 2008, Docking is not a prerequisite but a temporal constraint for fusion of secretory granules, Traffic, 9, 1191, 10.1111/j.1600-0854.2008.00744.x · DOI
  9. Yi, 2024, Distinct Sub- to Superdiffuse Insulin Granule Transport Behaviors in beta-Cells Are Strongly Affected by Granule Age, J. Phys. Chem. B, 128, 6246, 10.1021/acs.jpcb.4c01403 · DOI
  10. Hickey, 2009, Proteins associated with immunopurified granules from a model pancreatic islet beta-cell system: proteomic snapshot of an endocrine secretory granule, J. Proteome Res., 8, 178, 10.1021/pr800675k · DOI
  11. Norris, 2024, Optimized Proteomic Analysis of Insulin Granules From MIN6 Cells Identifies Scamp3, a Novel Regulator of Insulin Secretion and Content, Diabetes, 73, 2045, 10.2337/db24-0355 · DOI
  12. Kreutzberger, 2020, Distinct insulin granule subpopulations implicated in the secretory pathology of diabetes types 1 and 2, eLife, 9, 10.7554/eLife.62506 · DOI
  13. Huang, 2021, Defective insulin maturation in patients with type 2 diabetes, Eur. J. Endocrinol., 185, 565, 10.1530/EJE-21-0144 · DOI
  14. Yau, 2021, Targeting the insulin granule for modulation of insulin exocytosis, Biochem. Pharmacol., 194, 10.1016/j.bcp.2021.114821 · DOI
  15. Rafelski, 2008, Building the cell: design principles of cellular architecture, Nat. Rev. Mol. Cell Biol., 9, 593, 10.1038/nrm2460 · DOI
  16. Mirvis, 2026, A scoping study of the whole-cell imaging literature: a foundational corpus, potential for mesoscale data synthesis, and implications for standardization of an emerging field., BMC Biology., 24, 91, 10.1186/s12915-026-02556-0 · DOI
  17. Barekatain, 2024, Insulator-based dielectrophoresis-assisted separation of insulin secretory vesicles, eLife, 13, 10.7554/eLife.74989 · DOI
  18. Loconte, 2022, Soft X-ray tomography to map and quantify organelle interactions at the mesoscale, Structure, 30, 510, 10.1016/j.str.2022.01.006 · DOI
  19. White, 2020, Visualizing subcellular rearrangements in intact beta cells using soft x-ray tomography, Sci. Adv., 6, 10.1126/sciadv.abc8262 · DOI
  20. Deshmukh, 2024, Subcellular Feature-Based Classification of alpha and beta Cells Using Soft X-ray Tomography, Cells, 13, 869, 10.3390/cells13100869 · DOI
  21. Chen, 2022, A protocol for full-rotation soft X-ray tomography of single cells, STAR Protoc., 3, 10.1016/j.xpro.2022.101176 · DOI
  22. Do, 2015, Imaging and characterizing cells using tomography, Arch. Biochem. Biophys., 581, 111, 10.1016/j.abb.2015.01.011 · DOI
  23. Deshmukh, 2025, Secretory stimuli distinctly regulate insulin secretory granule maturation through structural remodeling, Structure, 33, 1831, 10.1016/j.str.2025.07.022 · DOI · Paper page
  24. Lee, 2024, Quantitative subcellular reconstruction reveals a lipid mediated inter-organelle biogenesis network, Nat. Cell Biol., 26, 57, 10.1038/s41556-023-01297-4 · DOI
  25. Cho, 2022, OpenCell: Endogenous tagging for the cartography of human cellular organization, Science, 375, 10.1126/science.abi6983 · DOI · Paper page
  26. Pfeifer, 2015, Quantitative analysis of mouse pancreatic islet architecture by serial block-face SEM, J. Struct. Biol., 189, 44, 10.1016/j.jsb.2014.10.013 · DOI
  27. Müller, 2021, 3D FIB-SEM reconstruction of microtubule-organelle interaction in whole primary mouse beta cells, J. Cell Biol., 220, 10.1083/jcb.202010039 · DOI
  28. Rao, 2020, Determination of secretory granule maturation times in pancreatic islet β-cells by serial block-face electron microscopy, J. Struct. Biol., 212, 10.1016/j.jsb.2020.107584 · DOI
  29. Autin, 2022, Integrative structural modelling and visualisation of a cellular organelle, QRB Discov., 3, e11, 10.1017/qrd.2022.10 · DOI
  30. Hennig, 2007, Cluster-wise assessment of cluster stability, Comput. Stat. Data Anal., 52, 258, 10.1016/j.csda.2006.11.025 · DOI · Paper page
  31. Fortunato, 2016, Community detection in networks: A user guide, Phys. Rep., 659, 1, 10.1016/j.physrep.2016.09.002 · DOI · Paper page
  32. Schlegel, 2024, Whole-brain annotation and multi-connectome cell typing of Drosophila, Nature, 634, 139, 10.1038/s41586-024-07686-5 · DOI · Paper page
  33. Zhang, 2020, Visualizing insulin vesicle neighborhoods in beta cells by cryo-electron tomography, Sci. Adv., 6, 10.1126/sciadv.abc8258 · DOI
  34. Müller, 2017, A 4D view on insulin secretory granule turnover in the beta-cell, Diabetes Obes. Metabol., 19, 107, 10.1111/dom.13015 · DOI
  35. Proks, 2002, Sulfonylurea stimulation of insulin secretion, Diabetes, 51, 368, 10.2337/diabetes.51.2007.S368 · DOI · Paper page
  36. Haddad, 2024, New-Generation Glucokinase Activators: Potential Game-Changers in Type 2 Diabetes Treatment, Int. J. Mol. Sci., 25, 571, 10.3390/ijms25010571 · DOI
  37. Sakuma, 2016, Fasiglifam (TAK-875) has dual potentiating mechanisms via Galphaq-GPR40/FFAR1 signaling branches on glucose-dependent insulin secretion, Pharmacol. Res. Perspect., 4, 10.1002/prp2.237 · DOI
  38. Negoro, 2010, Discovery of TAK-875: A Potent, Selective, and Orally Bioavailable GPR40 Agonist, ACS Med. Chem. Lett., 1, 290, 10.1021/ml1000855 · DOI
  39. Burant, 2013, Activation of GPR40 as a therapeutic target for the treatment of type 2 diabetes, Diabetes Care, 36, 175, 10.2337/dcS13-2037 · DOI
  40. Seino, 2010, GIP and GLP-1, the two incretin hormones: Similarities and differences, J. Diabetes Investig., 1, 8, 10.1111/j.2040-1124.2010.00022.x · DOI · Paper page
  41. Gomi, 2005, Granuphilin molecularly docks insulin granules to the fusion machinery, J. Cell Biol., 171, 99, 10.1083/jcb.200505179 · DOI
  42. Gaisano, 2014, Here come the newcomer granules, better late than never, Trends Endocrinol. Metabol., 25, 381, 10.1016/j.tem.2014.03.005 · DOI · Paper page
  43. Izumi, 2023, Multiple pathways and independent functional pools in insulin granule exocytosis, Genes Cells, 28, 471, 10.1111/gtc.13029 · DOI
  44. Gilbert, 2018, Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study, Lancet, 391, 1775, 10.1016/S0140-6736(18)30668-8 · DOI
  45. Gower, 1971, A General Coefficient of Similarity and Some of Its Properties, Biometrics, 27, 857, 10.2307/2528823 · DOI
  46. Potdar, 2025, Pancreatic β cell models for screening insulin secretagogues and cytotoxicity, J. Appl. Toxicol., 45, 89, 10.1002/jat.4658 · DOI
  47. Topalidou, 2020, EIPR1 controls dense-core vesicle cargo retention and EARP complex localization in insulin-secreting cells, Mol. Biol. Cell, 31, 59, 10.1091/mbc.E18-07-0469 · DOI
  48. Lucocq, 2015, Systems biology in 3D space – enter the morphome, Trends Cell Biol., 25, 59, 10.1016/j.tcb.2014.09.008 · DOI
  49. Son, 2024, Morphomics via next-generation electron microscopy, J. Mol. Cell Biol., 15, 10.1093/jmcb/mjad081 · DOI
  50. Unterauer, 2024, Spatial proteomics in neurons at single-protein resolution, Cell, 187, 1785, 10.1016/j.cell.2024.02.045 · DOI
  51. Wang, 2021, Live-cell imaging of glucose-induced metabolic coupling of beta and alpha cell metabolism in health and type 2 diabetes. Commun, Biol., 4, 594
  52. Kravčenko, 2024, Molecular architecture of synaptic vesicles, Proc. Natl. Acad. Sci. USA, 121, 10.1073/pnas.2407375121 · DOI · Paper page
  53. Rosengren, 2008, Why Treatment Fails in Type 2 Diabetes, PLoS Med., 5, 10.1371/journal.pmed.0050215 · DOI
  54. Fahy, 2024, Laboratory based correlative cryo-soft X-ray tomography and cryo-fluorescence microscopy, Methods Cell Biol., 187, 293, 10.1016/bs.mcb.2024.02.033 · DOI
  55. Kievits, 2022, How innovations in methodology offer new prospects for volume electron microscopy, J. Microsc., 287, 114, 10.1111/jmi.13134 · DOI
  56. Kremer, 1996, Computer visualization of three-dimensional image data using IMOD, J. Struct. Biol., 116, 71, 10.1006/jsbi.1996.0013 · DOI
  57. Erozan, 2024, Automated 3D cytoplasm segmentation in soft X-ray tomography, iScience, 27, 10.1016/j.isci.2024.109856 · DOI
  58. Parkinson, 2012, Automatic alignment and reconstruction of images for soft X-ray tomography, J. Struct. Biol., 177, 259, 10.1016/j.jsb.2011.11.027 · DOI
  59. Van der Walt, 2014, scikit-image: image processing in Python, PeerJ, 2, 10.7717/peerj.453 · DOI
  60. McInnes, 2018, Umap: Uniform manifold approximation and projection for dimension reduction, arXiv
  61. Becht, 2018, Dimensionality reduction for visualizing single-cell data using UMAP, Nat. Biotechnol., 37, 38, 10.1038/nbt.4314 · DOI · Paper page
  62. Wickham, 2011, ggplot2, WIREs Computational Stats., 3, 180, 10.1002/wics.147 · DOI
  63. D’Orazio, 2015, Integration and imputation of survey data in R: the StatMatch package, Romanian Statistical Review, 63, 57
  64. Dixon, 2003, VEGAN, a package of R functions for community ecology, J. Veg. Sci., 14, 927, 10.1111/j.1654-1103.2003.tb02228.x · DOI
  65. Suckale, 2010, The insulin secretory granule as a signaling hub, Trends Endocrinol. Metabol., 21, 599, 10.1016/j.tem.2010.06.003 · DOI · Paper page
  66. Ahlqvist, 2018, Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables, Lancet Diabetes Endocrinol., 6, 361, 10.1016/S2213-8587(18)30051-2 · DOI · Paper page
  67. Whitaker, 2022, Persistent COVID-19 symptoms in a community study of 606,434 people in England, Nat. Commun., 13, 1957, 10.1038/s41467-022-29521-z · DOI
  68. Wagner, 2021, Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes, Nat. Med., 27, 49, 10.1038/s41591-020-1116-9 · DOI · Paper page
  69. Hammarlund, 2007, Open Syntaxin Docks Synaptic Vesicles, PLoS Biol., 5, 10.1371/journal.pbio.0050198 · DOI

Metadata

Authors
Chang, K., Deshmukh, A., Verma, R., Loconte, V., & White, K. L.
Affiliations
University of Southern California (US)
Corresponding author
White, Kate L.
ORCID
Venue
Structure
Publisher
Elsevier BV
Published
2026-06
Volume
34
Issue
6
Pages
977-987.e2
ISSN
0969-2126 (ISSN-L)
DOI
10.1016/j.str.2026.03.005
PMC
PMC12458229
OpenAlex
W4414255444
Cited by
3
References
5
Citations by year
2026: 3
FWCI
19.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Structure (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Europe PMC (PubMed Central) (submittedVersion, cc-by-nc-nd)
Funders
  • National Institutes of Health
  • National Institute of General Medical Sciences (R35GM154893, P30GM138441)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular transport and secretion
  • Physical Sciences › Physics and Astronomy › Radiation › Advanced X-ray Imaging Techniques
Keywords
insulin secretory granules, pancreatic beta cells, secretory granule maturation, soft X-ray tomography, volume electron microscopy, unsupervised clustering, proinsulin processing, insulin secretion

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Chang, K., Deshmukh, A., Verma, R., Loconte, V., & White, K. L. (2026). Classifying biophysical subpopulations of insulin secretory granules using quantitative whole-cell structure analysis. Structure. https://doi.org/10.1016/j.str.2026.03.005