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

Area of Science:

• Cell Biology
• Biophysics
• Endocrinology

Background:

• Pancreatic beta cells produce and store insulin in insulin secretory granules (ISGs).
• ISGs mature through biophysical remodeling, creating diverse subpopulations with varying molecular and spatial features.
• Systematic methods for defining these ISG subpopulations are currently underdeveloped.

Purpose of the Study:

• To develop and apply systematic methods for classifying insulin secretory granule (ISG) subpopulations.
• To investigate the dynamic remodeling of ISG subpopulations in response to secretory stimuli.
• To integrate multiple imaging techniques for a comprehensive understanding of ISG heterogeneity.

Main Methods:

• Soft X-ray Tomography (SXT) was used to quantitatively measure ISG biochemical density in whole beta cells.
• Unsupervised clustering classified ISG subpopulations based on molecular density, size, and spatial positioning.
• Volume Electron Microscopy (vEM) was utilized to characterize primary beta cells, integrating findings with SXT data.

Main Results:

• A classification framework for ISG subpopulations was established using SXT and unsupervised clustering.
• Exogenous insulin secretory stimuli induced dynamic shifts in ISG subpopulations towards mature and releasable states.
• Integrating SXT and vEM provided insights into ISG heterogeneity that were not accessible by either method alone.

Conclusions:

• The developed methodology provides a robust framework for defining and analyzing ISG subpopulations.
• Dynamic remodeling of ISG subpopulations is responsive to external secretory signals.
• This approach offers a foundation for developing therapeutic strategies to enhance beneficial ISG subpopulations for improved insulin secretion.