Auto-AVS: A Novel Framework for Automated Autophagic Vacuole Simulation

Abstract

Transmission electron microscopy (TEM) is widely used to quantify autophagic bodies in yeast, but ultra-thin sectioning provides only two-dimensional cross-sections of inherently 3D structures. Inferring the underlying size and number of autophagic bodies therefore requires assumptions about their geometry, spatial organization, and distribution within the vacuole. We introduce Auto-AVS, an open-source computational stereology framework that combines configurable 3D autophagic-vacuole simulation, optional CompuCell3D-based body deformation, virtual TEM sectioning, and statistical comparison with experimental data. We first validate the virtual sectioning pipeline against analytical stereology and an existing spherical R-based simulation. We then use Auto-AVS to examine whether explicitly modeling autophagic-body deformation alters observed section statistics and find no consistent measurable effect under the tested simulation parameters. Finally, we apply the framework to experimental wild-type and mutant TEM datasets. Auto-AVS produces body-size estimates closely consistent with conventional stereological estimates, while body-number fitting reveals substantial discrepancies between simulated and experimental distributions despite similar average count estimates. These discrepancies implicate spatial clustering and vacuole geometry as important limitations of current body-number models. Rather than replacing conventional stereology, Auto-AVS provides a computational framework for testing its underlying assumptions and determining which aspects of 3D organization affect inference from two-dimensional microscopy.