Auto-AVS: A Novel Framework for Automated Autophagic Vacuole Simulation
- 1Eastern Michigan University
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.
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BibTeX
@misc{bleta2026autoavs,
title = {{Auto-AVS: A Novel Framework for Automated Autophagic Vacuole Simulation}},
author = {S. Bleta and P. Dunning and A. Ross and S. K. Backues and O. Spantidi},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.21.760762},
url = {https://www.biorxiv.org/content/10.64898/2026.10.21.760762v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Auto-AVS: A Novel Framework for Automated Autophagic Vacuole Simulation
AU - S. Bleta
AU - P. Dunning
AU - A. Ross
AU - S. K. Backues
AU - O. Spantidi
PY - 2026
DA - 2026/10/21
PB - bioRxiv
DO - 10.64898/2026.10.21.760762
UR - https://www.biorxiv.org/content/10.64898/2026.10.21.760762v1
AB - 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.
ER -