{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Eastern Michigan University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "S. Bleta"
    },
    {
      "name": "P. Dunning"
    },
    {
      "name": "A. Ross"
    },
    {
      "affs": [
        0
      ],
      "name": "S. K. Backues"
    },
    {
      "name": "O. Spantidi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0bdgdzqgafqkk.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.21.760762",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760762v1.full",
  "id": "0bdgdzqgafqkk",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760762v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1304|Cell Image Analysis Techniques"
    ]
  ],
  "openalex": "W7223700492",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760762v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0bdgdzqgafqkk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:43Z",
      "native_id": "10.64898/2026.10.21.760762",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760762v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Auto-AVS: A Novel Framework for Automated Autophagic Vacuole Simulation",
  "title_text": "Auto-AVS: A Novel Framework for Automated Autophagic Vacuole Simulation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:43Z",
  "url": "https://research.draconitic.com/paper/0bdgdzqgafqkk",
  "venue": "bioRxiv",
  "versions": []
}