{
  "abstract": "Organelle morphology is increasingly recognized as a computationally accessible readout of molecular state, yet whether subcellular architecture can identify which signaling pathway is active, not merely whether a cell is perturbed, remains untested. Here we introduce SIGMA (SIgnaling via Golgi Morphology Analysis), a framework that reframes pathway identification as a morphological classification problem centered on the Golgi apparatus. SIGMA integrates imaging flow cytometry for population-scale Golgi profiling, teacher-student knowledge distillation to transfer fine morphological cues from limited high-resolution images to an efficient lower-resolution classifier, and mechanistic generalization testing with chemically distinct activators. Applied to MRC-5 fibroblasts across six conditions spanning four signaling cascades plus controls, SIGMA achieved 90.02% cell-level accuracy and correctly assigned five of seven structurally unrelated external compounds to their expected pathway class. These results establish Golgi morphology as a multiplexed, machine-readable fingerprint of signaling state.",
  "abstract_shown": true,
  "affiliations": [
    "University of Alabama at Birmingham"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "M. Fan"
    },
    {
      "name": "X. Zhang"
    },
    {
      "name": "B. Geng"
    },
    {
      "affs": [
        0
      ],
      "name": "P. Che"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0z81s89mk09f1.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.01.756010",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.01.756010v1.full",
  "id": "0z81s89mk09f1",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.01.756010v1",
  "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",
      "molecular biology"
    ],
    [
      "openalex",
      "1304|Cell Image Analysis Techniques"
    ]
  ],
  "openalex": "W7216551992",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.01.756010v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0z81s89mk09f1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:51Z",
      "native_id": "10.64898/2026.10.01.756010",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.01.756010v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:molecular biology",
      "subfield": "molecular-biology"
    }
  ],
  "title": "SIGMA: SIgnaling via Golgi Morphology Analysis for pathway-state classification",
  "title_text": "SIGMA: SIgnaling via Golgi Morphology Analysis for pathway-state classification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:51Z",
  "url": "https://research.draconitic.com/paper/0z81s89mk09f1",
  "venue": "bioRxiv",
  "versions": []
}