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bioRxiv · 10.64898/2026.10.01.756010

SIGMA: SIgnaling via Golgi Morphology Analysis for pathway-state classification

Fan, M.·Zhang, X.·Geng, B.·Che, P.
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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.

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Fan, M., Zhang, X., Geng, B., Che, P.. 2026-10-02. SIGMA: SIgnaling via Golgi Morphology Analysis for pathway-state classification. https://doi.org/10.64898/2026.10.01.756010

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