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bioRxiv · 10.1101/2025.11.11.687379

CryoSiam: self-supervised representation learning for automated analysis of cryo-electron tomograms

Stojanovska, F.·Sanchez, R. M.·Jensen, R. K.·Mahamid, J.·Kreshuk, A.·Zaugg, J. B.
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Abstract

Cryo-electron tomography (cryo-ET) enables visualization of macromolecular complexes in their native cellular context, but interpretation remains challenging due to high noise levels, missing information, and lack of ground-truth data. Here, we present CryoSiam (CRYO-electron tomography SIAMese networks), an open-source framework for self-supervised representation learning in cryo-ET. CryoSiam learns hierarchical representations of tomographic data spanning both voxel-level and subtomogram-level information. To train CryoSiam, we generated CryoETSim (CRYO-Electron Tomography SIMulated), a synthetic dataset that systematically models defocus variation, sample thickness, and molecular crowding. CryoSiam trained models transfer directly to experimental data without fine-tuning and support key aspects of cryo-ET data analysis, including tomogram denoising, segmentation of subcellular structures, and macromolecular detection and identification across both prokaryotic and eukaryotic systems. Publicly available pretrained models and the CryoETSim dataset provide a foundation for scalable and automated cryo-ET analysis.

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Stojanovska, F., Sanchez, R. M., Jensen, R. K., Mahamid, J., Kreshuk, A., Zaugg, J. B.. 2025-11-12. CryoSiam: self-supervised representation learning for automated analysis of cryo-electron tomograms. https://doi.org/10.1101/2025.11.11.687379

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