@article{20935,
  abstract     = {In situ cryo-electron tomography (cryo-ET) has emerged as the method of choice to investigate the structures of biomolecules in their native context. However, challenges remain for the efficient production and sharing of large-scale cryo-ET datasets. Here, we combined cryogenic plasma-based focused ion beam (cryo-PFIB) milling with recent advances in cryo-ET acquisition and processing to generate a dataset of 1,829 annotated tomograms of the green alga Chlamydomonas reinhardtii, which we provide as a community resource to drive method development and inspire biological discovery. To assay data quality, we performed subtomogram averaging of both soluble and membrane-bound complexes ranging in size from >3 MDa to ∼200 kDa, including 80S ribosomes, Rubisco, nucleosomes, microtubules, clathrin, photosystem II, and mitochondrial ATP synthase. The majority of these density maps reached sub-nanometer resolution, demonstrating the potential of this C. reinhardtii dataset as well as the promise of modern cryo-ET workflows and open data sharing to empower visual proteomics.},
  author       = {Kelley, Ron and Khavnekar, Sagar and Righetto, Ricardo D. and Heebner, Jessica and Obr, Martin and Zhang, Xianjun and Chakraborty, Saikat and Tagiltsev, Grigory and Michael, Alicia and Van Dorst, Sofie and Waltz, Florent and Mccafferty, Caitlyn L. and Lamm, Lorenz and Zufferey, Simon and Van Der Stappen, Philippe and Van Den Hoek, Hugo and Wietrzynski, Wojciech and Harar, Pavol and Wan, William and Briggs, John A.G. and Plitzko, Jürgen M. and Engel, Benjamin D. and Kotecha, Abhay},
  issn         = {1097-4164},
  journal      = {Molecular Cell},
  number       = {1},
  pages        = {213--230.e7},
  publisher    = {Elsevier},
  title        = {{Toward community-driven visual proteomics with large-scale cryo-electron tomography of Chlamydomonas reinhardtii}},
  doi          = {10.1016/j.molcel.2025.11.029},
  volume       = {86},
  year         = {2026},
}

@article{19443,
  abstract     = {In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training datasets. The protracted generation time of physics-based models, often employed to produce these datasets, limits their broad applicability. We introduce FakET, a method based on neural style transfer, capable of simulating the forward operator of any cryo transmission electron microscope. It can be used to adapt a synthetic training dataset according to reference data producing high-quality simulated micrographs or tilt-series. To assess the quality of our generated data, we used it to train a state-of-the-art localization and classification architecture and compared its performance with a counterpart trained on benchmark data. Remarkably, our technique matches the performance, boosts data generation speed 750x, uses 33x less memory, and scales well to typical transmission electron microscope detector sizes. It leverages GPU acceleration and parallel processing. The source code is available at https://github.com/paloha/faket/.},
  author       = {Harar, Pavol and Herrmann, Lukas and Grohs, Philipp and Haselbach, David},
  issn         = {1878-4186},
  journal      = {Structure},
  number       = {4},
  pages        = {820--827.e4},
  publisher    = {Elsevier},
  title        = {{FakET: Simulating cryo-electron tomograms with neural style transfer}},
  doi          = {10.1016/j.str.2025.01.020},
  volume       = {33},
  year         = {2025},
}