[{"intvolume":"        86","das_tickbox":"1","year":"2026","publication_status":"published","dataavailabilitystatement":"Raw EM data are available at the EMPIAR under accession code EMPIAR: EMPIAR-11830. Annotation and processing information for all 1,829 tomograms are provided in spreadsheet format.153 The following subtomogram averages have been deposited at the Electron Microscopy Data Bank (EMDB): 80S ribosome (EMDB: EMD-51847), nucleosome (EMDB: EMD-19906), PSII (EMDB: EMD-51731), Rubisco (EMDB: EMD-51848), microtubule (EMDB: EMD-51804), clathrin (EMDB: EMD-51789), and ATP synthase (EMDB: EMD-51802). Segmentations shown in Figures 2 and 3 are deposited on Zenodo (https://doi.org/10.5281/zenodo.15875785). Particle positions and orientations used for STA, along with all resources derived from this work, are available on GitHub (https://github.com/Chromatin-Structure-Rhythms-Lab/ChlamyAnnotations). Reconstructed tomograms and annotations are also available to explore interactively at the CZII Cryo-ET Data Portal (DS-10302, https://cryoetdataportal.czscience.com/datasets/10302/). Raw data for cryo-PFIB/SEM slice-and-view of a whole C. reinhardtii cell has also been deposited (EMPIAR: EMPIAR-11275).\r\n\r\nThis paper does not report original code.\r\n\r\nAny additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.","acknowledgement":"Calculations were performed at the Max Planck Institute of Biochemistry and the Raven Supercomputer of the Max Planck Computing and Data Facility (MPCDF) in Garching, Germany; at the sciCORE (http://scicore.unibas.ch/) scientific computing center at the University of Basel, Switzerland; and at Thermo Fisher Scientific, in Eindhoven, the Netherlands. This work was supported by Thermo Fisher Scientific. All lamella preparations and tilt-series collections used in this work were conducted at Thermo Fisher R&D facilities in Brno and Eindhoven, utilizing Arctis and Krios microscopes. This work was also supported by the ERC consolidator grant “cryOcean” (fulfilled by the Swiss State Secretariat for Education, Research and Innovation, M822.00045) as well as a Swiss Nanoscience Institute PhD school grant to B.D.E. and P.V.d.S., an EMBO long-term postdoctoral fellowship (ALTF-383-2022) to G.T., an SNSF Postdoctoral Fellowship (project 210561) to F.W., a Boehringer Ingelheim Fonds fellowship to L.L., and by the Max Planck Society to J.A.G.B. and J.M.P.","ddc":["570"],"scopus_import":"1","oa":1,"article_type":"original","month":"01","date_created":"2026-01-04T23:01:36Z","researchdata_availability":"yes","title":"Toward community-driven visual proteomics with large-scale cryo-electron tomography of Chlamydomonas reinhardtii","department":[{"_id":"AlMi"}],"quality_controlled":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"PlanS_conform":"1","citation":{"mla":"Kelley, Ron, et al. “Toward Community-Driven Visual Proteomics with Large-Scale Cryo-Electron Tomography of Chlamydomonas Reinhardtii.” <i>Molecular Cell</i>, vol. 86, no. 1, Elsevier, 2026, p. 213–230.e7, doi:<a href=\"https://doi.org/10.1016/j.molcel.2025.11.029\">10.1016/j.molcel.2025.11.029</a>.","chicago":"Kelley, Ron, Sagar Khavnekar, Ricardo D. Righetto, Jessica Heebner, Martin Obr, Xianjun Zhang, Saikat Chakraborty, et al. “Toward Community-Driven Visual Proteomics with Large-Scale Cryo-Electron Tomography of Chlamydomonas Reinhardtii.” <i>Molecular Cell</i>. Elsevier, 2026. <a href=\"https://doi.org/10.1016/j.molcel.2025.11.029\">https://doi.org/10.1016/j.molcel.2025.11.029</a>.","ieee":"R. Kelley <i>et al.</i>, “Toward community-driven visual proteomics with large-scale cryo-electron tomography of Chlamydomonas reinhardtii,” <i>Molecular Cell</i>, vol. 86, no. 1. Elsevier, p. 213–230.e7, 2026.","apa":"Kelley, R., Khavnekar, S., Righetto, R. D., Heebner, J., Obr, M., Zhang, X., … Kotecha, A. (2026). Toward community-driven visual proteomics with large-scale cryo-electron tomography of Chlamydomonas reinhardtii. <i>Molecular Cell</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.molcel.2025.11.029\">https://doi.org/10.1016/j.molcel.2025.11.029</a>","ama":"Kelley R, Khavnekar S, Righetto RD, et al. Toward community-driven visual proteomics with large-scale cryo-electron tomography of Chlamydomonas reinhardtii. <i>Molecular Cell</i>. 2026;86(1):213-230.e7. doi:<a href=\"https://doi.org/10.1016/j.molcel.2025.11.029\">10.1016/j.molcel.2025.11.029</a>","ista":"Kelley R, Khavnekar S, Righetto RD, Heebner J, Obr M, Zhang X, Chakraborty S, Tagiltsev G, Michael AK, Van Dorst S, Waltz F, Mccafferty CL, Lamm L, Zufferey S, Van Der Stappen P, Van Den Hoek H, Wietrzynski W, Harar P, Wan W, Briggs JAG, Plitzko JM, Engel BD, Kotecha A. 2026. Toward community-driven visual proteomics with large-scale cryo-electron tomography of Chlamydomonas reinhardtii. Molecular Cell. 86(1), 213–230.e7.","short":"R. Kelley, S. Khavnekar, R.D. Righetto, J. Heebner, M. Obr, X. Zhang, S. Chakraborty, G. Tagiltsev, A.K. Michael, S. Van Dorst, F. Waltz, C.L. Mccafferty, L. Lamm, S. Zufferey, P. Van Der Stappen, H. Van Den Hoek, W. Wietrzynski, P. Harar, W. Wan, J.A.G. Briggs, J.M. Plitzko, B.D. Engel, A. Kotecha, Molecular Cell 86 (2026) 213–230.e7."},"has_accepted_license":"1","publication_identifier":{"eissn":["1097-4164"],"issn":["1097-2765"]},"OA_place":"publisher","_id":"20935","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"213-230.e7","doi":"10.1016/j.molcel.2025.11.029","article_processing_charge":"Yes (in subscription journal)","volume":86,"language":[{"iso":"eng"}],"date_published":"2026-01-08T00:00:00Z","OA_type":"hybrid","file_date_updated":"2026-07-28T07:38:45Z","abstract":[{"lang":"eng","text":"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."}],"status":"public","issue":"1","publication":"Molecular Cell","file":[{"content_type":"application/pdf","creator":"dernst","file_size":26749637,"date_updated":"2026-07-28T07:38:45Z","file_id":"22599","file_name":"2026_MolecularCell_Kelley.pdf","access_level":"open_access","relation":"main_file","checksum":"96a2f8519124d1a0d9de8d2594bf7147","date_created":"2026-07-28T07:38:45Z","success":1}],"supplementarymaterial":"yes","oa_version":"Published Version","type":"journal_article","author":[{"last_name":"Kelley","full_name":"Kelley, Ron","first_name":"Ron"},{"full_name":"Khavnekar, Sagar","last_name":"Khavnekar","first_name":"Sagar"},{"first_name":"Ricardo D.","full_name":"Righetto, Ricardo D.","last_name":"Righetto"},{"first_name":"Jessica","full_name":"Heebner, Jessica","last_name":"Heebner"},{"full_name":"Obr, Martin","last_name":"Obr","orcid":"0000-0003-1756-6564","id":"4741CA5A-F248-11E8-B48F-1D18A9856A87","first_name":"Martin"},{"last_name":"Zhang","full_name":"Zhang, Xianjun","first_name":"Xianjun"},{"first_name":"Saikat","last_name":"Chakraborty","full_name":"Chakraborty, Saikat"},{"first_name":"Grigory","full_name":"Tagiltsev, Grigory","last_name":"Tagiltsev"},{"first_name":"Alicia","id":"6437c950-2a03-11ee-914d-d6476dd7b75c","orcid":"0000-0002-6080-839X","full_name":"Michael, Alicia","last_name":"Michael"},{"first_name":"Sofie","full_name":"Van Dorst, Sofie","last_name":"Van Dorst"},{"last_name":"Waltz","full_name":"Waltz, Florent","first_name":"Florent"},{"full_name":"Mccafferty, Caitlyn L.","last_name":"Mccafferty","first_name":"Caitlyn L."},{"first_name":"Lorenz","last_name":"Lamm","full_name":"Lamm, Lorenz"},{"full_name":"Zufferey, Simon","last_name":"Zufferey","first_name":"Simon"},{"last_name":"Van Der Stappen","full_name":"Van Der Stappen, Philippe","first_name":"Philippe"},{"last_name":"Van Den Hoek","full_name":"Van Den Hoek, Hugo","first_name":"Hugo"},{"first_name":"Wojciech","full_name":"Wietrzynski, Wojciech","last_name":"Wietrzynski"},{"last_name":"Harar","full_name":"Harar, Pavol","orcid":"0000-0001-5206-1794","id":"e03d953a-6e8c-11ef-99e4-f0717d385cd5","first_name":"Pavol"},{"first_name":"William","last_name":"Wan","full_name":"Wan, William"},{"first_name":"John A.G.","last_name":"Briggs","full_name":"Briggs, John A.G."},{"first_name":"Jürgen M.","full_name":"Plitzko, Jürgen M.","last_name":"Plitzko"},{"first_name":"Benjamin D.","last_name":"Engel","full_name":"Engel, Benjamin D."},{"last_name":"Kotecha","full_name":"Kotecha, Abhay","first_name":"Abhay"}],"publisher":"Elsevier","day":"08","date_updated":"2026-07-28T07:39:23Z"},{"isi":1,"day":"03","date_updated":"2025-09-30T11:13:02Z","type":"journal_article","publisher":"Elsevier","author":[{"orcid":"0000-0001-5206-1794","first_name":"Pavol","id":"e03d953a-6e8c-11ef-99e4-f0717d385cd5","full_name":"Harar, Pavol","last_name":"Harar"},{"full_name":"Herrmann, Lukas","last_name":"Herrmann","first_name":"Lukas"},{"full_name":"Grohs, Philipp","last_name":"Grohs","first_name":"Philipp"},{"first_name":"David","last_name":"Haselbach","full_name":"Haselbach, David"}],"status":"public","abstract":[{"text":"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/.","lang":"eng"}],"corr_author":"1","publication":"Structure","file":[{"success":1,"relation":"main_file","date_created":"2025-08-05T12:15:13Z","checksum":"f346bc357a66a88cca3d0eb95793fb73","creator":"dernst","file_size":4367530,"date_updated":"2025-08-05T12:15:13Z","file_id":"20130","file_name":"2025_Structure_Harar.pdf","access_level":"open_access","content_type":"application/pdf"}],"issue":"4","oa_version":"Published Version","article_processing_charge":"Yes (in subscription journal)","doi":"10.1016/j.str.2025.01.020","page":"820-827.e4","volume":33,"file_date_updated":"2025-08-05T12:15:13Z","language":[{"iso":"eng"}],"date_published":"2025-04-03T00:00:00Z","OA_type":"hybrid","publication_identifier":{"eissn":["1878-4186"],"issn":["0969-2126"]},"OA_place":"publisher","_id":"19443","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2025-03-23T23:01:27Z","department":[{"_id":"AlMi"}],"quality_controlled":"1","title":"FakET: Simulating cryo-electron tomograms with neural style transfer","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"PlanS_conform":"1","has_accepted_license":"1","pmid":1,"citation":{"mla":"Harar, Pavol, et al. “FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer.” <i>Structure</i>, vol. 33, no. 4, Elsevier, 2025, p. 820–827.e4, doi:<a href=\"https://doi.org/10.1016/j.str.2025.01.020\">10.1016/j.str.2025.01.020</a>.","chicago":"Harar, Pavol, Lukas Herrmann, Philipp Grohs, and David Haselbach. “FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer.” <i>Structure</i>. Elsevier, 2025. <a href=\"https://doi.org/10.1016/j.str.2025.01.020\">https://doi.org/10.1016/j.str.2025.01.020</a>.","ieee":"P. Harar, L. Herrmann, P. Grohs, and D. Haselbach, “FakET: Simulating cryo-electron tomograms with neural style transfer,” <i>Structure</i>, vol. 33, no. 4. Elsevier, p. 820–827.e4, 2025.","ama":"Harar P, Herrmann L, Grohs P, Haselbach D. FakET: Simulating cryo-electron tomograms with neural style transfer. <i>Structure</i>. 2025;33(4):820-827.e4. doi:<a href=\"https://doi.org/10.1016/j.str.2025.01.020\">10.1016/j.str.2025.01.020</a>","apa":"Harar, P., Herrmann, L., Grohs, P., &#38; Haselbach, D. (2025). FakET: Simulating cryo-electron tomograms with neural style transfer. <i>Structure</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.str.2025.01.020\">https://doi.org/10.1016/j.str.2025.01.020</a>","ista":"Harar P, Herrmann L, Grohs P, Haselbach D. 2025. FakET: Simulating cryo-electron tomograms with neural style transfer. Structure. 33(4), 820–827.e4.","short":"P. Harar, L. Herrmann, P. Grohs, D. Haselbach, Structure 33 (2025) 820–827.e4."},"ddc":["570"],"publication_status":"published","acknowledgement":"The IMP and D.H. are generously funded by Boehringer Ingelheim. We thank Julius Berner from the Mathematical Data Science group @ UniVie, Ilja Gubins and Marten Chaillet from the SHREC team, and the members of the Haselbach lab for helpful discussions.","scopus_import":"1","article_type":"original","oa":1,"month":"04","intvolume":"        33","related_material":{"link":[{"relation":"software","url":"https://github.com/paloha/faket/"}]},"year":"2025","external_id":{"isi":["001463196100001"],"pmid":["39947174"]}}]