---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '20935'
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.
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.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Ron
  full_name: Kelley, Ron
  last_name: Kelley
- first_name: Sagar
  full_name: Khavnekar, Sagar
  last_name: Khavnekar
- first_name: Ricardo D.
  full_name: Righetto, Ricardo D.
  last_name: Righetto
- first_name: Jessica
  full_name: Heebner, Jessica
  last_name: Heebner
- first_name: Martin
  full_name: Obr, Martin
  id: 4741CA5A-F248-11E8-B48F-1D18A9856A87
  last_name: Obr
  orcid: 0000-0003-1756-6564
- first_name: Xianjun
  full_name: Zhang, Xianjun
  last_name: Zhang
- first_name: Saikat
  full_name: Chakraborty, Saikat
  last_name: Chakraborty
- first_name: Grigory
  full_name: Tagiltsev, Grigory
  last_name: Tagiltsev
- first_name: Alicia
  full_name: Michael, Alicia
  id: 6437c950-2a03-11ee-914d-d6476dd7b75c
  last_name: Michael
  orcid: 0000-0002-6080-839X
- first_name: Sofie
  full_name: Van Dorst, Sofie
  last_name: Van Dorst
- first_name: Florent
  full_name: Waltz, Florent
  last_name: Waltz
- first_name: Caitlyn L.
  full_name: Mccafferty, Caitlyn L.
  last_name: Mccafferty
- first_name: Lorenz
  full_name: Lamm, Lorenz
  last_name: Lamm
- first_name: Simon
  full_name: Zufferey, Simon
  last_name: Zufferey
- first_name: Philippe
  full_name: Van Der Stappen, Philippe
  last_name: Van Der Stappen
- first_name: Hugo
  full_name: Van Den Hoek, Hugo
  last_name: Van Den Hoek
- first_name: Wojciech
  full_name: Wietrzynski, Wojciech
  last_name: Wietrzynski
- first_name: Pavol
  full_name: Harar, Pavol
  id: e03d953a-6e8c-11ef-99e4-f0717d385cd5
  last_name: Harar
  orcid: 0000-0001-5206-1794
- first_name: William
  full_name: Wan, William
  last_name: Wan
- first_name: John A.G.
  full_name: Briggs, John A.G.
  last_name: Briggs
- first_name: Jürgen M.
  full_name: Plitzko, Jürgen M.
  last_name: Plitzko
- first_name: Benjamin D.
  full_name: Engel, Benjamin D.
  last_name: Engel
- first_name: Abhay
  full_name: Kotecha, Abhay
  last_name: Kotecha
citation:
  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>
  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>
  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.
  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.
  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>.
  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.
das_tickbox: '1'
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."
date_created: 2026-01-04T23:01:36Z
date_published: 2026-01-08T00:00:00Z
date_updated: 2026-07-28T07:39:23Z
day: '08'
ddc:
- '570'
department:
- _id: AlMi
doi: 10.1016/j.molcel.2025.11.029
file:
- access_level: open_access
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  date_created: 2026-07-28T07:38:45Z
  date_updated: 2026-07-28T07:38:45Z
  file_id: '22599'
  file_name: 2026_MolecularCell_Kelley.pdf
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has_accepted_license: '1'
intvolume: '        86'
issue: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '01'
oa: 1
oa_version: Published Version
page: 213-230.e7
publication: Molecular Cell
publication_identifier:
  eissn:
  - 1097-4164
  issn:
  - 1097-2765
publication_status: published
publisher: Elsevier
quality_controlled: '1'
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Toward community-driven visual proteomics with large-scale cryo-electron tomography
  of Chlamydomonas reinhardtii
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 86
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '19443'
abstract:
- lang: eng
  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/.
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.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Pavol
  full_name: Harar, Pavol
  id: e03d953a-6e8c-11ef-99e4-f0717d385cd5
  last_name: Harar
  orcid: 0000-0001-5206-1794
- first_name: Lukas
  full_name: Herrmann, Lukas
  last_name: Herrmann
- first_name: Philipp
  full_name: Grohs, Philipp
  last_name: Grohs
- first_name: David
  full_name: Haselbach, David
  last_name: Haselbach
citation:
  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>'
  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.'
  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.'
  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>.'
  short: P. Harar, L. Herrmann, P. Grohs, D. Haselbach, Structure 33 (2025) 820–827.e4.
corr_author: '1'
date_created: 2025-03-23T23:01:27Z
date_published: 2025-04-03T00:00:00Z
date_updated: 2025-09-30T11:13:02Z
day: '03'
ddc:
- '570'
department:
- _id: AlMi
doi: 10.1016/j.str.2025.01.020
external_id:
  isi:
  - '001463196100001'
  pmid:
  - '39947174'
file:
- access_level: open_access
  checksum: f346bc357a66a88cca3d0eb95793fb73
  content_type: application/pdf
  creator: dernst
  date_created: 2025-08-05T12:15:13Z
  date_updated: 2025-08-05T12:15:13Z
  file_id: '20130'
  file_name: 2025_Structure_Harar.pdf
  file_size: 4367530
  relation: main_file
  success: 1
file_date_updated: 2025-08-05T12:15:13Z
has_accepted_license: '1'
intvolume: '        33'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 820-827.e4
pmid: 1
publication: Structure
publication_identifier:
  eissn:
  - 1878-4186
  issn:
  - 0969-2126
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/paloha/faket/
scopus_import: '1'
status: public
title: 'FakET: Simulating cryo-electron tomograms with neural style transfer'
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 33
year: '2025'
...