CoChat
Sign Up
PreprintCited by 11Cell Image Analysis TechniquesAdvanced X-ray Imaging TechniquesMedical Image Segmentation Techniques

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Automated segmentation of soft X-ray tomography: native cellular structure with sub-micron resolution at high throughput for whole-cell quantitative imaging in yeast

Open access
Chen, Jianhua, Mirvis, Mary, Ekman, Axel, Vanslembrouck, Bieke, Le Gros, Mark, Larabell, Carolyn, Marshall, Wallace F.·2024·Molecular Biology of the Cell
DOI 10.1101/2024.10.31.621371PMC PMC12509287

Preprint — published as 10.1091/mbc.e24-10-0486

Open this paper in CoChat — free accountOpen PDF

Abstract

Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at sub-optical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based auto-segmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline employs manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the 3D whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts. Significance Statement: Soft X-ray tomography offers many powerful features for whole-cell multi-organelle imaging, but, like other high resolution volumetric imaging modalities, is typically limited by low throughput due to laborious segmentation. Auto-segmentation for soft X-ray tomography overcomes this limitation, enabling statistical 3D morphometric analysis of multiple organelles in whole cells across cell populations. The combination of high 3D resolution of SXT data with statistically useful throughput represents an avenue for more thorough characterizations of cells in toto and opens new mesoscale biological questions and statistical whole-cell modeling of organelle and cell morphology, interactions, and responses to perturbations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2024.10.31.621371

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

  • Integrated intracellular organization and its variations in human iPS cells

    Viana, M. P., Chen, J., Knijnenburg, T. A., Vasan, R., Yan, C., Arakaki, J. E., Bailey, M., Berry, B., Borensztejn, A., Brown, E. M., et al. (2023) · Nature · doi:10.1038/s41586-022-05563-7 · Cited by 132 · cited 1×

    “…count organelle associations at the whole-cell scale from voxel co-localization in standard fluorescence data (Shai et al., 2018; Valm et al., 2017; Viana et al., 2023), while electron microscopy enables accurate surface area and distance measurements within limited volumes (Crabtree et al., 2024;…”
    BackgroundSelf-citation

Flagged first, then by citations

Research works printed but not linked: 88

  1. 10.1109/TPAMI.2024.3435571 · DOI
  2. 10.1083/jcb.107.4.1369 · DOI
  3. 10.1038/s41592-019-0582-9 · DOI
  4. 10.1111/j.1365-2818.2006.01706.x · DOI
  5. 10.1128/MCB.17.12.6847 · DOI
  6. 10.1083/jcb.200201002 · DOI
  7. 10.1016/j.bpj.2014.03.014 · DOI
  8. 10.1016/j.cub.2016.03.020 · DOI
  9. 10.1038/s41592-024-02241-6 · DOI
  10. 10.1093/nar/26.1.73 · DOI
  11. 10.1101/2021.03.29.437450 · DOI
  12. 10.1007/978-3-319-46723-8_49 · DOI · Paper page
  13. Cignoni, P. , Callieri, M. , Corsini, M. , Dellepiane, M. , Ganovelli, F. , & Ranzuglia, G . (2008). MeshLab: An Open-Source Mesh Processing Tool. Eurographics Italian Chapter Conference, 129–136.
  14. 10.1002/jcp.31340 · DOI
  15. 10.1016/j.abb.2015.01.011 · DOI
  16. 10.1371/journal.pcbi.1009155 · DOI · Paper page
  17. 10.1016/S0960-9822(02)00891-6 · DOI
  18. 10.1073/pnas.2209938120 · DOI
  19. 10.1080/15548627.2023.2270378 · DOI
  20. 10.1016/j.devcel.2016.10.022 · DOI
  21. 10.1111/boc.201600044 · DOI
  22. 10.1017/S1431927620023983 · DOI
  23. 10.1016/j.devcel.2014.06.007 · DOI
  24. 10.1016/j.isci.2024.109856 · DOI
  25. 10.1002/cnm.2532 · DOI
  26. 10.1126/science.1207385 · DOI · Paper page
  27. 10.1016/j.ejmp.2021.05.003 · DOI
  28. 10.1016/j.tibs.2020.02.010 · DOI
  29. 10.1073/pnas.2202695119 · DOI
  30. 10.15252/embr.201744815 · DOI
  31. 10.1038/s41586-020-2649-2 · DOI · Paper page
  32. 10.1101/2023.12.18.572249 · DOI
  33. 10.1038/nature11654 · DOI · Paper page
  34. 10.1038/nature02026 · DOI · Paper page
  35. 10.1109/MCSE.2007.55 · DOI · Paper page
  36. 10.1038/s41592-020-01008-z · DOI · Paper page
  37. 10.1038/emboj.2008.248 · DOI
  38. 10.1091/mbc.E06-10-0973 · DOI
  39. 10.1016/0262-8856(92)90076-F · DOI
  40. 10.1126/science.1175088 · DOI · Paper page
  41. 10.1371/journal.pbio.3000226 · DOI · Paper page
  42. 10.1107/S1600577514015033 · DOI
  43. 10.1016/j.sbi.2005.08.008 · DOI
  44. 10.1371/journal.pcbi.1007756 · DOI
  45. 10.1016/j.bpj.2019.11.3400 · DOI
  46. 10.1096/fj.202200253R · DOI
  47. 10.1016/j.str.2022.01.006 · DOI
  48. 10.1038/s41580-018-0094-y · DOI · Paper page
  49. 10.1007/978-1-4939-9102-0_11 · DOI
  50. 10.1073/pnas.051631998 · DOI
  51. 10.1091/mbc.7.5.825 · DOI
  52. 10.1016/j.tcb.2009.08.005 · DOI
  53. 10.25080/Majora-92bf1922-00a · DOI
  54. 10.1007/s00205-020-01497-4 · DOI
  55. 10.7554/eLife.02678 · DOI
  56. 10.7554/eLife.00422 · DOI · Paper page
  57. 10.1016/B978-0-12-388403-9.00007-2 · DOI
  58. 10.1371/journal.ppat.1010629 · DOI
  59. 10.1016/j.jbiotec.2009.03.014 · DOI
  60. 10.1093/bioinformatics/btt276 · DOI
  61. 10.1091/mbc.11.7.2445 · DOI
  62. 10.1016/j.jsb.2011.11.027 · DOI
  63. 10.1016/j.jsb.2008.02.003 · DOI
  64. 10.1073/pnas.1715832114 · DOI
  65. 10.1007/978-1-84882-891-9 · DOI
  66. 10.1002/cai2.33 · DOI
  67. 10.1038/s41586-021-04115-9 · DOI · Paper page
  68. 10.1016/j.cell.2024.04.035 · DOI
  69. 10.1016/j.ceb.2022.102086 · DOI
  70. 10.1091/mbc.E03-05-0297 · DOI
  71. 10.1371/journal.pcbi.1007962 · DOI
  72. 10.1038/nmeth.2019 · DOI · Paper page
  73. 10.1186/1471-2105-11-274 · DOI
  74. 10.1186/s13628-015-0018-6 · DOI
  75. 10.1038/s41467-018-03957-8 · DOI · Paper page
  76. 10.1016/j.jbc.2023.102973 · DOI
  77. 10.1109/ACCESS.2021.3086020 · DOI
  78. 10.1109/ICCV.1995.466848 · DOI
  79. 10.1038/s41467-023-44045-w · DOI
  80. 10.1002/yea.1834 · DOI
  81. 10.1038/s41467-021-21314-0 · DOI
  82. 10.1073/pnas.2025252118 · DOI
  83. 10.1038/nature22369 · DOI · Paper page
  84. 10.1101/2022.11.01.514705 · DOI
  85. 10.21105/joss.03021 · DOI · Paper page
  86. 10.1042/BST20180036 · DOI
  87. 10.1016/j.bpj.2018.11.3136 · DOI
  88. 10.1016/j.bpr.2022.100062 · DOI

Metadata

Authors
Chen, J., Mirvis, M., Ekman, A., Vanslembrouck, B., Le Gros, M., Larabell, C., & Marshall, W. F.
Affiliations
  • University of California, San Francisco (US)
  • Lawrence Berkeley National Laboratory (US)
Corresponding author
Marshall, Wallace F.
ORCID
Venue
Molecular Biology of the Cell
Publisher
openRxiv
Published
2024-11-01
Volume
36
Issue
10
Pages
ar132-ar132
ISSN
1059-1524 (ISSN-L)
DOI
10.1101/2024.10.31.621371
PMC
PMC12509287
OpenAlex
W4413780521
Cited by
11
References
1
Citations by year
2026: 5 · 2025: 6
FWCI
4.15
Type
journal article
Language
en
Open access
Licence
www.biorxiv.org/about/FAQ#license (vor)
Locations
  • Molecular Biology of the Cell (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • PubMed Central (submittedVersion)
Funders
  • National Science Foundation (1548297)
  • U.S. Department of Energy (DEAC02-5CH11231, DE-AC02-)
  • San Francisco State University
  • National Institutes of Health (DBI-1548297, GM130327)
  • University of California, San Diego
  • University of California, San Francisco (DBI-1548297)
  • National Institute of General Medical Sciences (1548297, P30GM138441, R35GM130327)
  • Biological and Environmental Research (DE-AC02-5CH11231)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Physical Sciences › Physics and Astronomy › Radiation › Advanced X-ray Imaging Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
soft X-ray tomography, automated image segmentation, deep learning, Saccharomyces cerevisiae, nuclear morphology, vacuoles, lipid droplets, VAC14, surface meshes, cell morphology

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review