IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007962.html
   My bibliography  Save this article

Reliable estimation of membrane curvature for cryo-electron tomography

Author

Listed:
  • Maria Salfer
  • Javier F Collado
  • Wolfgang Baumeister
  • Rubén Fernández-Busnadiego
  • Antonio Martínez-Sánchez

Abstract

Curvature is a fundamental morphological descriptor of cellular membranes. Cryo-electron tomography (cryo-ET) is particularly well-suited to visualize and analyze membrane morphology in a close-to-native state and molecular resolution. However, current curvature estimation methods cannot be applied directly to membrane segmentations in cryo-ET, as these methods cannot cope with some of the artifacts introduced during image acquisition and membrane segmentation, such as quantization noise and open borders. Here, we developed and implemented a Python package for membrane curvature estimation from tomogram segmentations, which we named PyCurv. From a membrane segmentation, a signed surface (triangle mesh) is first extracted. The triangle mesh is then represented by a graph, which facilitates finding neighboring triangles and the calculation of geodesic distances necessary for local curvature estimation. PyCurv estimates curvature based on tensor voting. Beside curvatures, this algorithm also provides robust estimations of surface normals and principal directions. We tested PyCurv and three well-established methods on benchmark surfaces and biological data. This revealed the superior performance of PyCurv not only for cryo-ET, but also for data generated by other techniques such as light microscopy and magnetic resonance imaging. Altogether, PyCurv is a versatile open-source software to reliably estimate curvature of membranes and other surfaces in a wide variety of applications.Author summary: Membrane curvature plays a central role in many cellular processes like cell division, organelle shaping and membrane contact sites. While cryo-electron tomography (cryo-ET) allows the visualization of cellular membranes in 3D at molecular resolution and close-to-native conditions, there is a lack of computational methods to quantify membrane curvature from cryo-ET data. Therefore, we developed a computational procedure for membrane curvature estimation from tomogram segmentations and implemented it in a software package called PyCurv. PyCurv converts a membrane segmentation, i.e. a set of voxels, into a surface, i.e. a mesh of triangles. PyCurv uses the local geometrical information to reliably estimate the local surface orientation, the principal (maximum and minimum) curvatures and their directions. PyCurv outperforms well-established curvature estimation methods, and it can also be applied to data generated by other imaging techniques.

Suggested Citation

  • Maria Salfer & Javier F Collado & Wolfgang Baumeister & Rubén Fernández-Busnadiego & Antonio Martínez-Sánchez, 2020. "Reliable estimation of membrane curvature for cryo-electron tomography," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-29, August.
  • Handle: RePEc:plo:pcbi00:1007962
    DOI: 10.1371/journal.pcbi.1007962
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007962
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007962&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1007962?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Arno Klein & Satrajit S Ghosh & Forrest S Bao & Joachim Giard & Yrjö Häme & Eliezer Stavsky & Noah Lee & Brian Rossa & Martin Reuter & Elias Chaibub Neto & Anisha Keshavan, 2017. "Mindboggling morphometry of human brains," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-40, February.
    2. Kishore R Mosaliganti & Ramil R Noche & Fengzhu Xiong & Ian A Swinburne & Sean G Megason, 2012. "ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes," PLOS Computational Biology, Public Library of Science, vol. 8(12), pages 1-14, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christopher Cyrus Kuhn & Nirakar Basnet & Satish Bodakuntla & Pelayo Alvarez-Brecht & Scott Nichols & Antonio Martinez-Sanchez & Lorenzo Agostini & Young-Min Soh & Junichi Takagi & Christian Biertümpf, 2023. "Direct Cryo-ET observation of platelet deformation induced by SARS-CoV-2 spike protein," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zachariah M. Reagh & Charan Ranganath, 2023. "Flexible reuse of cortico-hippocampal representations during encoding and recall of naturalistic events," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Nir Moneta & Mona M. Garvert & Hauke R. Heekeren & Nicolas W. Schuck, 2023. "Task state representations in vmPFC mediate relevant and irrelevant value signals and their behavioral influence," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    3. Yong He & Yunlong Meng & Hui Gong & Shangbin Chen & Bin Zhang & Wenxiang Ding & Qingming Luo & Anan Li, 2014. "An Automated Three-Dimensional Detection and Segmentation Method for Touching Cells by Integrating Concave Points Clustering and Random Walker Algorithm," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-15, August.
    4. Alexander Nitsch & Mona M. Garvert & Jacob L. S. Bellmund & Nicolas W. Schuck & Christian F. Doeller, 2024. "Grid-like entorhinal representation of an abstract value space during prospective decision making," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    5. Qi Huang & Zhibing Xiao & Qianqian Yu & Yuejia Luo & Jiahua Xu & Yukun Qu & Raymond Dolan & Timothy Behrens & Yunzhe Liu, 2024. "Replay-triggered brain-wide activation in humans," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    6. Farnaz Zamani Esfahlani & Joshua Faskowitz & Jonah Slack & Bratislav Mišić & Richard F. Betzel, 2022. "Local structure-function relationships in human brain networks across the lifespan," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    7. Rotem Botvinik-Nezer & Bogdan Petre & Marta Ceko & Martin A. Lindquist & Naomi P. Friedman & Tor D. Wager, 2024. "Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    8. Angelika Maurer & Julian Klein & Jannik Claus & Neeraj Upadhyay & Leonie Henschel & Jason Anthony Martin & Lukas Scheef & Marcel Daamen & Theresa Schörkmaier & Rüdiger Stirnberg & Tony Stöcker & Alexa, 2022. "Effects of a 6-Month Aerobic Exercise Intervention on Mood and Amygdala Functional Plasticity in Young Untrained Subjects," IJERPH, MDPI, vol. 19(10), pages 1-19, May.
    9. Benjamin Lahner & Kshitij Dwivedi & Polina Iamshchinina & Monika Graumann & Alex Lascelles & Gemma Roig & Alessandro Thomas Gifford & Bowen Pan & SouYoung Jin & N. Apurva Ratan Murty & Kendrick Kay & , 2024. "Modeling short visual events through the BOLD moments video fMRI dataset and metadata," Nature Communications, Nature, vol. 15(1), pages 1-26, December.
    10. Kristjan Kalm & Dennis Norris, 2021. "Sequence learning recodes cortical representations instead of strengthening initial ones," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-34, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1007962. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.