IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007902.html
   My bibliography  Save this article

Statistical analysis of 3D localisation microscopy images for quantification of membrane protein distributions in a platelet clot model

Author

Listed:
  • Sandra Mayr
  • Fabian Hauser
  • Sujitha Puthukodan
  • Markus Axmann
  • Janett Göhring
  • Jaroslaw Jacak

Abstract

We present the software platform 2CALM that allows for a comparative analysis of 3D localisation microscopy data representing protein distributions in two biological samples. The in-depth statistical analysis reveals differences between samples at the nanoscopic level using parameters such as cluster-density and -curvature. An automatic classification system combines multiplex and multi-level statistical approaches into one comprehensive parameter for similarity testing of the compared samples. We demonstrated the biological importance of 2CALM, comparing the protein distributions of CD41 and CD62p on activated platelets in a 3D artificial clot. Additionally, using 2CALM, we quantified the impact of the inflammatory cytokine interleukin-1β on platelet activation in clots. The platform is applicable to any other cell type and biological system and can provide new insights into biological and medical applications.Author summary: Single-molecule localisation microscopy (LM) became more accessible over the past years. Companies and research facilities developed instruments for 3D/2D LM, but there is a lack of comparison methods for LM-images. Our tool offers a new comparative analysis of LM data. Our system is capable of showing the difference on the level of single molecule clusters and provides information on differences between images on various scales. We determine if clusters formed of molecule localisations are comparable. We display the difference on density or the shape of these formed clusters for a certain dimension. Since the comparison is performed for all dimensions, the differences between cluster properties are observed very precisely. Thereby, we learn if clusters of molecules are formed and how they differ in both samples. For a fast/concluding comparison, we have added tools which derive a percentage of equality of both images based on all comparison results. The tool is useful for comparison of images of molecules in cells, which are expected to differ from each other (exemplified with platelets). The differences might be caused by drug treatment, different disease progress or other environmental/genetical issues. The analysis is performed at a single cell level as well as cell cluster level.

Suggested Citation

  • Sandra Mayr & Fabian Hauser & Sujitha Puthukodan & Markus Axmann & Janett Göhring & Jaroslaw Jacak, 2020. "Statistical analysis of 3D localisation microscopy images for quantification of membrane protein distributions in a platelet clot model," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-34, June.
    • Handle: RePEc:plo:pcbi00:1007902
    DOI: 10.1371/journal.pcbi.1007902
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007902
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007902&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1007902?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. Jasmine Burguet & Philippe Andrey, 2014. "Statistical Comparison of Spatial Point Patterns in Biological Imaging," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-12, February.
    2. Thibault Lagache & Alexandre Grassart & Stéphane Dallongeville & Orestis Faklaris & Nathalie Sauvonnet & Alexandre Dufour & Lydia Danglot & Jean-Christophe Olivo-Marin, 2018. "Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    3. William Day & Herbert Edelsbrunner, 1984. "Efficient algorithms for agglomerative hierarchical clustering methods," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 7-24, December.
    4. Oleg V. Kim & Rustem I. Litvinov & Mark S. Alber & John W. Weisel, 2017. "Quantitative structural mechanobiology of platelet-driven blood clot contraction," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    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. Claudiu Vinte & Marcel Ausloos, 2022. "The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator," Papers 2205.00104, arXiv.org.
    2. Lerato Lerato & Thomas Niesler, 2015. "Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-24, October.
    3. William Day & Herbert Edelsbrunner, 1985. "Investigation of proportional link linkage clustering methods," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 239-254, December.
    4. Monika Khandelwal & Sabha Sheikh & Ranjeet Kumar Rout & Saiyed Umer & Saurav Mallik & Zhongming Zhao, 2022. "Unsupervised Learning for Feature Representation Using Spatial Distribution of Amino Acids in Aldehyde Dehydrogenase (ALDH2) Protein Sequences," Mathematics, MDPI, vol. 10(13), pages 1-20, June.
    5. Alberto Fernández & Sergio Gómez, 2020. "Versatile Linkage: a Family of Space-Conserving Strategies for Agglomerative Hierarchical Clustering," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 584-597, October.
    6. C. Finden & A. Gordon, 1985. "Obtaining common pruned trees," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 255-276, December.
    7. Ryszard Tomaszewski & Jerzy Dajka, 2021. "Statistical image analysis and escort histograms in characterization of articular cartilage repair in a skeleton animal model," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-15, June.
    8. Quan Gan & Wang Chun Wei & David Johnstone, 2015. "A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1805-1821, November.
    9. Taneja, Anu & Arora, Anuja, 2019. "Modeling user preferences using neural networks and tensor factorization model," International Journal of Information Management, Elsevier, vol. 45(C), pages 132-148.
    10. Yuching Lu & Koki Tozuka & Goutam Chakraborty & Masafumi Matsuhara, 2021. "A Novel Item Cluster-Based Collaborative Filtering Recommendation System," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 327-346, November.
    11. Yimei Wang & Yongqian Liu & Li Li & David Infield & Shuang Han, 2018. "Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method," Energies, MDPI, vol. 11(4), pages 1-19, April.
    12. Cheng-Chun Lee & Mikel Maron & Ali Mostafavi, 2022. "Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
    13. Li, Daolun & Zhou, Xia & Xu, Yanmei & Wan, Yujin & Zha, Wenshu, 2023. "Deep learning-based analysis of the main controlling factors of different gas-fields recovery rate," Energy, Elsevier, vol. 285(C).
    14. Qiufang Shi & Xiaoyong Yan & Bin Jia & Ziyou Gao, 2020. "Freight Data-Driven Research on Evaluation Indexes for Urban Agglomeration Development Degree," Sustainability, MDPI, vol. 12(11), pages 1-16, June.
    15. Bajoulvand, Atena & Zargari Marandi, Ramtin & Daliri, Mohammad Reza & Sabzpoushan, Seyed Hojjat, 2017. "Analysis of folk music preference of people from different ethnic groups using kernel-based methods on EEG signals," Applied Mathematics and Computation, Elsevier, vol. 307(C), pages 62-70.
    16. Dongyun Nie & Michael Scriney & Xiaoning Liang & Mark Roantree, 2024. "From data acquisition to validation: a complete workflow for predicting individual customer lifetime value," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 321-341, June.
    17. Mirko Křivánek, 1986. "Computing the nearest neighbor interchange metric for unlabeled binary trees is NP-complete," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 55-60, March.
    18. Ji, Yuxuan & Geroliminis, Nikolas, 2012. "On the spatial partitioning of urban transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1639-1656.
    19. Zhang, Xiaolei & Ren, Yibin & Huang, Baoxiang & Han, Yong, 2018. "Analysis of time-varying characteristics of bus weighted complex network in Qingdao based on boarding passenger volume," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 376-394.
    20. Potoniec, Jedrzej & Sroka, Daniel & Pawlak, Tomasz P., 2022. "Continuous discovery of Causal nets for non-stationary business processes using the Online Miner," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1304-1320.

    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:pcbi00:1007902. 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.