IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0219892.html
   My bibliography  Save this article

Quantification and isolation of Bacillus subtilis spores using cell sorting and automated gating

Author

Listed:
  • Marianna Karava
  • Felix Bracharz
  • Johannes Kabisch

Abstract

The Gram-positive bacterium Bacillus subtilis is able to form endospores which have a variety of biotechnological applications. Due to this ability, B. subtilis is as well a model organism for cellular differentiation processes. Sporulating cultures of B. subtilis form sub-populations which include vegetative cells, sporulating cells and spores. In order to readily and rapidly quantify spore formation we employed flow cytometric and fluorescence activated cell sorting techniques in combination with nucleic acid fluorescent staining in order to investigate the distribution of sporulating cultures on a single cell level. Automated gating procedures using Gaussian mixture modeling (GMM) were employed to avoid subjective gating and allow for the simultaneous measurement of controls. We utilized the presented method for monitoring sporulation over time in germination deficient strains harboring different genome modifications. A decrease in the sporulation efficiency of strain Bs02018, utilized for the display of sfGFP on the spores surface was observed. On the contrary, a double knock-out mutant of the phosphatase gene encoding Spo0E and of the spore killing factor SkfA (Bs02025) exhibited the highest sporulation efficiency, as within 24 h of cultivation in sporulation medium, cultures of BS02025 already consisted of 80% spores as opposed to 18% for the control strain. We confirmed the identity of the different subpopulations formed during sporulation by employing sorting and microscopy.

Suggested Citation

  • Marianna Karava & Felix Bracharz & Johannes Kabisch, 2019. "Quantification and isolation of Bacillus subtilis spores using cell sorting and automated gating," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-15, July.
  • Handle: RePEc:plo:pone00:0219892
    DOI: 10.1371/journal.pone.0219892
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0219892
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0219892&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0219892?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. Lee, Gyemin & Scott, Clayton, 2012. "EM algorithms for multivariate Gaussian mixture models with truncated and censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2816-2829.
    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. Aldo M. Garay & Victor H. Lachos & Heleno Bolfarine & Celso R. B. Cabral, 2017. "Linear censored regression models with scale mixtures of normal distributions," Statistical Papers, Springer, vol. 58(1), pages 247-278, March.
    2. Michele Bavaro & Federico Tullio, 2023. "Intergenerational mobility measurement with latent transition matrices," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 25-45, March.
    3. Diego Tomassi & Liliana Forzani & Efstathia Bura & Ruth Pfeiffer, 2017. "Sufficient dimension reduction for censored predictors," Biometrics, The International Biometric Society, vol. 73(1), pages 220-231, March.
    4. Masahiro Kuroda & Zhi Geng & Michio Sakakihara, 2015. "Improving the vector $$\varepsilon $$ ε acceleration for the EM algorithm using a re-starting procedure," Computational Statistics, Springer, vol. 30(4), pages 1051-1077, December.
    5. Jaspers, Stijn & Aerts, Marc & Verbeke, Geert & Beloeil, Pierre-Alexandre, 2014. "A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 30-42.
    6. Pan, Yan & Jing, Yunteng & Wu, Tonghai & Kong, Xiangxing, 2022. "Knowledge-based data augmentation of small samples for oil condition prediction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2016. "Multivariate mixtures of Erlangs for density estimation under censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 429-455, July.
    8. Fung, Tsz Chai, 2022. "Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 180-198.
    9. Forzani, Liliana & García Arancibia, Rodrigo & Llop, Pamela & Tomassi, Diego, 2018. "Supervised dimension reduction for ordinal predictors," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 136-155.
    10. Semhar Michael & Tatjana Miljkovic & Volodymyr Melnykov, 2020. "Mixture modeling of data with multiple partial right-censoring levels," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 355-378, June.
    11. Gloria Gonzalez-Rivera & Yun Luo, 2020. "A Truncated Mixture Transition Model for Interval-valued Time Series," Working Papers 202005, University of California at Riverside, Department of Economics.
    12. Baran, Sándor, 2014. "Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 227-238.
    13. Laurent Bordes & Didier Chauveau, 2016. "Stochastic EM algorithms for parametric and semiparametric mixture models for right-censored lifetime data," Computational Statistics, Springer, vol. 31(4), pages 1513-1538, December.
    14. Bouveyron, Charles & Brunet-Saumard, Camille, 2014. "Model-based clustering of high-dimensional data: A review," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 52-78.
    15. Zhechun He, 2017. "Housing and Financial Asset Allocations of Heterogeneous Homeowners," Discussion Papers 17/07, Department of Economics, University of York.
    16. Bram Thijssen & Lodewyk F A Wessels, 2020. "Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-25, March.

    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:pone00:0219892. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.