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

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  • 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
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    References listed on IDEAS

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    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.
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