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Kraibooj K, Schoeler H, Svensson CM, et al. Automated quantification of the phagocytosis of Aspergillus fumigatus conidia by a novel image analysis algorithm. Front Microbiol. 2015;6:549doi: 10.3389/fmicb.2015.00549.
Kraibooj, K., Schoeler, H., Svensson, C. M., Brakhage, A. A., & Figge, M. T. (2015). Automated quantification of the phagocytosis of Aspergillus fumigatus conidia by a novel image analysis algorithm. Frontiers in microbiology, 6549. https://doi.org/10.3389/fmicb.2015.00549
Kraibooj, Kaswara, et al. "Automated quantification of the phagocytosis of Aspergillus fumigatus conidia by a novel image analysis algorithm." Frontiers in microbiology vol. 6 (2015): 549. doi: https://doi.org/10.3389/fmicb.2015.00549
Kraibooj K, Schoeler H, Svensson CM, Brakhage AA, Figge MT. Automated quantification of the phagocytosis of Aspergillus fumigatus conidia by a novel image analysis algorithm. Front Microbiol. 2015 Jun 09;6:549. doi: 10.3389/fmicb.2015.00549. eCollection 2015. PMID: 26106370; PMCID: PMC4460560.
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Front Microbiol. 2015 Jun 09;6:549. doi: 10.3389/fmicb.2015.00549. eCollection 2015.

Automated quantification of the phagocytosis of Aspergillus fumigatus conidia by a novel image analysis algorithm.

Frontiers in microbiology

Kaswara Kraibooj, Hanno Schoeler, Carl-Magnus Svensson, Axel A Brakhage, Marc Thilo Figge

Affiliations

  1. Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Germany ; Faculty of Biology and Pharmacy, Friedrich Schiller University Jena Jena, Germany.
  2. Faculty of Biology and Pharmacy, Friedrich Schiller University Jena Jena, Germany ; Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Germany.
  3. Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Germany.

PMID: 26106370 PMCID: PMC4460560 DOI: 10.3389/fmicb.2015.00549

Abstract

Studying the pathobiology of the fungus Aspergillus fumigatus has gained a lot of attention in recent years. This is due to the fact that this fungus is a human pathogen that can cause severe diseases, like invasive pulmonary aspergillosis in immunocompromised patients. Because alveolar macrophages belong to the first line of defense against the fungus, here, we conduct an image-based study on the host-pathogen interaction between murine alveolar macrophages and A. fumigatus. This is achieved by an automated image analysis approach that uses a combination of thresholding, watershed segmentation and feature-based object classification. In contrast to previous approaches, our algorithm allows for the segmentation of individual macrophages in the images and this enables us to compute the distribution of phagocytosed and macrophage-adherent conidia over all macrophages. The novel automated image-based analysis provides access to all cell-cell interactions in the assay and thereby represents a framework that enables comprehensive computation of diverse characteristic parameters and comparative investigation for different strains. We here apply automated image analysis to confocal laser scanning microscopy images of the two wild-type strains ATCC 46645 and CEA10 of A. fumigatus and investigate the ability of macrophages to phagocytose the respective conidia. It is found that the CEA10 strain triggers a stronger response of the macrophages as revealed by a higher phagocytosis ratio and a larger portion of the macrophages being active in the phagocytosis process.

Keywords: Aspergillus fumigatus; alveolar macrophages; automated image analysis; host-pathogen interaction; phagocytosis assay

References

  1. Clin Microbiol Rev. 2009 Jul;22(3):447-65 - PubMed
  2. Int J Comput Assist Radiol Surg. 2011 Jan;6(1):127-34 - PubMed
  3. Int J Med Microbiol. 2014 Jul;304(5-6):626-36 - PubMed
  4. PLoS One. 2011 May 05;6(5):e19591 - PubMed
  5. Nat Methods. 2014 Feb;11(2):119-20 - PubMed
  6. PLoS One. 2014 Jul 15;9(7):e101999 - PubMed
  7. Front Microbiol. 2012 Apr 26;3:129 - PubMed
  8. Genome Biol. 2006;7(10):R100 - PubMed
  9. PLoS One. 2014 Oct 31;9(10):e111630 - PubMed
  10. Mycoses. 2014 Dec;57 Suppl 3:56-66 - PubMed
  11. Cytometry A. 2015 Jun;87(6):503-12 - PubMed
  12. Cytometry A. 2014 Feb;85(2):126-39 - PubMed
  13. J Bacteriol. 1995 May;177(10):2781-8 - PubMed
  14. Curr Drug Targets. 2005 Dec;6(8):875-86 - PubMed
  15. Cytometry A. 2015 Jun;87(6):462-70 - PubMed
  16. Front Microbiol. 2011 May 03;2:96 - PubMed
  17. Annu Rev Immunol. 1999;17:593-623 - PubMed
  18. Annu Rev Microbiol. 2002;56:433-55 - PubMed
  19. Nature. 2009 Jan 22;457(7228):471-4 - PubMed
  20. Infect Immun. 2010 Jul;78(7):2995-3006 - PubMed
  21. J Clin Invest. 2003 Aug;112(3):389-97 - PubMed
  22. FEBS J. 2005 Jul;272(14):3615-30 - PubMed
  23. Annu Rev Biomed Eng. 2010 Aug 15;12 :315-44 - PubMed
  24. Clin Microbiol Rev. 1999 Apr;12(2):310-50 - PubMed

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