• DocumentCode
    1539450
  • Title

    Mapping Infected Cell Phenotype

  • Author

    Adiga, Umesh ; Bell, Brian L. ; Ponomareva, Larissa ; Taylor, Debbie ; Saldanha, Roland ; Nelson, Sandra ; Lamkin, Thomas J.

  • Author_Institution
    UES, Inc., Dayton, OH, USA
  • Volume
    59
  • Issue
    8
  • fYear
    2012
  • Firstpage
    2362
  • Lastpage
    2371
  • Abstract
    Quantitative modeling of the phenotypic changes in the host cell during the bacterial infection makes it possible to explore an empirical relation between the infection stages and the quantifiable host-cell phenotype. A statistically reliable model of this relation can facilitate therapeutic defense against threats due to natural and genetically engineered bacterium. In the preliminary experiment, we have collected several thousand cell images over a period of 72 h of infection with a 2-h sampling frequency that covers various stages of infection by Francisella tularenesis (Ft). Segmentation of macrophages in images was accomplished using a fully automatic, parallel region growing technique. Over two thousand feature descriptors for the host cell were calculated. Multidimensional scaling, followed by hierarchical clustering, was used to group the cells. Preliminary results show that the host-cell phenotype, as defined by the set of measureable features, groups into different classes that can be mapped to the stages of infection.
  • Keywords
    biological techniques; biology computing; cellular biophysics; image segmentation; microorganisms; Francisella tularenesis; automatic parallel region growing technique; bacterial infection; cell images; empirical relation; feature descriptors; genetically engineered bacterium; hierarchical clustering; host cell; infected cell phenotype mapping; macrophage image segmentation; measureable features; multidimensional scaling; quantifiable host-cell phenotype; quantitative modeling; statistically reliable model; therapeutic defense; Fluorescence; Image segmentation; Imaging; Manuals; Microorganisms; Shape; Biodefense; bioimaging; high content screening; image analytics; infection; Bacterial Load; Cells; Cells, Cultured; Cytological Techniques; Disease Progression; Host-Pathogen Interactions; Humans; Infection; Phenotype;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2012.2204746
  • Filename
    6217279