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
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;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2012.2204746