Poster abstracts

Poster number 35 submitted by Shareef Dabdoub

Exploiting biological coherency in classification and segmentation

Shareef Dabdoub (OSU Biophysics Program, The Ohio State University), Sheryl S. Justice (Microbial Pathogenesis, The Research Institute at Nationwide Childrens Hospital), William C. Ray (Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Childrens Hospital)

Abstract:
Uropathogenic Escherichia coli (UPEC) cause recurrent urinary tract infections by establishing intracellular colonies that are capable of evading host immune responses. A large component of UPEC’s evasion strategy is embodied in a morphological change whereby some members of the colony cease cell division, and adopt a multinucleated filamentous form. This form is able to resist phagocytosis, and provides the seed generation for perpetuating the infection after the host response subsides. Understanding the role of filamentation requires tracking the bacterial load in numerous bacterial colonies in three dimensions (3-D) over time, as well as identifying the population contribution from each of the three morphological states. Since several hundred colonies may be isolated from a test bladder, and hundreds of thousands of bacteria may be present in a colony, rapid quantitative computational visualization and analysis of the morphological types is a necessity. Current algorithms are uniformly aimed at “difficult” datasets with a great deal of noise obscuring the relevant features. However for data such as ours, without significant noise, their performance is hindered by the sophistication necessary to deal with complex data. We have developed a computationally simple approach for such 3-D datasets utilizing dynamically masked Gaussian blurring and domain-specific information based on the morphological and marker differences of the bacteria. This approach will provide accuracy, as well as performance, in characterizing the quantitative and morphological makeup of the bacterial colonies.

Keywords: Uropathogenic E coli, 3D visualization, Gaussian