Measuring cell surface elasticity on enteroaggregative Escherichia coli wild type and dispersin mutant by AFM.

Enteroaggregative Escherichia coli (EAEC) is pathogenic and produces severe diarrhea in humans. A mutant of EAEC that does not produce dispersin, a cell surface protein, is not pathogenic. It has been proposed that dispersin imparts a positive charge to the bacterial cell surface allowing the bacteria to colonize on the negatively charged intestinal mucosa. However, physical properties of the bacterial cell surface, such as rigidity, may be influenced by the presence of dispersin and may contribute to pathogenicity. Using the system developed in our laboratory for mounting and imaging bacterial cells by atomic force microscopy (AFM), in liquid, on gelatin coated mica surfaces, studies were initiated to measure cell surface elasticity. This was carried out in both wild type EAEC, that produces dispersin, and the mutant that does not produce dispersin. This was accomplished using AFM force-distance (FD) spectroscopy on the wild type and mutant grown in liquid or on solid medium. Images in liquid and in air of both the wild-type and mutant grown in liquid and on solid media are presented. This work represents an initial step in efforts to understand the pathogenic role of the dispersin protein in the wild-type bacteria.

Automated image analysis of atomic force microscopy images of rotavirus particles.

A variety of biological samples can be imaged by the atomic force microscope (AFM) under environments that range from vacuum to ambient to liquid. Generally imaging is pursued to evaluate structural features of the sample or perhaps identify some structural changes in the sample that are induced by the investigator. In many cases, AFM images of sample features and induced structural changes are interpreted in general qualitative terms such as markedly smaller or larger, rougher, highly irregular, or smooth. Various manual tools can be used to analyze images and extract more quantitative data, but this is usually a cumbersome process. To facilitate quantitative AFM imaging, automated image analysis routines are being developed. Viral particles imaged in water were used as a test case to develop an algorithm that automatically extracts average dimensional information from a large set of individual particles. The extracted information allows statistical analyses of the dimensional characteristics of the particles and facilitates interpretation related to the binding of the particles to the surface. This algorithm is being extended for analysis of other biological samples and physical objects that are imaged by AFM.

Automated analysis of fluorescence microscopy images to identify protein-protein interactions.

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.