CMEIASã Software for Computer-Assisted Microscopy ofMicrobial Communities

Liu, J., F-I Liu, E. Marshall, and F.B. Dazzo

Presented at the ASM at Snowbird (2000-08-02 to 2017-12-05 )

A major challenge in microbial ecology is to develop reliable and facile methods of computer-assisted microscopy that can analyze digital images of complex microbial communities at single cell resolution, and compute useful ecological characteristics of their organization and structure in situ without cultivation. Current systems are limited in that they can only perform object classification of up to 3 bacterial morphotypes: straight rods, spheres, and curved rods. Our goal is to develop user-friendly, interactive software that can extract the full information value present within digital images of microbial communities. Our system, called “CMEIASã” (Center for Microbial Ecology Image Analysis System), consists of plug-ins for the free, open-architecture UTHSCSA ImageTool software operating on a personal computer in a Windows NT environment. The first version of CMEIASã(soon to be released by internet download) can automatically classify each bacterial cell into one of 11 morphotypes (regular straight rods, cocci, spirals, curved rods, U-shaped rods, unbranched filaments, ellipsoids, clubs, prosthecate rods, rudimentary branched rods, and branched filaments) at an overall classification accuracy of  97%. The second version of CMEIASãcurrently under development has efficient semi-automated segmentation tools to reduce the image to objects of interest, various new measurement features to compute an unlimited degree of morphological diversity using the latest compilation of phylogenetically relevant information from Bergey’s Manual as reference, microbial abundance (concentration, biovolume, biomass C, biosurface area, cumulative hyphal length), various plotless, plot-based and geo-referenced spatial distribution relationships of the microbes, color recognition capability to extract phylogenetic, metabolic, and strain-specific autecological information from microbial communities using fluorescent molecular probes, and various Photoshop action sequences / Excel macros to facilitate routine image processing and data management tasks prior to and after image analysis. The data acquired from CMEIASãcan be exported into EcoStat and GS+ Geostatistics software to compute numerous ecological indices that characterize the microbial community under investigation. Our vision of CMEIASãis that it will become an accurate, robust, and user-friendly image analysis tool that can significantly enhance the ability to analyze whole community samples without cultivation, and provide wide application in studies of microbial ecology.

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