CMEIAS: A computer-aided system for the image analysis of bacterial morphotypes in microbial communities

A major challenge in microbial ecology is to develop reliable and facile me thods of computer-assisted microscopy that can analyze digital images of co mplex microbial communities at single cell resolution, and compute useful q uantitative characteristics of their organization and structure without cul tivation. Here we describe a computer-aided interactive system to analyze t he high degree of morphological diversity in growing microbial communities revealed by phase-contrast microscopy. The system, called "CMEIAS" (Center for Microbial Ecology Image Analysis System) consists of several custom plu g-ins for UTHSCSA ImageTool, a free downloadable image analysis program ope rating on a personal computer in a Windows NT environment. CMEIAS uses vari ous measurement features and two object classifiers to extract size and sha pe measurements of segmented, digital images of microorganisms and classify them into their appropriate morphotype. The first object classifier uses a single measurement feature to analyze relatively simple communities contai ning only a few morphotypes (e.g., regular rods, cocci, filaments). A secon d new hierarchical tree classifier uses an optimized subset of multiple mea surement features to analyze significantly more complex communities contain ing greater morphological diversity than ever before possible. This CMEIAS shape classifier automatically categorizes each cell into one of 11 predomi nant bacterial morphotypes, including cocci, spirals, curved rods, U-shaped rods, regular straight rods, unbranched filaments, ellipsoids, clubs, rods with extended prostheca, rudimentary branched rods, and branched filaments . The training and testing images for development and evaluation of the CME IAS classifier were obtained from 1,937 phase-contrast grayscale digital im ages of various diverse communities. The CMEIAS shape classifier had an acc uracy of 96.0% on a training set of 1,471 cells and 97.0% on a test set of 4,270 cells representing all 11 bacterial morphotype classes, indicating th at accurate classification of rich morphological diversity in microbial com munities is now possible. An interactive edit feature was added to address the main sources of error in automatic shape classification, enabling the o perator to inspect the assigned morphotype of each bacterium based on visua l recognition of its distinctive pseudocolor, reassign it to another morpho type class if necessary, and add up to five other morphotypes to the classi fication scheme. The shape classifier reports on the number and types of di fferent morphotypes present and the abundance among each of them, thus prov iding the data needed to compute the morphological diversity within the mic robial community. An example of how CMEIAS can augment the analysis of micr obial community structure is illustrated by studies of morphological divers ity as an indicator of dynamic ecological succession following a nutrient s hift-up perturbation in two continuously fed, anaerobic bioreactors with mo rphologically distinct start communities. Various steps to minimize the lim itations of computer-assisted microscopy to classify bacterial morphotypes using CMEIAS are described. In summary, CMEIAS is an accurate, robust, flex ible semiautomatic computing tool that can significantly enhance the abilit y to quantitate bacterial morphotype diversity and should serve as a useful adjunct to the analysis of microbial community structure. This first versi on of CMEIAS will be released as free, downloadable plug-ins so it can prov ide wide application in studies of microbial ecology.