CMEIAS 3.0: Advanced Computational Tools of Image Analysis Software Designed to Strengthen Microscopy-Based Approaches for Understanding Microbial Ecology

Dazzo, F., J. Liu, G. Tang, G. Zhu, C. Gross, C. Reddy, C. Monosmith, M. Li, D. Nasr, C. Passmore, L. Shan, C. Meyers, S. Gantner, R. Bollempalli, R. Peretz, D. McGarrell, Y. Yanni, A. Smucker, S. Nakano, et al.

Presented at the All Scientist Poster Reception (2007-05-14 to 2007-05-14 )

A major challenge in microbial ecology is to develop reliable 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. To address this challenge, our team of microbiologists, mathematicians and computer scientists has been developing image analysis software that can extract the full information content in digital images of actively growing microbial communities, thereby strengthening microscopy-based methods for understanding microbial ecology. Our system, called CMEIAS (Center for Microbial Ecology Image Analysis System) includes plug-ins for object analysis and classification operating within UTHSCSA ImageTool running in Windows 2000/XP Pro, plus a suite of software applications including stand-alone programs for color image segmentation, creating image quadrats, and cluster analysis of object feature measurements; action sequences to edit images of microbial communities in Adobe Photoshop; Excel addins to process and statistically analyze image analysis data, and various support documents including user manuals, sample images and interactive training tutorials.

Our first release version (v. 1.27) of CMEIAS analyzes images, reports object counts, sizes and shapes, plus performs a supervised classification of all major plus several minor microbial morphotypes (equivalent to 98% of the genera in Bergey’s Manual 9th ed.) in 14-dimensional space at frequencies of > 0.1% with 97% accuracy, and is available for free download at our CMEIAS website <http://cme.msu.edu/cmeias/>. A CMEIAS ver. 1.28 upgrade is scheduled for release in summer ’07 and includes new user support files in a fully-featured Help file, updated user manual and expanded training tutorials that have undergone major revision based on feedback from an international team of beta-testers.

The CMEIAS v. 3.0 upgrade is at various stages of development. Its platform for image analysis is designed to extract five major ecologically relevant types of information from microbial communities in digital images: (i) classification of morphological diversity, using v. 1.27 algorithms combined with up to 42 additional measurement attributes of cell size, shape, luminance, and spatial distribution; (ii) microbial abundance, using cell counts, dilution adjusted concentration, hyphal length, cumulative biovolume, biomass C, and biosurface area of the entire community or of each individual morphotype within the community; (iii) metabolic/physiological activity using differential color staining, (iv) autecology/phylogeny using fluorescent molecular probes and color image segmentation, and (v) spatial distribution using various plotless, plot-based and geostatistical analysis parameters. CMEIAS output data are typically exported into MS Excel, EcoStat and GS+ Geostatistics to compute various ecological statistics that further characterize microbial community structure, activity and spatial distribution.

Recent developments for CMEIAS v. 3.0 include:

• Four image processing tools to help prepare images for analysis. A stand-alone CMEIAS Color Segmentation program interactively samples color pixels of the foreground microbes of interest, then finds each cell’s boundary, and finally creates a new RGB segmented output image containing these colored microbes in a noise-free background (99%+ accuracy). Its primary use will be to facilitate segmentation of foreground microbes of interest within complex RGB digital images where color differentiation really counts, e.g., immunofluorescence, FISH, activity/viability stains, reporter gene expression, Gram stains, etc. CMEIAS Actions operate in Adobe Photoshop / Image Processing ToolKit are being developed to semi-automate the editing of foreground and background pixels in brightfield, phase contrast, fluorescence and SEM images of a variety of microbial communities (97.1% accuracy). A CMEIAS Object Separation Plugin operating in ImageTool automatically splits touching microbial cells within thresholded images (95.8% accuracy). A stand-alone CMEIAS Quadrat Maker application facilitates the optimization of grid dimensions that divide an image into smaller, constant size quadrats for spatial sampling of local microbial density, and then prepares images of each quadrat ready for plot-based and geostatistical spatial distribution analyses.

• Three statistical computation programs to process and analyze CMEIAS data extracted from images. Two of these applications operate within MS Excel® and the third is a stand-alone program. The CMEIAS Data Preparation Macro compiles and concatenates CMEIAS object analysis and classification data, computes descriptive statistics, and automatically prepares the input tables of data for further analysis. The CMEIAS Data Analysis Addin performs frequency distribution analyses with user-selected bin parameters on object analysis data, analyzes object classification datasets built from multiple images to determine if the sample size is sufficient to estimate morphotype diversity, computes and plots a wide variety of diversity indices and similarity coefficients to compare community structures, and analyzes plot-less, plot-based, and georeferenced data for spatial distribution analysis. The CMEIAS Size Border Cluster Analysis program is a stand-alone statistical analysis application that defines the “best cut” size borders that separate clusters of object analysis data in order to build the user-defined and default size border files that work together with the CMEIAS-3 classifier to classify the diversity of Operational Morphological Units in microbial communities. The CMEIAS Upper Size Border tool in this program performs a Monte Carlo cluster analysis to optimize the upper class limits for the user-defined size border file that is tailored for diversity analysis of the specific community being examined. The flexible design built into the user-defined size border file and these cluster analysis applications expands the ability of the CMEIAS-3 OMU classifier to recognize and classify an almost unlimited range of diversity optimized for the specific community under investigation. The CMEIAS High-Low Size Border tool in this program analyzes population range data extracted by CMEIAS and from the Bergey’s Manual database, and Barbaraidentifies the least-overlapping border that separates clusters of data for each measurement feature needed to build the default size border file. That file is intended for general use to classify OMUs in a wide variety of communities and habitats, rather than custom-tailored for any specific community.

Additional notables in CMEIAS v.3.0: Eight out of 18 different microbial biovolume formulas have been identified as most accurate in measuring this cell abundance parameter for all microbial morphotypes recognized by CMEIAS. A CMEIAS Cluster Index has been introduced to measure the degree to which each cell is spatially aggregated with its neighbors as a Z variate for geostatistical spatial distribution analyses of colonization behavior. Several CMEIAS measurement features of object analysis (Empirical Distribution, % Substratum Coverage, Spatial Randomness, 1st and 2nd Nearest Neighbor Distances) have been added to analyze spatial distribution patterns of microbial colonization in situ. Mean Radius, Maximum Radius, and Aerial Porosity attributes have been added for image analysis of microbial biofilms. Other measurement features that discriminate biofilm architectures are currently being evaluated. A CMEIAS Script has been built to report the area of the AOI polygon for cumulative object analysis. Also, a feature has been added to the CMEIAS Cumulative Object Analyzer that can assign the Cartesian coordinates for quadrat image postings at their geometric center, mean center of objects weighted by their local density, or randomized for spatial analysis.

Recent collaborative projects using CMEIAS include in situ analysis of the autecological biogeography of candidate biofertilizer inoculant strains of rhizobia that promote rice growth, microbial community analyses of corn leaf surfaces (normal vs. genetically engineered BT-corn), distribution and abundance of active communities within soil aggregates, seasonal succession and food-web dynamics of epilithic microbial biofilm communities on streambed rocks in Japanese freshwater streams, bacterioplankton diversity in pristine Italian Alpine lakes, in situ spatial scale of bacterial cell-to-cell communication (quorum sensing), and community structure of normal vaginal microflora following perturbations associated with bacterial vaginosis.

In summary, CMEIAS-based applications have potential for filling major gaps in our understanding of microbial ecology by providing accurate, robust and user-friendly computing tools that can extract ecologically important, quantitative information from digital images of microbes, at spatial scales relevant to their diversity, abundance and distribution in situ. The awesome computational power of CMEIAS used in conjunction with microscopy at single-cell resolution adds an exciting new dimension to microbial community analysis, and it is especially valuable when combined with molecular-based and other methods of polyphasic analysis. CMEIAS tools are admirably suited for microbial community analyses currently under investigation in this LTER program focused on row crop agriculture and in other collaborative studies including microbial community analysis conducted in other LTER programs. Announcements of progress in CMEIAS development, publications of studies using this software, and new releases are provided at our website .

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