Dazzo, F.B., J. Liu, A. Jain, A. Prabhu, C. Reddy, M. Wadekar, R. Peretz, R. Bollempalli, D. Trione, E. Marshall, J. Zurdo, H. Hammoud, J. Wang, M. Li, D. McGarrell, J. Maya-Flores, S. Gantner, C. Dowling, A. B. Gomaa, and Y. Yanni
Presented at the All Scientist Meeting (2004-10-08 )
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 have 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) contains image analysis and object classification plug-ins for UTHSCSA ImageTool running in Win NT4.0/2000, stand-alone programs for image segmentation in color, making image quadrats, and cluster analysis of object feature measurements, action sequences to edit images of microbial communities in Adobe Photoshop, Excel macros to process and statistically analyze image analysis data, plus training tutorials and operator manual documents.Our first release version (v1.27) of CMEIAS analyzes images and 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 > 0.1% with 97% accuracy, and is available at our CMEIAS website <http://cme.msu.edu/cmeias/>.The CMEIAS v3.0 upgrade is designed to extract 5 major ecologically relevant types of information from microbial communities in digital images: (i) classification of morphological diversity, using v1.27 algorithms combined with up to 42 additional measurement attributes of cell size, shape, luminance, and spatial distribution; (ii) microbial abundance, using cell size, counts, dilution adjusted concentration, hyphal length, cumulative biovolume, biomass C, 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 color recognition with fluorescent molecular probes, and (v) spatial distribution, using numerous 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.Recent developments for CMEIAS v3.0 include:Four image processing tools to help prepare images for analysis. A stand-alone CMEIAS Color Recognition program interactively samples color pixels of the foreground microbes of interest, then finds each cell’s boundary and finally creates a new RGB image with these colored microbes in a noise-free background (96.7% accuracy). Its primary use will be to facilitate segmentation of foreground microbes of interest within digital images where color differentiation really counts. CMEIAS Action Palette Sequences operate in Adobe Photoshop /ImageProcessing ToolKit to semi-automate the editing of foreground and background pixels in phase contrast, fluorescence and SEM grayscale images of microbial communities (97.1% accuracy). A CMEIAS Object Separation Plugin operating in ImageTool automatically splits touching microbial cells within binary images (95.8% accuracy). A stand-alone CMEIAS Quadrat Maker program interactively divides an image into an optimized set of smaller quadrat mages for preparing image stacks used in plot-based spatial distribution analyses.Four statistical computation tools to process and analyze data extracted from images. Three macros have been prepared to work with acquired image analysis data imported into MS Excel. The CMEIAS Data Preparation macro compiles, concatenates, performs descriptive statistics and graphically displays object analysis and classification data, preparing them for further analysis by ecological statistics. The CMEIAS Sampling Statistics macro analyzes datasets containing multiple images to determine if the sample size is sufficient to estimate community morphotype diversity. The CMEIAS Action Performance macro computes performance accuracy for newly developed action sequences by analyzing object analysis and morphotype classification data extracted from the corresponding edited images of microbes. A stand-alone CMEIAS Size Border tool performs a Monte Carlo cluster analysis on object analysis datasets containing up to 42 user-selected measurement attributes of cell size, shape, luminosity, and/or local spatial attributes, and reports the information needed to build a statistically defendable, customized size border file that would define and classify all the diversity of Operational Morphological Units in a set of community images using the CMEIAS-3 classifier.Additional notables in CMEIAS v.3.0: Eight out of 18 different Microbial Biovolume formulas have been identified as most accurate and implemented to measure this cell abundance parameter for all microbial morphotypes recognized by CMEIAS. A CMEIAS Cluster Index has been introduced to measure the proximity of aggregated cells surrounding each individual microbe as a Z variate for geostatistical spatial distribution analyses of colonization behavior. Mean Radius, Maximum Radius, and Aerial Porosity measurement feature attributes have been added to analyze microbial biofilm structure/development/function. Several new CMEIAS measurement features of object analysis (Empirical Distribution, % Microbial Cover, Spatial Randomness, 1st and 2nd Nearest Neighbor Distances) have been added to analyze spatial distribution patterns of microbial colonization in situ. 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 posting centers that are weighted by the local density of objects within it 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 of microbial biofilm communities in streambed rocks of Japanese freshwater streams, diversity of bacterioplankton in pristine Italian Alpine lakes, and measurements of the in situ calling distances and spatial gradients for quorum sensing cell-to-cell communication during microbial colonization of roots.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 in situ at spatial scales relevant to the microbes themselves. It adds a powerful new dimension to examining microbial communities, and is especially valuable when combined with molecular-based and other methods of polyphasic analysis. Announcements of progress in CMEIAS development and release are provided at our <http://cme.msu.edu/cmeias/> website.
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