Dazzo, F.B., J. Liu, A. Prabhu, C. Reddy, M. Wadekar, R. Peretz, R. Bollempalli, D. Trione, E. Marshall, J. Zurdo, H. Hammoud, J. Wang, M. Li, D. McGarrell, A. Gore, J. Maya-Flores, S. Gantner, and N. Hollingsworth
Presented at the All Scientist Meeting (2003-09-12 )
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. We are addressing this challenge by developing new integrative software tools designed to 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 classification plug-ins for UTHSCSA ImageTool running in Win NT4.0/2000, stand-alone programs for color segmentation of microbes and object size cluster analysis, action sequences to edit images in Adobe Photoshop, macros to process and statistically analyze data in MS Excel, and various training tutorials / Help files and operator manual documents. CMEIAS v1.27 can automatically classify bacterial morphotypes at 0.1% frequency with 97% accuracy. A website for CMEIAS (http://cme.msu.edu/cmeias/) is under construction for our first release version (v1.27) of CMEIAS program files along with an operator manual. An upgraded CMEIAS v3.0 is being developed to extract 5 ecologically relevant parameters of microbial communities in situ in digital images: (i) classify their morphological diversity at a complexity level equivalent to 98% of the genera described in Bergey’s Manual of Determinative Bacteriology, (ii) measure microbial abundance based on cell density, biovolume and biomass carbon, (iii) quantify microbial metabolic activity based on color recognition, (iv) analyze microbial autecology and phylogeny based on color recognition using molecular probes, and (v) analyze spatial patterns of microbial colonization at single-cell resolution. CMEIAS output data can also be exported into EcoStat and GS+ Geostatistics to compute additional ecological statistics that further characterize microbial community structure. Recent developments for CMEIAS v3.0 are as follows:Three image segmentation tools have been built to edit images for analysis in ImageTool. A stand-alone CMEIAS Color Recognition program interactively samples up to 200 color pixels of the microbes of interest, then finds each cell’s boundary and finally creates a new RGB image with these colored microbes surrounded by a noise-free background. This new program has an overall accuracy of 96.7% and will likely revolutionize the in situ quantitative image analysis of bacteria differentiated by color for immunofluorescence, FISH, total abundance and metabolic activity. Action Palette Sequences operating in Adobe Photoshop have been built to edit foreground and background pixels in typical images of microbes acquired using phase contrast LM, fluorescence microscopy and SEM, and these actions perform with an overall accuracy of 97.1%. A CMEIAS Object Separation plugin operating in ImageTool automatically splits touching microbial cells before image analysis and has an overall accuracy of 95.8%.Three CMEIAS macros have been developed to prepare and compute image analysis data imported into MS Excel. The Data Preparation macro compiles, concatenates, and graphically displays object analysis and classification data, preparing them for further analysis by ecological statistics. The Sampling Statistics macro analyzes datasets containing multiple images to determine if the sample size is sufficient to estimate community morphotype diversity. The 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.Several new CMEIAS measurement features of object analysis have been developed to analyze microbial abundance and spatial distribution patterns in situ. 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 in microbial biofilms. A CMEIAS Size Border tool analyzes cell size data and returns the upper and lower border limits of their optimized cell size clusters for classification of Operational Morphotype Units. Two CMEIAS script macros have been built to perform quadrat-based spatial distribution analyses of microbes colonizing surfaces. One script provides a transparent grid overlay of the image to be analyzed, and the second reports the area of the AOI polygon for cumulative object analysis.Recent projects using CMEIAS in the Dazzo lab include in situ microbial community analyses of corn leaf surfaces (normal vs. genetically engineered BT-corn), soil aggregates, Japanese freshwater streams, pristine Italian Alpine lakes, plus autecological biogeography of rice growth-promoting rhizobia and measurements of the calling distances and spatial gradients for quorum sensing cell-to-cell communication during microbial colonization of roots.Our vision is for CMEIAS to become an accurate, robust and user-friendly software tool that can significantly enhance the analysis of whole community samples without cultivation, thereby creating many new applications for quantitative studies of in situ microbial ecology at spatial scales relevant to the microbes themselves.
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