FB Dazzo, J Liu, K Kwatra, C Gross, N Philips, C Monosmith, K Klemmer, Z Ji, C Gidcumb, R Shah, D McGarrell, I Folland, I Ganesan, P Smith, Y Yanni, S Gantner, P Jha, K Card, S Lundback, B Niccum, S Zamani, I Leader, D Stawkey, B Patel, A Jones, and T Abukar
Dept. of Microbiology and Molecular Genetics Michigan State University
Presented at the All Scientists Meeting (2015-04-15 to 2015-04-16 )
A major challenge in microbial ecology is to develop reliable methods of computer-assisted microscopy that can process and analyze complex digital images of microbial populations and 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 ecologically relevant information of actively growing microbial populations and communities in digital images. The long-range mission of this project is to develop and release a comprehensive, innovative suite of bioimage informatics analysis software applications designed to strengthen quantitative, microscopy-based approaches for understanding microbial ecology, at various spatial scales relevant to the individual microbes and their ecological niches. Our software package, called CMEIAS (Center for Microbial Ecology Image Analysis System) consists of new and improved computing tools for various stages of image bioinformatics, including image acquisition, processing and segmentation, object analysis and classification, data processing, statistical analysis and exploratory data mining. When finalized, the various software applications and their documentations (refereed journal publications, thoroughly illustrated user manuals, help topic search files, audio-visual training tutorials with accompanying test images) are released as free downloads at our MSU CMEIAS website http://cme.msu.edu/cmeias
The first release version of CMEIAS (v. 1.27) operates within UTHSCSA ImageTool v. 1.27 in a PC running Windows 32-bit operating system. It can analyze foreground objects in images, reports their numerical abundance and 27 attributes of size, shape and luminosity, provides a 1-dimensional object classifier using a single, user-specified measurement attribute and 15 upper bin limits (16 classes), and also features a unique semi-automatic supervised hierarchical tree classifier of all major (cocci, spirals, curved rods, regular rods, prosthecates, branched and unbranched filaments) and several minor (U-rods, ellipsoids, clubs and rudimentary branched rods) microbial morphotypes, equivalent to 98% of the genera described in Bergey’s Manual 9th ed. The CMEIAS Morphotype Classifier works in 14-dimensional space, measures changes in abundance of each microbial morphotype present within dynamically growing communities at frequencies down to 0.01% [depending on the sample size] with 97% accuracy, and can add 5 additional (rare) morphotypes if present in the community.
Several major upgrades of CMEIAS software applications are at various stages of development. At the core of the suite is a CMEIAS-IT ver. 3.10 upgrade of the UTHSCSA ImageTool host program with numerous revisions that improve its user-friendly operation, significantly improved design and organization of tab pages to select the measurement features for the object analysis / cumulative object analysis / object classification plugins, 10 new toolbar shortcuts for repetitive tasks, a minimum – maximum object size filter, expanded range of image zoom features, upgrades of manual object analysis routines, expanded range of upper bin limits for the 1-dimensional object classifier, and an expanded help menu providing user support. This CMEIAS-IT upgrade operates in both 32-bit and 64-bit Windows operating systems.
Image processing routines featured in ImageTool v3.0 include contrast / brightness adjustment, sharpening, background subtraction, median smoothing, color-to-grayscale conversion, stack averaging, histogram stretch, neighborhood convolution, and a manual pixel editor. The CMEIAS-IT v3.10 upgrade includes 2 image-editing tools to facilitate the segmentation of objects in images prior to analysis. First, a stand-alone CMEIAS Color Segmentation software application has been developed and released with improved technologies of image processing to facilitate the accurate segmentation of foreground microbial objects within complex RGB digital images where color differentiation really matters most. This application is designed to assist studies that use color classifications to reveal important quantitative information on the ecophysiology of microorganisms at single-cell resolution, e.g., understanding bacterial individuality to explore the mechanisms through which ecological systems work, how individual cells interact with each other and their environment, and tests of the emerging theory of individual-based modeling and ecology which predict that individual cell variation is a major driver of population structure and function. It features a flexible, user-defined color tolerance setting to interactively sample a training set of local color pixels representing the microbes of interest, finds each cell’s boundary, and finally creates a new RGB segmented output image containing these foreground colored microbes in a noise-free background. Its performance has been extensively tested on many environmentally complex color images analyzed pixel-by-pixel one at a time, and operates with ~99% accuracy. This software application also features other image processing routines to fill vacant holes within objects, removes pixel noise by dilate/erode, splits images into their RGB / HIS / YUV color model channels, pseudocolors object features differing in brightness to assist in defining their contour (especially important in fluorescence microscopy), and sets thresholds for object size and brightness filtering. Second, a CMEIAS Object Separation plugin has been developed to automatically split touching objects (e.g., microbial aggregates) in thresholded images. It performs with 99% accuracy and has a unique design that avoids the deletion of foreground pixels resulting in erroneous size reduction of the segmented objects, unlike other automated object separation tools (e.g., watershed) featured in other image processing software that correspondingly perform with 76% accuracy.
The digital image analysis plug-ins of the CMEIAS-IT v3.10 upgrade are designed to extract 5 categories of information important to microbial ecology. These include features to analyze: (i) microbial abundance, reporting cell counts, spatial density, % substratum coverage, dilution adjusted concentration, filamentous length, biovolume, biomass C, and biosurface area of each individual cell, each individual population of morphotypes or the entire community; (ii) microbial ecophysiology differentiated by size / shape / color / microdensitometry, e.g., allometric scaling / metabolic rate, viability, resource ecology, productivity, cell communication and other cell-cell interactions; (iii) microbial autecology / phylogeny using fluorescent molecular probes (e.g., immunofluorescence, FISH) or autofluorescent cell components (e.g., GFP, F420) and color image segmentation especially useful in studies of gene expression in situ; (iv) microbial in situ spatial pattern analysis, reporting information on local spatial density, nearest neighbor relationships, colonization behavior, biogeography, and spatial autocorrelation; and (v) microbial morphology, reporting information on community diversity, morphological adaptations, bacteriovory stress responses, and community succession and resilience following environmental perturbation.
The object analysis core of the CMEIAS-IT v3.10 upgrade can currently extract 71 different measurement attributes of cell size, shape, luminance, and spatial distribution from each individual foreground object, and 56 attributes of cumulative object analysis of global landscape, microbial abundance, and spatial ecology attributes of microbial assemblage images in biofilms. Notable among the measurement attributes of CMEIAS Object Analysis are 8 thoroughly tested, biovolume formulas to accurately measure this body size feature for each microbial morphotype classified by CMEIAS, attributes that employ user-specified variables in a universal allometric scaling formula to compute biomass C from biovolume data, attributes of luminosity for in situ microdensitometry of biofilm texture, gene expression and metabolic activity, and attributes of biogeography that test if their patterns of spatial distribution deviate from complete randomness, and if so (most often the case), provide statistically defendable predictions of their cooperative vs. conflict colonization behavior within the landscape domain.
A major research activity has been to rank the ability of CMEIAS measurement attributes to discriminate biofilm architecture at multiple spatial scales. CMEIAS metrics of landscape ecology that exhibit high discriminating power in 2-dimension biofilm analysis include % Substratum Coverage, Areal Porosity, Relative Porosity, Mean Circular Intensity, Edge Density, Mean / Maximum Diffusion Radii, and Patch Length / Shape / Cohesion / Index. Also, CMEIAS luminosity metrics provide discerning information on 3-dimensional biofilm architecture, including data to explore discriminating features for surface texture analysis and intensity of gene expression using fluorescent reporters.
In addition to the above features to analyze the landscape ecology of microbial biofilms, the CMEIAS Spatial Analysis module can analyze microbial biogeography across multiple spatial scales, including their in situ spatial patterns of biofilm colonization on surfaces. These analyses include plot-less point pattern, plot-based quadrat-lattice and geostatistical analyses of the null hypothesis of spatial randomness, from which their colonization behavior can be deduced. Object analysis attributes can report the location of each individual microbial object (Cartesian X, Y coordinates relative to a landmark origin), shortest linear distances to their 1st and 2nd nearest neighbors, cumulative empirical distribution function of the 1st nearest neighbor distance, and a Cluster Index that reports the intensity to which each cell is clustered near its neighbors. Cumulative Object Analysis attributes provide quadrat-based metrics of local spatial density using area-weighted abundance of cell concentration / cumulative length / biovolume / biomass C / biosurface area, distance between random points and nearest neighbors for spatial pattern analysis, and postings of the quadrat X, Y centroid and its geometric centroid weighted by local object density. CMEIAS georeferenced data can be analyzed geostatistically to test for and mathematically model the spatially autocorrelated intensities of the selected Z-variate for each cell (e.g., cell biovolume, cluster index), test for anisotropy in their spatial autocorrelation, measure the real-world range of effective separation distance that defines the spatial scale of influence in microbial cell-cell interactions, and produce statistically defendable 2-D and 3-D kriging maps of their heterogeneity in Z-variate intensity interpolated over the entire georeferenced landscape domain, even in areas not sampled.
The CMEIAS Spatial Analysis module is supported by a script for plot-based spatial analysis of foreground objects enclosed within a user-defined polygon area of interest instead of the entire image. A second featured script assists with replicated sampling for point-pattern spatial analysis. Also, a stand-alone CMEIAS Quadrat Maker software application has been developed to facilitate the optimization of grid lattice dimensions that divide an image into smaller, constant size contiguous quadrats for high-resolution spatial resolution of local microbial density, produces an optimized / indexed image with identification labels of each quadrat in the spatial domain, and then cuts a copy of the original image into size-optimized individual quadrat images with column-row numbered annotation defined by the optimized grid that are ready for image stack construction followed by plot-based and geostatistical spatial distribution analyses.
A powerful CMEIAS JFrad software program has been developed to compute 11 discriminating fractal dimensions along coastlines of microbiofilm colonies in single images using a semi-automated wizard design, or multiple images using a fully automated batch analysis. The CMEIAS outputs of fractal geometry provide quantitative insights on the complexity of microbial biofilm architecture and type / intensity of colonization behavior resulting from the scale-dependent heterogeneous fractal variability in limiting resource partitioning, and reflects the high efficiency at which cells position themselves when faced with the interactive forces of microbial coexistence to optimize their allocation of nutrient resources on a local scale.
The CMEIAS Object Classification module features 5 different object classifiers. The Single Variable Classifier sorts foreground objects based on division of a scale defined by a single user-selected metric of size, shape, luminosity or spatial proximity. CMEIAS ver. 3.10 is upgraded to include 20 upper class limits to define the decision boundaries separating each bin class for this classifier. The unique Morphotype Classifier is a hierarchical tree classifier that works in 14-dimensional space as described above for CMEIAS ver. 1.27 and has an improved design of the reported output table. The unique Operational Morphological Unit (OMU) Classifier is a supervised hierarchical tree classifier that sub-classifies each population of morphotypes in the community based on a pre-established set of cell size features defining the morphological signature of each cell at 0.2 um resolution. Its rules of classification use a multilinear matrix of upper bin limits optimized as the least-overlapping borders that separate bin clusters for each measurement attribute used in the classification scheme that are defined by the default and the user-defined size border files. The default size border file is derived from statistical best-cut cluster analyses of microbial cell size data in the published knowledge base of taxonomically described microorganisms, and currently distinguishes 890+ OMUs. The user-defined size border file is custom built to optimize the classification of all statistically distinguishable OMU’s present in the specific community under investigation, regardless of whether they have been described previously. Two other unique Object Multiclassifiers combine the awesome computing powers of CMEIAS Object Analysis and Morphotype / OMU classification reported at individual, single-cell resolution. They are of the semi-supervised type, associating the supervised morphotype classifier or the OMU classifier with a user-selected set of additional measurement attributes available in the Object Analysis plugin. These 2 classifiers produce an output table of mixed data reporting both object analysis and classification results for each individual microbial object that can then be evaluated by exploratory morphotype-weighted cluster analyses and data mining techniques to compute their statistically defined classification based on the (dis)similarities between microbial community structures. Various measures of ecophysiology (e.g., allometric scaling, metabolic activity, membrane integrity, resource ecology, spatial positioning reflecting colonization behavior, nutrient uptake efficiency, predatory bacteriovory, etc.) can be statistically inferred from the output data acquired by these individual object classification-analysis modules. These multiclassifiers significantly expand the ability of CMEIAS to compare the diversity and ecophysiology of microbial communities in situ at single-cell resolution.
Several CMEIAS computing tools support its object classification module. A powerful Size Border Cluster Analysis Tool (Sbcat) is designed to identify clusters with optimized upper bin limits of the OMU sub-classifications. It uses a Monte Carlo 1,000-iterated simulation to perform an exploratory, multilinear unsupervised cluster analysis of each data array of size/shape attributes, and ranks the statistically significant schemes with least overlapping upper class limits that separate statistically acceptable clusters within each array. Each upper class limit in highly ranked cluster schemes is then validated using the Single Variable object classifier, and the resultant cluster series model of optimized “best cut top solution” upper bin limits is then coded into the user-defined size border file to operate the OMU classifier. This flexible CMEIAS design expands the user’s ability to recognize and classify the full range of statistically validated OMU diversity that is optimized for the specific community under investigation. Also supporting the classifiers are an improved set of easily recognized pseudocolor assignments for each morphotype in the rendered classification output image, and an Object Label edit tool that interactively reassigns objects to the best fit class when they are located at the real-world continuum between classification borders, creates new morphotype classes if necessary, and reconstructs new images of morphotype-specific populations derived from classified community images containing multiple morphotypes. This latter feature is helpful when building user-defined size border files.
A CMEIAS Data Toolpack Com-addin application is being built to compile, tabulate, analyze, graph and compute numerous ecological statistics on CMEIAS population and community analysis data within Microsoft Excel. The program concatenates CMEIAS object analysis and classification data extracted from multiple images within the same community dataset, and builds the properly formatted input tables of data for further statistical analyses. It then computes descriptive statistics, optimized bin limits for frequency distribution analyses, plots tolerance envelopes of confidence intervals to determine if the sample size (number of images and cell objects found within them) is sufficient to estimate diversity, constructs ranked and relative abundance plots of community structure, and computes / tabulates / plots numerous alpha and beta diversity indices and distance coefficients to compare the richness, dominance, evenness, diversity, and dis(similarity) of the analyzed microbial communities, as commonly done in studies of community ecology, including their stability, resilience and succession.
Recent collaborative microbial ecology studies using CMEIAS include analysis of the autecological biogeography and colonization behavior of rhizobial biofertilizer inoculant strains that significantly promote the growth and grain yield of rice (the world’s most important crop), seasonal succession and food-web dynamics of microbes in pristine Alpine lakes and epilithic microbial biofilm communities on streambed rocks in freshwater streams, discrimination of biofilm architecture / diversity / eco-physiology / spatial ecology of freshwater microbial community assemblages with emphasis on how the substratum physicochemistry impacts on biofilm development, in situ spatial scale of bacterial cell-to-cell communication (quorum sensing) at single-cell resolution, analysis of microbial cell-size distribution at various depths within soil aggregates, comparisons of microbial communities that develop on leaves of field-grown corn with or without the genetically engineered BT insecticide, and shifts in community structure of human vaginal microflora in health and bacterial vaginosis disease.
In summary, CMEIAS-based applications can help to fill major gaps in our understanding of microbial ecology by providing well-documented, accurate, robust and user-friendly computing tools that extract ecologically important, quantitative information from digital images of microbes, at multiple spatial scales relevant to their diversity, abundance, ecophysiology and in situ spatial distribution without the need for cultivation. The awesome computational power of CMEIAS used in conjunction with computer-assisted 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 KBS-LTER program focused on row crop agriculture and in other studies of microbial community analysis conducted in other LTER programs. For instance, we have initiated studies using computer-assisted microscopy and CMEIAS image analysis to examine diatom diversity and the epiphytic microbial communities that develop on dominant aquatic plants (lily pad, saw grass and periphyton) in the Florida everglades ecosystem as a link to the Florida Coastal Everglades LTER program hosted at Florida International University. Finally, the CMEIAS website (http://cme.msu.edu/cmeias/) includes a continuously updated webpage entitled “Publications using CMEIAS” containing hyperlinked entries that describe various aspects of CMEIAS software development and its worldwide use in research applications as background information for this extended summary.Back to meeting | Show |