Dazzo FB, Yanni Y, Liu J, Kwatra K, Jain A, Gross C, Philips N, Monosmith C, Klemmer K, Ji Z, Niccum B, Ganesan I, DeSilva N, McGarrell D, Folland I, Card K, Jha P, Lundback S, Tan W, Upadhye M, Stawkey D, Jones A, Gusfa D, Prater K, Bakir N, Sexton R, Shears M, Makhoul A, and Radosa K
Microbiology & Molecular Genetics
Presented at the All Scientist Meeting and Investigators Field Tour (2016-09-16 to 2016-09-17 )
CMEIAS v4.0: Advanced computational tools of bioimage informatics software designed to strengthen microscopy‐ based approaches for understanding microbial ecology
FB Dazzo, Y Yanni, J Liu, K Kwatra, A Jain, C Gross, N Philips, C Monosmith, K Klemmer, Z Ji, B Niccum, I Ganesan, N DeSilva, D McGarrell, I Folland, K Card, P Jha, S Lundback, W Tan, M Upadhye, D Stawkey, A Jones, D Gusfa, K Prater, N Bakir, R. Sexton, M. Shears, and A. Makhoul
Dept. of Microbiology & Molecular Genetics, Michigan State Univ., East Lansing MI 48824
A major challenge in microbial ecology is to develop reliable methods of computer-assisted microscopy that can process and analyze complex digital images of actively growing microbial populations and communities (including unculturable microbes) that reveal important insights on their phenotypic characterization without the need for laboratory cultivation, and at spatial scales enabling analysis of single cells and their interacting neighbors occupying local ecological niches. To address this challenge, our team of microbiologists, mathematicians and computer scientists has been developing and releasing a comprehensive suite of bioimage informatics software technologies that strengthen quantitative microscopy-based approaches to support microbial ecology research. The information gained by analysis of digital images of microbial populations and communities can bridge with other modern genotypic technologies to fill gaps of information on the in situ ecology of microbial assemblages. Our bioimage informatics software, called CMEIAS (Center for Microbial Ecology Image Analysis System) consists of new and improved computing tools for image acquisition, processing and segmentation, object analysis and classification, data processing, statistical analysis and exploratory data mining. This research offers unique opportunities for students to participate in creative and exploratory studies of scientific software development for quantitative computational biology with wide applications in microbial ecology. When finalized, the software tools and their comprehensive documentations are released as free downloads at our MSU CMEIAS website http://cme.msu.edu/cmeias/.
The first release version of CMEIAS (v. 1.28) operates within the UTHSCSA ImageTool v. 1.27 host in a PC running Windows 32-bit and 64-bit operating systems. It can analyze foreground objects in digital 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 96% of the genera described in Bergey’s Manual 9th ed. The CMEIAS Morphotype Classifier works in 14- dimensional space, measures the richness and abundance of each microbial morphotype present within images of dynamically growing communities at 97% accuracy, and can add up to 5 additional (rare) morphotypes if present in the community under investigation.
Image processing routines featured in the ImageTool host program 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 software suite includes 2 image-editing tools to facilitate the segmentation of objects in images prior to analysis. A stand-alone CMEIAS Color Segmentation software application has been developed, documented and released with improved technologies for accurate segmentation of foreground objects within complex RGB digital images where color differentiation really matters most, e.g., ecophysiological studies of organisms in situ. It uses a novel, color-based brightness threshold algorithm to interactively sample a training set of local color pixels representing the foreground objects of interest, then implements user-defined tolerance settings to identify each object’s boundary differentiated by its unique color range, and finally creates a new RGB segmented output image containing these color-classified foreground objects in a noise-free background. Its performance operates with ~99% accuracy when tested on complex color environmental images of microorganisms. The color segmentation software also features other image processing routines to fill vacant holes within objects, remove pixel noise at the object’s contour by dilate/erode, split images into their RGB / HIS / YUV color model channels, pseudocolor object features differing in brightness to assist in defining their contour (especially useful to eliminate object haloes in fluorescence microscopy), and set thresholds for object size and brightness filtering. Our second image processing tool (CMEIAS Object Separation) is a plugin that automatically splits touching objects (e.g., within microbial cell aggregates) in thresholded images. Its unique algorithm performs with 98+% accuracy and avoids deletion of foreground pixels that erroneously reduces the size of the segmented objects, unlike the watershed object separation tool commonly featured in other image processing software.
Several components of the next major CMEIAS upgrade (ver. 4.0) are at various stages of development. At its core is the CMEIAS upgrade of the UTHSCSA ImageTool host program with numerous improvements in user-friendly operation, enhanced interface design to set the preferences / open the images / select the measurement features for image analysis, 11 new toolbar shortcuts of commonly used image calibration / analysis / classification routines, a minimum – maximum object size filter, expanded size range of image zoom ratios and object annotations, upgrades of the 7 manual object analysis routines, and an expanded help menu providing quick access to several CMEIAS user support documents and demonstration tools.
Many new analytical features have been introduced into the CMEIAS dynamic library-linked (dll) extension plugins that operate within the CMEIAS-ImageTool ver. 4.0 upgrade. These plugin tools represent significant transformations providing breakthrough technologies including modules of image analysis with substantially increased discrimination of microbial (i) biodiversity based on the statistically significant heterogeneity of their morphological signatures defined at 0.2 m spatial resolution; (ii) abundance; (iii) ecophysiology, and (iv) in situ spatial ecology. We prepared interactive tutorial scripts to introduce the analytical features for each of these modules, plus several other software tools to support the processing and analysis of data outputs from them in the CMEIAS upgrade.
The upgraded Object Analysis plugin can extract up to 74 (41 are new) metrics that discriminate shape, size, luminosity and spatial features for each object. This plugin assists studies to reveal quantitative information on 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. This plugin is ideal for studies of microbial autecology when based on cell tracking methods using specific fluorescent molecular probes (e.g., immunofluorescence, FISH) or autofluorescent cell components (e.g., GFP, F420). Its abundance metrics can analyze object density and biomass intensity to examine community dominance / evenness / conditional rarity, seasonal productivity and food-web dynamics.
Notable among its measurement attributes are 8 biovolume formulas optimized to accurately measure cell body size for each microbial morphotype classified by CMEIAS. Its ecophysiology module employs user-specified variables to examine microbial surface area:volume adaptations, changes in their nutrient resource apportionment and utilization, a universal allometric scaling formula to compute their biomass C and biovolume-weighted metabolic rates, luminosity for in situ microdensitometry of biofilm surface texture, and color-differentiated changes in cell viability, intensity of gene expression, cell-cell communication and specific enzyme activities.
A new Cumulative Object Analysis plugin features 38 discriminating metrics of landscape ecology, global abundance, and spatial patterns for all foreground objects (e.g., single cells, microcolony biofilms) within the same image. Its metrics of global landscape ecology provide insights on the relationships among spatial patterns, nutrient apportionment, processes / scale
/complexity / connectivity of microcolony biofilm patches within the landscape domain. Examples of discriminating CMEIAS metrics for landscape ecology include % substratum coverage, areal and relative porosities, patch area distribution statistics, mean circular intensity, edge density, mean / maximum diffusion radii, and indices of patch length / shape / cohesion / connectivity. Its global abundance module enables measurement of cumulative microbial lengths providing insights on intensity of bacteriovory selection pressures and morphological stress responses, cumulative biovolume / biosurface area / biomass carbon / spatial density of foreground objects providing insights on their nutrient apportionment and productivity, and calculation of dilution-adjusted object concentrations in measured sample volumes for individual populations of morphotypes or the entire community.
The CMEIAS Spatial Analysis module can analyze microbial biogeography across multiple spatial scales, including attributes for plot-less point pattern, plot-based quadrat-lattice and geostatistical analyses. A major use of this module is to test the null hypothesis of spatial randomness for the 2-dimensional position of organisms within the landscape, from which the type and intensity of their colonization behavior can be deduced. When applied to images of microbial biofilms, these analyses can statistically test if their patterns of spatial distribution deviate from complete randomness, and if so (most often the case), provide statistically defendable predictions of their in situ cooperative vs. conflicting interactions within the landscape domain. Object analysis attributes can report the georeferenced 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 indicating the intensity to which each cell is clustered near its neighbors. Geostatistical analysis of these georeferenced data can 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 and angular preference in their
spatial autocorrelation, measure the range of effective separation distances that define the real- world spatial scale of influence that individual cells or microcolony patches exert in microbial cell-cell interactions and resource ecology, and produce statistically defendable pseudocolored, kriging maps of their heterogeneity in user-selected Z-variate intensities interpolated over the entire georeferenced landscape domain, even in areas not sampled. Its spatial 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 each quadrat’s X, Y centroid and its geometric centroid weighted by local object density.
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 CMEIAS script assists with replicated/iterated sampling of image stacks for point-pattern spatial analysis (distance from the image-center, object-weighted center, or random-center to nearest object). Also, a stand-alone CMEIAS Quadrat Maker software application has been developed and released to optimize the grid dimensions that divide landscape images of microbial biofilms into smaller, constant size contiguous quadrats for high- resolution spatial pattern analysis of their local density, then produces an optimized / indexed image with identification labels of each quadrat in the spatial domain, and finally transforms a
copy of the original landscape 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.
The upgraded CMEIAS Object Classification module now 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. Any one of 60 metrics can be utilized for this 1-dimensional object classifier. It also has an expanded range that now can include up to 20 upper class limits to define the decision boundaries separating each bin class. The unique Morphotype Classifier (mentioned earlier) remains functional in this plugin upgrade as a hierarchical tree classifier that works in 14-dimensional space and with an improved design of the reported output table. A new supervised hierarchical tree classifier has been introduced with ability to subclassify each morphotype population in the image into its Operational Morphological Units (OMU) based on a pre-established set of cell size features that define the morphological signature of each cell at 0.2 um resolution. Its rules of classification use a multilinear matrix of predefined upper bin limits optimized as the least- overlapping borders that separate bin clusters for each measurement attribute used in the classification scheme. These upper bin limits are coded into two (“default” and “user-defined”) size border files from which the user selects to operate the OMU classifier. 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 it 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.
The two other unique object multiclassifiers are designed to combine the awesome computing power of the CMEIAS object analysis plugin together with morphotype / OMU classification.
They report each individual organism’s morphology class together with any additional combination of 60+ discriminating characteristics of its shape, size, luminosity or point-pattern attributes to analyze community biodiversity, resilience, succession and spatial ecology. These data can then be evaluated by exploratory morphotype-weighted cluster analyses and data mining techniques to compute their statistically defined subclassification based on the (dis)similarities between microbial community structures. Various measures of ecophysiology (e.g., allometric scaling, metabolic activity, membrane integrity and viability, resource ecology, spatial positioning reflecting colonization behavior, nutrient uptake efficiency, predatory bacteriovory, dominance vs. conditional rarity, community succession and resilience following environmental perturbation, species-area and distance decay relationships) 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 and microbiomes in situ at single-cell resolution.
Several CMEIAS computing tools support its modules of object classification. A powerful Size Border Cluster Analysis Tool had been developed to assist in identifying the optimal design of pattern recognition rules for the OMU classifier. It uses a 1,000-iterated simulation to perform an exploratory, multilinear unsupervised cluster analysis of each data array of discriminating size/shape attributes, and then ranks the statistically significant schemes with least overlapping decision boundaries of upper class limits that separate statistically acceptable clusters within each array of metrics. Each upper class limit in highly ranked cluster schemes is then validated using the Single Variable object classifier (described earlier), 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 distinguished by their assigned pseudocolors. This latter feature is also helpful when building user-defined size border files.
A powerful CMEIAS JFrad software program has been developed, documented and released to discriminate complex biofilm architectures based on the uniqueness of their self-similar fractal geometry. It does so by computing 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. This featured level of fractal analysis provides sufficient opportunities for statistical data mining, especially when the most discriminating method of fractal analysis (among many choices) is not known beforehand. The CMEIAS JFrad 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 maximize and compete for their allocation of nutrient resources on a local scale.
A CMEIAS Data Preparation program has been developed to concatenate 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 analyses in other ecological statistics programs. A CMEIAS Data Toolpack com-addin application is being built to perform numerous ecological statistics on CMEIAS population and community analysis data within Microsoft Excel. It computes descriptive statistics, optimizes bin widths 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.
Recent collaborative microbial ecology studies using CMEIAS include analysis of the autecological biogeography and intensities of colonization behavior of rhizobial biofertilizer inoculant strains that significantly promote the growth and grain yield of rice (the world’s most important crop), how substratum physicochemistry impacts on freshwater biofilm architectures, development / diversity / ecophysiology / spatial ecology of microbial communities developed on field-grown corn leaves 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 of bioimage informatics 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 software technologies for computer-assisted microscopy at single-cell resolution adds an exciting new dimension to microbial community analysis, and it is especially valuable when bridged to other methods of polyphasic analysis. We maintain a CMEIAS project website (http://cme.msu.edu/cmeias/) that provides access to freely-available copyrighted programs, support documents including refereed journal publications, thoroughly illustrated user manuals, help topics search files, audio-visual demos, interactive training tutorials with accompanying test images, a periodically updated webpage entitled “Publications using CMEIAS” with hyperlinked entries describing CMEIAS and its worldwide use in research applications, and contact information.Get poster
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