In Situ Ecophysiology of Microbial Biofilm Communities Analyzed by CMEIAS Computer-Assisted Microscopy at Single-Cell Resolution

Abstract

This paper describes the utility of CMEIAS (Center for Microbial Ecology Image Analysis System) computer-assisted microscopy to extract data from accurately segmented images that provide 63 different insights into the ecophysiology of microbial populations and communities within biofilms and other habitats. Topics include quantitative assessments of: (i) morphological diversity as an indicator of impacts that substratum physicochemistries have on biofilm community structure and dominance-rarity relationships among populations; (ii) morphotype-specific distributions of biovolume body size that relate microbial allometric scaling, metabolic activity and growth physiology; (iii) fractal geometry of optimal cellular positioning for efficient utilization of allocated nutrient resources; (iv) morphotype-specific stress responses to starvation, environmental disturbance and bacteriovory predation; (v) patterns of spatial distribution indicating positive and negative cell-cell interactions affecting their colonization behavior; and (vi) significant methodological improvements to increase the accuracy of color-discriminated ecophysiology, e.g., differentiation of cell viability based on cell membrane integrity, cellular respiratory activity, phylogenetically differentiated substrate utilization, and N-acyl homoserine lactone-mediated cell-cell communication by bacteria while colonizing plant roots. The intensity of these ecophysiological attributes commonly varies at the individual cell level, emphasizing the importance of analyzing them at single-cell resolution and the proper spatial scale at which they occur in situ.

Use of CMEIAS Image Analysis Software to Accurately Compute Attributes of Cell Size, Morphology, Spatial Aggregation and Color Segmentation that Signify in Situ Ecophysiological Adaptations in Microbial Biofilm Communities

“…The extracted data can be analyzed by geospatial statistics to define the biogeography of microbial cells during biofilm development, and to generate predictive models of their colonization behavior that define the intensity of cell-cell interactions and the spatial scales of their niche [4]. Geostatistical analysis requires that a relevant Z-variate be assigned to each georeferenced site sampled.…”

“…Geostatistical analysis requires that a relevant Z-variate be assigned to each georeferenced site sampled. This parameter is a non-binary, quantitative attribute that is continuously distributed in the landscape, and the location of each sampling point (e.g., individual microbe) must be indicated at defined X, Y Cartesian coordinates relative to the (0, 0) landmark origin within the same domain [4,20]. We created the CMEIAS cluster index as the major Z-variate for geospatial analyses of microbial biofilm colonization behavior [21].…”

“…We created the CMEIAS cluster index as the major Z-variate for geospatial analyses of microbial biofilm colonization behavior [21]. This unique metric is computed as the inverse of the first nearest neighbor distance [21], and the spatial scale of its ecological influence typically lies in the micrometer range [4]. The magnitude of this index indicates the strength of each microbe's clustered distribution relative to other microbes in its local environment [4] and is a sensor of positive, spatially autocorrelated cell interactions that cooperatively promote bacterial colonization behavior in situ [4,21,22].…”

“…This unique metric is computed as the inverse of the first nearest neighbor distance [21], and the spatial scale of its ecological influence typically lies in the micrometer range [4]. The magnitude of this index indicates the strength of each microbe's clustered distribution relative to other microbes in its local environment [4] and is a sensor of positive, spatially autocorrelated cell interactions that cooperatively promote bacterial colonization behavior in situ [4,21,22]. A high cluster index is assigned to cells in aggregated distributions that assist in positive cell-to-cell interactions, enabling promotion of their growth into microcolony biofilms [4,21,22].…”

“…Its value is increased when combined with computer-assisted digital image analysis [1,2] to quantify the abundance, ecophysiology, morphotaxa diversity, and spatial distribution of microbes in relation to their interacting neighbors and local environments. These tools can enhance the polyphasic taxonomy approach to analyze complex microbial communities by providing direct visual feedback while minimizing the quantification biases associated with other community analysis methods [1,3,4].…”