Accuracy of Biovolume Formulas for CMEIAS Computer-Assisted Microscopy and Body Size Analysis of Morphologically Diverse Microbial Populations and Communities

Abstract

Cell biovolume is a commonly used metric of microbial abundance analyzed by computer-assisted microscopy, but the accuracies of most biovolume formulas have not been validated by ground truth data. We examined the accuracy of 17 biovolume formulas by comparing the computed volumes of 3D models representing 11 microbial morphotypes (cocci, spirals, curved rods, U-shaped rods, regular straight rods, unbranched filaments, ellipsoids, clubs, prosthecates, rudimentary branched rods, and branched filaments) to the volume displacement of the same objects as ground truth. As anticipated, formula accuracy was significantly influenced by the morphotype examined. A few formulas performed very accurately (> 95 %), especially those that adapted to the cell's shape, whereas others were consistently inaccurate or only accurate for one or two morphotypes. As an example of application, indices of morphological diversity in a freshwater biofilm assemblage were shown to be significantly different when microbial abundance among morphotype classes was measured as biovolume body mass rather than cell counts. Spatial analysis of biovolume body mass can also provide insights on the in situ ecophysiological attributes among individuals in microbial populations and communities, including their spatially autocorrelated allometric scaling interrelationships between body size, metabolic activity, resource apportionment and use, food web dynamics, and various cell-cell interactions affecting their growth and colonization behavior within spatially structured biofilm landscapes. This improved computing technology of biovolume algorithms with proven accuracy identifies which formula(s) should be used to compute microbial biovolumes in 2D images of morphologically diverse communities acquired by conventional phase-contrast light microscopy at single-cell resolution.

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 Simpson diversity index is more influenced by the most abundant class and therefore is higher than the Shannon diversity index [4,30]. Biovolume-weighted measures of abundance result in higher evenness indices since few larger morphotypes will counterbalance smaller, more numerically abundant morphotypes [4,15,30]. Conversely, the Berger-Parker Dominance index is greater when individual counts are used to measure abundance because it reflects the proportional importance of the most abundant classes.…”

“…The results clearly indicated the strong influence that morphology had on accurate computation of each object's volume. A common feature shared by the two most accurate algorithms (shown below) is their use of computer vision to intelligently adapt the biovolume formula to the classification of the object's shape in computing their cell lengths and widths [15]. When applied to properly segmented images, these two most-accurate biovolume algorithms performed with >98% accuracy for all morphotype populations and >96% accuracy for communities containing various combinations of these same morphotype populations [15].…”

“…A common feature shared by the two most accurate algorithms (shown below) is their use of computer vision to intelligently adapt the biovolume formula to the classification of the object's shape in computing their cell lengths and widths [15]. When applied to properly segmented images, these two most-accurate biovolume algorithms performed with >98% accuracy for all morphotype populations and >96% accuracy for communities containing various combinations of these same morphotype populations [15]. This shape-adaptive strategy ultimately improves their accuracy to compute biovolumes for the diverse range of cell morphotypes in actively growing microbial communities that have high morphological diversity, as commonly occurs in nutritionally enriched habitats that support microbial growth.…”

“…Recently, we evaluated the accuracy of 17 different formulas to compute the biovolume of 11 different microbial morphotypes that are accurately classified by CMEIAS, including all of the major and most of the minor morphotypes that develop in natural and managed microbial communities [15]. Object biovolumes computed by 2-D digital image analysis using each formula were compared to ground truth data acquired by volume displacement measurements of 3-D model objects representing each morphotype.…”

“…In addition to its importance as a quantitative metric of abundance among morphotype classes, cell biovolume body size can provide insights on several other relevant ecophysiological functions and activities that drive community biodiversity [4,15,30,33,34,37,47]. First, it ranks individuals within the very wide range of body size (sometimes by several orders of magnitude) for the microbial component in ecosystems.…”