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Organisms display an immense variety of shapes, sizes, and reproductive strategies. At microscopic scales, bacterial cell morphology and growth dynamics are adaptive traits that influence the spatial organization of microbial communities. In one such community—the human dental plaque biofilm—a network of filamentousCorynebacterium matruchotiicells forms the core of bacterial consortia known as hedgehogs, but the processes that generate these structures are unclear. Here, using live-cell time-lapse microscopy and fluorescent D-amino acids to track peptidoglycan biosynthesis, we report an extraordinary example of simultaneous multiple division within the domainBacteria. We show thatC. matruchotiicells elongate at one pole through tip extension, similar to the growth strategy of soil-dwellingStreptomycesbacteria. Filaments elongate rapidly, at rates more than five times greater than other closely related bacterial species. Following elongation, many septa form simultaneously, and each cell divides into 3 to 14 daughter cells, depending on the length of the mother filament. The daughter cells then nucleate outgrowth of new thinner vegetative filaments, generating the classic “whip handle” morphology of this taxon. Our results expand the known diversity of bacterial cell cycles and help explain how this filamentous bacterium can compete for space, access nutrients, and form important interspecies interactions within dental plaque. 

Abstract MotivationSpectral unmixing methods attempt to determine the concentrations of different fluorophores present at each pixel location in an image by analyzing a set of measured emission spectra. Unmixing algorithms have shown great promise for applications where samples contain many fluorescent labels; however, existing methods perform poorly when confronted with autofluorescence-contaminated images. ResultsWe propose an unmixing algorithm designed to separate fluorophores with overlapping emission spectra from contamination by autofluorescence and background fluorescence. First, we formally define a generalization of the linear mixing model, called the affine mixture model (AMM), that specifically accounts for background fluorescence. Second, we use the AMM to derive an affine nonnegative matrix factorization method for estimating fluorophore endmember spectra from reference images. Lastly, we propose a semi-blind sparse affine spectral unmixing (SSASU) algorithm that uses knowledge of the estimated endmembers to learn the autofluorescence and background fluorescence spectra on a per-image basis. When unmixing real-world spectral images contaminated by autofluorescence, SSASU greatly improved proportion indeterminacy as compared to existing methods for a given relative reconstruction error. Availability and implementationThe source code used for this paper was written in Julia and is available with the test data at https://github.com/brossetti/ssasu.