We have previously presented our work in developing and applying a commercial digital holographic microscopy (DHM) system for volumetric, 3D characterization of bacterial motility. The system was applied to simple biological systems, i.e., single bacterial species, to demonstrate its effectiveness. We are now applying DHM to more realistic conditions, including multiple bacterial types, to differentiate the species of interest for investigating their interactions. Our workflow for species classification and motility characterization combines DHM and machine learning. Specifically, our DHM instrument acquires holograms of single bacterial species and mixtures of different species, the software extracts in-focus images of individual bacteria and their trajectories, and deep convolutional neural network models are constructed and trained using the in-focus images and then deployed to the mixture data for classification. The motility and morphology of the predicted species in the mixture is consistent with the measurements from isolates, verifying the effectiveness of the developed workflow. This work showcases the application of DHM to investigate complex biological systems.
KEYWORDS: Particles, Bacteria, Digital holography, Microscopy, Holography, 3D image processing, GPU based image processing, Detection and tracking algorithms, Software development
Digital holographic microscopy (DHM) has extensive applications in measuring the movement/dynamics of various particles (e.g., tracer particles and biological samples). Specifically, inline DHM has been used for different types of bacteria, but few have explored its application to soil bacteria, whose participation in metabolic processes and interaction with plant tissues affect plant growth in the rhizosphere. In the present study, we have developed a DHM instrument and GPU-enabled hologram processing software to retrieve three-dimensional (3D) motility data of free-swimming Azospirillum brasilense, a model soil bacterium. The dimensions of the sample volume are 0.69 mm × 0.59 mm in the in-plane directions and 1.00 mm in the depth direction, respectively. With a 20X magnification and a 12 frames per second imaging rate, the moving bacteria are spatiotemporally resolved. Firstly, the raw holograms are preprocessed to enhance the interference fringes of the bacteria. Subsequently, by volumetric reconstruction, thresholding, and segmentation, the 3D coordinates of each bacterium recorded in the hologram are extracted. Finally, the coordinates determined in sequential holograms are linked using particle tracking to form the trajectories. The characteristics extracted from the 3D trajectories quantitatively revealed the differences in the motility patterns of the wildtype and mutant strains of the bacteria, providing unique insights on the dominant motility patterns, which are otherwise unavailable from conventional microscopy. Therefore, we have demonstrated the capabilities of our DHM system as a powerful tool for studying the chemotaxis of soil bacteria involved in the interaction with plant roots.
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