Non-invasive single-cell morphometry in living bacterial biofilms

Mingxing Zhang, Ji Zhang, Yibo Wang, Jie Wang, Alecia M. Achimovich, Scott T. Acton and Andreas Gahlmann ()
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Mingxing Zhang: University of Virginia
Ji Zhang: University of Virginia
Yibo Wang: University of Virginia
Jie Wang: University of Virginia
Alecia M. Achimovich: University of Virginia School of Medicine
Scott T. Acton: University of Virginia
Andreas Gahlmann: University of Virginia

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract Fluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. Accurate cell detection and cellular shape measurement in densely packed biofilms are challenging because of the limited resolution and low signal to background ratios (SBRs) in fluorescence microscopy images. In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images. In BCM3D, deep convolutional neural networks (CNNs) are trained using simulated biofilm images with experimentally realistic SBRs, cell densities, labeling methods, and cell shapes. We systematically evaluate the segmentation accuracy of BCM3D using both simulated and experimental images. Compared to state-of-the-art bacterial cell segmentation approaches, BCM3D consistently achieves higher segmentation accuracy and further enables automated morphometric cell classifications in multi-population biofilms.

Date: 2020
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DOI: 10.1038/s41467-020-19866-8

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