Non-invasive single-cell morphometry in living bacterial biofilms

TLDR

Fluorescence microscopy can measure live cells and communities, yet its full potential for tracking individual cell behavior and phenotypic changes in dense, three‑dimensional bacterial biofilms remains unrealized because of limited resolution and low signal‑to‑background ratios. This study introduces Bacterial Cell Morphometry 3D (BCM3D), an image‑analysis workflow that merges deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3‑D fluorescence images. BCM3D trains deep convolutional neural networks on simulated biofilm images that incorporate realistic signal‑to‑background ratios, cell densities, labeling methods, and cell shapes, and it systematically evaluates segmentation accuracy on both simulated and experimental data. Compared with existing bacterial cell segmentation methods, BCM3D consistently delivers higher accuracy and enables automated morphometric classification of cells in multi‑population biofilms.

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.