Non-invasive single-cell morphometry in living bacterial biofilms.
Nat Commun
; 11(1): 6151, 2020 12 01.
Article
in En
| MEDLINE
| ID: mdl-33262347
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Bacteria
/
Biofilms
/
Imaging, Three-Dimensional
/
Microscopy, Fluorescence
Type of study:
Evaluation_studies
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2020
Type:
Article
Affiliation country:
United States