Non-invasive single-cell morphometry in living bacterial biofilms.
Non-invasive single-cell morphometry in living bacterial biofilms.
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DOI：
10.1038/s41467-020-19866-8
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发表时间：
2020-12-01
影响因子：
16.6
通讯作者：
Gahlmann A
中科院分区：
文献类型：
--
作者：
Zhang M;Zhang J;Wang Y;Wang J;Achimovich AM;Acton ST;Gahlmann A
荧光显微镜能够对活细胞和细胞群落进行空间和时间测量。然而，这一潜力尚未完全实现的调查个人的细胞行为和表型变化的致密，三维（3D）细菌生物膜。由于荧光显微镜图像中有限的分辨率和低的信号与背景比（SBR），在密集堆积的生物膜中精确的细胞检测和细胞形状测量是具有挑战性的。在这项工作中，我们提出了细菌细胞形态测量3D（BCM3D），这是一种图像分析工作流程，将深度学习与数学图像分析相结合，以准确地分割和分类3D荧光图像中的单个细菌细胞。在BCM3D中，使用模拟生物膜图像训练深度卷积神经网络（CNN），这些图像具有实验上真实的SBR，细胞密度，标记方法和细胞形状。我们使用模拟和实验图像系统地评估了BCM3D的分割精度。与最先进的细菌细胞分割方法相比，BCM3D始终实现了更高的分割精度，并进一步实现了多种群生物膜中的自动形态测量细胞分类。在密集的细菌生物膜中准确检测细胞是具有挑战性的。在这里，作者报告了一种能够在3D荧光图像中准确分割和分类单个细菌细胞的图像分析管道：细菌细胞形态测定3D（BCM3D）。
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. Accurate cell detection in dense bacterial biofilms is challenging. Here, the authors report an image analysis pipeline that is able to accurately segment and classify single bacterial cells in 3D fluorescence images: Bacterial Cell Morphometry 3D (BCM3D).