An Automated Image Analysis Method for Segmenting Fluorescent Bacteria in Three Dimensions.

Single-cell fluorescence imaging is a powerful technique for studying inherently heterogeneous biological processes. To correlate a genotype or phenotype to a specific cell, images containing a population of cells must first be properly segmented. However, a proper segmentation with minimal user input becomes challenging when cells are clustered or overlapping in three dimensions. We introduce a new analysis package, Seg-3D, for the segmentation of bacterial cells in three-dimensional (3D) images, based on local thresholding, shape analysis, concavity-based cluster splitting, and morphology-based 3D reconstruction. The reconstructed cell volumes allow us to directly quantify the fluorescent signals from biomolecules of interest within individual cells. We demonstrate the application of this analysis package in 3D segmentation of individual bacterial pathogens invading host cells. We believe Seg-3D can be an efficient and simple program that can be used to analyze a wide variety of single-cell images, especially for biological systems involving random 3D orientation and clustering behavior, such as bacterial infection or colonization.