MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis

Abstract

Single-cell analysis of bacteria and subcellular protein localization dynamics has shown that bacteria have elaborate life cycles, cytoskeletal protein networks and complex signal transduction pathways driven by localized proteins. The volume of multidimensional images generated in such experiments and the computation time required to detect, associate and track cells and subcellular features pose considerable challenges, especially for high-throughput experiments. There is therefore a need for a versatile, computationally efficient image analysis tool capable of extracting the desired relationships from images in a meaningful and unbiased way. Here, we present MicrobeJ, a plug-in for the open-source platform ImageJ(1). MicrobeJ provides a comprehensive framework to process images derived from a wide variety of microscopy experiments with special emphasis on large image sets. It performs the most common intensity and morphology measurements as well as customized detection of poles, septa, fluorescent foci and organelles, determines their subcellular localization with subpixel resolution, and tracks them over time. Because a dynamic link is maintained between the images, measurements and all data representations derived from them, the editor and suite of advanced data presentation tools facilitates the image analysis process and provides a robust way to verify the accuracy and veracity of the data.

Figure 1. The MicrobeJ GUI and workflow

(a) The MicrobeJ GUI: This interface contains several tabs that allow users to set up an experiment (Experiment), select or load a set of images (Images), define parameters for bacterial cell detection and analysis (Bacteria), define parameters for fluorescent foci detection and analysis (Maxima), define and use templates (Templates), and track any errors that might arise during analysis (Log). (b) To start, the user can simply drag and drop an image file or stack from a disk onto the MicrobeJ GUI (1), here a stack of images with C. crescentus cells expressing the polarly localized PleC-GFP. Once the images are loaded and selected, the user can test the bacterial cell detection settings. At any time, the user can also select dedicated options such as cell segmentation (Segmentation), fluorescence intensity measurements (Intensity), cellular type detection (Type), features detection (Feature), (Branching), assign poles polarity based on any particle’s property (Polarity), measure number of particles within a defined distance of cell (Contact), or display cell profile along chosen axis (Profile) (2). To detect fluorescent foci, the user can test the maxima detection settings and further refine using another set of dedicated exclusion filters. The user can also select dedicated options such as the sub-pixel resolution (Gaussian Fit, not shown) or fluorescence intensity measurement (Intensity, not shown) (3). When both bacterial cells and fluorescent foci are detected, the user can associate them hierarchically using the association panel (4). At that point the user can either manually add, edit or delete any particles detected or missed during the detection process, as in this example of a cell contour being re-adjusted using the active handles distributed along the polygon (5), or run the analysis on the stack of images (6) and obtain the raw data (see Supplementary Fig. 4).

Figure 2. MicrobeJ can detect various cell morphologies

(a) Representative phase contrast images of (1) C. crescentus cells, (2) Streptomyces venezuelae cells, (3) Streptococcus pneumoniae cells, (4) Rhodopseudomonas palustris cells, (5) Agrobacterium tumefaciens branched mutant cells, (6) Bacillus subtilis cells, (7) Cephalexin-treated C. crescentus cells, (8) Asticcacaulis biprosthecum cells, (9) 2 week old, viable, late stationary phase-adapted C. crescentus cells, (10) Vegetative S. venezuelae cells, (11) long-stalked morphotype Prosthecomicrobium hirshii cells, and (12) Rhodomicrobium sp. cells. The bacterial cell contours (green) and their corresponding medial axes (orange) are shown. A. biprosthecum stalks, detected and associated with the cell using the filament detection option, are shown in blue (8). Scale bar = 1µm. (b–d) In this experiment, C. crescentus CB15 cells (red contours), C. crescentus cells expressing a cytoplasmic GFP (mini-Tn7(Gm)gfp) (cyan contours), B. subtilis vegetative cells (magenta contours), B. subtilis spores (yellow contours), Lactococcus lactis cells (orange contours), and Escherichia coli MG1655 cells (green contours) were mixed together and imaged on an agarose pad. Cellular types were defined using the morphological and signal properties based on the specified criteria (c). The relative abundance of each type is shown (d).

Figure 3. Detection and quantification of the localization of FtsZ-YFP and the constriction site during the cell cycle of C. crescentus

(a–d) All the representations depicted here are created from within the Results Interface of MicrobeJ. (a) Sequence of representative phase contrast and fluorescent overlay images showing several round of division from a single C. crescentus cell expressing FtsZ-YFP. Time points are indicated for each time frame (Scale bar = 1 µm). The bacterial cell contours (green), their corresponding medial axes (orange), the position of fluorescent septum (red) and the position of the constriction (orange) are shown. Septa were detected using the intensity profile of pixels from the fluorescent image along the medial axis of the particle. Constrictions were detected using the width profile measured along the medial axis of the particle. The orientation of the medial axis of each cell, marked by the position of the pole polygon (cyan), is determined based on the position of the constriction relative to the cell center. (b) Distribution of the time delay between the first appearances of the fluorescence septum (red), the constriction (orange), and cell division (n=352). (c) Demographic representation of the FtsZ-YFP fluorescence intensities and the cellular widths measured along the medial axis of the cells. Cells are sorted according to their length. The Y-axis of each demograph represents the relative position along the cell body, where 0 represents mid-cell and 1 or −1 the cell poles. The 1 pole is the pole marked by the pole polygon shown in (a). (d) The distribution of the localization of the septa (red) and the constriction (orange) relative to the cell center (n=1521).

Figure 4. The automated and manual segmentation processes

(a–d) Sequence of representative phase contrast images showing several rounds of division from a group of C. crescentus cells harboring division defects. Time points are indicated for each time frame (Scale bar = 1 µm). (b–d) The particle contours computed by MicrobeJ at different steps of the segmentation process: before the automated segmentation process (b), after the automated segmentation process (c), and after the manual correction (d). Rejected particles are shown in red, while accepted particles are shown in green. (c) For clarity, the phase contrast images and their respective particle contours that did not require manual correction, were hidden. The red arrows highlight the cell that needed manual segmentation. (d) The green arrows highlight the result of the manual segmentation. (e) A montage of straightened phase contrast images (left panel) and fluorescence overlays showing the dynamic localization of FtsZ-YFP in a filamentous cell over time. Pictures were taken every 5 min. Black arrows highlight the two constrictions where FtsZ-YFP localizes alternatively until the cell divides.