ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data

Abstract

Single-molecule fluorescence microscopy studies of bacteria provide unique insights into the mechanisms of cellular processes and protein machineries in ways that are unrivalled by any other technique. With the cost of microscopes dropping and the availability of fully automated microscopes, the volume of microscopy data produced has increased tremendously. These developments have moved the bottleneck of throughput from image acquisition and sample preparation to data analysis. Furthermore, requirements for analysis procedures have become more stringent given the requirement of various journals to make data and analysis procedures available. To address this we have developed a new data analysis package for analysis of fluorescence microscopy data of rod-like cells. Our software ColiCoords structures microscopy data at the single-cell level and implements a coordinate system describing each cell. This allows for the transformation of Cartesian coordinates of both cellular images (e.g. from transmission light or fluorescence microscopy) and single-molecule localization microscopy (SMLM) data to cellular coordinates. Using this transformation, many cells can be combined to increase the statistical significance of fluorescence microscopy datasets of any kind. Coli-Coords is open source, implemented in the programming language Python, and is extensively documented. This allows for modifications for specific needs or to inspect and publish data analysis procedures. By providing a format that allows for easy sharing of code and associated data, we intend to promote open and reproducible research. The source code and documentation can be found via the project's GitHub page.

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

“…Cell segmentation is a complex problem that extends beyond microbiological research; thus, many solutions are currently available in image-analysis programs 8,9,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] . Most of these solutions use traditional image-processing techniques such as intensity thresholding to segment isolated cells; however, this approach performs poorly on cells in close contact and it requires image-by-image tuning to optimize parameters.…”

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

“…Cell segmentation is a complex problem that extends beyond microbiological research, thus many solutions are currently available in image analysis programs (8,9,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27). Most of these solutions use traditional image processing techniques such as the application of an intensity threshold to segment isolated cells; however, this approach does not perform well for cells in close contact and it requires extensive parameter-tuning in order to optimize for a given cell type.…”

SyMBac: Synthetic Micrographs for Accurate Segmentation of Bacterial Cells using Deep Neural Networks

“…It must be noted that we do not perform any post-processing on the training data or the segmentation results which would affect the shape of the mask. After generation of the masks, information on cell curvature, radius (width) and length are calculated using the Colicoords Python library [35] if further analysis is desired.…”

“…If membrane dyes are the only way to measure width due to experimental constraints (e.g the lack of a phase contrast objective), then packages such as ColiCoords [2] are recommended, as they can mitigate the error incurred by this method by allowing the user to choose the method of perimeter estimation.…”