Exploiting geometric similarity for statistical quantification of fluorescence spatial patterns in bacterial colonies

Background Currently the combination of molecular tools, imaging techniques and analysis software offer the possibility of studying gene activity through the use of fluorescent reporters and infer its distribution within complex biological three-dimensional structures. For example, the use of Confocal Scanning Laser Microscopy (CSLM) is a regularly-used approach to visually inspect the spatial distribution of a fluorescent signal. Although a plethora of generalist imaging software is available to analyze experimental pictures, the development of tailor-made software for every specific problem is still the most straightforward approach to perform the best possible image analysis. In this manuscript, we focused on developing a simple methodology to satisfy one particular need: automated processing and analysis of CSLM image stacks to generate 3D fluorescence profiles showing the average distribution detected in bacterial colonies grown in different experimental conditions for comparison purposes. Results The presented method processes batches of CSLM stacks containing three-dimensional images of an arbitrary number of colonies. Quasi-circular colonies are identified, filtered and projected onto a normalized orthogonal coordinate system, where a numerical interpolation is performed to obtain fluorescence values within a spatially fixed grid. A statistically representative three-dimensional fluorescent pattern is then generated from this data, allowing for standardized fluorescence analysis regardless of variability in colony size. The proposed methodology was evaluated by analyzing fluorescence from GFP expression subject to regulation by a stress-inducible promoter. Conclusions This method provides a statistically reliable spatial distribution profile of fluorescence detected in analyzed samples, helping the researcher to establish general correlations between gene expression and spatial allocation under differential experimental regimes. The described methodology was coded into a MATLAB script and shared under an open source license to make it accessible to the whole community.