Toward Automated Analysis of Biofilm Architecture: Bias Caused by Extraneous Confocal Laser Scanning Microscopy Images

Abstract

An increasing number of studies utilize confocal laser scanning microscopy (CLSM) for in situ visualization of biofilms and rely on the use of image analysis programs to extract quantitative descriptors of architecture. Recently, designed programs have begun incorporating procedures to automatically determine threshold values for three-dimensional CLSM image stacks. We have found that the automated threshold calculation is biased when a stack contains images lacking pixels of biological significance. Consequently, we have created the novel program Auto PHLIP-ML to resolve this bias by iteratively excluding extraneous images based on their area coverage of biomass. A procedure was developed to identify the optimal percent area coverage value used for extraneous image removal (PACVEIR). The optimal PACVEIR was defined to occur when the standard deviation of mean thickness, determined from replicate image stacks, was at a maximum, because it more accurately reflected inherent structural variation. Ten monoculture biofilms of either Ralstonia eutropha JMP228n::gfp or Acinetobacter sp. strain BD413 were tested to verify the routine. All biofilms exhibited an optimal PACVEIR between 0 and 1%. Prior to the exclusion of extraneous images, JMP228n::gfp appeared to develop more homogeneous biofilms than BD413. However, after the removal of extraneous images, JMP228n::gfp biofilms were found to form more heterogeneous biofilms. Similarly, JMP228n::gfp biofilms grown on glass surfaces vis-à-vis polyethylene membranes produced significantly different architectures after extraneous images had been removed but not when such images were included in threshold calculations. This study shows that the failure to remove extraneous images skewed a seemingly objective analysis of biofilm architecture and significantly altered statistically derived conclusions.

Extracellular Polymeric Substance Architecture Influences Natural Genetic Transformation of Acinetobacter baylyi in Biofilms

“…Semiautomated image analysis was performed utilizing the programs Auto PHLIP-ML (26) and PHLIP (27). Auto PHLIP-ML (available at http://sourceforge.net/projects/auto-phlip-ml/) calculates an Otsu threshold for image stacks not biased by extraneous images (images without pixels of biological significance).…”

“…Auto PHLIP-ML (available at http://sourceforge.net/projects/auto-phlip-ml/) calculates an Otsu threshold for image stacks not biased by extraneous images (images without pixels of biological significance). Extraneous images are identified and removed based on their area coverage of biomass as described by Merod et al (26). The percent area coverage value used for extraneous image removal (PACVEIR) identifying the substratum was set at 1%.…”

Identification and Characterization of an Acinetobacter baumannii Biofilm-Associated Protein

“…The slides were loaded onto the motorized preheated stage of the CLSM system, and image stacks were acquired every 60 min for 14 h from two nonoverlapping fields of view at ϫ400 magnification, covering a total slide surface area of 10 5 m 2 , in order to obtain a representative sample of the biofilm (21). The CLSM image stacks were manually edited to remove extraneous images (defined as any image including and below those containing reflections of the glass coverslip and any images including and above the first image to contain no bright pixels representing bacterial cells) to minimize bias during quantitative image analysis (29). The manually edited image stacks were analyzed by using the Image Structure Analyzer-3D program to calculate 20 parameters describing the three-dimensional biofilm structure (2), including biovolume and average thickness at each time point for each field of view.…”

Evidence for persisters in Staphylococcus epidermidis RP62a planktonic cultures and biofilms

“…Recently, however, the application of CLSM shed a new light on the study of biofilms, providing a more accurate depiction of the heterogeneous architecture of these bacterial populations (Donlan, 2002). CLSM allows for non-destructive, in situ, three-dimensional investigation of biofilms in their naturally hydrated state and for detecting fluorescence from specific constituents such as live/dead cells or extracellular polymeric substances (Merod et al, 2007).…”