Biofilm viability checker: An open-source tool for automated biofilm viability analysis from confocal microscopy images

Abstract

Quantifying biofilm formation on surfaces is challenging because traditional microbiological methods, such as total colony-forming units (CFUs), often rely on manual counting. These are laborious, resource intensive techniques, more susceptible to human error. Confocal laser scanning microscopy (CLSM) is a high-resolution technique that allows 3D visualisation of biofilm architecture. In combination with a live/dead stain, it can be used to quantify biofilm viability on both transparent and opaque surfaces. However, there is little consensus on the appropriate methodology to apply in confocal micrograph processing. In this study, we report the development of an image analysis approach to repeatably quantify biofilm viability and surface coverage. We also demonstrate its use for a range of bacterial species and translational applications. This protocol has been created with ease of use and accessibility in mind, to enable researchers who do not specialise in computational techniques to be confident in applying these methods to analyse biofilm micrographs. Furthermore, the simplicity of the method enables the user to adapt it for their bespoke needs. Validation experiments demonstrate the automated analysis is robust and accurate across a range of bacterial species and an improvement on traditional microbiological analysis. Furthermore, application to translational case studies show the automated method is a reliable measurement of biomass and cell viability. This approach will ensure image analysis is an accessible option for those in the microbiology and biomaterials field, improve current detection approaches and ultimately support the development of novel strategies for preventing biofilm formation by ensuring comparability across studies.

Evaluation of the biofilm life cycle between Candida albicans and Candida tropicalis

“…Ltda., Quito, Ecuador) was used to fix the sample in place. The samples were stored protected from light at room temperature (25°C) until epifluorescence microscopy was performed within the first hour ( Rosenberg et al., 2019 ; Mountcastle et al., 2021 ).…”

“…These results were expressed as the number of cells ± standard deviation per square centimeter ( N cells/cm 2 ± SD) by dividing the previous total number of cells and their deviation over the glass surface area in square centimeter (4.84 cm 2 ). In EM, the percentages of dead and alive cells within images were measured through ImageJ by Fiji ( Schindelin et al., 2012 ) (version 1.57) using the macros Biofilms Viability checker proposed by Mountcastle et al. (2021) and the plugin MorphoLibJ ( Legland et al., 2016 ), while the total cell counting in DAPI images was processed by a sequence of modules forming a pipeline designed for this purpose in Cell Profiler software ( Mcquin et al., 2018 ), an open-source software, version 4.2.1 (available from the Broad Institute at ), the applied pipeline of which can be reviewed in the Supplementary Material .…”

DNase inhibits early biofilm formation in Pseudomonas aeruginosa- or Staphylococcus aureus-induced empyema models

“…The surface morphology and three-dimensional structure of the biofilms could be observed using CLSM ( Luo et al., 2018 ), and CLSM coupled with live/dead fluorescent staining is a powerful tool for the analysis of biofilm structure ( Lacotte et al., 2022 ). The Fiji macro analysis method was used in ImageJ to calculate CLSM bacterial viability, which is an automated image analysis technique that has shown reliable measurements of biomass and cell viability ( Mountcastle et al., 2021 ). Many bespoke software, such as Imaris ( Chávez de Paz et al., 2008 ), COMSTAT ( Yin et al., 2022 ), PHLIP ( Lukas et al., 2006 ), and BiofilmQ ( Hartmann et al., 2021 ), have been used to quantitatively analyze confocal images.…”

“…The images were captured and processed using Leica LAS X software, Version core 3.5.7 (Leica, Wetzlar, Germany). Biofilm viability was evaluated using the Fiji macro (ImageJ) method as previously described ( Mountcastle et al., 2021 ). Three points were randomly selected for the examination of every sample, and biomass and average thickness were assessed using COMSTAT2.1 software.…”

Isolation and identification of Biofilm-producing E. coli from drinking water.

“…Each sample containing dislodged bacteria was serially diluted ten times. To calculate the accurate count of the biofilm population, a 10 ml inoculum was seeded 15,16 .…”