Enhanced analysis of bacteria susceptibility in connected biofilms

A common method for visualizing bacterial biofilms is through confocal laser scanning microscopy images. Current software packages separate connected-biofilm bacteria from unconnected bacteria, such as planktonic or dispersed bacteria, but do not save both image sequences, making interpretation of the two bacterial populations difficult. Thus we report the development of an algorithm to save separate image sequences and enable qualitative and quantitative evaluation of each bacterial population. To improve bacterial viability assessment using a membrane integrity dye, a colocalization algorithm was also developed. This assigns colocalized pixels to the dead bacteria population, rather than to both the live and dead bacteria groups. Visually, this makes it clearer to distinguish a green live bacteria pixel from a yellow colocalized dead bacteria pixel. This algorithm also aids in the quantification of viability for connected-biofilm bacteria and unconnected bacteria to investigate susceptibility of each population to antimicrobials. The utility of these algorithms was demonstrated with Pseudomonas aeruginosa biofilms treated with ciprofloxacin hydrochloride. Results from this study indicate that quantification with colocalization adjustment can prevent underestimation of dead bacteria. These improvements in image processing will enable researchers to visually differentiate connected-biofilm and unconnected bacteria in a single image and to quantify these populations independently for viability without double counting the colocalized image pixels.