Cytoaura vision: a quantitative imaging platform for real-time assessment of bacterial turbidity and culture dynamics.

Abstract

Bacterial growth quantification plays a critical role in antimicrobial susceptibility testing, environmental microbial monitoring, and antimicrobial drug discovery. Here, we describe Cytoaura Vision, a cost-effective label-free imaging platform for quantitative and spatial analysis of bacterial growth in standard 96-well microtiter plates. The system comprises controlled illumination, high-resolution imaging, computational image correction, and automated pixel-intensity analysis. The validation on serial bacterial dilution (P. aeruginosa PAO1) revealed that the image-derived pixel intensity increased in a concentration-dependent manner, supporting the use of the imaging signal as a quantitative readout of the growth-associated turbidity. Time-resolved imaging captured the growth trajectories over 0-270 min, while heatmap and pixel-intensity distribution analyses provided complementary data on the temporal and spatial trends in the image-derived signal. The growth trajectories were further fitted to the Logistic, Gompertz and Modified Gompertz models using goodness-of-fit statistics (R2 and RMSE). For the four experimental groups, the Logistic model achieved R2 values of 0.903, 0.955 and 0.979 respectively, and we compared it against Gompertz and Modified Gompertz models, which yielded R2 values across the corresponding groups. Model suitability was assessed according to goodness-of-fit statistics, whereas AUC offered a cumulative metric reflecting the overall signal exposure during the experiment. The platform was further benchmarked for antibiotic-induced modulation using gentamicin, where the treated cultures exhibited diminished signal accumulation relative to the bacterial control, thereby indirectly assessing the antimicrobial effect within 2-3 h of treatment. These results demonstrate the platform's prowess in resolving growth-associated transitions and antibiotic-mediated suppression through label-free time-resolved imaging. The platform offers a scalable and affordable solution for quantitative microbial growth analysis and has the potential to impact antimicrobial research, compound screening, and other plate-based biological assays.