Integrating optical coherence tomography and bioluminescence with predictive modeling for quantitative assessment of methicillin-resistant S. aureus biofilms.

Significance: Methicillin-resistant Staphylococcus aureus (MRSA) biofilm infections present a critical challenge in orthopedic trauma surgery and are notoriously resistant to systemic antibiotic therapy. Noninvasive, quantitative imaging methods are urgently needed to assess biofilm burden and therapeutic efficacy, especially for emerging photodynamic therapy (PDT) strategies.

Aim: We aim to establish a quantitative framework using a combined bioluminescence and optical coherence tomography (OCT) imaging approach to correlate bioluminescent signal with viable MRSA burden in both planktonic and biofilm states and to determine how biofilm density and structure influence this relationship.

Approach: Bioluminescent MRSA (SAP231-luxCDABE) was cultured in planktonic and biofilm forms using in vitro growth models in 24-well plates and custom macrofluidic devices, respectively. Bacteria bioluminescence intensity (BLI), counted colony-forming units (CFU), and OCT-based biofilm thickness measurements were collected to construct linear regression models to evaluate how well BLI alone, or combined with biofilm density (CFU/volume), predicts bacterial counts across culture conditions.

Results: Bioluminescence strongly correlated with CFU in planktonic cultures ( $R^2=0.98$ ). In biofilms, BLI per CFU decreased with density, indicating metabolic downregulation, and BLI alone was less reliable ( $R^2=0.59$ ). Incorporating biofilm density (CFU/volume) improved prediction ( $R^2=0.84$ ). A joint model for both states showed excellent fit ( $R^2=0.985$ ), but the biofilm versus planktonic group remained a significant factor ( $p=0.002$ ), revealing systematic differences. This highlights the need for a mixed-model approach that segments subvolumes by morphological features to improve accurate, generalizable CFU estimation across both growth states.

Conclusions: Bioluminescence alone underestimates bacterial burden in dense, metabolically suppressed MRSA biofilms. The combination of BLI with OCT-derived structural metrics enables accurate, nondestructive quantification of viable bacterial load. This approach provides a robust toolset for preclinical evaluation of antimicrobial therapies, particularly for optimizing PDT dosimetry and assessing biofilm response in translational infection models.