A novel MIR imaging approach for precise detection of S. epidermidis biofilms in seconds

Abstract

The impact of microbial biofilm growth poses a threat to both human health and the performance of industrial systems, manifesting as a global crisis with noteworthy economic implications for modern society. Exploring new methods and alternative approaches for the detection of biofilm signatures are imperative for developing optimized and cost-effective strategies that can help to identify early-stage biofilm formation. Clinical diagnostic technologies are constantly looking for more affordable, practical and faster methods of prevention and detection of chronic infections in periprosthetic joint infections (PJIs), which are often characterized by biofilm formation on implant surfaces. Staphylococcus epidermidis (SE) is especially known for its strong biofilm production and is considered a leading cause of biomaterial-associated infections, including PJIs. Implant-associated infections are severe and difficult to treat, therefore it is crucial to continue identifying bacterial biomarkers that contribute to its structural stability and attachment to implant surfaces. This study presents a pioneering approach for fast spectral detection of biofilm formation with a novel mid-infrared (MIR) scanning system. To highlight the advantages of our MIR system, we performed a comparative analysis with measurements from a commercially available Fourier-transform infrared (FTIR) scanner. We have assessed SE biofilms grown for 3 days comparing the processing times between a commercially available infrared (IR) scanning system (∼8 h/cm²), and our innovative scanning approach with rapid self-built MIR detection, achieving a reduction in scanning time to seconds. K-means clustering analysis identified pronounced differences in distribution of clusters, representing a significant variation between biofilm producing (RP62A) and non-biofilm producing (ATCC 12228) bacterial strains. The distribution serves as a critical tool for identifying biofilm phenotypes, particularly where poly-N-acetylglucosamine (PNAG), a key constituent of extracellular polymeric substances (EPS) in S. epidermidis, represents the dominant mass fraction in the samples analyzed by our infrared (IR) scanning systems. In addition to faster processing times, our novel MIR system demonstrated significantly higher sensitivity compared to FTIR, enabling clear differentiation between the chemical signatures of biofilm and planktonic strains. The corresponding novel approach integrates advanced data analytics with a newly designed rapid MIR prototype, enabling optimized and swift detection of biofilm signatures. These signatures, now recognized as critical targets in diagnosing complex infections, provide an alternative to traditional microbial detection methods in clinical diagnostics.