Species-specific discrimination of bacterial biofilms using a ratiometric fluorescence sensor array and machine...

Area of Science:

• Microbiology
• Analytical Chemistry
• Biotechnology

Background:

• Biofilms are bacterial communities with diverse extracellular matrix (ECM) compositions.
• Accurate biofilm identification is critical for treating biofilm-associated infections.
• Current identification methods are time-consuming, costly, and require specialized resources.

Purpose of the Study:

• To develop a streamlined and accurate method for bacterial biofilm identification.
• To evaluate the efficacy of a paper-based ratiometric sensor array coupled with machine learning for biofilm analysis.
• To address the limitations of traditional biofilm detection techniques.

Main Methods:

• Utilized a paper-based sensor array with pre-adsorbed fluorescent dyes (3-hydroxyflavone derivatives).
• Analyzed fluorescence signals upon interaction with bacterial biofilms using machine learning algorithms (LDA, neural networks, SVM, Naïve Bayes).
• Tested sensor performance on eight bacterial species, including clinical isolates.

Main Results:

• Achieved 97.5% classification accuracy for distinguishing between eight biofilm species.
• Detected bacterial concentrations as low as OD600 = 0.002 o.u.
• Demonstrated >95% accuracy in differentiating planktonic bacteria from biofilms and >85% accuracy for clinical species identification.

Conclusions:

• The paper-based sensor array provides high-precision biofilm identification.
• This method offers a promising tool for advancing biofilm research and clinical diagnostics.
• The sensor array presents a rapid, accurate, and accessible alternative to traditional biofilm identification methods.