Orthogonal Biochemical Sensing for Concentration-Independent Bacterial Fingerprinting.

Area of Science:

• Biochemistry
• Microbiology
• Sensor Technology

Background:

• Conventional bacterial identification methods struggle with nonspecific interactions and bulk population analysis, obscuring unique bacterial characteristics.
• Existing techniques often yield concentration-dependent results, hindering precise biochemical interpretation and fingerprinting.

Purpose of the Study:

• To develop a novel, concentration-independent bacteria identification (CIBI) sensor array.
• To achieve bacterial identification at the single-cell level, generating distinct biochemical fingerprints.
• To enable rapid and reliable clinical diagnostics for bacterial infections.

Main Methods:

• Utilized an array with three sensing modules: unnatural d-amino acid probes for peptidoglycan synthesis pathways and a phenylboronic acid probe for surface polysaccharides.
• Employed single-cell analysis, measuring per-cell fluorescence from approximately 10,000 individually interrogated bacteria.
• Integrated machine learning algorithms, including a random forest algorithm, for bacterial strain identification and prediction.

Main Results:

• Achieved concentration-independent bacterial profiling by analyzing single-cell responses.
• Accurately identified nine bacterial strains (at 10^5 CFU/mL) with 92.2% accuracy in under 100 minutes.
• Demonstrated high accuracy in identifying pathogen-spiked urinary tract infection samples (95.2%, improving to 97.6% with random forest).

Conclusions:

• The CIBI strategy provides a robust platform for rapid and reliable bacterial identification and diagnostics.
• Single-cell analysis overcomes limitations of bulk population detection, enabling distinct bacterial fingerprinting.
• The developed sensor array shows significant potential for clinical applications, including the identification of unknown bacterial strains.