Multiarray sensors with pattern recognition for the detection, classification, and differentiation of bacteria at subspecies and strain levels.
This work describes the integration of a fully autonomous electrochemical biosensor with pattern recognition techniques for the detection and classification of bacteria at subspecies and strain level. The system provides a continuous, real-time monitoring of bacteria activity upon exposure to antibiotics. The system utilizes 96-well-type electrodes array (DOX-dissolved oxygen sensor) with principal component analysis (PCA) for rapid and routine classification of different classes of bacteria and related strains. A representative sample of a section of the bacteria kingdom has been analyzed and classified using the proposed DOX-PCA system, including the following: Corynebacterium glutamicum, Micrococcus luteus, Staphylococcus epidermidis, Yersinia ruckeri, Escherichia adecarboxylata, Comamonas acidovorans, Alcaligenes odorans, Bacillus globigii, and three strains of Escherichia coli (K12, SM10, ATCC 25922). The new classification scheme is based on the hypothesis that, under identical experimental conditions, various bacteria consume oxygen at different rates and are affected in different ways by selected antibiotics. Thus, the response of the individual electrode in the array is indirectly altered, compared to that of cells growing on medium, by the addition of the antibiotic. By using three different antibiotics in separate wells, a unique fingerprint can be created for a specific bacterium. With the proposed DOX-PCA system, classification of bacteria was achieved at subspecies and strain level in real time. This study represents a basic research tool that may allow researchers to rapidly detect, quantify, and classify bacteria type at subspecies and strain levels.
Karasinski
Jason
J
Department of Chemistry, State University of New York-Binghamton, P.O. Box 6000, Binghamton, New York 13902, USA.
Andreescu
Silvana
S
Sadik
Omowunmi A
OA
Lavine
Barry
B
Vora
Mehul N
MN
D000667:Ampicillin / Q000494:pharmacology; D000900:Anti-Bacterial Agents / Q000494:pharmacology; D001419:Bacteria / Q000145:classification* / Q000187:drug effects / Q000302:isolation & purification* / Q000378:metabolism; D015373:Bacterial Typing Techniques / Q000295:instrumentation / Q000379:methods; D015374:Biosensing Techniques / Q000295:instrumentation*; D002701:Chloramphenicol / Q000494:pharmacology; D004563:Electrochemistry / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D013752:Tetracycline / Q000494:pharmacology
D000900:Anti-Bacterial Agents; D002701:Chloramphenicol; D000667:Ampicillin; D013752:Tetracycline