Selective Single-Bacterium Analysis and Motion Tracking Based on Conductive Bulk-Surface Imprinting

Conductive molecular imprinting (MI) shows great potential in enhancing the selectivity of electrochemical bacterial assays, but its efficiency is hindered due to the rigid long-conjugated structure and imprecise specific recognition sites. It is thus urgent to activate the surface MI with clear specific recognition sites toward the bacteria and to develop a single-bacterium monitoring technique for precisely verifying the MI efficiency microscopically. Herein, using lipopolysaccharides and Escherichia coli (E. coli) cells as the surface and bulk templates, respectively, an ideal monomer is successfully predicted by the density functional theory, and the MI with clear and high-precision recognition sites for bacterial matching is prepared. A deep learning-assisted single-bacteria movement trajectory tracking method is developed, and the trained model can effectively recognize and track the movement paths and velocities of both single and group bacteria. Accordingly, the surface MI capture process of the specific recognition sites for E. coli is systematically monitored and analyzed, opening the way for establishing a multidimensional system for characterizing the selective capture process of single and group bacteria by MI polymers. Moreover, the as-prepared electrochemical sensors accomplish the rapid, sensitive sensing of E. coli with a detection limit of 10 CFU/mL and a 433%-increased selectivity, which could promote the development of finer-grained bacterial imprinting techniques and smart bacterial biosensors.