Investigation of Escherichia coli Capture and Detection Using Quartz Crystal Microbalance with Dissipation Monitoring: Comparative Assessment of Label-Free and Labeled Strategies

Reliable detection of Escherichia coli in raw water remains a major challenge for environmental surveillance, owing to the biological complexity of bacterial cells and the variability introduced by strain diversity, biorecognition entity, and sample composition. Here, we present a systematic investigation of E. coli capture and detection strategies using quartz crystal microbalance with dissipation monitoring (QCM-D) as a surface-sensitive analytical tool. Two representative strains (E. coli O157:H7 and O6) and two anti-E. coli antibodies were evaluated under standardized experimental conditions, comparing two distinct approaches: a "label-free" strategy based on antibody immobilization, and a "labelled" strategy combining nonspecific bacterial capture with subsequent strain-specific immuno-recognition. Our findings show that detection performance varies not only with strain identity and antibody selection, but also with the detection strategy itself, with some strain-antibody pairs producing distinct responses depending on surface configuration when attached to the surface under different configurations. Although the label-free approach enabled detection of the O157: H7 with EAb1 to a detection limit around 1.5 × 105 CFU/mL, its performance was inconsistent between strains and strongly influenced by the viscoelastic properties of the bacterial suspension, which can mask frequency changes indicating mass addition. In contrast, the labeled strategy provided consistent and more sensitive detection, achieving limits of detection of 2.5 × 103 CFU/mL. The improved robustness and interpretability of this configuration, together with the possibility of multiplexing through sequential antibody injections, make it a versatile approach. Moreover, we show that bacterial lysis further enhances sensitivity by improving antigen accessibility, underlining the critical role of sample preparation in biosensor performance. Collectively, these findings establish a framework for the rational design of functionalization schemes for biosensors targeting E. coli and provide transferable insights into antibody functionalization and detection strategies beyond QCM-D.