With new advances in infectious disease, antifouling surfaces, and environmental microbiology research comes the need to understand and control the accumulation and attachment of bacterial cells on a surface. Thus,...

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Whole cell bioreporters, such as bacterial cells, can be used for environmental and clinical sensing of specific analytes. However, the current methods implemented to observe such bioreporters in the form of chemotactic responses heavily rely on microscope analysis, fluorescent labels, and hard-to-scale microfluidic devices. Herein, we demonstrate that chemotaxis can be detected within minutes using intrinsic optical measurements of silicon femtoliter well arrays (FMAs). This is done via phase-shift reflectometric interference spectroscopic measurements (PRISM) of the wells, which act as silicon diffraction gratings, enabling label-free, real-time quantification of the number of trapped bacteria cells in the optical readout. By generating unsteady chemical gradients over the wells, we first demonstrate that chemotaxis toward attractants and away from repellents can be easily differentiated based on the signal response of PRISM. The lowest concentration of chemorepellent to elicit an observed bacterial response was 50 mM, whereas the lowest concentration of chemoattractant to elicit a response was 10 mM. Second, we employed PRISM, in combination with a computational approach, to rapidly scan for and identify a novel synthetic histamine chemoreceptor strain. Consequently, we show that by using a combined computational design approach, together with a quantitative, real-time, and label-free detection method, it is possible to manufacture and characterize novel synthetic chemoreceptors in Escherichia coli (E. coli).

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The peculiarities of surface-bound bacterial cells are often overshadowed by the study of planktonic cells in clinical microbiology. Thus, we employ phase-shift reflectometric interference spectroscopic measurements to observe the interactions between bacterial cells and abiotic, microstructured material surfaces in a label-free, real-time manner. Both material characteristics (i.e., substrate surface charge and wettability) and characteristics of the bacterial cells (i.e., motility, cell charge, biofilm formation, and physiology) drive bacteria to adhere to a particular surface. We conclude that the attachment of bacterial cells to a surface is determined by the culmination of numerous factors. When specific characteristics of the bacteria are met with factors of the surface, enhanced cell attachment and biofilm formation occur. Such knowledge can be exploited to predict antibiotic efficacy, biofilm development, enhance biosensor development, as well as prevent biofouling.