A glucose meter interface for point-of-care gene circuit-based diagnostics.

Recent advances in cell-free synthetic biology have given rise to gene circuit-based sensors with the potential to provide decentralized and low-cost molecular diagnostics. However, it remains a challenge to deliver this sensing capacity into the hands of users in a practical manner. Here, we leverage the glucose meter, one of the most widely available point-of-care sensing devices, to serve as a universal reader for these decentralized diagnostics. We describe a molecular translator that can convert the activation of conventional gene circuit-based sensors into a glucose output that can be read by off-the-shelf glucose meters. We show the development of new glucogenic reporter systems, multiplexed reporter outputs and detection of nucleic acid targets down to the low attomolar range. Using this glucose-meter interface, we demonstrate the detection of a small-molecule analyte; sample-to-result diagnostics for typhoid, paratyphoid A/B; and show the potential for pandemic response with nucleic acid sensors for SARS-CoV-2.

On-chip phenotypic investigation of combinatory antibiotic effects by generating orthogonal concentration gradients.

Combinatory therapy using two or more kinds of antibiotics is attracting considerable attention for inhibiting multi-drug resistant pathogenic bacteria. Although the therapy mostly leads to more powerful antimicrobial effects than using a single antibiotic (synergy), interference may arise from certain antibiotic combinations, resulting in the antimicrobial effect being suppressed (antagonism). Here, we present a microfluidic-based phenotypic screening chip to investigate combinatory antibiotic effects by automatically generating two orthogonal concentration gradients on a bacteria-trapping agarose gel. Computational simulations and fluorescence experiments together verify the simultaneous establishment of 121 concentration combinations, facilitating on-chip drug testing with stability and efficiency. Against Gram-negative bacteria, Pseudomonas aeruginosa, our chip allows the measurement of phenotypic growth levels, and enables various types of analyses for all antibiotic pairs to be conducted in 7 h. Furthermore, by providing a specific amount of susceptibility data, our chip enables the two reference models, Loewe additivity and Bliss independence, to be implemented, which classify the antibiotic interaction types into synergy or antagonism. These results suggest the efficacy of our chip as a cell-based drug screening platform for exploring the underlying pharmacological patterns of antibiotic interactions, with potential applications in guidance in clinical therapies and in screening other cell-type agents.