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Updated: Jul 26, 2025

Stress-induced Antibiotic Susceptibility Testing on a Chip
Published on: January 8, 2014
Single-cell microfluidics enabled dynamic evaluation of drug combinations on antibiotic resistance bacteria
Xiaobo Li1, Yanqing Song2, Xiuzhao Chen3
1School of Chemical Engineering and Technology, Key Laboratory of Systems Bioengineering (Ministry of Education), Frontiers Science Center for Synthetic Biology, Tianjin University, Tianjin, 300072, China; James Watt School of Engineering, University of Glasgow, G12 8LT, UK.
Abstract:
The rapid spread of antibiotic resistance has become a significant threat to global health, yet the development of new antibiotics is outpaced by emerging new resistance. To treat multidrug-resistant bacteria and prolong the lifetime of existing antibiotics, a productive strategy is to use combinations of antibiotics and/or adjuvants. However, evaluating drug combinations is primarily based on end-point checkerboard measurements, which provide limited information to study the mechanism of action and the discrepancies in the clinical outcomes. Here, single-cell microfluidics is used for rapid evaluation of the efficacy and mode of action of antibiotic combinations within 3 h. Focusing on multidrug-resistant Acinetobacter baumannii, the combination between berberine hydrochloride (BBH, as an adjuvant) and carbapenems (meropenem, MEM) or β-lactam antibiotic is evaluated. Real-time tracking of individual cells to programmable delivered antibiotics reveals multiple phenotypes (i.e., susceptible, resistant, and persistent cells) with fidelity. Our study discovers that BBH facilitates the accumulation of antibiotics within cells, indicating synergistic effects (FICI = 0.5). For example, the combination of 256 mg/L BBH and 16 mg/L MEM has a similar killing effect (i.e., the inhibition rates >90%) as the MIC of MEM (64 mg/L). Importantly, the synergistic effect of a combination can diminish if the bacteria are pre-stressed with any single drug. Such information is vital for understanding the underlying mechanisms of combinational treatments. Overall, our platform provides a promising approach to evaluate the dynamic and heterogenous response of a bacterial population to antibiotics, which will facilitate new drug discovery and reduce emerging antibiotic resistance.
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