Poster Y20 - Single Cell Antimicrobial Susceptibility Testing with Drop-Based Microfluidics

Saturday, October 26, 2024

10:00 AM - 11:00 AM EST

Location: Exhibit Hall E, F & G

Presenting Author(s)

Maureen Pelissero (she/her/hers)

PhD Candidate
Mayo Clinic Graduate School of Biomedical Sciences
Rochester, Minnesota, United States

Co-Author(s)

CH

Carter Hoffman

PhD Candidate
Montana State University
rochester, Minnesota, United States

Primary Investigator(s)

CC

Connie Chang, PhD

Associate Professor
Mayo Clinic
Rochester, Minnesota, United States

Introduction: Antimicrobial resistance (AMR) poses a critical global health threat due to the overuse and misuse of antimicrobials. With increased AMR, there is decreased treatment efficacy, creating adverse clinical and economic outcomes including increased treatment failure, hospital stay duration, cost of care, and death. The current gold standard for antimicrobial susceptibility testing (AST) is minimum inhibitory concentration (MIC) which is the lowest concentration of antibiotic required to prevent microbial growth. AST is a population-level analysis, which probes a dense culture of bacteria thus is insensitive to resistant subgroups. In addition, this test requires significant cellular proliferation to produce a detectable growth signal, resulting in a time to result of 24-120 hours. Addressing these limitations could result in improved patient outcomes and reduced societal burden. Our lab utilizes drop-based microfluidics which is a technology capable of creating and analyzing picoliter-sized drops at rates of thousands per second. Drops act as microreactors housing individual cells for analysis. Additionally, we can utilize microfluidic sorting to isolate cells for further downstream analysis.

Materials and

Methods: We utilize drop-based microfluidics in combination with a custom-built droplet fluorescence detection stand to perform detection and sorting of droplets. We perform rapid AST with droplets containing individual bacterial cells, growth medium, antibiotics, and an antibiotic identifier. Cell growth is evaluated via fluorescence intensity. We have recently analyzed cell growth of green fluorescent protein expressing Escherichia coli in the presence of aminoglycosides, tetracycline, ciprofloxacin, chloramphenicol, and erythromycin . Additionally, we have sorted E. coli. Using live/dead stains (RSG/Draq7) we will evaluate the growth of Pseudomonas syringae in the presence of tailocins.

Results, Conclusions, and Discussions: We have successfully utilized drop-based microfluidics in combination with a custom-built droplet fluorescence detection stand to detect fluorescence signals within individual drops. Our work has established sorting of droplets containing dye with an average false positive rate of 0.01 and average false negative rate of 0.05. In addition, individual E. coli cells in droplets have also been sorted with an average false positive rate of 0.03 and average false negative rate of 0.23. Drops were sorted at a rate of 1 kHz and collected for 20 minutes. False positive and negative calculations were performed via image analysis on an epifluorescent microscope. A minimum of five experiments were performed with at least 1400 droplets analyzed (mean = 1900 droplets) per experiment.

Our custom-built droplet fluorescence detection stand is not capable of analyzing non-fluorescently labelled samples which is necessary for studying clinical isolates. Future work involves the development of an optofluidic device which guides light through multiple integrated optical fibers that will enable measurements of cell growth in droplets via cell-scattering. This device will be capable of assaying multiple antibiotic combinations, with detection sensitivities down to single cells in drops. Additionally, resistant cells can be sorted in drops and identified, to provide informed diagnostics for therapeutic approaches in a timely manner. The use of microfluidics for AST is expected to increase fidelity by quantifying individual cell responses, revealing heterogenous behavior of previously masked subgroups when analyzed via standard AST methods. We also expect our platform to enhance sensitivity by enabling the early detection of bacterial divisions through single-cell analysis, reducing result turnaround time significantly compared to traditional methods that require up to 1 week of culturing. The development of a diagnostic platform which can provide rapid results representative of the entire bacterial population will be a powerful tool in addressing the threat of AMR.

Acknowledgements (Optional):