# High-Throughput, Massively Parallel Antimicrobial Resistance Surveillance Using Drop-Based Microfluidics

> **NIH NIH R21** · MONTANA STATE UNIVERSITY - BOZEMAN · 2022 · $106,899

## Abstract

A fundamental challenge in administering effective treatments for infectious diseases caused by bacteria is the rapid identification of antimicrobial resistance and the efficacy of single or combinatorial drug treatments, including the lowest concentrations or combinations of antibiotics required to prevent bacterial growth. We propose to overcome this by engineering an easy-to-use platform based on miniaturization of microbial cultivation using microfluidic drops that can be tagged, tracked, and evaluated in scalable and massively parallel designs. Standard antimicrobial susceptibility testing (AST) platforms typically require 18-72 hours to generate susceptibility results. The primary research objective of this proposal is to apply droplet-based microfluidics to perform rapid AST screening of P. aeruginosa clinical isolates in under four hours. Droplet-based microfluidics is a technology in which picoliter-sized volumes are created and assayed at rates of up to thousands per second. These drops serve as individual microreactors that can contain single cells. The ability to isolate single cells and discrete combinations of antibiotics within picoliter-sized microreactor volumes will allow for rapid and early detection of antibiotic susceptibility with high resolution and fidelity. This method will enable the ability to detect and quantify subpopulations of single cells that are normally below the limit of detection of standard drug assays. To achieve this goal, we have two specific aims: (1) High-throughput minimum inhibitory concentration screening will be performed by multiplexed assaying with a barcoded droplet library, and (2) A microfluidic device with integrated optical fibers will be engineered for sensitive detection of barcode and cell signals, which will enable the platform to be portable and readily adaptable to clinical settings. This novel, scalable technology for high-throughput antimicrobial susceptibility testing in bacteria can greatly decrease the diagnosis time of bacterial infections such as sepsis or urinary-tract infections.

## Key facts

- **NIH application ID:** 10357953
- **Project number:** 5R21AI151923-02
- **Recipient organization:** MONTANA STATE UNIVERSITY - BOZEMAN
- **Principal Investigator:** Connie B Chang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $106,899
- **Award type:** 5
- **Project period:** 2021-02-22 → 2023-04-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10357953

## Citation

> US National Institutes of Health, RePORTER application 10357953, High-Throughput, Massively Parallel Antimicrobial Resistance Surveillance Using Drop-Based Microfluidics (5R21AI151923-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10357953. Licensed CC0.

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