Martina Graf, Arjun Sarkar, Carl-Magnus Svensson, Anne-Sophie Munser, Sven Schröder, Elke Mülle, Sundar Hengoju, Marc Thilo Figge, Miriam A. Rosenbaum
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Rosenbaum","doi":"10.1016/j.jare.2025.09.047","DOIUrl":null,"url":null,"abstract":"<h3>Introduction</h3>There is an urgent need for rapid, high-throughput phenotypic antimicrobial susceptibility testing (AST) capable of assessing a microbial sample’s susceptibility to multiple antibiotics.<h3>Objectives</h3>In this study, we have established a multiplexed rapid AST platform that employs droplet microfluidics for high-throughput single-cell based analysis, 2D angle-resolved light scattering for growth detection, and fluorescence detection via optical fibers to identify the antibiotic condition within each droplet.<h3>Methods</h3>For this, multiple antibiotic conditions are coded with fluorescence dyes and encapsulated with single cells to enable the testing of multiple antibiotics in a single experiment. We utilize convolutional neural networks (CNNs) and statistical models to assess the growth of various <em>Staphylococcus aureus</em> strains and determine the probability of susceptibility to different antibiotics.<h3>Results</h3>Our platform achieved a 95<!-- --> <!-- -->% categorical agreement with the disc diffusion reference method after just three hours of incubation, demonstrating the same level of accuracy as the established VITEK 2 system for the tested strains and antibiotics. Notably, our platform reduced the incubation time by 5–11 h compared to VITEK 2 and by 13–17 h compared to the gold standard disc diffusion method.<h3>Conclusions</h3>With the presented innovations, our technology takes a big step towards realizing true phenotypic determination of antibiotic resistance profiles for timely antimicrobial treatment decisions.","PeriodicalId":14952,"journal":{"name":"Journal of Advanced Research","volume":"16 1","pages":""},"PeriodicalIF":13.0000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiplexed, rapid phenotypic antibiotic susceptibility testing based on angle-resolved light scattering imaging of microfluidic droplets\",\"authors\":\"Martina Graf, Arjun Sarkar, Carl-Magnus Svensson, Anne-Sophie Munser, Sven Schröder, Elke Mülle, Sundar Hengoju, Marc Thilo Figge, Miriam A. 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We utilize convolutional neural networks (CNNs) and statistical models to assess the growth of various <em>Staphylococcus aureus</em> strains and determine the probability of susceptibility to different antibiotics.<h3>Results</h3>Our platform achieved a 95<!-- --> <!-- -->% categorical agreement with the disc diffusion reference method after just three hours of incubation, demonstrating the same level of accuracy as the established VITEK 2 system for the tested strains and antibiotics. 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Multiplexed, rapid phenotypic antibiotic susceptibility testing based on angle-resolved light scattering imaging of microfluidic droplets
Introduction
There is an urgent need for rapid, high-throughput phenotypic antimicrobial susceptibility testing (AST) capable of assessing a microbial sample’s susceptibility to multiple antibiotics.
Objectives
In this study, we have established a multiplexed rapid AST platform that employs droplet microfluidics for high-throughput single-cell based analysis, 2D angle-resolved light scattering for growth detection, and fluorescence detection via optical fibers to identify the antibiotic condition within each droplet.
Methods
For this, multiple antibiotic conditions are coded with fluorescence dyes and encapsulated with single cells to enable the testing of multiple antibiotics in a single experiment. We utilize convolutional neural networks (CNNs) and statistical models to assess the growth of various Staphylococcus aureus strains and determine the probability of susceptibility to different antibiotics.
Results
Our platform achieved a 95 % categorical agreement with the disc diffusion reference method after just three hours of incubation, demonstrating the same level of accuracy as the established VITEK 2 system for the tested strains and antibiotics. Notably, our platform reduced the incubation time by 5–11 h compared to VITEK 2 and by 13–17 h compared to the gold standard disc diffusion method.
Conclusions
With the presented innovations, our technology takes a big step towards realizing true phenotypic determination of antibiotic resistance profiles for timely antimicrobial treatment decisions.
期刊介绍:
Journal of Advanced Research (J. Adv. Res.) is an applied/natural sciences, peer-reviewed journal that focuses on interdisciplinary research. The journal aims to contribute to applied research and knowledge worldwide through the publication of original and high-quality research articles in the fields of Medicine, Pharmaceutical Sciences, Dentistry, Physical Therapy, Veterinary Medicine, and Basic and Biological Sciences.
The following abstracting and indexing services cover the Journal of Advanced Research: PubMed/Medline, Essential Science Indicators, Web of Science, Scopus, PubMed Central, PubMed, Science Citation Index Expanded, Directory of Open Access Journals (DOAJ), and INSPEC.