Combining deep learning and droplet microfluidics for rapid and label-free antimicrobial susceptibility testing of colistin

Efficient tools for rapid antibiotic susceptibility testing (AST) are crucial for appropriate use of antibiotics, especially colistin, which is now often considered a last resort therapy with extremely drug resistant Gram-negative bacteria. Here, we developed a rapid, easy and miniaturized colistin...

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Veröffentlicht in:Biosensors & bioelectronics 2024-08, Vol.257, p.116301-116301, Article 116301
Hauptverfasser: Riti, Justine, Sutra, Guillaume, Naas, Thierry, Volland, Hervé, Simon, Stéphanie, Perez-Toralla, Karla
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Sprache:eng
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Zusammenfassung:Efficient tools for rapid antibiotic susceptibility testing (AST) are crucial for appropriate use of antibiotics, especially colistin, which is now often considered a last resort therapy with extremely drug resistant Gram-negative bacteria. Here, we developed a rapid, easy and miniaturized colistin susceptibility assay based on microfluidics, which allows for culture and high-throughput analysis of bacterial samples. Specifically, a simple microfluidic platform that can easily be operated was designed to encapsulate bacteria in nanoliter droplets and perform a fast and automated bacterial growth detection in 2 h, using standardized samples. Direct bright-field imaging of compartmentalized samples proved to be a faster and more accurate detection method as compared to fluorescence-based analysis. A deep learning powered approach was implemented for the sensitive detection of the growth of several strains in droplets. The DropDeepL AST method (Droplet and Deep learning-based method for AST) developed here allowed the determination of the colistin susceptibility profiles of 21 fast-growing Enterobacterales (E. coli and K. pneumoniae), including clinical isolates with different resistance mechanisms, showing 100 % categorical agreement with the reference broth microdilution (BMD) method performed simultaneously. Direct AST of bacteria in urine samples on chip also provided accurate results in 2 h, without the need of complex sample preparation procedures. This method can easily be implemented in clinical microbiology laboratories, and has the potential to be adapted to a variety of antibiotics, especially for last-line antibiotics to optimize treatment of patients infected with multi-drug resistant strains. [Display omitted] •Simplified workflow for bacteria encapsulation and detection in nanoliter droplets.•Automated and deep learning powered approach for droplet classification.•Digital susceptibility testing for colistin using clinical isolates in 2 hours.•Categorization of bacteria strains in complete agreement with the reference method.•Direct urine analysis on chip for rapid detection of antimicrobial resistance.
ISSN:0956-5663
1873-4235
DOI:10.1016/j.bios.2024.116301