BactoSpin: Novel Technology for Rapid Bacteria Detection and Antibiotic Susceptibility Testing

Shumeiko, Vlad; Hidas, Guy; Nowogrodski, Chen; Pinto, Yariv; Gofrit, Ofer N; Duvdevani, Mordechai; Shoseyov, Oded

doi:10.3390/s21175902

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…The main functions included (i) bacteria detection within 5 min at concentration down to 5 × 10 3 cells per mL ( E. coli cells spiked into urine), and (ii) AST in less than 2 h on a valve-less multi-channel chip with dried antibiotic ( E. coli and K. pneumoniae clinical isolates). 295 Needs et al emphasized the potential of miniaturised broth microdilution for simplified AST and developed a fluoropolymer microcapillary film approach. E. coli and K. pneumoniae uropathogenic isolates were seeded in a well plate for colorimetric growth detection by means of a 10-plex test strip that was dipped into the reservoirs.…”

Section: Urinary Tract Infectionsmentioning

confidence: 99%

Microfluidic systems for infectious disease diagnostics

Lehnert,

Gijs

2024

Lab Chip

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Section: Urinary Tract Infectionsmentioning

confidence: 99%

Microfluidic systems for infectious disease diagnostics

Lehnert,

Gijs

2024

Lab Chip

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Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium

Tran,

Sridhar,

Reece

et al. 2024

Nat Commun

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Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Current susceptibility testing approaches limit our ability to rapidly distinguish between antimicrobial-susceptible and -resistant organisms. Salmonella Typhimurium (S. Typhimurium) is an enteric pathogen responsible for severe gastrointestinal illness and invasive disease. Despite widespread resistance, ciprofloxacin remains a common treatment for Salmonella infections, particularly in lower-resource settings, where the drug is given empirically. Here, we exploit high-content imaging to generate deep phenotyping of S. Typhimurium isolates longitudinally exposed to increasing concentrations of ciprofloxacin. We apply machine learning algorithms to the imaging data and demonstrate that individual isolates display distinct growth and morphological characteristics that cluster by time point and susceptibility to ciprofloxacin, which occur independently of ciprofloxacin exposure. Using a further set of S. Typhimurium clinical isolates, we find that machine learning classifiers can accurately predict ciprofloxacin susceptibility without exposure to it or any prior knowledge of resistance phenotype. These results demonstrate the principle of using high-content imaging with machine learning algorithms to predict drug susceptibility of clinical bacterial isolates. This technique may be an important tool in understanding the morphological impact of antimicrobials on the bacterial cell to identify drugs with new modes of action.

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Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium

Baker,

Tran,

Srid

et al. 2023

Preprint

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Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Presently we rely on exposing single organisms to an antimicrobial and growth to determine susceptibility; throughput and interpretation hinder our ability to rapidly distinguish between antimicrobial-susceptible and -resistant organisms isolated from clinical samples. Salmonella Typhimurium (S. Typhimurium) is an enteric pathogen responsible for severe gastrointestinal illness in immunocompetent individuals and can also cause invasive disease in immunocompromised people. Despite widespread resistance, ciprofloxacin remains a common treatment, particularly in lower-resource settings, where the drug is given empirically. Here, we exploited high-content imaging to generate deep phenotyping of various S. Typhimurium isolates longitudinally exposed to increasing concentrations of ciprofloxacin. We applied machine learning algorithms to the resulting imaging data and demonstrated that individual isolates display distinct growth and morphological characteristics that clustered by time point and susceptibility to ciprofloxacin, which occurred independently of ciprofloxacin exposure. We used a further set of S. Typhimurium clinical isolates to test the ability of our algorithm to distinguish between ciprofloxacin-susceptible and -resistant isolates. We found that a random forest classifier could accurately predict how the organism would respond to ciprofloxacin without exposure to it or any prior knowledge of ciprofloxacin susceptibility. These results provide the first proof-of-principle for the use of high-content imaging with machine learning algorithms to predict drug susceptibility of clinical bacterial isolates. This technique can be exploited to identify drug-resistant bacteria more rapidly and accurately and may be an important tool in understanding the phenotypic impact of antimicrobials on the bacterial cell in order to identify drugs with new modes of action.

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BactoSpin: Novel Technology for Rapid Bacteria Detection and Antibiotic Susceptibility Testing

Cited by 3 publications

References 28 publications

Microfluidic systems for infectious disease diagnostics

Microfluidic systems for infectious disease diagnostics

Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium

Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium

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