Bacterial classification and antibiotic susceptibility testing on an integrated microfluidic platform.
Alexandros A SklavounosCarine R NemrShana O KelleyAaron R WheelerPublished in: Lab on a chip (2021)
With the prevalence of bacterial infections and increasing levels of antibiotic resistance comes the need for rapid and accurate methods for bacterial classification (BC) and antibiotic susceptibility testing (AST). Here we demonstrate the use of the fluid handling technique digital microfluidics (DMF) for automated and simultaneous BC and AST using growth metabolic markers. Custom instrumentation was developed for this application including an integrated heating module and a machine-learning-enabled low-cost colour camera for real-time absorbance and fluorescent sample monitoring on multipurpose devices. Antibiotic dilutions along with sample handling, mixing and incubation at 37 °C were all pre-programmed and processed automatically. By monitoring the metabolism of resazurin, resorufin beta-D-glucuronide and resorufin beta-D-galactopyranoside to resorufin, BC and AST were achieved in under 18 h. AST was validated in two uropathogenic E. coli strains with antibiotics ciprofloxacin and nitrofurantoin. BC was performed independently and simultaneously with ciprofloxacin AST for E. coli, K. pneumoniae, P. mirabilis and S. aureus. Finally, a proof-of-concept multiplexed system for breakpoint testing of two antibiotics, as well as E. coli and coliform classification was investigated with a multidrug-resistant E. coli strain. All bacteria were correctly identified, while AST and breakpoint test results were in essential and category agreement with reference methods. These results show the versatility and accuracy of this all-in-one microfluidic system for analysis of bacterial growth and phenotype.
Keyphrases
- machine learning
- escherichia coli
- deep learning
- high throughput
- low cost
- multidrug resistant
- single cell
- artificial intelligence
- pseudomonas aeruginosa
- circulating tumor cells
- big data
- klebsiella pneumoniae
- convolutional neural network
- risk factors
- biofilm formation
- high resolution
- staphylococcus aureus
- atomic force microscopy
- quantum dots
- living cells
- acinetobacter baumannii