Abstract
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. This journal is
Original language | English (US) |
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Pages (from-to) | 4208-4222 |
Number of pages | 15 |
Journal | Lab on a Chip |
Volume | 21 |
Issue number | 21 |
DOIs | |
State | Published - Nov 7 2021 |
Funding
This research was supported in part by the Canadian Institutes of Health Research (Foundation grant no. FDN-148415) and the Natural Sciences and Engineering Research Council of Canada (Discovery grant no. RGPIN-2016-06090 and RGPIN 2019-04867). The research was also supported by the Society for Laboratory Automation and Screening (SLAS), under award number: SLASFG2019; any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of SLAS. Figure components were created with BioRender.com. We thank Dr. Azza Al-Mahrouki (Centre for Pharm. Oncology, Univ. Toronto) for assistance with Cytation 5 multi-mode reader measurements. We thank Prof. Stefan Nagl and Qiu Wenting (Hong Kong Univ. Sci. Technol.), and Dr. Michael D. M. Dryden, Dr. Erica Y. Scott, Dr. Mark P. Pereira and Harrison S. Edwards (Univ. Toronto) for fruitful discussions. We thank Joshua Dahmer and Louise Dupoiron (Univ. Toronto) for technical support. AAS thanks the Centre for Research and Applications in Fluidic Technologies (CRAFT) for a graduate fellowship, and ARW thanks the Canada Research Chair (CRC) program for a CRC.
ASJC Scopus subject areas
- Bioengineering
- Biochemistry
- General Chemistry
- Biomedical Engineering