Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging

Abstract

The emergence and spread of antibiotic-resistant bacteria are aggravated by incorrect prescription and use of antibiotics. A core problem is that there is no sufficiently fast diagnostic test to guide correct antibiotic prescription at the point of care. Here, we investigate if it is possible to develop a point-of-care susceptibility test for urinary tract infection, a disease that 100 million women suffer from annually and that exhibits widespread antibiotic resistance. We capture bacterial cells directly from samples with low bacterial counts (10 4 cfu/mL) using a custom-designed microfluidic chip and monitor their individual growth rates using microscopy. By averaging the growth rate response to an antibiotic over many individual cells, we can push the detection time to the biological response time of the bacteria. We find that it is possible to detect changes in growth rate in response to each of nine antibiotics that are used to treat urinary tract infections in minutes. In a test of 49 clinical uropathogenic Escherichia coli (UPEC) isolates, all were correctly classified as susceptible or resistant to ciprofloxacin in less than 10 min. The total time for antibiotic susceptibility testing, from loading of sample to diagnostic readout, is less than 30 min, which allows the development of a point-of-care test that can guide correct treatment of urinary tract infection.point of care | UTI | AST | antibiotic | resistance | microfluidic

A Comprehensive Review of the Present and Future Antibiotic Susceptibility Testing (AST) Systems

“…The growth rate calculations are done for each individual cell traps in reference rows (which do not contain any antibiotics) and treatment rows (containing antibiotics at different concentrations). By averaging and normalizing the growth rate against reference population, they can detect the response to antibiotic treatment populations [28]. This method was used to determine the AST for E coli with respect to nine different antibiotics commonly used for Urinary Tract Infections (UTIs).…”

Identification and Antibiotic‐Susceptibility Profiling of Infectious Bacterial Agents: A Review of Current and Future Trends

“…By comparing the average growth rate of the treated population to the reference population, the system detects growth changes as fast as the biological response to the antibiotic. By measuring single cells dividing, it monitors the real‐time response to an antibiotic . This tool was evaluated directly on urine clinical samples with bacterial loads of 10 4 CFU mL −1 , the lower range for clinically relevant UTIs.…”

In situ genotyping of a pooled strain library after characterizing complex phenotypes

“…We describe the microfluidic chip design, production, and operation in Baltekin et al (2017). The chip is designed to rapidly capture individual bacterial cells from liquid growth cultures and exchange the liquid media around the cells effectively while keeping the captured cells in place throughout the experiment.…”

“…In the morning, cells were diluted 1:200 in M9 + 0.2% Glucose + 1× RPMI + 50 lg/ml kanamycin + 0.85 g/l Pluronic F108 and grown for 2 h at 37°C shaking at 200 rpm, at which point cells were flown into the chip and into the cell channels where they are caught by the 300 nm constriction at the end of the cell channels [as described in (Baltekin et al, 2017)]. The cells were grown in the chip overnight in M9 + 0.2% Glucose + 1× RPMI + 50 lg/ml kanamycin + 0.85 g/l Pluronic + 0.1 ng/ll aTc at 30°C, and then imaged.…”

“…Before segmentation, the image of a trap designed to be without cells was deducted from all traps imaged in phase contrast as described in Baltekin et al (2017). Using the detected cell outlines, lineages were constructed using the Baxter algorithm (Magnusson et al, 2015) where Jaccard indices between consecutive cell outlines were used to score migration and division events.…”