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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.

In this work, we present a proof-of-principle experiment that extends advanced live cell microscopy to the scale of pool-generated strain libraries. We achieve this by identifying the genotypes for individual cells in situ after a detailed characterization of the phenotype. The principle is demonstrated by single-molecule fluorescence time-lapse imaging of Escherichia coli strains harboring barcoded plasmids that express a sgRNA which suppresses different genes in the E.coli genome through dCas9 interference. In general, the method solves the problem of characterizing complex dynamic phenotypes for diverse genetic libraries of cell strains. For example, it allows screens of how changes in regulatory or coding sequences impact the temporal expression, location, or function of a gene product, or how the altered expression of a set of genes impacts the intracellular dynamics of a labeled reporter.

Blood stream infection (BSI) is related to high mortality and morbidity. Early antimicrobial therapy is crucial in treating patients with BSI. The most common Gram-negative bacteria causing BSI is Escherichia coli. Targeted effective treatment of patients with BSI is only possible if it is based on antibiotic susceptibility testing (AST) data after blood culture positivity. However, there are very few methods available for rapid phenotypic AST and the fastest method takes 4 h. Here we analyzed the performance of a 30 min ultra-rapid method for AST of E. coli directly from positive blood cultures (BC). In total, 51 positive BC with E. coli were studied, and we evaluated the ultra-rapid method directly on positive BC as well as on E. coli colonies cultured on agar plates. The results obtained by the new method were compared with disk diffusion.The method provided accurate AST result in 30 min to Ciprofloxacin and Gentamicin for 92% and 84% of the positive BC samples, respectively. For E. coli isolates retrieved from agar plates, 86% and 96% of the AST results were accurate for Ciprofloxacin and Gentamicin, respectively, after 30 min of assay time. When time to result was modulated in-silico from 30 to 60 minutes for the agar plate samples, accuracy of AST results went up to 92% for Ciprofloxacin and to 100% for Gentamicin.The present study shows that the method is reliable and delivers ultra-rapid AST data in 30 minutes directly from positive BC and as well as from agar plates.