Bloodstream infections (BSIs) cause >500,000 infections and >80,000 deaths per year in North America. The length of time between the onset of symptoms and administration of appropriate antimicrobials is directly linked to mortality rates. It currently takes 2–5 days to identify BSI pathogens and measure their susceptibility to antimicrobials – a timeline that directly contributes to preventable deaths. To address this, we demonstrate a rapid metabolic preference assay (MPA) that uses the pattern of metabolic fluxes observed in ex-vivo microbial cultures to identify common pathogens and determine their antimicrobial susceptibility profiles. In a head-to-head race with a leading platform (VITEK 2, BioMérieux) used in diagnostic laboratories, MPA decreases testing timelines from 40 hours to under 20. If put into practice, this assay could reduce septic shock mortality and reduce the use of broad spectrum antibiotics.

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Metabolomics is a mainstream approach for investigating the metabolic underpinnings of complex biological phenomena and is increasingly being applied to large-scale studies involving hundreds or thousands of samples. Although metabolomics methods are robust in smaller-scale studies, they can be challenging to apply to larger cohorts due to the inherent variability of liquid chromatography mass spectrometry (LC-MS). Much of this difficulty results from the time-dependent changes in the LC-MS system, which affects both the qualitative and quantitative performances of the instrument. Herein, we introduce an analytical strategy for addressing this problem in large-scale microbial studies. Our approach quantifies microbial boundary fluxes using two zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns that are plumbed to enable offline column equilibration. Using this strategy, we show that over 397 common metabolites can be resolved in 4.5 min per sample and that metabolites can be quantified with a median coefficient of variation of 0.127 across 1100 technical replicates. We illustrate the utility of this strategy via an analysis of 960 strains of Staphylococcus aureus isolated from bloodstream infections. These data capture the diversity of metabolic phenotypes observed in clinical isolates and provide an example of how large-scale investigations can leverage our novel analytical strategy.

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Electrochemical nanobiosensors are ultrasensitive tools used for detection and monitoring of various markers in biofluids. In the absence of reliable techniques for large‐scale production of reproducible nanomaterial structures on the electrodes, they are created individually in batch‐production. This has become a substantial hurdle in the practical implementation of electrochemical nanobiosensors. An automated microfluidic‐based platform (NanoChip) is presented for reproducible and scalable formation of complex nanomaterial constructs with a defined order of nanocomposites and biomaterials to create ultrasensitive nanobiosensors. The automated liquid handling system of the setup delivers reagents to electrodes inserted temporarily into the chip for modifying their surfaces by depositing different nanomaterials. The NanoChip platform is used for the creation of a multilayer nanocomposite structure on the electrode surface. These reproducible nanobiosensors are used for detecting breast cancer cells in the blood. The nanobiosensors offered a dynamic detection range of 10 to 5 × 106 cells mL−1. Performance of sensors produced from NanoChip shows similar selectivity and operational range along with improved sensitivity and reproducibility compared to sensors developed using batch process. These features make automated Nanochip technology a versatile tool for producing nanosensors for the ultrasensitive detection of various markers in biomedical, clinical, energy, and environmental applications.