Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry.

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.

CFD based analysis of 3D printed nasopharyngeal swabs for COVID-19 diagnostics.

BACKGROUND AND OBJECTIVE: Additive manufacturing of nasopharyngeal (NP) swabs using 3D printing technology presents a viable alternative to address the immediate shortage problem of standard flock-headed swabs for rapid COVID-19 testing. Recently, several geometrical designs have been proposed for 3D printed NP swabs and their clinical trials are already underway. During clinical testing of the NP swabs, one of the key criteria to compare the efficacy of 3D printed swabs with traditional swabs is the collection efficiency. In this study, we report a numerical framework to investigate the collection efficiency of swabs utilizing the computational fluid dynamics (CFD) approach. METHODS: Three-dimensional computational domain comprising of NP swab dipped in the liquid has been considered in this study to mimic the dip test procedure. The volume of fluid (VOF) method has been employed to track the liquid-air interface as the NP swab is pulled out of the liquid. The governing equations of the multiphase model have been solved utilizing finite-volume-based ANSYS Fluent software by imposing appropriate boundary conditions. Taguchi's based design of experiment analysis has also been conducted to evaluate the influence of geometric design parameters on the collection efficiency of NP swabs. The developed model has been validated by comparing the numerically predicted collection efficiency of different 3D printed NP swabs with the experimental findings. RESULTS: Numerical predictions of the CFD model are in good agreement with the experimental results. It has been found that there prevails huge variability in the collection efficiency of the 3D printed designs of NP swabs available in the literature, ranging from 2 µl to 120 µl. Furthermore, even the smallest alteration in the geometric design parameter of the 3D printed NP swab results in significant changes in the amount of fluid captured. CONCLUSIONS: The proposed framework would assist in quantifying the collection efficiency of the 3D printed designs of NP swabs, rapidly and at a low cost. Moreover, we demonstrate that the developed framework can be extended to optimize the designs of 3D printed swabs to drastically improve the performances of the existing designs and achieve comparable efficacy to that of conventionally manufactured swabs.

Metabolic preference assay for rapid diagnosis of bloodstream infections.

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.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation.

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes - the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system-the Microbial Containment Device (MCD)-that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Emerging technologies and commercial products in exosome-based cancer diagnosis and prognosis.

Academic and industrial groups worldwide have reported technological advances in exosome-based cancer diagnosis and prognosis. However, the potential translation of these emerging technologies for research and clinical settings remains unknown. This work overviews the role of exosomes in cancer diagnosis and prognosis, followed by a survey on emerging exosome technologies, particularly microfluidic advances for the isolation and detection of exosomes in cancer research. The advantages and drawbacks of each of the technologies used for the isolation, detection and engineering of exosomes are evaluated to address their clinical challenges for cancer diagnosis and prognosis. Furthermore, commercial platforms for exosomal detection and analysis are introduced, and their performance and impact on cancer diagnosis and prognosis are assessed. Also, the risks associated with the further development of the next generation of exosome devices are discussed. The outcome of this work could facilitate recognizing deliverable Exo-devices and technologies with unprecedented functionality and predictable manufacturability for the next-generation of cancer diagnosis and prognosis.