RESUMEN
Elucidation and monitoring of biomarkers continues to expand because of their medical value and potential to reduce healthcare costs. For example, biomarkers are used extensively to track physiology associated with drug addiction, disease progression, aging, and industrial processes. While longitudinal analyses are of great value from a biological or healthcare perspective, the cost associated with replicate analyses is preventing the expansion of frequent routine testing. Frequent testing could deepen our understanding of disease emergence and aid adoption of personalized healthcare. To address this need, we have developed a system for measuring metabolite abundance from raw biofluids. Using a metabolite extraction chip (MEC), based upon diffusive extraction of small molecules and metabolites from biofluids using microfluidics, we show that biologically relevant markers can be measured in blood and urine. Previously it was shown that the MEC could be used to track metabolic changes in real-time. We now demonstrate that the device can be adapted to high-throughput screening using standard liquid chromatography mass spectrometry instrumentation (LCMS). The results provide insight into the sensitivity of the system and its application for the analysis of human biofluids. Quantitative analysis of clinical predictors including nicotine, caffeine, and glutathione are described.
RESUMEN
The real-time quantification of changes in intracellular metabolic activities has the potential to vastly improve upon traditional transcriptomics and metabolomics assays for the prediction of current and future cellular phenotypes. This is in part because intracellular processes reveal themselves as specific temporal patterns of variation in metabolite abundance that can be detected with existing signal processing algorithms. Although metabolite abundance levels can be quantified by mass spectrometry (MS), large-scale real-time monitoring of metabolite abundance has yet to be realized because of technological limitations for fast extraction of metabolites from cells and biological fluids. To address this issue, we have designed a microfluidic-based inline small molecule extraction system, which allows for continuous metabolomic analysis of living systems using MS. The system requires minimal supervision, and has been successful at real-time monitoring of bacteria and blood. Feature-based pattern analysis of Escherichia coli growth and stress revealed cyclic patterns and forecastable metabolic trajectories. Using these trajectories, future phenotypes could be inferred as they exhibit predictable transitions in both growth and stress related changes. Herein, we describe an interface for tracking metabolic changes directly from blood or cell suspension in real-time.