Analysis of raw biofluids by mass spectrometry using microfluidic diffusion-based separation. Issue 3 (16th December 2016)

• Record Type: Journal Article
• Authors: Heinemann, Joshua; Noon, Brigit; Willems, Daniel; Budeski, Katherine; Bothner, Brian
• Is Part Of: Analytical methods. Volume 9:Issue 3(2017)
• Page Start: 385
• Page End: 392
• Publication Date: 2016-12-16
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/c6ay02827f
• Languages: English
• ISSNs: 1759-9660

Abstract

Abstract : Elucidation and monitoring of biomarkers continues to expand because of their medical value and potential to reduce healthcare costs. Abstract : 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.