Recent advances in metabolomics technologies have made possible simultaneous analysis of hundreds of metabolites by mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR). As the """"""""gateway"""""""" to meaningful metabolomics analysis, sample processing has critical issues not widely addressed in metabolomics studies, such as 1) sample integrity;2) reproducibility and range of metabolite recovery;3) tools for extending metabolite coverage, enabling high throughput &robust metabolite analysis, and facilitating de novo structure elucidation of unknowns. Our Center (CREAM) has developed test-proven protocols for processing a wide selection of biospecimens with the aim of maintaining biochemical integrity and reproducible recovery of metabolites in large-scale. These protocols have been applied to many stable isotope-resolved metabolomics (SIRM) studies to reveal key reprogrammed metabolic events in human diseases. We have also shown the utility of chemoselective (CS) tagging in enriching low-level, labile, and volatile carbonyl metabolites for high throughput analysis by direct infusion Fourier transform-ion cyclotron resonance-mass spectrometry (FTICR-MS). Our protocols can be further enhanced in terms of throughput and coverage of previously inaccessible metabolites. We will support sample processing activities for the U24 RCMRC with three Specific Aims: SA1. To process a wide range of biospecimens researched by Clients. These range from cells, tissues, media, biofluids to animals. We have provided this service since 2007 and will greatly expand the operation with added resources including sample preparation for in vivo NMR. SA2. To implement microfluidics-based subcellular/cell separation and metabolite extraction. We will test and refine microfiuidic platforms for separation of subcellular particles, cell sorting, and metabolite extraction to offer more efficient, speedy, and high throughput sample processing than existing methods. SA3. To implement profiling and multiplexed quantification of metabolites and their labeling patterns by FTICR- MS in a wide range of biological systems using chemoselective (CS) tagging of functional groups (FG). We will implement the CS capture approach (funded by a separate Common Funds R01) for enriching previously inaccessible metabolites with carbonyl, amino, thiol, and diol FG for robust identification and multiplexed analysis by FTICR-MS and NMR.

Public Health Relevance

Sound sample processing is a prerequisite to metabolomic studies, which have tremendous potential for promoting large-scale functional understanding of human diseases. Such understanding will ultimately enhance our ability to prevent, diagnose and treat intractable diseases such as cancer. The proposed sample processing plan should help expand and facilitate meaningful metabolomic analysis and interpretation.

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University of Kentucky
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