Abstract
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties of metabolism. Here we extend high-resolution-magic angle spinning (HR-MAS) to enable continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) and provide analysis tools for these data. First, we reproduced a result in human chronic lymphoid leukemia cells by using isotope-edited CIVM-NMR to rapidly and unambiguously demonstrate unidirectional flux in branched-chain amino acid metabolism. We then collected untargeted CIVM-NMR datasets for Neurospora crassa, a classic multicellular model organism, and uncovered dynamics between central carbon metabolism, amino acid metabolism, energy storage molecules, and lipid and cell wall precursors. Virtually no sample preparation was required to yield a dynamic metabolic fingerprint over hours to days at ~4-min temporal resolution with little noise. CIVM-NMR is simple and readily adapted to different types of cells and microorganisms, offering an experimental complement to kinetic models of metabolism for diverse biological systems.
Highlights
Metabolic time-series data are invaluable for the development and validation of high-quality models that accurately describe the dynamics of metabolism (Montana et al, 2011; Link et al, 2014; Sefer et al, 2016)
We extended high-resolution-magic angle spinning (HR-MAS) to real-time continuous in vivo measurements of metabolism in cells
We found that CIVM-NMR was easier but faster and more conclusive than traditional approaches for flux measurements in human cell cultures
Summary
Metabolic time-series data are invaluable for the development and validation of high-quality models that accurately describe the dynamics of metabolism (Montana et al, 2011; Link et al, 2014; Sefer et al, 2016). While many studies employ sample preparation and extraction approaches effectively, direct or in vivo measurements are fundamentally simpler to obtain and interpret. While carefully designed (Rhoades et al, 2017) and executed studies with large sample sizes yield powerful insights into the dynamics of biological systems (Sengupta et al, 2016; Krishnaiah et al, 2017; Cannon et al, 2018), continuous and repeated measurements on the same living sample are invaluable for monitoring and confirming these dynamics
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