Stable isotope tracers are widely used to quantify metabolic rates, and yet a limited number of studies have considered the impact of analytical error on estimates of flux. For example, when estimating the contribution of de novo lipogenesis, one typically measures a minimum of four isotope ratios, i.e., the precursor and product labeling pre- and posttracer administration. This seemingly simple problem has 1 correct solution and 80 erroneous outcomes. In this report, we outline a methodology for evaluating the effect of error propagation on apparent physiological endpoints. We demonstrate examples of how to evaluate the influence of analytical error in case studies concerning lipid and protein synthesis; we have focused on (2)H2O as a tracer and contrast different mass spectrometry platforms including GC-quadrupole-MS, GC-pyrolysis-IRMS, LC-quadrupole-MS, and high-resolution FT-ICR-MS. The method outlined herein can be used to determine how to minimize variations in the apparent biology by altering the dose and/or the type of tracer. Likewise, one can facilitate biological studies by estimating the reduction in the noise of an outcome that is expected for a given increase in the number of replicate injections.
Keywords: Cardiovascular disease; Diabetes; Dyslipidemia; Isotope ratio mass spectrometry; Lipogenesis; Mass isotopomer distribution analysis; Protein synthesis.
© 2015 Elsevier Inc. All rights reserved.