Optimizing the Use of
Quality Control Samples for
Signal Drift Correction in Large-Scale Urine Metabolic Profiling Studies
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Abstract
The evident importance of metabolic profiling for biomarker
discovery
and hypothesis generation has led to interest in incorporating this
technique into large-scale studies, e.g., clinical and molecular phenotyping
studies. Nevertheless, these lengthy studies mandate the use of analytical
methods with proven reproducibility. An integrated experimental plan
for LC–MS profiling of urine, involving sample sequence design
and postacquisition correction routines, has been developed. This
plan is based on the optimization of the frequency of analyzing identical
quality control (QC) specimen injections and using the QC intensities
of each metabolite feature to construct a correction trace for all
the samples. The QC-based methods were tested against other current
correction practices, such as total intensity normalization. The evaluation
was based on the reproducibility obtained from technical replicates
of 46 samples and showed the feature-based signal correction (FBSC)
methods to be superior to other methods, resulting in ∼1000
and 600 metabolite features with coefficient of variation (CV) <
15% within and between two blocks, respectively. Additionally, the
required frequency of QC sample injection was investigated and the
best signal correction results were achieved with at least one QC
injection every 2 h of urine sample injections (<i>n</i> = 10). Higher rates of QC injections (1 QC/h) resulted in slightly
better correction but at the expense of longer total analysis time