Patterned Signal Ratio Biases in Mass Spectrometry-Based Quantitative Metabolomics.

Despite the well-known nonlinear response of electrospray ionization (ESI) in mass spectrometry (MS)-based analysis, its complicated response patterns and negative impact on quantitative comparison are still understudied. We showcase in this work that the patterns of nonlinear ESI response are feature-dependent and can cause significant compression or inflation to signal ratios. In particular, our metabolomics study of serial diluted human urine samples showed that over 72% and 16% metabolic features suffered ratio compression and inflation, respectively, whereas only 12% of the signal ratios represent real metabolic concentration ratios. More importantly, these ratio compression and inflation largely exist in the linear response ranges, suggesting that it cannot be resolved by simply diluting the sample solutions to the linear ESI response ranges. Furthermore, we demonstrated that a polynomial regression model that converts MS signals to sample injection amounts can correct the biased ratios and, surprisingly, outperform the linear regression model in both data fitting and data prediction. Therefore, we proposed a metabolic ratio correction (MRC) strategy to minimize signal ratio bias in untargeted metabolomics for accurate quantitative comparison. In brief, by using the data of serial diluted quality control (QC) samples, we applied a cross-validation strategy to determine the best regression model, between linear and polynomial, for each metabolic feature and to convert the measured MS intensities to QC injection amounts for accurate metabolic ratio calculation. Both the studies of human urine samples and a metabolomics application supported that our MRC approach is very efficient in correcting the biased signal ratios. This novel insight of patterned ESI nonlinear response and MRC workflow can significantly benefit the downstream statistical comparison and biological interpretation for untargeted metabolomics.