Fold-Change Compression: An Unexplored But Correctable Quantitative Bias Caused by Nonlinear Electrospray Ionization Responses in Untargeted Metabolomics

The nonlinear signal response
of electrospray ionization (ESI) presents a critical limitation for
mass spectrometry (MS)-based quantitative analysis. In the field of
metabolomics research, this issue has largely remained unaddressed;
MS signal intensities are usually directly used to calculate fold
changes for quantitative comparison. In this work, we demonstrate
that, due to the nonlinear ESI response, signal intensity ratios of
a metabolic feature calculated between two samples may not reflect
their real metabolic concentration ratios (i.e., fold-change compression),
implying that conventional fold-change calculations directly using
MS signal intensities can be misleading. In this regard, we developed
a quality control (QC) sample-based signal calibration workflow to
overcome the quantitative bias caused by the nonlinear ESI response.
In this workflow, calibration curves for every metabolic feature are
first established using a QC sample injected in serial injection volumes.
The MS signals of each metabolic feature are then calibrated to their
equivalent QC injection volumes for comparative analysis. We demonstrated
this novel workflow in a targeted metabolite analysis, showing that
the accuracy of fold-change calculations can be significantly improved.
Furthermore, in a metabolomic comparison of the bone marrow interstitial
fluid samples from leukemia patients before and after chemotherapy,
an additional 59 significant metabolic features were found with fold
changes larger than 1.5, and an additional 97 significant metabolic
features had fold changes corrected by more than 0.1. This work enables
high-quality quantitative analysis in untargeted metabolomics, thus
providing more confident biological hypotheses generation.