Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics

TLDR

The study highlights the lack of a clear trend among normalization techniques, underscoring the need for further investigation into label‑free proteomics bias correction. The authors evaluated central tendency, linear regression, locally weighted regression, and quantile normalization methods on LC‑FTICR MS peptide data from standard, Deinococcus radiodurans, and mouse striatum samples, assessing extraneous variability before and after normalization. Normalization eliminated statistical differences between replicate runs, with linear regression consistently ranking first or second among methods, demonstrating its effectiveness in reducing systematic bias in label‑free proteomics. Keywords: proteomics, normalization, relative quantification, Fourier transform ion cyclotron resonance mass spectrometry, extraneous variability, bias.

Abstract

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC−FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias to some degree, assigned ranks among the techniques revealed that for most LC−FTICR−MS analyses linear regression normalization ranked either first or second. However, the lack of a definitive trend among the techniques suggested the need for additional investigation into adapting normalization approaches for label-free proteomics. Nevertheless, this study serves as an important step for evaluating approaches that address systematic biases related to relative quantification and label-free proteomics. Keywords: proteomics • normalization • relative quantification • Fourier transform ion cyclotron resonance mass spectrometry (FTICR−MS) • extraneous variability • bias