The great importance of normalization of LC–MS data for highly‐accurate non‐targeted metabolomics

Abstract

The non-targeted metabolomics analysis of biological samples is very important to understand biological functions and diseases. LC combined with electrospray ionization-based MS has been a powerful tool and widely used for metabolomic analyses. However, the ionization efficiency of electrospray ionization fluctuates for various unexpected reasons such as matrix effects and intraday variations of the instrument performances. To remove these fluctuations, normalization methods have been developed. Such techniques include increasing the sensitivity, separating coeluting components and normalizing the ionization efficiencies. Normalization techniques allow simultaneously correcting of the ionization efficiencies of the detected metabolite peaks and achieving quantitative non-targeted metabolomics. In this review paper, we focused on these normalization methods for non-targeted metabolomics by LC-MS.

High‐resolution gas chromatography/mass spectrometry metabolomics of non‐human primate serum

“…Furthermore, algorithms that account for the variation in measurement error with intensity were found to provide the most accurate estimates of differential abundance in mass spectrometry . Use of the total MS signal is one of the better methods for the non‐targeted metabolomic and proteomic analysis of biological fluid samples, and is a very common data‐processing method toward normalization . However, the total number of metabolites identified were higher for the 2D‐GC/TOF‐MS platform.…”

“…43 Use of the total MS signal is one of the better methods for the non-targeted metabolomic and proteomic analysis of biological fluid samples, and is a very common data-processing method toward normalization. 44 However, the total number of metabolites identified were higher for the 2D-GC/TOF-MS platform. Using HR 1D-GC/Orbitrap-MS only on EI capabilities we identified fewer metabolites ( Figure 2D).…”

Recognition of Multiomics-Based Molecule-Pattern Biomarker for Precise Prediction, Diagnosis, and Prognostic Assessment in Cancer

“…Therefore, metabolomics is a methodology used to study metabolome, refers to identification of biochemical and molecular features of metabolome, among different metabolite interactions between genetic/environmental factors and metabolites, and to assessment of biochemical mechanisms associated with a given conditions like different pathophysiological processes [94]. Generally, two strategies, targeted and untargeted methods, are mainly employed to detect variations in a metabolome [95,96]. Targeted metabolomics method concentrates on quantification of the variations of the hypothesis-driven known metabolite profiling (like metabolites that are produced from one or more unknown pathways) between or among groups, followed by multivariate statistical analysis and establishment of mathematical model [95,97].…”

“…Up to now, the familiar techniques used for targeted metabolomics are the triple quadrupole mass spectrometry (QqQ-MS) in the SRM/MRM modes with optimized sample extraction and liquid chromatography-mass spectrometry (LC-MS) conditions [98,99]. The untargeted metabolomics is different from targeted method, which shows in these aspects, such as no hypothesis-driven strategy, and the whole comprehensive study variations of metabolome in a biological system without bias for exploration of metabolite biomarkers for impactful prediction, diagnosis, and prognostic assessment [80,96]. The current techniques used to qualify and quantify the metabolomic variations are nuclear magnetic resonance (NMR)-based methods and mass spectrometry (MS)-based methods [88,[100][101][102].…”

Biomedical Chromatography 2019

“…Their results suggested several biochemical pathways that were adversely impacted and appeared to be relevant to understanding substance abuse involving dextromethorphan. Toshimasa Toyo’oka and co‐workers at the University of Shizuoka presented an outstanding review of the various types of normalization that can be applied to data when conducting non‐targeted metabolomics (Mizuno et al, ). This article is a valuable resource to anyone working in this field.…”