Abstract

When developing a sample preparation protocol for LC–MS untargeted metabolomics of a new sample matrix unfamiliar to the laboratory, selection of a suitable injection concentration is rarely described. Here we developed a simple workflow to address this issue prior to untargeted LC–MS metabolomics using pig adipose tissue and liver tissue. Bi-phasic extraction was performed to enable simultaneous optimisation of parameters for analysis of both lipids and polar extracts. A series of diluted pooled samples were analysed by LC–MS and used to evaluate signal linearity. Suitable injected concentrations were determined based on both the number of reproducible features and linear features. With our laboratory settings, the optimum concentrations of tissue mass to reconstitution solvent of liver and adipose tissue lipid fractions were found to be 125 mg/mL and 7.81 mg/mL respectively, producing 2811 (ESI+) and 4326 (ESI−) linear features from liver, 698 (ESI+) and 498 (ESI−) linear features from adipose tissue. For analysis of the polar fraction of both tissues, 250 mg/mL was suitable, producing 403 (ESI+) and 235 (ESI−) linear features from liver, 114 (ESI+) and 108 (ESI−) linear features from adipose tissue. Incorrect reconstitution volumes resulted in either severe overloading or poor linearity in our lipid data, while too dilute polar fractions resulted in a low number of reproducible features (<50) compared to hundreds of reproducible features from the optimum concentration used. Our study highlights on multiple matrices and multiple extract and chromatography types, the critical importance of determining a suitable injected concentration prior to untargeted LC–MS metabolomics, with the described workflow applicable to any matrix and LC–MS system.

Highlights

  • Untargeted metabolomics is becoming more widespread as a powerful tool for biomarker discovery and determination of metabolic changes associated with exposure, diet, and disease at a systemMetabolites 2019, 9, 124; doi:10.3390/metabo9070124 www.mdpi.com/journal/metabolitesMetabolites 2019, 9, 124 level [1,2,3,4,5]

  • We developed a simple workflow for the determination of suitable injected concentrations for animal tissues metabolomics and lipidomics analyses, to maximise the number of features that fall within the linear range of analysis

  • The present study utilised lipid and polar metabolite extracts from two different tissues as examples to demonstrate a workflow for the selection of injected concentration for liquid chromatography–mass spectrometry (LC–MS) analysis

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Summary

Introduction

Metabolites 2019, 9, 124 level [1,2,3,4,5] This involves measuring as many metabolites as possible, both the known and unknown molecules present in a biological sample, followed by data pre-processing to extract chemometric information and relative intensities of features from the spectral data, and subsequent analysis with multivariate/univariate statistical methods to identify discriminant features between groups of interest [6]. Injected concentration can be associated with overloading, signal saturation or features falling below detection limit and, is a factor that can affect data quality and reproducibility [16]. A robust metabolomics method requires not just that a maximum number of metabolites is detected, and that they fall within the linear dynamic range of the method to allow direct comparison of metabolites between samples [6]. Not accounting for nonlinearity of the measurements will severely affect the biological interpretation of the results

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