In-Vial Dual Extraction for Direct LC-MS Analysis of Plasma for Comprehensive and Highly Reproducible Metabolic Fingerprinting.

Abstract

Metabolic fingerprinting of biological tissues has become an important area of research, particularly in the biomarker discovery field. Methods have inherent analytical variation, and new approaches are necessary to ensure that the vast numbers of intact metabolites present in biofluids are detected. Here, we describe an in-vial dual extraction (IVDE) method and a direct injection method that shows the total number of features recovered to be over 4500 from a single 20 μL plasma aliquot. By applying a one-step extraction consisting of a lipophilic and hydrophilic layer within a single vial insert, we showed that analytical variation was decreased. This was achieved by reducing sample preparation stages including procedures of drying and transfers. The two phases in the vial, upper and lower, underwent HPLC-QTOF analysis on individually customized LC gradients in both positive and negative ionization modes. A 60 min lipid profiling HPLC-QTOF method for the lipophilic phase was specifically developed, enabling the separation and putative identification of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, and sterols. The aqueous phase of the extract underwent direct injection onto a 45 min gradient, enabling the detection of both polarities. The IVDE method was compared to two traditional extraction methods. The first method was a two-step ether evaporation and IPA resuspension, and the second method was a methanol precipitation typically used in fingerprinting studies. The IVDE provided a 378% increase in reproducible features when compared to evaporation and a 269% increase when compared to the precipitate and inject method. As a proof of concept, the method was applied to an animal model of diabetes. A 2-fold increase in discriminant metabolites was found when comparing diabetic and control rats with IVDE. These discriminant metabolites accounted for around 600 entities, out of which 388 were identified in available databases.

Simultaneous extraction of metabolome and lipidome with methyl tert-butyl ether from a single small tissue sample for ultra-high performance liquid chromatography/mass spectrometry

“…Previously, only the polar fraction of MTBE extracts has been used for non-targeted metabolomic analyses, a strategy that has been described for plant material and plasma [28,29]. Aiming to apply non-targeted metabolomics to MTBE extracts of mammalian tissue and at the same time increasing the number of detectable metabolites, we extracted 10 mg of freeze-dried liver (6 replicates) and subjected aliquots of the mix and of the polar fraction to nontargeted metabolomic analyses.…”

Multimodal Metabolomics Combining UPLC-qToF-MS and GC-MS Data in Plasma and Brain Tissue

“…The in vial dual extractions (IVDE) were performed as previously described for plasma [14] and brain tissue [13], the workflow is summarised in. After all of the LC-MS analysis had been performed …”

“…The HILIC and reversed phase LC-MS analysis was performed on a Waters ultra-performance liquid chromatogrpahy (UPLC) system coupled to a quadrupole rime-of-flight (Q-ToF) mass spectrometer (Waters, Milford, MA, USA) as described previously [13,14]. GC-MS analysis was carried out on a …”

“…To this end we developed a strategy that enables 6 analytical assays (4 LC-MS and 2 GC-MS) to be applied to a single in-vial dual extraction utilising as little as 3mg of brain tissue [13] or 20µl of plasma [14]. The method was designed to analyse samples in the future such as human brain, prioritising the information acquired over the length of the analysis.…”

LC–MS based global metabolite profiling: the necessity of high data quality

“…Sampling and sample preparation as well as the method used during LC-MS analysis have a profound effect on the likelihood of generating analytically reliable hypotheses (Gika et al 2014a, b;Vuckovic 2012). A number of publications has focused on assessing and measuring data quality for global metabolite profiling (Gika et al 2016;Naz et al 2014;Naz et al 2013a;Naz et al 2013b;Pandher et al 2009;Simón-Manso et al 2013;Telu et al 2016;Tulipani et al 2013Tulipani et al , 2015Whiley et al 2012), however most of these only focus on a particular step in the workflow.…”