Development and Application of Multidimensional Lipid Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity

Abstract

The implication of lipid dysregulation in diseases, toxic exposure outcomes, and inflammation has brought great interest to lipidomic studies. However, lipids have proven to be analytically challenging due to their highly isomeric nature and vast concentration ranges in biological matrices. Therefore, multidimensional techniques such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation, and mass spectrometry (LC-IMS-CID-MS) have been implemented to separate lipid isomers as well as provide structural information and increased identification confidence. These data sets are however extremely large and complex, resulting in challenges for data processing and annotation. Here, we have overcome these challenges by developing sample-specific multidimensional lipid libraries using the freely available software Skyline. Specifically, the human plasma library developed for this work contains over 500 unique lipids and is combined with adapted Skyline functions such as indexed retention time (iRT) for retention time prediction and IMS drift time filtering for enhanced selectivity. For comparison with other studies, this database was used to annotate LC-IMS-CID-MS data from a NIST SRM 1950 extract. The same workflow was then utilized to assess plasma and bronchoalveolar lavage fluid (BALF) samples from patients with varying degrees of smoke inhalation injury to identify lipid-based patient prognostic and diagnostic markers.

An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies

“…Accordingly, we applied the concept of iRT, which was initially established for proteomics 20 and recently applied to lipids. 21 In brief, two to three compounds per lipid class that are of high intensity and easily distinguished from nearby peaks were chosen as indexing compounds. When importing all data files into Skyline in centroid mode, the peaks for the indexing compounds were consistently picked correctly.…”

“…Among those compounds that showed retention-time drifts, lysophosphatidylcholines and lysophosphatidylethanolamines were most pronounced in specific samples within each batch, which may be attributed to matrix effects (Figure S4). Accordingly, we applied the concept of iRT, which was initially established for proteomics and recently applied to lipids . In brief, two to three compounds per lipid class that are of high intensity and easily distinguished from nearby peaks were chosen as indexing compounds.…”

Recent developments in data acquisition, treatment and analysis with ion mobility‐mass spectrometry for lipidomics

“…The low reproducibility of RT hampers its interpretation, but the high reproducibility of CCS makes the difference (ΔCCS) between the experimental CCS and the reference ones (contained in databases or predicted by computational tools) ideal for novel researchers that might not have enough experience to use the RT to identify the analyzed features, but they can easily interpret the ΔCCS. Thus, the CCS similarity increases the confidence in the annotation of putative candidates of features, although the high correlation between the CCS and the m/z values [14,48,53,101] hinders the unique identification of metabolites using the information coming from LC-IM-MS experiments. The CCS value together with the m/z is not sufficient to uniquely identify features, especially in biological samples with a large number of metabolites present without prior knowledge, but it provides hints about which one is more plausible.…”

“…CCS information combined with the new acquisition and data processing approaches enables 8051 lipids from 117 lipid subclasses to be identified between Level 1 (identified by standard compound) and Level 3.2 (accurate mass spectrum and number of carbons confirmed). • Skyline [48,107] • Lipid4DAnalyzer, previously known as LipidIMMS [66], is an expert system processing multidimensional information from the mass spectrometer (m/z), the separation techniques (RT, CCS) and the fragmentation spectra (MS/MS) for lipid identification. The tool covers 4 superclasses, 25 classes and 267,716 in silico lipid structures.…”

Tricky Isomers—The Evolution of Analytical Strategies to Characterize Plasmalogens and Plasmanyl Ether Lipids

“…There are both targeted and untargeted mass spectrometric approaches, and both have their specific advantages and disadvantages in terms of reliability, reproducibility, and information content. These are based on either direct infusion (shotgun) lipidomics ( Han and Gross, 2005 ; Surma et al, 2021 ) or mass spectrometric detection after different types of pre-separation, including gas or liquid chromatography ( Mawatari et al, 2007 ; Fauland et al, 2011 ; Lísa et al, 2017 ), capillary electrophoresis ( Zhang et al, 2017 ; Ly et al, 2021 ), ion mobility separation ( Vasilopoulou et al, 2020 ; Kirkwood et al, 2022 ), and supercritical fluid chromatography ( Lísa et al, 2017 ; Schoeny et al, 2020 ). In this section, we will focus on how to tackle the analytical challenge to discriminate between plasmanyl and plasmenyl lipids.…”