A relative quantitative positive/negative ion switching method for untargeted lipidomics via high resolution LC-MS/MS from any biological source

Abstract

Introduction: Advances in high-resolution mass spectrometry have created renewed interest for studying global lipid biochemistry in disease and biological systems.

Objectives: Here, we present an untargeted 30 min. LC-MS/MS platform that utilizes positive/negative polarity switching to perform unbiased data dependent acquisitions (DDA) via higher energy collisional dissociation (HCD) fragmentation to profile more than 1000-1500 lipid ions mainly from methyl-tert-butyl ether (MTBE) or chloroform:methanol extractions.

Methods: The platform uses C₁₈ reversed-phase chromatography coupled to a hybrid QExactive Plus/HF Orbitrap mass spectrometer and the entire procedure takes ~10 h from lipid extraction to identification/quantification for a data set containing 12 samples (~4 h for a single sample). Lipids are identified by both accurate precursor ion mass and fragmentation features and quantified using Lipid-Search and Elements software.

Results: Using this approach, we are able to profile intact lipid ions from up to 18 different main lipid classes and 66 subclasses. We show several studies from different biological sources, including cultured cancer cells, resected tissues from mice such as lung and breast tumors and biological fluids such as plasma and urine.

Conclusions: Using mouse embryonic fibroblasts, we showed that TSC2^-/- KD significantly abrogates lipid biosynthesis and that rapamycin can rescue triglyceride (TG) lipids and we show that SREBP^-/- shuts down lipid biosynthesis significantly via mTORC1 signaling pathways. We show that in mouse EGFR driven lung tumors, a large number of TGs and phosphatidylmethanol (PMe) lipids are elevated while some phospholipids (PLs) show some of the largest decrease in lipid levels from ~ 2000 identified lipid ions. In addition, we identified more than 1500 unique lipid species from human blood plasma.

Keywords: Biomarkers; Cancer; Disease; LC-MS/MS; Lipidomics; Mass spectrometry; Polarity switching; Profiling; Quantification; Shotgun.

Fig. 1

Lipidomics platform schematic. Overview of the untargeted LC-MS/MS experiment for lipid metabolite profiling via data dependent acquisition (DDA) using positive/negative switching from a single 28 min reversed phase column run while routinely identifying and quantifying more than 1000 compounds

Fig. 2

LipidSearch software workflow. a RAW data files from the high resolution QExactive Plus are loaded into the LipidSearch software which then integrates the MS1 peak areas, and then proceeds to interpret the MS2 fragment ions for lipid ion identification based on an internal database query. The lipid molecules are then scored to accept the top scoring lipid and rated using a quality assessment of A–D. During the peak alignment step, the lipid ion peak areas are compared across the sample set and relative quantification is performed. b Lipids are identified based on the major structural units of lipid molecules including the lipid backbone, head group, fatty acid chain composition and adduct ions within each polarity mode. For this to be successful, high mass accuracy is necessary

Fig. 3

Lipid standards and quantitative validation. a The bar plot with error bars from three replicate injections for the MS1 peak are as show the quantitative accuracy of the lipid standards LPC, SM and TG mix from 4 different concentration dilutions (1:1, 1:2, 1:4 and 1:8); the 1:1 conc. = 125 μg/mL. b The SM lipid standard fatty acid profile with relative peak areas and error bars. c The relative peak areas correlate with the injected sample concentration on the level of single lipid ions as seen with the sphingomyelin lipid SM(16:0/24:1) which include CV values. d The same single ion quantification of the phosphatidylinositol PI(18:0/20:4)

Fig. 4

Lipid class and fatty acid profiles. a The overall lipid class profile across all identified lipids from extracts (~5 × 10⁵ cells injected) of TSC2−/− MEF cells from three biological replicates per condition. PC lipids are the most abundant in MEFs followed by PE, SM, TG, DG, PI and PS. Across the four cellular conditions, TG was the most responsive by increasing upon stimulation with 20nM of the TOR inhibitor Rapamycin (condition S3) as compared to untreated TSC2−/− cells (condition S1). b The fatty acid profile for the TG lipid class showing that the majority of the fatty acid chains in triglycerides in MEFs contain the basic lipid building blocks of palmitate, palmitoleate, oleate and stearate. The data also shows that the majority of the 249 TG lipid ions are elevated when TSC2−/− cells are treated with Rapamycin

Fig. 5

Biostatistical analysis of TSC2 cell lipidomics data. a PC1 vs. PC2 PCA clustering analysis of TSC2+/+ mouse embryonic fibroblast cells, TSC2−/− cells, TSC2−/− with siSREBP cells and TSC2−/− with Rapamycin treated cells. The different sample types cluster distinctly and the biological replicates cluster tightly. b Sample clustering using the WARD method showing that each TSC2 sample type and replicates cluster as expected. c The basic lipid biosynthesis showing the major enzymes and pathways including the metabolic pathways and signaling pathways via mTOR leading to fatty acid synthesis via ACC, FAS and SREBP regulation. d A heat map of the same biological sample conditions as in A and B showing specific groups of lipid classes and their regulation

Fig. 6

Global lipid regulation in lung tumor tissue. a A scatterplot (Log10 Intensity vs. Log2 Ratio) showing the distribution of 2,062 identified lipid ions via LipidSearch from lipids extracted from three biological replicates of both T790M EGFR driven mouse lung tumor and normal mouse lung tissue extracts (~5 mg injected). b A bar plot of the most regulated lipid ions from the lung tumor and normal lung tissue experiment. The most regulated lipids suggest that a higher concentration of TG lipids is upregulated while more phospholipids are down-regulated