Sample Prep for Lipidomic Mass Spec

Lipidomics, one of the ‘omics to emerge as a separate discipline, is finally getting the respect it deserves. This is in large part thanks to mass spectrometry (MS), which provides unambiguous identification, even for highly branched isomers. An MS result, however, is only as good as the sample.

Lipids are ubiquitous in living organisms and involved in most critical cellular processes. Lipids’ hydrophobicity helps them serve as barriers (e.g., biological membranes) that protect the inner components and workings of cells.

Structure defines preparation

Although they serve varying functions and are chemically heterogeneous, all lipids contain hydrocarbon chains constructed from either fatty acid, sphingosine, or isoprene building blocks. A lipid categorization system devised in 2005 classifies lipids into eight categories: fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterols, prenol lipids, saccharolipids, and polyketides, each with sub-classifications. More than 40,000 entries exist in The LIPID MAPS® Structure Database, a publicly accessible relational database of structures and annotations of biologically relevant lipids.

That all lipids dissolve in apolar solvents simplifies lipid sample preparation somewhat, but care is still required to maximize recovery, particularly of low-abundance species. The first step, sample acquisition, must occur rapidly or at sub-freezing temperatures to avoid enzymatic and chemical modifications to critical species. Generally, the more complex the sample milieu (e.g., blood vs. plant material) the more urgent this concern, particularly when oxidation is a concern.

Sample homogeneity is generally not an issue with liquid samples like bodily fluids, but becomes critical with tissues, cells, and many other solid samples. Thorough homogenization (or lysis for cells) provides extraction solvents with access to all lipids in the sample. Homogenization methods include grinding, crushing frozen tissue, bead-based milling, or any of dozens of commercially available homogenizers.

Liquid–liquid extraction achieves two goals: simplifying the sample through the elimination of proteins, genetic material, and other lipid-insoluble species, and to enrich the sample in analytes of interest. The composition of extraction solvents may be tailored to the analysis step, for example MS, chromatography, or separation followed by mass analysis.

Liquid-liquid extraction is the most commonly use sample preparation method in lipidomics. Among the several approaches are the Folch protocol and Bligh/Dyer methods. Both are based on partitioning the sample between methanol and chloroform. Methanol disrupts associations between lipids and proteins, while extracting the freed-up lipids into the chloroform layer. Bligh/Dyer also uses water to induce phase separation.

Many variations on binary or ternary phase extraction have been tried for preparing plant and animal samples for lipidomic analysis. Tweaks include the addition of a hydrochloric acid or ammonium acetate extraction step, water-saturated butanol with or without hexane extraction, extraction with petroleum ether, boiling with isopropyl alcohol followed by Bligh-Dyer, or a multi-step extraction method using isopropanol, chloroform, methanol, and water.

A method borrowed

Some general instrumental methods developed for proteomics or metabolomics work for lipids as well. For example, last year SCIEX introduced the ZenoTOF 7600 system, an accurate-mass liquid chromatography MS/MS system that employs the SCIEX Zeno trap plus novel ion fragmentation technology, electron activated dissociation (EAD). These features improve sensitivity, according to the company. Pulsing the Zeno trap overcomes the traditional duty cycle challenges of orthogonal TOF technology, delivering much-improved sensitivity to enable the routine detection of significant, low-abundant molecules.

With the ability to scan high molecular weight regions for proteomics, and the low end for metabolites, ZenoTOF fully characterizes individual lipids, including the location of unsaturation and side chain composition.

According to Maryam Goudarzi, Ph.D., Senior Manager of Metabolomics and Lipidomics at SCIEX, accurate lipid ID with high isomeric structural specificity are major hurdles to overcome in lipidomics. “Another challenge is accurate quantification of low abundance lipid species. Fibrous tissue and soil are two of the most challenging samples to prepare for analysis, but once this step is set, all samples go through individualized, optimized workflows to ensure high confidence in structural and quantitative data.”

Goudarzi explains the significance of the new analysis platform. “Until recently, the depth of metabolomics coverage was limited by sample size, MS/MS spectral richness, and sensitivity to quantitate low abundance lipids. This system addresses all three challenges and provides fast, deep lipid profiling through next-generation high resolution mass spectrometry. The addition of EAD uncovers key, unique MS/MS fragments that otherwise would be hidden while the Zeno trap significantly boosts quantitation of low-abundance metabolites. This is most evident in recently acquired data where we saw a twofold increase in the number of annotations on one-tenth the sample load compared to ‘SWATH DIA’.”

SWATH DIA is a SCIEX “Data Independent Acquisition” technique that allows comprehensive detection and quantitation of virtually every detectable compound in a sample. Zeno SWATH DIA is the next-generation version of the technique. “It serves as a powerful approach to marry untargeted and targeted lipidomics through deep, comprehensive, and quantitative metabolomics coverage,” Goudarzi tells Biocompare.

Filling in knowledge gaps

One reason so many ‘omics disciplines have sprung up is that none provides a complete picture of a test subject’s status. Hence the emergence of multi-omics. Many of these methods are in direct response to specific needs of drug developers. For example, in 2020 Hepion Pharmaceuticals, which specializes in liver diseases, introduced AI-POWR™ (Artificial Intelligence—Precision Medicine; Omics), an artificial intelligence (AI) platform for genomics, proteomics, metabolomics, transcriptomics, and lipidomics. AI-POWR assists in identifying and characterizing novel drug targets and biomarkers as they relate to Hepion’s ongoing program to develop therapies for non-alcoholic steatohepatitis. AI-POWR accesses structural information from publicly available databases and in-house multi-omic “big data” sets.

“Classical individual ‘omic studies result in the isolation of only one level of biological action,” explains Daren Ure, Ph.D., Chief Scientific Officer. “Any ‘omics analysis can identify drug and disease effects that may serve as biomarkers or pharmacodynamic endpoints. However, the combination of ‘omics is what reveals full biological effects and helps determine causal relationships. In fact, lipidomics analyses during our phase 2a study were not that helpful until they were integrated into a multi-omic analysis with transcriptomics, selected proteomics, and clinical traits. Only then were specific networks readily discernible. Our multi-omic analyses include RNA sequencing for transcriptomics, protein concentrations for proteomics, and lipid concentrations for lipidomics. These are analyzed separately with subsequent integration through multi-omic techniques.”

Hepion has developed multi-omic pipelines specifically for their methodology, but use external labs to measure the individual analytes. Owl, a metabolomics firm, runs Hepion’s lipidomic analyses from study subject serum, while transcriptomics are assessed through analysis of RNA-stabilized whole blood. Hepion performs total RNA sequencing using ribo-depletion, which allows sequencing of mRNA, pre-mRNA, and ncRNA on an Illumina NovaSeq instrument. To date, proteomics has focused on measuring protein in tissue and whole blood using mass spectrometry, with some collagen biomarkers quantified through competitive ELISA.

Hepion’s integrational approach involves several statistical treatments depending on the data type. Different tools are used, for example, in single ‘omics vs. multi-omics N-integration or multi-omics P-integration. N-integration refers to the same samples run through multiple ‘omics methods, whereas P-integration applies to separate studies using the same predictors.

Sample preparation for lipidomic MS studies is simplified by lipids’ apolar nature, but experimenters must process samples rapidly to minimize chemical degradation, and throughout the process avoid reagents that could interfere with MS analysis. Taking these precautions will assure the most reliable results, whether lipidomics is a standalone goal or part of a larger multi-omic investigation.