Lipidomics: A Comprehensive Overview

Lipidomics is a scientific discipline investigating the molecular diversity, structural characteristics, and functional roles of lipids within biological systems, as well as their involvement in physiological and pathological mechanisms. Beyond serving as essential constituents of cellular membranes, lipids participate critically in processes such as cellular signaling, energy homeostasis, and metabolic modulation. The evolution of mass spectrometry (MS) technologies has positioned lipidomics as a cornerstone of systems biology, enabling precise characterization of lipid species at unprecedented resolution. This review provides a systematic examination of lipidomics, addressing its core components: lipid classification, analytical methodologies, biological and clinical applications, experimental workflows, computational data interpretation, and accessible lipidomic databases.

Classification of Lipidomics Approaches

Based on methodological frameworks and analytical objectives, lipidomics is broadly categorized into targeted and untargeted strategies, each with distinct operational paradigms and applications.

1. Targeted Lipidomics

• Definition: Focuses on precise quantification of predefined lipid species or classes.
• Key Attributes:
  • Exceptional sensitivity and specificity for known lipid targets.
  • Optimized for hypothesis-driven validation studies.

2. Untargeted Lipidomics

• Definition: Systematically characterizes the complete lipid repertoire within biological specimens without a priori target restrictions.
• Key Attributes:
  • High-throughput capability for global lipidome profiling.
  • Discovery-oriented design for novel lipid identification.

If you want to know more about the difference between metabolomics and lipidomics, please refer to "Metabonomics and Lipidomics: Correlation and Difference".

Untargeted lipidomics workflow and data processing (Xiong L et al., 2024).

Methodological Framework in Lipidomics Research

Lipidomics employs an integrated workflow spanning sample preparation to computational analysis, utilizing advanced technologies to decipher the complexity of lipid dynamics. Below is a structured overview of key methodologies and innovations:

1. Sample Preparation Strategies

Extraction Techniques

• Solvent-based approaches:
  • Chloroform-methanol mixtures (e.g., Folch protocol) preferentially isolate polar lipid classes (phospholipids, sphingolipids).
  • Methyl tert-butyl ether (MTBE) demonstrates enhanced efficiency for nonpolar species (e.g., triglycerides).
• Microscale extraction: Microfluidic platforms enable high-yield lipid recovery from limited samples (e.g., single-cell lipidomics).

Purification Methods

• Solid-phase extraction (SPE):
  • C18 columns effectively eliminate phospholipid contaminants.
  • Silica columns selectively enrich polar lipids.
• Liquid-liquid partitioning: Hexane-isopropanol systems separate neutral lipids (e.g., cholesteryl esters).

Derivatization Protocols

• Fatty acid methylation: BSTFA-mediated derivatization enhances GC-MS compatibility for volatile analysis.
• Hydroxyl group stabilization: Silanization improves analytical robustness for lipids like triacylglycerols.

2. Mass Spectrometry (MS) Platforms

Ionization Techniques

• MALDI-TOF/TOF: Enables spatial lipid mapping in tissues (e.g., tumor lipid heterogeneity via imaging).
• APCI/ESI: Facilitates concurrent detection of polar/nonpolar lipids in biofluids (e.g., plasma lipidome).

Mass Analyzers

• Orbitrap Fusion Lumos: Delivers ultra-high mass accuracy (<1 ppm) and multi-stage fragmentation capabilities, enabling detailed structural elucidation (e.g., sn-positional isomer discrimination).
• QTRAP 6500: Combines ion trapping with MRM for sensitive quantification of low-abundance lipids (e.g., ceramide flux during apoptosis).

Acquisition Modes

• Data-dependent (DDA): Prioritizes unknown lipid discovery (e.g., oxidized species).
• Data-independent (DIA): Enhances reproducibility in large-scale studies via All-ion Fragmentation.

If you want to know more about lipidomics mass spectrometry, please refer to "Comprehensive Analysis of Lipidomics Using Mass Spectrometry".

3. Computational Lipidomics

Preprocessing Tools

• XCMS Online: Mitigates batch effects via automated peak alignment and normalization.
• Progenesis QI: Integrates multi-platform data (LC-MS, GC-MS) for comprehensive lipid coverage.

Annotation Approaches

• High-resolution MS/MS: Combines exact mass (<5 ppm error) with fragmentation patterns (e.g., CID) for lipid subclass identification.
• AI-driven annotation: Deep learning models (e.g., LipidAI) accelerate characterization of novel lipids.

Biological Interpretation

• Pathway analysis: KEGG/LipidMaps-based enrichment identifies dysregulated routes (e.g., arachidonate-mediated inflammation).
• Network modeling: Cytoscape visualizes lipid-metabolite-gene interactions (e.g., ACSL1 regulatory hubs).

4. Cutting-Edge Innovations and Challenges

• Spatially Resolved Lipidomics: MALDI/DESI-MSI reveals region-specific lipid alterations (e.g., glioma margin lipid dysregulation).
• Single-Cell Analysis: Microfluidic-nanoESI platforms resolve lipid heterogeneity (e.g., drug-resistant cancer cell signatures).
• Standardization Needs: Inconsistent nomenclature (e.g., sn-position annotation) and data harmonization require global initiatives (e.g., LIPID MAPS consortia).

Workflow for Lipidomics Analysis

Lipidomics investigations require systematic experimental design and multidisciplinary technological integration to decode metabolic regulatory networks. Below is a refined workflow encompassing critical experimental and analytical phases:

1. Specimen Acquisition and Stabilization

Sample-Specific Protocols

• Biofluids (plasma/serum): Rapid centrifugation to remove cellular debris, supplemented with antioxidant additives (e.g., EDTA) to inhibit lipid peroxidation.
• Tissues: Flash-freezing in liquid nitrogen followed by −80°C storage to prevent enzymatic degradation (e.g., phospholipase-mediated hydrolysis).
• Cellular specimens: Immediate metabolic quenching post-harvest (e.g., methanol/chloroform deactivation) to arrest enzymatic activity.

Storage Optimization

• Aliquot samples to avoid freeze-thaw-induced vesicle disruption; utilize cryovials for hermetic sealing.

2. Advanced Sample Preparation

Extraction Innovations

• MTBE-based protocols: Enhance polar/nonpolar lipid co-extraction efficiency, outperforming Folch methods for short-chain lipid recovery.
• Microfluidic enrichment: Minimizes contamination while maximizing yield for trace specimens (e.g., single-cell lipidomics).

Purification Advancements

• Molecularly imprinted polymers (MIPs): Selective isolation of target lipids (e.g., oxidized phospholipids) by excluding high-abundance interferents.

Derivatization techniques

• Reductive stabilization (NaBH₄) of free fatty acids for GC-MS reliability.
• Isotopically labeled derivatization (¹³C) for metabolic flux tracing (e.g., β-oxidation kinetics).

3. Mass Spectrometry Configuration

Ionization Strategies

• ESI polarity switching: Positive mode for phosphatidylcholines (PC), negative mode for phosphatidylserines (PS).
• Ambient imaging: DESI-MSI enables direct spatial lipid profiling (e.g., tumor margin heterogeneity).

Analyzer Capabilities

• Orbitrap Exploris 480: Ultrahigh resolution (1,000,000 FWHM) resolves sn-positional isomers (e.g., PC 16:0/18:1 vs. 18:1/16:0).
• Q-TOF hybrids: Deliver<1 ppm mass accuracy with MS/MS fragmentation for novel lipid annotation.

Acquisition Modes

• DIA-based acquisition: Fixed m/z windows (1 Da) enable high-throughput quantification (e.g., clinical lipidome screening).
• Intelligent DDA: Adaptive fragmentation ranges optimize discovery of unknown lipids (e.g., oxidized species).

For more information about MS-Lipomics, please refer to "Comprehensive Analysis of Lipidomics Using Mass Spectrometry".

4. Computational Pipeline

Data Harmonization

• ProteoWizard: Converts vendor-specific raw data (e.g., Thermo RAW→mzML) for cross-platform compatibility.
• LOESS normalization: Corrects batch effects across large datasets.

Annotation Frameworks

• Multi-database integration: Cross-reference LIPID MAPS (structures), HMDB (pathways), and MoNA (spectral libraries).
• Deep learning: CNN models (e.g., LipidCNN) predict structural features (e.g., glycerol backbone isomers).

Biological Contextualization

• Isotope flux modeling: Quantify lipid turnover rates using ¹³C-tracers (e.g., palmitate β-oxidation).
• Network pharmacology: Cytoscape-based "lipid-metabolite-gene-drug" networks identify regulatory hubs (e.g., SREBP-1c targets).

5. Quality Assurance & Standardization

Internal Standardization

• Isotopically labeled standards (¹³C-PC 16:0/18:1): Correct extraction/ionization biases.
• Class-specific surrogates (PC 15:0/18:1-d7): Monitor instrument performance drift.

Benchmarking Tools

• LIQA Toolkit: Implements ComBat algorithms for batch effect removal.
• NIST SRM 1950: Validates method accuracy via certified plasma lipid reference ranges.

Simplified workflow of untargeted lipidomics using serum and CSF (Galper J et l., 2024).

Lipidomics Data Analytics and Database

The systematic integration of computational frameworks and database resources forms the foundational pillars for investigating dynamic fluctuations within complex lipidomes. This requires synergistic application of multidimensional analytical tools to ensure precise molecular annotation and biological contextualization. Below is a structured overview of methodological advancements and resource developments:

1. Advanced Data Analytics

Preprocessing Innovations

• Peak Detection Algorithms:
  • XCMS Online: Enhances low-abundance lipid detection via non-parametric statistical approaches (LOESS/Wavelet-based smoothing).
  • Progenesis QI: Facilitates cross-platform data integration (LC-MS/GC-MS) through alignment optimization.
• Noise Reduction Strategies:
  • Wavelet Decomposition: Suppresses high-frequency noise artifacts while preserving lipid-specific spectral features.
  • Robust LOESS Regression: Resists outlier interference in complex matrices (e.g., plasma, tissue homogenates).
• Normalization Techniques:
  • Total Ion Current (TIC): System-wide normalization for studies lacking internal standards.
  • Endogenous Lipid Standards: Utilizes stable species (e.g., PC 16:0/18:1) for relative quantitation.

Lipid Annotation Frameworks

• Multidimensional Fragmentation Analysis:
  • Collision-induced dissociation (CID) and higher-energy collisional dissociation (HCD) differentiate isomeric species (e.g., PE 36:1 vs. PE 34:2).
  • Computational Fragmentation Prediction Tools: LIPID MAPS-derived algorithms simulate lipid cleavage patterns.
• Novel Lipid Discovery:
  • Deep Neural Networks: Models like LipidCNN predict structural features (e.g., sn-positional isomers in sphingomyelins).
  • Hierarchical Fragmentation Trees: MS-Tree infers lipid backbones via tiered fragment analysis.

Statistical and Biological Interpretation

• Multivariate Statistical Modeling:
  • OPLS-DA: Discriminates intergroup lipid variations (e.g., LysoPC 18:0 elevation in hepatocellular carcinoma).
  • t-SNE and UMAP Algorithms: Visualize high-dimensional lipid datasets (e.g., diabetic vs. healthy cohorts).
• Pathway and Network Analysis:
  • KEGG Pathway Mapper: Identifies metabolic routes enriched with differentially abundant lipids (e.g., arachidonate-inflammatory axis).
  • Cytoscape Plugins: Construct interaction networks (lipid-gene-metabolite) to pinpoint regulatory nodes (e.g., ACSL1 in fatty acid oxidation).

2. Evolving Database Ecosystem

Core Resources

• LIPID MAPS:
  • Structural Taxonomy: Encompasses 37 lipid categories (e.g., glycerophospholipids, sphingolipids) and subclasses (PE, PC).
  • Pathway Mapping: Details biosynthesis/degradation routes (e.g., Kennedy pathway).
• HMDB:
  • Clinical Annotations: Documents lipid alterations in pathologies (e.g., oxidized phospholipids in atherosclerosis).
  • Isotope Tracing: Supports flux analysis (e.g., ¹³C-palmitate β-oxidation kinetics).
• LipidBlast:
  • Curated MS/MS Spectral Repository: 10,000+ reference spectra for targeted identification.

Specialized and Emerging Databases

• SwissLipids: Structural Prediction Algorithms: Generates candidate structures for unannotated sn-positional variants.
• Lipidomics Commons: Collaborative Data Repository: FAIR-compliant platform for dataset sharing and reuse.
• JLipidDB: Population-Specific Lipid Signatures: Captures East Asian lipid profiles influenced by dietary omega-3 intake.

For more information about lipidomics database, please refer to "Overview of Lipidomics Databases".

AI-Driven Annotation Tools

• LipidMatch: Open-source annotation pipeline leveraging ML algorithms (>90% accuracy for unknowns).
• DeepLipid: Transformer-based architectures predict spectral patterns to accelerate novel lipid discovery.

3. Methodological Integration

Applications of Lipidomics in Biomedical and Life Sciences

Lipidomics has emerged as a transformative discipline with broad applications across biological research and clinical practice, driving advancements in four key domains:

1. Disease Pathogenesis and Biomarker Discovery

• Diagnostic Biomarker Identification: Comparative lipidomic profiling between physiological and pathological states enables the detection of disease-associated lipid signatures. e.g., This investigation employed untargeted lipidomic profiling integrated with machine learning algorithms to screen for serum biomarkers associated with polycystic ovary syndrome (PCOS). The analysis identified phosphatidylinositol PI(18:0/20:3)-H and plasmenyl phosphatidylethanolamine PE(18:1p/22:6)-H as candidate diagnostic biomarkers. The predictive model incorporating these lipids achieved an area under the curve (AUC) of 0.815 in the validation cohort, demonstrating 74% accuracy and 88% specificity, highlighting their potential utility for PCOS diagnosis (Chen JY et al., 2025).
• Mechanistic Insights: Elucidates lipid metabolic dysregulation in pathologies. e.g., This investigation employed untargeted lipidomic profiling to characterize lipid metabolic reprogramming driven by PIK3CA hotspot mutations (E545K and H1047R) in breast cancer models. Cells harboring these mutations exhibited pronounced elevations in oncogenic lipid mediators, including sialylated gangliosides and pro-metastatic lipid classes (e.g., triglycerides, ceramides), alongside depletion of tumor-suppressive lipids such as lysophosphatidylcholines and phosphatidylcholines. These alterations confirm that PI3K/AKT pathway activation promotes malignant phenotypes through systemic lipidomic remodeling, establishing a direct link between oncogenic signaling and lipid metabolism dysregulation in cancer progression (Jung JH et al., 2023).

2. Pharmaceutical Innovation

• Therapeutic Target Validation: Investigates pharmacological modulation of lipid pathways. e.g., This investigation elucidated the pharmacological mechanism of Ramulus Mori alkaloids (SZ-As) in treating non-alcoholic fatty liver disease (NAFLD) using an HFD/STZ-induced murine model. SZ-As markedly attenuated body weight gain and hepatic lipid accumulation, suppressing lipogenic pathways while stimulating fatty acid β-oxidation, thermogenic activity, and choline metabolic processes. The intervention elevated cytoprotective lipid species, notably lysophosphatidylcholines (LPCs), downregulated pro-inflammatory mediators via TNF-α pathway suppression, and reduced pathogenic triglyceride accumulation. Therapeutic effects appeared mediated through systemic remodeling of glycerophospholipid metabolism and modulation of lipid-inflammatory crosstalk, offering mechanistic insights to support natural product-based NAFLD therapeutics (Wang F et al., 2023).
• Toxin assessment: monitoring the lipid proteomic changes in metabolism caused by toxins. e.g., This investigation employed oral cantharidin (CTD) administration to establish a murine model of hepatotoxicity, integrating biochemical profiling, histopathological examination, and lipidomic analysis to delineate CTD's liver injury mechanisms. CTD exposure significantly elevated serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels, driving hepatocellular necrosis and inflammatory infiltration. Lipidomic profiling revealed dysregulation of 58 lipid species, including upregulated lysophosphatidylcholines (e.g., LPC 20:4) and phosphatidylcholines (e.g., PC 22:6e/2:0), alongside downregulated cholesteryl esters (e.g., CE 16:0) and triacylglycerols (e.g., TAG 18:1/18:1/20:0). These alterations were linked to perturbations in glycerophospholipid, glycerolipid, and glycosylphosphatidylinositol (GPI)-anchored metabolic networks. Mechanistically, GPI pathway hyperactivity induced phosphatidylethanolamine (PE) accumulation, synergizing with glucose-lipid metabolic disarray to destabilize hepatic lipid homeostasis and amplify liver injury (Li S et al., 2024).

3. Nutritional Science and Metabolic Health

• Dietary Impact Studies: Analyzes lipidomic adaptations to dietary interventions. e.g., This investigation evaluated the metabolic consequences of prolonged low-sodium dietary regimens in LDL receptor-deficient murine models. Over a 90-day intervention, mice receiving a 0.06% sodium diet exhibited significant metabolic perturbations, including increased body weight (+9%), elevated plasma triglycerides (+51%), hyperglycemia (+19%), and heightened insulin resistance (+46%). Skeletal muscle analysis revealed marked upregulation of lipid uptake/oxidation markers (Fabp3: +106%, Prkaa1: +46%, Cpt1: +74%), alongside notable increases in glycerophospholipids (phosphatidylcholine: +68%; phosphatidylinositol: +90%) and free fatty acids (+59%). Concurrently, cardiac adipose tissue mass decreased (-41%), while acyl carnitine levels rose. These findings demonstrate that chronic sodium restriction exacerbates glucolipid metabolic dysregulation through muscle-specific lipidomic remodeling—characterized by glycerophospholipid-fatty acid imbalance—thereby underscoring the potential risks of stringent sodium limitation in precipitating metabolic dysfunction (Pinto PR et al., 2021).
• Metabolic Disorder Research: Explores links between lipid metabolism and conditions such as obesity and type 2 diabetes. e.g., This investigation delineated the dynamic remodeling of lipid metabolic networks in type 2 diabetes mellitus (T2DM) through integrative untargeted and targeted lipidomic profiling. Analysis of a cohort comprising 155 individuals identified 44 significantly altered lipid species in newly diagnosed T2DM patients versus healthy controls, with 29 lipidomic alterations observed in high-risk subjects. These perturbations were characterized by dysregulation in sphingolipid, phosphatidylcholine, and sterol ester metabolic axes, implicating these pathways in driving insulin resistance and oxidative damage. Notably, 13 lipid species exhibiting progressive abundance shifts correlating with disease duration were identified, offering candidate biomarkers for early detection and disease progression tracking. These findings underscore the central role of lipid metabolic reprogramming in T2DM pathogenesis, bridging molecular dysregulation to clinical disease trajectories (Feng L et al., 2024).

4. Plant and Microbial Lipid Biology

• Plant Lipidomics: Investigates lipid roles in growth regulation, stress adaptation and lipid-mediated defense mechanisms. e.g., This investigation utilized proteomic approaches to elucidate the temporal dynamics of triglyceride (TAG) biosynthesis in rapeseed (Brassica napus) during seed development. At the peak lipid accumulation phase (27 days after flowering, DAF), distinct molecular profiles emerged between TAG and diacylglycerol (DAG)—TAG demonstrated enrichment in stearic acid and long-chain fatty acids, whereas DAG remained undetectable. This differentiation underscores the predominance of the DGAT pathway in TAG biosynthesis. Phospholipid analysis revealed metabolic autonomy between phosphatidylcholine (PC) and phosphatidylethanolamine (PE), with PC's sn-2 position preferentially incorporating unsaturated fatty acids. Comparative analysis confirmed DGAT's superior efficiency over PDAT in TAG production, while PDAT potentially facilitates the integration of specialized fatty acids, including hydroxylated variants. These insights establish a foundation for genetically enhancing oil yield in crops through targeted pathway engineering (Woodfield HK et al., 2018).
• Microbial Lipidomics: Deciphers pathogen-host interactions through lipid signaling and antimicrobial resistance mechanisms. e.g., This pioneering investigation conducted the first comprehensive analysis of oral/gut microbial communities and lipidomic profiles in COVID-19 patients. Patients with active infections exhibited marked reductions in oral/fecal microbial diversity, characterized by depleted butyrate-producing species and enriched lipopolysaccharide-generating bacteria. A diagnostic model utilizing eight oral microbial markers (seven fecal biomarkers) demonstrated robust efficacy (87.24% accuracy) in cross-regional validation, with 92.11% precision in identifying IgG-positive suspected cases. Lipidomic profiling revealed significant depletion of sphingomyelins (e.g., SM d40:4) and monoacylglycerols (33:5) during active infection, contrasted by enrichment of 122 lipid species—including plasmenyl phosphatidylcholine (36:4p)—during convalescence (Ren Z et al., 2021).

Future Directions in Lipidomics Development

As a pivotal discipline within systems biology, lipidomics is poised for transformative advancements across three strategic domains:

1. Analytical Methodology Innovation

Advancements will focus on pioneering high-sensitivity platforms with enhanced mass resolution and detection limits, enabling precise characterization of low-abundance lipid species and structural isomers.

2. Computational Framework Advancement

Next-generation bioinformatic tools will be developed to streamline lipid annotation workflows, leveraging machine learning algorithms to improve annotation reliability and quantification precision across diverse biological matrices.

3. Multi-Omics Synergy

Strategic integration with complementary omics disciplines (e.g., genomics, proteomics) will unravel systemic interactions between lipid networks and molecular pathways, fostering holistic insights into biological regulation and disease mechanisms.

References

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2. Jung JH, Yang DQ, Song H, Wang X, Wu X, Kim KP, Pandey A, Byeon SK. "Characterization of Lipid Alterations by Oncogenic PIK3CA Mutations Using Untargeted Lipidomics in Breast Cancer." OMICS. 2023 Jul;27(7):327-335. doi: 10.1089/omi.2023.0076
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