Untargeted vs. Targeted Lipidomics—Understanding the Differences

Lipidomics is the large-scale study of pathways and networks of cellular lipids in biological systems. Lipids are a diverse group of molecules that play essential roles in cell structure, energy storage, and signaling. By analyzing lipid profiles, researchers can gain insights into cellular processes, disease mechanisms, and potential therapeutic targets. Lipidomics has applications in various fields, including cancer research, neurobiology, cardiovascular studies, and metabolic disorders.

Untargeted lipidomics and targeted lipidomics are two approaches used in lipidomics, the study of lipids and their functions in biological systems.

A workflow of MS-based analytical strategies for untargeted, targeted and pseudotargeted lipidomics. (Xu et al., 2020)

Untargeted Lipidomics

Untargeted lipidomics is a comprehensive, unbiased approach aimed at identifying and quantifying as many lipid species as possible within a biological sample. This exploratory technique does not predefine the lipids of interest, thereby allowing for the discovery of novel and unexpected lipid species.

Methodology of Untargeted Lipidomics

Sample Preparation

The process of untargeted lipidomics begins with meticulous sample preparation to ensure accurate and reproducible results. Key steps include:

• Extraction: Lipids are extracted from biological samples (e.g., blood, tissues) using solvents like chloroform-methanol or methyl tert-butyl ether (MTBE). This step separates lipids from other biomolecules such as proteins and nucleic acids.
• Derivatization (if necessary): Some lipids may require chemical modification to enhance their detection and quantification by mass spectrometry.

Analytical Techniques

Mass Spectrometry (MS)

Mass spectrometry is the cornerstone of untargeted lipidomics due to its high sensitivity, specificity, and ability to analyze complex lipid mixtures. Key MS techniques include:

• Time-of-Flight (TOF) MS: Provides high mass accuracy and resolution, making it ideal for identifying and quantifying a broad range of lipid species.
• Orbitrap MS: Known for its high resolution and mass accuracy, Orbitrap MS is widely used for detailed lipidome analysis.

Chromatography

Chromatographic techniques are often combined with mass spectrometry to separate lipid species before detection:

• Liquid Chromatography (LC): LC is frequently used to separate lipids based on their hydrophobicity. Reversed-phase LC (RPLC) and hydrophilic interaction chromatography (HILIC) are common methods.
• Gas Chromatography (GC): GC is used for the analysis of volatile lipids, such as fatty acids, after derivatization to increase their volatility.

Data Acquisition and Processing

Data acquisition in untargeted lipidomics involves the simultaneous detection of multiple lipid species, generating vast and complex datasets. Key steps in data processing include:

• Data Deconvolution: Separates overlapping signals to identify individual lipid species.
• Peak Identification: Matches detected peaks to lipid databases based on mass-to-charge ratios (m/z) and retention times.
• Quantification: Relative or absolute quantification of lipid species using internal standards.

Bioinformatics and Data Analysis

The complexity of untargeted lipidomics data necessitates advanced bioinformatics tools for interpretation. Key aspects include:

• Lipid Identification: Using software tools and lipid databases (e.g., LipidMaps, HMDB) to identify lipid species based on MS data.
• Statistical Analysis: Applying statistical methods to compare lipid profiles between different sample groups, identifying significant changes and potential biomarkers.
• Pathway Analysis: Mapping identified lipids to metabolic pathways to understand their biological roles and implications.

Applications of Untargeted Lipidomics

Hypothesis Generation: Use these profiles to identify patterns and correlations that may suggest novel biological mechanisms or pathways involved in disease processes.

Biomarker Discovery: Identify lipid species that are significantly altered, serving as potential diagnostic or prognostic biomarkers.

Disease Mechanism Elucidation: Reveal changes in lipid metabolism associated with various diseases, providing insights into disease mechanisms. For instance, alterations in lipid profiles have been linked to metabolic disorders, cardiovascular diseases, and neurodegenerative diseases.

Drug Development and Therapeutic Target Identification: Identify new therapeutic targets. Additionally, it can be used to monitor the effects of drug treatment on lipid metabolism, contributing to the development of more effective therapies.

Advantages of Untargeted Lipidomics

Comprehensive Profiling: Untargeted lipidomics offers a broad, unbiased view of the lipidome, capturing a wide range of lipid species. This comprehensive profiling is crucial for discovering novel lipids and understanding the full scope of lipid metabolism.

Discovery of Novel Lipids: The unbiased nature of untargeted lipidomics allows for the identification of previously unknown lipid species, expanding our knowledge of the lipidome and its biological significance.

Limitations of Untargeted Lipidomics

Data Complexity: The vast amount of data generated by untargeted lipidomics can be overwhelming and requires sophisticated bioinformatics tools for analysis and interpretation. This complexity can pose challenges in accurately identifying and quantifying lipid species.

Need for Advanced Bioinformatics: Interpreting the data from untargeted lipidomics requires robust bioinformatics infrastructure and expertise. The development and maintenance of these resources can be resource-intensive and require specialized knowledge.

Targeted Lipidomics

Targeted lipidomics is a focused analytical approach that quantifies specific lipid species within a biological sample. This method is hypothesis-driven and involves the precise measurement of predefined lipids, which are selected based on prior knowledge or findings from untargeted studies. By concentrating on known lipids, targeted lipidomics achieves higher sensitivity and specificity compared to untargeted approaches.

Methodology of Targeted Lipidomics

Sample Preparation

Similar to untargeted lipidomics, targeted lipidomics begins with careful sample preparation to ensure accuracy and reproducibility. Key steps include:

• Extraction: Lipids are extracted from biological samples using solvents like chloroform-methanol or methyl tert-butyl ether (MTBE). The extraction process isolates lipids from proteins, nucleic acids, and other biomolecules.
• Internal Standards: Stable isotope-labeled internal standards are added to samples to ensure accurate quantification and correct for any variations in extraction and analysis.

Analytical Techniques

Mass Spectrometry (MS)

Mass spectrometry is the primary analytical technique used in targeted lipidomics due to its precision and sensitivity. Key MS methods include:

• Multiple Reaction Monitoring (MRM): MRM, also known as selected reaction monitoring (SRM), is highly specific and sensitive. It monitors predefined precursor-product ion transitions for each target lipid, enabling precise quantification.
• Stable Isotope Dilution: The use of stable isotope-labeled internal standards ensures accurate quantification by correcting for matrix effects and variations in sample preparation and analysis.

Chromatography

Chromatography is used to separate lipids before MS analysis, enhancing specificity and reducing matrix effects:

• Liquid Chromatography (LC): Reversed-phase LC (RPLC) is commonly used to separate lipid species based on hydrophobicity. This technique is compatible with a wide range of lipids and is often coupled with MRM for targeted analysis.
• Gas Chromatography (GC): GC is used for volatile lipids, such as fatty acids, which may require derivatization to enhance their volatility and detectability.

Data Acquisition and Processing

Data acquisition in targeted lipidomics involves the precise measurement of specific lipid species. Key steps in data processing include:

• Peak Integration: Quantifying the area under the curve (AUC) for each target lipid peak in the chromatogram.
• Normalization: Using internal standards to normalize the data and correct for any variations in sample preparation and analysis.
• Quantification: Calculating the concentration of each target lipid based on calibration curves generated with known standards.

Bioinformatics and Data Analysis

Targeted lipidomics generates more manageable datasets compared to untargeted approaches, but still requires careful analysis. Key aspects include:

• Lipid Identification: Confirming the identity of target lipids based on their MS/MS spectra and retention times.
• Statistical Analysis: Comparing lipid concentrations between different sample groups to identify significant changes.
• Pathway Analysis: Mapping quantified lipids to metabolic pathways to understand their biological roles and implications.

Applications of Targeted Lipidomics

Hypothesis Testing: Targeted lipidomics is ideal for hypothesis testing, allowing researchers to validate findings from untargeted lipidomics or other studies. By focusing on specific lipids, it provides precise and reliable data to confirm or refute hypotheses.

Clinical Diagnostics: Enable the accurate quantification of lipid biomarkers, facilitating early diagnosis, disease monitoring, and personalized treatment strategies. For example, specific lipid profiles can serve as biomarkers for metabolic disorders, cardiovascular diseases, and cancer.

Therapeutic Monitoring: By tracking changes in specific lipid species, researchers can assess the efficacy of treatments and identify potential side effects, leading to more effective and safer therapies.

Mechanistic Studies: In mechanistic studies, targeted lipidomics provides detailed insights into the roles of specific lipids in biological processes. By quantifying key lipids, researchers can elucidate their functions in signaling pathways, membrane dynamics, and energy metabolism, contributing to a deeper understanding of cellular and molecular mechanisms.

Advantages of Targeted Lipidomics

High Sensitivity and Specificity: This ensures accurate and reliable quantification, even at low concentrations, making it suitable for detecting subtle changes in lipid metabolism.

Accurate Quantification: The use of stable isotope-labeled internal standards and precise analytical techniques enables accurate quantification of target lipids

Focused Analysis: By concentrating on specific lipids, targeted lipidomics generates more manageable datasets compared to untargeted approaches.

Limitations of Targeted Lipidomics

Limited Scope: Targeted lipidomics is restricted to predefined lipid species, potentially overlooking unknown or unexpected lipids. This limitation can result in a biased view of the lipidome and may miss important lipid alterations.

Potential Bias: The focus on known lipids can introduce bias, as it relies on prior knowledge and assumptions about the lipidome. This can limit the discovery of novel lipids and unexpected lipid changes.

Different Purposes of Experiments in Untargeted/Targeted Lipidomics

Untargeted lipidomics is a comprehensive approach that aims to identify and quantify all lipid species present in a sample, without prior knowledge of the specific lipid species of interest, and regardless of their known or unknown presence. This method provides a comprehensive overview of the lipid profile in a sample but may be limited by sensitivity, specificity, and the ability to accurately identify and quantify all lipid species. Untargeted lipidomics is commonly used in areas such as disease diagnosis, drug development, and fundamental biology research.

Targeted lipidomics is a hypothesis-driven approach that focuses on the analysis of specific lipids or lipid classes of interest. This approach provides quantification of the targeted lipids with high precision and accuracy, and is commonly used in areas such as biomarker discovery, pharmacokinetics and pharmacodynamics studies, and nutritional research. However, it provides a limited view of the lipid profile, and may miss other important lipids present in the sample.

The choice between untargeted lipidomics and targeted lipidomics depends on the research question and the specific goals of the analysis.

In general, untargeted lipidomics is best suited for exploratory studies, while targeted lipidomics is best suited for more focused and specific studies. Typically, untargeted lipidomics screening for differential lipids is followed by target validation using targeted lipidomics. The validation of untargeted lipidomics findings using targeted lipidomics provides increased confidence in the results and allows for more robust conclusions to be drawn from the lipidomics data.

Techniques and Instruments Used in Untargeted/Targeted Lipidomics

The primary instruments used for untargeted lipidomics and targeted lipidomics are mass spectrometers (MS) and liquid chromatography (LC) systems.

Untargeted lipidomics often employs liquid chromatography-mass spectrometry (LC-MS) techniques, such as shotgun lipidomics or liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) to identify and quantify all lipid species present in a sample.

• Shotgun lipidomics is a specific type of LC-MS that is focused on identifying and quantifying all lipid species in a sample. Shotgun lipidomics uses high-resolution mass spectrometry (HR-MS) to identify and quantify all lipid species present in a sample, without prior knowledge of the specific lipid species of interest. In this approach, lipids are extracted from a sample, separated by liquid chromatography, and then analyzed by HR-MS. The high-resolution mass spectrometry allows for the identification of many lipid species simultaneously.
• LC-MS/MS is a comprehensive and untargeted approach that uses liquid chromatography in combination with tandem mass spectrometry (MS/MS) to identify and quantify all lipid species in a sample. In this approach, lipids are extracted from a sample, separated by liquid chromatography, and then analyzed by tandem mass spectrometry. LC-MS/MS can be used in a "data-dependent" or "data-independent" manner. In a data-dependent manner, the mass spectrometer automatically switches between different analysis modes, such as full scan and product ion scan, to acquire information about the lipids in a sample. In a data-independent manner, the mass spectrometer collects information about all lipid species present in a sample without prior selection, providing a comprehensive overview of the lipid profile.

Targeted lipidomics primarily employs liquid chromatography-tandem mass spectrometry (LC-MS/MS) techniques. In this approach, the lipid species of interest are pre-selected and specific mass transitions are monitored using multiple reaction monitoring (MRM) or selected reaction monitoring (SRM) to accurately quantify the targeted lipids.

Examples of mass spectrometers used in lipidomics include quadrupole time-of-flight (Q-TOF), Orbitrap, and linear ion trap (LIT) mass spectrometers. Examples of liquid chromatography systems include reversed-phase LC, normal-phase LC, and hydrophilic interaction LC (HILIC).

The lipidomics workflow, including all essential steps from sample to biological outcome (Züllig et al., 2020)

At Creative Proteomics, we offer state-of-the-art targeted and untargeted lipidomics services to support a wide range of research needs. Our experienced team of scientists use advanced mass spectrometry and chromatography instrumentation to provide high-quality and reliable lipidomics data. Whether you need to focus on specific lipids of interest or gain a comprehensive view of the lipidome, our targeted and untargeted lipidomics services are tailored to meet your unique requirements. With years of experience in lipidomics research and a commitment to delivering accurate and reliable results, Creative Proteomics is the trusted partner for all your lipidomics needs.

References

1. Xu, Tianrun, et al. "Recent advances in analytical strategies for mass spectrometry-based lipidomics." Analytica chimica acta 1137 (2020): 156-169.
2. Züllig, Thomas, Martin Trötzmüller, and Harald C. Köfeler. "Lipidomics from sample preparation to data analysis: a primer." Analytical and bioanalytical chemistry 412 (2020): 2191-2209.

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