Lipidomic Analysis: A Beginner's Guide to Workflows and Data Interpretation

Lipidomic analysis is the large-scale study of lipid species within a biological system, using mass spectrometry as the primary analytical technology to identify and quantify the lipidome. For researchers new to the field, the workflow spans sample preparation, liquid-liquid or solid-phase extraction, chromatography, mass spectrometry acquisition, and data processing through various software packages. This guide provides a practical orientation to the decisions required at each stage, with emphasis on data quality assurance, adequate reporting, and the interpretation limits that define credible conclusions.

At a Glance

Lipidomics workflows differ fundamentally in their analytical goals, and the choice between targeted and untargeted approaches shapes every downstream decision. The table below summarizes the primary workflow options, their typical applications, and the key considerations for each.

Scope and Reader Context

This guide addresses researchers, students, analysts, and life-science professionals who are beginning to work with lipidomics data. The content covers the complete experimental pipeline from sample collection through data interpretation, with attention to the practical decisions that determine whether results are reproducible and biologically meaningful. The focus is on mass spectrometry-based lipidomics, which is the dominant analytical platform for this field. Nuclear magnetic resonance approaches exist but are less commonly used for large-scale lipid profiling.

The intended outcome is the ability to evaluate a lipidomics workflow critically, to understand what each stage contributes to data quality, and to recognize the limitations that constrain interpretation. This guide does not provide a specific laboratory protocol, because the appropriate choices depend on sample type, biological question, and available instrumentation. Instead, it provides the conceptual framework for making those choices and for assessing the quality of published lipidomics data.

Core Principles of Lipidomics

Lipids are natural substances found in all living organisms and are involved in many biological functions. Imbalances in lipid metabolism are linked to various diseases such as obesity, diabetes, or cardiovascular disease. Lipids comprise thousands of chemically distinct species, making them a challenge to analyze because of their great structural diversity. This structural diversity means that no single analytical method can capture the entire lipidome, and every workflow involves tradeoffs between coverage, specificity, and throughput.

The analytical challenge is compounded by the dynamic range of lipid concentrations in biological samples. Structural lipids such as phospholipids are abundant, while signaling lipids may be present at very low levels. The chemical diversity of lipids also creates analytical difficulties, because isomeric species can have identical masses but different biological functions. These factors explain why lipidomics requires careful attention to sample preparation, separation science, and mass spectrometry parameters.

Technological improvements in chromatography, high-resolution mass spectrometry, and bioinformatics have made it possible to perform global lipidomics analyses that allow the concomitant detection, identification, and relative quantification of hundreds of lipid species. The field has matured to the point where standardized workflows exist for common sample types, but the choice of workflow remains a critical decision that affects the biological conclusions that can be drawn.

Sample Preparation and Lipid Extraction

Sample preparation is the first and often most consequential stage of the lipidomics workflow. The goal is to extract lipids from the biological matrix while minimizing degradation, contamination, and loss of specific lipid classes. The choice of extraction method depends on the sample type and the lipid classes of interest.

Sample Types and Preprocessing

The sample type determines the preprocessing requirements. Plasma and serum require consideration of fasting time, handling procedures, and storage conditions, because these factors influence measured lipid levels. Tissue samples require homogenization before extraction, which obscures the spatial distribution of lipids within the tissue. Cells require washing to remove media components that could interfere with analysis.

For clinical studies, the high sensitivity of measured lipid levels to analytical, physiological, and environmental factors must be taken into account when designing the studies. Confounding factors such as age, gender, fasting time, and handling procedures can affect blood lipid metabolites. Researchers should document these variables and control them across experimental groups.

Liquid-Liquid and Solid-Phase Extraction

Liquid-liquid extraction is the most common approach for lipid isolation. The method partitions lipids into an organic phase while proteins and polar metabolites remain in the aqueous phase. The choice of solvents affects which lipid classes are recovered, and no single solvent system extracts all lipids with equal efficiency.

Solid-phase extraction provides an alternative that can separate lipid classes based on their polarity. This approach is more selective but requires method development for each sample type. The choice between liquid-liquid and solid-phase extraction should be based on the lipid classes of interest and the downstream analytical method.

Internal Standards and Quality Control

Internal standards are essential for quantitative lipidomics. These are isotopically labeled or structurally similar lipids that are added at known concentrations before extraction. They correct for losses during sample preparation and for matrix effects during mass spectrometry analysis. The selection of internal standards should match the lipid classes being measured.

Quality control samples should be included throughout the workflow. These include pooled biological samples that are analyzed repeatedly to monitor instrument performance and extraction consistency. Blank samples are necessary to identify contamination from solvents, glassware, and the instrument itself. The importance of optimization steps in addressing challenges from lipid contamination, especially in blanks, has been demonstrated in single-cell lipidomics applications.

Mass Spectrometry Acquisition

Mass spectrometry is the core analytical technology for lipidomics. The choice of instrument and acquisition mode determines the type of information that can be obtained about each lipid species.

High-Resolution Mass Spectrometry

High-resolution mass spectrometry enables untargeted lipidomics by providing accurate mass measurements that support putative identification. Orbitrap and quadrupole time-of-flight instruments are commonly used for this purpose. These instruments can detect hundreds of lipid species in a single run, but the identification confidence depends on the mass accuracy and the availability of tandem mass spectrometry data.

Liquid chromatography coupled to high-resolution mass spectrometry is the most common configuration for untargeted lipidomics. The chromatographic separation reduces matrix effects and provides an additional dimension of separation that improves identification confidence. Normal-phase chromatography separates lipids by class, while reversed-phase chromatography separates by fatty acyl chain length and degree of unsaturation.

Targeted Acquisition with Multiple Reaction Monitoring

Targeted lipidomics uses triple quadrupole instruments operating in multiple reaction monitoring mode. This approach provides high sensitivity and specificity for predefined lipid species. The method requires knowledge of the mass transitions for each lipid, which are typically established using authentic standards or published methods.

A targeted workflow using liquid chromatography-mass spectrometry can analyze over 1,000 species of lipids from five classes in a single run on the LC-MS. This includes sphingolipids, cholesteryl esters, neutral lipids, phospholipids, and fatty acids. Partial elucidation of the identity of neutral lipid aliphatic chains can be performed with this simple LC-MS setup.

Shotgun Lipidomics and Differential Mobility Spectrometry

Shotgun lipidomics involves direct infusion of the lipid extract into the mass spectrometer without chromatographic separation. This approach is faster than LC-based methods and avoids the potential for chromatographic artifacts. However, it is more susceptible to matrix effects and ion suppression.

Differential mobility spectrometry is highly useful for shotgun lipidomic analysis because it overcomes difficulties in measuring isobaric species within a complex lipid sample and allows for acyl tail characterization of phospholipid species. The workflow presents technical challenges, including the need to tune the DMS before every batch to update compensative voltages settings within the method.

Ion Mobility-Mass Spectrometry

Ion mobility adds an orthogonal separation dimension that can distinguish isomeric lipids that have identical masses but different structures. High-resolution ion mobility strategies provide marked improvements to resolving power that can differentiate small structural differences characteristic of isomers.

High-resolution demultiplexing utilizes multiplexed drift tube ion mobility spectrometry with post-acquisition algorithmic deconvolution to access high ion mobility resolutions while retaining the measurement precision inherent to the drift tube technique. In untargeted workflows, total lipid features were found to increase by 2.5-fold with HRdm compared to demultiplexed DTIMS as a consequence of more isomeric lipids being resolved.

Spatial Lipidomics

Conventional lipidomics methods based on liquid chromatography-mass spectrometry require tissue homogenization, which obscures the spatial distribution of lipids and precludes the analysis of their heterogeneity within complex tissue microenvironments. Spatial omics technologies such as mass spectrometry imaging and laser capture microdissection provide tools for the in situ and visual investigation of lipid spatial distribution.

Spatial lipidomics breaks through the bottleneck of losing spatial information in traditional methods and opens a path for further exploration of disease mechanisms and the discovery of new biomarkers in the spatial dimension. The approach faces challenges in quantitative accuracy, isomer identification, and spatial localization precision.

Data Processing and Software

Data processing transforms raw mass spectrometry data into a matrix of lipid identities and abundances. This stage involves feature detection, alignment, identification, and quantification. The choice of software affects the results, and different tools may produce different outcomes from the same raw data.

Feature Detection and Alignment

Feature detection identifies peaks in the mass spectrometry data that correspond to putative lipid species. This requires algorithms that can distinguish true signals from noise and background. Alignment matches features across samples so that the same lipid is compared consistently.

The output of feature detection is a data matrix with rows corresponding to lipid features and columns corresponding to samples. This matrix is the input for statistical analysis. The quality of this matrix depends on the parameters used for feature detection, and these parameters should be documented and reported.

Lipid Identification

Lipid identification assigns a chemical structure to each detected feature. This is the most challenging step in the workflow because of the structural diversity of lipids and the prevalence of isomeric species. Identification confidence is typically categorized by the level of evidence, with authentic standard comparison providing the highest confidence and accurate mass alone providing the lowest.

Software tools such as LipidSearch and LIQUID are used for lipid identification and quantification. These tools use databases of known lipid structures and fragmentation patterns to assign identities. The LIPID MAPS classification system provides a standardized nomenclature for lipid species.

Data Normalization and Batch Correction

Normalization corrects for differences in sample amount, extraction efficiency, and instrument response. Internal standards can be used to normalize the data matrix, providing normalized concentration values per lipids and samples. This is essential for quantitative comparisons across samples.

Batch effects are systematic variations that arise from instrument drift, reagent changes, and other technical factors. These effects can obscure biological differences and must be corrected before statistical analysis. Batch effect correction methods are available in specialized software tools.

Software Platforms for Lipidomics Data Analysis

ADViSELipidomics is a novel Shiny app for preprocessing, analyzing and visualizing lipidomics data. It handles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data from the Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species using LIPID MAPS classification and, together with information available on the samples, performs several exploratory and statistical analyses.

When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix, providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundant lipids in simple and complex experimental designs, dealing with batch effect correction. The tool has a user-friendly graphical user interface and supports an extensive series of interactive graphics.

Targeted versus Untargeted Approaches

The choice between targeted and untargeted lipidomics is a fundamental decision that shapes the entire workflow. Each approach has distinct strengths and limitations, and the choice should be guided by the biological question.

Untargeted Lipidomics

Untargeted lipidomics aims to detect and identify as many lipid species as possible without prior knowledge of which lipids are relevant. This approach is suited for discovery studies where the goal is to identify lipid species that differ between conditions. The output is a broad profile of the lipidome, but the identification confidence is lower than in targeted approaches.

Global discovery lipidomics can provide comprehensive chemical information toward understanding the intricacies of metabolic lipid disorders such as dyslipidemia. However, the isomeric complexity of lipid species remains an analytical challenge. Orthogonal separation strategies such as ion mobility can be inserted into liquid chromatography-mass spectrometry untargeted lipidomic workflows for additional isomer separation and high-confidence annotation.

Targeted Lipidomics

Targeted lipidomics measures a predefined set of lipid species with high sensitivity and specificity. This approach is suited for hypothesis-driven studies where the relevant lipids are known in advance. The method uses authentic standards or established mass transitions to achieve quantitative accuracy.

Targeted approaches are commonly used in clinical research where specific lipid classes are implicated in disease. The method provides absolute or relative quantification with lower limits of detection than untargeted approaches. However, the coverage is limited to the predefined lipid list, and unexpected lipid changes will not be detected.

Choosing Between Approaches

The choice between targeted and untargeted approaches depends on the research question, the available instrumentation, and the required quantitative accuracy. Untargeted approaches are appropriate for discovery and hypothesis generation, while targeted approaches are appropriate for validation and clinical application. Some studies use both approaches sequentially, with untargeted analysis identifying candidate lipids that are subsequently validated by targeted quantification.

Data Interpretation and Statistical Analysis

Data interpretation is the stage where biological meaning is extracted from the lipidomics data matrix. This involves statistical analysis to identify differentially abundant lipids, pathway analysis to place these changes in biological context, and careful consideration of the limitations of the data.

Exploratory Data Analysis

Exploratory analysis provides an overview of the data structure and identifies patterns and outliers. Principal component analysis is commonly used to visualize the relationships between samples and to identify clustering by experimental group. This analysis can reveal batch effects, sample contamination, and other technical issues before formal statistical testing.

Differential Abundance Analysis

Differential abundance analysis identifies lipid species that differ significantly between experimental groups. The choice of statistical test depends on the experimental design, the number of samples, and the distribution of the data. Multiple testing correction is essential because hundreds of lipids are tested simultaneously, and the false discovery rate should be controlled.

The results of differential analysis are often visualized as volcano plots, which display the magnitude of change against the statistical significance for each lipid. These plots provide a quick overview of the most promising candidate lipids for further investigation.

Pathway Analysis

Pathway analysis is one of the most commonly used approaches for the functional interpretation of metabolomics data. The approach places differentially abundant lipids in the context of known metabolic pathways, providing insight into the biological processes that are altered.

The approach is not well standardized, and the impact of different methodologies on the functional outcome is not well understood. The consideration of non-human native enzymatic reactions, such as those from microbiota, can affect the results. The exclusion of non-human native reactions led to detached and poorly represented reaction networks and to loss of information. The consideration of connectivity between pathways led to better emphasis of certain central metabolites in the network, however, it occasionally overemphasized the hub compounds.

Researchers should be aware of the capabilities and shortcomings of the currently used pathway analysis practices in metabolomics. The choice of pathway database and analysis method can affect the biological conclusions, and results should be interpreted with caution.

Interpretation Limits

Lipidomics data interpretation is constrained by several factors. Incomplete structural annotation is a common limitation, because many detected features cannot be assigned to a specific lipid structure. Isomeric ambiguity means that a single feature may represent multiple lipid species with different biological functions. Platform dependence means that results may differ between laboratories and instruments.

The gap between statistical discrimination and mechanistic validation is a frequent limitation. A lipid that differs between conditions is not necessarily causally involved in the biological process. Direct evidence linking specific lipid species to particular biological outcomes remains limited in many applications.

Workflow Diagram

The following diagram illustrates the major stages of a lipidomics workflow and the decisions required at each stage.

Sample Collection
    |
    v
Sample Preprocessing (fasting time, handling, storage)
    |
    v
Lipid Extraction (liquid-liquid or solid-phase)
    |
    v
Internal Standards and Quality Control Samples
    |
    v
Mass Spectrometry Acquisition
    |
    +--> Untargeted (high-resolution MS, LC-MS, ion mobility)
    |
    +--> Targeted (multiple reaction monitoring, triple quadrupole)
    |
    +--> Shotgun (direct infusion, differential mobility spectrometry)
    |
    v
Data Processing (feature detection, alignment, identification)
    |
    v
Normalization and Batch Correction
    |
    v
Statistical Analysis (exploratory, differential abundance)
    |
    v
Pathway Analysis and Biological Interpretation
    |
    v
Reporting and Data Deposition

Practical Implementation Steps

For researchers beginning a lipidomics study, the following steps provide a structured approach to workflow design and execution.

Step 1: Define the Biological Question

The biological question determines the choice of workflow. Discovery studies require untargeted approaches, while validation studies require targeted quantification. The sample type, number of samples, and available instrumentation should be considered at this stage.

Step 2: Design the Study

The study design should account for the high sensitivity of measured lipid levels to analytical, physiological, and environmental factors. Confounding factors such as age, gender, fasting time, and handling procedures should be controlled or documented. The number of biological and technical replicates should be sufficient for the planned statistical analysis.

Step 3: Validate the Extraction Method

The extraction method should be validated for the specific sample type. Recovery of representative lipid classes should be assessed using internal standards. Blank samples should be analyzed to identify contamination sources.

Step 4: Optimize the Mass Spectrometry Method

The mass spectrometry method should be optimized for the lipid classes of interest. This includes chromatographic separation, ionization parameters, and acquisition settings. For shotgun approaches, the differential mobility spectrometry settings should be tuned before every batch.

Step 5: Process the Data with Documented Parameters

Data processing parameters should be documented and reported. The choice of software and settings affects the results, and transparency is essential for reproducibility. The data matrix should be inspected for quality before statistical analysis.

Step 6: Perform Statistical Analysis with Appropriate Corrections

Statistical analysis should account for multiple testing and batch effects. The choice of test should match the experimental design and data distribution. Results should be visualized to identify patterns and outliers.

Step 7: Interpret Results with Awareness of Limitations

Biological interpretation should acknowledge the limitations of the data. Incomplete annotation, isomeric ambiguity, and the gap between statistical discrimination and mechanistic validation should be discussed. Pathway analysis results should be interpreted with awareness of methodological shortcomings.

Step 8: Report and Deposit Data

Data reporting should follow community standards for lipidomics. The FAIR Guiding Principles provide a framework for making data findable, accessible, interoperable, and reusable. Data deposition in public repositories supports reproducibility and enables meta-analyses.

Records and Measurements

Accurate record keeping is essential for reproducible lipidomics. The following records should be maintained for each study.

Sample Metadata

Sample metadata includes the source of each sample, collection date, handling procedures, storage conditions, and any relevant clinical or biological information. This information is essential for interpreting the results and for identifying confounding factors.

Extraction Records

Extraction records document the method used, the solvents and reagents, the internal standards added, and the quality control samples included. Any deviations from the standard protocol should be recorded.

Instrument Records

Instrument records document the mass spectrometry settings, the chromatographic conditions, and the tuning and calibration results. For shotgun approaches, the differential mobility spectrometry settings should be recorded for each batch.

Data Processing Records

Data processing records document the software versions, the parameters used for feature detection and identification, and the normalization and batch correction methods. These records are essential for reproducing the analysis.

Quality Control Records

Quality control records document the performance of the instrument and the extraction process over time. This includes the results from pooled quality control samples, blanks, and internal standards. Trends in these measurements can identify emerging problems.

Common Failure Patterns

Several recurring problems can compromise lipidomics studies. Recognizing these patterns early can prevent wasted effort and invalid conclusions.

Contamination from Blanks

Lipid contamination is a common problem, especially in the analysis of low-abundance samples. Contamination can arise from solvents, glassware, plasticware, and the instrument itself. The importance of optimization steps in addressing challenges from lipid contamination, especially in blanks, has been demonstrated in single-cell applications. Blank samples should be analyzed throughout the workflow to identify contamination sources.

Batch Effects

Batch effects are systematic variations that arise from instrument drift, reagent changes, and other technical factors. These effects can obscure biological differences and lead to false conclusions. Batch effect correction methods should be applied when the experiment includes multiple batches.

Isomeric Ambiguity

Many lipid features represent multiple isomeric species with identical masses but different structures. This ambiguity limits the biological interpretation of the data. Ion mobility separation can resolve some isomers, but not all. Researchers should acknowledge this limitation in their interpretation.

Overinterpretation of Pathway Analysis

Pathway analysis results can be misleading if the methodological limitations are not recognized. The consideration of non-human native reactions and the interconnectivity of pathways affect the results. The overemphasis of hub compounds can lead to incorrect conclusions about the importance of specific metabolites.

Inadequate Quality Control

The absence of quality control samples, blanks, and internal standards compromises the validity of the results. Quality control should be integrated throughout the workflow, not added as an afterthought.

Limitations and Professional Escalation Criteria

Lipidomics has inherent limitations that should be acknowledged in any study. The following limitations are common across workflows.

Structural Annotation Limits

Incomplete structural annotation is a universal limitation. Many detected features cannot be assigned to a specific lipid structure, and the confidence of identification varies by level of evidence. Researchers should report the confidence level for each identified lipid.

Quantitative Accuracy

Quantitative accuracy depends on the availability of authentic standards and the appropriateness of internal standards. Relative quantification is more common than absolute quantification, and the results should be interpreted accordingly.

Platform Dependence

Results may differ between platforms, laboratories, and software versions. This platform dependence limits the comparability of studies and underscores the need for standardized reporting.

Correlation versus Causation

Lipidomics data provide correlational evidence, not causal evidence. A lipid that differs between conditions is not necessarily involved in the biological process. Direct evidence linking specific lipid species to particular biological outcomes remains limited in many applications.

Professional Escalation Criteria

Researchers should seek expert consultation when the following situations arise:

• The data show unexpected patterns that cannot be explained by the experimental design
• The quality control samples show trends that indicate instrument or extraction problems
• The statistical analysis produces results that conflict with established biological knowledge
• The pathway analysis produces results that are difficult to interpret or that overemphasize hub compounds
• The study involves clinical samples or regulatory decisions that require validated methods

Safety and Regulatory Context

Lipidomics research involving human samples is subject to ethical and regulatory requirements. Researchers should ensure that the study has appropriate ethical approval and that participant consent is obtained. The handling of human samples should follow institutional biosafety guidelines.

Data sharing and deposition should follow applicable policies. The NIH Genomic Data Sharing Policy provides a framework for the sharing of genomic data, and similar principles apply to other omics data. The FAIR Guiding Principles provide a framework for making data findable, accessible, interoperable, and reusable.

Researchers should be aware that lipidomics data may be subject to data protection regulations, especially when the data can be linked to individual participants. De-identification of samples and data should be performed according to institutional policies.

Frequently Asked Questions

What is the difference between targeted and untargeted lipidomics?

Targeted lipidomics measures a predefined set of lipid species with high sensitivity and specificity using methods such as multiple reaction monitoring. Untargeted lipidomics aims to detect and identify as many lipid species as possible without prior knowledge of which lipids are relevant, using high-resolution mass spectrometry. The choice depends on whether the research question is hypothesis-driven or discovery-oriented.

How do I choose the right lipid extraction method?

The choice of extraction method depends on the sample type and the lipid classes of interest. Liquid-liquid extraction is the most common approach and partitions lipids into an organic phase. Solid-phase extraction provides more selective separation of lipid classes. The method should be validated for the specific sample type, and recovery should be assessed using internal standards.

What are internal standards and why are they important?

Internal standards are isotopically labeled or structurally similar lipids added at known concentrations before extraction. They correct for losses during sample preparation and for matrix effects during mass spectrometry analysis. Internal standards are essential for quantitative lipidomics and should match the lipid classes being measured.

How do I handle batch effects in lipidomics data?

Batch effects are systematic variations that arise from instrument drift, reagent changes, and other technical factors. They can obscure biological differences and must be corrected before statistical analysis. Software tools such as ADViSELipidomics can deal with batch effect correction. The experimental design should also include measures to minimize batch effects, such as randomizing sample order and including pooled quality control samples.

What level of identification confidence is needed for lipidomics?

Identification confidence depends on the level of evidence. Authentic standard comparison provides the highest confidence, while accurate mass alone provides the lowest. Researchers should report the confidence level for each identified lipid and acknowledge the limitations of the identification. Isomeric ambiguity means that a single feature may represent multiple lipid species.

How do I interpret pathway analysis results from lipidomics data?

Pathway analysis places differentially abundant lipids in the context of known metabolic pathways. The approach is not well standardized, and the impact of different methodologies on the functional outcome is not well understood. The consideration of non-human native reactions and the interconnectivity of pathways affect the results. Results should be interpreted with awareness of these methodological shortcomings.

What quality control samples should I include in a lipidomics study?

Quality control samples include pooled biological samples analyzed repeatedly to monitor instrument performance and extraction consistency, blank samples to identify contamination, and internal standards to correct for losses and matrix effects. The importance of optimization steps in addressing challenges from lipid contamination, especially in blanks, has been demonstrated in single-cell applications.

How should I report lipidomics data for publication?

Data reporting should follow community standards for lipidomics. The FAIR Guiding Principles provide a framework for making data findable, accessible, interoperable, and reusable. Data deposition in public repositories supports reproducibility and enables meta-analyses. The methods should be reported in sufficient detail to allow replication, including extraction protocols, mass spectrometry settings, and data processing parameters.

Related Bioinformatics Guides

• Alternative Splicing Analysis from RNA-Seq Data
• Orchestrating Bioinformatics at Scale: Workflows, Containers, and Cloud Infrastructures
• ChIP-Seq Bioinformatics Workflows
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• Gene Ontology (GO) and Enrichment Analysis

References and Further Reading

• EMBL-EBI Training. European Bioinformatics Institute.
• NCBI Data Resources. National Center for Biotechnology Information.
• Genomic Data Sharing Policy. National Institutes of Health.
• The FAIR Guiding Principles. Scientific Data.
• ADViSELipidomics: a workflow for analyzing lipidomics data.. Bioinformatics (Oxford, England), 2022.
• Optimizing the lipidomics workflow for clinical studies--practical considerations.. Analytical and bioanalytical chemistry, 2015.
• A DMS Shotgun Lipidomics Workflow Application to Facilitate High-Throughput, Comprehensive Lipidomics.. Journal of the American Society for Mass Spectrometry, 2021.
• Lipidomics in Biomarker Research.. Handbook of experimental pharmacology, 2022.
• Lipidomics Workflow for Analyzing Lipid Profiles Using Multiple Reaction Monitoring (MRM) in Liver Homogenate of Mice with Non-alcoholic Steatohepatitis (NASH).. Bio-protocol, 2023.
• Single-Cell Lipidomics: An Automated and Accessible Microfluidic Workflow Validated by Capillary Sampling.. Analytical chemistry, 2024.
• An Untargeted Lipidomics Workflow Incorporating High-Resolution Demultiplexing (HRdm) Drift Tube Ion Mobility-Mass Spectrometry.. Journal of the American Society for Mass Spectrometry, 2024.
• Towards spatial lipid profiling by using mass spectrometry: analytical challenges and applications.. 2026.
• Mass Spectrometry-Based Lipidomics in Coffee: Linking Lipid Transformation to Flavor Formation and Quality Control.. 2026.
• Metabolic Pathway Analysis: Advantages and Pitfalls for the Functional Interpretation of Metabolomics and Lipidomics Data. Biomolecules, 2023.
• Lipid profile migration during the tilapia muscle steaming process revealed by a transactional analysis between MS data and lipidomics data. npj Science of Food, 2021.
• Lipidomics from sample preparation to data analysis: a primer. Analytical and Bioanalytical Chemistry, 2019.
• A robust lipidomics workflow for mammalian cells, plasma, and tissue using liquid-chromatography high-resolution tandem mass spectrometry. Methods in Molecular Biology, 2017.
• A multidimensional 1 H NMR lipidomics workflow to address chemical food safety issues. Metabolomics, 2018.
• Global UHPLC/HRMS lipidomics workflow for the analysis of lymphocyte suspension cultures. Neuromethods, 2017.
• NIST lipidomics workflow questionnaire: an assessment of community-wide methodologies and perspectives. Metabolomics, 2018.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.