Targeted vs Untargeted Lipidomics: Key Differences and How to Choose

1. Targeted and Untargeted Lipidomics: Core Concepts

1.1 What Is Untargeted Lipidomics?

Untargeted lipidomics is a discovery-oriented strategy designed to survey a broad portion of the measurable lipidome without restricting the experiment to a short, predefined list of lipid species. LC coupled to high-resolution mass spectrometry (HRMS) is commonly used to acquire full-scan data, with data-dependent acquisition (DDA) or data-independent acquisition (DIA) supplying tandem MS information for structural annotation. The resulting dataset can retain expected lipids as well as features that were not selected before analysis, which is useful when the affected lipid pathways or candidate species are not yet known (Xu et al., 2020; Smirnov et al., 2021).

"Untargeted" does not mean that every lipid in a sample is measured or identified. Extraction chemistry, chromatography, ionization, mass range, acquisition settings, abundance, and database coverage all define the analytical window. A detected feature is also not automatically a confidently identified lipid. Untargeted studies are therefore most informative when broad discovery is paired with transparent annotation levels and appropriate follow-up for high-priority candidates (Kofeler et al., 2021).

1.2 What Is Targeted Lipidomics?

Targeted lipidomics is a focused mass spectrometry strategy that measures a predefined set of lipid species, lipid classes, or pathway-related analytes selected before data acquisition. Because the analytical targets are known in advance, acquisition parameters can be optimized for their detection and measurement. Multiple reaction monitoring (MRM) on triple-quadrupole mass spectrometers is widely used for targeted lipid analysis by monitoring selected precursor-product ion transitions, while parallel reaction monitoring (PRM) on high-resolution mass spectrometers provides an alternative targeted acquisition approach. This focused design is particularly useful for testing defined biological hypotheses, examining specific lipid pathways, verifying candidate lipid biomarkers, and measuring established lipid endpoints across large sample cohorts (Xu et al., 2020; Anh et al., 2024).

Targeted lipidomics is not a synonym for absolute quantification. A targeted assay may report relative abundance, semi-quantitative values, or absolute concentrations depending on the standards, calibration strategy, response characterization, and validation applied. Quantification level is therefore a separate method-design decision (Koelmel et al., 2019; Kofeler et al., 2021).

2. Targeted vs Untargeted Lipidomics: Key Analytical Differences

Targeted and untargeted lipidomics use the same broad analytical chain - extraction, separation, ionization, mass analysis, and data processing - but allocate analytical effort differently. Untargeted methods preserve breadth for discovery; targeted methods use prior knowledge to concentrate measurement on selected analytes. That difference propagates into coverage, acquisition efficiency, structural interpretation, quantification, and downstream data handling (Züllig et al., 2020).

2.1 Lipid Coverage and MS Acquisition Strategy

Untargeted lipidomics distributes acquisition across a large m/z and retention-time space. HRMS can detect many coexisting signals, while DDA or DIA provides fragmentation information for selected precursors or broader precursor windows. That breadth creates discovery value, but analytical attention is not equal across every feature. Low-intensity species may receive limited MS/MS information in DDA, and DIA produces multiplexed fragment data that require more complex interpretation. Feature tables can also contain adducts, isotopes, in-source fragments, and redundant signals that must be resolved during processing (Smirnov et al., 2021).

Targeted acquisition narrows this problem. Monitoring selected transitions or precursor ions allows method development and acquisition time to be concentrated on lipids that matter to the hypothesis. Under an appropriately optimized assay, this can improve selectivity, precision, and sensitivity for those analytes. The cost of that focus is reduced discovery space: a lipid outside the target list will generally not become a new finding simply because it is present. Coverage should therefore be judged against the research question, not by the largest possible feature count.

2.2 Lipid Identification and Structural Confidence

Lipid identification is a major reason why targeted-versus-untargeted comparisons cannot be reduced to "many lipids versus few lipids." Lipid structures are hierarchical. A signal may support a species-level annotation based on total carbon number and unsaturation, while stronger evidence may identify individual fatty acyl chains, sn-positions, double-bond positions, or stereochemistry. The LIPID MAPS shorthand framework links the reported name to the structural information actually supported by the analytical data (Liebisch et al., 2020).

Untargeted datasets often contain lipids at different identification levels because MS/MS quality, abundance, separation, and reference spectra vary across features. Targeted methods can strengthen identification of selected analytes through characteristic transitions, retention behavior, authentic standards, and purpose-built chromatography. Even so, a targeted transition does not prove that every structural isomer has been resolved. Isobaric species, sn-position isomers, and double-bond positional isomers may share precursor or fragment ions and can require additional separation or structure-specific methods. The useful question is therefore how much structure the method supports, not simply whether a lipid has been assigned a name.

2.3 Lipid Quantification: Relative, Semi-Quantitative, and Absolute

Quantification level is independent of the targeted-versus-untargeted label. Relative quantification is appropriate when the main goal is comparing signal changes across groups. Semi-quantitative workflows use internal standards to improve comparability and estimate abundance, but a class-matched standard does not necessarily reproduce the response of every molecular species in that class. Ionization response can vary with chain length, unsaturation, bond type, matrix, and chromatographic conditions (Koelmel et al., 2019; Kofeler et al., 2021).

Absolute or accurate quantitative measurement requires stronger analyte-specific evidence, typically including authentic reference materials, calibration curves, suitable internal standards - ideally stable-isotope analogues where available - and characterization of analytical response. The Lipidomics Standards Initiative distinguishes quantification levels according to how well the internal standard and response characterization match the analyte. Targeting defines what is measured; calibration and validation define what quantitative claim can be made.

3. How to Choose Between Targeted and Untargeted Lipidomics

The most useful selection rule is to start from the decision the dataset must support. Discovery studies need room for unexpected biology; confirmatory studies need dependable measurement of specified analytes. Between those endpoints, coverage, structural confidence, sensitivity, cohort size, and quantitative requirements have to be balanced rather than maximized independently.

3.1 Broad Lipid Discovery and Exploratory Studies

Untargeted lipidomics is a logical starting point when the mechanism is poorly defined or the study aims to map broad lipid remodeling after a disease state, genetic perturbation, environmental exposure, dietary intervention, or drug treatment. Retaining signals outside an initial candidate list allows the dataset to reveal lipid classes or molecular species that were not part of the original hypothesis. This is valuable in early-stage projects where selecting a narrow panel too soon could exclude informative biology.

A discovery design still needs boundaries. No extraction or LC-MS method captures the complete lipidome, and broad datasets can contain features with uncertain annotation. The objective should therefore be framed as broad measurable lipid profiling followed by prioritization of reproducible, biologically relevant, and structurally supportable candidates.

3.2 Defined Lipid Targets and Pathway-Focused Studies

Targeted lipidomics becomes more efficient when prior evidence has already narrowed the biological question. A study focused on a defined ceramide panel, selected phospholipid species, eicosanoids, or another established lipid pathway can use analyte-specific acquisition and QC instead of devoting much of the dataset to unrelated features. This is useful for testing a mechanistic hypothesis, reproducing a reported lipid signature, or comparing the same endpoints across treatment groups.

The target list still has to be analytically realistic. Different lipid subclasses can have distinct extraction, stability, ionization, and structural-resolution requirements. The strongest targeted designs therefore define the biological targets, expected concentration range, required identification level, and desired quantitative output together.

3.3 Sensitivity and Quantitative Precision Requirements

A focused strategy is often preferable when the central analytes are low in abundance, occur in a complex matrix, or require consistent quantitative comparison across samples. Targeted acquisition can be optimized around retention windows, precursor-product transitions, collision conditions, and analyte-relevant standards, allowing more analytical effort to be directed to the signals that matter. This is especially useful for lipid mediators or other biologically important species that may be difficult to characterize reliably in a broad discovery run (Xu et al., 2020; Anh et al., 2024).

Sensitivity remains a property of the complete assay. Extraction recovery, matrix effects, chromatographic separation, isomeric interference, internal-standard selection, and calibration all influence the final result. Targeted analysis is therefore most appropriate when the study requires a defined performance goal for known lipids and the method can be optimized around those lipids and the sample matrix.

3.4 Large-Cohort Lipidomics Studies

Cohort size alone does not determine the strategy. Large untargeted studies are feasible, but long acquisition series require rigorous randomization, pooled QC samples, monitoring of analytical drift, and methods for recognizing or correcting batch effects. Otherwise, technical variation can become entangled with the biological comparison (Burla et al., 2018; Smirnov et al., 2021).

When a large cohort already has predefined lipid endpoints, a targeted or broad predefined-panel strategy can reduce unnecessary feature complexity and make cross-sample measurement more focused. Reproducibility, missingness, batch consistency, and quantitative precision then become central selection criteria. Study stage is the key distinction: large cohorts used for discovery still benefit from breadth, while cohorts used to test established candidates usually benefit from a more constrained analytical space.

3.5 Lipid Biomarker Discovery and Verification

Biomarker research often uses both strategies at different stages. Untargeted lipidomics can screen a broad signal space and identify candidate lipids associated with a phenotype. Candidates that survive statistical, biological, and structural review can then move into a targeted workflow for focused measurement. A discovery association does not make a lipid a validated biomarker; identity, analytical performance, reproducibility, and performance in independent samples remain separate questions.

Planning these stages separately is useful. Discovery prioritizes coverage and unbiased comparison. Verification prioritizes a locked target list, stronger identity evidence, defined QC criteria, and a quantitative scheme matched to the intended claim.

4. Combining Targeted and Untargeted Lipidomics

Targeted and untargeted lipidomics are complementary when a project moves from an open biological question to a defined analytical endpoint. Combining them does not require measuring every sample twice. A common design uses an untargeted dataset to generate and prioritize candidates, then develops a focused method for the smaller set of lipids tested in additional samples or later experiments.

4.1 Untargeted Discovery to Targeted Verification

The transition from discovery to verification should narrow both biological and analytical uncertainty. Candidate lipids can be filtered for reproducibility, effect consistency, biological relevance, and confidence of structural annotation. High-priority candidates may then be confirmed with stronger MS/MS evidence, retention-time matching, or authentic standards where appropriate. A subsequent targeted assay defines the analytes, transitions, integration rules, internal standards, and calibration level required by the verification question. This preserves the exploratory value of untargeted lipidomics without carrying a large feature space into every later experiment.

4.2 Broad-Panel and Pseudotargeted Lipidomics

Some workflows sit between classical untargeted discovery and narrowly targeted assays. Pseudotargeted approaches can derive a large set of transitions from prior untargeted data and then acquire those transitions in a more focused manner, while broad predefined panels may monitor hundreds or thousands of known lipid species. These designs can improve consistency across a large known measurement space while retaining more breadth than a small pathway-specific assay (Xu et al., 2020).

Terminology is not fully standardized across laboratories. "Pseudotargeted," "widely targeted," "broad-panel targeted," and "quantitative lipidomics" can describe different combinations of prior knowledge, acquisition mode, identification evidence, and quantification. The method itself - what is predefined, how signals are acquired, how lipids are identified, and how abundance is calculated - is more informative than the label alone.

5. Targeted vs Untargeted Lipidomics FAQs

Does untargeted lipidomics measure the entire lipidome?

No. Untargeted lipidomics aims for broad discovery, but the observable lipid space is constrained by extraction, chromatography, ionization, mass range, abundance, acquisition settings, and data processing. "Untargeted" describes the absence of a fixed analyte list; it does not mean complete coverage of every lipid molecule in the sample (Kofeler et al., 2021).

Does targeted lipidomics always provide absolute quantification?

No. Targeted lipidomics can be relative, semi-quantitative, or absolute. Absolute concentration measurements require an appropriate calibration strategy and analyte-relevant standards. A targeted MRM assay using class-specific internal standards without species-specific calibration may provide robust semi-quantitative comparisons but should not automatically be described as absolute quantification (Koelmel et al., 2019).

Which approach is more sensitive?

For predefined analytes, a well-optimized targeted method can often achieve greater sensitivity and selectivity because acquisition and method development are focused on those compounds. The comparison is conditional: matrix effects, separation, lipid chemistry, ionization, instrument configuration, and assay optimization can all change practical sensitivity.

Is untargeted lipidomics better for biomarker discovery?

Untargeted lipidomics is usually the stronger starting point when candidate biomarkers are unknown because it surveys a broader signal space and can reveal unexpected lipid associations. Those signals remain candidates until identity and analytical behavior are sufficiently supported. Targeted analysis is then useful for focused verification across additional samples.

Can targeted and untargeted lipidomics be used in the same study?

Yes. A staged design can use untargeted lipidomics to generate candidates and targeted lipidomics to test a selected subset. The transition should define which candidates move forward, what identification evidence is required, and what quantitative endpoint the targeted stage must support.

Does detecting more lipids mean a better lipidomics study?

No. A larger feature count increases discovery space but does not by itself establish higher data quality. Reproducibility, QC performance, missingness, structural identification confidence, appropriate quantification, and relevance to the study question also matter. Current lipidomics reporting initiatives emphasize transparent documentation of these dimensions rather than a single coverage number (Kopczynski et al., 2024).