Lipidomics Workflow Research Articles

Mapping Lipid C═C Isomer Profiles of Human Gut Bacteria through a Novel Structural Lipidomics Workflow Assisted by Chemical Epoxidation.

The unsaturated lipids produced by human gut bacteria have an extraordinary range of structural diversity, largely because of the isomerism of the carbon-carbon double bond (C═C) in terms of its position and stereochemistry. Characterizing distinct C═C configurations poses a considerable challenge in research, primarily owing to limitations in current bioanalytical methodologies. This study developed a novel structural lipidomics workflow by combining MELDI (meta-chloroperoxybenzoic acid epoxidation for lipid double-bond identification) with liquid chromatography-tandem mass spectrometry for C═C characterization. We utilized this workflow to quantitatively assess more than 50 C═C positional and cis/trans isomers of fatty acids and phospholipids from selected human gut bacteria. Strain-specific isomer profiles revealed unexpectedly high productivity of trans-10-octadecenoic acid by Enterococcus faecalis, Bifidobacterium longum, and Lactobacillus acidophilus among numerous trans-fatty acid isomers produced by gut bacteria. Isotope-tracking experiments suggested that gut bacteria produce trans-10-octadecenoic acid through the isomeric biotransformation of oleic acid in vitro and that such isomeric biotransformation of dietary oleic acid is dependent on the presence of gut bacteria in vivo.

An Untargeted Lipidomics Workflow Incorporating High-Resolution Demultiplexing (HRdm) Drift Tube Ion Mobility-Mass Spectrometry.

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 (IM), can be inserted into liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomic workflows for additional isomer separation and high-confidence annotation, and the emergence of high-resolution ion mobility (HRIM) strategies provides marked improvements to the resolving power (Rp > 200) that can differentiate small structural differences characteristic of isomers. One such HRIM strategy, high-resolution demultiplexing (HRdm), utilizes multiplexed drift tube ion mobility spectrometry (DTIMS) with post-acquisition algorithmic deconvolution to access high IM resolutions while retaining the measurement precision inherent to the drift tube technique; however, HRdm has yet to be utilized in untargeted studies. In this manuscript, a proof-of-concept study using ATP10D dysfunctional murine models was investigated to demonstrate the utility of HRdm-incorporated untargeted lipidomic analysis pipelines. 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. An example lipid, PC 36:5, was found to be significantly higher in dysfunctional ATP10D mice with two resolved peaks observed by HRdm that were absent in both the functional ATP10D mice and the standard demultiplexed DTIMS acquisition mode. The benefits of utilizing HRdm for discerning isomeric lipids in untargeted workflows have the potential to enhance our analytical understanding of lipids related to disease complexity and biologically relevant studies.

FAIMS Shotgun Lipidomics for Enhanced Class- and Charge-State Separation Complemented by Automated Ganglioside Annotation.

The analysis of gangliosides is extremely challenging, given their structural complexity, lack of reference standards, databases, and software solutions. Here, we introduce a fast 6 min high field asymmetric ion mobility spectrometry (FAIMS) shotgun lipidomics workflow, along with a dedicated software solution for ganglioside detection. By ramping FAIMS compensation voltages, ideal ranges for different ganglioside classes were obtained. FAIMS revealed both class- and charge-state separation behavior based on the glycan headgroup moiety. The number of sialic acids attached to the glycan moiety correlates positively with their preferred charge states, i.e., trisialylated gangliosides were mainly present as [M - 3H]3- ions, whereas [M - 4H]4- and [M - 5H]5- ions were observed for GQ1 and GP1. For data evaluation, we developed a shotgun/FAIMS extension for the open-source Lipid Data Analyzer (LDA), enabling automated annotation of gangliosides up to the molecular lipid species level. This extension utilized combined orthogonal fragmentation spectra from CID, HCD, and 213 nm UVPD ion activation methods and covers 29 ganglioside classes, including acetylated and fucosylated modifications. With our new workflow and software extension 117 unique gangliosides species were identified in porcine brain extracts. While conventional shotgun lipidomics favored the observation of singly charged ganglioside species, the utilization of FAIMS made multiply charged lipid species accessible, resulting in an increased number of detected species, primarily due to an improved signal-to-noise ratio arising from FAIMS charge state filtering. Therefore, this FAIMS-driven workflow, complemented by new software capabilities, offers a promising strategy for complex ganglioside and glycosphingolipid characterization in shotgun lipidomics.

MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization.

Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional property of an ion that can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating the CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C-labeled lipids, derived from a U13C-labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R Markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C-labeled lipids per lipid class-adduct combination across 10 lipid classes without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C-labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary data set, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.

Microwave-Assisted Extraction/UHPLC-Q-Orbitrap-MS-Based Lipidomic Workflow for Comprehensive Study of Lipids in Soft Cheese.

In this work, Microwave-Assisted Extraction (MAE) was proposed as an alternative and environmentally friendly technique in lipidomics to study the lipid fingerprint of soft cheeses, such as mozzarella. For method development, a first step concerning an evaluation of extraction solvents was carried out via testing three different mixtures, including methanol/ethyl acetate, isopropanol/ethyl acetate, and ethanol/ethyl acetate, at a 1:2 v/v ratio. The latter was chosen as a solvent mixture for subsequent method optimization. MAE conditions, in terms of solvent volume, time, and temperature, were explored to define their effects on extraction capability through a full factorial experimental design. The best compromise to extract more lipids at the same time was obtained with 24 mL g-1 for solvent-to-solid ratio, 65 °C for temperature, and 18 min for time. Lipid analyses were conducted by UHPLC-Q-Orbitrap-MS associated with multivariate statistics. The developed lipidomic workflow allowed for the extraction of over 400 lipids grouped into 18 different subclasses. The results confirmed that MAE is a suitable technique for lipid extraction in the omics approach with high efficiency, even using low-cost and less toxic solvents. Moreover, a comprehensive structure characterization of extracted lipids, in terms of fatty acid composition and regiochemistry, was carried out.

Studies of Labware Contamination during Lipid Extraction in Mass Spectrometry-Based Lipidome Analysis.

In lipidomic analysis, plasticware is increasingly being used for lipid extraction and other sample processing procedures over glassware. However, a systematic investigation of the consequences of plasticware use on mass spectrometry (MS)-based lipidome analysis is lacking. In this work, we present an analytical approach for detecting and comparing solvent and labware contaminants encountered in lipidomic workflows. It is shown that the contaminant profiles varied widely between microcentrifuge tubes from different manufacturers. The most suitable polypropylene tubes tested introduced 847 labware-originating contaminant m/z's when three different manufacturing batches were tested for Folch lipid extractions. Of particular concern is that 21 primary amide and fatty acid surfactants were introduced that were identical to biological endogenous lipids, 16 of which had not been previously reported as leachables from polypropylene materials. Alternatively, the use of borosilicate glassware and PTFE-lined screw caps introduced 98 different contaminant m/z's across three manufacturing batches tested for Folch extractions. Despite the overwhelming number of labware contaminants introduced, current databases and literature only facilitated the identification of 32 contaminants. To address the dearth of publicly available contaminant information, we provide a comprehensive labware contamination repository containing high-resolution m/z values, adductation information, retention times, and MS/MS spectra. This resource should prove to be valuable for researchers in detecting and distinguishing contaminants from analytes of interest. A companion paper presents a detailed study of how labware contamination can lead to ion-suppression effects on coeluting lipids and interference in the analysis of endogenous lipids, such as those from human sera.

Spatial lipidomics maps brain alterations associated with mild traumatic brain injury.

Traumatic brain injury (TBI) is a global public health problem with 50-60 million incidents per year, most of which are considered mild (mTBI) and many of these repetitive (rmTBI). Despite their massive implications, the pathologies of mTBI and rmTBI are not fully understood, with a paucity of information on brain lipid dysregulation following mild injury event(s). To gain more insight on mTBI and rmTBI pathology, a non-targeted spatial lipidomics workflow utilizing high resolution mass spectrometry imaging was developed to map brain region-specific lipid alterations in rats following injury. Discriminant multivariate models were created for regions of interest including the hippocampus, cortex, and corpus callosum to pinpoint lipid species that differentiated between injured and sham animals. A multivariate model focused on the hippocampus region differentiated injured brain tissues with an area under the curve of 0.99 using only four lipid species. Lipid classes that were consistently discriminant included polyunsaturated fatty acid-containing phosphatidylcholines (PC), lysophosphatidylcholines (LPC), LPC-plasmalogens (LPC-P) and PC potassium adducts. Many of the polyunsaturated fatty acid-containing PC and LPC-P selected have never been previously reported as altered in mTBI. The observed lipid alterations indicate that neuroinflammation and oxidative stress are important pathologies that could serve to explain cognitive deficits associated with rmTBI. Therapeutics which target or attenuate these pathologies may be beneficial to limit persistent damage following a mild brain injury event.

High-Throughput Microbore LC-MS Lipidomics to Investigate APOE Phenotypes.

Microflow liquid chromatography interfaced with mass spectrometry (μLC-MS/MS) is increasingly applied for high-throughput profiling of biological samples and has been proven to have an acceptable trade-off between sensitivity and reproducibility. However, lipidomics applications are scarce. We optimized a μLC-MS/MS system utilizing a 1 mm inner diameter × 100 mm column coupled to a triple quadrupole mass spectrometer to establish a sensitive, high-throughput, and robust single-shot lipidomics workflow. Compared to conventional lipidomics methods, we achieve a ∼4-fold increase in response, facilitating quantification of 351 lipid species from a single iPSC-derived cerebral organoid during a 15 min LC-MS analysis. Consecutively, we injected 303 samples over ∼75 h to prove the robustness and reproducibility of the microflow separation. As a proof of concept, μLC-MS/MS analysis of Alzheimer's disease patient-derived iPSC cerebral organoid reveals differential lipid metabolism depending on APOE phenotype (E3/3 vs E4/4). Microflow separation proves to be an environmentally friendly and cost-effective method as it reduces the consumption of harmful solvents. Also, the data demonstrate robust, in-depth, high-throughput performance to enable routine clinical or biomedical applications.

Q-RAI data-independent acquisition for lipidomic quantitative profiling

Untargeted lipidomics has been increasingly adopted for hypothesis generation in a biological context or discovery of disease biomarkers. Most of the current liquid chromatography mass spectrometry (LC–MS) based untargeted methodologies utilize a data dependent acquisition (DDA) approach in pooled samples for identification and MS-only acquisition for semi-quantification in individual samples. In this study, we present for the first time an untargeted lipidomic workflow that makes use of the newly implemented Quadrupole Resolved All-Ions (Q-RAI) acquisition function on the Agilent 6546 quadrupole time-of-flight (Q-TOF) mass spectrometer to acquire MS2 spectra in data independent acquisition (DIA) mode. This is followed by data processing and analysis on MetaboKit, a software enabling DDA-based spectral library construction and extraction of MS1 and MS2 peak areas, for reproducible identification and quantification of lipids in DIA analysis. This workflow was tested on lipid extracts from human plasma and showed quantification at MS1 and MS2 levels comparable to multiple reaction monitoring (MRM) targeted analysis of the same samples. Analysis of serum from Ceramide Synthase 2 (CerS2) null mice using the Q-RAI DIA workflow identified 88 lipid species significantly different between CerS2 null and wild type mice, including well-characterized changes previously associated with this phenotype. Our results show the Q-RAI DIA as a reliable option to perform simultaneous identification and reproducible relative quantification of lipids in exploratory biological studies.

Lipidomic studies reveal two specific circulating phosphatidylcholines as surrogate biomarkers of the omega-3 index

Optimal dietary intake of omega-3 long-chain polyunsaturated fatty acids (n3-LCPUFAs) is critical to human health across the lifespan. However, omega-3 index (O3I) determination is not routinely assessed due to complicated procedures for n3-LCPUFA analysis from the phospholipid (PL) fraction of erythrocytes. Herein, a high-throughput method for lipidomics based on multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry was applied to identify circulating PLs as surrogate biomarkers of O3I in two randomized placebo-controlled trials. An untargeted lipidomic data workflow using a subgroup analysis of serum extracts from sunflower oil versus high-dose fish oil (FO)-supplemented participants revealed that ingested n3-LCPUFAs were primarily distributed as their phosphatidylcholines (PCs) relative to other PL classes. In both high-dose FO (5.0 g/day) and EPA-only trials (3.0 g/day), PC (16:0_20:5) was the most responsive PL, whereas PC (16:0_22:6) was selective to DHA-only supplementation. We also demonstrated that the sum concentration of both these PCs in fasting serum or plasma samples was positively correlated to the O3I following FO (r = 0.708, P = 1.02 × 10−11, n = 69) and EPA- or DHA-only supplementation (r = 0.768, P = 1.01 × 10−33, n = 167). Overall, DHA was more effective in improving the O3I (ΔO3I = 4.90 ± 1.33%) compared to EPA (ΔO3I = 2.99 ± 1.19%) in young Canadian adults who had a poor nutritional status with an O3I (3.50 ± 0.68%) at baseline. Our method enables the rapid assessment of the O3I by directly measuring two circulating PC species in small volumes of blood, which may facilitate screening applications for population and precision health.

Untargeted hair lipidomics: comprehensive evaluation of the hair-specific lipid signature and considerations for retrospective analysis.

Lipidomics investigates the composition and function of lipids, typically employing blood or tissue samples as the primary study matrices. Hair has recently emerged as a potential complementary sample type to identify biomarkers in early disease stages and retrospectively document an individual's metabolic status due to its long detection window of up to several months prior to the time of sampling. However, the limited coverage of lipid profiling presented in previous studies has hindered its exploitation. This study aimed to evaluate the lipid coverage of hair using an untargeted liquid chromatography-high-resolution mass spectrometry lipidomics platform. Two distinct three-step exhaustive extraction experiments were performed using a hair metabolomics one-phase extraction technique that has been recently optimized, and the two-phase Folch extraction method which is recognized as the gold standard for lipid extraction in biological matrices. The applied lipidomics workflow improved hair lipid coverage, as only 99 species could be annotated using the one-phase extraction method, while 297 lipid species across six categories were annotated with the Folch method. Several lipids in hair were reported for the first time, including N-acyl amino acids, diradylglycerols, and coenzyme Q10. The study suggests that hair lipids are not solely derived from de novo synthesis in hair, but are also incorporated from sebum and blood, making hair a valuable matrix for clinical, forensic, and dermatological research. The improved understanding of the lipid composition and analytical considerations for retrospective analysis offers valuable insights to contextualize untargeted hair lipidomic analysis and facilitate the use of hair in translational studies.

Using MetaboAnalyst to introduce undergraduates to lipidomic analysis and lipid remodeling in barley roots.

Lipidomics is a discipline that focuses on the identification and quantification of lipids. Although a part of the larger omics field, lipidomics requires specific approaches for the analysis and biological interpretation of datasets. This article presents a series of activities for introducing undergraduate microbiology students to lipidomic analysis through tools from the web-based platform MetaboAnalyst. The students perform a complete lipidomic workflow, which includes experiment design, data processing, data normalization, and statistical analysis of molecular phospholipid species obtained from barley roots exposed to Fusarium macroconidia. The input data are provided by the teacher, but students also learn about the methods through which they were originally obtained (untargeted liquid chromatography coupled with mass spectrometry). The ultimate aim is for students to understand the biological significance of phosphatidylcholine acyl editing. The chosen methodology allows users who are not proficient in statistics to make a comprehensive analysis of quantitative lipidomic datasets. We strongly believe that virtual activities based on the analysis of such datasets should be incorporated more often into undergraduate courses, in order to improve students' data-handling skills for omics sciences.

Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry.

Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes

Type 2 diabetes mellitus (DM) poses a major burden for the treatment and control of tuberculosis (TB). Characterization of the underlying metabolic perturbations in DM patients with TB infection would yield insights into the pathophysiology of TB-DM, thus potentially leading to improvements in TB treatment. In this study, a multimodal metabolomics and lipidomics workflow was applied to investigate plasma metabolic profiles of patients with TB and TB-DM. Significantly different biological processes and biomarkers in TB-DM vs. TB were identified using a data-driven, knowledge-based framework. Changes in metabolic and signaling pathways related to carbohydrate and amino acid metabolism were mainly captured by amide HILIC column metabolomics analysis, while perturbations in lipid metabolism were identified by the C18 metabolomics and lipidomics analysis. Compared to TB, TB-DM exhibited elevated levels of bile acids and molecules related to carbohydrate metabolism, as well as the depletion of glutamine, retinol, lysophosphatidylcholine, and phosphatidylcholine. Moreover, arachidonic acid metabolism was determined as a potentially important factor in the interaction between TB and DM pathophysiology. In a correlation network of the significantly altered molecules, among the central nodes, chenodeoxycholic acid was robustly associated with TB and DM. Fatty acid (22:4) was a component of all significant modules. In conclusion, the integration of multimodal metabolomics and lipidomics provides a thorough picture of the metabolic changes associated with TB-DM. The results obtained from this comprehensive profiling of TB patients with DM advance the current understanding of DM comorbidity in TB infection and contribute to the development of more effective treatment.

Systematic untargeted UHPLC-Q-TOF-MS based lipidomics workflow for improved detection and annotation of lipid sub-classes in serum.

Taking into consideration the challenges of lipid analytics, present study aims to design thebest high-throughputworkflow for detection and annotation oflipids. Serum lipid profiling was performed on CSH-C18 and EVO-C18 columns using UHPLC Q-TOF-MS and generated lipid features were annotated based on m/z and fragment ion using different software. Better detection of features was observedin CSH-C18 than EVO-C18 with enhanced resolution except for Glycerolipids (triacylglycerols) and Sphingolipids (sphingomyelin). The study revealed an optimized untargeted Lipidomics-workflow with comprehensive lipidprofiling(CSH-C18 column) and confirmatory annotation (LipidBlast).

Comprehensive Lipidomic Workflow for Multicohort Population Phenotyping Using Stable Isotope Dilution Targeted Liquid Chromatography-Mass Spectrometry.

Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, requiring advanced methods that are accurate and robust to batch-to-batch and interday analytical variation. Herein, an optimized comprehensive targeted workflow for plasma and serum lipid quantification is presented, combining stable isotope internal standard dilution, automated sample preparation, and ultrahigh performance liquid chromatography-tandem mass spectrometry with rapid polarity switching to target 1163 lipid species spanning 20 subclasses. The resultant method is robust to common sources of analytical variation including blood collection tubes, hemolysis, freeze-thaw cycles, storage stability, analyte extraction technique, interinstrument variation, and batch-to-batch variation with 820 lipids reporting a relative standard deviation of <30% in 1048 replicate quality control plasma samples acquired across 16 independent batches (total injection count = 6142). However, sample hemolysis of ≥0.4% impacted lipid concentrations, specifically for phosphatidylethanolamines (PEs). Low interinstrument variability across two identical LC-MS systems indicated feasibility for intra/inter-lab parallelization of the assay. In summary, we have optimized a comprehensive lipidomic protocol to support rigorous analysis for large-scale, multibatch applications in precision medicine. The mass spectrometry lipidomics data have been deposited to massIVE: data set identifiers MSV000090952 and 10.25345/C5NP1WQ4S.

Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples

Lipidomics encompassing automated lipid extraction, a four-dimensional (4D) feature selection strategy for confident lipid annotation as well as reproducible and cross-validated quantification can expedite clinical profiling. Here, we determine 4D descriptors (mass to charge, retention time, collision cross section, and fragmentation spectra) of 200 lipid standards and 493 lipids from reference plasma via trapped ion mobility mass spectrometry to enable the implementation of stringent criteria for lipid annotation. We use 4D lipidomics to confidently annotate 370 lipids in reference plasma samples and 364 lipids in serum samples, and reproducibly quantify 359 lipids using level-3 internal standards. We show the utility of our 4D lipidomics workflow for high-throughput applications by reliable profiling of intra-individual lipidome phenotypes in plasma, serum, whole blood, venous and finger-prick dried blood spots.

Recent methodological developments in data-dependent analysis and data-independent analysis workflows for exhaustive lipidome coverage

Untargeted lipidomics applied to biological samples typically involves the coupling of separation methods to high-resolution mass spectrometry (HRMS). Getting an exhaustive coverage of the lipidome with a high confidence in structure identification is still highly challenging due to the wide concentration range of lipids in complex matrices and the presence of numerous isobaric and isomeric species. The development of innovative separation methods and HRMS(/MS) acquisition workflows helped improving the situation but issues still remain regarding confident structure characterization. To overcome these issues, thoroughly optimized MS/MS acquisition methods are needed. For this purpose, different methodologies have been developed to enable MS and MS/MS acquisition in parallel. Those methodologies, derived from the proteomics, are referred to Data Dependent Acquisition (DDA) and Data Independent Acquisition (DIA). In this context, this perspective paper presents the latest developments of DDA- and DIA-based lipidomic workflows and lists available bioinformatic tools for the analysis of resulting spectral data.

Lipid Spectrum Generator: A Simple Script for the Generation of Accurate In Silico Lipid Fragmentation Spectra.

Due to the complexity of lipids in nature, the use of in silico generated spectral libraries to identify lipid species from mass spectral data has become an integral part of many lipidomic workflows. However, many in silico libraries are either limited in usability or their capacity to represent lipid species. Here, we introduce Lipid Spectrum Generator, an open-source in silico spectral library generator specifically designed to aid in the identification of lipids in liquid chromatography-tandem mass spectrometry analysis.

Serum Lipidomic Screen Identifies Key Metabolites, Pathways, and Disease Classifiers in Crohn's Disease.

There is an unmet medical need for biomarkers that capture host and environmental contributions in inflammatory bowel diseases (IBDs). This study aimed at testing the potential of circulating lipids as disease classifiers given their major roles in inflammation. We applied a previously validated comprehensive high-resolution liquid chromatography-mass spectrometry-based untargeted lipidomic workflow covering 25 lipid subclasses to serum samples from 100 Crohn's disease (CD) patients and 100 matched control subjects. Findings were replicated and expanded in another 200 CD patients and 200 control subjects. Key metabolites were tested for associations with disease behavior and location, and classification models were built and validated. Their association with disease activity was tested using an independent cohort of 42 CD patients. We identified >70 metabolites with strong association (P < 1 × 10-4, q < 5 × 10-4) to CD. Highly performing classification models (area under the curve > 0.84-0.97) could be built with as few as 5 to 9 different metabolites, representing 6 major correlated lipid clusters. These classifiers included a phosphatidylethanolamine ether (O-16:0/20:4), a sphingomyelin (d18:1/21:0) and a cholesterol ester (14:1), a very long-chain dicarboxylic acid [28:1(OH)] and sitosterol sulfate. These classifiers and correlated lipids indicate a dysregulated metabolism in host cells, notably in peroxisomes, as well as dysbiosis, oxidative stress, compromised inflammation resolution, or intestinal membrane integrity. A subset of these were associated with disease behavior or location. Untargeted lipidomic analyses uncovered perturbations in the circulating human CD lipidome, likely resulting from multiple pathogenic mechanisms. Models using as few as 5 biomarkers had strong disease classifier characteristics, supporting their potential use in diagnosis or prognosis.