LC/MS/MS Method for Quantification of Acylcarnitines and Carnitine Intermediates

Applications | 2026 | Agilent TechnologiesInstrumentation
LC/MS, LC/MS/MS, LC/QQQ
Industries
Clinical Research
Manufacturer
Agilent Technologies

Significance of the topic

Acylcarnitines are central metabolites linking fatty acid β-oxidation, amino acid catabolism and mitochondrial function. Their concentrations and acyl-chain distributions are used as biomarkers in metabolic disease, inflammatory conditions, microbiome-associated perturbations and in assessments of drug-induced mitochondrial liabilities. A robust, high-throughput, derivatization-free assay for broad acylcarnitine coverage in biologically relevant matrices (plasma and feces) therefore addresses an important need in basic research, translational biomarker studies, DMPK and safety assessment workflows.

Aims and overview of the study

This application note presents a standardized LC/MS/MS workflow for the quantification of 32 acylcarnitines (C0–C20) in plasma and fecal matrices without chemical derivatization. The method aims to provide wide coverage (short-, medium- and long-chain species including isomers), high sensitivity and reproducibility, and compatibility with high-throughput targeted metabolomics and integrated omics platforms to enable harmonized data generation across studies.

Methodology

  • Sample preparation (plasma): 20 µL pooled plasma mixed with 20 µL labelled internal standards (20 ng/mL in MeOH) and 60 µL isopropanol with 0.3% formic acid. Vortex 5 min, centrifuge 10,000 × g, 10 min, 4 °C. Supernatant 20 µL diluted with 80 µL water prior to injection.
  • Sample preparation (feces): ~120 mg fecal material in bead tubes, extracted with ice-cold MeOH:H2O (8:2, v/v) containing internal standards to 100 mg/mL, homogenized, stored overnight at −80 °C, centrifuged 20,000 × g for 20 min at 4 °C. Supernatant dried and reconstituted by adding methanol first (20 µL) then water (80 µL) to total 100 µL; mixing performed after each step to ensure solubilization of long-chain acylcarnitines.
  • Calibration ranges: plasma 0.01–500 ng/mL (MeOH:H2O 8:2); feces 0.1–500 nmol/L. Stable isotope labelled standards were added at 20 ng/mL for absolute quantitation; where isotopically labelled standards were not available, the closest retention-time/chain-length labeled analog was used.
  • Chromatography: reversed-phase separation on Agilent ZORBAX RRHD Eclipse Plus C18 (2.1 × 100 mm, 1.8 µm). Column temperature 15 °C, autosampler 4 °C, injection 2 µL. Gradient optimized to baseline-resolve key isomeric pairs (notably C4, C5, C6) within a 21-minute total run time including re-equilibration.
  • Mass spectrometry: positive electrospray ionization with dynamic multiple reaction monitoring (dMRM). Two transitions monitored per analyte (quantifier and qualifier); product ion m/z 85.1 used as primary quantifier for most acylcarnitines, except L-carnitine (m/z 43.1) and deoxycarnitine (m/z 45.1).
  • Data handling: acquisition and processing using Agilent MassHunter (Acquisition 12.2 with Compound/Source Optimizer and Quantitative Analysis 12.0). Calibration-at-a-glance and automated visualization facilitate rapid review of linearity and recovery across analytes.

Used instrumentation

  • Agilent 1290 Infinity II Bio LC System.
  • Agilent ZORBAX RRHD Eclipse Plus C18 column (2.1 × 100 mm, 1.8 µm).
  • Agilent 6495D triple quadrupole LC/MS with iFunnel technology.
  • Agilent MassHunter software suite (Acquisition 12.2, Quantitative Analysis 12.0).

Main results and discussion

  • Coverage and separation: The method resolved 32 acylcarnitines from C0 to C20 in a single run, including baseline separation of important isomeric pairs (e.g., butyryl/isobutyryl C4; valeryl/isovaleryl/2-methylbutyryl C5), achieved without ion-pairing reagents.
  • Sensitivity and linearity: Calibration curves showed excellent linearity for all analytes with R2 > 0.99 across the stated ranges (plasma 0.01–500 ng/mL; feces 0.1–500 nmol/L), supporting quantitation across biologically relevant concentrations.
  • Precision and recovery: Inter-injection reproducibility produced RSDs < 10% for most analytes. Extraction recoveries ranged roughly from 80% to 122% (most near ~90%), indicating efficient and consistent extraction for both plasma and fecal matrices.
  • Robustness and throughput: Total runtime was 21 minutes, enabling moderate-to-high throughput. The omission of derivatization and ion-pairing reagents simplifies sample prep, reduces contamination risk, shortens turnaround time and improves instrument uptime—advantages for DMPK and bioanalytical labs.
  • Biological demonstration: In a small pilot, fecal profiles from IBD subjects showed trends toward decreased L-carnitine and several acylcarnitines relative to controls, illustrating the method’s potential for hypothesis generation in microbiome- and inflammation-related studies (not powered for statistical inference in the pilot).

Benefits and practical applications

  • Derivatization-free quantification reduces complexity, variability and total sample handling time.
  • Comprehensive single-run coverage (C0–C20) streamlines workflows for mitochondrial biology, metabolic disease research, biomarker discovery and DMPK safety screening.
  • Baseline separation of isomers reduces misidentification risk and improves mechanistic interpretation of metabolic signatures.
  • Integration with standardized omics platforms and MassHunter software supports harmonized, scalable data generation across labs and studies.

Future trends and potential uses

  • Wider adoption in translational and clinical studies: standardized, reproducible assays such as this facilitate multi-site studies and longitudinal cohorts assessing mitochondrial and metabolic biomarkers.
  • Expansion of labeled standard panels: improving availability of isotopically labelled isomer-specific standards will enhance quantitative accuracy for less abundant or isomeric species.
  • Automation and sample tracking: coupling this workflow with automated extraction and high-throughput plate handling will increase throughput for large-scale screening and drug safety programs.
  • Integration with multi-omics: linking targeted acylcarnitine data with lipidomics, proteomics and metagenomics will improve mechanistic insights into host–microbiome metabolic interactions.
  • Complementary high-resolution MS: combining targeted triple-quadrupole quantitation with high-resolution MS for discovery can refine identifications and detect unexpected metabolites or adducts.

Conclusion

The described LC/MS/MS workflow provides a practical, high-performing assay for quantifying a broad range of acylcarnitines in plasma and feces without derivatization. It achieves excellent linearity, reproducibility and recovery while resolving key isomeric species. The method’s simplicity, throughput and compatibility with standardized omics platforms make it well suited for research contexts spanning basic metabolism studies, biomarker discovery, translational research and DMPK safety assessment.

References

  1. Luna C.; et al. A clinically validated method to separate and quantify underivatized acylcarnitines and carnitine metabolic intermediates using mixed-mode chromatography with tandem mass spectrometry. Journal of Chromatography A 2022, 1663, 462749.
  2. Giesbertz P.; et al. An LC-MS/MS Method to Quantify Acylcarnitine Species Including Isomeric and Odd-Numbered Forms in Plasma and Tissues. Journal of Lipid Research 2015, 56(10), 2029–2039.
  3. Lemons J. M. S.; et al. Excess Dietary Sugar Alters Colonocyte Metabolism and Impairs the Gut Barrier. Cellular and Molecular Gastroenterology and Hepatology 2023.
  4. Adams S. H. Emerging Perspectives on Essential Amino Acid Metabolism in Obesity and the Metabolic Syndrome. Annual Review of Nutrition 2011, 31, 507–528.
  5. Schooneman M. G.; Vaz F. M.; Houten S. M.; Soeters M. R. Acylcarnitines: Reflecting or Inflicting Insulin Resistance? Diabetes 2013, 62(1), 1–8.
  6. Rinaldo P.; et al. Clinical and Biochemical Features of Fatty Acid Oxidation Disorders. Clinical Chemistry 2008, 54(4), 701–719.
  7. Hoyles L.; et al. Molecular Phenomics and Metagenomics of Hepatic Steatosis in Non-Diabetic Obese Women. Nature Microbiology 2018, 3, 537–550.
  8. McCoin C. S.; Knotts T. A.; Adams S. H. Acylcarnitines—Old Actors Auditioning for New Roles in Metabolic Physiology. American Journal of Physiology - Endocrinology and Metabolism 2015, 308(11), E978–E989.
  9. Minkler P. E.; et al. Quantification of Malonyl-Coenzyme A in Tissue Specimens by High-Performance Liquid Chromatography/Mass Spectrometry. Analytical Biochemistry 2008, 376(1), 31–39.

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