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Bioanalytical LC-MS Quantification of Itaconic Acid: A Potential Metabolic Biomarker of Inflammation

Applications | 2019 | WatersInstrumentation
LC/MS, LC/MS/MS, LC/QQQ
Industries
Metabolomics, Clinical Research
Manufacturer
Waters

Summary

Importance of the topic


Itaconic acid is a dicarboxylic metabolite derived from the tricarboxylic acid cycle, increasingly studied as a biomarker of inflammation and immune activation. Endogenous plasma levels in healthy individuals are low but can rise tenfold under conditions such as sepsis, rheumatoid arthritis, and gestational diabetes. Reliable quantification of this polar organic acid in biological samples is critical for drug development, diagnostics, and clinical research.

Objectives and overview of the study


This work aimed to develop and validate a fast, sensitive, and robust bioanalytical LC-MS/MS method for quantifying itaconic acid in human plasma. The approach integrates simplified pass-through sample cleanup, reversed-phase UPLC separation on a sub-2 µm HSS T3 column, and tandem quadrupole mass spectrometry to achieve a lower limit of quantification of 0.5 ng/mL across a broad dynamic range.

Methodology and instrumentation


Sample preparation employed the Ostro™ 96-well plate for simultaneous protein precipitation and phospholipid removal. A 200 µL plasma aliquot was mixed with acetonitrile containing 1 % formic acid and an isotopically labelled internal standard, then eluted under vacuum, evaporated, and reconstituted in acidic methanol:water.

Chromatographic separation used an ACQUITY™ UPLC HSS T3 column (2.1×100 mm, 1.8 µm) at 50 °C with a linear gradient of 0.1 % formic acid in water (mobile phase A) and acetonitrile (mobile phase B). The flow rate was 0.6 mL/min, with a total run time of 5 minutes.

Detection was performed on a Xevo™ TQ-XS tandem quadrupole MS in negative electrospray ionization using multiple reaction monitoring. Transitions monitored were 129.1→85.1 and 129.1→41.1 for itaconic acid, and 133.9→89.0 and 133.9→44.1 for the internal standard.

Main results and discussion


The HSS T3 column provided robust retention and baseline resolution of the isobaric isomer aconitic acid. Ostro sample cleanup removed over 95 % of phospholipids, compared to traditional protein precipitation, while maintaining analyte recovery above 85 % and matrix effects below 5 %.

Calibration curves in plasma were linear from 0.5 to 100 ng/mL (r2>0.999) with accuracies of 85–115 % and coefficients of variation below 15 %. Intra- and inter-day precision and accuracy met bioanalytical validation criteria. Measured endogenous itaconic acid across five plasma lots ranged from 0.45 to 1.31 ng/mL with inter-day CVs ≤8 %.

Benefits and practical applications


  • The combined Ostro cleanup and UPLC-MS/MS workflow enables high throughput and minimal phospholipid interference, improving instrument longevity.
  • Sensitivity down to 0.5 ng/mL supports detection of both baseline and elevated biomarker levels.
  • Fast analysis (5 minutes per sample) suits clinical and pharmaceutical screening applications.

Future trends and opportunities


Integration of this method into targeted metabolomics panels can broaden biomarker discovery in inflammatory diseases. Automation of the sample preparation workflow and coupling with high-resolution MS imaging or rapid online SPE may further increase throughput and data richness. Application to large cohort studies will clarify itaconic acid’s clinical utility.

Conclusion


The presented LC-MS/MS assay delivers a simple, rapid, and highly sensitive approach for quantifying itaconic acid in human plasma. The use of Ostro 96-well plates and an HSS T3 column ensures robust cleanup and chromatographic separation, meeting rigorous bioanalytical performance standards and facilitating biomarker research.

Reference


  1. Michelucci A., Hiller K., Cordes T. et al. Annual Review of Nutrition 2015, 35, 451–473.
  2. Meiser J., Krämer L., Jäger C. et al. Oncotarget 2018, 9(63), 32098–32107.
  3. Michopoulos F., Karagianni N., Whalley N.M. et al. Journal of Proteome Research 2016, 15(12), 4579–4590.
  4. de Seymour J., Conlon C., Sulek K. et al. Acta Diabetologica 2014, 51, 887.

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