Identification and Quantitative Analysis of Egg Allergen Peptides Using Data Independent Ion Mobility Mass Spectrometry

Significance of the Topic

Egg proteins are among the most common triggers of food allergies. Accurate detection and quantification of egg-derived allergens in both raw and processed foods are vital for consumer safety and regulatory compliance. Advanced mass spectrometry approaches that combine high sensitivity, specificity, and quantitative capability in a single analysis can streamline allergen screening workflows in research and quality control laboratories.

Study Objectives and Overview

This study aimed to discover and quantify diagnostic peptides from major egg allergens in raw and cooked egg samples. A label-free proteomics workflow was applied to identify peptide markers that could later inform targeted multiple reaction monitoring (MRM) methods. Comparisons were made between raw and thermally treated egg proteins, and a retail cake mix matrix was used to assess method performance under realistic food-processing conditions.

Methodology and Sample Preparation

Proteins from raw and boiled egg samples were extracted with phosphate-buffered saline, quantified by BCA assay, reduced, alkylated, and digested overnight with trypsin. After filtration and acidification, digests were analyzed by liquid chromatography–high-definition mass spectrometry in a data-independent acquisition mode with alternating low- and elevated-energy scans.

Instrumentation Used

• ACQUITY UPLC M-Class system with a Peptide BEH C18 nanoACQUITY column (75 µm × 150 mm, 1.7 µm, 130 Å)
• SYNAPT G2-Si Q-TOF mass spectrometer equipped with ion mobility (HDMS⁽ᴱ⁾)
• Progenesis QI for Proteomics software for peptide alignment, database searching (Gallus gallus UniProt), and label-free quantification

Key Results and Discussion

Using HDMS⁽ᴱ⁾, 95 proteins were identified in raw egg and 84 in cooked egg, including five known allergens. Relative concentrations (ng/µL) for raw versus cooked extracts were approximately:

• Ovomucoid: 344 → 333
• Ovalbumin: 44 → 44
• Apovitellenin: 13 → 5
• Ovotransferrin: 30 → 6
• Lysozyme: 6.5 → 1

Ion mobility separation markedly reduced background interference, enabling clear identification of overlapping precursors and fragments. Sequence coverage for ovalbumin exceeded 50% even in a cake matrix. A dilution series of cooked egg peptides spiked into cookie matrix (500 to 1 ppm) confirmed quantitative performance down to low‐ppm levels. Analysis of a FAPAS cake-mix proficiency test sample yielded an ovalbumin concentration of 58 ppm, in agreement with ELISA results (mean 47.7 ppm).

Benefits and Practical Applications

• Simultaneous qualitative and quantitative data from a single LC-MS run
• Enhanced specificity and confidence through ion mobility separation
• Robust performance in complex food matrices
• Ability to discover allergen markers for future targeted assays and multi-allergen screening

Future Trends and Potential Applications

Integration of ion mobility into routine allergen screens will promote high‐throughput, multiplexed analyses across diverse food products. Discovered peptides can feed MRM assay development for regulatory monitoring. Advances in software automation and machine learning will further streamline data processing and improve detection limits for trace allergens.

Conclusion

This work demonstrates that data-independent acquisition with ion mobility (HDMS⁽ᴱ⁾) offers a powerful label-free proteomics approach for discovery and quantification of egg allergens. The method delivers high specificity, adequate sensitivity in complex matrices, and lays the foundation for targeted multi-allergen assays.

Reference

• [1] Commission Directive No 2007/68/EC of 27 November 2007 amending Annex IIIa to Directive 2000/13/EC. Official Journal L310, 28/11/2007, pp. 1–14.
• [2] Li GZ, Vissers JP, Silva JC, Golick D, Gorenstein MV, Geromanos SJ. Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics. 2009 Mar;9(6):1696–1719.