Allergy against birch pollen is among the most common causes of spring pollinosis in Europe and is diagnosed and treated using extracts from natural sources. Quality control is crucial for safe and effective diagnosis and treatment. However, current methods are very difficult to standardize and do not address individual allergen or isoallergen composition. MS provides information regarding selected proteins or the entire proteome and could overcome the aforementioned limitations. We studied the proteome of birch pollen, focusing on allergens and isoallergens, to clarify which of the 93 published sequence variants of the major allergen, Bet v 1, are expressed as proteins within one source material in parallel. The unexpectedly complex Bet v 1 isoallergen composition required manual data interpretation and a specific design of databases, as current database search engines fail to unambiguously assign spectra to highly homologous, partially identical proteins. We identified 47 non-allergenic proteins and all 5 known birch pollen allergens, and unambiguously proved the existence of 18 Bet v 1 isoallergens and variants by manual data analysis. This highly complex isoallergen composition raises questions whether isoallergens can be ignored or must be included for the quality control of allergen products, and which data analysis strategies are to be applied.

Birch pollen allergy is diagnosed and treated with aqueous extracts from birch pollen, which contain a mixture of allergens and nonallergenic proteins, including large numbers of closely related sequence variants, so-called iso-allergens of the major allergen, Bet v 1. The quality of therapeutic and diagnostic allergen products largely depends on the allergen and iso-allergen composition. Several biochemical methods are currently applied to detect and quantify allergens and to record protein profiles without differentiating between iso-allergens. Mass spectrometry (MS) may entirely replace these technologies, as it allows sequence specific identification and quantification of proteins and protein profiles including sequence variants in one run. However, the protein inference problem still hampers the automatic assignment of peptide sequences to proteins, consequently impeding the quantification of sequence variants. Therefore, the aim of the study was to set up semitargeted analyses of label-free MS data that allow unambiguous identification and quantification of birch pollen allergens and nonallergenic proteins. We combined data independent acquisition with manual assignment of predefined target sequences for quantification of iso-allergens and automatic quantification of other allergens and nonallergenic proteins. The quantitative data for birch pollen allergens and sequence variants of Bet v 1 were further confirmed by multiple reaction monitoring.

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