Intelligent screening for food allergens by Scout triggered MRM

Holly Lee¹ and David Cox¹

¹SCIEX, Canada

Abstract

This technical note demonstrates a novel acquisition mode, scout triggered multiple reaction monitoring (stMRM)¹, for the LC-MS/MS-based screening of allergens in foods. Diagnostic marker peptides triggered the acquisition of confirmatory peptides for an entire allergen group, only when they were present at levels above predefined thresholds in the food sample (Figure 1). This greatly reduced the number of concurrent MRM transitions monitored during the LC run, while maintaining optimal duty cycle for analytical reproducibility. Here, stMRM was used to avoid unnecessary triggering of allergens not detectable above relevant levels in food samples. The resulting cycle time savings provided increased flexibility for retention time (RT) window scheduling and the expansion to larger analyte panels without sacrificing analytical precision. 

Key benefits of stMRM for food allergen screening
 

• Intelligent triggering to minimize MRM concurrency: Duty cycle was improved for allergens present in foods, while avoiding wasting acquisition time on those below thresholds 
• More reliable peak integration: Cycle time savings from stMRM improved data collection across LC peaks to produce better-defined peaks for integration 
• Increased MRM multiplexing and retention time window robustness: Reduced MRM concurrency can accommodate new analytes or more confirmatory transitions and enable larger RT windows to mitigate against RT shifts 
• Easy method development with SCIEX OS software: User-friendly features simplified and streamlined the development process of a stMRM method

Introduction

Mandatory allergen labeling is the most effective and proactive measure to ensure food safety for allergic consumers. The top major allergens are largely aligned worldwide, with some variations of specific allergens due to local regulations. For example, the European Union (EU) has one of the most comprehensive lists including lesser-known allergens like celery, mustard and lupin,² while the US recently added sesame for mandatory labeling.³ However, concerns about cross-contamination into unlabeled foods during production have resulted in excessive use of precautionary allergen labeling by manufacturers to avoid litigation.⁴ This, combined with the emerging risk of de novo sensitization to alternative proteins in novel foods,⁵ necessitates the development of robust and accurate methods for multi-allergen quantitation. 

Although enzyme-linked immunosorbent assays (ELISAs) are the most common approach for allergen quantitation, targeted MRM-based mass spectrometry assays are rising in popularity due to their sensitivity, selectivity and multiplexing capability. While ELISA is typically limited to single-allergen detection, MRM can simultaneously detect multiple proteins and easily incorporate additional peptides from post-translational modifications that occur during food processing.⁶ Analyte redundancy is critical for minimizing false negatives from non-specific and unreliable peptides during allergen screening. To ensure the target peptides are specific to the allergen and robust to modifications from food processing, a final MRM method may monitor 12–20 transitions per allergen assuming 2 proteins per allergen, 2 peptides per protein and 3–5 of the most intense and reproducible transitions per peptide.⁶ Initial method development may require screening even more transitions (up to 100s) from proteotypic peptides derived from discovery proteomics or published databases in the literature. While scheduled MRM (sMRM) has significantly improved MRM multiplexing by only monitoring transitions around specific RTs, duty cycle and RT maintenance are increasingly challenged with expanding allergen panels.

In this technical note, a targeted allergen screening assay was developed using stMRM, a novel MRM acquisition mode previously applied for proteomics,⁷ pesticides⁸ and metabolomics studies.⁹

Methods

Samples and reagents: The reagents used are listed in the SCIEX vMethod application for food allergen testing.¹⁰ Foods with the targeted allergens listed in the ingredients were purchased and extracted to determine peptide RTs. Food samples included raw nuts, butter derived from peanuts and the individual tree nuts, baked goods, mustard, a soybean paste, oatmeal, sesame soup, mochi and raw pasta.

Sample preparation: Prior to extraction, each food item was either homogenized into a paste by a food processor or ground into powder by a mortar and pestle. The extraction followed the procedure specified in the SCIEX vMethod.¹⁰ Briefly, 1 g of food was defatted with 2 x 5 mL hexane, followed by centrifugation at 4000 g for 20 minutes to discard the supernatant containing the lipids. After drying under nitrogen gas, the sample was combined with 3.8 mL of extraction buffer (50 mM Trizma base in 2 M urea) and 100 µL of denaturant (20% w/v, octyl β-D-glucopyranoside) and vigorously shaken for 30 minutes. After centrifuging at 4000 g for 20 min, 500 µL of the supernatant was transferred to a 2 mL microcentrifuge tube and mixed with 20 µL of the reducing agent (50 mM Tris-(2-carboxyethyl)-phosphine) to reduce the protein disulfide bonds. The mixture was then incubated at 60^oC for 1 hour. After cooling to room temperature, the sample was mixed with 10 µL of the cysteine-blocking agent (100 mM methyl methane-thiosulfonate) and incubated for another 15 minutes. Protein digestion occurred by incubating the sample with 425 µL of digestion buffer (5 mM calcium chloride in 100 mM ammonium bicarbonate) and 20 µL of trypsin overnight at 37^oC. Digestion was quenched by the addition of 30 µL of formic acid. After centrifuging at 12,000 g for 10 minutes, 400 µL of the supernatant was filtered using a 10 kDa MWCO filter and the filtrate was stored at -20^oC until LC-MS/MS analysis.

Chromatography: Chromatographic separation was performed on a Shimadzu Nexera Prominence LC system using a Kinetex C18 as the analytical column (100 x 3.0 mm, 2.6 µm, Phenomenex P/N 00D-4462-Y0). A flow rate of 0.3 mL/min, an injection volume of 2 µL and a column temperature of 30^oC were used. The LC gradient used is shown in Table 1.

Mass spectrometry: Analysis was performed using sMRM and stMRM Group modes in positive electrospray ionization mode on the SCIEX 7500 system. Table 2 shows the source and gas parameters used in both MRM modes. Optimized MRM parameters for the target peptide transitions were obtained from the SCIEX vMethod¹⁰ and published literature.

Table 3 lists the earliest eluting marker peptides of each allergen group, their RTs, MRM transitions, trigger thresholds and the number of their corresponding dependent transitions from the confirmatory peptides. Dynamic background subtraction (DBS) prioritizes triggering marker transitions that change in intensity above a user-specified threshold during the elution of a chromatographic peak. This option was enabled to set more consistent trigger thresholds despite changing analyte concentrations and matrices.

Data processing: Data acquisition and processing were performed using the SCIEX OS software, version 3.1.

Developing a food allergen screening method using stMRM Group mode

The MS Method Editor in SCIEX OS software introduced a new mode of MRM acquisition called stMRM Group mode (Figure 2A). This mode operates like sMRM, but with the enhanced capability to designate marker transitions to trigger dependent transitions eluting at the same or later RTs. In this mode, the mass table has new columns such as Super group ID (2B), Trigger (2C), Trigger threshold (2D) and Triggered by: Compound ID (2E). 

The Super group ID column groups marker and dependent transitions together based on a predefined relationship, such as precursor/degradation product, parent drug/metabolite and protein/peptide. Here, the Super group ID column specified the allergen proteins, while the Group ID and Compound ID columns specified the peptides and their corresponding MRM transitions, respectively. This also provided another level of analyte grouping for filtering results to expedite data review.

The earliest eluting peptide within each allergen super group is designated as the marker by the Trigger column with their corresponding MRM transitions also identified in the Triggered by: Compound ID column. When the marker transitions exceed their user-specified trigger thresholds, data acquisition turns on for the entire allergen super group, which includes the later-eluting confirmatory peptide transitions. As shown in Figure 2F, only the egg marker peptide exceeded its trigger threshold, which triggered all of the egg allergen confirmatory peptides, while the peanut and milk super groups were not acquired. This ensures that cycle time is preserved and not spent on unnecessary data collection when the allergen peptides are not present at relevant levels. 

Leveraging this intelligent triggering algorithm, an stMRM method comprised of marker and confirmatory peptides was developed and compared against sMRM for the screening of 16 allergens in different food samples.

Leveraging improved cycle time performance for more flexible RT window scheduling and MRM multiplexing

Figure 3 demonstrates the potential improvement in cycle time performance in stMRM compared to sMRM. Although RT windows help control MRM concurrency during sMRM analysis, data acquisition still occurs continuously regardless of whether the target analytes are present, resulting in a fixed cycle time range throughout the run. This means the cycle time is used to monitor the peptide transitions even if the allergens are absent or present at irrelevant levels. Furthermore, cycle time performance deteriorates if the panel is expanded to include more transitions from new allergens or additional confirmatory peptides. In stMRM, the cycle time range is lower due to the selective triggering of allergen markers above their specified thresholds. In most foods where only a few allergens are present, stMRM provides significant cycle time savings that can be leveraged to further optimize the screening assay.

The resulting cycle time saved in stMRM enables increased flexibility for RT scheduling, as shown by the sMRM summary plot in Figure 2G. For example, the RT window can be widened to minimize the risk of re-injections from RT shifts that often occur in complex food matrices. Alternatively, unscheduled acquisition is useful for screening candidate peptide transitions with unknown RTs to determine their suitability for method inclusion. Here, the degree of RT scheduling can be optimized by examining the estimated cycle time range (2H). These minimum and maximum cycle times are calculated based on the two extremes that assume only a single or all allergen super groups are triggered during acquisition, respectively. 

Another benefit of lower cycle times from stMRM is increased flexibility for MRM multiplexing. The SCIEX vMethod for LC-MS/MS screening for food allergens contained 92 transitions, including 4 transitions that were recently added for sesame.¹¹ Using stMRM, the allergen panel was expanded to 241 transitions from the addition of extra confirmatory peptides for the existing allergens and other new allergens like wheat, oat, mustard and tropomyosin.

Improved data quality from optimized triggering 

Faster acquisition from lower cycle times in stMRM results in more data points across the LC peak. Figure 4 shows the positive hits obtained for the confirmatory peptides in a mustard sample in stMRM and sMRM modes. In addition to the mustard peptide transitions, sMRM also acquired data for almost 200 additional peptide transitions even though their corresponding allergens were not present in the sample. In contrast, stMRM only triggered data acquisition for the confirmatory dependent transitions of the mustard peptides without redundant monitoring of other peptides whose markers did not exceed their trigger thresholds. This optimized triggering improved the cycle time for stMRM by ~50%, which resulted in more data points acquired across each LC peak, as compared to sMRM. Similar results were obtained for the positive detection of wheat and tropomyosin allergens in a shrimp chip (Figure 5). Again, the selective triggering in stMRM lowered cycle times to ~50% of those achieved in sMRM, resulting in a 2-fold increase in the number of data points collected across each LC peak.

An optimal duty cycle is critical to ensure sufficient data points are collected across each LC peak for reproducible integration. This becomes even more important for less sensitive analytes or those at lower concentrations where analytical precision may suffer from poorly sampled peaks.

Application of stMRM to food allergen screening

Food samples with specific allergens listed in the ingredients were selected for screening using stMRM. Table 4 lists the positive hits, all of which match at least one of the allergens specified in the ingredients claim on the food product label. In addition, stMRM did not acquire data for the allergens that are not listed nor expected to be present in the food sample. The expansion of the analyte panel also identified several new allergens that were not previously included in the SCIEX vMethod. For example, a shrimp chip extract exhibited positive hits for the peptides specific to wheat and tropomyosin, the latter being a known allergen for shellfish. Another interesting sample was the gochujang hot pepper paste, a common ingredient in Korean cuisine, that is made from fermented soybeans. In addition to soy, stMRM also detected the peptides for the wheat and peanut allergens, both of which were not listed on the product label. This demonstrates the effectiveness of stMRM for allergen detection in commercial food samples

Conclusion
 

• Intelligent triggering in stMRM reduced MRM concurrency and improved cycle time performance by avoiding unnecessary acquisition
  
  
• Lower cycle times in stMRM resulted in a higher rate of peak sampling, more flexibility for RT window scheduling and increased MRM multiplexing
  
  
• Application of stMRM to commercial food products successfully identified allergens that were known to be present or listed in the ingredients claim

References
 

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