No abstract

Background: When nanoparticles (NPs) are applied into a biological fluid, such as blood, proteins bind rapidly to their surface forming a so-called “protein corona”. These proteins are strongly attached to the NP surface and confers them a new biological identity that is crucial for the biological response in terms of body biodistribution, cellular uptake, and toxicity. The corona is dynamic in nature and it is well known that the composition varies in dependence of the physicochemical properties of the NPs. In the present study we investigated the protein corona that forms around poly(lactide-co-glycolide) (PLGA) NPs at different serum concentrations using two substantially different serum types, namely fetal bovine serum (FBS) and human serum. The corona was characterized by means of sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE), Bradford protein assay, zeta potential measurements, and liquid chromatography–mass spectrometry/mass spectrometry (LC–MS/MS). Additionally, the time-dependent cell interaction of PLGA NPs in the absence or presence of a preformed protein corona was assessed by in vitro incubation experiments with the human liver cancer cell line HepG2. Results: Our data revealed that the physiological environment critically affects the protein adsorption on PLGA NPs with significant impact on the NP–cell interaction. Under comparable conditions the protein amount forming the protein corona depends on the serum type used and the serum concentration. On PLGA NPs incubated with either FBS or human serum a clear difference in qualitative corona protein composition was identified by SDS-PAGE and LC–MS/MS in combination with bioinformatic protein classification. In the case of human serum a considerable change in corona composition was observed leading to a concentration-dependent desorption of abundant proteins in conjunction with an adsorption of high-affinity proteins with lower abundance. Cell incubation experiments revealed that the respective corona composition showed significant influence on the resulting nanoparticle–cell interaction. Conclusion: Controlling protein corona formation is still a challenging task and our data highlight the need for a rational future experimental design in order to enable a prediction of the corona formation on nanoparticle surfaces and, therefore, the resulting biodistribution in the body.

Food allergies have emerged as a global problem over the last few decades; therefore, reliable and sensitive analytical methods to ensure food safety for allergic consumers are required. The application of mass spectrometry is of growing interest in this field and several procedures based on low resolution tandem mass spectrometry using single tryptic peptides as analytical targets have recently been described. However, a comprehensive survey of marker peptides for the development of multi-methods is still missing, as is a consensus guide to marker identification. In this study, we therefore report a consistent approach to the development of liquid chromatography-mass spectrometry (LC-MS) multi-screening methods for the detection of allergens in food matrices. Proteotypic peptides were identified by a shotgun proteomics approach and verified through a thorough investigation of specificity and sensitivity. On the basis of this procedure, we identified 44 suitable tryptic marker peptides from six allergenic nut species and developed the first analytical LC-MS method for the detection of trace nut contaminations in processed foods using high resolution mass spectrometry (HRMS). The analysis of spiked matrix samples gave limits of detection (LODs) below 10 μg/g for several nuts; these LODs are comparable with routinely used methods such as ELISA and PCR. Notably, the HRMS approach can be used in an untargeted fashion to identify multiple allergens also retrospectively. In conclusion, we present here the so far largest consensus set of analytical markers from nut allergens and to the best of our knowledge the first multi-allergen method based on LC-HRMS.

Food allergies have become a global challenge to food safety in industrialized countries in recent years. With governmental monitoring and legislation moving towards the establishment of threshold allergen doses, there is a need for sensitive and quantitative analytical methods for the determination of allergenic food contaminants. Targeted proteomics employing liquid chromatography-mass spectrometry (LC-MS) has emerged as a promising technique that offers increased specificity and reproducibility compared to antibody and DNA-based technologies. As the detection of trace levels of allergenic food contaminants also demands excellent sensitivity, we aimed to significantly increase the analytical performance of LC-MS by utilizing multiple reaction monitoring cubed (MRM) technology. Following a bottom-up proteomics approach, including a straightforward sample preparation process, 38 MRM experiments specific to 18 proteotypic peptides were developed and optimized. This permitted the highly specific identification of peanut, almond, cashew, hazelnut, pistachio, and walnut. The analytical performance of the method was assessed for three relevant food matrices with different chemical compositions. Limits of detection were around 1 μg/g or below in fortified matrix samples, not accounting for the effects of food processing. Compared to an MRM-based approach, the MRM-based method showed an increase in sensitivity of up to 30-fold. Regression analysis demonstrated high linearity of the MRM signal in spiked matrix samples together with robust intersample reproducibility, confirming that the method is highly applicable for quantitative purposes. To the best of our knowledge, we describe here the most sensitive LC-MS multi-method for food allergen detection thus far. In addition, this is the first study that systematically compares MRM with MRM for the analysis of complex foods. Graphical abstract Allergen detection by MRM.

No abstract