Non-targeted and suspect analyses with liquid chromatography/electrospray/high-resolution mass spectrometry (LC/ESI/HRMS) are gaining importance as they enable identification of hundreds or even thousands of compounds in a single sample. Here, we present an approach to address the challenge to quantify compounds identified from LC/HRMS data without authentic standards. The approach uses random forest regression to predict the response of the compounds in ESI/HRMS with a mean error of 2.2 and 2.0 times for ESI positive and negative mode, respectively. We observe that the predicted responses can be transferred between different instruments via a regression approach. Furthermore, we applied the predicted responses to estimate the concentration of the compounds without the standard substances. The approach was validated by quantifying pesticides and mycotoxins in six different cereal samples. For applicability, the accuracy of the concentration prediction needs to be compatible with the effect (e.g. toxicology) predictions. We achieved the average quantification error of 5.4 times, which is well compatible with the accuracy of the toxicology predictions.

Circulating in blood, extracellular vesicles (EVs) and lipoprotein particles (LPs) have diagnostic and prognostic value. To unambiguously define their functions, separation protocols need to be developed. However, because of their similar size and density, traditional approaches to separate EVs and LPs often fail to provide the required resolution. Further development and standardization of affinity-based protocols is necessary and a quantitative method is needed to assess the efficiency of LPs depletion from EVs samples. In the present study, we propose the simultaneous quantification of three groups of pro-teins by mass spectrometry as a toolbox to evaluate prospective separation protocols. We generated 15N-labeled internal standards for quantification of (i) EVs-specific proteins, (ii) all classes and subclasses of apolipoproteins constituting LPs, and (iii) several major serum proteins. These standards were then used in multiple reaction monitoring assay to evaluate the performance of size exclusion chromatography, heparin-Sepharose, lipopolysaccharide-Sepharose, (2-hydroxypropyl)-β-cyclodextrin-Sepharose, and concanavalin A-Sepharose in separating serum EVs and LPs. The efficiency of a resin to sepa-rate EVs from non-EVs substances could be jeopardized by simultaneous EVs aggregation. Therefore, dynamic light scatter-ing analysis was used in this study in addition to the proteomic toolbox when making a recommendation to use particular resin for EVs isolation. Based on our measurements, we concluded that none of the individual separation protocols used in this study resulted in LPs-free EVs and combination of two protocols may be complex due to low EVs yield. Overall, this further points to the importance of proposed proteomic toolbox for the future evaluation of EVs separation protocols.