Non-target time trend screening: A novel data reduction strategy for detecting emerging contaminants.
Non-targeted mass spectrometry-based approaches for detecting novel xenobiotics in biological samples are hampered by the occurrence of naturally fluctuating endogenous substances, which are difficult to distinguish from environmental contaminants. Here, we investigate a data reduction strategy for datasets derived from a biological time series. The objective is to flag reoccurring peaks in the time series based on increasing peak intensities, thereby reducing peak lists to only those which may be associated with emerging bioaccumulative contaminants. As a result, compounds with increasing concentrations are flagged while compounds displaying random, decreasing, or steady-state time trends are removed. As an initial proof of concept, we created artificial time trends by fortifying human whole blood samples with isotopically labelled standards. Different scenarios were investigated: eight model compounds had a continuously increasing trend in the last two to nine time points, and four model compounds had a trend that reached steady state after an initial increase. Each time series was investigated at three fortification levels and one unfortified series. Following extraction, analysis by ultra performance liquid chromatography high-resolution mass spectrometry, and data processing, a total of 21,700 aligned peaks were obtained. Peaks displaying an increasing trend were filtered from randomly fluctuating peaks using time trend ratios and Spearman's rank correlation coefficients. The first approach was successful in flagging model compounds spiked at only two to three time points, while the latter approach resulted in all model compounds ranking in the top 11 % of the peak lists. Compared to initial peak lists, a combination of both approaches reduced the size of datasets by 80-85 %. Overall, non-target time trend screening represents a promising data reduction strategy for identifying emerging bioaccumulative contaminants in biological samples. Graphical abstract Using time trends to filter out emerging contaminants from large peak lists.
Extending analysis of environmental pollutants in human urine towards screening for suspected compounds.
Today there is a large difference in the number of chemicals of commerce and the number of chemicals being monitored in environmental and human samples. During the last decades suspect screening methods have been developed to increase the number of monitored analytes. Peaks detected during high resolution mass spectrometry full scan measurements are compared to a list of suspect chemicals with known exact masses. These methods, however, have so far focused on environmental samples. Thus we present a method development for a suspect screening of human urine samples. The sample preparation techniques and instrumental analysis were tested by target chemicals with a wide range of properties. A combination of direct injection and QuEChERS extraction followed by liquid chromatography coupled to high resolution mass spectrometry was able to detect 33 of the 40 spiked target compounds at 30-120% absolute recovery. For suspect evaluation peaks were deconvoluted and aligned with the software MZmine followed by R script processing. Comparing detected and in-silico fragmentation, nine suspect chemicals could be tentatively identified in a pooled human urine sample and four of these were confirmed by a reference standard.