Combining a Deconvolution and a Universal Library Search Algorithm for the Nontarget Analysis of Data-Independent Acquisition Mode Liquid Chromatography−High-Resolution Mass Spectrometry Results

Nontarget analysis is considered one of the most comprehensive tools for the identification of unknown compounds in a complex sample analyzed via liquid chromatography coupled to high-resolution mass spectrometry (LC–HRMS). Due to the complexity of the data generated via LC–HRMS, the data-dependent acquisition mode, which produces the MS² spectra of a limited number of the precursor ions, has been one of the most common approaches used during nontarget screening. However, data-independent acquisition mode produces highly complex spectra that require proper deconvolution and library search algorithms. We have developed a deconvolution algorithm and a universal library search algorithm (ULSA) for the analysis of complex spectra generated via data-independent acquisition. These algorithms were validated and tested using both semisynthetic and real environmental data. A total of 6000 randomly selected spectra from MassBank were introduced across the total ion chromatograms of 15 sludge extracts at three levels of background complexity for the validation of the algorithms via semisynthetic data. The deconvolution algorithm successfully extracted more than 60% of the added ions in the analytical signal for 95% of processed spectra (i.e., 3 complexity levels multiplied by 6000 spectra). The ULSA ranked the correct spectra among the top three for more than 95% of cases. We further tested the algorithms with 5 wastewater effluent extracts for 59 artificial unknown analytes (i.e., their presence or absence was confirmed via target analysis). These algorithms did not produce any cases of false identifications while correctly identifying ∼70% of the total inquiries. The implications, capabilities, and the limitations of both algorithms are further discussed

Pearson correlation coefficients between FPC scores for the ecstasy (MDMA) loads and simple summary measures.

<p>*Overall mean of the log-transformed data throughout the seven day week.</p><p>**Difference: mean of the log-transformed data (weekend) minus mean of the log-transformed data (weekdays).</p><p>Pearson correlation coefficients between FPC scores for the ecstasy (MDMA) loads and simple summary measures.</p

FANOVA F-permutation test plot separately for each drug and for each explanatory variable.

<p>2.1–2.2 show how the p-value of the permutation F-test changes, as different values of longitude are chosen as grouping; 2.3–2.4 show how the p-value of the permutation F-test changes, as different values of latitude are chosen as grouping; 2.5–2.6 show how the p-value of the permutation F-test changes, as different values of density are chosen as grouping; 2.7–2.8 show how the p-value of the permutation F-test changes, as different values of relative size are chosen as grouping; 2.9–2.10 show how the p-value of the permutation F-test changes, as different values of gross domestic product (GDP) are chosen as grouping.</p

Wastewater drug loads for 42 European cities throughout the week.

<p>*Statistically significant difference between weekday (Mon-Fri) and weekend (Sat-Sun) loads using the Wilcox test (p-value < 0.001)</p><p>**No statistically significant difference between weekday (Mon-Fri) and weekend (Sat-Sun) loads using the Wilcox test</p><p>(p-value = 0.369)</p><p>The data sets supporting the table are freely available [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138669#pone.0138669.ref017" target="_blank">17</a>].</p><p>Wastewater drug loads for 42 European cities throughout the week.</p

Raw data, individual curves and results from the FPCA for each drug.

<p>1.1–1.2 shows the raw data for each drug; 1.3–1.4 shows the raw data (light grey) with the individually fitted curves (dark grey) and the mean of these curves (black); 1.5–1.10 shows the mean of the fitted curves (solid line) and how the shape of an individual curve differs from the mean curve if a multiple of the principal component curve is added to (+ +) or subtracted from (- -) the mean curve. The multiples correspond to one SD of the FPC1, FPC2 and FPC3 scores, respectively.</p

Multiple regression analyses with functional principal component scores for ecstasy (MDMA) as dependent variable and longitude, latitude, gross domestic product, population density and relative size of the city as explanatory variables.

<p>* Akaike's information criterion.</p><p>a Number taken from <a href="http://en.wikipedia.org/wiki/List_of_countries_by_GDP_%28nominal%29_per_capita" target="_blank">http://en.wikipedia.org/wiki/List_of_countries_by_GDP_%28nominal%29_per_capita</a>.</p><p>b Number of inhabitants in city divided by the urban area in square kilometres.</p><p>c Number of inhabitants in city divided by the number of inhabitants in the country.</p><p>Multiple regression analyses with functional principal component scores for ecstasy (MDMA) as dependent variable and longitude, latitude, gross domestic product, population density and relative size of the city as explanatory variables.</p