IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets.

Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies (n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.idsl.me), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential 12C and 13C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m/z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA.

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets.

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

Decadal Differences in Emerging Halogenated Contaminant Profiles in Great Lakes Top Predator Fish.

Legacy halogenated contaminants have been monitored in the Great Lakes for decades, but there are many additional unknown halogenated contaminants potentially affecting the Great Lakes ecosystem. To address this concern, lake trout (Salvelinus namaycush) were collected in 2005/2006 and 2015/2016 from each lake and screened for previously unidentified compounds. The isotopic profile deconvoluted chromatogram algorithm was used to isolate unknown halogenated components using high-resolution mass spectrometry data files generated by an atmospheric pressure gas chromatography-quadrupole time-of-flight mass spectrometer operated in positive and negative modes. The temporal and spatial differences in the newly detected features were used to isolate new potential contaminants. Decadal differences in the unknown halogenated compounds (or features) were compared with the total polychlorinated biphenyl concentration trends. Greater than 2000 unknown halogenated features were detected. As expected, Lake Superior contained the lowest number of unknown halogenated features, whereas Lake Ontario contained the highest. Unknown features tended to have fewer Cl and/or Br atoms compared to traditional legacy contaminant features typically monitored. Diverse patterns of unknown halogenated compounds between lakes suggested that there continues to be unidentified sources of halogenated contaminants in the Great Lakes missed by current monitoring programs.

Automated Isotopic Profile Deconvolution for High Resolution Mass Spectrometric Data (APGC-QToF) from Biological Matrices.

An isotopic profile matching algorithm, the isotopic profile deconvoluted chromatogram (IPDC), was developed to screen for a wide variety of organic compounds in high-resolution mass spectrometry (HRMS) data acquired from instruments with resolution power as low as 22 000 fwhm. The algorithm initiates the screening process by generating a series of C/Br/Cl/S isotopic patterns consistent with the profiles of approximately 3 million molecular formulas for compounds with potentially persistent, bioaccumulative, and toxic (PBT) properties. To evaluate this algorithm, HRMS data were screened using these seed profiles to isolate relevant chlorinated and/or brominated compounds. Data reduction techniques included mass defect filtering and retention time prediction from estimated boiling points predicted using molecular formulas and reasonable elemental conformations. A machine learning classifier was also developed using spectrometric and chromatographic variables to minimize false positives. A scoring system was developed to rank candidate molecular formulas for an isotopic feature. The IPDC algorithm was applied to a Lake Michigan lake trout extract analyzed by atmospheric pressure gas chromatography-quadrupole time-of-flight (APGC-QToF) mass spectrometry in positive and negative modes. The IPDC algorithm detected isotopic features associated with legacy contaminants and a series of unknown halogenated features. The IPDC algorithm resolved 313 and 855 halogenated features in positive and negative modes, respectively, in Lake Michigan lake trout.

Comprehensive Emerging Chemical Discovery: Novel Polyfluorinated Compounds in Lake Michigan Trout.

A versatile screening algorithm capable of efficiently searching liquid chromatographic/mass spectrometric data for unknown compounds has been developed using a combination of open source and generic computing software packages. The script was used to search for select novel polyfluorinated contaminants in Great Lakes fish. However, the framework is applicable whenever full-scan, high-resolution mass spectral and chromatographic data are collected. Target compound classes are defined and a matrix of candidates is generated that includes mass spectral profiles and likely fragmentation pathways. The initial calibration was performed using a standard solution of known linear perfluoroalkyl acids. Once validated, Lake Michigan trout data files were analyzed for polyfluoroalkyl acids using the algorithm referencing 3570 possible compounds including C4-C10 perfluoro- and polyfluoroalkyl, polyfluorochloroalkyl acids and sulfonates, and potential ether forms. The results suggest the presence of 30 polyfluorinated chemical formulas which have not been previously reported in the literature. The identified candidates included mono- to hexafluoroalkyl carboxylic acids, mono- and trifluoroalkyl carboxylic acid ethers, and novel polyfluoroalkyl sulfonates. Candidate species identified in lake trout were qualified using theoretical isotopic profile matching, characteristic fragmentation patterns based on known linear perfluoroalkyl acid (PFAA) fragmentation, and retention time reproducibility among replicate extractions and injections. In addition, the relative retention times of multiple species within a compound class were compared based on theoretical octanol-water partition coefficients.