Tattoo Pigment Identification in Inks and Skin Biopsies of Adverse Reactions by Complementary Elemental and Molecular Bioimaging with Mass Spectral Library Matching.

Tattooing has become increasingly popular throughout society. Despite the recognized issue of adverse reactions in tattoos, regulations remain challenging with limited data available and a missing positive list. The diverse chemical properties of mostly insoluble inorganic and organic pigments pose an outstanding analytical challenge, which typically requires extensive sample preparation. Here, we present a multimodal bioimaging approach combining micro X-ray fluorescence (µXRF) and laser desorption ionization-mass spectrometry (LDI-MS) to detect the elemental and molecular composition in the same sample. The pigment structures directly absorb the laser energy, eliminating the need for matrix application. A computational data processing workflow clusters spatially resolved LDI-MS scans to merge redundant information into consensus spectra, which are then matched against new open mass spectral libraries of tattoo pigments. When applied to 13 tattoo inks and 68 skin samples from skin biopsies in adverse tattoo reactions, characteristic signal patterns of isotopes, ion adducts, and in-source fragments in LDI-MS1 scans yielded confident compound annotations across various pigment classes. Combined with µXRF, pigment annotations were achieved for all skin samples with 14 unique structures and 2 inorganic pigments, emphasizing the applicability to larger studies. The tattoo-specific spectral libraries and further information are available on the tattoo-analysis.github.io website.

Fluorine-Specific Detection Using ICP-MS Helps to Identify PFAS Degradation Products in Nontargeted Analysis.

Although several per- and polyfluoroalkyl substances (PFAS) have been banned and classified as substances of very high concern by the European Chemicals Agency, similar chemicals remain widely used compounds to date. Even though more than 4700 PFASs may occur in the environment, only 40-50 compounds are routinely determined in targeted analysis by ESI-MS using isotopically labeled standards. Nontargeted analysis using high resolution (HR) molecular mass spectrometry suffers from a lack of data mining algorithms for identification and often low ionization efficiency of the compounds. An additional problem for quantification is the potential lack of suitable species specific standards. Here, we demonstrate the usefulness of a hard ionization source (ICP-MS/MS) as a fluorine-specific detector in combination with ESI-MS for the identification of fluorine containing compounds. Simultaneous hyphenation of HPLC-ICP-MS/MS with HR-ESI-MS is applied to evaluate biodegradation products of organofluorine compounds by sewage sludge. The data are analyzed in a nontarget approach using MZmine. Due to the fluorine-specific detection by ICP-MS/MS, more than 5000 peaks (features) of the ESI-MS were reduced to 15 features. Of these, one was identified as a PFAS degradation compound of fluorotelomer alcohol (8:2 FTOH) without using targeted analysis. The feasibility of the detection of organofluorine metabolites using a fluorine-specific detection was demonstrated using a model compound and can thus be applied to new experiments and unknown organofluorine containing samples in the future.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Electrochemistry-mass spectrometry bridging the gap between suspect and target screening of valsartan transformation products in wastewater treatment plant effluent.

Degradation of xenobiotics in wastewater treatment plants may lead to the formation of transformation products with higher persistence or increased (eco-)toxic potential compared to the parent compounds. Accordingly, the identification of transformation products from wastewater treatment plant effluents has gained increasing attention. Here, we show the potential of electrochemistry hyphenated to liquid chromatography and mass spectrometry for the prediction of oxidative degradation in wastewater treatment plants using the antihypertensive drug valsartan as a model compound. This approach identifies seven electrochemical transformation products of valsartan, which are used to conduct a suspect screening in effluent of the main wastewater treatment plant in the city of Münster in Germany. Apart from the parent compound valsartan, an electrochemically predicted transformation product, the N-dealkylated ETP336, is detected in wastewater treatment plant effluent. Subsequently, a targeted liquid chromatographytandem mass spectrometry method for the detection of valsartan and its electrochemical transformation products is set up. Here, electrochemical oxidation is used to generate reference materials of the transformation products in situ by hyphenating electrochemistry online to a triple quadrupole mass spectrometer. Using this setup, multiple reaction monitoring transitions are set up without the need for laborious and costly synthesis and isolation of reference materials for the transformation products. The targeted method is then applied to extracts from wastewater treatment plant effluent and the presence of ETP336 and valsartan in the samples is verified. The presented workflow can be used to set up targeted analysis methods for previously unknown transformation products even without the need for expensive high-resolution mass spectrometers.

On-tissue dataset-dependent MALDI-TIMS-MS2 bioimaging.

Trapped ion mobility spectrometry (TIMS) adds an additional separation dimension to mass spectrometry (MS) imaging, however, the lack of fragmentation spectra (MS2) impedes confident compound annotation in spatial metabolomics. Here, we describe spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF), a dataset-dependent acquisition strategy that augments TIMS-MS imaging datasets with MS2 spectra. The fragmentation experiments are systematically distributed across the sample and scheduled for multiple collision energies per precursor ion. Extendable data processing and evaluation workflows are implemented into the open source software MZmine. The workflow and annotation capabilities are demonstrated on rat brain tissue thin sections, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enables on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and maps the (un)known chemical space by molecular networking. The SIMSEF algorithm and data analysis pipelines are open source and modular to provide a community resource.