No abstract

Even though raw mass spectrometry data is information rich, the vast majority of the data is underutilized. The ability to interrogate these rich datasets is handicapped by the limited capability and flexibility of existing software. We introduce the Mass Spec Query Language (MassQL) that addresses these issues by enabling an expressive set of mass spectrometry patterns to be queried directly from raw data. MassQL is an open-source mass spectrometry query language for flexible and mass spectrometer manufacturer-independent mining of MS data. We envision the flexibility, scalability, and ease of use of MassQL will empower the mass spectrometry community to take fuller advantage of their mass spectrometry data and accelerate discoveries.

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.

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-MS 1 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.

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, where 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 a rat brain tissue thin section, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enabled on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and mapped 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.