Precast Gelatin-Based Molds for Tissue Embedding Compatible with Mass Spectrometry Imaging

Preparation of tissue for matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) generally involves embedding the tissue followed by freezing and cryosectioning, usually between 5 and 25 μm thick, depending on the tissue type and the analyte(s) of interest. The brain is approximately 60% fat; it therefore lacks rigidity and poses structural preservation challenges during sample preparation. Histological sample preparation procedures are generally transferable to MALDI-MSI; however, there are various limitations. Optimal cutting temperature compound (OCT) is commonly used to embed and mount fixed tissue onto the chuck inside the cryostat during cryosectioning. However, OCT contains potential interferences that are detrimental to MALDI-MSI, while fixation is undesirable for the analysis of some analytes either due to extraction or chemical modification (i.e., polar metabolites). Therefore, a method for both fixed and fresh tissue compatible with MALDI-MSI and histology is desirable to increase the breadth of analyte(s), maintain the topographies of the brain, and provide rigidity to the fragile tissue while eliminating background interference. The method we introduce uses precast gelatin-based molds in which a whole mouse brain is embedded, flash frozen, and cryosectioned in preparation for mass spectrometry imaging (MSI)

Expanding Per- and Polyfluoroalkyl Substances Coverage in Nontargeted Analysis Using Data-Independent Analysis and IonDecon

Per- and polyfluoroalkyl substances (PFAS) are widespread, persistent environmental contaminants that have been linked to various health issues. Comprehensive PFAS analysis often relies on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC HRMS) and molecular fragmentation (MS/MS). However, the selection and fragmentation of ions for MS/MS analysis using data-dependent analysis results in only the topmost abundant ions being selected. To overcome these limitations, All Ions fragmentation (AIF) can be used alongside data-dependent analysis. In AIF, ions across the entire m/z range are simultaneously fragmented; hence, precursor–fragment relationships are lost, leading to a high false positive rate. We introduce IonDecon, which filters All Ions data to only those fragments correlating with precursor ions. This software can be used to deconvolute any All Ions files and generates an open source DDA formatted file, which can be used in any downstream nontargeted analysis workflow. In a neat solution, annotation of PFAS standards using IonDecon and All Ions had the exact same false positive rate as when using DDA; this suggests accurate annotation using All Ions and IonDecon. Furthermore, deconvoluted All Ions spectra retained the most abundant peaks also observed in DDA, while filtering out much of the artifact peaks. In complex samples, incorporating AIF and IonDecon into workflows can enhance the MS/MS coverage of PFAS (more than tripling the number of annotations in domestic sewage). Deconvolution in complex samples of All Ions data using IonDecon did retain some false fragments (fragments not observed when using ion selection, which were not isotopes or multimers), and therefore DDA and intelligent acquisition methods should still be acquired when possible alongside All Ions to decrease the false positive rate. Increased coverage of PFAS can inform on the development of regulations to address the entire PFAS problem, including both legacy and newly discovered PFAS