A new tissue sample embedding and processing method is presented that provides downstream compatibility with numerous different histological, molecular biology, and analytical techniques. The methodology is based on the low temperature embedding of fresh frozen specimens into a hydrogel matrix composed of hydroxypropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) and sectioning using a cryomicrotome. The hydrogel was expected not to interfere with standard tissue characterization methods, histologically or analytically. We assessed the compatibility of this protocol with various mass spectrometric imaging methods including matrix-assisted laser desorption ionization (MALDI), desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS). We also demonstrated the suitability of the universal protocol for extraction based molecular biology techniques such as rt-PCR. The integration of multiple analytical modalities through this universal sample preparation protocol offers the ability to study tissues at a systems biology level and directly linking results to tissue morphology and cellular phenotype.

There is an increasing need in the pharmaceutical industry to reduce drug failure at late stage and thus reduce the cost of developing a new medicine. Since most drug targets are intracellular, this requires a better understanding of the drug disposition within a cell. Secondary ion mass spectrometry has been identified as a potentially important technique to do this, as it is label-free and allows imaging in 3D with subcellular resolution and recent studies have shown promise for amiodarone. An important analytical parameter is sensitivity, and we measure this in a bovine liver homogenate reference sample for 20 drugs representing important class types relevant to the pharmaceutical industry. We also measure the sensitivity for pure drug and show, for the first time, that the secondary ion mass spectrometry (SIMS) positive ionization efficiency for small molecules is a simple power-law relationship to the log P value. This discovery will be important for advancing the understanding of the SIMS ionization process in small molecules that has, until now, been elusive. This simple relationship is found to hold true for drug doped in the bovine liver homogenate reference sample, except for fluticasone, nicardipine, and sorafenib which suffer from severe matrix suppression. This relationship provides a simple semiempirical method to determine drug sensitivity for positive secondary ions. Furthermore, we show, on chosen models, how the use of different solvents during sample preparation can affect the ionization of analytes.

OrbiSIMS is a recently developed instrument for label‐free imaging of chemicals with micron spatial resolution and high mass resolution. We report a cryogenic workflow for OrbiSIMS (Cryo‐OrbiSIMS) that improves chemical detection of lipids and other biomolecules in tissues. Cryo‐OrbiSIMS boosts ionization yield and decreases ion‐beam induced fragmentation, greatly improving the detection of biomolecules such as triacylglycerides. It also increases chemical coverage to include molecules with intermediate or high vapor pressures, such as free fatty acids and semi‐volatile organic compounds (SVOCs). We find that Cryo‐OrbiSIMS reveals the hitherto unknown localization patterns of SVOCs with high spatial and chemical resolution in diverse plant, animal, and human tissues. We also show that Cryo‐OrbiSIMS can be combined with genetic analysis to identify enzymes regulating SVOC metabolism. Cryo‐OrbiSIMS is applicable to high resolution imaging of a wide variety of non‐volatile and semi‐volatile molecules across many areas of biomedicine.

We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the identification of peptide sample TOF-SIMS spectra by machine learning. More than 1000 time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of six peptide model samples (one of them was a test sample) were collected using 27 TOF-SIMS instruments from 25 institutes of six countries, the U. S., the U. K., Germany, China, South Korea, and Japan. Because peptides have systematic and simple chemical structures, they were selected as model samples. The intensity of peaks in every TOF-SIMS spectrum was extracted using the same peak list and normalized to the total ion count. The spectra of the test peptide sample were predicted by Random Forest with 20 amino acid labels. The accuracy of the prediction for the test spectra was 0.88. Although the prediction of an unknown peptide was not perfect, it was shown that all of the amino acids in an unknown peptide can be determined by Random Forest prediction and the TOF-SIMS spectra. Moreover, the prediction of peptides, which are included in the training spectra, was almost perfect. Random Forest also suggests specific fragment ions from an amino acid residue Q, whose fragment ions detected by TOF-SIMS have not been reported, in the important features. This study indicated that the analysis using Random Forest, which enables translation of the mathematical relationships to chemical relationships, and the multi labels representing monomer chemical structures, is useful to predict the TOF-SIMS spectra of an unknown peptide.

Chemical imaging techniques are increasingly being used in combination to achieve a greater understanding of a sample. This is especially true in the case of mass spectrometry imaging (MSI), where the use of different ionization sources allows detection of different classes of molecules across a range of spatial resolutions. There has been significant recent effort in the development of data fusion algorithms that attempt to combine the benefits of multiple techniques, such that the output provides additional information that would have not been present or obvious from the individual techniques alone. However, the majority of the data fusion methods currently in use rely on image registration to generate the fused data and therefore can suffer from artifacts caused by interpolation. Here, we present a method for data fusion that does not incorporate interpolation-based artifacts into the final fused data, applied to data acquired from multiple chemical imaging modalities. The method is evaluated using simulated data and a model polymer blend sample, before being applied to biological samples of mouse brain and lung.