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With colorectal cancer (CRC) being the second most common cause of cancer-related deaths worldwide1, there is an urgent need for better diagnostic tools and new, more targeted therapies. Here we used genetically engineered mouse models (GEMMs), and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that unsupervised metabolic profiling can stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging (MSI) to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-mutant CRC, and propose one of its enzymes, i.e. adenosylhomocysteinase (AHCY), as a new therapeutic target. Collectively, we show that the profound genotype-dependent alterations in both lipid and small molecule metabolism in CRC may be exploited for tissue classification with no need for ion identification, and we applied further data analysis to expose a novel metabolic vulnerability of CRC.

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.

Here we show a new and significant application area for mass spectrometry imaging. The potential for fingerprints to reveal drug use has been widely reported, with potential applications in forensics and workplace drug testing. However, one unsolved issue is the inability to distinguish between drug administration and contamination by contact. Previous work using bulk mass spectrometry analysis has shown that this distinction can only be definitively made if the hands are washed prior to sample collection. Here, we illustrate how three mass spectrometry imaging approaches, desorption electrospray ionisation (DESI), matrix assisted laser desorption ionisation (MALDI) and time of flight secondary ion mass spectrometry (ToF-SIMS) can be used to visualise fingerprints at different pixel sizes, ranging from the whole fingerprint down to the pore structure. We show how each of these magnification scales can be used to distinguish between cocaine use and contact. We also demonstrate the first application of water cluster SIMS to a fingerprint sample, which was the sole method tested here that was capable of detecting excreted drug metabolites in fingerprints, while providing spatial resolution sufficient to resolve individual pore structure. We show that after administration of cocaine, lipids and salts in the fingerprint ridges spatially correlate with the cocaine metabolite, benzoylecgonine. In contrast after contact, we have observed that cocaine and its metabolite show a poor spatial correlation with the flow of the ridges.

Atmospheric pressure ionization methods confer a number of advantages over more traditional vacuum based techniques, in particular ease of hyphenation to a range of mass spectrometers. For atmospheric pressure matrix assisted desorption/ionization (AP-MALDI), several ion sources, operating in a range of geometries have been reported. Most of these platforms have, to date, generally demonstrated relatively low ion yields and/or poor ion transmission compared to vacuum sources. To improve the detection of certain ions, we have developed a second-generation transmission mode (TM) AP-MALDI imaging platform with in-line plasma postionization using the commercially available SICRIT device, replacing the previously used low temperature plasma probe from our developmental AP-TM-MALDI stage. Both plasma devices produce a significant ionization enhancement for a range of compounds, but the overall higher enhancement obtained by the SICRIT device in addition to the ease of installation and the minimal need for optimization presents this commercially available tool as an attractive method for simple postionization in AP-MALDI MSI.

Matrix-assisted laser desorption ionization (MALDI) operated at atmospheric pressure has been shown to be a promising technique for mass spectrometry imaging of biological tissues at high spatial resolution. Recent studies have shown several orders of magnitude improvement in sensitivity afforded by coupling with a low-temperature plasma (LTP) for postionization. In this work we report the first results from "matrix-free" imaging using our atmospheric pressure (AP) transmission mode (TM) (MA)LDI source with LTP postionization. Direct MSI analysis of murine testis with no sample preparation after tissue sectioning enabled imaging of a range of lipid classes at pixel sizes of 25 μm. We compared results from the matrix-free methods with MALDI experiments in which the matrix was applied on top, underneath, or layered as a sandwich. The sandwich preparation was found to lead to ion yields approximately 2- or 3-fold higher than the other methods, indicating that the addition of a light absorbing matrix remains beneficial. Nonetheless, LDI methods confer a range of advantages, and the sensitivity improvements provided by postionization strategies are a promising step toward high-efficiency laser sampling under ambient conditions.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a powerful label-free technique for mapping the spatial distribution of biomolecules directly from tissue. However, like most other MSI techniques, it suffers from low ionization yields and ion suppression effects for biomolecules that might be of interest for a specific application at hand. Recently, a form of laser postionization was introduced (coined MALDI-2) that critically boosts the ion yield for many glyco- and phospholipids by several orders of magnitude and makes the detection of further biomolecular species possible. While the MALDI-2 technique is being increasingly applied by the MSI community, it is still only implemented in fine vacuum ion sources in a pressure range of about 1-10 mbar. Here, we show the first implementation of the technique to a custom-built atmospheric pressure ion source coupled to an Orbitrap Elite system. We present results from parameter optimization of MALDI-2 at atmospheric pressure, compare our findings to previously published fine vacuum data, and show first imaging results from mouse cerebellum with a 20 μm pixel size. Our findings broaden the feasibility of the technique to overall more flexible atmospheric pressure ion sources.

High dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods.One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

Rationale Examining surface protein conformations, and especially achieving this with spatial resolution, is an important goal. The recently discovered ionization processes offer spatial-resolution measurements similar to matrix-assisted laser desorption/ionization (MALDI) and produce charge states similar to electrospray ionization (ESI) extending higher-mass protein applications directly from surfaces on high-performance mass spectrometers. Studying a well-interrogated protein by ion mobility spectrometry-mass spectrometry (IMS-MS) to access effects on structures using a solid vs. solvent matrix may provide insights. Methods Ubiquitin was studied by IMS-MS using new ionization processes with commercial and homebuilt ion sources and instruments (Waters SYNAPT G2(S)) and homebuilt 2 m drift-tube instrument; MS™ sources). Mass-to-charge and drift-time (td )-measurements are compared for ubiquitin ions obtained by inlet and vacuum ionization using laserspray ionization (LSI), matrix- (MAI) and solvent-assisted ionization (SAI), respectively, and compared with those from ESI under conditions that are most comparable. Results Using the same solution conditions with SYNAPT G2(S) instruments, td -distributions of various ubiquitin charge states from MAI, LSI, and SAI are similar to those from ESI using a variety of solvents, matrices, extraction voltages, a laser, and temperature only, showing subtle differences in more compact features within the elongated distribution of structures. However, on a homebuilt drift-tube instrument, within the elongated distribution of structures, both similar and different td -distributions are observed for ubiquitin ions obtained by MAI and ESI. MAI-generated ions are frequently narrower in their td -distributions. Conclusions Direct comparisons between ESI and the new ionization methods operational directly from surfaces suggest that the protein in its solution structure prior to exposure to the ionization event is either captured (frozen out) at the time of crystallization, or that the protein in the solid matrix is associated with sufficient solvent to maintain the solution structure, or, alternatively, that the observed structures are those related to what occurs in the gas phase with ESI- or MAI-generated ions and not with the solution structures.

Rationale The developments of new ionization technologies based on processes previously unknown to mass spectrometry (MS) have gained significant momentum. Herein we address the importance of understanding these unique ionization processes, demonstrate the new capabilities currently unmet by other methods, and outline their considerable analytical potential. Methods The inlet and vacuum ionization methods of solvent-assisted ionization (SAI), matrix-assisted ionization (MAI), and laserspray ionization can be used with commercial and dedicated ion sources producing ions from atmospheric or vacuum conditions for analyses of a variety of materials including drugs, lipids, and proteins introduced from well plates, pipet tips and plate surfaces with and without a laser using solid or solvent matrices. Mass spectrometers from various vendors are employed. Results Results are presented highlighting strengths relative to ionization methods of electrospray ionization (ESI) and matrix-assisted laser desorption/ionization. We demonstrate the utility of multi-ionization platforms encompassing MAI, SAI, and ESI and enabling detection of what otherwise is missed, especially when directly analyzing mixtures. Unmatched robustness is achieved with dedicated vacuum MAI sources with mechanical introduction of the sample to the sub-atmospheric pressure (vacuum MAI). Simplicity and use of a wide array of matrices are attained using a conduit (inlet ionization), preferably heated, with sample introduction from atmospheric pressure. Tissue, whole blood, urine (including mouse, chicken, and human origin), bacteria strains and chemical on-probe reactions are analyzed directly and, especially in the case of vacuum ionization, without concern of carryover or instrument contamination. Conclusions Examples are provided highlighting the exceptional analytical capabilities associated with the novel ionization processes in MS that reduce operational complexity while increasing speed and robustness, achieving mass spectra with low background for improved sensitivity, suggesting the potential of this simple ionization technology to drive MS into areas currently underserved, such as clinical and medical applications.

An unexpected ionization process was recently discovered for use in mass spectrometry in which no added energy is required to convert condensed-phase molecules to ESI-like gas-phase ions. This process offers the opportunity to greatly simplify ionization and reduce energy requirements for use with portable mass spectrometers. Here we designed and constructed minimal ion sources specifically for the new ionization methods, and demonstrate conversion of compounds directly to gas-phase ions with high sensitivity using matrix-assisted ionization without a laser, voltage, or heat. A variety of inlets and sample supports were tested for sample introduction with this minimalistic source, include a contactless ‘vacuum cleaner’ approach for analyzing samples as well as laser ablation for high spatial resolution. The analysis is astonishingly simple and the mass spectra surprisingly free of background ions. Analytes tested range from small molecules (e.g. drugs) to nonvolatile compounds (e.g. proteins). These developments are potentially useful in a variety of vendor-independent applications, especially where simplicity, miniaturization, or automation are desirable attributes.

Exceptional ion mobility spectrometry mass spectrometry (IMS-MS) developments by von Helden, Jarrold, and Clemmer provided technology that gives a view of chemical/biological compositions previously not achievable. The ionization method of choice used with IMS-MS has been electrospray ionization (ESI). In this special issue contribution, we focus on fundamentals of heretofore unprecedented means for transferring volatile and nonvolatile compounds into gas-phase ions singly and multiply charged. These newer ionization processes frequently lead to different selectivity relative to ESI and, together with IMS-MS, may provide a more comprehensive view of chemical compositions directly from their original environment such as surfaces, e.g., tissue. Similarities of results using solvent- and matrix-assisted ionization are highlighted, as are differences between ESI and the inlet ionization methods, especially with mixtures such as bacterial extracts. Selectivity using different matrices is discussed, as are results which add to our fundamental knowledge of inlet ionization as well as pose additional avenues for inquiry. IMS-MS provides an opportunity for comparison studies relative to ESI and will prove valuable using the new ionization technologies for direct analyses. Our hypothesis is that some ESI-IMS-MS applications will be replaced by the new ionization processes and by understanding mechanistic aspects to aid enhanced source and method developments this will be hastened.