Atmospheric-Pressure Infrared Laser-Ablation Plasma-Postionization Mass Spectrometry Imaging of Formalin-Fixed Paraffin-Embedded (FFPE) and Fresh-Frozen Tissue Sections with No Sample Preparation.

Mass spectrometry imaging (MSI) encompasses a powerful suit of techniques which provide spatially resolved atomic and molecular information from almost any sample type. MSI is now widely used in preclinical research to provide insight into metabolic phenotypes of disease. Typically, fresh-frozen tissue preparations are considered optimal for biological MSI and other traditional preservation methods such as formalin fixation, alone or with paraffin embedding (FFPE), are considered less optimal or even incompatible. Due to the prevalence of FFPE tissue storage, particularly for rare and therefore high-value tissue samples, there is substantial motivation for optimizing MSI methods for analysis of FFPE tissue. Here, we present a novel modality, atmospheric-pressure infrared laser-ablation plasma postionization (AP-IR-LA-PPI), with the first proof-of-concept examples of MSI for FFPE and fresh-frozen tissues, with no post-sectioning sample preparation. We present ion images from FFPE and fresh tissues in positive and negative ion modes. Molecular annotations (via the Metaspace annotation engine) and on-tissue MS/MS provide additional confidence that the detected ions arise from a broad range of metabolite and lipid classes from both FFPE and fresh-frozen tissues.

Chemical intervention in plant sugar signalling increases yield and resilience.

The pressing global issue of food insecurity due to population growth, diminishing land and variable climate can only be addressed in agriculture by improving both maximum crop yield potential and resilience. Genetic modification is one potential solution, but has yet to achieve worldwide acceptance, particularly for crops such as wheat. Trehalose-6-phosphate (T6P), a central sugar signal in plants, regulates sucrose use and allocation, underpinning crop growth and development. Here we show that application of a chemical intervention strategy directly modulates T6P levels in planta. Plant-permeable analogues of T6P were designed and constructed based on a 'signalling-precursor' concept for permeability, ready uptake and sunlight-triggered release of T6P in planta. We show that chemical intervention in a potent sugar signal increases grain yield, whereas application to vegetative tissue improves recovery and resurrection from drought. This technology offers a means to combine increases in yield with crop stress resilience. Given the generality of the T6P pathway in plants and other small-molecule signals in biology, these studies suggest that suitable synthetic exogenous small-molecule signal precursors can be used to directly enhance plant performance and perhaps other organism function.

Modality Agnostic Model for Spatial Resolution in Mass Spectrometry Imaging: Application to MALDI MSI Data.

Image resolution in mass spectrometry imaging (MSI) is governed by the sampling probe, the motion of the stage relative to the probe, and the noise inherent for the sample and instrumentation employed. A new image formation model accounting for these variables is presented here. The model shows that the size of the probe, stage velocity, and the rate at which the probe consumes material from the surface govern the amount of blur present in the image. However, the main limiting factor for resolution is the signal-to-noise ratio (SNR). To evaluate blurring and noise effects, a new computational method for measuring lateral resolution in MSI is proposed. A spectral decomposition of the observed image signal and noise is used to determine a resolution number. To evaluate this technique, a silver step edge was prepared. This device was imaged at different pixels sizes using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). A modulation transfer function (MTF) and a noise power spectrum (NPS) were computed for each single-ion image, and resolution was defined as the point of intersection between the MTF and the NPS. Finally, the algorithm was also applied to a MALDI MSI tissue data set.

Implications of Peak Selection in the Interpretation of Unsupervised Mass Spectrometry Imaging Data Analyses.

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.

Atmospheric Pressure MALDI Mass Spectrometry Imaging Using In-Line Plasma Induced Postionization.

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.

Correlative Hyperspectral Imaging Using a Dimensionality-Reduction-Based Image Fusion Method.

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.

Imaging drugs, metabolites and biomarkers in rodent lung: a DESI MS strategy for the evaluation of drug-induced lipidosis.

Within drug development and pre-clinical trials, a common, significant and poorly understood event is the development of drug-induced lipidosis in tissues and cells. In this manuscript, we describe a mass spectrometry imaging strategy, involving repeated analysis of tissue sections by DESI MS, in positive and negative polarities, using MS and MS/MS modes. We present results of the detected distributions of the administered drug, drug metabolites, lipid molecules and a putative marker of lipidosis, di-docosahexaenoyl (22:6)-bis(monoacylglycerol) phosphate (di-22:6-BMP). A range of strategies have previously been reported for detection, isolation and identification of this compound, which is an isomer of di-docosahexaenoic (22:6 n-3) phosphatidylglycerol (di-22:6 PG), a commonly found lipid that acts as a surfactant in lung tissues. We show that MS imaging using MS/MS can be used to differentiate these compounds of identical mass, based upon the different distributions of abundant fragment ions. Registration of images of these fragments, and detected drugs and metabolites, is presented as a new method for studying drug-induced lipidosis in tissues. Graphical abstract.

A methodological inter-comparison study on the detection of surface contaminant sodium dodecyl sulfate applying ambient- and vacuum-based techniques.

Biomedical devices are complex products requiring numerous assembly steps along the industrial process chain, which can carry the potential of surface contamination. Cleanliness has to be analytically assessed with respect to ensuring safety and efficacy. Although several analytical techniques are routinely employed for such evaluation, a reliable analysis chain that guarantees metrological traceability and quantification capability is desirable. This calls for analytical tools that are cascaded in a sensible way to immediately identify and localize possible contamination, both qualitatively and quantitatively. In this systematic inter-comparative approach, we produced and characterized sodium dodecyl sulfate (SDS) films mimicking contamination on inorganic and organic substrates, with potential use as reference materials for ambient techniques, i.e., ambient mass spectrometry (AMS), infrared and Raman spectroscopy, to reliably determine amounts of contamination. Non-invasive and complementary vibrational spectroscopy techniques offer a priori chemical identification with integrated chemical imaging tools to follow the contaminant distribution, even on devices with complex geometry. AMS also provides fingerprint outputs for a fast qualitative identification of surface contaminations to be used at the end of the traceability chain due to its ablative effect on the sample. To absolutely determine the mass of SDS, the vacuum-based reference-free technique X-ray fluorescence was employed for calibration. Convex hip liners were deliberately contaminated with SDS to emulate real biomedical devices with an industrially relevant substance. Implementation of the aforementioned analytical techniques is discussed with respect to combining multimodal technical setups to decrease uncertainties that may arise if a single technique approach is adopted. Graphical abstract ᅟ.

Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images.

Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach that uses a graph-based algorithm with a two-phase sampling method that overcomes this limitation. We demonstrate the algorithm on a range of sample types and show that it can segment anatomical features that are not identified using commonly employed algorithms in MSI, and we validate our results on synthetic MSI data. We show that the algorithm is robust to fluctuations in data quality by successfully clustering data with a designed-in variance using data acquired with varying laser fluence. Finally, we show that this method is capable of generating accurate segmentations of large MSI data sets acquired on the newest generation of MSI instruments and evaluate these results by comparison with histopathology.

Investigating MALDI MSI parameters (Part 1) - A systematic survey of the effects of repetition rates up to 20kHz in continuous raster mode.

Recent developments in laser performance, combined with the desire for increases in detected ion intensity and throughput, have led to the adoption of high repetition-rate diode-pumped solid-state (DPSS) lasers in matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). Previous studies have demonstrated a more complex relationship between detected ion intensity, stage raster speed and laser pulse repetition rate than the simple linear relationship between number of pulses and detected ion intensity that might be expected. Here we report, for the first time, the interrelated influence of varying laser energy, repetition rate and stage raster speed on detected ion intensity. Thin films of PC 34:1 lipid standard and murine brain tissue with CHCA are analysed by continuous stage raster MALDI MSI. Contrary to previous reports, the optimum laser repetition rate is found to be dependent on both laser energy and stage raster speed and is found to be as high as 20kHz under some conditions. The effects of different repetition rates and raster speeds are also found to vary for different ion species within MALDI MSI of tissue and so may be significant when either targeting specific molecules or seeking to minimize bias. A clear dependence on time between laser pulses is also observed indicating the underlying mechanisms may be related to on-plate hysteresis-exhibiting processes such as matrix chemical modification.

Investigating MALDI MSI parameters (Part 2) - On the use of a mechanically shuttered trigger system for improved laser energy stability.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is now widely used to desorb, ionize and detect molecules from complex samples and tissue sections. The detected ion intensity within MALDI MS and MSI is intimately linked to the laser energy per pulse incident upon the sample during analysis. Laser energy/power stability can be significantly affected by the manner in which the laser is operated. High-repetition rate diode-pumped solid-state (DPSS) lasers are being increasingly adopted to enable high-throughput MALDI MSI analysis. Within this work two different laser-triggering setups are used to demonstrate the effect of laser energy instabilities due to spiking and thermal control phenomena and a setup with a shutter to remove these effects. The effect of non-equilibrium laser operation on MALDI MSI data versus the more stable laser pulse energy of the shutter-triggered system is demonstrated in thin films of α-cyano-4-hydroxycinnamic acid (CHCA) and for imaging of murine brain tissue sections. Significant unwanted variations in absolute and relative detected ion intensity are shown where energy variation is introduced by these phenomena, which return to equilibrium within the setup employed here over timescales relevant to MALDI MS analysis.

SpectralAnalysis: Software for the Masses.

The amount of data produced by spectral imaging techniques, such as mass spectrometry imaging, is rapidly increasing as technology and instrumentation advances. This, combined with an increasingly multimodal approach to analytical science, presents a significant challenge in the handling of large data from multiple sources. Here, we present software that can be used through the entire analysis workflow, from raw data through preprocessing (including a wide range of methods for smoothing, baseline correction, normalization, and image generation) to multivariate analysis (for example, memory efficient principal component analysis (PCA), non-negative matrix factorization (NMF), maximum autocorrelation factor (MAF), and probabilistic latent semantic analysis (PLSA)), for data sets acquired from single experiments to large multi-instrument, multimodality, and multicenter studies. SpectralAnalysis was also developed with extensibility in mind to stimulate development, comparisons, and evaluation of data analysis algorithms.

Neutralized chimeric avidin binding at a reference biosensor surface.

We describe the development of a reference biosensor surface, based upon a binary mixture of oligo-ethylene glycol thiols, one of which has biotin at the terminus, adsorbed onto gold as self-assembled monolayers (SAMs). These surfaces were analyzed in detail by X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) to establish the relationship between the thiol solution composition and the surface composition and structure. We report the use of argon cluster primary ions for the analysis of PEG-thiols, establishing that the different thiols are intimately mixed and that SIMS may be used to measure surface composition of thiol SAMs on gold with a detection limit better than 1% fractional coverage. The adsorption of neutralized chimeric avidin to these surfaces was measured simultaneously using ellipsometry and QCM-D. Comparison of the two measurements demonstrates the expected nonlinearity of the frequency response of the QCM but also reveals a strong variation in the dissipation signal that correlates with the surface density of biotin. These variations are most likely due to the difference in mechanical response of neutralized chimeric avidin bound by just one biotin moiety at low biotin density and two biotin moieties at high density. The transition between the two modes of binding occurs when the average spacing of biotin ligands approaches the diameter of the avidin molecule.

Characterisation of a micro-plasma for ambient mass spectrometry imaging.

Results are presented on the characterisation and optimisation of a non-thermal atmospheric pressure micro-plasma ion source used for ambient mass spectrometry imaging. The geometry of the experiment is optimised to produce the most intense and stable ion signals. Signal stabilities (relative standard deviation) of 2.3-6.5% are achieved for total ion current measurements from chromatograms. Parameters are utilised to achieve MS imaging by raster scanning of PTFE/glass samples with a spatial resolution of 147 ± 31 µm. A systematic study of resolution as a function of acquisition parameters was also undertaken to underpin future technique development. Mass spectra are obtained from PTFE/glass sample edges in negative ion mode and used to construct images to calculate the spatial resolution. Images are constructed using the intensity variation of the dominant ion observed in the PTFE spectrum. Mass spectra originating from the polymer are dominated by three series of ions in a m/z spectral window from 200-500 Da. These ions are each separated by 50 Da and have the chemical formula [C2F + [CF2]n](-), [CF + [CF2]n + O](-) and [CF + [CF2]n + O3](-). The mechanism for the generation of these ions appears to be a polymer chain scission followed by ionisation by atmospheric ion adduction. Positive and negative ion mode mass spectra of personal care products, amino acids and pharmaceuticals, dominated by the proton abstracted/protonated molecular ion, highlight the potential areas of application for such a device. Further to this end a mass spectral image of cardamom seeds, constructed using the variation in intensity of possible fragments of the 1,8-cineole molecule, is included to reveal the potential application to the imaging of foods and other biological materials.

Evaluation of Inlet Temperature with Three Sprayer Designs for Desorption Electrospray Ionization Mass Spectrometry Tissue Analysis.

Mass spectrometry imaging (MSI) allows for the spatially resolved detection of endogenous and exogenous molecules and atoms in biological samples, typically prepared as thin tissue sections. Desorption electrospray ionization (DESI) is one of the most commonly utilized MSI modalities in preclinical research. DESI ion source technology is still rapidly evolving, with new sprayer designs and heated inlet capillaries having recently been incorporated in commercially available systems. In this study, three iterations of DESI sprayer designs are evaluated: (1) the first, and until recently only, commercially available Waters sprayer; (2) a developmental desorption electro-flow focusing ionization (DEFFI)-type sprayer; and (3) a prototype of the newly released Waters commercial sprayer. A heated inlet capillary is also employed, allowing for controlled inlet temperatures up to 500 °C. These three sprayers are evaluated by comparative tissue imaging analyses of murine testes across this temperature range. Single ion intensity versus temperature trends are evaluated as exemplar cases for putatively identified species of interest, such as lactate and glutamine. A range of trends are observed, where intensities follow either increasing, decreasing, bell-shaped, or other trends with temperature. Data for all sprayers show approximately similar trends for the ions studied, with the commercial prototype sprayer (sprayer version 3) matching or outperforming the other sprayers for the ions investigated. Finally, the mass spectra acquired using sprayer version 3 are evaluated by uniform manifold approximation and projection (UMAP) and k-means clustering. This approach is shown to provide valuable insight that is complementary to the presented univariate evaluation for reviewing the parameter space in this study. Full spectral temperature optimization data are provided as supporting data to enable other researchers to design experiments that are optimal for specific ions.

Memory efficient principal component analysis for the dimensionality reduction of large mass spectrometry imaging data sets.

A memory efficient algorithm for the computation of principal component analysis (PCA) of large mass spectrometry imaging data sets is presented. Mass spectrometry imaging (MSI) enables two- and three-dimensional overviews of hundreds of unlabeled molecular species in complex samples such as intact tissue. PCA, in combination with data binning or other reduction algorithms, has been widely used in the unsupervised processing of MSI data and as a dimentionality reduction method prior to clustering and spatial segmentation. Standard implementations of PCA require the data to be stored in random access memory. This imposes an upper limit on the amount of data that can be processed, necessitating a compromise between the number of pixels and the number of peaks to include. With increasing interest in multivariate analysis of large 3D multislice data sets and ongoing improvements in instrumentation, the ability to retain all pixels and many more peaks is increasingly important. We present a new method which has no limitation on the number of pixels and allows an increased number of peaks to be retained. The new technique was validated against the MATLAB (The MathWorks Inc., Natick, Massachusetts) implementation of PCA (princomp) and then used to reduce, without discarding peaks or pixels, multiple serial sections acquired from a single mouse brain which was too large to be analyzed with princomp. Then, k-means clustering was performed on the reduced data set. We further demonstrate with simulated data of 83 slices, comprising 20,535 pixels per slice and equaling 44 GB of data, that the new method can be used in combination with existing tools to process an entire organ. MATLAB code implementing the memory efficient PCA algorithm is provided.

Formal lithium fixation improves direct analysis of lipids in tissue by mass spectrometry.

Mass spectrometry imaging is a powerful method for imaging and in situ characterization of lipids in thin tissue sections. Structural elucidation of lipids is often achieved via collision induced dissociation, and lithium-lipid adducts have been widely reported as providing the most structurally informative fragment ions. We present a method for the incorporation of lithium salts into tissue imaging experiments via fixation of samples in formal lithium solutions. The method is suitable for preparation of single tissue sections, or as an immersion fixation method for whole tissue blocks or organs prior to sectioning. We compare lithium adduct detection and MALDI-MSI of murine brain from analysis of tissues prepared in different ways. Tissues prepared in formal solutions containing lithium or sodium salts before coating in matrix via air-spray deposition are compared with fresh samples coated in lithium-doped matrix preparations by either dry-coating or air-spray deposition. Sample preparation via fixation in formal lithium is shown to yield the highest quality images of lithium adducts, resulting in acquisition of more informative product ion spectra in MALDI MS/MS profiling and imaging experiments. Finally, the compatibility of formal lithium solutions with standard histological staining protocols (hemotoxylin and eosin, Van Giessen and Oil Red O) is demonstrated in a study of human liver tissue.

Hyperspectral visualization of mass spectrometry imaging data.

The acquisition of localized molecular spectra with mass spectrometry imaging (MSI) has a great, but as yet not fully realized, potential for biomedical diagnostics and research. The methodology generates a series of mass spectra from discrete sample locations, which is often analyzed by visually interpreting specifically selected images of individual masses. We developed an intuitive color-coding scheme based on hyperspectral imaging methods to generate a single overview image of this complex data set. The image color-coding is based on spectral characteristics, such that pixels with similar molecular profiles are displayed with similar colors. This visualization strategy was applied to results of principal component analysis, self-organizing maps and t-distributed stochastic neighbor embedding. Our approach for MSI data analysis, combining automated data processing, modeling and display, is user-friendly and allows both the spatial and molecular information to be visualized intuitively and effectively.

Repeat MALDI MS imaging of a single tissue section using multiple matrices and tissue washes.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) techniques are continually being assessed with a view to improving the quality of information obtained from a given sample. A single tissue section will typically only be analyzed once by MALDI MSI and is then either used for histological staining or discarded. In this study, we explore the idea of repeat analysis of a single tissue section by MALDI MSI as a route toward improving sensitivity, structural characterization, and diversity of detected analyte classes. Repeat analysis of a single tissue section from a fresh frozen mouse brain is investigated with both α-cyano-4-hydroxycinnamic acid (CHCA) and para-nitroaniline (PNA). Repeat analysis is then applied to the acquisition of MALDI MSI and MALDI tandem mass spectrometry imaging employing collision induced dissociation (MS/MS imaging employing CID) from a formalin-fixed mouse brain section. Finally, both lipid and protein data are acquired from the same tissue section via repeat analysis utilizing CHCA, sinapinic acid (SA), and a tissue wash step. PNA was found to outperform CHCA as a matrix for repeat analysis; multiple lipids were identified using MS/MS imaging; both lipid and protein images were successfully acquired from a single tissue section. Figure Repeat analysis by MALDI MS imaging of a single tissue section is investigated with multiple matrices and tissue washes to provide increased molecular information from a single tissue section.

Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target.

The genomic landscape of colorectal cancer (CRC) is shaped by inactivating mutations in tumour suppressors such as APC, and oncogenic mutations such as mutant KRAS. Here we used genetically engineered mouse models, 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 untargeted metabolic profiling can be applied to stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging 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-deficient CRC. Loss of Apc in the mouse intestine was found to be sufficient to drive expression of one of its enzymes, adenosylhomocysteinase (AHCY), which was also found to be transcriptionally upregulated in human CRC. Targeting of AHCY function impaired growth of APC-deficient organoids in vitro, and prevented the characteristic hyperproliferative/crypt progenitor phenotype driven by acute deletion of Apc in vivo, even in the context of mutant Kras. Finally, pharmacological inhibition of AHCY reduced intestinal tumour burden in ApcMin/+ mice indicating its potential as a metabolic drug target in CRC.