Visualization and data exploration of chromosome conformation capture data using Voronoi diagrams with v3c-viz

Chromosome conformation capture (3C) sequencing approaches, like Hi-C or micro-C, allow for an unbiased view of chromatin interactions. Most analysis methods rely on so-called interaction matrices, which are derived from counting read pairs in bins of fixed size. Here, we propose the Voronoi diagram, as implemented in Voronoi for chromosome conformation capture data visualization (v3c-viz) to visualize 3C data. The Voronoi diagram corresponds to an adaptive-binning strategy that adapts to the local densities of points. In this way, visualization of data obtained by moderate sequencing depth pinpoint many, if not most, interesting features such as high frequency contacts. The favorable visualization properties of the Voronoi diagram indicate that the Voronoi diagram as density estimator can be used to identify high frequency contacts at a resolution approaching the typical size of enhancers and promoters. v3c-viz is available at https://github.com/imbbLab/v3c-viz .

Screening of volatile organic compounds (VOCs) from liquid fungal cultures using ambient mass spectrometry

The potential of fungi for use as biotechnological factories in the production of a range of valuable metabolites, such as enzymes, terpenes, and volatile aroma compounds, is high. Unlike other microorganisms, fungi mostly secrete secondary metabolites into the culture medium, allowing for easy extraction and analysis. To date, the most commonly used technique in the analysis of volatile organic compounds (VOCs) is gas chromatography, which is time and labour consuming. We propose an alternative ambient screening method that provides rapid chemical information for characterising the VOCs of filamentous fungi in liquid culture using a commercially available ambient dielectric barrier discharge ionisation (DBDI) source connected to a quadrupole-Orbitrap mass spectrometer. The effects of method parameters on measured peak intensities of a series of 8 selected aroma standards were optimised with the best conditions being selected for sample analysis. The developed method was then deployed to the screening of VOCs from samples of 13 fungal strains in three different types of complex growth media showing clear differences in VOC profiles across the different media, enabling determination of best culturing conditions for each compound-strain combination. Our findings underline the applicability of ambient DBDI for the direct detection and comparison of aroma compounds produced by filamentous fungi in liquid culture.

MALDI mass spectrometry imaging: From constituents in fresh food to ingredients, contaminants and additives in processed food

Mass Spectrometry imaging (MS imaging) provides spatial information for a wide range of compound classes in different sample matrices. We used MS imaging to investigate the distribution of components in fresh and processed food, including meat, dairy and bakery products. The MS imaging workflow was optimized to cater to the specific properties and challenges of the individual samples. We successfully detected highly nonpolar and polar constituents such as beta-carotene and anthocyanins, respectively. For the first time, the distributions of a contaminant and a food additive were visualized in processed food. We detected acrylamide in German gingerbread and investigated the penetration of the preservative natamycin into cheese. For this purpose, a new data analysis tool was developed to study the penetration of analytes from uneven surfaces. Our results show that MS imaging has great potential in food analysis to provide relevant information about components' distributions, particularly those underlying official regulations.

Do Anti-tuberculosis Drugs Reach Their Target?─High-Resolution Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Provides Information on Drug Penetration into Necrotic Granulomas

Tuberculosis (TB) is characterized by mycobacteria-harboring centrally necrotizing granulomas. The efficacy of anti-TB drugs depends on their ability to reach the bacteria in the center of these lesions. Therefore, we developed a mass spectrometry (MS) imaging workflow to evaluate drug penetration in tissue. We employed a specific mouse model that─in contrast to regular inbred mice─strongly resembles human TB pathology. Mycobacterium tuberculosis was inactivated in lung sections of these mice by γ-irradiation using a protocol that was optimized to be compatible with high spatial resolution MS imaging. Different distributions in necrotic granulomas could be observed for the anti-TB drugs clofazimine, pyrazinamide, and rifampicin at a pixel size of 30 μm. Clofazimine, imaged here for the first time in necrotic granulomas of mice, showed higher intensities in the surrounding tissue than in necrotic granulomas, confirming data observed in TB patients. Using high spatial resolution drug and lipid imaging (5 μm pixel size) in combination with a newly developed data analysis tool, we found that clofazimine does penetrate to some extent into necrotic granulomas and accumulates in the macrophages inside the granulomas. These results demonstrate that our imaging platform improves the predictive power of preclinical animal models. Our workflow is currently being applied in preclinical studies for novel anti-TB drugs within the German Center for Infection Research (DZIF). It can also be extended to other applications in drug development and beyond. In particular, our data analysis approach can be used to investigate diffusion processes by MS imaging in general.

Method To Visualize the Intratumor Distribution and Impact of Gemcitabine in Pancreatic Ductal Adenocarcinoma by Multimodal Imaging

Gemcitabine (dFdC) is a common treatment for pancreatic cancer; however, it is thought that treatment may fail because tumor stroma prevents drug distribution to tumor cells. Gemcitabine is a pro-drug with active metabolites generated intracellularly; therefore, visualizing the distribution of parent drug as well as its metabolites is important. A multimodal imaging approach was developed using spatially coregistered mass spectrometry imaging (MSI), imaging mass cytometry (IMC), multiplex immunofluorescence microscopy (mIF), and hematoxylin and eosin (H&E) staining to assess the local distribution and metabolism of gemcitabine in tumors from a genetically engineered mouse model of pancreatic cancer (KPC) allowing for comparisons between effects in the tumor tissue and its microenvironment. Mass spectrometry imaging (MSI) enabled the visualization of the distribution of gemcitabine (100 mg/kg), its phosphorylated metabolites dFdCMP, dFdCDP and dFdCTP, and the inactive metabolite dFdU. Distribution was compared to small-molecule ATR inhibitor AZD6738 (25 mg/kg), which was codosed. Gemcitabine metabolites showed heterogeneous distribution within the tumor, which was different from the parent compound. The highest abundance of dFdCMP, dFdCDP, and dFdCTP correlated with distribution of endogenous AMP, ADP, and ATP in viable tumor cell regions, showing that gemcitabine active metabolites are reaching the tumor cell compartment, while AZD6738 was located to nonviable tumor regions. The method revealed that the generation of active, phosphorylated dFdC metabolites as well as treatment-induced DNA damage primarily correlated with sites of high proliferation in KPC PDAC tumor tissue, rather than sites of high parent drug abundance.

An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities

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Mass spectrometry imaging (MSI) is a powerful molecular imaging technique.

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Integration with other imaging modalities is essential in clinical MSI.

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Image integration is performed by image registration techniques.

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Technical potential of image registration in MSI has not been fully exploited.

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Roadmap proposed to improve registration accuracy.

Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.

Multi-modal molecular imaging maps the correlation between tumor microenvironments and nanomedicine distribution

Gaining insight into the heterogeneity of nanoparticle drug distribution within tumors would improve both design and clinical translation of nanomedicines. There is little data showing the spatio-temporal behavior of nanomedicines in tissues as current methods are not able to provide a comprehensive view of the nanomedicine distribution, released drug or its effects in the context of a complex tissue microenvironment. Methods: A new experimental approach which integrates the molecular imaging and bioanalytical technologies MSI and IMC was developed to determine the biodistribution of total drug and drug metabolite delivered via PLA-PEG nanoparticles and to overlay this with imaging of the nanomedicine in the context of detailed tumor microenvironment markers. This was used to assess the nanomedicine AZD2811 in animals bearing three different pre-clinical PDX tumors. Results: This new approach delivered new insights into the nanoparticle/drug biodistribution. Mass spectrometry imaging was able to differentiate the tumor distribution of co-dosed deuterated non-nanoparticle-formulated free drug alongside the nanoparticle-formulated drug by directly visualizing both delivery approaches within the same animal or tissue. While the IV delivered free drug was uniformly distributed, the nanomedicine delivered drug was heterogeneous. By staining for multiple biomarkers of the tumor microenvironment on the same tumor sections using imaging mass cytometry, co-registering and integrating data from both imaging modalities it was possible to determine the features in regions with highest nanomedicine distribution. Nanomedicine delivered drug was associated with regions higher in macrophages, as well as more stromal regions of the tumor. Such a comparison of complementary molecular data allows delineation of drug abundance in individual cell types and in stroma. Conclusions: This multi-modal imaging solution offers researchers a better understanding of drug and nanocarrier distribution in complex tissues and enables data-driven drug carrier design.

Holistic Characterization of a Salmonella Typhimurium Infection Model Using Integrated Molecular Imaging

A more complete and holistic view on host-microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of Salmonella Typhimurium infection in the liver of a mouse model using the S. Typhimurium strains SL3261 and SL1344. This approach enables correlation of tissue morphology and specific cell phenotypes with molecular images of tissue metabolism. IMC revealed a marked increase in immune cell markers and localization in immune aggregates in infected tissues. A correlative computational method (network analysis) was deployed to find metabolic features associated with infection and revealed metabolic clusters of acetyl carnitines, as well as phosphatidylcholine and phosphatidylethanolamine plasmalogen species, which could be associated with pro-inflammatory immune cell types. By developing an IMC marker for the detection of Salmonella LPS, we were further able to identify and characterize those cell types which contained S. Typhimurium.

Method to Investigate the Distribution of Water-Soluble Drug-Delivery Systems in Fresh Frozen Tissues Using Imaging Mass Cytometry

Imaging mass cytometry (IMC) offers the opportunity to image metal- and heavy halogen-containing xenobiotics in a highly multiplexed experiment with other immunochemistry-based reagents to distinguish uptake into different tissue structures or cell types. However, in practice, many xenobiotics are not amenable to this analysis, as any compound which is not bound to the tissue matrix will delocalize during aqueous sample-processing steps required for IMC analysis. Here, we present a strategy to perform IMC experiments on a water-soluble polysarcosine-modified dendrimer drug-delivery system (S-Dends). This strategy involves two consecutive imaging acquisitions on the same tissue section using the same instrumental platform, an initial laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MSI) experiment followed by tissue staining and a standard IMC experiment. We demonstrated that settings can be found for the initial ablation step that leave sufficient residual tissue for subsequent antibody staining and visualization. This workflow results in lateral resolution for the S-Dends of 2 μm followed by imaging of metal-tagged antibodies at 1 μm.

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

The amino acid transporter SLC7A5 is required for efficient growth of KRAS-mutant colorectal cancer

Oncogenic KRAS mutations and inactivation of the APC tumor suppressor co-occur in colorectal cancer (CRC). Despite efforts to target mutant KRAS directly, most therapeutic approaches focus on downstream pathways, albeit with limited efficacy. Moreover, mutant KRAS alters the basal metabolism of cancer cells, increasing glutamine utilization to support proliferation. We show that concomitant mutation of Apc and Kras in the mouse intestinal epithelium profoundly rewires metabolism, increasing glutamine consumption. Furthermore, SLC7A5, a glutamine antiporter, is critical for colorectal tumorigenesis in models of both early- and late-stage metastatic disease. Mechanistically, SLC7A5 maintains intracellular amino acid levels following KRAS activation through transcriptional and metabolic reprogramming. This supports the increased demand for bulk protein synthesis that underpins the enhanced proliferation of KRAS-mutant cells. Moreover, targeting protein synthesis, via inhibition of the mTORC1 regulator, together with Slc7a5 deletion abrogates the growth of established Kras-mutant tumors. Together, these data suggest SLC7A5 as an attractive target for therapy-resistant KRAS-mutant CRC.

MALDI-2 at Atmospheric Pressure-Parameter Optimization and First Imaging Experiments

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.

The Influence of MS Imaging Parameters on UV-MALDI Desorption and Ion Yield

Ultraviolet matrix-assisted laser desorption/ionization mass spectrometry imaging (UV-MALDI MSI) is a widely used technique for imaging molecular distributions within biological systems. While much work exists concerning desorption in UV-MALDI MS, the effects of commonly varied parameters for imaging applications (repetition rate, use of continuous raster mode and raster speed), which determine spatial resolution and limits of detection for the technique, remain largely unknown. We use multiple surface characterization modalities to obtain quantitative measurements of material desorption and analyte ion yield in thin film model systems of two matrix compounds, arising from different UV-MALDI MSI sampling conditions. Observed changes in resulting ablation feature point to matrix-dependent spatial resolution and laser-induced matrix modification effects. Analyte ion yields of 10^-9 to 10^-6 are observed. Complex changes in ion yield, between spot and raster sampling and arising from varied laser repetition rate and raster speed, are observed. Graphical Abstract.

Error-Free Data Visualization and Processing through imzML and mzML Validation

Open data formats are key to facilitating data processing, sharing, and integration. The imzML format ( http://imzml.org/ ) has drastically improved these aspects of mass spectrometry imaging data. Efficient processing of data depends on data sets which are consistent and adhere to the specifications; however, this is not always the case. Here we present a validation tool for data stored in both imzML and the HUPO-PSI mass spectrometery counterpart, mzML, to identify any deviations from the published (i)mzML standard which could cause issues for the user when visualizing or processing data. The tool is released in two forms, a graphical user interface (GUI) for ease of use, and a command line version to fit into existing workflows and pipelines. When certain known issues are encountered, such as the presence of negative values for the location of the binary data, the validator resolves the issue automatically upon saving. The GUI version of the validator also allows editing of the metadata included within the (i)mzML files in order to resolve inconsistencies. We also present a means of performing conditional validation on the metadata within (i)mzML files, where user-defined rules are validated against depending on whether specific metadata are present (or not). For example, if the MALDI term is present, then additional rules related to MALDI (such as the requirement of inclusion of laser parameters) can be validated against this. This enables a flexible and more thorough automated validation of (i)mzML data. Such a system is necessary for validating data against more comprehensive sets of metadata such as minimum reporting guidelines or metadata requirements prior to submission and acceptance of data to data repositories. We demonstrate how this tool can be used to validate against the proposed minimum reporting guidelines in MSI as well as institute specific metadata criteria. The validator tool is endorsed for validation of imzML ( http://imzml.org/ ) and mzML ( http://www.psidev.info/mzml ) and is made available through the respective Web sites. The validator is also released as open source under Mozilla Public License 2.0 at https://gitlab.com/imzML/imzMLValidator .

Abstract 5661: A multi modal mass spectrometry imaging strategy to profile the metabolic hallmarks of colorectal cancer

New methods are required to image the metabolic hallmarks of cancerous tissues and correlate this information with patient tumour pathology, cancer phenotype and treatment outcome. Mass spectrometry imaging (MSI) offers a powerful suite of techniques for imaging metabolites, drugs and proteins in cells and tissues. Here we present a multi-modal MSI strategy and use it to profile PDX, GEMM and biopsy samples.

Human and murine colon and small intestine samples were sectioned onto glass slides and stored at -80°C until use. MALDI-MSI (Waters and Bruker) was carried out with pixel sizes between 10 and 50 microns, DESI (Waters) at 20-50 microns and SIMS (IONTOF) at approximately 200 nm per pixel. Data were converted from proprietary format to imzML and analysed in MatLab (2015b, MathWorks) via the SpectralAnalysis software package using non-negative matrix factorisation (NMF) and principal component analysis (PCA). Further reduction and segmentation was achieved using stacked autoencoders and t-distributed stochastic neighbour embedding (t-SNE). To assign the peaks detected to molecular identities, peaks picked from mean spectra were matched to the human metabolome database (HMDB) using custom Matlab scripts. Possible adducts were queried against the database masses, and filtered based on comparison of expected and observed mass, and correlation between the images of the monoisotopic mass and 13C isotope image. Preliminary data show the successful preparation and analysis of multiple GEMM and human tissue samples across several sites by MALDI-MSI, DESI-MSI and SIMS imaging. Converting data to imzML and combining into large, single multi-file images enabled multivariate analysis (MVA) to be performed simultaneously within the same comparison. Of these MVA methods, mass spectrometry imaging (MSI) in combination with t-SNE has been shown to differentiate tumour subpopulations linked with patient outcomes. Current t-SNE methods suffer from three main limitations as compared to other MVA methods; poor computational scaling to large datasets, inability to add new data into the embedding, and inability to return to spectral contributions. By combining t-SNE performed on a subset of data and learning the embedding via deep learning, almost limitless size datasets can be analysed by t-SNE, new data can be incorporated into the embedding, and the spectral contributions towards the three dimensional space can be determined.

Database matching on the data from these GEMM samples to the HMDB reveals over 1000 possible molecular assignments, primarily structural lipids, within 10 ppm mass accuracy and with isotope image correlations above 0.6. From these methods, comparisons can be made between the unique molecules detected in different tissues, as well as the relative changes in intensity of those detected between different tissues.

Citation Format: Josephine Bunch, Rory T. Steven, Adam J. Taylor, Spencer A. Thomas, Alan M. Race, Alex Dexter, Gregory Hamm, Nicole Strittmatter, Rasmus Havelund, Renata F. Soares, Andrew D. Campbell, Owen J. Sansom, Richard J. Goodwin, Zoltan Takats. A multi modal mass spectrometry imaging strategy to profile the metabolic hallmarks of colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5661.

MALDI Imaging of Liquid Extraction Surface Analysis Sampled Tissue

Combined mass spectrometry imaging methods in which two different techniques are executed on the same sample have recently been reported for a number of sample types. Such an approach can be used to examine the sampling effects of the first technique with a second, higher resolution method and also combines the advantages of each technique for a more complete analysis. In this work matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) was used to study the effects of liquid extraction surface analysis (LESA) sampling on mouse brain tissue. Complementary multivariate analysis techniques including principal component analysis, non-negative matrix factorization, and t-distributed stochastic neighbor embedding were applied to MALDI MS images acquired from tissue which had been sampled by LESA to gain a better understanding of localized tissue washing in LESA sampling. It was found that MALDI MS images could be used to visualize regions sampled by LESA. The variability in sampling area, spatial precision, and delocalization of analytes in tissue induced by LESA were assessed using both single-ion images and images provided by multivariate analysis.

Probing the Relationship Between Detected Ion Intensity, Laser Fluence, and Beam Profile in Thin Film and Tissue in MALDI MSI

Matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) is increasingly widely used to provide information regarding molecular location within tissue samples. The nature of the photon distribution within the irradiated region, the laser beam profile, and fluence, will significantly affect the form and abundance of the detected ions. Previous studies into these phenomena have focused on circular-core optic fibers or Gaussian beam profiles irradiating dried droplet preparations, where peptides were employed as the analyte of interest. Within this work, we use both round and novel square core optic fibers of 100 and 50 μm diameter to deliver the laser photons to the sample. The laser beam profiles were recorded and analyzed to quantify aspects of the photon distributions and their relation to the spectral data obtained with each optic fiber. Beam profiles with a relatively small number of large beam profile features were found to give rise to the lowest threshold fluence. The detected ion intensity versus fluence relationship was investigated, for the first time, in both thin films of α-cyano-4-hydroxycinnamic acid (CHCA) with phosphatidylcholine (PC) 34:1 lipid standard and in CHCA coated murine tissue sections for both the square and round optic fibers in continuous raster imaging mode. The fluence threshold of ion detection was found to occur at between ~14 and ~64 J/m(2) higher in tissue compared with thin film for the same lipid, depending upon the optic fiber employed. The image quality is also observed to depend upon the fluence employed during image acquisition. Graphical Abstract ᅟ.

Analysis of Urine, Oral fluid and Fingerprints by Liquid Extraction Surface Analysis Coupled to High Resolution MS and MS/MS - Opportunities for Forensic and Biomedical Science

Liquid Extraction Surface Analysis (LESA) is a new, high throughput tool for ambient mass spectrometry. A solvent droplet is deposited from a pipette tip onto a surface and maintains contact with both the surface and the pipette tip for a few seconds before being re-aspirated. The technique is particularly suited to the analysis of trace materials on surfaces due to its high sensitivity and low volume of sample removal. In this work, we assess the suitability of LESA for obtaining detailed chemical profiles of fingerprints, oral fluid and urine, which may be used in future for rapid medical diagnostics or metabolomics studies. We further show how LESA can be used to detect illicit drugs and their metabolites in urine, oral fluid and fingerprints. This makes LESA a potentially useful tool in the growing field of fingerprint chemical analysis, which is relevant not only to forensics but also to medical diagnostics. Finally, we show how LESA can be used to detect the explosive material RDX in contaminated artificial fingermarks.

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.

para-Nitroaniline is a promising matrix for MALDI-MS imaging on intermediate pressure MS systems

para-Nitroaniline (PNA) is presented as a promising matrix for matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) on an intermediate-pressure ion source (~1 Torr) QqTOF instrument using an Nd:YVO4 laser operated at 5 kHz. An imaging study was carried out to determine the utility of PNA at this pressure by analyzing 14 tissue sections. We demonstrate acquisition of high-quality imaging data over a 6-h period in the ion source. In this study, comparisons were made between PNA and α-cyano-4-hydroxycinnamic acid (CHCA) in positive ion mode to demonstrate the utility of PNA in these circumstances. PNA performed as well as or better than CHCA in terms of lipid ion intensities, resulting in lower levels of ion fragmentation and in lower incidences of analyte migration at the edges of the tissue sections when using airspray matrix deposition.

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.