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Supplementary Figure S2. This supplementary figure depicts the non-correlation of average erlotinib peak intensities with survival or tumor grading or overall morphological feature

Supplemental material and methods file

Highlights • Lipids change overtime in normal fascial healing in the early post-surgery period. • Specific lipid species are correlated with the changes of inflammation cells and fibroblasts. • Lipid species in the present study are considered as predictive markers for the formation of incisional hernia., Background Failure of fascial healing in the abdominal wall can result in incisional hernia, which is one of the most common complications after laparotomy. Understanding the molecular healing process of abdominal fascia may provide lipid markers of incisional hernia or therapeutic targets that allow prevention or treatment of incisional hernias. Purpose This study aims to investigate temporal and in situ changes of lipids during the normal healing process of abdominal fascia in the first postoperative week. Methods Open hemicolectomy was performed in a total of 35 Wistar rats. The midline fascia was closed identically for all rats using a single continuous suturing technique. These animals were sacrificed with equal numbers (n = 5) at each of 7-time points (6, 12, 24, 48, 72, 120, and 168 h. The local and temporal changes of lipids were examined with mass spectrometry imaging and correlated to histologically scored changes during healing using hematoxylin and eosin staining. Results Two phosphatidylcholine lipid species (PC O-38:5 and PC 38:4) and one phosphatidylethanolamine lipid (PE O-16:1_20:4) were found to significantly correlate with temporal changes of inflammation. A phosphatidylcholine (PC 32:0) and a monosialodihexosylganglioside (GM3 34:1;2) were found to correlate with fibroblast cell growth. Conclusion Glycerophospholipids and gangliosides are strongly involved in the normal healing process of abdominal fascia and their locally fluctuating concentrations are considered as potential lipid markers and therapeutic targets of fascial healing.

Mass spectrometry imaging (MSI) has become an indispensible tool for spatially resolved molecular investigation of tissues. One of the key application areas is biomedical research, where matrix-assisted laser desorption/ionization (MALDI) MSI is predominantly used due to its high-throughput capability, flexibility in the molecular class to investigate, and ability to achieve single cell spatial resolution. While many of the initial technical challenges have now been resolved, so-called batch effects, a phenomenon already known from other omics fields, appear to significantly impede reliable comparison of data from particular midsized studies typically performed in translational clinical research. This critical insight will discuss at what levels (pixel, section, slide, time, and location) batch effects can manifest themselves in MALDI-MSI data and what consequences this might have for biomarker discovery or multivariate classification. Finally, measures are presented that could be taken to recognize and/or minimize these potentially detrimental effects, and an outlook is provided on what is still needed to ultimately overcome these effects.

Mass spectrometry imaging (MSI) enables the visualization of molecular distributions on complex surfaces. It has been extensively used in the field of biomedical research to investigate healthy and diseased tissues. Most of the MSI studies are conducted in a 2D fashion where only a single slice of the full sample volume is investigated. However, biological processes occur within a tissue volume and would ideally be investigated as a whole to gain a more comprehensive understanding of the spatial and molecular complexity of biological samples such as tissues and cells. Mass spectrometry imaging has therefore been expanded to the 3D realm whereby molecular distributions within a 3D sample can be visualized. The benefit of investigating volumetric data has led to a quick rise in the application of single-sample 3D-MSI investigations. Several experimental and data analysis aspects need to be considered to perform successful 3D-MSI studies. In this review, we discuss these aspects as well as ongoing developments that enable 3D-MSI to be routinely applied to multi-sample studies.

The large‐scale and label‐free molecular characterization of single cells in their natural tissue habitat remains a major challenge in molecular biology. We present a method that integrates morphometric image analysis to delineate and classify individual cells with their single‐cell‐specific molecular profiles. This approach provides a new means to study spatial biological processes such as cancer field effects and the relationship between morphometric and molecular features., Understanding how cells regulate their bioenergetic demands, adjust to the microenvironment, or respond to stimuli is the aim of molecular biology. Yet, methods able to describe the molecular content, histological identity, and spatial context of a single cell do not exist. The presented method integrates morphometric image analysis to delineate and classify individual cells with their cell‐specific molecular profiles obtained by MALDI‐MSI.

BACKGROUND Although aberrant glycosylation is recognized as a hallmark of cancer, glycosylation in clinical breast cancer (BC) metastasis has not yet been studied. While preclinical studies show that the glycocalyx coating of cancer cells is involved in adhesion, migration, and metastasis, glycosylation changes from primary tumor (PT) to various metastatic sites remain unknown in patients. METHODS We investigated N-glycosylation profiles in 17 metastatic BC patients from our rapid autopsy program. Primary breast tumor, lymph node metastases, multiple systemic metastases, and various normal tissue cores from each patient were arranged on unique single-patient tissue microarrays (TMAs). We performed mass spectrometry imaging (MSI) combined with extensive pathology annotation of these TMAs, which enabled spatially differentiated cell-based analysis of N-glycosylation patterns in metastatic BC. RESULTS N-glycan abundance increased during metastatic progression independent of BC subtype and treatment regimen, with high-mannose glycans most frequently elevated in BC metastases, followed by fucosylated and complex glycans. Bone metastasis, however, displayed increased core-fucosylation and decreased high-mannose glycans. Consistently, N-glycosylated proteins and N-glycan biosynthesis genes were differentially expressed during metastatic BC progression, with reduced expression of EpCAM and mannose-trimming enzymes and elevated N-glycan branching and sialylation enzymes in BC metastases versus PT. CONCLUSION We show for the first time in patients that N-glycosylation of breast cancer cells undergoing metastasis occurs in a metastatic site-specific manner, supporting the clinical importance of high-mannose, fucosylated, and complex N-glycans as future diagnostic markers and therapeutic targets in metastatic BC. FUNDING United States National Institutes of Health grants NIH R01CA213428, R01CA213492, T32CA193145, Dutch Province Limburg "LINK", European Union ERA-NET TRANSCAN2-643638.

Cancer & metabolism 9, 26 (2021). doi:10.1186/s40170-021-00262-9, Published by Biomed Central, London

Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.

Mass spectrometry imaging (MSI) is an analytical technique for the unlabeled and multiplex imaging of molecules in biological tissue sections. It therefore enables the spatial and molecular annotations of tissues complementary to histology. It has already been shown that MSI can guide subsequent material isolation technologies such as laser microdissection (LMD) to enable a more in-depth molecular characterization of MSI-highlighted tissue regions. However, with MSI now reaching spatial resolutions at the single-cell scale, there is a need for a precise co-registration between MSI and the LMD. As proof-of-principle, MSI of lipids was performed on a breast cancer tissue followed by a segmentation of the data to detect molecularly distinct segments within its tumor areas. After image processing of the segmentation results, the coordinates of the MSI-detected segments were passed to the LMD system by three co-registration steps. The errors of each co-registration step were quantified and the total error was found to be less than 13 μm. With this link established, MSI data can now accurately guide LMD to excise MSI-defined regions of interest for subsequent extract-based analyses. In our example, the excised tissue material was then subjected to ultrasensitive microproteomics in order to determine predominant molecular mechanisms in each of the MSI-highlighted intratumor segments. This work shows how the strengths of MSI, histology, and extract-based omics can be combined to enable a more comprehensive molecular characterization of in situ biological processes. Electronic supplementary material The online version of this article (10.1007/s00216-019-01983-z) contains supplementary material, which is available to authorized users.

In quantitative mass spectrometry imaging (MSI), the gold standard adds a single structural homologue of the target compound at a known concentration to the sample. This internal standard enables to map the detected intensity of the target molecule against an external calibration curve. This approach, however, ignores local noise levels and disproportional ion suppression effects, which might depend on the concentration of the target compound. To overcome these issues, we propose a novel approach that applies several isotopically labeled versions, each at a different concentration, to the sample. This allows creating individual internal calibration curves for every MSI pixel. As proof of principle, we have quantified an endogenous peptide of histone H4 by matrix-assisted laser desorption/ionization-QMSI (MALDI-QMSI), using a mixture of three isotopically labeled versions. The usage of a fourth label allowed us to compare the gold standard to our multilabel approach. We observed substantial heterogeneity in ion suppression across the tissue, which disclosed itself as varying slopes in the per-pixel regression analyses. These slopes were histology-dependent and differed from each other by up to a factor of 4. The results were validated by liquid chromatography-mass spectrometry (LC-MS), exhibiting a high agreement between LC-MS and MALDI-Q : MSI (Pearson correlation r = 0.87). A comparison between the multilabel and single-label approaches revealed a higher accuracy for the multilabel method when the local target compound concentration differed too much from the concentration of the single label. In conclusion, we show that the multilabel approach provides superior quantitation compared to a single-label approach, in case the target compound is inhomogeneously distributed at a wide concentration range in the tissue.

Cells often adopt different phenotypes, dictated by tissue-specific or local signals such as cell-cell and cell-matrix contacts or molecular micro-environment. This holds in extremis for macrophages with their high phenotypic plasticity. Their broad range of functions, some even opposing, reflects their heterogeneity, and a multitude of subsets has been described in different tissues and diseases. Such micro-environmental imprint cannot be adequately studied by single-cell applications as cells are detached from their context, while histology-based assessment lacks the phenotypic depth due to limitations in marker combination. Here, we present a novel, integrative approach in which 15-color multispectral imaging allows comprehensive cell classification based on multi-marker expression patterns, followed by downstream analysis pipelines to link their phenotypes to contextual, micro-environmental cues such as their cellular (“community”) and metabolic (“local lipidome”) niches in complex tissue. The power of this approach is illustrated for myeloid subsets and associated lipid signatures in murine atherosclerotic plaque.

Background Metabolic reprogramming is a common phenomenon in tumorigenesis and tumor progression. Amino acids are important mediators in cancer metabolism, and their kinetics in tumor tissue are far from being understood completely. Mass spectrometry imaging is capable to spatiotemporally trace important endogenous metabolites in biological tissue specimens. In this research, we studied L-[ring-13C6]-labeled phenylalanine and tyrosine kinetics in a human non-small cell lung carcinoma (NSCLC) xenografted mouse model using matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI). Methods We investigated the L-[ring-13C6]-Phenylalanine (13C6-Phe) and L-[ring-13C6]-Tyrosine (13C6-Tyr) kinetics at 10 min (n = 4), 30 min (n = 3), and 60 min (n = 4) after tracer injection and sham-treated group (n = 3) at 10 min in mouse-xenograft lung tumor tissues by MALDI-FTICR-MSI. Results The dynamic changes in the spatial distributions of 19 out of 20 standard amino acids are observed in the tumor tissue. The highest abundance of 13C6-Phe was detected in tumor tissue at 10 min after tracer injection and decreased progressively over time. The overall enrichment of 13C6-Tyr showed a delayed temporal trend compared to 13C6-Phe in tumor caused by the Phe-to-Tyr conversion process. Specifically, 13C6-Phe and 13C6-Tyr showed higher abundances in viable tumor regions compared to non-viable regions. Conclusions We demonstrated the spatiotemporal intra-tumoral distribution of the essential aromatic amino acid 13C6-Phe and its de-novo synthesized metabolite 13C6-Tyr by MALDI-FTICR-MSI. Our results explore for the first time local phenylalanine metabolism in the context of cancer tissue morphology. This opens a new way to understand amino acid metabolism within the tumor and its microenvironment. Supplementary Information The online version contains supplementary material available at 10.1186/s40170-021-00262-9.

Levels of zinc, along with its mechanistically related metabolites citrateand aspartate, are widely reported as reduced in prostate cancer compared to healthytissue and are therefore pointed out as potential cancer biomarkers. Previously, it hasonly been possible to analyze zinc and metabolites by separate detection methods.Through matrix-assisted laser desorption/ionization mass spectrometry imaging (MSI),we were for thefirst time able to demonstrate, in two different sample sets (n= 45 andn= 4), the simultaneous spatial detection of zinc, in the form of ZnCl3−, together withcitrate, aspartate, andN-acetylaspartate on human prostate cancer tissues. The reliabilityof the ZnCl3−detection was validated by total zinc determination using laser ablationinductively coupled plasma MSI on adjacent serial tissue sections. Zinc, citrate, andaspartate were correlated with each other (ranger= 0.46 to 0.74) and showed asignificant reduction in cancer compared to non-cancer epithelium (p< 0.05, log2foldchange range:−0.423 to−0.987), while no significant difference between cancer andstroma tissue was found. Simultaneous spatial detection of zinc and its metabolites is notonly a valuable tool for analyzing the role of zinc in prostate metabolism but might alsoprovide a fast and simple method to detect zinc, citrate, and aspartate levels as abiomarker signature for prostate cancer diagnostics and prognostics. This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.

Mass spectrometry imaging (MSI) has provided many results with translational character, which still have to be proven robust in large patient cohorts and across different centers. Although formalin-fixed paraffin-embedded (FFPE) specimens are most common in clinical practice, no MSI multicenter study has been reported for FFPE samples. Here, we report the results of the first round robin MSI study on FFPE tissues with the goal to investigate the consequences of inter- and intracenter technical variation on masking biological effects. A total of four centers were involved with similar MSI instrumentation and sample preparation equipment. A FFPE multi-organ tissue microarray containing eight different types of tissue was analyzed on a peptide and metabolite level, which enabled investigating different molecular and biological differences. Statistical analyses revealed that peptide intercenter variation was significantly lower and metabolite intercenter variation was significantly higher than the respective intracenter variations. When looking at relative univariate effects of mass signals with statistical discriminatory power, the metabolite data was more reproducible across centers compared to the peptide data. With respect to absolute effects (cross-center common intensity scale), multivariate classifiers were able to reach on average > 90% accuracy for peptides and > 80% for metabolites if trained with sufficient amount of cross-center data. Overall, our study showed that MSI data from FFPE samples could be reproduced to a high degree across centers. While metabolite data exhibited more reproducibility with respect to relative effects, peptide data-based classifiers were more directly transferable between centers and therefore more robust than expected. Graphical abstractᅟ Electronic supplementary material The online version of this article (10.1007/s00216-018-1216-2) contains supplementary material, which is available to authorized users.

Background: Barrett's esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic hematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE. Methods: Biopsies from 57 patients were considered, divided into non-dysplastic (n = 15), LGD non-progressive (n = 14), LGD progressive (n = 14), and HGD (n = 14). MSI experiments were conducted at 50 x 50 mu m spatial resolution per pixel corresponding to a tile size of 96x96 pixels in the co-registered H&E images, making a total of 144,823 tiles for the whole dataset. Results: ML models were trained to distinguish epithelial tissue from stroma with area-under-the-curve (AUC) values of 0.89 (MSI) and 0.95 (H&E)) and dysplastic grade (AUC of 0.97 (MSI) and 0.85 (H&E)) on a tile level, and low-grade progressors from non-progressors on a patient level (accuracies of 0.72 (MSI) and 0.48 (H&E)). Conclusions: In summary, while the H&E-based classifier was best at distinguishing tissue types, the MSI-based model was more accurate at distinguishing dysplastic grades and patients at progression risk, which demonstrates the complementarity of both approaches. Data are available via ProteomeXchange with identifier PXD028949.

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

// Thomas Kunzke 1 , Benjamin Balluff 2 , Annette Feuchtinger 1 , Achim Buck 1 , Rupert Langer 3 , Birgit Luber 4 , Florian Lordick 5 , Horst Zitzelsberger 6 , Michaela Aichler 1 and Axel Walch 1 1 Research Unit Analytical Pathology, Helmholtz Zentrum Munchen, Oberschleisheim, Germany 2 Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Maastricht, The Netherlands 3 Institute of Pathology, University of Bern, Bern, Switzerland 4 Institute of Pathology, Technische Universitat Munchen, Munich, Germany 5 University Cancer Center Leipzig, University Clinic Leipzig, Leipzig, Germany 6 Research Unit Radiation Cytogenetics, Helmholtz Zentrum Munchen, Oberschleisheim, Germany Correspondence to: Axel Walch, email: axel.walch@helmholtz-muenchen.de Keywords: glycans, gastric cancer, formalin-fixed paraffin-embedded tissue, MALDI, mass spectrometry imaging Received: September 14, 2016 Accepted: June 02, 2017 Published: July 10, 2017 ABSTRACT Glycosylation in cancer is a highly dynamic process that has a significant impact on tumor biology. Further, the attachment of aberrant glycan forms is already considered a hallmark of the disease state. Mass spectrometry has become a prominent approach to analyzing glycoconjugates. Specifically, matrix-assisted laser desorption/ionisation -mass spectrometric imaging (MALDI-MSI) is a powerful technique that combines mass spectrometry with histology and enables the spatially resolved and label-free detection of glycans. The most common approach to the analysis of glycans is the use of mass spectrometry adjunct to PNGase F digestion and other chemical reactions. In the current study, we perform the analysis of formalin-fixed, paraffin-embedded (FFPE) tissues for natively occurring bioactive glycan fragments without prior digestion or chemical reactions using MALDI-FT-ICR-MSI. We examined 106 primary resected gastric cancer patient tissues in a tissue microarray and correlated native-occurring fragments with clinical endpoints, therapeutic targets such as epidermal growth factor receptor (EGFR) and HER2/neu expressions and the proliferation marker MIB1. The detection of a glycosaminoglycan fragment in tumor stroma regions was determined to be an independent prognostic factor for gastric cancer patients. Native glycan fragments were significantly linked to the expression of EGFR, HER2/neu and MIB1. In conclusion, we are the first to report the in situ detection of native-occurring bioactive glycan fragments in FFPE tissues that influence patient outcomes. These findings highlight the significance of glycan fragments in gastric cancer tumor biology and patient outcome.

Scope In biomedical research, mass spectrometry imaging (MSI) can obtain spatially-resolved molecular information from tissue sections. Especially matrix-assisted laser desorption/ionization (MALDI) MSI offers, depending on the type of matrix, the detection of a broad variety of molecules ranging from metabolites to proteins, thereby facilitating the collection of multilevel molecular data. Lately, integrative clustering techniques have been developed that make use of the complementary information of multilevel molecular data in order to better stratify patient cohorts, but which have not yet been applied in the field of MSI. Materials and Methods In this study, the potential of integrative clustering is investigated for multilevel molecular MSI data to subdivide cancer patients into different prognostic groups. Metabolomic and peptidomic data are obtained by MALDI-MSI from a tissue microarray containing material of 46 esophageal cancer patients. The integrative clustering methods Similarity Network Fusion, iCluster, and moCluster are applied and compared to non-integrated clustering. Conclusion The results show that the combination of multilevel molecular data increases the capability of integrative algorithms to detect patient subgroups with different clinical outcome, compared to the single level or concatenated data. This underlines the potential of multilevel molecular data from the same subject using MSI for subsequent integrative clustering.

Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images are created through a grid-wise interrogation of individual spots by mass spectrometry across a surface. Classical statistical tests for within-sample comparisons fail as close-by measurement spots violate the assumption of independence of these tests, which can lead to an increased false-discovery rate. For spatial data this effect is referred to as spatial autocorrelation. In this study we investigated spatial autocorrelation in three different matrix-assisted laser desorption/ionization MSI datasets. These datasets cover different molecular classes (metabolites/drugs, lipids, and proteins) and different spatial resolutions ranging from 20 µm to 100 µm. Significant spatial autocorrelation was detected in all three datasets and found to increase with decreasing pixel size. To enable statistical testing for differences in mass signal intensities between regions of interest within MSI datasets, we propose the use of Conditional Autoregressive (CAR) models. We show that by accounting for spatial autocorrelation, discovery rates (i.e. the ratio between the features identified and the total number of features) could be reduced between 21% and 69%. The reliability of this approach was validated by control mass signals based on prior knowledge. In light of the advent of larger MSI datasets based on either an increased spatial resolution or 3D datasets, accounting for effects due to spatial autocorrelation becomes even more indispensable. Here we propose a generic and easily applicable workflow to enable within-sample statistical comparisons.

The combination of high heterogeneity, both intratumoral and intertumoral, with their rarity has made diagnosis, prognosis of high-grade sarcomas difficult. There is an urgent need for more objective molecular biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets. Mass spectrometry imaging (MSI) has amply demonstrated its ability to identify potential new markers for patient diagnosis, survival, metastasis and response to therapy in cancer research. In this study, we investigated the ability of MALDI-MSI of proteins to distinguish between high-grade osteosarcoma (OS), leiomyosarcoma (LMS), myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) (Ntotal = 53). We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival. Twenty diagnostic protein signals were found characteristic for specific tumors (p ≤ 0.05), amongst them acyl-CoA-binding protein (m/z 11 162), macrophage migration inhibitory factor (m/z 12 350), thioredoxin (m/z 11 608) and galectin-1 (m/z 14 633) were assigned. Another nine protein signals were found to be associated with overall survival (p ≤ 0.05), including proteasome activator complex subunit 1 (m/z 9753), indicative for non-OS patients with poor survival; and two histone H4 variants (m/z 11 314 and 11 355), indicative of poor survival for LMS patients.

In research and clinical settings, formalin-fixed and paraffin-embedded (FFPE) tissue specimens are collected routinely and therefore this material constitutes a highly valuable source to gather insight in metabolic changes of diseases. Among mass spectrometry techniques to examine, the molecular Content of FFPE tissue, mass spectrometry imaging (MSI) is the most appropriate when morphological and histological features are to be related to metabolic information. Currently, high-resolution mass spectrometers are widely used for metabolomics studies. However, with regards to matrix-assisted laser desorption/ionization (MALDI) MSI, no study has so far addressed the necessity of instrumental mass resolving power in terms of clinical diagnosis and prognosis using archived FFPE tissue. For this matter we performed for the first time a comprehensive comparison between a high mass resolution Fourier-transform ion cyclotron resonance (FTICR) mass spectrometer and a time-of-flight (TOP) instrument with lower mass resolving power. Spectra analysis revealed that about one-third of the detected peaks remained unresolved by MALDI-TOF, which led to a 3-5 times lower number of m/z features compared to FTICR measurements. Overlaid peak information and background noise in TOF images made a precise assignment of molecular attributes to morphological features more difficult and limited classification approaches. This clearly demonstrates the need for high-mass resolution capabilities for metabolite imaging. Nevertheless, MALDI-TOF allowed reproducing and verifying individual markers identified previously by MALDI-FTICR MSI. The systematic comparison gives rise to a synergistic combination of the different MSI platforms for high-throughput discovery and validation of biomarkers.

The high ion signals produced by many lipids in mass spectrometry imaging (MSI) make them an ideal molecular class to study compositional changes throughout tissue sections and their relationship with disease. However, the large extent of structural diversity observed in the lipidome means optimal ion signal for different lipid classes is often obtained in opposite polarities. In this work we demonstrate how new high speed MALDI-MSI technologies combined with precise laser position control enables the acquisition of positive and negative ion mode lipid data from the same tissue section much faster than is possible with other MSI instruments. Critically, using this approach we explicitly demonstrate how such dual polarity acquisitions provide more information regarding molecular composition and spatial distributions throughout biological tissues. For example, in applying this approach to the zebra finch songbird brain we reveal the high abundance of DHA containing phospholipids (PC in positive mode and PE, PS in negative ion mode) in the nuclei that control song learning behaviour. To make the most of dual polarity data from single tissues we have also developed a pLSA-based multivariate analysis technique that includes both positive and negative ion data in the classification approach. In doing so the correlation amongst different lipid classes that ionise best in opposite polarities and contribute to certain spatial patterns within the tissue can be directly revealed. To demonstrate we apply this approach to studying the lipidomic changes throughout the tumor microenvironment within xenografts from a lung cancer model.

Mass spectrometry imaging (MSI) allows investigating the spatial distribution of chemical compounds directly in biological tissues. As the analytical depth of MSI is limited, MSI needs to be coupled to more sensitive local extraction-based omics approaches to achieve a comprehensive molecular characterization. For this, it is important to retain the spatial information provided by MSI for follow-up omics studies. It has been shown that regiospecific MSI data can be used to guide a laser microdissection system for ultra-sensitive liquid chromatography-mass spectrometry (LC-MS) analyses. So far, this combination has required separate and specialized mass spectrometry (MS) instrumentation. Recent advances in dual-source instrumentation, harboring both matrix assisted laser/desorption ionization (MALDI) and electrospray ionization (ESI) sources, promise state-of-the-art MSI and liquid-based proteomic capabilities on the same MS instrument. This study demonstrates that such an instrument can offer both fast lipid-based MSI at high mass and high lateral resolution and sensitive LC-MS on local protein extracts from the exact same tissue section.

Mass spectrometry imaging (MSI) has emerged as a powerful tool in biomedical research to reveal the localization of a broad scale of compounds ranging from metabolites to proteins in diseased tissues, such as malignant tumors. MSI is most commonly used for the two-dimensional imaging of tissues from multiple patients or for the three-dimensional (3D) imaging of tissue from a single patient. These applications are potentially introducing a sampling bias on a sample or patient level, respectively. The aim of this study is therefore to investigate the consequences of sampling bias on sample representativeness and on the precision of biomarker discovery for histological grading of human bladder cancers by MSI. We therefore submitted formalin-fixed paraffin-embedded tissues from 14 bladder cancer patients with varying histological grades to 3D analysis by matrix-assisted laser desorption/ionization (MALDI) MSI. We found that, after removing 20% of the data based on novel outlier detection routines for 3D-MSI data based on the evaluation of digestion efficacy and z-directed regression, on average 33% of a sample has to be measured in order to obtain sufficient coverage of the existing biological variance within a tissue sample.Significance: In this study, 3D MALDI-MSI is applied for the first time on a cohort of bladder cancer patients using formalin-fixed paraffin-embedded (FFPE) tissue of bladder cancer resections. This work portrays the reproducibility that can be achieved when employing an optimized sample preparation and subsequent data evaluation approach. Our data shows the influence of sampling bias on the variability of the results, especially for a small patient cohort. Furthermore, the presented data analysis workflow can be used by others as a 3D FFPE data-analysis pipeline working on multi-patient 3D-MSI studies.

Mass spectrometry imaging (MSI) is a technique which is gaining increasing interest in biomedical research due to its capacity to visualize molecules in tissues. First applied to the field of clinical proteomics, its potential for metabolite imaging in biomedical studies is now being recognized. Here we describe how to set up experiments for mass spectrometry imaging of metabolites in clinical tissues and how to tackle most of the obstacles in the subsequent analysis of the data.

Mass spectrometry imaging (MSI) is a technique which is gaining increasing interest in biomedical research due to its capacity to visualize molecules in tissues. First applied to the field of clinical proteomics, its potential for metabolite imaging in biomedical studies is now being recognized. Here we describe how to set up experiments for mass spectrometry imaging of metabolites in clinical tissues and how to tackle most of the obstacles in the subsequent analysis of the data.

Subclinical atherosclerosis cannot be predicted and novel therapeutic targets are needed. The molecular anatomy of healthy and atherosclerotic tissue is pursued to identify ongoing molecular changes in atherosclerosis development. Mass Spectrometry Imaging (MSI) accounts with the unique advantage of analyzing proteins and metabolites (lipids) while preserving their original localization; thus two dimensional maps can be obtained. Main molecular alterations were investigated in a rabbit model in response to early development of atherosclerosis. Aortic arterial layers (intima and media) and calcified regions were investigated in detail by MALDI-MSI and proteins and lipids specifically defining those areas of interest were identified. These data further complement main findings previously published in J Proteomics (M. Martin-Lorenzo et al., J. Proteomics. (In press); M. Martin-Lorenzo et al., J. Proteomics 108 (2014) 465–468.) [1,2].

The molecular anatomy of healthy and atherosclerotic tissue is pursued here to identify ongoing molecular changes in atherosclerosis development. Subclinical atherosclerosis cannot be predicted and novel therapeutic targets are needed. Mass spectrometry imaging (MSI) is a novel unexplored ex vivo imaging approach in CVD able to provide in-tissue molecular maps. A rabbit model of early atherosclerosis was developed and high-spatial-resolution MALDI-MSI was applied to comparatively analyze histologically-based arterial regions of interest from control and early atherosclerotic aortas. Specific protocols were applied to identify lipids and proteins significantly altered in response to atherosclerosis. Observed protein alterations were confirmed by immunohistochemistry in rabbit tissue, and additionally in human aortas. Molecular features specifically defining different arterial regions were identified. Localized in the intima, increased expression of SFA and lysolipids and intimal spatial organization showing accumulation of PI, PG and SM point to endothelial dysfunction and triggered inflammatory response. TG, PA, SM and PE-Cer were identified specifically located in calcified regions. Thymosin β4 (TMSB4X) protein was upregulated in intima versus media layer and also in response to atherosclerosis. This overexpression and localization was confirmed in human aortas. In conclusion, molecular histology by MS Imaging identifies spatial organization of arterial tissue in response to atherosclerosis.

We present the first analytical approach to demonstrate the in situ imaging of metabolites from formalin-fixed, paraffin-embedded (FFPE) human tissue samples. Using high-resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR MSI), we conducted a proof-of-principle experiment comparing metabolite measurements from FFPE and fresh frozen tissue sections, and found an overlap of 72% amongst 1700 m/z species. In particular, we observed conservation of biomedically relevant information at the metabolite level in FFPE tissues. In biomedical applications, we analysed tissues from 350 different cancer patients and were able to discriminate between normal and tumour tissues, and different tumours from the same organ, and found an independent prognostic factor for patient survival. This study demonstrates the ability to measure metabolites in FFPE tissues using MALDI-FT-ICR MSI, which can then be assigned to histology and clinical parameters. Our approach is a major technical, histochemical, and clinicopathological advance that highlights the potential for investigating diseases in archived FFPE tissues.

Mass spectrometry imaging (MSI) has been widely used for the direct molecular assessment of tissue samples and has demonstrated great potential to complement current histopathological methods in cancer research. It is now well established that tissue preparation is key to a successful MSI experiment; for histologically heterogeneous tumor tissues, other parts of the workflow are equally important to the experiment's success. To demonstrate these facets here we describe a matrix-assisted laser desorption/ionization MSI biomarker discovery investigation of high-grade, complex karyotype sarcomas, which often have histological overlap and moderate response to chemo-/radio-therapy. Multiple aspects of the workflow had to be optimized, ranging from the tissue preparation and data acquisition protocols, to the post-MSI histological staining method, data quality control, histology-defined data selection, data processing and statistical analysis. Only as a result of developing every step of the biomarker discovery workflow was it possible to identify a panel of protein signatures that could distinguish between different subtypes of sarcomas or could predict patient survival outcome. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. (C) 2016 Elsevier B.V. All rights reserved.

Metabolites can be an important read-out of disease. The identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients is one of the main current research aspects. Mass spectrometry has become the technique of choice for metabolomics studies, and mass spectrometry imaging (MSI) enables their visualization in patient tissues. In this study, we used MSI to identify prognostic metabolite biomarkers in high grade sarcomas; 33 high grade sarcoma patients, comprising osteosarcoma, leiomyosarcoma, myxofibrosarcoma, and undifferentiated pleomorphic sarcoma were analyzed. Metabolite MSI data were obtained from sections of fresh frozen tissue specimens with matrix-assisted laser/desorption ionization (MALDI) MSI in negative polarity using 9-aminoarcridine as matrix. Subsequent annotation of tumor regions by expert pathologists resulted in tumor-specific metabolite signatures, which were then tested for association with patient survival. Metabolite signals with significant clinical value were further validated and identified by high mass resolution Fourier transform ion cyclotron resonance (FTICR) MSI. Three metabolite signals were found to correlate with overall survival (m/z 180.9436 and 241.0118) and metastasis-free survival (m/z 160.8417). FTICR-MSI identified m/z 241.0118 as inositol cyclic phosphate and m/z 160.8417 as carnitine. Graphical Abstractᅟ Electronic supplementary material The online version of this article (doi:10.1007/s13361-016-1544-4) contains supplementary material, which is available to authorized users.

Angewandte Chemie / International edition 56(25), 7146-7150 (2017). doi:10.1002/anie.201702669, Published by Wiley-VCH, Weinheim