Publications by authors named "Benjamin Balluff"

53 Publications

Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging.

Comput Biol Med 2021 Oct 4;138:104918. Epub 2021 Oct 4.

Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6202 AZ, Maastricht, The Netherlands.

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 × 50 μm 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.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104918DOI Listing
October 2021

Mass spectrometry imaging of L-[ring-C]-labeled phenylalanine and tyrosine kinetics in non-small cell lung carcinoma.

Cancer Metab 2021 Jun 11;9(1):26. Epub 2021 Jun 11.

Maastricht MultiModal Molecular Imaging institute (M4I), Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.

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-C]-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-C]-Phenylalanine (C-Phe) and L-[ring-C]-Tyrosine (C-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 C-Phe was detected in tumor tissue at 10 min after tracer injection and decreased progressively over time. The overall enrichment of C-Tyr showed a delayed temporal trend compared to C-Phe in tumor caused by the Phe-to-Tyr conversion process. Specifically, C-Phe and C-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 C-Phe and its de-novo synthesized metabolite C-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.
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http://dx.doi.org/10.1186/s40170-021-00262-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193875PMC
June 2021

Batch Effects in MALDI Mass Spectrometry Imaging.

J Am Soc Mass Spectrom 2021 Mar 1;32(3):628-635. Epub 2021 Feb 1.

Maastricht MultiModal Molecular Imaging Institute (M4i), Maastricht University, 6229 ER Maastricht, The Netherlands.

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.
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http://dx.doi.org/10.1021/jasms.0c00393DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944567PMC
March 2021

Spatial differentiation of metabolism in prostate cancer tissue by MALDI-TOF MSI.

Cancer Metab 2021 Jan 29;9(1). Epub 2021 Jan 29.

Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.

Background: Prostate cancer tissues are inherently heterogeneous, which presents a challenge for metabolic profiling using traditional bulk analysis methods that produce an averaged profile. The aim of this study was therefore to spatially detect metabolites and lipids on prostate tissue sections by using mass spectrometry imaging (MSI), a method that facilitates molecular imaging of heterogeneous tissue sections, which can subsequently be related to the histology of the same section.

Methods: Here, we simultaneously obtained metabolic and lipidomic profiles in different prostate tissue types using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MSI. Both positive and negative ion mode were applied to analyze consecutive sections from 45 fresh-frozen human prostate tissue samples (N = 15 patients). Mass identification was performed with tandem MS.

Results: Pairwise comparisons of cancer, non-cancer epithelium, and stroma revealed several metabolic differences between the tissue types. We detected increased levels of metabolites crucial for lipid metabolism in cancer, including metabolites involved in the carnitine shuttle, which facilitates fatty acid oxidation, and building blocks needed for lipid synthesis. Metabolites associated with healthy prostate functions, including citrate, aspartate, zinc, and spermine had lower levels in cancer compared to non-cancer epithelium. Profiling of stroma revealed higher levels of important energy metabolites, such as ADP, ATP, and glucose, and higher levels of the antioxidant taurine compared to cancer and non-cancer epithelium.

Conclusions: This study shows that specific tissue compartments within prostate cancer samples have distinct metabolic profiles and pinpoint the advantage of methodology providing spatial information compared to bulk analysis. We identified several differential metabolites and lipids that have potential to be developed further as diagnostic and prognostic biomarkers for prostate cancer. Spatial and rapid detection of cancer-related analytes showcases MALDI-TOF MSI as a promising and innovative diagnostic tool for the clinic.
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http://dx.doi.org/10.1186/s40170-021-00242-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7847144PMC
January 2021

Multilabel Per-Pixel Quantitation in Mass Spectrometry Imaging.

Anal Chem 2021 01 29;93(3):1393-1400. Epub 2020 Dec 29.

Maastricht Multimodal Molecular Imaging Institute (M4I), Maastricht University, 6200 MD Maastricht, The Netherlands.

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-Q-MSI (MALDI-Q-MSI), 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 = 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.
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http://dx.doi.org/10.1021/acs.analchem.0c03186DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7871324PMC
January 2021

Atheroma-Specific Lipids in and Mice Using 2D and 3D Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

J Am Soc Mass Spectrom 2020 Sep 12;31(9):1825-1832. Epub 2020 Aug 12.

Maastricht Multimodal Molecular Imaging Institute (M4I), Maastricht University, 6200 MD Maastricht, The Netherlands.

Atherosclerosis is the major contributor to cardiovascular diseases. It is a spatially and temporally complex inflammatory disease, in which intravascular accumulation of a plethora of lipids is considered to play a crucial role. To date, both the composition and local distribution of the involved lipids have not been thoroughly mapped yet. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) enables analyzing and visualizing hundreds of lipid molecules within the plaque while preserving each lipid's specific location. In this study, we aim to identify and verify aortic plaque-specific lipids with high-spatial-resolution 2D and 3D MALDI-MSI common to high-fat-diet-fed low-density lipoprotein receptor deficient () mice and chow-fed apolipoprotein E deficient () mice, the two most widely used animal models for atherosclerosis. A total of 11 lipids were found to be significantly and specifically colocalized to the plaques in both mouse models. These were identified and belong to one sphingomyelin (SM), three lysophosphatidic acids (LPA), four lysophosphatidylcholines (LPC), two lysophosphatidylethanolamines (LPE), and one lysophosphatidylinositol (LPI). While these lysolipids and SM 34:0;2 were characteristic of the atherosclerotic aorta plaque itself, LPI 18:0 was mainly localized in the necrotic core of the plaque.
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http://dx.doi.org/10.1021/jasms.0c00070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472746PMC
September 2020

MS Imaging-Guided Microproteomics for Spatial Omics on a Single Instrument.

Proteomics 2020 12 19;20(23):e1900369. Epub 2020 Aug 19.

Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, Maastricht, 6229 ER, The Netherlands.

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.
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http://dx.doi.org/10.1002/pmic.201900369DOI Listing
December 2020

Morphometric Cell Classification for Single-Cell MALDI-Mass Spectrometry Imaging.

Angew Chem Int Ed Engl 2020 09 17;59(40):17447-17450. Epub 2020 Aug 17.

Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Universiteitssingel 50, 6200 MD, Maastricht, The Netherlands.

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.
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http://dx.doi.org/10.1002/anie.202007315DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540554PMC
September 2020

Simultaneous Detection of Zinc and Its Pathway Metabolites Using MALDI MS Imaging of Prostate Tissue.

Anal Chem 2020 02 28;92(4):3171-3179. Epub 2020 Jan 28.

Department of Circulation and Medical Imaging , Norwegian University of Science and Technology (NTNU) , 7491 Trondheim , Norway.

Levels of zinc, along with its mechanistically related metabolites citrate and aspartate, are widely reported as reduced in prostate cancer compared to healthy tissue and are therefore pointed out as potential cancer biomarkers. Previously, it has only been possible to analyze zinc and metabolites by separate detection methods. Through matrix-assisted laser desorption/ionization mass spectrometry imaging (MSI), we were for the first time able to demonstrate, in two different sample sets ( = 45 and = 4), the simultaneous spatial detection of zinc, in the form of ZnCl, together with citrate, aspartate, and -acetylaspartate on human prostate cancer tissues. The reliability of the ZnCl detection was validated by total zinc determination using laser ablation inductively coupled plasma MSI on adjacent serial tissue sections. Zinc, citrate, and aspartate were correlated with each other (range = 0.46 to 0.74) and showed a significant reduction in cancer compared to non-cancer epithelium ( < 0.05, log fold change range: -0.423 to -0.987), while no significant difference between cancer and stroma tissue was found. Simultaneous spatial detection of zinc and its metabolites is not only a valuable tool for analyzing the role of zinc in prostate metabolism but might also provide a fast and simple method to detect zinc, citrate, and aspartate levels as a biomarker signature for prostate cancer diagnostics and prognostics.
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http://dx.doi.org/10.1021/acs.analchem.9b04903DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584334PMC
February 2020

Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges.

Clin Chem Lab Med 2020 06;58(6):914-929

Unit Health, Flemish Institute for Technological Research (VITO), Mol, Belgium.

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.
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http://dx.doi.org/10.1515/cclm-2019-0858DOI Listing
June 2020

Precise co-registration of mass spectrometry imaging, histology, and laser microdissection-based omics.

Anal Bioanal Chem 2019 Sep 1;411(22):5647-5653. Epub 2019 Jul 1.

Maastricht Multimodal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.

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.
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http://dx.doi.org/10.1007/s00216-019-01983-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6704276PMC
September 2019

Integrative Clustering in Mass Spectrometry Imaging for Enhanced Patient Stratification.

Proteomics Clin Appl 2019 01 4;13(1):e1800137. Epub 2019 Jan 4.

Maastricht MultiModal Molecular Imaging institute (M4I), Maastricht University, 6229 ER, Maastricht, The Netherlands.

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.
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http://dx.doi.org/10.1002/prca.201800137DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6590511PMC
January 2019

Round robin study of formalin-fixed paraffin-embedded tissues in mass spectrometry imaging.

Anal Bioanal Chem 2018 Sep 3;410(23):5969-5980. Epub 2018 Jul 3.

The Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, Pigeon Hole 57, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.

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 abstract ᅟ.
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http://dx.doi.org/10.1007/s00216-018-1216-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096706PMC
September 2018

Mass Spectrometry Imaging of Metabolites.

Methods Mol Biol 2018 ;1730:345-357

Fondazione Pisana per la Scienza ONLUS, Pisa, Italy.

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.
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http://dx.doi.org/10.1007/978-1-4939-7592-1_26DOI Listing
December 2018

Native glycan fragments detected by MALDI-FT-ICR mass spectrometry imaging impact gastric cancer biology and patient outcome.

Oncotarget 2017 Sep 10;8(40):68012-68025. Epub 2017 Jul 10.

Research Unit Analytical Pathology, Helmholtz Zentrum München, OberschleiΔheim, Germany.

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 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.
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http://dx.doi.org/10.18632/oncotarget.19137DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5620232PMC
September 2017

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations.

Analyst 2017 Jul;142(15):2690-2712

Maastricht MultiModal Molecular Imaging (M4I) institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.

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.
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http://dx.doi.org/10.1039/c7an00565bDOI Listing
July 2017

Detection of Localized Hepatocellular Amino Acid Kinetics by using Mass Spectrometry Imaging of Stable Isotopes.

Angew Chem Int Ed Engl 2017 06 11;56(25):7146-7150. Epub 2017 May 11.

Department of General Surgery (NUTRIM), Maastricht University, Postbus 616, 6200 MD, Maastricht, The Netherlands.

Mass spectrometry imaging (MSI) simultaneously detects and identifies the spatial distribution of numerous molecules throughout tissues. Currently, MSI is limited to providing a static and ex vivo snapshot of highly dynamic systems in which molecules are constantly synthesized and consumed. Herein, we demonstrate an innovative MSI methodology to study dynamic molecular changes of amino acids within biological tissues by measuring the dilution and conversion of stable isotopes in a mouse model. We evaluate the method specifically on hepatocellular metabolism of the essential amino acid l-phenylalanine, associated with liver diseases. Crucially, the method reveals the localized dynamics of l-phenylalanine metabolism, including its in vivo hydroxylation to l-tyrosine and co-localization with other liver metabolites in a time course of samples from different animals. This method thus enables the dynamics of localized biochemical synthesis to be studied directly from biological tissues.
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http://dx.doi.org/10.1002/anie.201702669DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099435PMC
June 2017

Prognostic Metabolite Biomarkers for Soft Tissue Sarcomas Discovered by Mass Spectrometry Imaging.

J Am Soc Mass Spectrom 2017 02 21;28(2):376-383. Epub 2016 Nov 21.

Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.

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 Abstract ᅟ.
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http://dx.doi.org/10.1007/s13361-016-1544-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5227002PMC
February 2017

Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data.

Proc Natl Acad Sci U S A 2016 10 10;113(43):12244-12249. Epub 2016 Oct 10.

Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands; Pattern Recognition and Bioinformatics Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, 2600 AA Delft, The Netherlands.

The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.
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http://dx.doi.org/10.1073/pnas.1510227113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5087072PMC
October 2016

High nuclear expression of proteasome activator complex subunit 1 predicts poor survival in soft tissue leiomyosarcomas.

Clin Sarcoma Res 2016 1;6:17. Epub 2016 Oct 1.

Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.

Background: Previous studies on high grade sarcomas using mass spectrometry imaging showed proteasome activator complex subunit 1 (PSME1) to be associated with poor survival in soft tissue sarcoma patients. PSME1 is involved in immunoproteasome assembly for generating tumor antigens presented by MHC class I molecules. In this study, we aimed to validate PSME1 as a prognostic biomarker in an independent and larger series of soft tissue sarcomas by immunohistochemistry.

Methods: Tissue microarrays containing leiomyosarcomas (n = 34), myxofibrosarcomas (n = 14), undifferentiated pleomorphic sarcomas (n = 15), undifferentiated spindle cell sarcomas (n = 4), pleomorphic liposarcomas (n = 4), pleomorphic rhabdomyosarcomas (n = 2), and uterine leiomyomas (n = 7) were analyzed for protein expression of PSME1 using immunohistochemistry. Survival times were compared between high and low expression groups using Kaplan-Meier analysis. Cox regression models as multivariate analysis were performed to evaluate whether the associations were independent of other important clinical covariates.

Results: PSME1 expression was variable among soft tissue sarcomas. In leiomyosarcomas, high expression was associated with overall poor survival (p = 0.034), decreased metastasis-free survival (p = 0.002) and lower event-free survival (p = 0.007). Using multivariate analysis, the association between PSME1 expression and metastasis-free survival was still significant (p = 0.025) and independent of the histological grade.

Conclusions: High expression of PSME1 is associated with poor metastasis-free survival in soft tissue leiomyosarcoma patients, and might be used as an independent prognostic biomarker.
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http://dx.doi.org/10.1186/s13569-016-0057-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5045577PMC
October 2016

An experimental guideline for the analysis of histologically heterogeneous tumors by MALDI-TOF mass spectrometry imaging.

Biochim Biophys Acta Proteins Proteom 2017 Jul 8;1865(7):957-966. Epub 2016 Oct 8.

Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands; Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands; Fondazione Pisana per la Scienza ONLUS, Pisa, Italy. Electronic address:

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.
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http://dx.doi.org/10.1016/j.bbapap.2016.09.020DOI Listing
July 2017

High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue.

Nat Protoc 2016 08 14;11(8):1428-43. Epub 2016 Jul 14.

Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany.

Formalin-fixed and paraffin-embedded (FFPE) tissue specimens are the gold standard for histological examination, and they provide valuable molecular information in tissue-based research. Metabolite assessment from archived tissue samples has not been extensively conducted because of a lack of appropriate protocols and concerns about changes in metabolite content or chemical state due to tissue processing. We present a protocol for the in situ analysis of metabolite content from FFPE samples using a high-mass-resolution matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR-MSI) platform. The method involves FFPE tissue sections that undergo deparaffinization and matrix coating by 9-aminoacridine before MALDI-MSI. Using this platform, we previously detected ∼1,500 m/z species in the mass range m/z 50-1,000 in FFPE samples; the overlap compared with fresh frozen samples is 72% of m/z species, indicating that metabolites are largely conserved in FFPE tissue samples. This protocol can be reproducibly performed on FFPE tissues, including small samples such as tissue microarrays and biopsies. The procedure can be completed in a day, depending on the size of the sample measured and raster size used. Advantages of this approach include easy sample handling, reproducibility, high throughput and the ability to demonstrate molecular spatial distributions in situ. The data acquired with this protocol can be used in research and clinical practice.
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http://dx.doi.org/10.1038/nprot.2016.081DOI Listing
August 2016

Spatial Autocorrelation in Mass Spectrometry Imaging.

Anal Chem 2016 06 24;88(11):5871-8. Epub 2016 May 24.

Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University , Universiteitssingel 50, P.O. Box 616, 6200 MD Maastricht, The Netherlands.

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 data sets. These data sets cover different molecular classes (metabolites/drugs, lipids, and proteins) and different spatial resolutions ranging from 20 to 100 μm. Significant spatial autocorrelation was detected in all three data sets and found to increase with decreasing pixel size. To enable statistical testing for differences in mass signal intensities between regions of interest within MSI data sets, 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 data sets based on either an increased spatial resolution or 3D data sets, 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.
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http://dx.doi.org/10.1021/acs.analchem.6b00672DOI Listing
June 2016

High-grade sarcoma diagnosis and prognosis: Biomarker discovery by mass spectrometry imaging.

Proteomics 2016 06;16(11-12):1802-13

Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.

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.
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http://dx.doi.org/10.1002/pmic.201500514DOI Listing
June 2016

How Suitable is Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight for Metabolite Imaging from Clinical Formalin-Fixed and Paraffin-Embedded Tissue Samples in Comparison to Matrix-Assisted Laser Desorption/Ionization-Fourier Transform Ion Cyclotron Resonance Mass Spectrometry?

Anal Chem 2016 05 2;88(10):5281-9. Epub 2016 May 2.

Research Unit Analytical Pathology, Helmholtz Zentrum München , Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany.

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 (TOF) 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.
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http://dx.doi.org/10.1021/acs.analchem.6b00460DOI Listing
May 2016

More from less: high-throughput dual polarity lipid imaging of biological tissues.

Analyst 2016 Jun 4;141(12):3832-41. Epub 2016 Apr 4.

M4I, The Maastricht Multimodal Molecular Imaging Institute, 6229 ER Maastricht, The Netherlands.

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.
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http://dx.doi.org/10.1039/c6an00169fDOI Listing
June 2016

Modeling Therapy Response and Spatial Tissue Distribution of Erlotinib in Pancreatic Cancer.

Mol Cancer Ther 2016 05 28;15(5):1145-52. Epub 2016 Jan 28.

2. Medizinische Klinik, Technische Universität München, Munich, Germany. German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany. Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK) Partner Site Essen, West German Cancer Center, University Hospital Essen, Essen, Germany.

Pancreatic ductal adenocarcinoma (PDAC) is likely the most aggressive and therapy-resistant of all cancers. The aim of this study was to investigate the emerging technology of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) as a powerful tool to study drug delivery and spatial tissue distribution in PDAC. We utilized an established genetically engineered mouse model of spontaneous PDAC to examine the distribution of the small-molecule inhibitor erlotinib in healthy pancreas and PDAC. MALDI IMS was utilized on sections of single-dose or long-term-treated mice to measure drug tissue distribution. Histologic and statistical analyses were performed to correlate morphology, drug distribution, and survival. We found that erlotinib levels were significantly lower in PDAC compared with healthy tissue (P = 0.0078). Survival of long-term-treated mice did not correlate with overall levels of erlotinib or with overall histologic tumor grade but did correlate both with the percentage of atypical glands in the cancer (P = 0.021, rs = 0.59) and the level of erlotinib in those atypical glands (P = 0.019, rs = 0.60). The results of this pilot study present MALDI IMS as a reliable technology to study drug delivery and spatial distribution of compounds in a preclinical setting and support drug imaging-based translational approaches. Mol Cancer Ther; 15(5); 1145-52. ©2016 AACR.
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http://dx.doi.org/10.1158/1535-7163.MCT-15-0165DOI Listing
May 2016

Lipid and protein maps defining arterial layers in atherosclerotic aorta.

Data Brief 2015 Sep 24;4:328-31. Epub 2015 Jun 24.

Department of Immunology, IIS-Fundacion Jimenez Diaz, UAM, REDinREN, Madrid, Spain.

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].
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http://dx.doi.org/10.1016/j.dib.2015.06.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4510571PMC
September 2015

Molecular anatomy of ascending aorta in atherosclerosis by MS Imaging: Specific lipid and protein patterns reflect pathology.

J Proteomics 2015 Aug 12;126:245-51. Epub 2015 Jun 12.

Department of Immunology, IIS-Fundación Jiménez Díaz, UAM, REDinREN, Madrid, Spain. Electronic address:

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.
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http://dx.doi.org/10.1016/j.jprot.2015.06.005DOI Listing
August 2015

High-resolution MALDI-FT-ICR MS imaging for the analysis of metabolites from formalin-fixed, paraffin-embedded clinical tissue samples.

J Pathol 2015 Sep 30;237(1):123-32. Epub 2015 Jun 30.

Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany.

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.
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http://dx.doi.org/10.1002/path.4560DOI Listing
September 2015
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