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Föll MC, Volkmann V, Enderle-Ammour K, Timme S, Wilhelm K, Guo D, Vitek O, Bronsert P, Schilling O. Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework. Clin Proteomics 2022; 19:8. [PMID: 35439943 PMCID: PMC9016955 DOI: 10.1186/s12014-022-09347-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 04/04/2022] [Indexed: 11/24/2022] Open
Abstract
Background Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens and thus bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of a urothelial carcinoma dataset. Methods Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle infiltrating urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. Results Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle infiltrating urothelial tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: stromal tissue was characterized by collagen peptides and tumor tissue by histone peptides. Immunohistochemistry confirmed an increased histone H2A abundance in the tumor compared to the stroma tissues. The muscle-infiltration status was distinguished via MSI by peptides from intermediate filaments such as cytokeratin 7 in non-muscle infiltrating carcinomas and vimentin in muscle-infiltrating urothelial carcinomas, which was confirmed by immunohistochemistry. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links. Conclusion Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.
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Affiliation(s)
- Melanie Christine Föll
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany. .,Khoury College of Computer Sciences, Northeastern University, Boston, USA.
| | - Veronika Volkmann
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany
| | - Kathrin Enderle-Ammour
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany
| | - Sylvia Timme
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany.,Core Facility for Histopathology and Digital Pathology, Faculty of Medicine, Medical Center - University of Freiburg, 79106, Freiburg, Germany
| | - Konrad Wilhelm
- Department of Urology, Center for Surgery, Medical Center, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | - Peter Bronsert
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany.,German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Freiburg, Germany
| | - Oliver Schilling
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany.,German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
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2
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Balluff B, Heeren RM, Race AM. An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities. J Mass Spectrom Adv Clin Lab 2022; 23:26-38. [PMID: 35156074 PMCID: PMC8821033 DOI: 10.1016/j.jmsacl.2021.12.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 01/25/2023] Open
Abstract
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. Integration with other imaging modalities is essential in clinical MSI. Image integration is performed by image registration techniques. Technical potential of image registration in MSI has not been fully exploited. Roadmap proposed to improve registration accuracy.
Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.
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3
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Denti V, Andersen MK, Smith A, Bofin AM, Nordborg A, Magni F, Moestue SA, Giampà M. Reproducible Lipid Alterations in Patient-Derived Breast Cancer Xenograft FFPE Tissue Identified with MALDI MSI for Pre-Clinical and Clinical Application. Metabolites 2021; 11:577. [PMID: 34564393 PMCID: PMC8467053 DOI: 10.3390/metabo11090577] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 08/18/2021] [Accepted: 08/24/2021] [Indexed: 12/20/2022] Open
Abstract
The association between lipid metabolism and long-term outcomes is relevant for tumor diagnosis and therapy. Archival material such as formalin-fixed and paraffin embedded (FFPE) tissues is a highly valuable resource for this aim as it is linked to long-term clinical follow-up. Therefore, there is a need to develop robust methodologies able to detect lipids in FFPE material and correlate them with clinical outcomes. In this work, lipidic alterations were investigated in patient-derived xenograft of breast cancer by using a matrix-assisted laser desorption ionization mass spectrometry (MALDI MSI) based workflow that included antigen retrieval as a sample preparation step. We evaluated technical reproducibility, spatial metabolic differentiation within tissue compartments, and treatment response induced by a glutaminase inhibitor (CB-839). This protocol shows a good inter-day robustness (CV = 26 ± 12%). Several lipids could reliably distinguish necrotic and tumor regions across the technical replicates. Moreover, this protocol identified distinct alterations in the tissue lipidome of xenograft treated with glutaminase inhibitors. In conclusion, lipidic alterations in FFPE tissue of breast cancer xenograft observed in this study are a step-forward to a robust and reproducible MALDI-MSI based workflow for pre-clinical and clinical applications.
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Affiliation(s)
- Vanna Denti
- Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20854 Vedano al Lambro, MB, Italy; (V.D.); (A.S.); (F.M.)
| | - Maria K. Andersen
- Department of Circulation and Medical Imaging, NTNU–Norwegian University of Science and Technology, 7491 Trondheim, Norway;
| | - Andrew Smith
- Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20854 Vedano al Lambro, MB, Italy; (V.D.); (A.S.); (F.M.)
| | - Anna Mary Bofin
- Department of Clinical and Molecular Medicine, NTNU–Norwegian University of Science and Technology, 7491 Trondheim, Norway; (A.M.B.); (S.A.M.)
| | - Anna Nordborg
- Department of Biotechnology and Nanomedicine, SINTEF, 7034 Trondheim, Norway;
| | - Fulvio Magni
- Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20854 Vedano al Lambro, MB, Italy; (V.D.); (A.S.); (F.M.)
| | - Siver Andreas Moestue
- Department of Clinical and Molecular Medicine, NTNU–Norwegian University of Science and Technology, 7491 Trondheim, Norway; (A.M.B.); (S.A.M.)
- Department of Pharmacy, Nord University, 8026 Bodø, Norway
| | - Marco Giampà
- Department of Clinical and Molecular Medicine, NTNU–Norwegian University of Science and Technology, 7491 Trondheim, Norway; (A.M.B.); (S.A.M.)
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4
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Boskamp T, Casadonte R, Hauberg-Lotte L, Deininger S, Kriegsmann J, Maass P. Cross-Normalization of MALDI Mass Spectrometry Imaging Data Improves Site-to-Site Reproducibility. Anal Chem 2021; 93:10584-10592. [PMID: 34297545 DOI: 10.1021/acs.analchem.1c01792] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an established tool for the investigation of formalin-fixed paraffin-embedded (FFPE) tissue samples and shows a high potential for applications in clinical research and histopathological tissue classification. However, the applicability of this method to serial clinical and pharmacological studies is often hampered by inevitable technical variation and limited reproducibility. We present a novel spectral cross-normalization algorithm that differs from the existing normalization methods in two aspects: (a) it is based on estimating the full statistical distribution of spectral intensities and (b) it involves applying a non-linear, mass-dependent intensity transformation to align this distribution with a reference distribution. This method is combined with a model-driven resampling step that is specifically designed for data from MALDI imaging of tryptic peptides. This method was performed on two sets of tissue samples: a single human teratoma sample and a collection of five tissue microarrays (TMAs) of breast and ovarian tumor tissue samples (N = 241 patients). The MALDI MSI data was acquired in two labs using multiple protocols, allowing us to investigate different inter-lab and cross-protocol scenarios, thus covering a wide range of technical variations. Our results suggest that the proposed cross-normalization significantly reduces such batch effects not only in inter-sample and inter-lab comparisons but also in cross-protocol scenarios. This demonstrates the feasibility of cross-normalization and joint data analysis even under conditions where preparation and acquisition protocols themselves are subject to variation.
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Affiliation(s)
- Tobias Boskamp
- Bruker Daltonics GmbH & Co. KG, 28359 Bremen, Germany.,Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
| | | | - Lena Hauberg-Lotte
- Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
| | | | - Jörg Kriegsmann
- Proteopath, 54296 Trier, Germany.,Center for Histology, Cytology and Molecular Diagnostic, 54296 Trier, Germany
| | - Peter Maass
- Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
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5
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Vos DRN, Ellis SR, Balluff B, Heeren RMA. Experimental and Data Analysis Considerations for Three-Dimensional Mass Spectrometry Imaging in Biomedical Research. Mol Imaging Biol 2021; 23:149-159. [PMID: 33025328 PMCID: PMC7910367 DOI: 10.1007/s11307-020-01541-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/12/2020] [Accepted: 09/10/2020] [Indexed: 10/26/2022]
Abstract
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.
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Affiliation(s)
- D R N Vos
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
| | - S R Ellis
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
- Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, 2522, Australia
| | - B Balluff
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
| | - R M A Heeren
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
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6
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RamalloGuevara C, Paulssen D, Popova AA, Hopf C, Levkin PA. Fast Nanoliter-Scale Cell Assays Using Droplet Microarray-Mass Spectrometry Imaging. Adv Biol (Weinh) 2021; 5:e2000279. [PMID: 33729695 DOI: 10.1002/adbi.202000279] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/23/2020] [Indexed: 12/21/2022]
Abstract
In pharmaceutical research and development, cell-based assays are primarily used with readout that rely on fluorescence-based and other label-dependent techniques for analysis of different cellular processes. Superhydrophobic-hydrophilic droplet microarrays (DMA) and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) have recently emerged as key technologies for miniaturized high-throughput cell assays and for label-free molecular high-content drug profiling, respectively. Here, nanoliter-scale cell assays are integrated on DMAs with MALDI-MS imaging (MALDI-MSI) approaches to a droplet microarray-mass spectrometry imaging (DMA-MSI) platform. Using A549 lung cancer cells, concentration-response profiling of a pharmaceutical compound, the fatty acid synthase inhibitor GSK2194069, are demonstrated. Direct cell culture on DMAs enables combination of microscopy and high speed, high molecular content analysis using MALDI-MSI. Miniaturization of array spots down to 0.5 mm confining 40 nL droplets allows for MALDI imaging analysis of as few as ten cells per spot. Partial automation ensures a fast sample preparation workflow. Taken together, the integrated DMA-MSI platform that combines MALDI-MSI, as a label-free analytical readout, with the miniaturized droplet microarray platform is a valuable complement to high throughput cell-based assays technologies.
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Affiliation(s)
- Carina RamalloGuevara
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim, 68163, Germany
| | - Dorothea Paulssen
- Karlsruhe Institute of Technology (KIT), Institute of Biological and Chemical Systems - Functional Molecular Systems (IBCS-FMS), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany
| | - Anna A Popova
- Karlsruhe Institute of Technology (KIT), Institute of Biological and Chemical Systems - Functional Molecular Systems (IBCS-FMS), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany
| | - Carsten Hopf
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim, 68163, Germany
| | - Pavel A Levkin
- Karlsruhe Institute of Technology (KIT), Institute of Biological and Chemical Systems - Functional Molecular Systems (IBCS-FMS), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany
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7
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Balluff B, Hopf C, Porta Siegel T, Grabsch HI, Heeren RMA. Batch Effects in MALDI Mass Spectrometry Imaging. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:628-635. [PMID: 33523675 PMCID: PMC7944567 DOI: 10.1021/jasms.0c00393] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
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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Affiliation(s)
- Benjamin Balluff
- Maastricht
MultiModal Molecular Imaging Institute (M4i), Maastricht University, 6229 ER Maastricht, The Netherlands
- Mailing address: Dr. Benjamin Balluff,
Maastricht University, Maastricht MultiModal Molecular Imaging institute
(M4I), Universiteitssingel 50, 6229 ER Maastricht, The Netherlands;
Phone: +31 43 388 1251;
| | - Carsten Hopf
- Center
for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, 68163 Mannheim, Germany
| | - Tiffany Porta Siegel
- Maastricht
MultiModal Molecular Imaging Institute (M4i), Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Heike I. Grabsch
- Department
of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC+), 6229 HX Maastricht, The Netherlands
- Pathology
and Data Analytics, Leeds Institute of Medical Research at St. James’s, University of Leeds, LS9 7TF Leeds, U.K.
| | - Ron M. A. Heeren
- Maastricht
MultiModal Molecular Imaging Institute (M4i), Maastricht University, 6229 ER Maastricht, The Netherlands
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8
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Enzlein T, Cordes J, Munteanu B, Michno W, Serneels L, De Strooper B, Hanrieder J, Wolf I, Chávez-Gutiérrez L, Hopf C. Computational Analysis of Alzheimer Amyloid Plaque Composition in 2D- and Elastically Reconstructed 3D-MALDI MS Images. Anal Chem 2020; 92:14484-14493. [PMID: 33138378 DOI: 10.1021/acs.analchem.0c02585] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
MALDI mass spectrometry imaging (MSI) enables label-free, spatially resolved analysis of a wide range of analytes in tissue sections. Quantitative analysis of MSI datasets is typically performed on single pixels or manually assigned regions of interest (ROIs). However, many sparse, small objects such as Alzheimer's disease (AD) brain deposits of amyloid peptides called plaques are neither single pixels nor ROIs. Here, we propose a new approach to facilitate the comparative computational evaluation of amyloid plaque-like objects by MSI: a fast PLAQUE PICKER tool that enables a statistical evaluation of heterogeneous amyloid peptide composition. Comparing two AD mouse models, APP NL-G-F and APP PS1, we identified distinct heterogeneous plaque populations in the NL-G-F model but only one class of plaques in the PS1 model. We propose quantitative metrics for the comparison of technical and biological MSI replicates. Furthermore, we reconstructed a high-accuracy 3D-model of amyloid plaques in a fully automated fashion, employing rigid and elastic MSI image registration using structured and plaque-unrelated reference ion images. Statistical single-plaque analysis in reconstructed 3D-MSI objects revealed the Aβ1-42Arc peptide to be located either in the core of larger plaques or in small plaques without colocalization of other Aβ isoforms. In 3D, a substantially larger number of small plaques were observed than that indicated by the 2D-MSI data, suggesting that quantitative analysis of molecularly diverse sparsely-distributed features may benefit from 3D-reconstruction. Data are available via ProteomeXchange with identifier PXD020824.
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Affiliation(s)
- Thomas Enzlein
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany.,KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Jonas Cordes
- Faculty of Computer Science, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, Mannheim 68163, Germany
| | - Bogdan Munteanu
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany
| | - Wojciech Michno
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, House V3, Mölndal 43180, Sweden.,Department of Neuroscience, Physiology and Pharmacology, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Lutgarde Serneels
- KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Bart De Strooper
- KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium.,UK Dementia Research Institute at UCL, University College London, London WC1E 6BT U.K
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, House V3, Mölndal 43180, Sweden.,Department of Neurodegenerative Diseases, University College London Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
| | - Ivo Wolf
- Faculty of Computer Science, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, Mannheim 68163, Germany
| | - Lucía Chávez-Gutiérrez
- KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Carsten Hopf
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany
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9
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Föll MC, Moritz L, Wollmann T, Stillger MN, Vockert N, Werner M, Bronsert P, Rohr K, Grüning BA, Schilling O. Accessible and reproducible mass spectrometry imaging data analysis in Galaxy. Gigascience 2019; 8:giz143. [PMID: 31816088 PMCID: PMC6901077 DOI: 10.1093/gigascience/giz143] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 09/10/2019] [Accepted: 11/10/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. FINDINGS We have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Furthermore, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research. CONCLUSION The Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access, together with high levels of reproducibility and transparency.
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Affiliation(s)
- Melanie Christine Föll
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Biology, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
| | - Lennart Moritz
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
| | - Thomas Wollmann
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Maren Nicole Stillger
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Biology, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
- Institute of Molecular Medicine and Cell Research, Faculty of Medicine, University of Freiburg, Stefan-Meier-Straße 17, 79104 Freiburg, Germany
| | - Niklas Vockert
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Martin Werner
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
| | - Peter Bronsert
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
| | - Karl Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Björn Andreas Grüning
- Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Oliver Schilling
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
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10
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Condina MR, Mittal P, Briggs MT, Oehler MK, Klingler-Hoffmann M, Hoffmann P. Egg White as a Quality Control in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI). Anal Chem 2019; 91:14846-14853. [PMID: 31660720 DOI: 10.1021/acs.analchem.9b03091] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The strength of MALDI-MSI is to analyze and visualize spatial intensities of molecular features from an intact tissue. The distribution of the intensities can then be visualized within a single tissue section or compared in between sections, acquired consecutively. This method can be reliably used to reveal physiological structures and has the potential to identify molecular details, which correlate with biological outcomes. MALDI-MSI implementation in clinical laboratories requires the ability to ensure method quality and validation to meet diagnostic expectations. To be able to get consistent qualitative and quantitative results, standardized sample preparation and data acquisition are of highest priority. We have previously shown that the deposition of internal standards onto the tissue section during sample preparation can be used to improve the mass accuracy of monitored m/z features across the sample. Here, we present the use of external and internal controls for the quality check of sample preparation and data acquisition, which is particularly relevant when either many spectra are acquired during a single MALDI-MSI experiment or data from independent experiments are processed together. To monitor detector performance and sample preparation, we use egg white as an external control for peptide and N-glycan MALDI-MSI throughout the experiment. We have also identified endogenous peptides from cytoskeletal proteins, which can be reliably monitored in gynecological tissue samples. Lastly, we summarize our standard quality control workflow designed to produce reliable and comparable MALDI-MSI data from single sections and tissue microarrays (TMAs).
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Affiliation(s)
- Mark R Condina
- Future Industries Institute , The University of South Australia , Adelaide , South Australia 5095 , Australia
| | - Parul Mittal
- Adelaide Proteomics Centre , The University of Adelaide , Adelaide , South Australia 5005 , Austrailia
| | - Matthew T Briggs
- Future Industries Institute , The University of South Australia , Adelaide , South Australia 5095 , Australia
| | - Martin K Oehler
- Discipline of Obstetrics and Gynaecology, Adelaide Medical School, Robinson Research Institute , The University of Adelaide , Adelaide , South Australia 5000 , Australia.,Department of Gynaecological Oncology , Royal Adelaide Hospital , Adelaide , South Australia 5005 , Australia
| | - Manuela Klingler-Hoffmann
- Future Industries Institute , The University of South Australia , Adelaide , South Australia 5095 , Australia
| | - Peter Hoffmann
- Future Industries Institute , The University of South Australia , Adelaide , South Australia 5095 , Australia
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11
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Ucal Y, Coskun A, Ozpinar A. Quality will determine the future of mass spectrometry imaging in clinical laboratories: the need for standardization. Expert Rev Proteomics 2019; 16:521-532. [DOI: 10.1080/14789450.2019.1624165] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Yasemin Ucal
- School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Abdurrahman Coskun
- School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Aysel Ozpinar
- School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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12
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Ly A, Longuespée R, Casadonte R, Wandernoth P, Schwamborn K, Bollwein C, Marsching C, Kriegsmann K, Hopf C, Weichert W, Kriegsmann J, Schirmacher P, Kriegsmann M, Deininger S. Site-to-Site Reproducibility and Spatial Resolution in MALDI-MSI of Peptides from Formalin-Fixed Paraffin-Embedded Samples. Proteomics Clin Appl 2019; 13:e1800029. [PMID: 30408343 PMCID: PMC6590241 DOI: 10.1002/prca.201800029] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 10/23/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE To facilitate the transition of MALDI-MS Imaging (MALDI-MSI) from basic science to clinical application, it is necessary to analyze formalin-fixed paraffin-embedded (FFPE) tissues. The aim is to improve in situ tryptic digestion for MALDI-MSI of FFPE samples and determine if similar results would be reproducible if obtained from different sites. EXPERIMENTAL DESIGN FFPE tissues (mouse intestine, human ovarian teratoma, tissue microarray of tumor entities sampled from three different sites) are prepared for MALDI-MSI. Samples are coated with trypsin using an automated sprayer then incubated using deliquescence to maintain a stable humid environment. After digestion, samples are sprayed with CHCA using the same spraying device and analyzed with a rapifleX MALDI Tissuetyper at 50 µm spatial resolution. Data are analyzed using flexImaging, SCiLS, and R. RESULTS Trypsin application and digestion are identified as sources of variation and loss of spatial resolution in the MALDI-MSI of FFPE samples. Using the described workflow, it is possible to discriminate discrete histological features in different tissues and enabled different sites to generate images of similar quality when assessed by spatial segmentation and PCA. CONCLUSIONS AND CLINICAL RELEVANCE Spatial resolution and site-to-site reproducibility can be maintained by adhering to a standardized MALDI-MSI workflow.
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Affiliation(s)
- Alice Ly
- Bruker Daltonik GmbHBremenGermany
| | - Rémi Longuespée
- Institute of PathologyUniversity Hospital HeidelbergHeidelbergGermany
| | | | | | | | | | - Christian Marsching
- Center for Biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS)Mannheim University of Applied SciencesMannheimGermany
| | - Katharina Kriegsmann
- Department of HematologyOncology and RheumatologyUniversity Hospital HeidelbergHeidelbergGermany
| | - Carsten Hopf
- Center for Biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS)Mannheim University of Applied SciencesMannheimGermany
| | - Wilko Weichert
- Institute of PathologyTechnical University of MunichMunichGermany
| | | | - Peter Schirmacher
- Institute of PathologyUniversity Hospital HeidelbergHeidelbergGermany
| | - Mark Kriegsmann
- Institute of PathologyUniversity Hospital HeidelbergHeidelbergGermany
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13
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Vaysse PM, Heeren RMA, Porta T, Balluff B. Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations. Analyst 2018. [PMID: 28642940 DOI: 10.1039/c7an00565b] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
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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Affiliation(s)
- Pierre-Maxence Vaysse
- Maastricht MultiModal Molecular Imaging (M4I) institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging (M4I) institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
| | - Tiffany Porta
- Maastricht MultiModal Molecular Imaging (M4I) institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
| | - Benjamin Balluff
- Maastricht MultiModal Molecular Imaging (M4I) institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
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14
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Vos DRN, Jansen I, Lucas M, Paine MRL, de Boer OJ, Meijer SL, Savci-Heijink CD, Marquering HA, de Bruin DM, Heeren RMA, Ellis SR, Balluff B. Strategies for managing multi-patient 3D mass spectrometry imaging data. J Proteomics 2018; 193:184-191. [PMID: 30343012 DOI: 10.1016/j.jprot.2018.10.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 09/26/2018] [Accepted: 10/17/2018] [Indexed: 01/30/2023]
Abstract
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.
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Affiliation(s)
- D R N Vos
- The Maastricht Multimodal Molecular Imaging Institute (M4I), Maastricht University, 6229 ER Maastricht, the Netherlands
| | - I Jansen
- Department of Urology, Department of Biomedical Engineering & Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - M Lucas
- Department of Biomedical Engineering & Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - M R L Paine
- The Maastricht Multimodal Molecular Imaging Institute (M4I), Maastricht University, 6229 ER Maastricht, the Netherlands
| | - O J de Boer
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - S L Meijer
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - C D Savci-Heijink
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - H A Marquering
- Department of Biomedical Engineering & Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - D M de Bruin
- Department of Urology, Department of Biomedical Engineering & Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - R M A Heeren
- The Maastricht Multimodal Molecular Imaging Institute (M4I), Maastricht University, 6229 ER Maastricht, the Netherlands
| | - S R Ellis
- The Maastricht Multimodal Molecular Imaging Institute (M4I), Maastricht University, 6229 ER Maastricht, the Netherlands
| | - B Balluff
- The Maastricht Multimodal Molecular Imaging Institute (M4I), Maastricht University, 6229 ER Maastricht, the Netherlands.
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15
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Dilillo M, Heijs B, McDonnell LA. Mass spectrometry imaging: How will it affect clinical research in the future? Expert Rev Proteomics 2018; 15:709-716. [PMID: 30203995 DOI: 10.1080/14789450.2018.1521278] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
INTRODUCTION Mass spectrometry imaging (MSI) is a label free, multiplex imaging technology able to simultaneously record the distributions of 100's to 1000's of species, and which may be configured to study metabolites, lipids, glycans, peptides, and proteins simply by changing the tissue preparation protocol. Areas covered: The capability of MSI to complement established histopathological practice through the identification of biomarkers for differential diagnosis, patient prognosis, and response to therapy; the capability of MSI to annotate tissues on the basis of each pixel's mass spectral signature; the development of reproducible MSI through multicenter studies. Expert commentary: We discuss how MSI can be combined with microsampling/microdissection technologies in order to investigate, with more depth of coverage, the molecular changes uncovered by MSI.
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Affiliation(s)
| | - Bram Heijs
- b Center for Proteomics and Metabolomics , Leiden University Medical Center , Leiden , The Netherlands
| | - Liam A McDonnell
- a Fondazione Pisana per la Scienza ONLUS , Pisa , Italy.,b Center for Proteomics and Metabolomics , Leiden University Medical Center , Leiden , The Netherlands
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16
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Weigt D, Sammour DA, Ulrich T, Munteanu B, Hopf C. Automated analysis of lipid drug-response markers by combined fast and high-resolution whole cell MALDI mass spectrometry biotyping. Sci Rep 2018; 8:11260. [PMID: 30050068 PMCID: PMC6062520 DOI: 10.1038/s41598-018-29677-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 07/13/2018] [Indexed: 12/20/2022] Open
Abstract
Recent advances in matrix-assisted laser desorption/ionization (MALDI) mass spectrometry have enabled whole cell-MALDI mass spectrometry biotyping of drug-treated cultured cells for rapid monitoring of known abundant pharmacodynamic protein markers such as polyacetylated histones. In contrast, generic and automated analytical workflows for discovery of such pharmacodynamic markers, in particular lipid markers, and their use in cellular tests of drug-like compounds are still lacking. Here, we introduce such a workflow and demonstrate its utility for cellular drug-response monitoring of BCR-ABL tyrosine kinase inhibitors in K562 leukemia cells: First, low-molecular mass features indicating drug responses are computationally extracted from groups of MALDI-TOF mass spectra. Then, the lipids/metabolites corresponding to these features are identified by MALDI-Fourier transformation mass spectrometry. To demonstrate utility of the method, we identify the potassium adduct of phosphatidylcholine PC(36:1) as well as heme B, a marker for erythroid differentiation, as markers for a label-free MALDI MS-based test of cellular responses to BCR-ABL inhibitors. Taken together, these results suggest that MALDI-TOF mass spectrometry of lipids and other low molecular mass metabolites could support cell-based drug profiling.
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Affiliation(s)
- David Weigt
- Center for biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
- HBIGS International Graduate School of Molecular and Cellular Biology, Heidelberg University, Im Neuenheimer Feld 501, 69120, Heidelberg, Germany
| | - Denis A Sammour
- Center for biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Timon Ulrich
- Center for biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Bogdan Munteanu
- Center for biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Carsten Hopf
- Center for biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany.
- HBIGS International Graduate School of Molecular and Cellular Biology, Heidelberg University, Im Neuenheimer Feld 501, 69120, Heidelberg, Germany.
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17
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Buck A, Heijs B, Beine B, Schepers J, Cassese A, Heeren RMA, McDonnell LA, Henkel C, Walch A, Balluff B. Round robin study of formalin-fixed paraffin-embedded tissues in mass spectrometry imaging. Anal Bioanal Chem 2018; 410:5969-5980. [PMID: 29968108 PMCID: PMC6096706 DOI: 10.1007/s00216-018-1216-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/14/2018] [Accepted: 06/21/2018] [Indexed: 12/12/2022]
Abstract
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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Affiliation(s)
- Achim Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München, 85764, Oberschleißheim, Germany
| | - Bram Heijs
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Birte Beine
- Medizinisches Proteom-Center, Ruhr-Universität Bochum, 44801, Bochum, Germany
- Leibniz-Institut für Analytische Wissenschaften - ISAS-e.V, 44139, Dortmund, Germany
| | - Jan Schepers
- Department of Methodology and Statistics, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
| | - Alberto Cassese
- Department of Methodology and Statistics, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
| | - Ron M A Heeren
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, Pigeon Hole 57, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - Liam A McDonnell
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
- Fondazione Pisana per la Scienza ONLUS, 56017, Pisa, Italy
| | - Corinna Henkel
- Medizinisches Proteom-Center, Ruhr-Universität Bochum, 44801, Bochum, Germany
- Bruker Daltonik, Bremen, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München, 85764, Oberschleißheim, Germany
| | - Benjamin Balluff
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Maastricht University, Universiteitssingel 50, Pigeon Hole 57, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
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18
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Dilillo M, Pellegrini D, Ait-Belkacem R, de Graaf EL, Caleo M, McDonnell LA. Mass Spectrometry Imaging, Laser Capture Microdissection, and LC-MS/MS of the Same Tissue Section. J Proteome Res 2017. [DOI: 10.1021/acs.jproteome.7b00284] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Marialaura Dilillo
- Fondazione Pisana per la Scienza ONLUS, 56121 Pisa, Italy
- Department of Chemistry
and Industrial Chemistry, University of Pisa, 56126 Pisa, Italy
| | - Davide Pellegrini
- Fondazione Pisana per la Scienza ONLUS, 56121 Pisa, Italy
- NEST, Scuola Normale Superiore di Pisa, 56127 Pisa, Italy
| | | | | | | | - Liam A. McDonnell
- Fondazione Pisana per la Scienza ONLUS, 56121 Pisa, Italy
- Center for Proteomics
and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
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Abstract
In the last decade mass spectrometry imaging has developed rapidly, in terms of multiple new instrumentation innovations, expansion of target molecules, and areas of application. Mass spectrometry imaging has already had a substantial impact in cancer research, uncovering biomolecular changes associated with disease progression, diagnosis, and prognosis. Many new approaches are incorporating the use of readily available formalin-fixed paraffin-embedded cancer tissues from pathology centers, including tissue blocks, biopsy specimens, and tumor microarrays. It is also increasingly used in drug formulation development as an inexpensive method to determine the distributions of drugs and their metabolites. In this chapter, we offer a perspective in the current and future methodological developments and how these may open up new vistas for cancer research.
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