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Zhu E, Xie Q, Huang X, Zhang Z. Application of spatial omics in gastric cancer. Pathol Res Pract 2024; 262:155503. [PMID: 39128411 DOI: 10.1016/j.prp.2024.155503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/25/2024] [Accepted: 07/27/2024] [Indexed: 08/13/2024]
Abstract
Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.
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Affiliation(s)
- Erran Zhu
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Qi Xie
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Xinqi Huang
- Excellent Class, Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Zhiwei Zhang
- Cancer Research Institute of Hengyang Medical College, University of South China; Key Laboratory of Cancer Cellular and Molecular Pathology of Hunan; Department of Pathology, Department of Pathology of Hengyang Medical College, University of South China; The First Affiliated Hospital of University of South China, China.
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2
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Shah M, Guo L, Xu X, Deng L, Lu K, Dong J, Zhao C, Xu J. eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity. J Proteome Res 2024; 23:3088-3095. [PMID: 38690713 DOI: 10.1021/acs.jproteome.3c00764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.
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Affiliation(s)
- Mudassir Shah
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Lei Guo
- Interdisciplinary Institute of Medical Engineering, Fuzhou University, Fuzhou 350108, China
| | - Xiangnan Xu
- School of Business and Economics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, Nanchang 330013, China
| | - Keyi Lu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Jiyang Dong
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Jingjing Xu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
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Stillger MN, Li MJ, Hönscheid P, von Neubeck C, Föll MC. Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma. Proteomics 2024; 24:e2300001. [PMID: 38402423 DOI: 10.1002/pmic.202300001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 02/10/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.
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Affiliation(s)
- Maren Nicole Stillger
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Mujia Jenny Li
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Institute for Pharmaceutical Sciences, University of Freiburg, Freiburg, Germany
| | - Pia Hönscheid
- Institute of Pathology, Faculty of Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases, Partner Site Dresden, German Cancer Research Center Heidelberg, Dresden, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cläre von Neubeck
- Department of Particle Therapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
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Bitto V, Hönscheid P, Besso MJ, Sperling C, Kurth I, Baumann M, Brors B. Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors. NPJ Syst Biol Appl 2024; 10:57. [PMID: 38802379 PMCID: PMC11130291 DOI: 10.1038/s41540-024-00385-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Mass spectrometry imaging (MSI) allows to study cancer's intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery.
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Affiliation(s)
- Verena Bitto
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Heidelberg, Germany.
- Faculty for Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Pia Hönscheid
- National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Hospital Carl Gustav Carus (UKD), Technische Universität Dresden, Institute of Pathology, Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - María José Besso
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christian Sperling
- National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Hospital Carl Gustav Carus (UKD), Technische Universität Dresden, Institute of Pathology, Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ina Kurth
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Michael Baumann
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Medical Faculty Heidelberg and Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
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Vutov V, Dickhaus T. Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry. Biom J 2023; 65:e2100328. [PMID: 36029271 DOI: 10.1002/bimj.202100328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 05/12/2022] [Accepted: 07/04/2022] [Indexed: 11/12/2022]
Abstract
Large-scale hypothesis testing has become a ubiquitous problem in high-dimensional statistical inference, with broad applications in various scientific disciplines. One relevant application is constituted by imaging mass spectrometry (IMS) association studies, where a large number of tests are performed simultaneously in order to identify molecular masses that are associated with a particular phenotype, for example, a cancer subtype. Mass spectra obtained from matrix-assisted laser desorption/ionization (MALDI) experiments are dependent, when considered as statistical quantities. False discovery proportion (FDP) estimation and control under arbitrary dependency structure among test statistics is an active topic in modern multiple testing research. In this context, we are concerned with the evaluation of associations between the binary outcome variable (describing the phenotype) and multiple predictors derived from MALDI measurements. We propose an inference procedure in which the correlation matrix of the test statistics is utilized. The approach is based on multiple marginal models. Specifically, we fit a marginal logistic regression model for each predictor individually. Asymptotic joint normality of the stacked vector of the marginal regression coefficients is established under standard regularity assumptions, and their (limiting) correlation matrix is estimated. The proposed method extracts common factors from the resulting empirical correlation matrix. Finally, we estimate the realized FDP of a thresholding procedure for the marginal p-values. We demonstrate a practical application of the proposed workflow to MALDI IMS data in an oncological context.
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Affiliation(s)
- Vladimir Vutov
- Institute for Statistics, University of Bremen, Bremen, Germany
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Kanter F, Lellmann J, Thiele H, Kalloger S, Schaeffer DF, Wellmann A, Klein O. Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks. Cancers (Basel) 2023; 15:cancers15030686. [PMID: 36765644 PMCID: PMC9913229 DOI: 10.3390/cancers15030686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 01/25/2023] Open
Abstract
Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.
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Affiliation(s)
- Frederic Kanter
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
| | - Jan Lellmann
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
- Correspondence: (J.L.); (O.K.)
| | - Herbert Thiele
- Fraunhofer Institute for Digital Medicine MEVIS, 23562 Luebeck, Germany
| | - Steve Kalloger
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David F. Schaeffer
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Pancreas Centre BC, Vancouver, BC V5Z 1G1, Canada
- Division of Anatomic Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada
| | - Axel Wellmann
- Institute of Pathology, Wittinger Strasse 14, 29223 Celle, Germany
| | - Oliver Klein
- BIH Center for Regenerative Therapies, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
- Correspondence: (J.L.); (O.K.)
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Mrukwa G, Polanska J. DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data. BMC Bioinformatics 2022; 23:538. [PMID: 36503372 PMCID: PMC9743550 DOI: 10.1186/s12859-022-05093-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 12/01/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured. RESULTS We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets-2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial [Formula: see text] effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)). CONCLUSIONS DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik .
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Affiliation(s)
- Grzegorz Mrukwa
- grid.6979.10000 0001 2335 3149Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland ,Netguru, Małe Garbary 9, 61-756 Poznań, Poland
| | - Joanna Polanska
- grid.6979.10000 0001 2335 3149Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
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Hu H, Laskin J. Emerging Computational Methods in Mass Spectrometry Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203339. [PMID: 36253139 PMCID: PMC9731724 DOI: 10.1002/advs.202203339] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/17/2022] [Indexed: 05/10/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
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Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
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Prasad M, Postma G, Franceschi P, Buydens LMC, Jansen JJ. Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI). Sci Rep 2022; 12:15687. [PMID: 36127378 PMCID: PMC9489880 DOI: 10.1038/s41598-022-19365-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/29/2022] [Indexed: 11/18/2022] Open
Abstract
For the extraction of spatially important regions from mass spectrometry imaging (MSI) data, different clustering methods have been proposed. These clustering methods are based on certain assumptions and use different criteria to assign pixels into different classes. For high-dimensional MSI data, the curse of dimensionality also limits the performance of clustering methods which are usually overcome by pre-processing the data using dimension reduction techniques. In summary, the extraction of spatial patterns from MSI data can be done using different unsupervised methods, but the robust evaluation of clustering results is what is still missing. In this study, we have performed multiple simulations on synthetic and real MSI data to validate the performance of unsupervised methods. The synthetic data were simulated mimicking important spatial and statistical properties of real MSI data. Our simulation results confirmed that K-means clustering with correlation distance and Gaussian Mixture Modeling clustering methods give optimal performance in most of the scenarios. The clustering methods give efficient results together with dimension reduction techniques. From all the dimension techniques considered here, the best results were obtained with the minimum noise fraction (MNF) transform. The results were confirmed on both synthetic and real MSI data. However, for successful implementation of MNF transform the MSI data requires to be of limited dimensions.
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Affiliation(s)
- Mridula Prasad
- IMM/Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ, Nijmegen, The Netherlands.,Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010, San Michele all' Adige, Italy
| | - Geert Postma
- IMM/Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ, Nijmegen, The Netherlands.
| | - Pietro Franceschi
- Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010, San Michele all' Adige, Italy
| | - Lutgarde M C Buydens
- IMM/Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ, Nijmegen, The Netherlands
| | - Jeroen J Jansen
- IMM/Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ, Nijmegen, The Netherlands
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Peak learning of mass spectrometry imaging data using artificial neural networks. Nat Commun 2021; 12:5544. [PMID: 34545087 PMCID: PMC8452737 DOI: 10.1038/s41467-021-25744-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 08/18/2021] [Indexed: 02/07/2023] Open
Abstract
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.
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Murta T, Steven RT, Nikula CJ, Thomas SA, Zeiger LB, Dexter A, Elia EA, Yan B, Campbell AD, Goodwin RJA, Takáts Z, Sansom OJ, Bunch J. Implications of Peak Selection in the Interpretation of Unsupervised Mass Spectrometry Imaging Data Analyses. Anal Chem 2021; 93:2309-2316. [PMID: 33395266 DOI: 10.1021/acs.analchem.0c04179] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.
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Affiliation(s)
- Teresa Murta
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Rory T Steven
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Chelsea J Nikula
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Spencer A Thomas
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Lucas B Zeiger
- Cancer Research UK Beatson Institute, Glasgow G61 1BD, U.K
- Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Glasgow G61 1QH, U.K
| | - Alex Dexter
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Efstathios A Elia
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Bin Yan
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | | | - Richard J A Goodwin
- Imaging and AI, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB4 0WG, U.K
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, U.K
| | - Zoltan Takáts
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Owen J Sansom
- Cancer Research UK Beatson Institute, Glasgow G61 1BD, U.K
- Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Glasgow G61 1QH, U.K
| | - Josephine Bunch
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
- The Rosalind Franklin Institute, Oxfordshire OX11 0FA, U.K
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12
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Prasad M, Postma G, Franceschi P, Morosi L, Giordano S, Falcetta F, Giavazzi R, Davoli E, Buydens LMC, Jansen J. A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data. Gigascience 2020; 9:6006351. [PMID: 33241286 PMCID: PMC7688471 DOI: 10.1093/gigascience/giaa131] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/28/2020] [Accepted: 11/01/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection. RESULTS The number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method. CONCLUSIONS In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.
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Affiliation(s)
- Mridula Prasad
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands.,Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010 San Michele all' Adige, Italy
| | - Geert Postma
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
| | - Pietro Franceschi
- Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010 San Michele all' Adige, Italy
| | - Lavinia Morosi
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Silvia Giordano
- Mass Spectrometry Laboratory, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Francesca Falcetta
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Raffaella Giavazzi
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Enrico Davoli
- Mass Spectrometry Laboratory, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Lutgarde M C Buydens
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
| | - Jeroen Jansen
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
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13
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Nia AM, Shavkunov A, Ullrich RL, Emmett MR. 137Cs γ Ray and 28Si Irradiation Induced Murine Hepatocellular Carcinoma Lipid Changes in Liver Assessed by MALDI-MSI Combined with Spatial Shrunken Centroid Clustering Algorithm: A Pilot Study. ACS OMEGA 2020; 5:25164-25174. [PMID: 33043195 PMCID: PMC7542585 DOI: 10.1021/acsomega.0c03047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 09/07/2020] [Indexed: 06/11/2023]
Abstract
Characterization of lipids by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is of great interest because not only are lipids important structural molecules in both the cell and internal organelle membranes, but they are also important signaling molecules. MALDI-MSI combined with spatial image segmentation has been previously used to identify tumor heterogeneities within tissues with distinct anatomical regions such as the brain. However, there has been no systematic study utilizing MALDI-MSI combined with spatial image segmentation to assess the tumor microenvironment in the liver. Here, we present that image segmentation can be used to evaluate the tumor microenvironment in the liver. In particular, to better understand the molecular mechanisms of irradiation-induced hepatic carcinogenesis, we used MALDI-MSI in the negative ion mode to identify lipid changes 12 months post exposure to low dose 28Si and 137Cs γ ray irradiation. We report here the changes in the lipid profiles of male C3H/HeNCrl mice liver tissues after exposure to irradiation and analyzed using the spatial shrunken centroid clustering algorithm. These findings provide valuable information as astronauts will be exposed to high-charge high-energy (HZE) particles and low-energy γ-ray irradiation during deep space travel. Even at low doses, exposure to these irradiations can lead to cancer. Previous studies infer that irradiation of mice with low-dose HZE particles induces oxidative damage and microenvironmental changes that are thought to play roles in the pathophysiology of hepatocellular carcinoma.
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Affiliation(s)
- Anna M. Nia
- Biochemistry
and Molecular Biology, The University of
Texas Medical Branch, Galveston, Texas 77555, United States
| | - Alexander Shavkunov
- Pharmacology
and Toxicology, The University of Texas
Medical Branch, Galveston, Texas 77555, United States
| | - Robert L. Ullrich
- The
Radiation Effects Research Foundation (RERF), Hiroshima and Nagasaki 732-0815, Japan
| | - Mark R. Emmett
- Biochemistry
and Molecular Biology, The University of
Texas Medical Branch, Galveston, Texas 77555, United States
- Pharmacology
and Toxicology, The University of Texas
Medical Branch, Galveston, Texas 77555, United States
- Radiation
Oncology, The University of Texas Medical
Branch, Galveston, Texas 77555, United
States
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14
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Goodwin RJA, Takats Z, Bunch J. A Critical and Concise Review of Mass Spectrometry Applied to Imaging in Drug Discovery. SLAS DISCOVERY 2020; 25:963-976. [PMID: 32713279 DOI: 10.1177/2472555220941843] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
During the past decade, mass spectrometry imaging (MSI) has become a robust and versatile methodology to support modern pharmaceutical research and development. The technologies provide data on the biodistribution, metabolism, and delivery of drugs in tissues, while also providing molecular maps of endogenous metabolites, lipids, and proteins. This allows researchers to make both pharmacokinetic and pharmacodynamic measurements at cellular resolution in tissue sections or clinical biopsies. Despite drug imaging within samples now playing a vital role within research and development (R&D) in leading pharmaceutical companies, however, the challenges in turning compounds into medicines continue to evolve as rapidly as the technologies used to discover them. The increasing cost of development of new and emerging therapeutic modalities, along with the associated risks of late-stage program attrition, means there is still an unmet need in our ability to address an increasing array of challenging bioanalytical questions within drug discovery. We require new capabilities and strategies of integrated imaging to provide context for fundamental disease-related biological questions that can also offer insights into specific project challenges. Integrated molecular imaging and advanced image analysis have the opportunity to provide a world-class capability that can be deployed on projects in which we cannot answer the question with our battery of established assays. Therefore, here we will provide an updated concise review of the use of MSI for drug discovery; we will also critically consider what is required to embed MSI into a wider evolving R&D landscape and allow long-lasting impact in the industry.
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Affiliation(s)
- Richard J A Goodwin
- Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.,Institute of Infection, Immunity, and Inflammation, College of Medical, Veterinary, and Life Sciences, University of Glasgow, UK
| | - Zoltan Takats
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.,The Rosalind Franklin Institute, Oxfordshire, UK
| | - Josephine Bunch
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.,The Rosalind Franklin Institute, Oxfordshire, UK.,National Physical Laboratory, Teddington, London, UK
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15
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Verbeeck N, Caprioli RM, Van de Plas R. Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. MASS SPECTROMETRY REVIEWS 2020; 39:245-291. [PMID: 31602691 PMCID: PMC7187435 DOI: 10.1002/mas.21602] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/27/2018] [Indexed: 05/20/2023]
Abstract
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.
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Affiliation(s)
- Nico Verbeeck
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Aspect Analytics NVGenkBelgium
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT)KU LeuvenLeuvenBelgium
| | - Richard M. Caprioli
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
- Department of ChemistryVanderbilt UniversityNashvilleTN
- Department of PharmacologyVanderbilt UniversityNashvilleTN
- Department of MedicineVanderbilt UniversityNashvilleTN
| | - Raf Van de Plas
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
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16
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Guo D, Bemis K, Rawlins C, Agar J, Vitek O. Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues. Bioinformatics 2019; 35:i208-i217. [PMID: 31510675 PMCID: PMC6612871 DOI: 10.1093/bioinformatics/btz345] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
MOTIVATION Mass spectrometry imaging (MSI) characterizes the spatial distribution of ions in complex biological samples such as tissues. Since many tissues have complex morphology, treatments and conditions often affect the spatial distribution of the ions in morphology-specific ways. Evaluating the selectivity and the specificity of ion localization and regulation across morphology types is biologically important. However, MSI lacks algorithms for segmenting images at both single-ion and spatial resolution. RESULTS This article contributes spatial-Dirichlet Gaussian mixture model (DGMM), an algorithm and a workflow for the analyses of MSI experiments, that detects components of single-ion images with homogeneous spatial composition. The approach extends DGMMs to account for the spatial structure of MSI. Evaluations on simulated and experimental datasets with diverse MSI workflows demonstrated that spatial-DGMM accurately segments ion images, and can distinguish ions with homogeneous and heterogeneous spatial distribution. We also demonstrated that the extracted spatial information is useful for downstream analyses, such as detecting morphology-specific ions, finding groups of ions with similar spatial patterns, and detecting changes in chemical composition of tissues between conditions. AVAILABILITY AND IMPLEMENTATION The data and code are available at https://github.com/Vitek-Lab/IonSpattern. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Kylie Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Catherine Rawlins
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Jeffrey Agar
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
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17
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Mass Spectrometry Imaging and Integration with Other Imaging Modalities for Greater Molecular Understanding of Biological Tissues. Mol Imaging Biol 2019; 20:888-901. [PMID: 30167993 PMCID: PMC6244545 DOI: 10.1007/s11307-018-1267-y] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Over the last two decades, mass spectrometry imaging (MSI) has been increasingly employed to investigate the spatial distribution of a wide variety of molecules in complex biological samples. MSI has demonstrated its potential in numerous applications from drug discovery, disease state evaluation through proteomic and/or metabolomic studies. Significant technological and methodological advancements have addressed natural limitations of the techniques, i.e., increased spatial resolution, increased detection sensitivity especially for large molecules, higher throughput analysis and data management. One of the next major evolutions of MSI is linked to the introduction of imaging mass cytometry (IMC). IMC is a multiplexed method for tissue phenotyping, imaging signalling pathway or cell marker assessment, at sub-cellular resolution (1 μm). It uses MSI to simultaneously detect and quantify up to 30 different antibodies within a tissue section. The combination of MSI with other molecular imaging techniques can also provide highly relevant complementary information to explore new scientific fields. Traditionally, classical histology (especially haematoxylin and eosin–stained sections) is overlaid with molecular profiles obtained by MSI. Thus, MSI-based molecular histology provides a snapshot of a tissue microenvironment and enables the correlation of drugs, metabolites, lipids, peptides or proteins with histological/pathological features or tissue substructures. Recently, many examples combining MSI with other imaging modalities such as fluorescence, confocal Raman spectroscopy and MRI have emerged. For instance, brain pathophysiology has been studied using both MRI and MSI, establishing correlations between in and ex vivo molecular imaging techniques. Endogenous metabolite and small peptide modulation were evaluated depending on disease state. Here, we review advanced ‘hot topics’ in MSI development and explore the combination of MSI with established molecular imaging techniques to improve our understanding of biological and pathophysiological processes.
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18
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Establishment and characterization of a novel cell line, NCC-MFS1-C1, derived from a patient with myxofibrosarcoma. Hum Cell 2019; 32:214-222. [DOI: 10.1007/s13577-018-00233-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Accepted: 12/08/2018] [Indexed: 01/10/2023]
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19
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Hoffmann F, Umbreit C, Krüger T, Pelzel D, Ernst G, Kniemeyer O, Guntinas-Lichius O, Berndt A, von Eggeling F. Identification of Proteomic Markers in Head and Neck Cancer Using MALDI-MS Imaging, LC-MS/MS, and Immunohistochemistry. Proteomics Clin Appl 2018; 13:e1700173. [PMID: 30411850 DOI: 10.1002/prca.201700173] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 10/29/2018] [Indexed: 12/12/2022]
Abstract
PURPOSE The heterogeneity of squamous cell carcinoma tissue greatly complicates diagnosis and individualized therapy. Therefore, characterizing the heterogeneity of tissue spatially and identifying appropriate biomarkers is crucial. MALDI-MS imaging (MSI) is capable of analyzing spatially resolved tissue biopsies on a molecular level. EXPERIMENTAL DESIGN MALDI-MSI is used on snap frozen and formalin-fixed and paraffin-embedded (FFPE) tissue samples from patients with head and neck cancer (HNC) to analyze m/z values localized in tumor and nontumor regions. Peptide identification is performed using LC-MS/MS and immunohistochemistry (IHC). RESULTS In both FFPE and frozen tissue specimens, eight characteristic masses of the tumor's epithelial region are found. Using LC-MS/MS, the peaks are identified as vimentin, keratin type II, nucleolin, heat shock protein 90, prelamin-A/C, junction plakoglobin, and PGAM1. Lastly, vimentin, nucleolin, and PGAM1 are verified with IHC. CONCLUSIONS AND CLINICAL RELEVANCE The combination of MALDI-MSI, LC-MS/MS, and subsequent IHC furnishes a tool suitable for characterizing the molecular heterogeneity of tissue. It is also suited for use in identifying new representative biomarkers to enable a more individualized therapy.
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Affiliation(s)
- Franziska Hoffmann
- Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany
| | - Claudia Umbreit
- Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany.,Institute of Forensic Medicine, Section Pathology, Jena University Hospital, Jena, Germany
| | - Thomas Krüger
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
| | - Daniela Pelzel
- Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany
| | - Günther Ernst
- Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany
| | - Olaf Kniemeyer
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
| | | | - Alexander Berndt
- Institute of Forensic Medicine, Section Pathology, Jena University Hospital, Jena, Germany
| | - Ferdinand von Eggeling
- Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany.,Institute of Physical Chemistry, Friedrich Schiller University, Jena, Germany
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20
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Ràfols P, Vilalta D, Brezmes J, Cañellas N, Del Castillo E, Yanes O, Ramírez N, Correig X. Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications. MASS SPECTROMETRY REVIEWS 2018; 37:281-306. [PMID: 27862147 DOI: 10.1002/mas.21527] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 10/11/2016] [Indexed: 06/06/2023]
Abstract
Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.
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Affiliation(s)
- Pere Ràfols
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Dídac Vilalta
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Jesús Brezmes
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Nicolau Cañellas
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Esteban Del Castillo
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Oscar Yanes
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Noelia Ramírez
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Xavier Correig
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
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21
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He J, Huang L, Tian R, Li T, Sun C, Song X, Lv Y, Luo Z, Li X, Abliz Z. MassImager: A software for interactive and in-depth analysis of mass spectrometry imaging data. Anal Chim Acta 2018. [PMID: 29530251 DOI: 10.1016/j.aca.2018.02.030] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Mass spectrometry imaging (MSI) has become a powerful tool to probe molecule events in biological tissue. However, it is a widely held viewpoint that one of the biggest challenges is an easy-to-use data processing software for discovering the underlying biological information from complicated and huge MSI dataset. Here, a user-friendly and full-featured MSI software including three subsystems, Solution, Visualization and Intelligence, named MassImager, is developed focusing on interactive visualization, in-situ biomarker discovery and artificial intelligent pathological diagnosis. Simplified data preprocessing and high-throughput MSI data exchange, serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets. It also offers diverse self-defined operations for visual processing, including multiple ion visualization, multiple channel superposition, image normalization, visual resolution enhancement and image filter. Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra, also the overlaid optical image guide, to directly find out the region-specific biomarkers. Moreover, automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling. Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image, which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer. All the features are integrated together in MassImager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis.
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Affiliation(s)
- Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Luojiao Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Runtao Tian
- Chemmind Technologies Co., Ltd., Beijing 100085, China
| | - Tiegang Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Chenglong Sun
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Xiaowei Song
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Yiwei Lv
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zhigang Luo
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Xin Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zeper Abliz
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; Center for Imaging and Systems Biology, Minzu University of China, Beijing 100081, China.
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22
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Abdelmoula WM, Pezzotti N, Hölt T, Dijkstra J, Vilanova A, McDonnell LA, Lelieveldt BPF. Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution. J Proteome Res 2018; 17:1054-1064. [PMID: 29430923 PMCID: PMC5838640 DOI: 10.1021/acs.jproteome.7b00725] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
![]()
Technological
advances in mass spectrometry imaging (MSI) have
contributed to growing interest in 3D MSI. However, the large size
of 3D MSI data sets has made their efficient analysis and visualization
and the identification of informative molecular patterns computationally
challenging. Hierarchical stochastic neighbor embedding (HSNE), a
nonlinear dimensionality reduction technique that aims at finding
hierarchical and multiscale representations of large data sets, is
a recent development that enables the analysis of millions of data
points, with manageable time and memory complexities. We demonstrate
that HSNE can be used to analyze large 3D MSI data sets at full mass
spectral and spatial resolution. To benchmark the technique as well
as demonstrate its broad applicability, we have analyzed a number
of publicly available 3D MSI data sets, recorded from various biological
systems and spanning different mass-spectrometry ionization techniques.
We demonstrate that HSNE is able to rapidly identify regions of interest
within these large high-dimensionality data sets as well as aid the
identification of molecular ions that characterize these regions of
interest; furthermore, through clearly separating measurement artifacts,
the HSNE analysis exhibits a degree of robustness to measurement batch
effects, spatially correlated noise, and mass spectral misalignment.
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Affiliation(s)
- Walid M Abdelmoula
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands.,Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School , Boston, Massachusetts 02115, United States
| | - Nicola Pezzotti
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | - Thomas Hölt
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | - Jouke Dijkstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands
| | - Anna Vilanova
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | | | - Boudewijn P F Lelieveldt
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands.,Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
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23
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Rae Buchberger A, DeLaney K, Johnson J, Li L. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. Anal Chem 2018; 90:240-265. [PMID: 29155564 PMCID: PMC5959842 DOI: 10.1021/acs.analchem.7b04733] [Citation(s) in RCA: 561] [Impact Index Per Article: 93.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Amanda Rae Buchberger
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Kellen DeLaney
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Jillian Johnson
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705, United States
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24
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Dexter A, Race AM, Steven RT, Barnes JR, Hulme H, Goodwin RJA, Styles IB, Bunch J. Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images. Anal Chem 2017; 89:11293-11300. [DOI: 10.1021/acs.analchem.7b01758] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Alex Dexter
- PSIBS
Doctoral Training Centre, University of Birmingham Edgbaston, Birmingham B15 2TT, United Kingdom
- National Physical
Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Alan M. Race
- National Physical
Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Rory T. Steven
- National Physical
Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Jennifer R. Barnes
- AstraZeneca, Drug Safety and Metabolism, Cambridge CB4 0WG, United Kingdom
| | - Heather Hulme
- AstraZeneca, Drug Safety and Metabolism, Cambridge CB4 0WG, United Kingdom
- University
of Glasgow, University Avenue, Glasgow, G12 8QQ, United Kingdom
| | | | - Iain B. Styles
- School
of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Josephine Bunch
- National Physical
Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
- School
of
Pharmacy, University of Nottingham, Nottingham, Nottinghamshire NG7 2RD, United Kingdom
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25
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Lou S, Balluff B, de Graaff MA, Cleven AHG, Briaire-de Bruijn I, Bovée JVMG, McDonnell LA. High-grade sarcoma diagnosis and prognosis: Biomarker discovery by mass spectrometry imaging. Proteomics 2017; 16:1802-13. [PMID: 27174013 DOI: 10.1002/pmic.201500514] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Revised: 05/04/2016] [Accepted: 05/09/2016] [Indexed: 12/24/2022]
Abstract
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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Affiliation(s)
- Sha Lou
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Benjamin Balluff
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.,Maastricht MultiModal Molecular Imaging Institute, Maastricht University, Maastricht, The Netherlands
| | - Marieke A de Graaff
- Department of Pathology, Leiden University, Medical Center, Leiden, The Netherlands
| | - Arjen H G Cleven
- Department of Pathology, Leiden University, Medical Center, Leiden, The Netherlands
| | | | - Judith V M G Bovée
- Department of Pathology, Leiden University, Medical Center, Leiden, The Netherlands
| | - Liam A McDonnell
- 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
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26
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Inglese P, McKenzie JS, Mroz A, Kinross J, Veselkov K, Holmes E, Takats Z, Nicholson JK, Glen RC. Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer. Chem Sci 2017; 8:3500-3511. [PMID: 28507724 PMCID: PMC5418631 DOI: 10.1039/c6sc03738k] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 02/18/2017] [Indexed: 12/14/2022] Open
Abstract
Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
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Affiliation(s)
- Paolo Inglese
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - James S McKenzie
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Anna Mroz
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - James Kinross
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Kirill Veselkov
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Elaine Holmes
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Zoltan Takats
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Jeremy K Nicholson
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Robert C Glen
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
- Centre for Molecular Informatics , Department of Chemistry , University of Cambridge , Cambridge , UK
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27
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Establishment of a novel cellular model for myxofibrosarcoma heterogeneity. Sci Rep 2017; 7:44700. [PMID: 28304377 PMCID: PMC5356330 DOI: 10.1038/srep44700] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 02/13/2017] [Indexed: 11/15/2022] Open
Abstract
Human cancers frequently display substantial intra-tumoural heterogeneity in virtually all distinguishable phenotypic features, such as cellular morphology, gene expression, and metastatic potential. In order to investigate tumour heterogeneity in myxofibrosarcoma, we established a novel myxofibrosarcoma cell line with two well defined sub-clones named MUG-Myx2a and MUG-Myx2b. The parental tumour tissue and both MUG-Myx2 cell lines showed the same STR profile. The fact that MUG-Myx2a showed higher proliferation activity, faster migration and enhanced tumourigenicity was of particular interest. NGS mutation analysis revealed corresponding mutations in the FGFR3, KIT, KDR and TP53 genes. In contrast, the MUG-Myx2a cell lines showed an additional PTEN mutation. Analysis of CNV uncovered a highly aberrant karyotype with frequent losses and gains in the tumour sample. The two MUG-Myx2 cell lines share several CNV features of the tumour tissue, while some CNVs are present only in the two cell lines. Furthermore, certain CNV gains and losses that are exclusive to either MUG-Myx2a or MUG-Myx2b, distinguish the two cell lines. As it is currently not possible to purchase two different sarcoma cell lines derived from the same patient, the novel myxofibrosarcoma cell lines MUG-Myx2a and MUG-Myx2b will be useful tools to study pathogenesis, tumour heterogeneity and treatment options.
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28
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Lou S, Balluff B, Cleven AHG, Bovée JVMG, McDonnell LA. Prognostic Metabolite Biomarkers for Soft Tissue Sarcomas Discovered by Mass Spectrometry Imaging. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2017; 28:376-383. [PMID: 27873216 PMCID: PMC5227002 DOI: 10.1007/s13361-016-1544-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 10/14/2016] [Accepted: 10/15/2016] [Indexed: 05/22/2023]
Abstract
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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Affiliation(s)
- Sha Lou
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Benjamin Balluff
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
- Maastricht MultiModal Molecular Imaging institute (M4I), Maastricht University, Maastricht, The Netherlands
| | - Arjen H G Cleven
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Judith V M G Bovée
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Liam A McDonnell
- 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.
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29
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Ucal Y, Durer ZA, Atak H, Kadioglu E, Sahin B, Coskun A, Baykal AT, Ozpinar A. Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2017; 1865:795-816. [PMID: 28087424 DOI: 10.1016/j.bbapap.2017.01.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 12/08/2016] [Accepted: 01/06/2017] [Indexed: 12/25/2022]
Abstract
Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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Affiliation(s)
- Yasemin Ucal
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Zeynep Aslıhan Durer
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Hakan Atak
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Elif Kadioglu
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Betul Sahin
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Abdurrahman Coskun
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Ahmet Tarık Baykal
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Aysel Ozpinar
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey.
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30
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Arentz G, Mittal P, Zhang C, Ho YY, Briggs M, Winderbaum L, Hoffmann MK, Hoffmann P. Applications of Mass Spectrometry Imaging to Cancer. Adv Cancer Res 2017; 134:27-66. [PMID: 28110654 DOI: 10.1016/bs.acr.2016.11.002] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Pathologists play an essential role in the diagnosis and prognosis of benign and cancerous tumors. Clinicians provide tissue samples, for example, from a biopsy, which are then processed and thin sections are placed onto glass slides, followed by staining of the tissue with visible dyes. Upon processing and microscopic examination, a pathology report is provided, which relies on the pathologist's interpretation of the phenotypical presentation of the tissue. Targeted analysis of single proteins provide further insight and together with clinical data these results influence clinical decision making. Recent developments in mass spectrometry facilitate the collection of molecular information about such tissue specimens. These relatively new techniques generate label-free mass spectra across tissue sections providing nonbiased, nontargeted molecular information. At each pixel with spatial coordinates (x/y) a mass spectrum is acquired. The acquired mass spectrums can be visualized as intensity maps displaying the distribution of single m/z values of interest. Based on the sample preparation, proteins, peptides, lipids, small molecules, or glycans can be analyzed. The generated intensity maps/images allow new insights into tumor tissues. The technique has the ability to detect and characterize tumor cells and their environment in a spatial context and combined with histological staining, can be used to aid pathologists and clinicians in the diagnosis and management of cancer. Moreover, such data may help classify patients to aid therapy decisions and predict outcomes. The novel complementary mass spectrometry-based methods described in this chapter will contribute to the transformation of pathology services around the world.
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Affiliation(s)
- G Arentz
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - P Mittal
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - C Zhang
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - Y-Y Ho
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - M Briggs
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia; ARC Centre for Nanoscale BioPhotonics (CNBP), University of Adelaide, Adelaide, SA, Australia
| | - L Winderbaum
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - M K Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - P Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia.
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31
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Gough A, Stern AM, Maier J, Lezon T, Shun TY, Chennubhotla C, Schurdak ME, Haney SA, Taylor DL. Biologically Relevant Heterogeneity: Metrics and Practical Insights. SLAS DISCOVERY 2017; 22:213-237. [PMID: 28231035 DOI: 10.1177/2472555216682725] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.
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Affiliation(s)
- Albert Gough
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Andrew M Stern
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - John Maier
- 3 Department of Family Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy Lezon
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Tong-Ying Shun
- 2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Chakra Chennubhotla
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Mark E Schurdak
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Steven A Haney
- 5 Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA
| | - D Lansing Taylor
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
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32
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Abstract
One of the big clinical challenges in the treatment of cancer is the different behavior of cancer patients under guideline therapy. An important determinant for this phenomenon has been identified as inter- and intratumor heterogeneity. While intertumor heterogeneity refers to the differences in cancer characteristics between patients, intratumor heterogeneity refers to the clonal and nongenetic molecular diversity within a patient. The deciphering of intratumor heterogeneity is recognized as key to the development of novel therapeutics or treatment regimens. The investigation of intratumor heterogeneity is challenging since it requires an untargeted molecular analysis technique that accounts for the spatial and temporal dynamics of the tumor. So far, next-generation sequencing has contributed most to the understanding of clonal evolution within a cancer patient. However, it falls short in accounting for the spatial dimension. Mass spectrometry imaging (MSI) is a powerful tool for the untargeted but spatially resolved molecular analysis of biological tissues such as solid tumors. As it provides multidimensional datasets by the parallel acquisition of hundreds of mass channels, multivariate data analysis methods can be applied for the automated annotation of tissues. Moreover, it integrates the histology of the sample, which enables studying the molecular information in a histopathological context. This chapter will illustrate how MSI in combination with statistical methods and histology has been used for the description and discovery of intratumor heterogeneity in different cancers. This will give evidence that MSI constitutes a unique tool for the investigation of intratumor heterogeneity, and could hence become a key technology in cancer research.
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33
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Computational Methods for Mass Spectrometry Imaging: Challenges, Progress, and Opportunities. HEALTH INFORMATION SCIENCE 2017. [DOI: 10.1007/978-3-319-44981-4_2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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34
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Buck A, Aichler M, Huber K, Walch A. In Situ Metabolomics in Cancer by Mass Spectrometry Imaging. Adv Cancer Res 2016; 134:117-132. [PMID: 28110648 DOI: 10.1016/bs.acr.2016.11.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Metabolomics is a rapidly evolving and a promising research field with the expectation to improve diagnosis, therapeutic treatment prediction, and prognosis of particular diseases. Among all techniques used to assess the metabolome in biological systems, mass spectrometry imaging is the method of choice to qualitatively and quantitatively analyze metabolite distribution in tissues with a high spatial resolution, thus providing molecular data in relation to cancer histopathology. The technique is ideally suited to study tissues molecular content and is able to provide molecular biomarkers or specific mass signatures which can be used in classification or the prognostic evaluation of tumors. Recently, it was shown that FFPE tissue samples are also suitable for metabolic analyses. This progress in methodology allows access to a highly valuable resource of tissues believed to widen and strengthen metabolic discovery-driven studies.
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Affiliation(s)
- A Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany
| | - M Aichler
- Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany
| | - K Huber
- Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany
| | - A Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany.
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35
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Karlsson O, Hanrieder J. Imaging mass spectrometry in drug development and toxicology. Arch Toxicol 2016; 91:2283-2294. [PMID: 27933369 PMCID: PMC5429351 DOI: 10.1007/s00204-016-1905-6] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Accepted: 11/24/2016] [Indexed: 11/25/2022]
Abstract
During the last decades, imaging mass spectrometry has gained significant relevance in biomedical research. Recent advances in imaging mass spectrometry have paved the way for in situ studies on drug development, metabolism and toxicology. In contrast to whole-body autoradiography that images the localization of radiolabeled compounds, imaging mass spectrometry provides the possibility to simultaneously determine the discrete tissue distribution of the parent compound and its metabolites. In addition, imaging mass spectrometry features high molecular specificity and allows comprehensive, multiplexed detection and localization of hundreds of proteins, peptides and lipids directly in tissues. Toxicologists traditionally screen for adverse findings by histopathological examination. However, studies of the molecular and cellular processes underpinning toxicological and pathologic findings induced by candidate drugs or toxins are important to reach a mechanistic understanding and an effective risk assessment strategy. One of IMS strengths is the ability to directly overlay the molecular information from the mass spectrometric analysis with the tissue section and allow correlative comparisons of molecular and histologic information. Imaging mass spectrometry could therefore be a powerful tool for omics profiling of pharmacological/toxicological effects of drug candidates and toxicants in discrete tissue regions. The aim of the present review is to provide an overview of imaging mass spectrometry, with particular focus on MALDI imaging mass spectrometry, and its use in drug development and toxicology in general.
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Affiliation(s)
- Oskar Karlsson
- Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institute, 171 76, Stockholm, Sweden.
- Department of Pharmaceutical Biosciences, Drug Safety and Toxicology, Uppsala University, 751 24, Uppsala, Sweden.
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, House V, 431 80, Mölndal, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, University College London, Queen Square, London, WC1N, UK
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36
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Bilkey J, Tata A, McKee TD, Porcari AM, Bluemke E, Woolman M, Ventura M, Eberlin MN, Zarrine-Afsar A. Variations in the Abundance of Lipid Biomarker Ions in Mass Spectrometry Images Correlate to Tissue Density. Anal Chem 2016; 88:12099-12107. [PMID: 28193010 DOI: 10.1021/acs.analchem.6b02767] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
While mass spectrometry (MS) imaging is widely used to investigate the molecular composition of ex vivo slices of cancerous tumors, little is known about how variations in the cellular properties of cancer tissue can influence cancer biomarker ion images. To better understand the basis for variations in the abundances of cancer biomarker ions seen in MS images of relatively homogeneous ex vivo tumor samples, sections of snap frozen human breast cancer murine xenografts were subjected to desorption electrospray ionization mass spectrometry (DESI-MS) imaging. Serial sections were then stained with hematoxylin and eosin (H&E) and subjected to detailed morphometric cellular analysis, using a commercial digital pathology platform augmented with custom-tailored image analysis algorithms developed in-house. Gross morphological heterogeneities due to stroma, vasculature, and noncancer cells were mapped in the tumor and found to not correlate with the areas of suppressed cancer biomarker abundance. Instead, the ion abundances of major breast cancer biomarkers were found to correlate with the cytoplasmic area of cancer cells that comprised the tumor tissue. Therefore, detailed cellular analyses can be used to rationalize subtle heterogeneities in ion abundance in MS images, not explained by the presence of gross morphological heterogeneities such as stroma.
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Affiliation(s)
- Jade Bilkey
- STTARR Innovation Centre, Princess Margaret Cancer Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Alessandra Tata
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Trevor D McKee
- STTARR Innovation Centre, Princess Margaret Cancer Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Andreia M Porcari
- ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of Campinas , Campinas, SP Brazil
| | - Emma Bluemke
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Manuela Ventura
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Marcos N Eberlin
- ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of Campinas , Campinas, SP Brazil
| | - Arash Zarrine-Afsar
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada.,Department of Medical Biophysics, University of Toronto ,101 College Street Suite 15-701, Toronto, Ontario M5G 1L7, Canada.,Department of Surgery, University of Toronto , 149 College Street, Toronto, Ontario M5T-1P5, Canada.,Keenan Research Centre for Biomedical Science, Li Ka-Shing Knowledge Institute, St. Michael's Hospital , 30 Bond Street, Toronto, Ontario M5B-1W8, Canada
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37
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Dexter A, Race AM, Styles IB, Bunch J. Testing for Multivariate Normality in Mass Spectrometry Imaging Data: A Robust Statistical Approach for Clustering Evaluation and the Generation of Synthetic Mass Spectrometry Imaging Data Sets. Anal Chem 2016; 88:10893-10899. [DOI: 10.1021/acs.analchem.6b02139] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Alex Dexter
- National Physical Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Alan M. Race
- National Physical Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
| | | | - Josephine Bunch
- National Physical Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
- School
of Pharmacy, University of Nottingham, Nottingham, Nottinghamshire NG7 2RD, United Kingdom
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38
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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; 113:12244-12249. [PMID: 27791011 DOI: 10.1073/pnas.1510227113] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
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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Race AM, Palmer AD, Dexter A, Steven RT, Styles IB, Bunch J. SpectralAnalysis: Software for the Masses. Anal Chem 2016; 88:9451-9458. [DOI: 10.1021/acs.analchem.6b01643] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Alan M. Race
- National
Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington, TW11 0LW, United Kingdom
- PSIBS
Doctoral Training Centre, School of Chemistry, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Andrew D. Palmer
- PSIBS
Doctoral Training Centre, School of Chemistry, University of Birmingham, Birmingham, B15 2TT, United Kingdom
- European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, 69117, Germany
| | - Alex Dexter
- National
Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington, TW11 0LW, United Kingdom
- PSIBS
Doctoral Training Centre, School of Chemistry, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Rory T. Steven
- National
Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington, TW11 0LW, United Kingdom
| | - Iain B. Styles
- PSIBS
Doctoral Training Centre, School of Chemistry, University of Birmingham, Birmingham, B15 2TT, United Kingdom
- School
of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Josephine Bunch
- National
Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington, TW11 0LW, United Kingdom
- School
of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, United Kingdom
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40
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Deng J, Wang L, Ni J, Beretov J, Wasinger V, Wu D, Duan W, Graham P, Li Y. Proteomics discovery of chemoresistant biomarkers for ovarian cancer therapy. Expert Rev Proteomics 2016; 13:905-915. [DOI: 10.1080/14789450.2016.1233065] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Junli Deng
- Cancer Care Centre, St George Hospital, Kogarah, Australia
- St George and Sutherland Clinical School, University of New South Wales (UNSW), Kensington, Australia
- Department of Gynecological Oncology, Henan Cancer Hospital, Zhengzhou, China
- Zhengzhou University, Zhengzhou, China
| | - Li Wang
- Department of Gynecological Oncology, Henan Cancer Hospital, Zhengzhou, China
- Zhengzhou University, Zhengzhou, China
| | - Jie Ni
- Cancer Care Centre, St George Hospital, Kogarah, Australia
- St George and Sutherland Clinical School, University of New South Wales (UNSW), Kensington, Australia
| | - Julia Beretov
- Cancer Care Centre, St George Hospital, Kogarah, Australia
- St George and Sutherland Clinical School, University of New South Wales (UNSW), Kensington, Australia
| | - Valerie Wasinger
- Mark Wainwright Analytical Centre, Bioanalytical Mass Spectrometry Facility, University of New South Wales (UNSW), Kensington, Australia
- School of Medical Sciences, University of New South Wales (UNSW), Kensington, Australia
| | - Duojia Wu
- Cancer Care Centre, St George Hospital, Kogarah, Australia
- St George and Sutherland Clinical School, University of New South Wales (UNSW), Kensington, Australia
| | - Wei Duan
- School of Medicine, Deakin University, Waurn Ponds, Australia
| | - Peter Graham
- Cancer Care Centre, St George Hospital, Kogarah, Australia
- St George and Sutherland Clinical School, University of New South Wales (UNSW), Kensington, Australia
| | - Yong Li
- Cancer Care Centre, St George Hospital, Kogarah, Australia
- St George and Sutherland Clinical School, University of New South Wales (UNSW), Kensington, Australia
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41
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Steven RT, Dexter A, Bunch J. Investigating MALDI MSI parameters (Part 2) – On the use of a mechanically shuttered trigger system for improved laser energy stability. Methods 2016; 104:111-7. [DOI: 10.1016/j.ymeth.2016.04.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 03/01/2016] [Accepted: 04/13/2016] [Indexed: 10/21/2022] Open
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42
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Paine MRL, Kim J, Bennett RV, Parry RM, Gaul DA, Wang MD, Matzuk MM, Fernández FM. Whole Reproductive System Non-Negative Matrix Factorization Mass Spectrometry Imaging of an Early-Stage Ovarian Cancer Mouse Model. PLoS One 2016; 11:e0154837. [PMID: 27159635 PMCID: PMC4861325 DOI: 10.1371/journal.pone.0154837] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 04/20/2016] [Indexed: 01/13/2023] Open
Abstract
High-grade serous carcinoma (HGSC) is the most common and deadliest form of ovarian cancer. Yet it is largely asymptomatic in its initial stages. Studying the origin and early progression of this disease is thus critical in identifying markers for early detection and screening purposes. Tissue-based mass spectrometry imaging (MSI) can be employed as an unbiased way of examining localized metabolic changes between healthy and cancerous tissue directly, at the onset of disease. In this study, we describe MSI results from Dicer-Pten double-knockout (DKO) mice, a mouse model faithfully reproducing the clinical nature of human HGSC. By using non-negative matrix factorization (NMF) for the unsupervised analysis of desorption electrospray ionization (DESI) datasets, tissue regions are segregated based on spectral components in an unbiased manner, with alterations related to HGSC highlighted. Results obtained by combining NMF with DESI-MSI revealed several metabolic species elevated in the tumor tissue and/or surrounding blood-filled cyst including ceramides, sphingomyelins, bilirubin, cholesterol sulfate, and various lysophospholipids. Multiple metabolites identified within the imaging study were also detected at altered levels within serum in a previous metabolomic study of the same mouse model. As an example workflow, features identified in this study were used to build an oPLS-DA model capable of discriminating between DKO mice with early-stage tumors and controls with up to 88% accuracy.
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Affiliation(s)
- Martin R. L. Paine
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, United States of America
| | - Jaeyeon Kim
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX, 77030, United States of America
| | - Rachel V. Bennett
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, United States of America
| | - R. Mitchell Parry
- Department of Computer Science, Appalachian State University, Boone, NC, 28608, United States of America
| | - David A. Gaul
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, United States of America
- School of Biology, Georgia Institute of Technology, Atlanta, GA, 30332, United States of America
- Integrated Cancer Research Center, Georgia Institute of Technology, Atlanta, GA, 30332, United States of America
| | - May D. Wang
- Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology, Atlanta, GA, 30332, United States of America
| | - Martin M. Matzuk
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX, 77030, United States of America
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, United States of America
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, United States of America
- Department of Pharmacology, Baylor College of Medicine, Houston, TX, 77030, United States of America
- Center for Drug Discovery, Baylor College of Medicine, Houston, TX, 77030, United States of America
- Center for Reproductive Medicine, Baylor College of Medicine, Houston, TX, 77030, United States of America
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, United States of America
- Integrated Cancer Research Center, Georgia Institute of Technology, Atlanta, GA, 30332, United States of America
- Institute of Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, 30332, United States of America
- * E-mail:
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43
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Widlak P, Mrukwa G, Kalinowska M, Pietrowska M, Chekan M, Wierzgon J, Gawin M, Drazek G, Polanska J. Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium - application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data. Proteomics 2016; 16:1613-21. [PMID: 27168173 PMCID: PMC5074322 DOI: 10.1002/pmic.201500458] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 01/11/2016] [Accepted: 02/24/2016] [Indexed: 01/16/2023]
Abstract
Intra-tumor heterogeneity is a vivid problem of molecular oncology that could be addressed by imaging mass spectrometry. Here we aimed to assess molecular heterogeneity of oral squamous cell carcinoma and to detect signatures discriminating normal and cancerous epithelium. Tryptic peptides were analyzed by MALDI-IMS in tissue specimens from five patients with oral cancer. Novel algorithm of IMS data analysis was developed and implemented, which included Gaussian mixture modeling for detection of spectral components and iterative k-means algorithm for unsupervised spectra clustering performed in domain reduced to a subset of the most dispersed components. About 4% of the detected peptides showed significantly different abundances between normal epithelium and tumor, and could be considered as a molecular signature of oral cancer. Moreover, unsupervised clustering revealed two major sub-regions within expert-defined tumor areas. One of them showed molecular similarity with histologically normal epithelium. The other one showed similarity with connective tissue, yet was markedly different from normal epithelium. Pathologist's re-inspection of tissue specimens confirmed distinct features in both tumor sub-regions: foci of actual cancer cells or cancer microenvironment-related cells prevailed in corresponding areas. Hence, molecular differences detected during automated segmentation of IMS data had an apparent reflection in real structures present in tumor.
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Affiliation(s)
- Piotr Widlak
- Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Grzegorz Mrukwa
- Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland
| | - Magdalena Kalinowska
- Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Monika Pietrowska
- Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Mykola Chekan
- Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Janusz Wierzgon
- Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Marta Gawin
- Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Grzegorz Drazek
- Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland
| | - Joanna Polanska
- Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland
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44
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Bodzon-Kulakowska A, Suder P. Imaging mass spectrometry: Instrumentation, applications, and combination with other visualization techniques. MASS SPECTROMETRY REVIEWS 2016; 35:147-69. [PMID: 25962625 DOI: 10.1002/mas.21468] [Citation(s) in RCA: 123] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2014] [Accepted: 01/23/2015] [Indexed: 05/18/2023]
Abstract
Imaging Mass Spectrometry (IMS) is strengthening its position as a valuable analytical tool. It has unique ability to identify structures and to unravel molecular changes that occur in the precisely defined part of the sample. These unique features open new possibilities in the field of various aspects of biological research. In this review we briefly discuss the main imaging mass spectrometry techniques, as well as the nature of biological samples and molecules, which might be analyzed by such methodology. Moreover, a novel approach, where different analytical techniques might be combined with the results of IMS study, is emphasized and discussed. With such a fast development of IMS and related methods, we can foresee the promising future of this technique.
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Affiliation(s)
- Anna Bodzon-Kulakowska
- Department of Biochemistry and Neurobiology, Faculty of Materials Sciences and Ceramics, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Piotr Suder
- Department of Biochemistry and Neurobiology, Faculty of Materials Sciences and Ceramics, AGH University of Science and Technology, 30-059 Krakow, Poland
- Academic Centre for Materials and Nanotechnology (ACMiN), AGH University of Science and Technology, 30-059 Krakow, Poland
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45
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Winderbaum LJ, Koch I, Gustafsson OJR, Meding S, Hoffmann P. Feature extraction for proteomics imaging mass spectrometry data. Ann Appl Stat 2015. [DOI: 10.1214/15-aoas870] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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46
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Fischer CR, Ruebel O, Bowen BP. An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses. Arch Biochem Biophys 2015; 589:18-26. [PMID: 26365033 DOI: 10.1016/j.abb.2015.08.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 08/21/2015] [Accepted: 08/28/2015] [Indexed: 10/23/2022]
Abstract
Mass spectrometry imaging (MSI) is used in an increasing number of biological applications. Typical MSI datasets contain unique, high-resolution mass spectra from tens of thousands of spatial locations, resulting in raw data sizes of tens of gigabytes per sample. In this paper, we review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Handling such data often requires specialized computational infrastructure, software, and expertise. OpenMSI, our recently described platform, makes it easy to explore and share MSI datasets via the web - even when larger than 50 GB. Here we describe the integration of OpenMSI with IPython notebooks for transparent, sharable, and replicable MSI research. An advantage of this approach is that users do not have to share raw data along with analyses; instead, data is retrieved via OpenMSI's web API. The IPython notebook interface provides a low-barrier entry point for data manipulation that is accessible for scientists without extensive computational training. Via these notebooks, analyses can be easily shared without requiring any data movement. We provide example notebooks for several common MSI analysis types including data normalization, plotting, clustering, and classification, and image registration.
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Affiliation(s)
- Curt R Fischer
- Life Sciences Division, Lawrence Berkeley National Lab, One Cyclotron Road, Berkeley CA 94720, USA
| | - Oliver Ruebel
- Computational Research Division, Lawrence Berkeley National Lab, USA
| | - Benjamin P Bowen
- Life Sciences Division, Lawrence Berkeley National Lab, One Cyclotron Road, Berkeley CA 94720, USA.
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47
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Wijetunge CD, Saeed I, Boughton BA, Spraggins JM, Caprioli RM, Bacic A, Roessner U, Halgamuge SK. EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data. Bioinformatics 2015; 31:3198-206. [DOI: 10.1093/bioinformatics/btv356] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Accepted: 06/04/2015] [Indexed: 11/13/2022] Open
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48
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Oetjen J, Veselkov K, Watrous J, McKenzie JS, Becker M, Hauberg-Lotte L, Kobarg JH, Strittmatter N, Mróz AK, Hoffmann F, Trede D, Palmer A, Schiffler S, Steinhorst K, Aichler M, Goldin R, Guntinas-Lichius O, von Eggeling F, Thiele H, Maedler K, Walch A, Maass P, Dorrestein PC, Takats Z, Alexandrov T. Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry. Gigascience 2015; 4:20. [PMID: 25941567 PMCID: PMC4418095 DOI: 10.1186/s13742-015-0059-4] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Accepted: 04/09/2015] [Indexed: 01/16/2023] Open
Abstract
Background Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. Findings High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. Conclusions With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets. Electronic supplementary material The online version of this article (doi:10.1186/s13742-015-0059-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Janina Oetjen
- MALDI Imaging Lab, University of Bremen, Bremen, Germany
| | - Kirill Veselkov
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Jeramie Watrous
- Department of Medicine, Biomedical Research Facility II, University of California, San Diego, USA
| | - James S McKenzie
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | | | | | | | - Nicole Strittmatter
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Anna K Mróz
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Franziska Hoffmann
- Institute of Physical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany ; Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany
| | - Dennis Trede
- Steinbeis Center SCiLS Research, Bremen, Germany ; SCiLS GmbH, Bremen, Germany
| | - Andrew Palmer
- European Molecular Biology Laboratory, Heidelberg, Germany
| | | | | | - Michaela Aichler
- Research Unit Analytical Pathology, Institute of Pathology, Helmholtz Center Munich, Munich, Germany
| | - Robert Goldin
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | | | - Ferdinand von Eggeling
- Institute of Physical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany ; Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany ; Leibnitz Institute of Photonic Technology (IPHT), Jena, Germany ; Jena Center for Soft Matter (JCSM), Friedrich-Schiller-University Jena, Jena, Germany
| | | | - Kathrin Maedler
- MALDI Imaging Lab, University of Bremen, Bremen, Germany ; Islet Research Lab, Center for Biomolecular Interactions, University of Bremen, Bremen, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, Institute of Pathology, Helmholtz Center Munich, Munich, Germany
| | - Peter Maass
- Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy & Pharmaceutical Sciences, University of California, San Diego, USA
| | - Zoltan Takats
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Theodore Alexandrov
- Steinbeis Center SCiLS Research, Bremen, Germany ; SCiLS GmbH, Bremen, Germany ; European Molecular Biology Laboratory, Heidelberg, Germany ; Skaggs School of Pharmacy & Pharmaceutical Sciences, University of California, San Diego, USA
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49
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Yang J, Rübel O, Prabhat, Mahoney MW, Bowen BP. Identifying Important Ions and Positions in Mass Spectrometry Imaging Data Using CUR Matrix Decompositions. Anal Chem 2015; 87:4658-66. [DOI: 10.1021/ac5040264] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Jiyan Yang
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| | - Oliver Rübel
- Computational
Research Division, Lawrence Berkeley Lab, One Cyclotron Road, Berkeley, California 94720, United States
| | - Prabhat
- Computational
Research Division, Lawrence Berkeley Lab, One Cyclotron Road, Berkeley, California 94720, United States
| | - Michael W. Mahoney
- International
Computer Science Institute and Department of Statistics, University of California, Berkeley, California 94720, United States
| | - Benjamin P. Bowen
- Life
Sciences Division, Lawrence Berkeley Lab, One Cyclotron Road, Berkeley, California 94720, United States
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50
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Palmer AD, Alexandrov T. Serial 3D imaging mass spectrometry at its tipping point. Anal Chem 2015; 87:4055-62. [PMID: 25817912 DOI: 10.1021/ac504604g] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Since biology is by and large a 3-dimensional phenomenon, it is hardly surprising that 3D imaging has had a significant impact on many challenges in the life sciences. Imaging mass spectrometry (MS) is a spatially resolved label-free analytical technique that recently maturated into a powerful tool for in situ localization of hundreds of molecular species. Serial 3D imaging MS reconstructs 3D molecular images from serial sections imaged with mass spectrometry. As such, it provides a novel 3D imaging modality inheriting the advantages of imaging MS. Serial 3D imaging MS has been steadily developing over the past decade, and many of the technical challenges have been met. Essential tools and protocols were developed, in particular to improve the reproducibility of sample preparation, speed up data acquisition, and enable computationally intensive analysis of the big data generated. As a result, experimental data is starting to emerge that takes advantage of the extra spatial dimension that 3D imaging MS offers. Most studies still focus on method development rather than on exploring specific biological problems. The future success of 3D imaging MS requires it to find its own niche alongside existing 3D imaging modalities through finding applications that benefit from 3D imaging and at the same time utilize the unique chemical sensitivity of imaging mass spectrometry. This perspective critically reviews the challenges encountered during the development of serial-sectioning 3D imaging MS and discusses the steps needed to tip it from being an academic curiosity into a tool of choice for answering biological and medical questions.
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Affiliation(s)
- Andrew D Palmer
- †European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.,‡Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
| | - Theodore Alexandrov
- †European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.,‡Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.,§SCiLS GmbH, 28359 Bremen, Germany.,∥Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California 92161, United States
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