1
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Kandanaarachchi S, Gardner W, Alexander DLJ, Muir BW, Chouinard PA, Crewther SG, Scurr DJ, Halliday M, Pigram PJ. Comparison of Tiling Artifact Removal Methods in Secondary Ion Mass Spectrometry Images. Anal Chem 2023; 95:17384-17391. [PMID: 37963228 PMCID: PMC10688221 DOI: 10.1021/acs.analchem.3c03887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/27/2023] [Accepted: 10/29/2023] [Indexed: 11/16/2023]
Abstract
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterization of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, standard ToF-SIMS instruments allow for the acquisition of adjoining tiles, which are acquired by rastering the primary ion beam. For such large area scans, tiling artifacts are a ubiquitous challenge, manifesting as intensity gradients across each tile and/or sudden changes in intensity between tiles. Such artifacts are thought to be related to a combination of sample charging, local detector sensitivity issues, and misalignment of the primary ion gun, among other instrumental factors. In this work, we investigated six different computational tiling artifact removal methods: tensor decomposition, multiplicative linear correction, linear discriminant analysis, seamless stitching, simple averaging, and simple interpolating. To ensure robustness in the study, we applied these methods to three hyperspectral ToF-SIMS data sets and one OrbiTrapSIMS data set. Our study includes a carefully designed statistical analysis and a quantitative survey that subjectively assessed the quality of the various methods employed. Our results demonstrate that while certain methods are useful and preferred more often, no one particular approach can be considered universally acceptable and that the effectiveness of the artifact removal method is strongly dependent on the particulars of the data set analyzed. As examples, the multiplicative linear correction and seamless stitching methods tended to score more highly on the subjective survey; however, for some data sets, this led to the introduction of new artifacts. In contrast, simple averaging and interpolation methods scored subjectively poorly on the biological data set, but more highly on the microarray data sets. We discuss and explore these findings in depth and present general recommendations given our findings to conclude the work.
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Affiliation(s)
| | - Wil Gardner
- Centre
for Materials and Surface Science and Department of Mathematical and
Physical Sciences, La Trobe University, Melbourne, Victoria 3086, Australia
| | | | | | - Philippe A. Chouinard
- School
of Psychology and Public Health, La Trobe
University, Melbourne, Victoria 3086, Australia
| | - Sheila G. Crewther
- School
of Psychology and Public Health, La Trobe
University, Melbourne, Victoria 3086, Australia
| | - David J. Scurr
- School
of Pharmacy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Mark Halliday
- Altos Laboratories, Cambridge Institute of Science, The Portway Building, Granta Park, Great Abington CB21 6GP, United
Kingdom
| | - Paul J. Pigram
- Centre
for Materials and Surface Science and Department of Mathematical and
Physical Sciences, La Trobe University, Melbourne, Victoria 3086, Australia
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2
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Grogan DP, Skelton HM, Fernandez AM, Gutekunst CANE, Gross RE. The laterodorsal tegmentum and seizure regulation: Revisiting the evidence. J Neurosci Res 2023; 101:256-262. [PMID: 36349730 DOI: 10.1002/jnr.25144] [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: 09/22/2022] [Accepted: 10/26/2022] [Indexed: 11/10/2022]
Abstract
Electrical deep brain stimulation (DBS) is now a routine treatment option for patients suffering from medically refractory epilepsy. DBS of the anterior nucleus of the thalamus (ANT) has proven to be effective but, despite its success, few patients experience complete cessation of seizure activity. However, improving the therapy is challenging because the mechanism underlying its action remains largely unknown. One angle on improving the effectiveness of ANT stimulation is to better understand the various anatomic regions that send projections to and through this area. Here, the authors utilized a connectomic atlas of the mouse brain to better understand the regions projecting to the ANT and were particularly interested by the presence of robust cholinergic projections from the laterodorsal tegmentum (LDT). A subsequent review of the literature resulted in limited studies, which presented convincing evidence supporting this region's role in seizure control present in acute rodent models of epilepsy. It is thus the purpose of this paper to encourage further research into the role of the LDT on seizure mitigation, with mechanistic effects likely stemming from its cholinergic projections to the ANT. While previous studies have laid a firm foundation supporting the role of this region in modulation of seizure activity, modern scientific methodology has yet to be applied to further elucidate the mechanisms and potential benefits associated with LDT stimulation in the epileptic population.
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Affiliation(s)
- Dayton P Grogan
- Department of Neurosurgery, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Henry M Skelton
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Alejandra M Fernandez
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Robert E Gross
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA.,Department of Neurology, Emory University, Atlanta, Georgia, USA
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3
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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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4
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Balluff B, Heeren RM, Race AM. An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities. J Mass Spectrom Adv Clin Lab 2022; 23:26-38. [PMID: 35156074 PMCID: PMC8821033 DOI: 10.1016/j.jmsacl.2021.12.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 01/25/2023] Open
Abstract
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. Integration with other imaging modalities is essential in clinical MSI. Image integration is performed by image registration techniques. Technical potential of image registration in MSI has not been fully exploited. Roadmap proposed to improve registration accuracy.
Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.
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5
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Tian S, Hou Z, Zuo X, Xiong W, Huang G. Automatic Registration of the Mass Spectrometry Imaging Data of Sagittal Brain Slices to the Reference Atlas. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1789-1797. [PMID: 34096712 DOI: 10.1021/jasms.1c00137] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The registration of the mass spectrometry imaging (MSI) data with mouse brain tissue slices from the atlases could perform automatic anatomical interpretation, and the comparison of MSI data in particular brain regions from different mice could be accelerated. However, the current registration of MSI data with mouse brain tissue slices is mainly focused on the coronal. Although the sagittal plane is able to provide more information about brain regions on a single histological slice than the coronal, it is difficult to directly register the complete sagittal brain slices of a mouse as a result of the more significant individualized differences and more positional shifts of brain regions. Herein, by adding the auxiliary line on the two brain regions of central canal (CC) and cerebral peduncle (CP), the registration accuracy of the MSI data with sagittal brain slices has been improved (∼2-5-folds for different brain regions). Moreover, the histological sections with different degrees deformation and different dyeing effects have been used to verify that this pipeline has a certain universality. Our method facilitates the rapid comparison of sagittal plane MSI data from different animals and accelerates the application in the discovery of disease markers.
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Affiliation(s)
- Shuangshuang Tian
- Department of Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
| | - Zhuanghao Hou
- Department of Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
| | - Xin Zuo
- School of Life Sciences, Neurodegenerative Disorder Research Center, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
| | - Wei Xiong
- School of Life Sciences, Neurodegenerative Disorder Research Center, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Guangming Huang
- Department of Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
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6
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Developmental and Degenerative Cerebellar Pathologies in Peroxisomal β-Oxidation Deficiency. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021. [PMID: 33417211 DOI: 10.1007/978-3-030-60204-8_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
The integrity of the cerebellum is exquisitely dependent on peroxisomal β-oxidation metabolism. Patients with peroxisomal β-oxidation defects commonly develop malformation, leukodystrophy, and/or atrophy of the cerebellum depending on the gene defect and on the severity of the mutation. By analyzing mouse models lacking the central peroxisomal β-oxidation enzyme, multifunctional protein-2 (MFP2), either globally or in selected cell types, insights into the pathomechanisms could be obtained. All mouse models developed ataxia, but the onset was earlier in global and neural-selective (Nestin) Mfp2-/- knockout mice as compared to Purkinje cell (PC)-selective Mfp2 knockouts.At the histological level, this was associated with developmental anomalies in global and Nestin-Mfp2-/- mice, including aberrant wiring of PCs by parallel and climbing fibers and altered electrical properties of PCs. In all mouse models, dystrophy of PC axons with swellings initiating in the deep cerebellar nuclei and evolving to the proximal axon, preceded death of PCs. These degenerative features are in part mediated by deficient peroxisomal β-oxidation within PCs but are accelerated when MFP2 is also absent from other neural cell types. The metabolic causes of the diverse cerebellar pathologies remain unknown.In conclusion, peroxisomal β-oxidation is required both for the development and for the maintenance of the cerebellum. This is mediated by PC autonomous and nonautonomous mechanisms.
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7
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Enzlein T, Cordes J, Munteanu B, Michno W, Serneels L, De Strooper B, Hanrieder J, Wolf I, Chávez-Gutiérrez L, Hopf C. Computational Analysis of Alzheimer Amyloid Plaque Composition in 2D- and Elastically Reconstructed 3D-MALDI MS Images. Anal Chem 2020; 92:14484-14493. [PMID: 33138378 DOI: 10.1021/acs.analchem.0c02585] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
MALDI mass spectrometry imaging (MSI) enables label-free, spatially resolved analysis of a wide range of analytes in tissue sections. Quantitative analysis of MSI datasets is typically performed on single pixels or manually assigned regions of interest (ROIs). However, many sparse, small objects such as Alzheimer's disease (AD) brain deposits of amyloid peptides called plaques are neither single pixels nor ROIs. Here, we propose a new approach to facilitate the comparative computational evaluation of amyloid plaque-like objects by MSI: a fast PLAQUE PICKER tool that enables a statistical evaluation of heterogeneous amyloid peptide composition. Comparing two AD mouse models, APP NL-G-F and APP PS1, we identified distinct heterogeneous plaque populations in the NL-G-F model but only one class of plaques in the PS1 model. We propose quantitative metrics for the comparison of technical and biological MSI replicates. Furthermore, we reconstructed a high-accuracy 3D-model of amyloid plaques in a fully automated fashion, employing rigid and elastic MSI image registration using structured and plaque-unrelated reference ion images. Statistical single-plaque analysis in reconstructed 3D-MSI objects revealed the Aβ1-42Arc peptide to be located either in the core of larger plaques or in small plaques without colocalization of other Aβ isoforms. In 3D, a substantially larger number of small plaques were observed than that indicated by the 2D-MSI data, suggesting that quantitative analysis of molecularly diverse sparsely-distributed features may benefit from 3D-reconstruction. Data are available via ProteomeXchange with identifier PXD020824.
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Affiliation(s)
- Thomas Enzlein
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany.,KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Jonas Cordes
- Faculty of Computer Science, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, Mannheim 68163, Germany
| | - Bogdan Munteanu
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany
| | - Wojciech Michno
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, House V3, Mölndal 43180, Sweden.,Department of Neuroscience, Physiology and Pharmacology, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Lutgarde Serneels
- KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Bart De Strooper
- KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium.,UK Dementia Research Institute at UCL, University College London, London WC1E 6BT U.K
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, House V3, Mölndal 43180, Sweden.,Department of Neurodegenerative Diseases, University College London Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
| | - Ivo Wolf
- Faculty of Computer Science, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, Mannheim 68163, Germany
| | - Lucía Chávez-Gutiérrez
- KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Carsten Hopf
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany
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8
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Ščupáková K, Balluff B, Tressler C, Adelaja T, Heeren RM, Glunde K, Ertaylan G. Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges. Clin Chem Lab Med 2020; 58:914-929. [PMID: 31665113 PMCID: PMC9867918 DOI: 10.1515/cclm-2019-0858] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 10/07/2019] [Indexed: 02/07/2023]
Abstract
Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.
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Affiliation(s)
- Klára Ščupáková
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Benjamin Balluff
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Caitlin Tressler
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tobi Adelaja
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ron M.A. Heeren
- Corresponding author: Ron M.A. Heeren, Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands,
| | - Kristine Glunde
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; and The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gökhan Ertaylan
- Unit Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
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9
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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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10
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González de San Román E, Manuel I, Ledent C, Chun J, Rodríguez de Fonseca F, Estivill-Torrús G, Santín LJ, Rodríguez Puertas R. CB 1 and LPA 1 Receptors Relationship in the Mouse Central Nervous System. Front Mol Neurosci 2019; 12:223. [PMID: 31607860 PMCID: PMC6761275 DOI: 10.3389/fnmol.2019.00223] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 09/03/2019] [Indexed: 01/29/2023] Open
Abstract
Neurolipids are a class of bioactive lipids that are produced locally through specific biosynthetic pathways in response to extracellular stimuli. Neurolipids are important endogenous regulators of neural cell proliferation, differentiation, oxidative stress, inflammation and apoptosis. Endocannabinoids (eCBs) and lysophosphatidic acid (LPA) are examples of this type of molecule and are involved in neuroprotection. The present study analyzes a possible relationship of the main receptor subtypes for both neurolipid systems that are present in the central nervous system, the CB1 and LPA1 receptors, by using brain slices from CB1 KO mice and LPA1-null mice. Receptor-mediated G protein activation and glycerophospholipid regulation of potential precursors of their endogenous neurotransmitters were measured by two different in vitro imaging techniques, functional autoradiography and imaging mass spectrometry (IMS), respectively. Possible crosstalk between CB1 and LPA1 receptors was identified in specific areas of the brain, such as the amygdala, where LPA1 receptor activity is upregulated in CB1 KO mice. More evidence of an interaction between both systems was that the CB1-mediated activity was clearly increased in the prefrontal cortex and cerebellum of LPA1-null mice. The eCB system was specifically over-activated in regions where LPA1 has an important signaling role during embryonic development. The modifications on phospholipids (PLs) observed in these genetically modified mice by using the IMS technique indicated the regulation of some of the PL precursors of both LPA and eCBs in specific brain areas. For example, phosphatidylcholine (PC) (36:1) was detected as a potential LPA precursor, and phosphatidylethanolamine (PE) (40:6) and PE (p18:0/22:6) as potential eCB precursors. The absence of the main cerebral receptors for LPA or eCB systems is able to induce modulation on the other at the levels of both signaling and synthesis of endogenous neurotransmitters, indicating adaptive responses between both systems during prenatal and/or postnatal development.
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Affiliation(s)
| | - Iván Manuel
- Department of Pharmacology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Catherine Ledent
- Institut de Recherche Interdisciplinaire en Biologie Humaine et Moléculaire, Université Libre de Bruxelles, Brussels, Belgium
| | - Jerold Chun
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States
| | - Fernando Rodríguez de Fonseca
- Instituto de Investigación Biomédica de Málaga-IBIMA, Málaga, Spain, 5 Unidad de Gestión Clínica de Salud Mental, Málaga, Spain.,Unidad de Gestión Clínica de Salud Mental, Hospital Regional Universitario de Málaga, Universidad de Málaga, Málaga, Spain
| | - Guillermo Estivill-Torrús
- Instituto de Investigación Biomédica de Málaga-IBIMA, Málaga, Spain, 5 Unidad de Gestión Clínica de Salud Mental, Málaga, Spain.,Unidad de Gestión Clínica de Neurociencias, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - Luis Javier Santín
- Instituto de Investigación Biomédica de Málaga-IBIMA, Málaga, Spain, 5 Unidad de Gestión Clínica de Salud Mental, Málaga, Spain.,Departamento de Psicobiología y Metodología de las Ciencias del Comportamiento, Universidad de Málaga, Málaga, Spain
| | - Rafael Rodríguez Puertas
- Department of Pharmacology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, Spain.,Neurodegenerative Diseases, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
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11
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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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12
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Van Malderen SJM, Van Acker T, Laforce B, De Bruyne M, de Rycke R, Asaoka T, Vincze L, Vanhaecke F. Three-dimensional reconstruction of the distribution of elemental tags in single cells using laser ablation ICP-mass spectrometry via registration approaches. Anal Bioanal Chem 2019; 411:4849-4859. [PMID: 30790022 DOI: 10.1007/s00216-019-01677-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 01/22/2019] [Accepted: 02/06/2019] [Indexed: 12/15/2022]
Abstract
This paper describes a workflow towards the reconstruction of the three-dimensional elemental distribution profile within human cervical carcinoma cells (HeLa), at a spatial resolution down to 1 μm, employing state-of-the-art laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) instrumentation. The suspended cells underwent a series of fixation/embedding protocols and were stained with uranyl acetate and an Ir-based DNA intercalator. A priori, laboratory-based absorption micro-computed tomography (μ-CT) was applied to acquire a reference frame of the morphology of the cells and their spatial distribution before sectioning. After CT analysis, a trimmed 300 × 300 × 300 μm3 block was sectioned into a sequential series of 132 sections with a thickness of 2 μm, which were subjected to LA-ICP-MS imaging. A pixel acquisition rate of 250 pixels s-1 was achieved, through a bidirectional scanning strategy. After acquisition, the two-dimensional elemental images were reconstructed using the timestamps in the laser log file. The synchronization of the data required an improved optimization algorithm, which forces the pixels of scans in different ablation directions to be spatially coherent in the direction orthogonal to the scan direction. The volume was reconstructed using multiple registration approaches. Registration using the section outline itself as a fiducial marker resulted into a volume which was in good agreement with the morphology visualized in the μ-CT volume. The 3D μ-CT volume could be registered to the LA-ICP-MS volume, consisting of 2.9 × 107 voxels, and the nucleus dimensions in 3D space could be derived.
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Affiliation(s)
- Stijn J M Van Malderen
- Atomic & Mass Spectrometry (A&MS) Research Unit, Department of Chemistry, Ghent University, Campus Sterre, Krijgslaan 281 - S12, 9000, Ghent, Belgium
| | - Thibaut Van Acker
- Atomic & Mass Spectrometry (A&MS) Research Unit, Department of Chemistry, Ghent University, Campus Sterre, Krijgslaan 281 - S12, 9000, Ghent, Belgium
| | - Brecht Laforce
- X-ray Microspectroscopy and Imaging (XMI) Research Unit, Department of Chemistry, Ghent University, Campus Sterre, Krijgslaan 281 - S12, 9000, Ghent, Belgium
| | - Michiel De Bruyne
- Department of Biomedical Molecular Biology and VIB Center for Inflammation Research, Ghent University, Technologiepark 71, 9052, Ghent, Belgium
- Ghent University Expertise Centre for Transmission Electron Microscopy and VIB BioImaging Core, Ghent University, Technologiepark 927, 9052, Ghent, Belgium
| | - Riet de Rycke
- Department of Biomedical Molecular Biology and VIB Center for Inflammation Research, Ghent University, Technologiepark 71, 9052, Ghent, Belgium
- Ghent University Expertise Centre for Transmission Electron Microscopy and VIB BioImaging Core, Ghent University, Technologiepark 927, 9052, Ghent, Belgium
| | - Tomoko Asaoka
- Department of Biomedical Molecular Biology and VIB Center for Inflammation Research, Ghent University, Technologiepark 71, 9052, Ghent, Belgium
| | - Laszlo Vincze
- X-ray Microspectroscopy and Imaging (XMI) Research Unit, Department of Chemistry, Ghent University, Campus Sterre, Krijgslaan 281 - S12, 9000, Ghent, Belgium
| | - Frank Vanhaecke
- Atomic & Mass Spectrometry (A&MS) Research Unit, Department of Chemistry, Ghent University, Campus Sterre, Krijgslaan 281 - S12, 9000, Ghent, Belgium.
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13
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Patterson NH, Tuck M, Van de Plas R, Caprioli RM. Advanced Registration and Analysis of MALDI Imaging Mass Spectrometry Measurements through Autofluorescence Microscopy. Anal Chem 2018; 90:12395-12403. [PMID: 30272960 DOI: 10.1021/acs.analchem.8b02884] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The correlation of imaging mass spectrometry (IMS) with histopathology can help relate novel molecular findings obtained through IMS to the well-characterized and validated histopathology knowledge base. The quality of correlation between these two modalities is limited by the quality of the spatial mapping that is obtained by registration of the two image types. In this work, we develop novel workflows for MALDI IMS-to-microscopy data registration and analysis using nondestructive IMS-compatible wide field autofluorescence (AF) microscopy combined with computational image registration. First, a substantially automated procedure for high-accuracy registration between IMS and microscopy data of the same section is described that explicitly links the MALDI laser ablation pattern imaged by microscopy to its corresponding IMS pixel. Subsequent examination of the registered data allows for high-confidence colocalization of image features between the two modalities, down to single-cell scales within tissue. Building on this IMS-microscopy spatial mapping, we furthermore demonstrate the automated spatial correlation between IMS measurements from serial sections. This AF-registration-driven inter-section analysis, using a combination of nonlinear AF-to-AF and IMS-to-AF image registrations, can be applied to tissue sections that are prepared and imaged with different sample preparations (e.g., lipids vs proteins) and/or that are measured using different spatial resolutions. Importantly, all registrations, whether within a single section or across serial sections, are entirely independent of the IMS intensity signal content and thus unbiased by it.
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Affiliation(s)
| | | | - Raf Van de Plas
- Delft Center for Systems and Control (DCSC) , Delft University of Technology , 2628 CD Delft , The Netherlands
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14
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Dilillo M, Heijs B, McDonnell LA. Mass spectrometry imaging: How will it affect clinical research in the future? Expert Rev Proteomics 2018; 15:709-716. [PMID: 30203995 DOI: 10.1080/14789450.2018.1521278] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
INTRODUCTION Mass spectrometry imaging (MSI) is a label free, multiplex imaging technology able to simultaneously record the distributions of 100's to 1000's of species, and which may be configured to study metabolites, lipids, glycans, peptides, and proteins simply by changing the tissue preparation protocol. Areas covered: The capability of MSI to complement established histopathological practice through the identification of biomarkers for differential diagnosis, patient prognosis, and response to therapy; the capability of MSI to annotate tissues on the basis of each pixel's mass spectral signature; the development of reproducible MSI through multicenter studies. Expert commentary: We discuss how MSI can be combined with microsampling/microdissection technologies in order to investigate, with more depth of coverage, the molecular changes uncovered by MSI.
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Affiliation(s)
| | - Bram Heijs
- b Center for Proteomics and Metabolomics , Leiden University Medical Center , Leiden , The Netherlands
| | - Liam A McDonnell
- a Fondazione Pisana per la Scienza ONLUS , Pisa , Italy.,b Center for Proteomics and Metabolomics , Leiden University Medical Center , Leiden , The Netherlands
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15
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González de San Román E, Bidmon HJ, Malisic M, Susnea I, Küppers A, Hübbers R, Wree A, Nischwitz V, Amunts K, Huesgen PF. Molecular composition of the human primary visual cortex profiled by multimodal mass spectrometry imaging. Brain Struct Funct 2018; 223:2767-2783. [PMID: 29633039 PMCID: PMC5995978 DOI: 10.1007/s00429-018-1660-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 03/29/2018] [Indexed: 12/14/2022]
Abstract
The primary visual cortex (area V1) is an extensively studied part of the cerebral cortex with well-characterized connectivity, cellular and molecular architecture and functions (for recent reviews see Amunts and Zilles, Neuron 88:1086-1107, 2015; Casagrande and Xu, Parallel visual pathways: a comparative perspective. The visual neurosciences, MIT Press, Cambridge, pp 494-506, 2004). In humans, V1 is defined by heavily myelinated fibers arriving from the radiatio optica that form the Gennari stripe in cortical layer IV, which is further subdivided into laminae IVa, IVb, IVcα and IVcβ. Due to this unique laminar pattern, V1 represents an excellent region to test whether multimodal mass spectrometric imaging could reveal novel biomolecular markers for a functionally relevant parcellation of the human cerebral cortex. Here we analyzed histological sections of three post-mortem brains with matrix-assisted laser desorption/ionization mass spectrometry imaging and laser ablation inductively coupled plasma mass spectrometry imaging to investigate the distribution of lipids, proteins and metals in human V1. We identified 71 peptides of 13 different proteins by in situ tandem mass spectrometry, of which 5 proteins show a differential laminar distribution pattern revealing the border between V1 and V2. High-accuracy mass measurements identified 123 lipid species, including glycerolipids, glycerophospholipids and sphingolipids, of which at least 20 showed differential distribution within V1 and V2. Specific lipids labeled not only myelinated layer IVb, but also IVa and especially IVc in a layer-specific manner, but also and clearly separated V1 from V2. Elemental imaging further showed a specific accumulation of copper in layer IV. In conclusion, multimodal mass spectrometry imaging identified novel biomolecular and elemental markers with specific laminar and inter-areal differences. We conclude that mass spectrometry imaging provides a promising new approach toward multimodal, molecule-based cortical parcellation.
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Affiliation(s)
- Estibaliz González de San Román
- Central Institute of Engineering, Electronics and Analytics, ZEA-3, Forschungszentrum Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute of Brain Research, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Hans-Jürgen Bidmon
- Cécile and Oskar Vogt Institute of Brain Research, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Milena Malisic
- Central Institute of Engineering, Electronics and Analytics, ZEA-3, Forschungszentrum Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute of Brain Research, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Iuliana Susnea
- Central Institute of Engineering, Electronics and Analytics, ZEA-3, Forschungszentrum Jülich, Jülich, Germany
| | - Astrid Küppers
- Central Institute of Engineering, Electronics and Analytics, ZEA-3, Forschungszentrum Jülich, Jülich, Germany
| | - Rene Hübbers
- Cécile and Oskar Vogt Institute of Brain Research, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-1, Forschungszentrum Jülich, Jülich, Germany
| | - Andreas Wree
- Institute of Anatomy, Rostock University Medical Center, Rostock, Germany
| | - Volker Nischwitz
- Central Institute of Engineering, Electronics and Analytics, ZEA-3, Forschungszentrum Jülich, Jülich, Germany
| | - Katrin Amunts
- Cécile and Oskar Vogt Institute of Brain Research, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- Institute of Neuroscience and Medicine, INM-1, Forschungszentrum Jülich, Jülich, Germany.
| | - Pitter F Huesgen
- Central Institute of Engineering, Electronics and Analytics, ZEA-3, Forschungszentrum Jülich, Jülich, Germany.
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16
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Abstract
Magnetic resonance imaging, positron emission tomography, and optical imaging have emerged as key tools to understand brain function and neurological disorders in preclinical mouse models. They offer the unique advantage of monitoring individual structural and functional changes over time. What remained unsolved until recently was to generate whole-brain microscopy data which can be correlated to the 3D in vivo neuroimaging data. Conventional histological sections are inappropriate especially for neuronal tracing or the unbiased screening for molecular targets through the whole brain. As part of the European Society for Molecular Imaging (ESMI) meeting 2016 in Utrecht, the Netherlands, we addressed this issue in the Molecular Neuroimaging study group meeting. Presentations covered new brain clearing methods, light sheet microscopes for large samples, and automatic registration of microscopy to in vivo imaging data. In this article, we summarize the discussion; give an overview of the novel techniques; and discuss the practical needs, benefits, and limitations.
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17
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Mahfouz A, Huisman SMH, Lelieveldt BPF, Reinders MJT. Brain transcriptome atlases: a computational perspective. Brain Struct Funct 2017; 222:1557-1580. [PMID: 27909802 PMCID: PMC5406417 DOI: 10.1007/s00429-016-1338-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 11/15/2016] [Indexed: 01/31/2023]
Abstract
The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. We provide a comprehensive overview of the various computational methods used to analyze brain transcriptome atlases.
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Affiliation(s)
- Ahmed Mahfouz
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
- Delft Bioinformatics Laboratory, Delft University of Technology, Delft, The Netherlands.
| | - Sjoerd M H Huisman
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Laboratory, Delft University of Technology, Delft, The Netherlands
| | - Boudewijn P F Lelieveldt
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Laboratory, Delft University of Technology, Delft, The Netherlands
| | - Marcel J T Reinders
- Delft Bioinformatics Laboratory, Delft University of Technology, Delft, The Netherlands
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18
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Van Malderen SJM, Laforce B, Van Acker T, Nys C, De Rijcke M, de Rycke R, De Bruyne M, Boone MN, De Schamphelaere K, Borovinskaya O, De Samber B, Vincze L, Vanhaecke F. Three-Dimensional Reconstruction of the Tissue-Specific Multielemental Distribution within Ceriodaphnia dubia via Multimodal Registration Using Laser Ablation ICP-Mass Spectrometry and X-ray Spectroscopic Techniques. Anal Chem 2017; 89:4161-4168. [DOI: 10.1021/acs.analchem.7b00111] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Stijn J. M. Van Malderen
- Department of Analytical
Chemistry, Ghent University, Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
| | - Brecht Laforce
- Department of Analytical
Chemistry, Ghent University, Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
| | - Thibaut Van Acker
- Department of Analytical
Chemistry, Ghent University, Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
| | - Charlotte Nys
- Department of Applied Ecology and Environmental Biology, Ghent University, Jozef Plateaustraat 22, 9000 Ghent, Belgium
| | - Maarten De Rijcke
- Department of Applied Ecology and Environmental Biology, Ghent University, Jozef Plateaustraat 22, 9000 Ghent, Belgium
| | | | | | - Matthieu N. Boone
- Department of Physics and Astronomy, Ghent University, Proeftuinstraat 86, 9000 Ghent, Belgium
| | - Karel De Schamphelaere
- Department of Applied Ecology and Environmental Biology, Ghent University, Jozef Plateaustraat 22, 9000 Ghent, Belgium
| | | | - Björn De Samber
- Department of Analytical
Chemistry, Ghent University, Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
| | - Laszlo Vincze
- Department of Analytical
Chemistry, Ghent University, Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
| | - Frank Vanhaecke
- Department of Analytical
Chemistry, Ghent University, Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
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19
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Verbeeck N, Spraggins JM, Murphy MJM, Wang HD, Deutch AY, Caprioli RM, Van de Plas R. Connecting imaging mass spectrometry and magnetic resonance imaging-based anatomical atlases for automated anatomical interpretation and differential analysis. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2017; 1865:967-977. [PMID: 28254588 DOI: 10.1016/j.bbapap.2017.02.016] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 02/13/2017] [Indexed: 12/16/2022]
Abstract
Imaging mass spectrometry (IMS) is a molecular imaging technology that can measure thousands of biomolecules concurrently without prior tagging, making it particularly suitable for exploratory research. However, the data size and dimensionality often makes thorough extraction of relevant information impractical. To help guide and accelerate IMS data analysis, we recently developed a framework that integrates IMS measurements with anatomical atlases, opening up opportunities for anatomy-driven exploration of IMS data. One example is the automated anatomical interpretation of ion images, where empirically measured ion distributions are automatically decomposed into their underlying anatomical structures. While offering significant potential, IMS-atlas integration has thus far been restricted to the Allen Mouse Brain Atlas (AMBA) and mouse brain samples. Here, we expand the applicability of this framework by extending towards new animal species and a new set of anatomical atlases retrieved from the Scalable Brain Atlas (SBA). Furthermore, as many SBA atlases are based on magnetic resonance imaging (MRI) data, a new registration pipeline was developed that enables direct non-rigid IMS-to-MRI registration. These developments are demonstrated on protein-focused FTICR IMS measurements from coronal brain sections of a Parkinson's disease (PD) rat model. The measurements are integrated with an MRI-based rat brain atlas from the SBA. The new rat-focused IMS-atlas integration is used to perform automated anatomical interpretation and to find differential ions between healthy and diseased tissue. IMS-atlas integration can serve as an important accelerator in IMS data exploration, and with these new developments it can now be applied to a wider variety of animal species and modalities. 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)
- Nico Verbeeck
- Delft Center for Systems and Control (DCSC), Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands.
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center (MSRC), Vanderbilt University, 465 21st Ave. South, 9160 Medical Research Building III, Nashville, TN 37240, USA; Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA; Department of Chemistry, Vanderbilt University, 7330 Stevenson Center Station B 351822, Nashville, TN 37235, USA.
| | - Monika J M Murphy
- Program in Neuroscience, Vanderbilt University, U-1205 Medical Research Building III, 465 21st Ave. South, Nashville, TN 37232, USA.
| | - Hui-Dong Wang
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1211 Medical Center Dr. Nashville, TN 37232, USA.
| | - Ariel Y Deutch
- Program in Neuroscience, Vanderbilt University, U-1205 Medical Research Building III, 465 21st Ave. South, Nashville, TN 37232, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1211 Medical Center Dr. Nashville, TN 37232, USA; Department of Pharmacology, Vanderbilt University, Nashville, 460B Preston Research Building, Nashville, TN 37232, USA.
| | - Richard M Caprioli
- Mass Spectrometry Research Center (MSRC), Vanderbilt University, 465 21st Ave. South, 9160 Medical Research Building III, Nashville, TN 37240, USA; Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA; Department of Chemistry, Vanderbilt University, 7330 Stevenson Center Station B 351822, Nashville, TN 37235, USA; Department of Medicine, Vanderbilt University Medical Center, 1161 21st Ave. South, D-3100 Medical Center North, Nashville, TN 37232, USA.
| | - Raf Van de Plas
- Delft Center for Systems and Control (DCSC), Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands; Mass Spectrometry Research Center (MSRC), Vanderbilt University, 465 21st Ave. South, 9160 Medical Research Building III, Nashville, TN 37240, USA; Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA.
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