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Zhang H, Lu KH, Ebbini M, Huang P, Lu H, Li L. Mass spectrometry imaging for spatially resolved multi-omics molecular mapping. NPJ IMAGING 2024; 2:20. [PMID: 39036554 PMCID: PMC11254763 DOI: 10.1038/s44303-024-00025-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024]
Abstract
The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future.
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Affiliation(s)
- Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Kelly H. Lu
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Malik Ebbini
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Penghsuan Huang
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
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2
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Stevens NC, Shen T, Martinez J, Evans VJB, Domanico MC, Neumann EK, Van Winkle LS, Fiehn O. Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.05.602264. [PMID: 39026864 PMCID: PMC11257454 DOI: 10.1101/2024.07.05.602264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluated the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. Lung lobes from male and female BALB/c mice were cryosectioned and acquired by high resolution mass spectrometry imaging (MSI). Processed MSI peak annotations were validated by LC-MS/MS data from scraped tissue slides and microdissected lung tissue. Images were normalized and segmented into clusters. Interestingly, segmented clusters overlapped with stained serial tissue sections, enabling statistical analysis across biological replicates for morphologically relevant lung regions. Spatially distinct lipids had higher overall degree of unsaturated fatty acids in distal lung regions compared to proximal regions. Furthermore, the airway and alveolar epithelium exhibited significantly decreased sphingolipid and glycerophospholipid abundance in females, but not in males. We demonstrate the potential role of lipid saturation in healthy lung function and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future MSI experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.
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3
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Bemis KA, Föll MC, Guo D, Lakkimsetty SS, Vitek O. Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis. Nat Methods 2023; 20:1883-1886. [PMID: 37996752 DOI: 10.1038/s41592-023-02070-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 10/06/2023] [Indexed: 11/25/2023]
Abstract
Cardinal v.3 is an open-source software for reproducible analysis of mass spectrometry imaging experiments. A major update from its previous versions, Cardinal v.3 supports most mass spectrometry imaging workflows. Its analytical capabilities include advanced data processing such as mass recalibration, advanced statistical analyses such as single-ion segmentation and rough annotation-based classification, and memory-efficient analyses of large-scale multitissue experiments.
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Affiliation(s)
- Kylie Ariel Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Melanie Christine Föll
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Institute of Surgical Pathology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | | | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
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4
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Abstract
Imaging mass spectrometry is a well-established technology that can easily and succinctly communicate the spatial localization of molecules within samples. This review communicates the recent advances in the field, with a specific focus on matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) applied on tissues. The general sample preparation strategies for different analyte classes are explored, including special considerations for sample types (fresh frozen or formalin-fixed,) strategies for various analytes (lipids, metabolites, proteins, peptides, and glycans) and how multimodal imaging strategies can leverage the strengths of each approach is mentioned. This work explores appropriate experimental design approaches and standardization of processes needed for successful studies, as well as the various data analysis platforms available to analyze data and their strengths. The review concludes with applications of imaging mass spectrometry in various fields, with a focus on medical research, and some examples from plant biology and microbe metabolism are mentioned, to illustrate the breadth and depth of MALDI IMS.
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Affiliation(s)
- Jessica L Moore
- Department of Proteomics, Discovery Life Sciences, Huntsville, Alabama 35806, United States
| | - Georgia Charkoftaki
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, Connecticut 06520, United States
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5
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Bemis KA, Föll MC, Guo D, Lakkimsetty SS, Vitek O. Cardinal v3 - a versatile open source software for mass spectrometry imaging analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.20.529280. [PMID: 36865170 PMCID: PMC9980127 DOI: 10.1101/2023.02.20.529280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Cardinal v3 is an open source software for reproducible analysis of mass spectrometry imaging experiments. A major update from its previous versions, Cardinal v3 supports most mass spectrometry imaging workflows. Its analytical capabilities include advanced data processing such as mass re-calibration, advanced statistical analyses such as single-ion segmentation and rough annotation-based classification, and memory-efficient analyses of large-scale multi-tissue experiments.
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Affiliation(s)
- Kylie Ariel Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | - Melanie Christine Föll
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Faculty of Medicine, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | | | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
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6
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Guo D, Föll MC, Bemis KA, Vitek O. A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images. Bioinformatics 2023; 39:btad067. [PMID: 36744928 PMCID: PMC9942547 DOI: 10.1093/bioinformatics/btad067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 11/23/2022] [Accepted: 02/06/2023] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Mass Spectrometry Imaging (MSI) analyzes complex biological samples such as tissues. It simultaneously characterizes the ions present in the tissue in the form of mass spectra, and the spatial distribution of the ions across the tissue in the form of ion images. Unsupervised clustering of ion images facilitates the interpretation in the spectral domain, by identifying groups of ions with similar spatial distributions. Unfortunately, many current methods for clustering ion images ignore the spatial features of the images, and are therefore unable to learn these features for clustering purposes. Alternative methods extract spatial features using deep neural networks pre-trained on natural image tasks; however, this is often inadequate since ion images are substantially noisier than natural images. RESULTS We contribute a deep clustering approach for ion images that accounts for both spatial contextual features and noise. In evaluations on a simulated dataset and on four experimental datasets of different tissue types, the proposed method grouped ions from the same source into a same cluster more frequently than existing methods. We further demonstrated that using ion image clustering as a pre-processing step facilitated the interpretation of a subsequent spatial segmentation as compared to using either all the ions or one ion at a time. As a result, the proposed approach facilitated the interpretability of MSI data in both the spectral domain and the spatial domain. AVAILABILITYAND IMPLEMENTATION The data and code are available at https://github.com/DanGuo1223/mzClustering. 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 02115, USA
| | - Melanie Christine Föll
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
- Institute for Surgical Pathology, Medical Center – University of Freiburg, Freiburg 79106, Germany
- Faculty of Medicine, University of Freiburg, Freiburg 79110, Germany
| | - Kylie Ariel Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
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7
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Guo L, Dong J, Xu X, Wu Z, Zhang Y, Wang Y, Li P, Tang Z, Zhao C, Cai Z. Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data. Anal Chem 2023; 95:1924-1932. [PMID: 36633187 PMCID: PMC9878502 DOI: 10.1021/acs.analchem.2c04045] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023]
Abstract
Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.
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Affiliation(s)
- Lei Guo
- Department
of Electronic Science, National Institute
for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361005, China
| | - Jiyang Dong
- Department
of Electronic Science, National Institute
for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361005, China
| | - Xiangnan Xu
- School
of Mathematics and Statistics, The University
of Sydney, Sydney, NSW 2006, Australia
| | - Zhichao Wu
- School
of Artificial Intelligence, Beijing Normal
University, Beijing 100875, China
| | - Yinbin Zhang
- Department
of Oncology, The Second Affiliated Hospital
of Medical College, Xi’an Jiaotong University, Xi’an, Shaanxi 710004, China
| | - Yongwei Wang
- Bruker
Scientific Technology Co., Ltd., Beijing 100086, China
| | - Pengfei Li
- Bruker
Scientific Technology Co., Ltd., Beijing 100086, China
| | - Zhi Tang
- School
of Public Health, Dongguan Key Laboratory of Environmental Medicine, Institute of Environmental Health, Guangdong Medical
University, Dongguan, Guangdong 523808, 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
- State
Key Laboratory of Environmental and Biological Analysis, Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Zongwei Cai
- State
Key Laboratory of Environmental and Biological Analysis, Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
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8
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Guo A, Chen Z, Li F, Luo Q. Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation. Gigascience 2022; 12:giad021. [PMID: 37039115 PMCID: PMC10087011 DOI: 10.1093/gigascience/giad021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/17/2023] [Accepted: 03/13/2023] [Indexed: 04/12/2023] Open
Abstract
Mass spectrometry imaging (MSI), which localizes molecules in a tag-free, spatially resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate regions of interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A deep learning-based algorithm is proposed to extract "histomorphological feature spectra" across an entire hematoxylin and eosin image. Clustering is then similarly performed to produce histology segmentation. Since ROIs originating from instrumental noise or artifacts would not be reproduced cross-modally, the consistency between histology and MSI segmentation becomes an effective measure of the biological validity of the results. So, #Clusters that maximize the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.
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Affiliation(s)
- Ang Guo
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhiyu Chen
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fang Li
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Qian Luo
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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9
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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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10
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Wang HYJ, Huang CY, Wei KC, Hung KC. A mass spectrometry imaging and lipidomic investigation reveals aberrant lipid metabolism in the orthotopic mouse glioma. J Lipid Res 2022; 63:100304. [PMID: 36273646 PMCID: PMC9761856 DOI: 10.1016/j.jlr.2022.100304] [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: 07/27/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 12/14/2022] Open
Abstract
Lipids perform multiple biological functions and reflect the physiology and pathology of cells, tissues, and organs. Here, we sought to understand lipid content in relation to tumor pathology by characterizing phospholipids and sphingolipids in the orthotopic mouse glioma using MALDI MS imaging (MSI) and LC-MS/MS. Unsupervised clustering analysis of the MALDI-MSI data segmented the coronal tumoral brain section into 10 histopathologically salient regions. Heterogeneous decrease of the common saturated phosphatidylcholines (PCs) in the tumor was accompanied by the increase of analogous PCs with one or two additional fatty acyl double bonds and increased lyso-PCs. Polyunsaturated fatty acyl-PCs and ether PCs highlighted the striatal tumor margins, whereas the distributions of other PCs differentiated the cortical and striatal tumor parenchyma. We detected a reduction of SM d18:1/18:0 and the heterogeneous mild increase of SM d18:1/16:0 in the tumor, whereas ceramides accumulated only in a small patch deep in the tumoral parenchyma. LC-MS/MS analyses of phospholipids and sphingolipids complemented the MALDI-MSI observation, providing a snapshot of these lipids in the tumor. Finally, the proposed mechanisms responsible for the tumoral lipid changes were contrasted with our interrogation of gene expression in human glioma. Together, these lipidomic results unveil the aberrant and heterogeneous lipid metabolism in mouse glioma where multiple lipid-associated signaling pathways underline the tumor features, promote the survival, growth, proliferation, and invasion of different tumor cell populations, and implicate the management strategy of a multiple-target approach for glioma and related brain malignancies.
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Affiliation(s)
- Hay-Yan J. Wang
- Department of Biological Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan,For correspondence: Hay-Yan J. Wang
| | - Chiung-Yin Huang
- Neuroscience Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan,Department of Neurosurgery, New Taipei Municipal TuCheng Hospital, New Taipei City, Taiwan
| | - Kuo-Chen Wei
- Neuroscience Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan,Department of Neurosurgery, New Taipei Municipal TuCheng Hospital, New Taipei City, Taiwan,Department of Neurosurgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan,School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Kuo-Chen Hung
- Department of Surgery, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Chang Gung University College of Medicine, Taiwan
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11
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Guo L, Liu X, Zhao C, Hu Z, Xu X, Cheng KK, Zhou P, Xiao Y, Shah M, Xu J, Dong J, Cai Z. iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data. Anal Chem 2022; 94:14522-14529. [PMID: 36223650 DOI: 10.1021/acs.analchem.2c01456] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.
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Affiliation(s)
- Lei Guo
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Xingxing Liu
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, China
| | - Zhenxing Hu
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Xiangnan Xu
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW2006, Australia
| | - Kian-Kai Cheng
- Innovation Centre in Agritechnology, Universiti Teknologi Malaysia, Muar, Johor84600, Malaysia
| | - Peng Zhou
- Department of Thyroid and Breast Surgery, Shenzhen Second People's Hospital, Shenzhen518025, China
| | - Yu Xiao
- Department of Thyroid and Breast Surgery, Shenzhen Second People's Hospital, Shenzhen518025, China
| | - Mudassir Shah
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Jingjing Xu
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong KongSAR999077, China
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12
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Dong Y, Aharoni A. Image to insight: exploring natural products through mass spectrometry imaging. Nat Prod Rep 2022; 39:1510-1530. [PMID: 35735199 DOI: 10.1039/d2np00011c] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Covering: 2017 to 2022Mass spectrometry imaging (MSI) has become a mature molecular imaging technique that is well-matched for natural product (NP) discovery. Here we present a brief overview of MSI, followed by a thorough discussion of different MSI applications in NP research. This review will mainly focus on the recent progress of MSI in plants and microorganisms as they are the main producers of NPs. Specifically, the opportunity and potential of combining MSI with other imaging modalities and stable isotope labeling are discussed. Throughout, we focus on both the strengths and weaknesses of MSI, with an eye on future improvements that are necessary for the progression of MSI toward routine NP studies. Finally, we discuss new areas of research, future perspectives, and the overall direction that the field may take in the years to come.
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Affiliation(s)
- Yonghui Dong
- Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
| | - Asaph Aharoni
- Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
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13
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Föll MC, Volkmann V, Enderle-Ammour K, Timme S, Wilhelm K, Guo D, Vitek O, Bronsert P, Schilling O. Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework. Clin Proteomics 2022; 19:8. [PMID: 35439943 PMCID: PMC9016955 DOI: 10.1186/s12014-022-09347-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 04/04/2022] [Indexed: 11/24/2022] Open
Abstract
Background Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens and thus bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of a urothelial carcinoma dataset. Methods Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle infiltrating urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. Results Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle infiltrating urothelial tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: stromal tissue was characterized by collagen peptides and tumor tissue by histone peptides. Immunohistochemistry confirmed an increased histone H2A abundance in the tumor compared to the stroma tissues. The muscle-infiltration status was distinguished via MSI by peptides from intermediate filaments such as cytokeratin 7 in non-muscle infiltrating carcinomas and vimentin in muscle-infiltrating urothelial carcinomas, which was confirmed by immunohistochemistry. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links. Conclusion Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.
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Affiliation(s)
- Melanie Christine Föll
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany. .,Khoury College of Computer Sciences, Northeastern University, Boston, USA.
| | - Veronika Volkmann
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany
| | - Kathrin Enderle-Ammour
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany
| | - Sylvia Timme
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany.,Core Facility for Histopathology and Digital Pathology, Faculty of Medicine, Medical Center - University of Freiburg, 79106, Freiburg, Germany
| | - Konrad Wilhelm
- Department of Urology, Center for Surgery, Medical Center, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | - Peter Bronsert
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany.,German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Freiburg, Germany
| | - Oliver Schilling
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center - University of Freiburg, Breisacher Straße 115a, 79106, FreiburgFreiburg, Germany.,German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
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14
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Accelerating strain phenotyping with desorption electrospray ionization-imaging mass spectrometry and untargeted analysis of intact microbial colonies. Proc Natl Acad Sci U S A 2021; 118:2109633118. [PMID: 34857637 DOI: 10.1073/pnas.2109633118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2021] [Indexed: 11/18/2022] Open
Abstract
Reading and writing DNA were once the rate-limiting step in synthetic biology workflows. This has been replaced by the search for the optimal target sequences to produce systems with desired properties. Directed evolution and screening mutant libraries are proven technologies for isolating strains with enhanced performance whenever specialized assays are available for rapidly detecting a phenotype of interest. Armed with technologies such as CRISPR-Cas9, these experiments are capable of generating libraries of up to 1010 genetic variants. At a rate of 102 samples per day, standard analytical methods for assessing metabolic phenotypes represent a major bottleneck to modern synthetic biology workflows. To address this issue, we have developed a desorption electrospray ionization-imaging mass spectrometry screening assay that directly samples microorganisms. This technology increases the throughput of metabolic measurements by reducing sample preparation and analyzing organisms in a multiplexed fashion. To further accelerate synthetic biology workflows, we utilized untargeted acquisitions and unsupervised analytics to assess multiple targets for future engineering strategies within a single acquisition. We demonstrate the utility of the developed method using Escherichia coli strains engineered to overproduce free fatty acids. We determined discrete metabolic phenotypes associated with each strain, which include the primary fatty acid product, secondary products, and additional metabolites outside the engineered product pathway. Furthermore, we measured changes in amino acid levels and membrane lipid composition, which affect cell viability. In sum, we present an analytical method to accelerate synthetic biology workflows through rapid, untargeted, and multiplexed metabolomic analyses.
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15
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Castellanos-Garcia LJ, Sikora KN, Doungchawee J, Vachet RW. LA-ICP-MS and MALDI-MS image registration for correlating nanomaterial biodistributions and their biochemical effects. Analyst 2021; 146:7720-7729. [PMID: 34821231 DOI: 10.1039/d1an01783g] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) imaging and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) are complementary methods that measure distributions of elements and biomolecules in tissue sections. Quantitative correlations of the information provided by these two imaging modalities requires that the datasets be registered in the same coordinate system, allowing for pixel-by-pixel comparisons. We describe here a computational workflow written in Python that accomplishes this registration, even for adjacent tissue sections, with accuracies within ±50 μm. The value of this registration process is demonstrated by correlating images of tissue sections from mice injected with gold nanomaterial drug delivery systems. Quantitative correlations of the nanomaterial delivery vehicle, as detected by LA-ICP-MS imaging, with biochemical changes, as detected by MALDI-MSI, provide deeper insight into how nanomaterial delivery systems influence lipid biochemistry in tissues. Moreover, the registration process allows the more precise images associated with LA-ICP-MS imaging to be leveraged to achieve improved segmentation in MALDI-MS images, resulting in the identification of lipids that are most associated with different sub-organ regions in tissues.
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Affiliation(s)
| | - Kristen N Sikora
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA.
| | - Jeerapat Doungchawee
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA.
| | - Richard W Vachet
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA.
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16
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Kotnala A, Anderson DM, Patterson NH, Cantrell LS, Messinger JD, Curcio CA, Schey KL. Tissue fixation effects on human retinal lipid analysis by MALDI imaging and LC-MS/MS technologies. JOURNAL OF MASS SPECTROMETRY : JMS 2021; 56:e4798. [PMID: 34881479 PMCID: PMC8711642 DOI: 10.1002/jms.4798] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/09/2021] [Accepted: 11/04/2021] [Indexed: 06/13/2023]
Abstract
Imaging mass spectrometry (IMS) allows the location and abundance of lipids to be mapped across tissue sections of human retina. For reproducible and accurate information, sample preparation methods need to be optimized. Paraformaldehyde fixation of a delicate multilayer structure like human retina facilitates the preservation of tissue morphology by forming methylene bridge crosslinks between formaldehyde and amine/thiols in biomolecules; however, retina sections analyzed by IMS are typically fresh-frozen. To determine if clinically significant inferences could be reliably based on fixed tissue, we evaluated the effect of fixation on analyte detection, spatial localization, and introduction of artifactual signals. Hence, we assessed the molecular identity of lipids generated by matrix-assisted laser desorption ionization (MALDI-IMS) and liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) for fixed and fresh-frozen retina tissues in positive and negative ion modes. Based on MALDI-IMS analysis, more lipid signals were observed in fixed compared with fresh-frozen retina. More potassium adducts were observed in fresh-frozen tissues than fixed as the fixation process caused displacement of potassium adducts to protonated and sodiated species in ion positive ion mode. LC-MS/MS analysis revealed an overall decrease in lipid signals due to fixation that reduced glycerophospholipids and glycerolipids and conserved most sphingolipids and cholesteryl esters. The high quality and reproducible information from untargeted lipidomics analysis of fixed retina informs on all major lipid classes, similar to fresh-frozen retina, and serves as a steppingstone towards understanding of lipid alterations in retinal diseases.
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Affiliation(s)
- Ankita Kotnala
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Birmingham, AL
| | - David M.G. Anderson
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Nathan Heath Patterson
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Lee S. Cantrell
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Jeffrey D. Messinger
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Birmingham, AL
| | - Christine A. Curcio
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Birmingham, AL
| | - Kevin L. Schey
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN
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17
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Cordes J, Enzlein T, Marsching C, Hinze M, Engelhardt S, Hopf C, Wolf I. M2aia-Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data. Gigascience 2021; 10:giab049. [PMID: 34282451 PMCID: PMC8290197 DOI: 10.1093/gigascience/giab049] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/19/2021] [Accepted: 06/25/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Mass spectrometry imaging (MSI) is a label-free analysis method for resolving bio-molecules or pharmaceuticals in the spatial domain. It offers unique perspectives for the examination of entire organs or other tissue specimens. Owing to increasing capabilities of modern MSI devices, the use of 3D and multi-modal MSI becomes feasible in routine applications-resulting in hundreds of gigabytes of data. To fully leverage such MSI acquisitions, interactive tools for 3D image reconstruction, visualization, and analysis are required, which preferably should be open-source to allow scientists to develop custom extensions. FINDINGS We introduce M2aia (MSI applications for interactive analysis in MITK), a software tool providing interactive and memory-efficient data access and signal processing of multiple large MSI datasets stored in imzML format. M2aia extends MITK, a popular open-source tool in medical image processing. Besides the steps of a typical signal processing workflow, M2aia offers fast visual interaction, image segmentation, deformable 3D image reconstruction, and multi-modal registration. A unique feature is that fused data with individual mass axes can be visualized in a shared coordinate system. We demonstrate features of M2aia by reanalyzing an N-glycan mouse kidney dataset and 3D reconstruction and multi-modal image registration of a lipid and peptide dataset of a mouse brain, which we make publicly available. CONCLUSIONS To our knowledge, M2aia is the first extensible open-source application that enables a fast, user-friendly, and interactive exploration of large datasets. M2aia is applicable to a wide range of MSI analysis tasks.
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Affiliation(s)
- Jonas Cordes
- Faculty of Computer Science, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
- Medical Faculty Mannheim, University Heidelberg, Theodor Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Thomas Enzlein
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Christian Marsching
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Marven Hinze
- Faculty of Computer Science, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Sandy Engelhardt
- Working Group “Artificial Intelligence in Cardiovascular Medicine” (AICM), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
| | - Carsten Hopf
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Ivo Wolf
- Faculty of Computer Science, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
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18
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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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19
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Zhang W, Claesen M, Moerman T, Groseclose MR, Waelkens E, De Moor B, Verbeeck N. Spatially aware clustering of ion images in mass spectrometry imaging data using deep learning. Anal Bioanal Chem 2021; 413:2803-2819. [PMID: 33646352 PMCID: PMC8007517 DOI: 10.1007/s00216-021-03179-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/25/2020] [Accepted: 01/15/2021] [Indexed: 11/12/2022]
Abstract
Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction. The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, is used to cluster ion images based on spatial expressions. We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters. Additionally, we introduce the relative isotope ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes. The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.
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Affiliation(s)
- Wanqiu Zhang
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001, Leuven, Belgium.
- Aspect Analytics NV, C-mine 12, 3600, Genk, Belgium.
| | - Marc Claesen
- Aspect Analytics NV, C-mine 12, 3600, Genk, Belgium
| | | | | | - Etienne Waelkens
- KU Leuven, Department of Cellular and Molecular Medicine, Campus Gasthuisberg O&N1, Herestraat 49, Box 901, 3000, Leuven, Belgium
| | - Bart De Moor
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001, Leuven, Belgium
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20
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Guo D, Föll MC, Volkmann V, Enderle-Ammour K, Bronsert P, Schilling O, Vitek O. Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations. Bioinformatics 2021; 36:i300-i308. [PMID: 32657378 PMCID: PMC7355295 DOI: 10.1093/bioinformatics/btaa436] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
MOTIVATION Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance. RESULTS To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states. AVAILABILITY AND IMPLEMENTATION The data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.
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Affiliation(s)
- Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Veronika Volkmann
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Kathrin Enderle-Ammour
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Peter Bronsert
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Medical Center - University of Freiburg.,German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), 79106 Freiburg, Germany
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
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21
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Hu H, Yin R, Brown HM, Laskin J. Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding. Anal Chem 2021; 93:3477-3485. [PMID: 33570915 PMCID: PMC7904669 DOI: 10.1021/acs.analchem.0c04798] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Spatial segmentation partitions mass spectrometry imaging (MSI) data into distinct regions, providing a concise visualization of the vast amount of data and identifying regions of interest (ROIs) for downstream statistical analysis. Unsupervised approaches are particularly attractive, as they may be used to discover the underlying subpopulations present in the high-dimensional MSI data without prior knowledge of the properties of the sample. Herein, we introduce an unsupervised spatial segmentation approach, which combines multivariate clustering and univariate thresholding to generate comprehensive spatial segmentation maps of the MSI data. This approach combines matrix factorization and manifold learning to enable high-quality image segmentation without an extensive hyperparameter search. In parallel, some ion images inadequately represented in the multivariate analysis were treated using univariate thresholding to generate complementary spatial segments. The final spatial segmentation map was assembled from segment candidates that were generated using both techniques. We demonstrate the performance and robustness of this approach for two MSI data sets of mouse uterine and kidney tissue sections that were acquired with different spatial resolutions. The resulting segmentation maps are easy to interpret and project onto the known anatomical regions of the tissue.
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Affiliation(s)
- Hang Hu
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Ruichuan Yin
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Hilary M Brown
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
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22
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Sekera ER, Saraswat D, Zemaitis KJ, Sim FJ, Wood TD. MALDI Mass Spectrometry Imaging in a Primary Demyelination Model of Murine Spinal Cord. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:2462-2468. [PMID: 32926612 PMCID: PMC8628303 DOI: 10.1021/jasms.0c00187] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Destruction of myelin, or demyelination, is a characteristic of traumatic spinal cord injury and pathognomonic for primary demyelinating pathologies such as multiple sclerosis (MS). The regenerative process known as remyelination, which can occur following demyelination, fails as MS progresses. Models of focal demyelination by local injection of gliotoxins have provided important biological insights into the demyelination/remyelination process. Here, injection of lysolecithin to induce spinal cord demyelination is investigated using matrix-assisted laser desorption/ionization mass spectrometry imaging. A segmentation analysis revealed changes to the lipid composition during lysolecithin-induced demyelination at the lesion site and subsequent remyelination over time. The results of this study can be utilized to identify potential myelin-repair mechanisms and in the design of therapeutic strategies to enhance myelin repair.
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Affiliation(s)
- Emily R Sekera
- Department of Chemistry, Natural Sciences Complex, University at Buffalo State University of New York, Buffalo, New York 14260-3000, United States
| | - Darpan Saraswat
- Department of Pharmacology & Toxicology, Jacobs School of Medicine and Biomedical Sciences, 955 Main Street, University at Buffalo State University of New York, Buffalo, New York 14203, United States
| | - Kevin J Zemaitis
- Department of Chemistry, Natural Sciences Complex, University at Buffalo State University of New York, Buffalo, New York 14260-3000, United States
| | - Fraser J Sim
- Department of Pharmacology & Toxicology, Jacobs School of Medicine and Biomedical Sciences, 955 Main Street, University at Buffalo State University of New York, Buffalo, New York 14203, United States
| | - Troy D Wood
- Department of Chemistry, Natural Sciences Complex, University at Buffalo State University of New York, Buffalo, New York 14260-3000, United States
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23
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Kelley AR, Colley M, Dyer S, Bach SBH, Zhu X, Perry G. Ethanol-Fixed, Paraffin-Embedded Tissue Imaging: Implications for Alzheimer's Disease Research. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:2416-2420. [PMID: 32803969 DOI: 10.1021/jasms.0c00195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Mass spectrometry imaging (MSI) is rapidly becoming a crucial tool in disease research. Fresh-frozen tissue is ideal for MSI because the protein and lipid structures are undisturbed by chemical fixatives; however, that means long-term preservation is limited. Formalin-fixed paraffin-embedded tissue has a virtually infinite shelf life, but whole proteins are difficult or impossible to image directly. To bridge this gap, we examine the use of ethanol-fixed, paraffin-embedded (EFPE) tissue for the localization of intact proteins and lipids and comment on implications in Alzheimer's disease (AD) research. The new sample preparation methods for EFPE tissues have allowed us to greatly broaden the information we can extract from MSI experiments. Our methods involve a xylene-free deparaffination for lipid analysis and an intact protein method for visualizing amyloid-beta plaques from human AD brain tissue. This unique combination streamlines the MSI sample preparation process while allowing for the most biologically and pathologically relevant information to be extracted from a single tissue source.
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Affiliation(s)
| | | | | | | | - Xiongwei Zhu
- Department of Pathology, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, United States
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24
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Tuck M, Blanc L, Touti R, Patterson NH, Van Nuffel S, Villette S, Taveau JC, Römpp A, Brunelle A, Lecomte S, Desbenoit N. Multimodal Imaging Based on Vibrational Spectroscopies and Mass Spectrometry Imaging Applied to Biological Tissue: A Multiscale and Multiomics Review. Anal Chem 2020; 93:445-477. [PMID: 33253546 DOI: 10.1021/acs.analchem.0c04595] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Michael Tuck
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Landry Blanc
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Rita Touti
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232-8575, United States
| | - Sebastiaan Van Nuffel
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Sandrine Villette
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Jean-Christophe Taveau
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Andreas Römpp
- Bioanalytical Sciences and Food Analysis, University of Bayreuth, Universitätsstraße 30, 95440 Bayreuth, Germany
| | - Alain Brunelle
- Laboratoire d'Archéologie Moléculaire et Structurale, LAMS UMR 8220, CNRS, Sorbonne Université, 4 Place Jussieu, 75005 Paris, France
| | - Sophie Lecomte
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nicolas Desbenoit
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
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25
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Möginger U, Marcussen N, Jensen ON. Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections. Oncotarget 2020; 11:3998-4015. [PMID: 33216824 PMCID: PMC7646834 DOI: 10.18632/oncotarget.27787] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 10/10/2020] [Indexed: 12/24/2022] Open
Abstract
Pathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub) types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections. In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n = 11), clear cell renal cell carcinoma (ccRCC, n = 12) and chromophobe renal cell carcinoma (ChRCC, n = 5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients. This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases.
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Affiliation(s)
- Uwe Möginger
- Department of Biochemistry & Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark.,Present address: Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park, Bagsværd, Denmark
| | - Niels Marcussen
- Institute for Pathology, Odense University Hospital, Odense, Denmark
| | - Ole N Jensen
- Department of Biochemistry & Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark
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26
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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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27
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Huang R, Liu Y, Zheng Y, Ye M. Optical frequency and phase information-based fusion approach for image rotation symmetry detection. OPTICS EXPRESS 2020; 28:18577-18595. [PMID: 32672156 DOI: 10.1364/oe.390224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/11/2020] [Indexed: 06/11/2023]
Abstract
Detecting an object using rotation symmetry property is widely applicable as most artificial objects have this property. However, current known techniques often fail due to using single symmetry energy. To tackle this problem, this paper proposes a novel method which consists of two steps: 1) Based on an optical image, two independent symmetry energies are extracted from the optical frequency space (RSS - Rotation Symmetry Strength) and phase space (SSD - Symmetry Shape Density). And, an optimized symmetry-energy-based fusion algorithm is creatively applied to these two energies to achieve a more comprehensive reflection of symmetry information. 2) In the fused symmetry energy map, the local region detection algorithm is used to realize the detection of multi-scale symmetry targets. Compared with known methods, the proposed method can get more multiple-scale (skewed, small-scale, and regular) rotation symmetry centers, and can significantly boost the performance of detecting symmetry properties with better accuracy. Experimental results confirm the performance of the proposed method, which is superior to the state-of-the-art methods.
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28
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Luu GT, Condren AR, Kahl LJ, Dietrich LE, Sanchez LM. Evaluation of Data Analysis Platforms and Compatibility with MALDI-TOF Imaging Mass Spectrometry Data Sets. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:1313-1320. [PMID: 32329613 PMCID: PMC7275808 DOI: 10.1021/jasms.0c00039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Imaging mass spectrometry (IMS) has proven to be a useful tool when investigating the spatial distributions of metabolites and proteins in a biological system. One of the biggest advantages of IMS is the ability to maintain the 3D chemical composition of a sample and analyze it in a label-free manner. However, acquiring the spatial information leads to an increase in data size. Due to the increased availability of commercial mass spectrometers capable of IMS, there has been an exciting development of different statistical tools that can help decipher the spatial relevance of an analyte in a biological sample. To address this need, software packages like SCiLS and the open source R package Cardinal have been designed to perform unbiased spectral grouping based on the similarity of spectra in an IMS data set. In this note, we evaluate SCiLS and Cardinal compatibility with MALDI-TOF IMS data sets of the Gram-negative pathogen Pseudomonas aeruginosa PA14. Both software were able to perform unsupervised segmentation with similar performance. There were a few notable differences which are discussed related to the identification of statistically significant features which required optimization of preprocessing steps, region of interest, and manual analysis.
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Affiliation(s)
- Gordon T. Luu
- Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, IL 60612
| | - Alanna R. Condren
- Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, IL 60612
| | - Lisa Juliane Kahl
- Department of Biological Sciences, Columbia University, New York, NY 10027
| | - Lars E.P. Dietrich
- Department of Biological Sciences, Columbia University, New York, NY 10027
| | - Laura M. Sanchez
- Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, IL 60612
- Corresponding Author,
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29
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Jones MA, Cho SH, Patterson NH, Van de Plas R, Spraggins JM, Boothby MR, Caprioli RM. Discovering New Lipidomic Features Using Cell Type Specific Fluorophore Expression to Provide Spatial and Biological Specificity in a Multimodal Workflow with MALDI Imaging Mass Spectrometry. Anal Chem 2020; 92:7079-7086. [PMID: 32298091 PMCID: PMC7456589 DOI: 10.1021/acs.analchem.0c00446] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Identifying the spatial distributions of biomolecules in tissue is crucial for understanding integrated function. Imaging mass spectrometry (IMS) allows simultaneous mapping of thousands of biosynthetic products such as lipids but has needed a means of identifying specific cell-types or functional states to correlate with molecular localization. We report, here, advances starting from identity marking with a genetically encoded fluorophore. The fluorescence emission data were integrated with IMS data through multimodal image processing with advanced registration techniques and data-driven image fusion. In an unbiased analysis of spleens, this integrated technology enabled identification of ether lipid species preferentially enriched in germinal centers. We propose that this use of genetic marking for microanatomical regions of interest can be paired with molecular information from IMS for any tissue, cell-type, or activity state for which fluorescence is driven by a gene-tracking allele and ultimately with outputs of other means of spatial mapping.
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Affiliation(s)
- Marissa A Jones
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, Tennessee 37235, United States
- Mass Spectrometry Research Center and Department of Biochemistry, Vanderbilt University Medical Center, 465 21st Avenue South, MRB III Suite 9160, Nashville, Tennessee 37232, United States
| | - Sung Hoon Cho
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, 1161 21st Avenue South, MCN AA-4214B, MCN A-5301, Nashville, Tennessee 37232, United States
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center and Department of Biochemistry, Vanderbilt University Medical Center, 465 21st Avenue South, MRB III Suite 9160, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Raf Van de Plas
- Mass Spectrometry Research Center and Department of Biochemistry, Vanderbilt University Medical Center, 465 21st Avenue South, MRB III Suite 9160, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Delft Center for Systems and Control (DCSC), Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center and Department of Biochemistry, Vanderbilt University Medical Center, 465 21st Avenue South, MRB III Suite 9160, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Mark R Boothby
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, 1161 21st Avenue South, MCN AA-4214B, MCN A-5301, Nashville, Tennessee 37232, United States
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Richard M Caprioli
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, Tennessee 37235, United States
- Mass Spectrometry Research Center and Department of Biochemistry, Vanderbilt University Medical Center, 465 21st Avenue South, MRB III Suite 9160, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, United States
- Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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30
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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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31
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Alexandrov T. Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence. Annu Rev Biomed Data Sci 2020; 3:61-87. [PMID: 34056560 DOI: 10.1146/annurev-biodatasci-011420-031537] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, USA
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32
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Geier B, Sogin EM, Michellod D, Janda M, Kompauer M, Spengler B, Dubilier N, Liebeke M. Spatial metabolomics of in situ host-microbe interactions at the micrometre scale. Nat Microbiol 2020; 5:498-510. [PMID: 32015496 DOI: 10.1038/s41564-019-0664-6] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 12/16/2019] [Indexed: 11/09/2022]
Abstract
Spatial metabolomics describes the location and chemistry of small molecules involved in metabolic phenotypes, defence molecules and chemical interactions in natural communities. Most current techniques are unable to spatially link the genotype and metabolic phenotype of microorganisms in situ at a scale relevant to microbial interactions. Here, we present a spatial metabolomics pipeline (metaFISH) that combines fluorescence in situ hybridization (FISH) microscopy and high-resolution atmospheric-pressure matrix-assisted laser desorption/ionization mass spectrometry to image host-microbe symbioses and their metabolic interactions. The metaFISH pipeline aligns and integrates metabolite and fluorescent images at the micrometre scale to provide a spatial assignment of host and symbiont metabolites on the same tissue section. To illustrate the advantages of metaFISH, we mapped the spatial metabolome of a deep-sea mussel and its intracellular symbiotic bacteria at the scale of individual epithelial host cells. Our analytical pipeline revealed metabolic adaptations of the epithelial cells to the intracellular symbionts and variation in metabolic phenotypes within a single symbiont 16S rRNA phylotype, and enabled the discovery of specialized metabolites from the host-microbe interface. metaFISH provides a culture-independent approach to link metabolic phenotypes to community members in situ and is a powerful tool for microbiologists across fields.
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Affiliation(s)
- Benedikt Geier
- Max Planck Institute for Marine Microbiology, Bremen, Germany.
| | - Emilia M Sogin
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Dolma Michellod
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Moritz Janda
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Mario Kompauer
- Institute of Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Giessen, Germany
| | - Bernhard Spengler
- Institute of Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Giessen, Germany
| | - Nicole Dubilier
- Max Planck Institute for Marine Microbiology, Bremen, Germany
- MARUM, University of Bremen, Bremen, Germany
| | - Manuel Liebeke
- Max Planck Institute for Marine Microbiology, Bremen, Germany.
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33
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Föll MC, Moritz L, Wollmann T, Stillger MN, Vockert N, Werner M, Bronsert P, Rohr K, Grüning BA, Schilling O. Accessible and reproducible mass spectrometry imaging data analysis in Galaxy. Gigascience 2019; 8:giz143. [PMID: 31816088 PMCID: PMC6901077 DOI: 10.1093/gigascience/giz143] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 09/10/2019] [Accepted: 11/10/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. FINDINGS We have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Furthermore, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research. CONCLUSION The Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access, together with high levels of reproducibility and transparency.
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Affiliation(s)
- Melanie Christine Föll
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Biology, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
| | - Lennart Moritz
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
| | - Thomas Wollmann
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Maren Nicole Stillger
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Biology, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
- Institute of Molecular Medicine and Cell Research, Faculty of Medicine, University of Freiburg, Stefan-Meier-Straße 17, 79104 Freiburg, Germany
| | - Niklas Vockert
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Martin Werner
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
| | - Peter Bronsert
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
| | - Karl Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Björn Andreas Grüning
- Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Oliver Schilling
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
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34
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Wehrli PM, Michno W, Blennow K, Zetterberg H, Hanrieder J. Chemometric Strategies for Sensitive Annotation and Validation of Anatomical Regions of Interest in Complex Imaging Mass Spectrometry Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2019; 30:2278-2288. [PMID: 31529404 PMCID: PMC6828630 DOI: 10.1007/s13361-019-02327-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 06/12/2019] [Accepted: 08/10/2019] [Indexed: 05/04/2023]
Abstract
Imaging mass spectrometry (IMS) is a promising new chemical imaging modality that generates a large body of complex imaging data, which in turn can be approached using multivariate analysis approaches for image analysis and segmentation. Processing IMS raw data is critically important for proper data interpretation and has significant effects on the outcome of data analysis, in particular statistical modeling. Commonly, data processing methods are chosen based on rational motivations rather than comparative metrics, though no quantitative measures to assess and compare processing options have been suggested. We here present a data processing and analysis pipeline for IMS data interrogation, processing and ROI annotation, segmentation, and validation. This workflow includes (1) objective evaluation of processing methods for IMS datasets based on multivariate analysis using PCA. This was then followed by (2) ROI annotation and classification through region-based active contours (AC) segmentation based on the PCA component scores matrix. This provided class information for subsequent (3) OPLS-DA modeling to evaluate IMS data processing based on the quality metrics of their respective multivariate models and for robust quantification of ROI-specific signal localization. This workflow provides an unbiased strategy for sensitive annotation of anatomical regions of interest combined with quantitative comparison of processing procedures for multivariate analysis allowing robust ROI annotation and quantification of the associated molecular histology.
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Affiliation(s)
- Patrick M Wehrli
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
| | - Wojciech Michno
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, UK
- Department of Neurodegenerative Disease, Queen Square Instritute of Neurology, University College London, London, UK
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden.
- Department of Neurodegenerative Disease, Queen Square Instritute of Neurology, University College London, London, UK.
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35
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Ellis BM, Fischer CN, Martin LB, Bachmann BO, McLean JA. Spatiochemically Profiling Microbial Interactions with Membrane Scaffolded Desorption Electrospray Ionization-Ion Mobility-Imaging Mass Spectrometry and Unsupervised Segmentation. Anal Chem 2019; 91:13703-13711. [PMID: 31600444 DOI: 10.1021/acs.analchem.9b02992] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Imaging the inventory of microbial small molecule interactions provides important insights into microbial chemical ecology and human medicine. Herein we demonstrate a new method for enhanced detection and analysis of metabolites present in interspecies interactions of microorganisms on surfaces. We demonstrate that desorption electrospray ionization-imaging mass spectrometry (DESI-IMS) using microporous membrane scaffolds (MMS) enables enhanced spatiochemical analyses of interacting microbes among tested sample preparation techniques. Membrane scaffolded DESI-IMS has inherent advantages compared to matrix-assisted laser desorption ionization (MALDI) and other IMS methods through direct IMS analyses of microbial chemistry in situ. This rapid imaging method yields sensitive MS analyses with unique m/z measurements when compared to liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS) via unmediated sampling by MMS DESI-IMS. Unsupervised segmentation imaging analysis of acquired DESI-IMS data reveals distinct chemical regions corresponding to intermicrobial phenomenon such as predation and communication. We validate the method by linking Myxovirescin A and DKxanthene-560 to their known biological roles of predation and phase variation, respectively. In addition to providing the first topographic locations of known natural products, we prioritize 54 unknown features using segmentation within the region of predation. Thus, DESI-IMS and unsupervised segmentation spatially annotates the known biology of myxobacteria and provides functional exploration of newly uncharacterized small molecules.
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Affiliation(s)
| | | | - Leroy B Martin
- Waters Corporation , 34 Maple Street , Milford , Massachusetts 01757 , United States
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rMSIKeyIon: An Ion Filtering R Package for Untargeted Analysis of Metabolomic LDI-MS Images. Metabolites 2019; 9:metabo9080162. [PMID: 31382415 PMCID: PMC6724114 DOI: 10.3390/metabo9080162] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 07/23/2019] [Accepted: 07/30/2019] [Indexed: 12/25/2022] Open
Abstract
Many MALDI-MS imaging experiments make a case versus control studies of different tissue regions in order to highlight significant compounds affected by the variables of study. This is a challenge because the tissue samples to be compared come from different biological entities, and therefore they exhibit high variability. Moreover, the statistical tests available cannot properly compare ion concentrations in two regions of interest (ROIs) within or between images. The high correlation between the ion concentrations due to the existence of different morphological regions in the tissue means that the common statistical tests used in metabolomics experiments cannot be applied. Another difficulty with the reliability of statistical tests is the elevated number of undetected MS ions in a high percentage of pixels. In this study, we report a procedure for discovering the most important ions in the comparison of a pair of ROIs within or between tissue sections. These ROIs were identified by an unsupervised segmentation process, using the popular k-means algorithm. Our ion filtering algorithm aims to find the up or down-regulated ions between two ROIs by using a combination of three parameters: (a) the percentage of pixels in which a particular ion is not detected, (b) the Mann–Whitney U ion concentration test, and (c) the ion concentration fold-change. The undetected MS signals (null peaks) are discarded from the histogram before the calculation of (b) and (c) parameters. With this methodology, we found the important ions between the different segments of a mouse brain tissue sagittal section and determined some lipid compounds (mainly triacylglycerols and phosphatidylcholines) in the liver of mice exposed to thirdhand smoke.
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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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Thompson CG, Rosen EP, Prince HMA, White N, Sykes C, de la Cruz G, Mathews M, Deleage C, Estes JD, Charlins P, Mulder LR, Kovarova M, Adamson L, Arora S, Dellon ES, Peery AF, Shaheen NJ, Gay C, Muddiman DC, Akkina R, Garcia JV, Luciw P, Kashuba ADM. Heterogeneous antiretroviral drug distribution and HIV/SHIV detection in the gut of three species. Sci Transl Med 2019; 11:eaap8758. [PMID: 31270274 PMCID: PMC8273920 DOI: 10.1126/scitranslmed.aap8758] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Revised: 02/28/2018] [Accepted: 11/09/2018] [Indexed: 12/14/2022]
Abstract
HIV replication within tissues may increase in response to a reduced exposure to antiretroviral drugs. Traditional approaches to measuring drug concentrations in tissues are unable to characterize a heterogeneous drug distribution. Here, we used mass spectrometry imaging (MSI) to visualize the distribution of six HIV antiretroviral drugs in gut tissue sections from three species (two strains of humanized mice, macaques, and humans). We measured drug concentrations in proximity to CD3+ T cells that are targeted by HIV, as well as expression of HIV or SHIV RNA and expression of the MDR1 drug efflux transporter in gut tissue from HIV-infected humanized mice, SHIV-infected macaques, and HIV-infected humans treated with combination antiretroviral drug therapy. Serial 10-μm sections of snap-frozen ileal and rectal tissue were analyzed by MSI for CD3+ T cells and MDR1 efflux transporter expression by immunofluorescence and immunohistochemistry, respectively. The tissue slices were analyzed for HIV/SHIV RNA expression by in situ hybridization and for antiretroviral drug concentrations by liquid chromatography-mass spectrometry. The gastrointestinal tissue distribution of the six drugs was heterogeneous. Fifty percent to 60% of CD3+ T cells did not colocalize with detectable drug concentrations in the gut tissue. In all three species, up to 90% of HIV/SHIV RNA was found to be expressed in gut tissue with no exposure to drug. These data suggest that there may be gut regions with little to no exposure to antiretroviral drugs, which may result in low-level HIV replication contributing to HIV persistence.
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Affiliation(s)
- Corbin G Thompson
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elias P Rosen
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Heather M A Prince
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nicole White
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Craig Sykes
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gabriela de la Cruz
- Division of Infectious Diseases, Center for AIDS Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michelle Mathews
- Division of Infectious Diseases, Center for AIDS Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Claire Deleage
- AIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, USA
| | - Jacob D Estes
- AIDS and Cancer Virus Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, USA
- Vaccine and Gene Therapy Institute, Oregon Health & Science University, Beaverton, OR, USA
| | - Paige Charlins
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Leila R Mulder
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Martina Kovarova
- Division of Infectious Diseases, Center for AIDS Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lourdes Adamson
- Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, CA, USA
| | - Shifali Arora
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Evan S Dellon
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Anne F Peery
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nicholas J Shaheen
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cynthia Gay
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David C Muddiman
- W.M. Keck FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, USA
| | - Ramesh Akkina
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA
| | - J Victor Garcia
- Division of Infectious Diseases, Center for AIDS Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul Luciw
- Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, CA, USA
| | - Angela D M Kashuba
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Yang E, Gamberi C, Chaurand P. Mapping the fly Malpighian tubule lipidome by imaging mass spectrometry. JOURNAL OF MASS SPECTROMETRY : JMS 2019; 54:557-566. [PMID: 31038251 DOI: 10.1002/jms.4366] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 04/10/2019] [Accepted: 04/23/2019] [Indexed: 05/20/2023]
Abstract
Matrix-assisted laser/desorption ionization imaging mass spectrometry (MALDI IMS) is an analytical technique for understanding the spatial distribution of biomolecules across a sample surface. Originally employed for mammalian tissues, this technology has been adapted to study specimens as diverse as microbes and cell cultures, food such as strawberries, and invertebrates including the vinegar fly Drosophila melanogaster. As an ideal model organism, Drosophila has brought greater understanding about conserved biological processes, organism development, and diseased states and even informed management practices of agriculturally and environmentally important species. Drosophila displays anatomically separated renal (Malpighian) tubules that are the physiological equivalent to the vertebrate nephron. Insect Malpighian tubules are also responsible for pesticide detoxification. In this article, we first describe an effective workflow and sample preparation method to study the phospholipid distribution of the Malpighian tubules that initially involves the manual microdissection of the tubules in saline buffer followed by a series of washes to remove excess salt and enhances the phospholipid signals prior to matrix deposition and IMS at 25-μm spatial resolution. We also established a complementary methodology for lipid IMS analysis of whole-body fly sections using a dual-polarity data acquisition approach at the same spatial resolution after matrix deposition by sublimation. Both procedures yield rich signal profiles from the major phospholipid classes. The reproducibility and high-quality results offered by these methodologies enable cohort studies of Drosophila through MALDI IMS.
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Affiliation(s)
- Ethan Yang
- Department of Chemistry, University of Montreal, Pavillon Roger-Gaudry, 2900, boul. Édouard-Montpetit, Montreal, QC, Canada, H3C 3J7
| | - Chiara Gamberi
- Biology Department, Concordia University, Montreal, QC, Canada, H4B 1R6
| | - Pierre Chaurand
- Department of Chemistry, University of Montreal, Pavillon Roger-Gaudry, 2900, boul. Édouard-Montpetit, Montreal, QC, Canada, H3C 3J7
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Woolman M, Zarrine-Afsar A. Platforms for rapid cancer characterization by ambient mass spectrometry: advancements, challenges and opportunities for improvement towards intrasurgical use. Analyst 2019; 143:2717-2722. [PMID: 29786708 DOI: 10.1039/c8an00310f] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Ambient Mass Spectrometry (MS) analysis is widely used to characterize biological and non-biological samples. Advancements that allow rapid analysis of samples by ambient methods such as Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) and Rapid Evaporative Ionization Mass Spectrometry (REIMS) are discussed. A short, non-comprehensive overview of ambient MS is provided that only contains example applications due to space limitations. A spatially encoded mass spectrometry analysis concept to plan cancer resection is introduced. The application of minimally destructive tissue ablation probes to survey the surgical field for sites of pathology using on-line analysis methods is discussed. The technological challenges that must be overcome for ambient MS to become a robust method for intrasurgical pathology assessments are reviewed.
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Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada.
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Erich K, Reinle K, Müller T, Munteanu B, Sammour DA, Hinsenkamp I, Gutting T, Burgermeister E, Findeisen P, Ebert MP, Krijgsveld J, Hopf C. Spatial Distribution of Endogenous Tissue Protease Activity in Gastric Carcinoma Mapped by MALDI Mass Spectrometry Imaging. Mol Cell Proteomics 2019; 18:151-161. [PMID: 30293968 PMCID: PMC6317471 DOI: 10.1074/mcp.ra118.000980] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 09/23/2018] [Indexed: 12/30/2022] Open
Abstract
Aberrant protease activity has been implicated in the etiology of various prevalent diseases including neurodegeneration and cancer, in particular metastasis. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has recently been established as a key technology for bioanalysis of multiple biomolecular classes such as proteins, lipids, and glycans. However, it has not yet been systematically explored for investigation of a tissue's endogenous protease activity. In this study, we demonstrate that different tissues, spray-coated with substance P as a tracer, digest this peptide with different time-course profiles. Furthermore, we reveal that distinct cleavage products originating from substance P are generated transiently and that proteolysis can be attenuated by protease inhibitors in a concentration-dependent manner. To show the translational potential of the method, we analyzed protease activity of gastric carcinoma in mice. Our MSI and quantitative proteomics results reveal differential distribution of protease activity - with strongest activity being observed in mouse tumor tissue, suggesting the general applicability of the workflow in animal pharmacology and clinical studies.
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Affiliation(s)
- Katrin Erich
- From the ‡Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163 Mannheim, Germany;; §Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163 Mannheim, Germany
| | - Kevin Reinle
- From the ‡Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163 Mannheim, Germany
| | - Torsten Müller
- ¶German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany;; ‡‡Heidelberg University, Medical Faculty, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Bogdan Munteanu
- From the ‡Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163 Mannheim, Germany
| | - Denis A Sammour
- From the ‡Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163 Mannheim, Germany;; §Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163 Mannheim, Germany
| | - Isabel Hinsenkamp
- ‖Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Tobias Gutting
- ‖Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Elke Burgermeister
- ‖Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Peter Findeisen
- **Institute of Clinical Chemistry, University Medical Center Mannheim of Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Matthias P Ebert
- ‖Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Jeroen Krijgsveld
- ¶German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany;; ‡‡Heidelberg University, Medical Faculty, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Carsten Hopf
- From the ‡Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163 Mannheim, Germany;; §Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163 Mannheim, Germany;.
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Kulkarni P, Dost M, Bulut ÖD, Welle A, Böcker S, Boland W, Svatoš A. Secondary ion mass spectrometry imaging and multivariate data analysis reveal co-aggregation patterns of Populus trichocarpa leaf surface compounds on a micrometer scale. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2018; 93:193-206. [PMID: 29117637 DOI: 10.1111/tpj.13763] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 10/13/2017] [Accepted: 10/23/2017] [Indexed: 05/23/2023]
Abstract
Spatially resolved analysis of a multitude of compound classes has become feasible with the rapid advancement in mass spectrometry imaging strategies. In this study, we present a protocol that combines high lateral resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging with a multivariate data analysis (MVA) approach to probe the complex leaf surface chemistry of Populus trichocarpa. Here, epicuticular waxes (EWs) found on the adaxial leaf surface of P. trichocarpa were blotted on silicon wafers and imaged using TOF-SIMS at 10 μm and 1 μm lateral resolution. Intense M+● and M-● molecular ions were clearly visible, which made it possible to resolve the individual compound classes present in EWs. Series of long-chain aliphatic saturated alcohols (C21 -C30 ), hydrocarbons (C25 -C33 ) and wax esters (WEs; C44 -C48 ) were clearly observed. These data correlated with the 7 Li-chelation matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis, which yielded mostly molecular adduct ions of the analyzed compounds. Subsequently, MVA was used to interrogate the TOF-SIMS dataset for identifying hidden patterns on the leaf's surface based on its chemical profile. After the application of principal component analysis (PCA), a small number of principal components (PCs) were found to be sufficient to explain maximum variance in the data. To further confirm the contributions from pure components, a five-factor multivariate curve resolution (MCR) model was applied. Two distinct patterns of small islets, here termed 'crystals', were apparent from the resulting score plots. Based on PCA and MCR results, the crystals were found to be formed by C23 or C29 alcohols. Other less obvious patterns observed in the PCs revealed that the adaxial leaf surface is coated with a relatively homogenous layer of alcohols, hydrocarbons and WEs. The ultra-high-resolution TOF-SIMS imaging combined with the MVA approach helped to highlight the diverse patterns underlying the leaf's surface. Currently, the methods available to analyze the surface chemistry of waxes in conjunction with the spatial information related to the distribution of compounds are limited. This study uses tools that may provide important biological insights into the composition of the wax layer, how this layer is repaired after mechanical damage or insect feeding, and which transport mechanisms are involved in deploying wax constituents to specific regions on the leaf surface.
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Affiliation(s)
- Purva Kulkarni
- Lehrstuhl für Bioinformatik, Friedrich Schiller University, Ernst-Abbe-Platz 2, 07743, Jena, Germany
- Research Group Mass Spectrometry, Max Planck Institute for Chemical Ecology, Hans-Knöll-Strasse 8, 07745, Jena, Germany
| | - Mina Dost
- Department of Bioorganic Chemistry, Max Planck Institute for Chemical Ecology, Hans-Knöll-Strasse 8, 07745, Jena, Germany
| | - Özgül Demir Bulut
- Institute of Functional Interfaces and Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany
| | - Alexander Welle
- Institute of Functional Interfaces and Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany
| | - Sebastian Böcker
- Lehrstuhl für Bioinformatik, Friedrich Schiller University, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Wilhelm Boland
- Department of Bioorganic Chemistry, Max Planck Institute for Chemical Ecology, Hans-Knöll-Strasse 8, 07745, Jena, Germany
| | - Aleš Svatoš
- Research Group Mass Spectrometry, Max Planck Institute for Chemical Ecology, Hans-Knöll-Strasse 8, 07745, Jena, Germany
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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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