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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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Rosas-Román I, Guillén-Alonso H, Moreno-Pedraza A, Winkler R. Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia. Anal Chem 2024; 96:3999-4004. [PMID: 38427332 PMCID: PMC10938284 DOI: 10.1021/acs.analchem.3c05853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/14/2024] [Accepted: 02/20/2024] [Indexed: 03/02/2024]
Abstract
Julia combines the virtues of high-level and low-level programming languages: The code is human-readable, and the performance of the created binaries competes with machine-orientated compilers. Thus, Julia is popular in "Big Data" sciences. Reading mass spectrometry (MS) data with Julia was impossible until now due to missing libraries. Here, we present a Julia library for importing mass spectrometry (MS) data in HUPO standard mzML and imzML formats and demonstrate its function with direct and ambient ionization MS, liquid chromatography-MS, and MS imaging data on standard platforms (Windows, Linux, and Mac OS). The processing speed of Julia for reading imzML MS imaging files was up to 214 times faster than the comparable code in R. Julia can remove bottlenecks for computationally demanding tasks in large-scale MS-Omics and MS imaging data processing workflows and supports their agile development. In addition, time-critical and complex data evaluation tasks become possible, such as following the real-time monitoring of biological processes and pattern recognition in large MS imaging projects. Our mzML/imzML libraries and code examples are available under the terms of the MIT license from https://github.com/CINVESTAV-LABI/julia_mzML_imzML.
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Affiliation(s)
- Ignacio Rosas-Román
- Universidad
de Guanajuato, División de Ciencias e Ingenierías, Loma del Bosque 103, Lomas del Campestre, 37150 León, Guanajuato, Mexico
| | - Héctor Guillén-Alonso
- Center
for Research and Advanced Studies (CINVESTAV) Irapuato, UGA-Langebio, Km.
9.6 Libramiento Norte Carr. Irapuato-León, 36824 Irapuato, Guanajuato, Mexico
- Department
of Biochemical Engineering, National Technological
Institute, 38010 Celaya, Guanajuato, Mexico
| | - Abigail Moreno-Pedraza
- Leibniz
Institute for Vegetable and Ornamental Crops (IGZ) e.V., Theodor-Echtermeyer-Weg 1, 14979, Großbeeren, Germany
- Institute
of Biodiversity, Friedrich Schiller University
Jena, Dornburger-Str.
159, 07743, Jena, Germany
| | - Robert Winkler
- Center
for Research and Advanced Studies (CINVESTAV) Irapuato, UGA-Langebio, Km.
9.6 Libramiento Norte Carr. Irapuato-León, 36824 Irapuato, Guanajuato, Mexico
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3
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Gaudêncio SP, Bayram E, Lukić Bilela L, Cueto M, Díaz-Marrero AR, Haznedaroglu BZ, Jimenez C, Mandalakis M, Pereira F, Reyes F, Tasdemir D. Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar Drugs 2023; 21:md21050308. [PMID: 37233502 DOI: 10.3390/md21050308] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.
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Affiliation(s)
- Susana P Gaudêncio
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
- UCIBIO-Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Engin Bayram
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Lada Lukić Bilela
- Department of Biology, Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Mercedes Cueto
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
| | - Ana R Díaz-Marrero
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
- Instituto Universitario de Bio-Orgánica (IUBO), Universidad de La Laguna, 38206 La Laguna, Spain
| | - Berat Z Haznedaroglu
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Carlos Jimenez
- CICA- Centro Interdisciplinar de Química e Bioloxía, Departamento de Química, Facultade de Ciencias, Universidade da Coruña, 15071 A Coruña, Spain
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, HCMR Thalassocosmos, 71500 Gournes, Crete, Greece
| | - Florbela Pereira
- LAQV, REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Fernando Reyes
- Fundación MEDINA, Avda. del Conocimiento 34, 18016 Armilla, Spain
| | - Deniz Tasdemir
- GEOMAR Centre for Marine Biotechnology (GEOMAR-Biotech), Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Am Kiel-Kanal 44, 24106 Kiel, Germany
- Faculty of Mathematics and Natural Science, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
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4
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Lin BJ, Kuo TC, Chung HH, Huang YC, Wang MY, Hsu CC, Yao PY, Tseng YJ. MSIr: Automatic Registration Service for Mass Spectrometry Imaging and Histology. Anal Chem 2023; 95:3317-3324. [PMID: 36724516 PMCID: PMC9933042 DOI: 10.1021/acs.analchem.2c04360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful tool that can be used to simultaneously investigate the spatial distribution of different molecules in samples. However, it is difficult to comprehensively analyze complex biological systems with only a single analytical technique due to different analytical properties and application limitations. Therefore, many analytical methods have been combined to extend data interpretation, evaluate data credibility, and facilitate data mining to explore important temporal and spatial relationships in biological systems. Image registration is an initial and critical step for multimodal imaging data fusion. However, the image registration of multimodal images is not a simple task. The property difference between each data modality may include spatial resolution, image characteristics, or both. The image registrations between MSI and different imaging techniques are often achieved indirectly through histology. Many methods exist for image registration between MSI data and histological images. However, most of them are manual or semiautomatic and have their prerequisites. Here, we built MSI Registrar (MSIr), a web service for automatic registration between MSI and histology. It can help to reduce subjectivity and processing time efficiently. MSIr provides an interface for manually selecting region of interests from histological images; the user selects regions of interest to extract the corresponding spectrum indices in MSI data. In the performance evaluation, MSIr can quickly map MSI data to histological images and help pinpoint molecular components at specific locations in tissues. Most registrations were adequate and were without excessive shifts. MSIr is freely available at https://msir.cmdm.tw and https://github.com/CMDM-Lab/MSIr.
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Affiliation(s)
- Bo-Jhang Lin
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
| | - Tien-Chueh Kuo
- The
Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei 10617, Taiwan
| | - Hsin-Hsiang Chung
- Department
of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Ying-Chen Huang
- Department
of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Ming-Yang Wang
- Department
of Surgery, National Taiwan University Hospital, Taipei 10002, Taiwan
| | - Cheng-Chih Hsu
- Department
of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Po-Yang Yao
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
| | - Yufeng Jane Tseng
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan,The
Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei 10617, Taiwan,Department
of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan,School of
Pharmacy, College of Medicine, National
Taiwan University, Taipei 10002, Taiwan,. Phone: +886.2.3366.4888#529. Fax: +886.2.23628167
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5
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Lim MJ, Yagnik G, Henkel C, Frost SF, Bien T, Rothschild KJ. MALDI HiPLEX-IHC: multiomic and multimodal imaging of targeted intact proteins in tissues. Front Chem 2023; 11:1182404. [PMID: 37201132 PMCID: PMC10187789 DOI: 10.3389/fchem.2023.1182404] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/14/2023] [Indexed: 05/20/2023] Open
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is one of the most widely used methods for imaging the spatial distribution of unlabeled small molecules such as metabolites, lipids and drugs in tissues. Recent progress has enabled many improvements including the ability to achieve single cell spatial resolution, 3D-tissue image reconstruction, and the precise identification of different isomeric and isobaric molecules. However, MALDI-MSI of high molecular weight intact proteins in biospecimens has thus far been difficult to achieve. Conventional methods normally require in situ proteolysis and peptide mass fingerprinting, have low spatial resolution, and typically detect only the most highly abundant proteins in an untargeted manner. In addition, MSI-based multiomic and multimodal workflows are needed which can image both small molecules and intact proteins from the same tissue. Such a capability can provide a more comprehensive understanding of the vast complexity of biological systems at the organ, tissue, and cellular levels of both normal and pathological function. A recently introduced top-down spatial imaging approach known as MALDI HiPLEX-IHC (MALDI-IHC for short) provides a basis for achieving this high-information content imaging of tissues and even individual cells. Based on novel photocleavable mass-tags conjugated to antibody probes, high-plex, multimodal and multiomic MALDI-based workflows have been developed to image both small molecules and intact proteins on the same tissue sample. Dual-labeled antibody probes enable multimodal mass spectrometry and fluorescent imaging of targeted intact proteins. A similar approach using the same photocleavable mass-tags can be applied to lectin and other probes. We detail here several examples of MALDI-IHC workflows designed to enable high-plex, multiomic and multimodal imaging of tissues at a spatial resolution as low as 5 µm. This approach is compared to other existing high-plex methods such as imaging mass cytometry, MIBI-TOF, GeoMx and CODEX. Finally, future applications of MALDI-IHC are discussed.
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Affiliation(s)
- Mark J. Lim
- AmberGen, Inc., Billerica, MA, United States
- *Correspondence: Mark J. Lim, ; Kenneth J. Rothschild,
| | | | | | | | - Tanja Bien
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Kenneth J. Rothschild
- AmberGen, Inc., Billerica, MA, United States
- Department of Physics and Photonics Center, Boston University, Boston, MA, United States
- *Correspondence: Mark J. Lim, ; Kenneth J. Rothschild,
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6
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Mrukwa G, Polanska J. DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data. BMC Bioinformatics 2022; 23:538. [PMID: 36503372 PMCID: PMC9743550 DOI: 10.1186/s12859-022-05093-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 12/01/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured. RESULTS We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets-2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial [Formula: see text] effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)). CONCLUSIONS DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik .
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Affiliation(s)
- Grzegorz Mrukwa
- grid.6979.10000 0001 2335 3149Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland ,Netguru, Małe Garbary 9, 61-756 Poznań, Poland
| | - Joanna Polanska
- grid.6979.10000 0001 2335 3149Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
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7
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Zhu X, Xu T, Peng C, Wu S. Advances in MALDI Mass Spectrometry Imaging Single Cell and Tissues. Front Chem 2022; 9:782432. [PMID: 35186891 PMCID: PMC8850921 DOI: 10.3389/fchem.2021.782432] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 11/17/2021] [Indexed: 12/26/2022] Open
Abstract
Compared with conventional optical microscopy techniques, mass spectrometry imaging (MSI) or imaging mass spectrometry (IMS) is a powerful, label-free analytical technique, which can sensitively and simultaneously detect, quantify, and map hundreds of biomolecules, such as peptides, proteins, lipid, and other organic compounds in cells and tissues. So far, although several soft ionization techniques, such as desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS) have been used for imaging biomolecules, matrix-assisted laser desorption/ionization (MALDI) is still the most widespread MSI scanning method. Here, we aim to provide a comprehensive review of MALDI-MSI with an emphasis on its advances of the instrumentation, methods, application, and future directions in single cell and biological tissues.
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Affiliation(s)
- Xiaoping Zhu
- Joint Research Centre for Engineering Biology, Zhejiang University-University of Edinburgh Institute, Zhejiang University, Haining, China
- Research Center of Siyuan Natural Pharmacy and Biotoxicology, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Tianyi Xu
- Joint Research Centre for Engineering Biology, Zhejiang University-University of Edinburgh Institute, Zhejiang University, Haining, China
- Research Center of Siyuan Natural Pharmacy and Biotoxicology, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Chen Peng
- Research Center of Siyuan Natural Pharmacy and Biotoxicology, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Shihua Wu
- Joint Research Centre for Engineering Biology, Zhejiang University-University of Edinburgh Institute, Zhejiang University, Haining, China
- Research Center of Siyuan Natural Pharmacy and Biotoxicology, College of Life Sciences, Zhejiang University, Hangzhou, China
- *Correspondence: Shihua Wu, ; Shihua Wu,
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Balluff B, Heeren RM, Race AM. An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities. J Mass Spectrom Adv Clin Lab 2022; 23:26-38. [PMID: 35156074 PMCID: PMC8821033 DOI: 10.1016/j.jmsacl.2021.12.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 01/25/2023] Open
Abstract
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. Integration with other imaging modalities is essential in clinical MSI. Image integration is performed by image registration techniques. Technical potential of image registration in MSI has not been fully exploited. Roadmap proposed to improve registration accuracy.
Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.
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Peak learning of mass spectrometry imaging data using artificial neural networks. Nat Commun 2021; 12:5544. [PMID: 34545087 PMCID: PMC8452737 DOI: 10.1038/s41467-021-25744-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 08/18/2021] [Indexed: 02/07/2023] Open
Abstract
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.
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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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11
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Rosas-Román I, Winkler R. Contrast optimization of mass spectrometry imaging (MSI) data visualization by threshold intensity quantization (TrIQ). PeerJ Comput Sci 2021; 7:e585. [PMID: 34179452 PMCID: PMC8205298 DOI: 10.7717/peerj-cs.585] [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: 10/09/2020] [Accepted: 05/18/2021] [Indexed: 06/13/2023]
Abstract
Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient discovery of 'regions of interest' (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values ('outliers') and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.
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12
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Newman DJ, Cragg GM. Plant Endophytes and Epiphytes: Burgeoning Sources of Known and "Unknown" Cytotoxic and Antibiotic Agents? PLANTA MEDICA 2020; 86:891-905. [PMID: 32023633 DOI: 10.1055/a-1095-1111] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
In the last 20 or so years, the influence of endophytes and, quite recently, epiphytes of plants upon the compounds found in those plants, which were usually assumed to be phytochemicals produced by the plant for a variety of reasons, often as a defense against predators, is becoming more evident, in particular in the case of antitumor agents originally isolated from plant sources, though antibiotic agents might also be found, particularly from epiphytes. In this review, we started with the first report in 1993 of a taxol-producing endophyte and then expanded the compounds discussed to include camptothecin, the vinca alkaloids, podophyllotoxin, and homoharringtonine from endophytic microbes and then the realization that maytansine is not a plant secondary metabolite at all, and that even such a well-studied plant such as Arabidopsis thaliana has a vast repertoire of potential bioactive agents in its leaf epiphytic bacteria. We have taken data from a variety of sources, including a reasonable history of these discoveries that were not given in recent papers by us, nor in other papers covering this topic. The sources included the Scopus database, but we also performed other searches using bibliographic tools, thus, the majority of the papers referenced are the originals, though we note some very recent papers that have built on previous results. We concluded with a discussion of the more modern techniques that can be utilized to "persuade" endophytes and epiphytes to switch on silent biosynthetic pathways and how current analytical techniques may aid in evaluating such programs. We also comment at times on some findings, particularly in the case of homoharringtonine, where there are repetitious data reports differing by a few years claiming the same endophyte as the producer.
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Affiliation(s)
- David J Newman
- NIH Special Volunteer, NCI Natural Products Branch, Wayne, PA, USA
| | - Gordon M Cragg
- NIH Special Volunteer, NCI Natural Products Branch, Gaithersburg, MD, USA
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13
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Lieb F, Boskamp T, Stark HG. Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers. J Proteomics 2020; 225:103852. [DOI: 10.1016/j.jprot.2020.103852] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/13/2020] [Accepted: 05/29/2020] [Indexed: 12/23/2022]
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14
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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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15
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Holzlechner M, Eugenin E, Prideaux B. Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer. Cancer Rep (Hoboken) 2019; 2:e1229. [PMID: 32729258 PMCID: PMC7941519 DOI: 10.1002/cnr2.1229] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 11/04/2019] [Accepted: 11/07/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue. RECENT FINDINGS Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers. CONCLUSION MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.
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Affiliation(s)
- Matthias Holzlechner
- Department of Neuroscience, Cell Biology, and AnatomyThe University of Texas Medical Branch at Galveston (UTMB)GalvestonTexas
| | - Eliseo Eugenin
- Department of Neuroscience, Cell Biology, and AnatomyThe University of Texas Medical Branch at Galveston (UTMB)GalvestonTexas
| | - Brendan Prideaux
- Department of Neuroscience, Cell Biology, and AnatomyThe University of Texas Medical Branch at Galveston (UTMB)GalvestonTexas
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16
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Ardestani A, Li S, Annamalai K, Lupse B, Geravandi S, Dobrowolski A, Yu S, Zhu S, Baguley TD, Surakattula M, Oetjen J, Hauberg-Lotte L, Herranz R, Awal S, Altenhofen D, Nguyen-Tran V, Joseph S, Schultz PG, Chatterjee AK, Rogers N, Tremblay MS, Shen W, Maedler K. Neratinib protects pancreatic beta cells in diabetes. Nat Commun 2019; 10:5015. [PMID: 31676778 PMCID: PMC6825211 DOI: 10.1038/s41467-019-12880-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Accepted: 10/02/2019] [Indexed: 02/06/2023] Open
Abstract
The loss of functional insulin-producing β-cells is a hallmark of diabetes. Mammalian sterile 20-like kinase 1 (MST1) is a key regulator of pancreatic β-cell death and dysfunction; its deficiency restores functional β-cells and normoglycemia. The identification of MST1 inhibitors represents a promising approach for a β-cell-protective diabetes therapy. Here, we identify neratinib, an FDA-approved drug targeting HER2/EGFR dual kinases, as a potent MST1 inhibitor, which improves β-cell survival under multiple diabetogenic conditions in human islets and INS-1E cells. In a pre-clinical study, neratinib attenuates hyperglycemia and improves β-cell function, survival and β-cell mass in type 1 (streptozotocin) and type 2 (obese Leprdb/db) diabetic mouse models. In summary, neratinib is a previously unrecognized inhibitor of MST1 and represents a potential β-cell-protective drug with proof-of-concept in vitro in human islets and in vivo in rodent models of both type 1 and type 2 diabetes. Type 1 as well as type 2 diabetes are characterized by a loss of insulin-producing β-cells. Here the authors show that the FDA-approved drug neratinib has beneficial effects on β-cell survival, insulin secretion, and glycemic control in mouse models of diabetes.
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Affiliation(s)
- Amin Ardestani
- Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany.
| | - Sijia Li
- Calibr at Scripps Research, La Jolla, CA, USA
| | - Karthika Annamalai
- Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany
| | - Blaz Lupse
- Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany
| | - Shirin Geravandi
- Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany
| | | | - Shan Yu
- Calibr at Scripps Research, La Jolla, CA, USA
| | - Siying Zhu
- Calibr at Scripps Research, La Jolla, CA, USA
| | | | | | - Janina Oetjen
- Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany.,Center for Industrial Mathematics, University of Bremen, Bremen, Germany.,MALDI Imaging Lab, University of Bremen, Bremen, Germany
| | - Lena Hauberg-Lotte
- Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Raquel Herranz
- Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany
| | - Sushil Awal
- Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany
| | - Delsi Altenhofen
- Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany
| | | | - Sean Joseph
- Calibr at Scripps Research, La Jolla, CA, USA
| | | | | | | | | | - Weijun Shen
- Calibr at Scripps Research, La Jolla, CA, USA.
| | - Kathrin Maedler
- Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany.
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17
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Han J, Permentier H, Bischoff R, Groothuis G, Casini A, Horvatovich P. Imaging of protein distribution in tissues using mass spectrometry: An interdisciplinary challenge. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2018.12.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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18
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Paine MRL, Liu J, Huang D, Ellis SR, Trede D, Kobarg JH, Heeren RMA, Fernández FM, MacDonald TJ. Three-Dimensional Mass Spectrometry Imaging Identifies Lipid Markers of Medulloblastoma Metastasis. Sci Rep 2019; 9:2205. [PMID: 30778099 PMCID: PMC6379434 DOI: 10.1038/s41598-018-38257-0] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 12/18/2018] [Indexed: 12/11/2022] Open
Abstract
Treatment for medulloblastoma (MB) — the most common malignant pediatric brain tumor — includes prophylactic radiation administered to the entire brain and spine due to the high incidence of metastasis to the central nervous system. However, the majority of long-term survivors are left with permanent and debilitating neurocognitive impairments as a result of this therapy, while the remaining 30–40% of patients relapse with terminal metastatic disease. Development of more effective targeted therapies has been hindered by our lack of understanding of the underlying mechanisms regulating the metastatic process in this disease. To understand the mechanism by which MB metastasis occurs, three-dimensional matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) experiments were performed on whole brains from a mouse model of human medulloblastoma. Analyzing the tumor and surrounding normal brain in its entirety enabled the detection of low abundance, spatially-heterogeneous lipids associated with tumor development. Boundaries of metastasizing and non-metastasizing primary tumors were readily defined, leading to the identification of lipids associated with medulloblastoma metastasis, including phosphatidic acids, phosphatidylethanolamines, phosphatidylserines, and phosphoinositides. These lipids provide a greater insight into the metastatic process and may ultimately lead to the discovery of biomarkers and novel targets for the diagnosis and treatment of metastasizing MB in humans.
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Affiliation(s)
- Martin R L Paine
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA.,Maastricht Multimodal Molecular Imaging Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, 6229ER, The Netherlands
| | - Jingbo Liu
- Aflac Cancer and Blood Disorders Center, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Danning Huang
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Shane R Ellis
- Maastricht Multimodal Molecular Imaging Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, 6229ER, The Netherlands
| | | | | | - Ron M A Heeren
- Maastricht Multimodal Molecular Imaging Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, 6229ER, The Netherlands.
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA. .,Integrated Cancer Research Center, Georgia Institute of Technology, Atlanta, GA, 30332, USA. .,Institute of Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
| | - Tobey J MacDonald
- Aflac Cancer and Blood Disorders Center, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
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19
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C Silva AS, Palmer A, Kovalev V, Tarasov A, Alexandrov T, Martens L, Degroeve S. Data-Driven Rescoring of Metabolite Annotations Significantly Improves Sensitivity. Anal Chem 2018; 90:11636-11642. [PMID: 30188119 DOI: 10.1021/acs.analchem.8b03224] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
When analyzing mass spectrometry imaging data sets, assigning a molecule to each of the thousands of generated images is a very complex task. Recent efforts have taken lessons from (tandem) mass spectrometry proteomics and applied them to imaging mass spectrometry metabolomics, with good results. Our goal is to go a step further in this direction and apply a well established, data-driven method to improve the results obtained from an annotation engine. By using a data-driven rescoring strategy, we are able to consistently improve the sensitivity of the annotation engine while maintaining control of statistics like estimated rate of false discoveries. All the code necessary to run a search and extract the additional features can be found at https://github.com/anasilviacs/sm-engine and to rescore the results from a search in https://github.com/anasilviacs/rescore-metabolites .
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Affiliation(s)
- Ana S C Silva
- VIB-UGent Center for Medical Biotechnology , Ghent , 9000 , Belgium.,Department of Biochemistry, Faculty of Medicine , Ghent University , Ghent , 9000 , Belgium.,Bioinformatics Institute Ghent , Ghent University , Ghent , 9000 , Belgium
| | - Andrew Palmer
- Structural and Computational Biology Unit , European Molecular Biology Laboratory , Heidelberg , 69117 Germany
| | - Vitaly Kovalev
- Structural and Computational Biology Unit , European Molecular Biology Laboratory , Heidelberg , 69117 Germany
| | - Artem Tarasov
- Structural and Computational Biology Unit , European Molecular Biology Laboratory , Heidelberg , 69117 Germany
| | - Theodore Alexandrov
- Structural and Computational Biology Unit , European Molecular Biology Laboratory , Heidelberg , 69117 Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology , Ghent , 9000 , Belgium.,Department of Biochemistry, Faculty of Medicine , Ghent University , Ghent , 9000 , Belgium.,Bioinformatics Institute Ghent , Ghent University , Ghent , 9000 , Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology , Ghent , 9000 , Belgium.,Department of Biochemistry, Faculty of Medicine , Ghent University , Ghent , 9000 , Belgium.,Bioinformatics Institute Ghent , Ghent University , Ghent , 9000 , Belgium
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20
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Parrot D, Papazian S, Foil D, Tasdemir D. Imaging the Unimaginable: Desorption Electrospray Ionization - Imaging Mass Spectrometry (DESI-IMS) in Natural Product Research. PLANTA MEDICA 2018; 84:584-593. [PMID: 29388184 PMCID: PMC6053038 DOI: 10.1055/s-0044-100188] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 12/15/2017] [Accepted: 12/27/2017] [Indexed: 05/06/2023]
Abstract
Imaging mass spectrometry (IMS) has recently established itself in the field of "spatial metabolomics." Merging the sensitivity and fast screening of high-throughput mass spectrometry with spatial and temporal chemical information, IMS visualizes the production, location, and distribution of metabolites in intact biological models. Since metabolite profiling and morphological features are combined in single images, IMS offers an unmatched chemical detail on complex biological and microbiological systems. Thus, IMS-type "spatial metabolomics" emerges as a powerful and complementary approach to genomics, transcriptomics, and classical metabolomics studies. In this review, we summarize the current state-of-the-art IMS methods with a strong focus on desorption electrospray ionization (DESI)-IMS. DESI-IMS utilizes the original principle of electrospray ionization, but in this case solvent droplets are rastered and desorbed directly on the sample surface. The rapid and minimally destructive DESI-IMS chemical screening is achieved at ambient conditions and enables the accurate view of molecules in tissues at the µm-scale resolution. DESI-IMS analysis does not require complex sample preparation and allows repeated measurements on samples from different biological sources, including microorganisms, plants, and animals. Thanks to its easy workflow and versatility, DESI-IMS has successfully been applied to many different research fields, such as clinical analysis, cancer research, environmental sciences, microbiology, chemical ecology, and drug discovery. Herein we discuss the present applications of DESI-IMS in natural product research.
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Affiliation(s)
- Delphine Parrot
- GEOMAR Centre for Marine Biotechnology, Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany
| | - Stefano Papazian
- GEOMAR Centre for Marine Biotechnology, Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany
| | - Daniel Foil
- GEOMAR Centre for Marine Biotechnology, Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany
| | - Deniz Tasdemir
- GEOMAR Centre for Marine Biotechnology, Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany
- Kiel University, Kiel, Germany
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21
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Sandrin TR, Demirev PA. Characterization of microbial mixtures by mass spectrometry. MASS SPECTROMETRY REVIEWS 2018; 37:321-349. [PMID: 28509357 DOI: 10.1002/mas.21534] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 03/09/2017] [Accepted: 03/09/2017] [Indexed: 05/27/2023]
Abstract
MS applications in microbiology have increased significantly in the past 10 years, due in part to the proliferation of regulator-approved commercial MALDI MS platforms for rapid identification of clinical infections. In parallel, with the expansion of MS technologies in the "omics" fields, novel MS-based research efforts to characterize organismal as well as environmental microbiomes have emerged. Successful characterization of microorganisms found in complex mixtures of other organisms remains a major challenge for researchers and clinicians alike. Here, we review recent MS advances toward addressing that challenge. These include sample preparation methods and protocols, and established, for example, MALDI, as well as newer, for example, atmospheric pressure ionization (API) techniques. MALDI mass spectra of intact cells contain predominantly information on the highly expressed house-keeping proteins used as biomarkers. The API methods are applicable for small biomolecule analysis, for example, phospholipids and lipopeptides, and facilitate species differentiation. MS hardware and techniques, for example, tandem MS, including diverse ion source/mass analyzer combinations are discussed. Relevant examples for microbial mixture characterization utilizing these combinations are provided. Chemometrics and bioinformatics methods and algorithms, including those applied to large scale MS data acquisition in microbial metaproteomics and MS imaging of biofilms, are highlighted. Select MS applications for polymicrobial culture analysis in environmental and clinical microbiology are reviewed as well.
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Affiliation(s)
- Todd R Sandrin
- School of Mathematical and Natural Sciences, Arizona State University, Phoenix, Arizona
| | - Plamen A Demirev
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
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22
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Abdelmoula WM, Pezzotti N, Hölt T, Dijkstra J, Vilanova A, McDonnell LA, Lelieveldt BPF. Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution. J Proteome Res 2018; 17:1054-1064. [PMID: 29430923 PMCID: PMC5838640 DOI: 10.1021/acs.jproteome.7b00725] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
![]()
Technological
advances in mass spectrometry imaging (MSI) have
contributed to growing interest in 3D MSI. However, the large size
of 3D MSI data sets has made their efficient analysis and visualization
and the identification of informative molecular patterns computationally
challenging. Hierarchical stochastic neighbor embedding (HSNE), a
nonlinear dimensionality reduction technique that aims at finding
hierarchical and multiscale representations of large data sets, is
a recent development that enables the analysis of millions of data
points, with manageable time and memory complexities. We demonstrate
that HSNE can be used to analyze large 3D MSI data sets at full mass
spectral and spatial resolution. To benchmark the technique as well
as demonstrate its broad applicability, we have analyzed a number
of publicly available 3D MSI data sets, recorded from various biological
systems and spanning different mass-spectrometry ionization techniques.
We demonstrate that HSNE is able to rapidly identify regions of interest
within these large high-dimensionality data sets as well as aid the
identification of molecular ions that characterize these regions of
interest; furthermore, through clearly separating measurement artifacts,
the HSNE analysis exhibits a degree of robustness to measurement batch
effects, spatially correlated noise, and mass spectral misalignment.
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Affiliation(s)
- Walid M Abdelmoula
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands.,Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School , Boston, Massachusetts 02115, United States
| | - Nicola Pezzotti
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | - Thomas Hölt
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | - Jouke Dijkstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands
| | - Anna Vilanova
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | | | - Boudewijn P F Lelieveldt
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands.,Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
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23
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24
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Matrix-Assisted Laser Desorption/Ionisation Mass Spectrometry Imaging in the Study of Gastric Cancer: A Mini Review. Int J Mol Sci 2017; 18:ijms18122588. [PMID: 29194417 PMCID: PMC5751191 DOI: 10.3390/ijms18122588] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 11/25/2017] [Accepted: 11/28/2017] [Indexed: 02/07/2023] Open
Abstract
Gastric cancer (GC) is one of the leading causes of cancer-related deaths worldwide and the disease outcome commonly depends upon the tumour stage at the time of diagnosis. However, this cancer can often be asymptomatic during the early stages and remain undetected until the later stages of tumour development, having a significant impact on patient prognosis. However, our comprehension of the mechanisms underlying the development of gastric malignancies is still lacking. For these reasons, the search for new diagnostic and prognostic markers for gastric cancer is an ongoing pursuit. Modern mass spectrometry imaging (MSI) techniques, in particular matrix-assisted laser desorption/ionisation (MALDI), have emerged as a plausible tool in clinical pathology as a whole. More specifically, MALDI-MSI is being increasingly employed in the study of gastric cancer and has already elucidated some important disease checkpoints that may help us to better understand the molecular mechanisms underpinning this aggressive cancer. Here we report the state of the art of MALDI-MSI approaches, ranging from sample preparation to statistical analysis, and provide a complete review of the key findings that have been reported in the literature thus far.
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25
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Prentice BM, Caprioli RM, Vuiblet V. Label-free molecular imaging of the kidney. Kidney Int 2017; 92:580-598. [PMID: 28750926 PMCID: PMC6193761 DOI: 10.1016/j.kint.2017.03.052] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 03/27/2017] [Accepted: 03/28/2017] [Indexed: 12/25/2022]
Abstract
In this review, we will highlight technologies that enable scientists to study the molecular characteristics of tissues and/or cells without the need for antibodies or other labeling techniques. Specifically, we will focus on matrix-assisted laser desorption/ionization imaging mass spectrometry, infrared spectroscopy, and Raman spectroscopy.
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Affiliation(s)
- Boone M Prentice
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA; Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Richard M Caprioli
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA; Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA; Departments of Pharmacology and Medicine, Vanderbilt University, Nashville, Tennessee, USA; Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA.
| | - Vincent Vuiblet
- Biophotonic Laboratory, UMR CNRS 7369 URCA, Reims, France; Nephropathology, Department of Biopathology Laboratory, CHU de Reims, Reims, France; Nephrology and Renal Transplantation department, CHU de Reims, Reims, France.
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26
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Jones EE, Quiason C, Dale S, Shahidi-Latham SK. Feasibility Assessment of a MALDI FTICR Imaging Approach for the 3D Reconstruction of a Mouse Lung. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2017; 28:1709-1715. [PMID: 28401432 DOI: 10.1007/s13361-017-1658-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Revised: 03/07/2017] [Accepted: 03/09/2017] [Indexed: 05/13/2023]
Abstract
Matrix assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) has proven to be a quick, robust, and label-free tool to produce two-dimensional (2D) ion-density maps representing the distribution of a variety of analytes across a tissue section of interest. In addition, three-dimensional (3D) imaging mass spectrometry workflows have been developed that are capable of visualizing these same analytes throughout an entire volume of a tissue rather than a single cross-section. Until recently, the use of Fourier transform ion cyclotron resonance (FTICR) mass spectrometers for 3D volume reconstruction has been impractical due to software limitations, such as inadequate capacity to manipulate the extremely large data files produced during an imaging experiment. Fortunately with recent software and hardware advancements, 3D reconstruction from MALDI FTICR IMS datasets is now feasible. Here we describe the first proof of principle study for a 3D volume reconstruction of an entire mouse lung using data collected on a FTICR mass spectrometer. Each lung tissue section was analyzed with high mass resolution and mass accuracy, and considered as an independent dataset. Each subsequent lung section image, or lung dataset, was then co-registered to its adjacent section to reconstruct a 3D volume. Volumes representing various endogenous lipid species were constructed, including sphingolipids and phosphatidylcholines (PC), and species confirmation was performed with on-tissue collision induced dissociation (CID). Graphical Abstract ᅟ.
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Affiliation(s)
- E Ellen Jones
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way MS 41-2a, South San Francisco, CA, 94080, USA
| | - Cristine Quiason
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way MS 41-2a, South San Francisco, CA, 94080, USA
| | - Stephanie Dale
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way MS 41-2a, South San Francisco, CA, 94080, USA
| | - Sheerin K Shahidi-Latham
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way MS 41-2a, South San Francisco, CA, 94080, USA.
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Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2017; 1865:946-956. [DOI: 10.1016/j.bbapap.2016.08.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Revised: 08/03/2016] [Accepted: 08/27/2016] [Indexed: 12/14/2022]
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Abstract
Summary We introduce matter, an R package for direct interactions with larger-than-memory datasets, stored in an arbitrary number of files of any size. matter is primarily designed for datasets in new and rapidly evolving file formats, which may lack extensive software support. matter enables a wide variety of data exploration and manipulation steps and is extensible to many bioinformatics applications. It supports reproducible research by minimizing the need of converting and storing data in multiple formats. We illustrate the performance of matter in conjunction with the Bioconductor package Cardinal for analysis of high-resolution, high-throughput mass spectrometry imaging experiments. Availability and implementation The package, vignettes and examples of applications in several areas of bioinformatics are available open-source at www.bioconductor.org under the Artistic-2.0 license.
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Affiliation(s)
- Kylie A Bemis
- College of Computer and Information Science, Northeastern University, Boston, MA USA 02115
| | - Olga Vitek
- College of Computer and Information Science, Northeastern University, Boston, MA USA 02115.,College of Science, Northeastern University, Boston, MA USA 02115
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Inglese P, McKenzie JS, Mroz A, Kinross J, Veselkov K, Holmes E, Takats Z, Nicholson JK, Glen RC. Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer. Chem Sci 2017; 8:3500-3511. [PMID: 28507724 PMCID: PMC5418631 DOI: 10.1039/c6sc03738k] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 02/18/2017] [Indexed: 12/14/2022] Open
Abstract
Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
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Affiliation(s)
- Paolo Inglese
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - James S McKenzie
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Anna Mroz
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - James Kinross
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Kirill Veselkov
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Elaine Holmes
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Zoltan Takats
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Jeremy K Nicholson
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Robert C Glen
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
- Centre for Molecular Informatics , Department of Chemistry , University of Cambridge , Cambridge , UK
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Petras D, Jarmusch AK, Dorrestein PC. From single cells to our planet-recent advances in using mass spectrometry for spatially resolved metabolomics. Curr Opin Chem Biol 2017; 36:24-31. [PMID: 28086192 DOI: 10.1016/j.cbpa.2016.12.018] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Revised: 12/10/2016] [Accepted: 12/15/2016] [Indexed: 11/16/2022]
Abstract
Spatial information in the form of 3D digital content has been increasingly integrated into our daily lives. Metabolomic studies parallel this trend with spatial and time resolved information being acquired. Mass spectrometry imaging (MSI), which combines qualitative and quantitative molecular information with spatial information, plays a crucial role in mass spectrometry-based metabolomics. The lateral spatial resolution obtained by MSI continues to improve and allows mass spectrometers to be used as molecular microscopes-enabling the exploration of the cellular and subcellular metabolome. Towards the other end of the scale, MS is also being used to map (image) molecules on our skin, habitats, and entire ecosystems. In this article, we provide a perspective of imaging mass spectrometry for metabolomic studies from the subcellular to planetary scale.
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Affiliation(s)
- Daniel Petras
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, United States
| | - Alan K Jarmusch
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, United States
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, United States.
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Ucal Y, Durer ZA, Atak H, Kadioglu E, Sahin B, Coskun A, Baykal AT, Ozpinar A. Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2017; 1865:795-816. [PMID: 28087424 DOI: 10.1016/j.bbapap.2017.01.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 12/08/2016] [Accepted: 01/06/2017] [Indexed: 12/25/2022]
Abstract
Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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Affiliation(s)
- Yasemin Ucal
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Zeynep Aslıhan Durer
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Hakan Atak
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Elif Kadioglu
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Betul Sahin
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Abdurrahman Coskun
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Ahmet Tarık Baykal
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Aysel Ozpinar
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey.
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Spatial Metabolite Profiling by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 965:291-321. [DOI: 10.1007/978-3-319-47656-8_12] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Spatial Molecular Architecture of the Microbial Community of a Peltigera Lichen. mSystems 2016; 1:mSystems00139-16. [PMID: 28028548 PMCID: PMC5183598 DOI: 10.1128/msystems.00139-16] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 11/17/2016] [Indexed: 11/25/2022] Open
Abstract
Microbial communities have evolved over centuries to live symbiotically. The direct visualization of such communities at the chemical and functional level presents a challenge. Overcoming this challenge may allow one to visualize the spatial distributions of specific molecules involved in symbiosis and to define their functional roles in shaping the community structure. In this study, we examined the diversity of microbial genes and taxa and the presence of biosynthetic gene clusters by metagenomic sequencing and the compartmentalization of organic chemical components within a lichen using mass spectrometry. This approach allowed the identification of chemically distinct sections within this composite organism. Using our multipronged approach, various fungal natural products, not previously reported from lichens, were identified and two different fungal layers were visualized at the chemical level. Microbes are commonly studied as individual species, but they exist as mixed assemblages in nature. At present, we know very little about the spatial organization of the molecules, including natural products that are produced within these microbial networks. Lichens represent a particularly specialized type of symbiotic microbial assemblage in which the component microorganisms exist together. These composite microbial assemblages are typically comprised of several types of microorganisms representing phylogenetically diverse life forms, including fungi, photosymbionts, bacteria, and other microbes. Here, we employed matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) imaging mass spectrometry to characterize the distributions of small molecules within a Peltigera lichen. In order to probe how small molecules are organized and localized within the microbial consortium, analytes were annotated and assigned to their respective producer microorganisms using mass spectrometry-based molecular networking and metagenome sequencing. The spatial analysis of the molecules not only reveals an ordered layering of molecules within the lichen but also supports the compartmentalization of unique functions attributed to various layers. These functions include chemical defense (e.g., antibiotics), light-harvesting functions associated with the cyanobacterial outer layer (e.g., chlorophyll), energy transfer (e.g., sugars) surrounding the sun-exposed cyanobacterial layer, and carbohydrates that may serve a structural or storage function and are observed with higher intensities in the non-sun-exposed areas (e.g., complex carbohydrates). IMPORTANCE Microbial communities have evolved over centuries to live symbiotically. The direct visualization of such communities at the chemical and functional level presents a challenge. Overcoming this challenge may allow one to visualize the spatial distributions of specific molecules involved in symbiosis and to define their functional roles in shaping the community structure. In this study, we examined the diversity of microbial genes and taxa and the presence of biosynthetic gene clusters by metagenomic sequencing and the compartmentalization of organic chemical components within a lichen using mass spectrometry. This approach allowed the identification of chemically distinct sections within this composite organism. Using our multipronged approach, various fungal natural products, not previously reported from lichens, were identified and two different fungal layers were visualized at the chemical level.
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Dexter A, Race AM, Styles IB, Bunch J. Testing for Multivariate Normality in Mass Spectrometry Imaging Data: A Robust Statistical Approach for Clustering Evaluation and the Generation of Synthetic Mass Spectrometry Imaging Data Sets. Anal Chem 2016; 88:10893-10899. [DOI: 10.1021/acs.analchem.6b02139] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Alex Dexter
- National Physical Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Alan M. Race
- National Physical Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
| | | | - Josephine Bunch
- National Physical Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom
- School
of Pharmacy, University of Nottingham, Nottingham, Nottinghamshire NG7 2RD, United Kingdom
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35
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Baker TC, Han J, Borchers CH. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry imaging. Curr Opin Biotechnol 2016; 43:62-69. [PMID: 27690313 DOI: 10.1016/j.copbio.2016.09.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 09/06/2016] [Indexed: 10/20/2022]
Abstract
Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a robust tool for spatially resolved analysis of biomolecules in situ. Recent advances in high ionization-efficiency MALDI matrices, new matrix deposition procedures, and the development of high spatial-resolution and high sensitivity MS instruments continue to drive new applications of MALDI-MSI, along with other MSI techniques, which allow us to visualize and determine the regio-specific and temporal changes in proteins, peptides, lipids, drug molecules, and metabolites within the tissues, cells and microorganisms. These provide researchers with a new route to the discovery of potential biomarkers of human disease and elucidation of the underlying biology of metabolic regulation, thus bringing our understanding of human health to a new level.
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Affiliation(s)
- Teesha C Baker
- University of Victoria-Genome British Columbia Proteomics Centre, #3101-4464 Markham St., Vancouver Island Technology Park, Victoria, BC V8Z 7X8, Canada; Department of Biochemistry and Microbiology, University of Victoria, Petch Building Room 207, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada
| | - Jun Han
- University of Victoria-Genome British Columbia Proteomics Centre, #3101-4464 Markham St., Vancouver Island Technology Park, Victoria, BC V8Z 7X8, Canada
| | - Christoph H Borchers
- University of Victoria-Genome British Columbia Proteomics Centre, #3101-4464 Markham St., Vancouver Island Technology Park, Victoria, BC V8Z 7X8, Canada; Department of Biochemistry and Microbiology, University of Victoria, Petch Building Room 207, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada.
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36
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Race AM, Palmer AD, Dexter A, Steven RT, Styles IB, Bunch J. SpectralAnalysis: Software for the Masses. Anal Chem 2016; 88:9451-9458. [DOI: 10.1021/acs.analchem.6b01643] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Alan M. Race
- National
Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington, TW11 0LW, United Kingdom
- PSIBS
Doctoral Training Centre, School of Chemistry, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Andrew D. Palmer
- PSIBS
Doctoral Training Centre, School of Chemistry, University of Birmingham, Birmingham, B15 2TT, United Kingdom
- European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, 69117, Germany
| | - Alex Dexter
- National
Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington, TW11 0LW, United Kingdom
- PSIBS
Doctoral Training Centre, School of Chemistry, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Rory T. Steven
- National
Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington, TW11 0LW, United Kingdom
| | - Iain B. Styles
- PSIBS
Doctoral Training Centre, School of Chemistry, University of Birmingham, Birmingham, B15 2TT, United Kingdom
- School
of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Josephine Bunch
- National
Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington, TW11 0LW, United Kingdom
- School
of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, United Kingdom
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Phalak P, Chen J, Carlson RP, Henson MA. Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species. BMC SYSTEMS BIOLOGY 2016; 10:90. [PMID: 27604263 PMCID: PMC5015247 DOI: 10.1186/s12918-016-0334-8] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 08/25/2016] [Indexed: 12/18/2022]
Abstract
Background Chronic wounds are often colonized by consortia comprised of different bacterial species growing as biofilms on a complex mixture of wound exudate. Bacteria growing in biofilms exhibit phenotypes distinct from planktonic growth, often rendering the application of antibacterial compounds ineffective. Computational modeling represents a complementary tool to experimentation for generating fundamental knowledge and developing more effective treatment strategies for chronic wound biofilm consortia. Results We developed spatiotemporal models to investigate the multispecies metabolism of a biofilm consortium comprised of two common chronic wound isolates: the aerobe Pseudomonas aeruginosa and the facultative anaerobe Staphylococcus aureus. By combining genome-scale metabolic reconstructions with partial differential equations for metabolite diffusion, the models were able to provide both temporal and spatial predictions with genome-scale resolution. The models were used to analyze the metabolic differences between single species and two species biofilms and to demonstrate the tendency of the two bacteria to spatially partition in the multispecies biofilm as observed experimentally. Nutrient gradients imposed by supplying glucose at the bottom and oxygen at the top of the biofilm induced spatial partitioning of the two species, with S. aureus most concentrated in the anaerobic region and P. aeruginosa present only in the aerobic region. The two species system was predicted to support a maximum biofilm thickness much greater than P. aeruginosa alone but slightly less than S. aureus alone, suggesting an antagonistic metabolic effect of P. aeruginosa on S. aureus. When each species was allowed to enhance its growth through consumption of secreted metabolic byproducts assuming identical uptake kinetics, the competitiveness of P. aeruginosa was further reduced due primarily to the more efficient lactate metabolism of S. aureus. Lysis of S. aureus by a small molecule inhibitor secreted from P. aeruginosa and/or P. aeruginosa aerotaxis were predicted to substantially increase P. aeruginosa competitiveness in the aerobic region, consistent with in vitro experimental studies. Conclusions Our biofilm modeling approach allows the prediction of individual species metabolism and interspecies interactions in both time and space with genome-scale resolution. This study yielded new insights into the multispecies metabolism of a chronic wound biofilm, in particular metabolic factors that may lead to spatial partitioning of the two bacterial species. We believe that P. aeruginosa lysis of S. aureus combined with nutrient competition is a particularly relevant scenario for which model predictions could be tested experimentally. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0334-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Poonam Phalak
- Department of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA
| | - Jin Chen
- Department of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA
| | - Ross P Carlson
- Department of Chemical and Biological Engineering and Center for Biofilm Engineering, Montana State University, Bozeman, MT, 59717, USA
| | - Michael A Henson
- Department of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA.
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Patterson NH, Doonan RJ, Daskalopoulou SS, Dufresne M, Lenglet S, Montecucco F, Thomas A, Chaurand P. Three-dimensional imaging MS of lipids in atherosclerotic plaques: Open-source methods for reconstruction and analysis. Proteomics 2016; 16:1642-51. [DOI: 10.1002/pmic.201500490] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2015] [Revised: 02/01/2016] [Accepted: 03/03/2016] [Indexed: 01/01/2023]
Affiliation(s)
| | - Robert J. Doonan
- Department of Medicine, Faculty of Medicine; McGill University; Montreal Quebec Canada
| | | | - Martin Dufresne
- Department of Chemistry; University of Montreal; Montreal Quebec Canada
| | - Sébastien Lenglet
- Unit of Toxicology; University Centre of Legal Medicine; Geneva-Lausanne Switzerland
| | - Fabrizio Montecucco
- First Clinic of Internal Medicine, Department of Internal Medicine; University of Genoa; Genoa Italy
- Division of Cardiology, Foundation for Medical Researches, Faculty of Medicine; University of Geneva; Geneva Switzerland
| | - Aurélien Thomas
- Unit of Toxicology; University Centre of Legal Medicine; Geneva-Lausanne Switzerland
- Faculty of Biology and Medicine; Lausanne University Hospital; University of Lausanne; Lausanne Switzerland
| | - Pierre Chaurand
- Department of Chemistry; University of Montreal; Montreal Quebec Canada
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Affiliation(s)
- Zhenpeng Zhou
- Department of Chemistry, Stanford University, Stanford, California 94305-5080, United States
| | - Jae Kyoo Lee
- Department of Chemistry, Stanford University, Stanford, California 94305-5080, United States
| | - Samuel C. Kim
- Department of Chemistry, Stanford University, Stanford, California 94305-5080, United States
| | - Richard N. Zare
- Department of Chemistry, Stanford University, Stanford, California 94305-5080, United States
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Gorzolka K, Kölling J, Nattkemper TW, Niehaus K. Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging. PLoS One 2016; 11:e0150208. [PMID: 26938880 PMCID: PMC4777520 DOI: 10.1371/journal.pone.0150208] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 02/10/2016] [Indexed: 12/11/2022] Open
Abstract
MALDI mass spectrometry imaging was performed to localize metabolites during the first seven days of the barley germination. Up to 100 mass signals were detected of which 85 signals were identified as 48 different metabolites with highly tissue-specific localizations. Oligosaccharides were observed in the endosperm and in parts of the developed embryo. Lipids in the endosperm co-localized in dependency on their fatty acid compositions with changes in the distributions of diacyl phosphatidylcholines during germination. 26 potentially antifungal hordatines were detected in the embryo with tissue-specific localizations of their glycosylated, hydroxylated, and O-methylated derivates. In order to reveal spatio-temporal patterns in local metabolite compositions, multiple MSI data sets from a time series were analyzed in one batch. This requires a new preprocessing strategy to achieve comparability between data sets as well as a new strategy for unsupervised clustering. The resulting spatial segmentation for each time point sample is visualized in an interactive cluster map and enables simultaneous interactive exploration of all time points. Using this new analysis approach and visualization tool germination-dependent developments of metabolite patterns with single MS position accuracy were discovered. This is the first study that presents metabolite profiling of a cereals' germination process over time by MALDI MSI with the identification of a large number of peaks of agronomically and industrially important compounds such as oligosaccharides, lipids and antifungal agents. Their detailed localization as well as the MS cluster analyses for on-tissue metabolite profile mapping revealed important information for the understanding of the germination process, which is of high scientific interest.
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Affiliation(s)
- Karin Gorzolka
- Proteome and Metabolome Research, Faculty of Biology, Center for Biotechnology (CeBiTec), Bielefeld, Germany
| | - Jan Kölling
- Biodata Mining, Faculty of Technology, Center for Biotechnology (CeBiTec), Bielefeld, Germany.,International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Bielefeld University, Bielefeld, Germany
| | - Tim W Nattkemper
- Biodata Mining, Faculty of Technology, Center for Biotechnology (CeBiTec), Bielefeld, Germany
| | - Karsten Niehaus
- Proteome and Metabolome Research, Faculty of Biology, Center for Biotechnology (CeBiTec), Bielefeld, Germany
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Abstract
Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.
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Affiliation(s)
- Michael A Henson
- Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, U.S.A.
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42
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The application of “-omics” technologies for the classification and identification of animals. ORG DIVERS EVOL 2015. [DOI: 10.1007/s13127-015-0234-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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43
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Oetjen J, Veselkov K, Watrous J, McKenzie JS, Becker M, Hauberg-Lotte L, Kobarg JH, Strittmatter N, Mróz AK, Hoffmann F, Trede D, Palmer A, Schiffler S, Steinhorst K, Aichler M, Goldin R, Guntinas-Lichius O, von Eggeling F, Thiele H, Maedler K, Walch A, Maass P, Dorrestein PC, Takats Z, Alexandrov T. Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry. Gigascience 2015; 4:20. [PMID: 25941567 PMCID: PMC4418095 DOI: 10.1186/s13742-015-0059-4] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Accepted: 04/09/2015] [Indexed: 01/16/2023] Open
Abstract
Background Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. Findings High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. Conclusions With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets. Electronic supplementary material The online version of this article (doi:10.1186/s13742-015-0059-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Janina Oetjen
- MALDI Imaging Lab, University of Bremen, Bremen, Germany
| | - Kirill Veselkov
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Jeramie Watrous
- Department of Medicine, Biomedical Research Facility II, University of California, San Diego, USA
| | - James S McKenzie
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | | | | | | | - Nicole Strittmatter
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Anna K Mróz
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Franziska Hoffmann
- Institute of Physical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany ; Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany
| | - Dennis Trede
- Steinbeis Center SCiLS Research, Bremen, Germany ; SCiLS GmbH, Bremen, Germany
| | - Andrew Palmer
- European Molecular Biology Laboratory, Heidelberg, Germany
| | | | | | - Michaela Aichler
- Research Unit Analytical Pathology, Institute of Pathology, Helmholtz Center Munich, Munich, Germany
| | - Robert Goldin
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | | | - Ferdinand von Eggeling
- Institute of Physical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany ; Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany ; Leibnitz Institute of Photonic Technology (IPHT), Jena, Germany ; Jena Center for Soft Matter (JCSM), Friedrich-Schiller-University Jena, Jena, Germany
| | | | - Kathrin Maedler
- MALDI Imaging Lab, University of Bremen, Bremen, Germany ; Islet Research Lab, Center for Biomolecular Interactions, University of Bremen, Bremen, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, Institute of Pathology, Helmholtz Center Munich, Munich, Germany
| | - Peter Maass
- Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy & Pharmaceutical Sciences, University of California, San Diego, USA
| | - Zoltan Takats
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Theodore Alexandrov
- Steinbeis Center SCiLS Research, Bremen, Germany ; SCiLS GmbH, Bremen, Germany ; European Molecular Biology Laboratory, Heidelberg, Germany ; Skaggs School of Pharmacy & Pharmaceutical Sciences, University of California, San Diego, USA
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44
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Jarmusch AK, Kerian KS, Pirro V, Peat T, Thompson CA, Ramos-Vara JA, Childress MO, Cooks RG. Characteristic lipid profiles of canine non-Hodgkin's lymphoma from surgical biopsy tissue sections and fine needle aspirate smears by desorption electrospray ionization – mass spectrometry. Analyst 2015; 140:6321-9. [DOI: 10.1039/c5an00825e] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Exploring lipid information characteristic of non-Hodgkin's lymphoma using DESI – mass spectrometry.
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Affiliation(s)
- Alan K. Jarmusch
- Department of Chemistry and Center for Analytical Instrumentation Development
- Purdue University
- 560 Oval Drive
- USA
| | - Kevin S. Kerian
- Department of Chemistry and Center for Analytical Instrumentation Development
- Purdue University
- 560 Oval Drive
- USA
| | - Valentina Pirro
- Department of Chemistry and Center for Analytical Instrumentation Development
- Purdue University
- 560 Oval Drive
- USA
| | - Tyler Peat
- Department of Comparative Pathobiology
- College of Veterinary Medicine
- Purdue University
- West Lafayette
- USA
| | - Craig A. Thompson
- Department of Comparative Pathobiology
- College of Veterinary Medicine
- Purdue University
- West Lafayette
- USA
| | - José A. Ramos-Vara
- Department of Comparative Pathobiology
- College of Veterinary Medicine
- Purdue University
- West Lafayette
- USA
| | - Michael O. Childress
- Department of Veterinary Clinical Sciences
- College of Veterinary Medicine
- Purdue University
- West Lafayette
- USA
| | - R. Graham Cooks
- Department of Chemistry and Center for Analytical Instrumentation Development
- Purdue University
- 560 Oval Drive
- USA
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