1
|
Hu S, Habib A, Xiong W, Chen L, Bi L, Wen L. Mass Spectrometry Imaging Techniques: Non-Ambient and Ambient Ionization Approaches. Crit Rev Anal Chem 2024:1-54. [PMID: 38889072 DOI: 10.1080/10408347.2024.2362703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Molecular information can be acquired from sample surfaces in real time using a revolutionary molecular imaging technique called mass spectrometry imaging (MSI). The technique can concurrently provide high spatial resolution information on the spatial distribution and relative proportion of many different compounds. Thus, many scientists have been drawn to the innovative capabilities of the MSI approach, leading to significant focus in various fields during the past few decades. This review describes the sampling protocol, working principle and applications of a few non-ambient and ambient ionization mass spectrometry imaging techniques. The non-ambient techniques include secondary ionization mass spectrometry and matrix-assisted laser desorption ionization, while the ambient techniques include desorption electrospray ionization, laser ablation electrospray ionization, probe electro-spray ionization, desorption atmospheric pressure photo-ionization and femtosecond laser desorption ionization. The review additionally addresses the advantages and disadvantages of ambient and non-ambient MSI techniques in relation to their suitability, particularly for biological samples used in tissue diagnostics. Last but not least, suggestions and conclusions are made regarding the challenges and future prospects of MSI.
Collapse
Affiliation(s)
- Shundi Hu
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
| | - Ahsan Habib
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
- Department of Chemistry, University of Dhaka, Dhaka, Bangladesh
| | - Wei Xiong
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
| | - La Chen
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
| | - Lei Bi
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
| | - Luhong Wen
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
| |
Collapse
|
2
|
Nassar AF, Nie X, Zhang T, Yeung J, Norris P, He J, Ogura H, Babar MU, Muldoon A, Libreros S, Chen L. Is Lipid Metabolism of Value in Cancer Research and Treatment? Part I- Lipid Metabolism in Cancer. Metabolites 2024; 14:312. [PMID: 38921447 PMCID: PMC11205345 DOI: 10.3390/metabo14060312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/12/2024] [Accepted: 05/16/2024] [Indexed: 06/27/2024] Open
Abstract
For either healthy or diseased organisms, lipids are key components for cellular membranes; they play important roles in numerous cellular processes including cell growth, proliferation, differentiation, energy storage and signaling. Exercise and disease development are examples of cellular environment alterations which produce changes in these networks. There are indications that alterations in lipid metabolism contribute to the development and progression of a variety of cancers. Measuring such alterations and understanding the pathways involved is critical to fully understand cellular metabolism. The demands for this information have led to the emergence of lipidomics, which enables the large-scale study of lipids using mass spectrometry (MS) techniques. Mass spectrometry has been widely used in lipidomics and allows us to analyze detailed lipid profiles of cancers. In this article, we discuss emerging strategies for lipidomics by mass spectrometry; targeted, as opposed to global, lipid analysis provides an exciting new alternative method. Additionally, we provide an introduction to lipidomics, lipid categories and their major biological functions, along with lipidomics studies by mass spectrometry in cancer samples. Further, we summarize the importance of lipid metabolism in oncology and tumor microenvironment, some of the challenges for lipodomics, and the potential for targeted approaches for screening pharmaceutical candidates to improve the therapeutic efficacy of treatment in cancer patients.
Collapse
Affiliation(s)
- Ala F. Nassar
- Department of Immunobiology, Yale University, West Haven, CT 06516, USA
| | - Xinxin Nie
- Department of Immunobiology, Yale University, West Haven, CT 06516, USA
| | - Tianxiang Zhang
- Department of Immunobiology, Yale University, West Haven, CT 06516, USA
| | - Jacky Yeung
- Department of Immunobiology, Yale University, West Haven, CT 06516, USA
| | - Paul Norris
- Sciex, 500 Old Connecticut Path, Framingham, MA 01701, USA
| | - Jianwei He
- Department of Immunobiology, Yale University, West Haven, CT 06516, USA
| | - Hideki Ogura
- Department of Microbiology, Hyogo Medical University, Nishinomiya 663-8501, Japan
| | - Muhammad Usman Babar
- Department of Pathology, Yale University, New Haven, CT 06520, USA
- Vascular Biology and Therapeutic Program, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Anne Muldoon
- Department of Immunobiology, Yale University, West Haven, CT 06516, USA
| | - Stephania Libreros
- Department of Pathology, Yale University, New Haven, CT 06520, USA
- Vascular Biology and Therapeutic Program, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Lieping Chen
- Department of Immunobiology, Yale University, West Haven, CT 06516, USA
| |
Collapse
|
3
|
Ellin NR, Guo Y, Miranda-Quintana RA, Prentice BM. Extended similarity methods for efficient data mining in imaging mass spectrometry. DIGITAL DISCOVERY 2024; 3:805-817. [PMID: 38638647 PMCID: PMC11022984 DOI: 10.1039/d3dd00165b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 03/19/2024] [Indexed: 04/20/2024]
Abstract
Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x, y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often contain negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of imaging mass spectrometry spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1 : 1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.
Collapse
Affiliation(s)
- Nicholas R Ellin
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
| | - Yingchan Guo
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
- Quantum Theory Project, University of Florida Gainesville FL 32611-7200 USA
| | - Boone M Prentice
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
| |
Collapse
|
4
|
Ellin NR, Miranda-Quintana RA, Prentice BM. Extended Similarity Methods for Efficient Data Mining in Imaging Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550838. [PMID: 37546817 PMCID: PMC10402165 DOI: 10.1101/2023.07.27.550838] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x , y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often containing negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of IMS spectra simultaneously. The linear complexity, O ( N ) , of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1:1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.
Collapse
Affiliation(s)
- Nicholas R Ellin
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
- Quantum Theory Project, University of Florida, Gainesville, FL, 32611-7200; USA
| | - Boone M Prentice
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
| |
Collapse
|
5
|
Xia T, Zhou F, Zhang D, Jin X, Shi H, Yin H, Gong Y, Xia Y. Deep-profiling of phospholipidome via rapid orthogonal separations and isomer-resolved mass spectrometry. Nat Commun 2023; 14:4263. [PMID: 37460558 DOI: 10.1038/s41467-023-40046-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Abstract
A lipidome comprises thousands of lipid species, many of which are isomers and isobars. Liquid chromatography-tandem mass spectrometry (LC-MS/MS), although widely used for lipidomic profiling, faces challenges in differentiating lipid isomers. Herein, we address this issue by leveraging the orthogonal separation capabilities of hydrophilic interaction liquid chromatography (HILIC) and trapped ion mobility spectrometry (TIMS). We further integrate isomer-resolved MS/MS methods onto HILIC-TIMS, which enable pinpointing double bond locations in phospholipids and sn-positions in phosphatidylcholine. This system profiles phospholipids at multiple structural levels with short analysis time (<10 min per LC run), high sensitivity (nM detection limit), and wide coverage, while data analysis is streamlined using a home-developed software, LipidNovelist. Notably, compared to our previous report, the system doubles the coverage of phospholipids in bovine liver and reveals uncanonical desaturation pathways in RAW 264.7 macrophages. Relative quantitation of the double bond location isomers of phospholipids and the sn-position isomers of phosphatidylcholine enables the phenotyping of human bladder cancer tissue relative to normal control, which would be otherwise indistinguishable by traditional profiling methods. Our research offers a comprehensive solution for lipidomic profiling and highlights the critical role of isomer analysis in studying lipid metabolism in both healthy and diseased states.
Collapse
Affiliation(s)
- Tian Xia
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Department of Chemistry, Tsinghua University, 100084, Beijing, China
| | - Feng Zhou
- Bytedance Technology Co., 201103, Shanghai, China
| | - Donghui Zhang
- State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Department of Precision Instrument, 100084, Beijing, China
| | - Xue Jin
- School of Pharmaceutical Sciences, Tsinghua University, 100084, Beijing, China
| | - Hengxue Shi
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Department of Chemistry, Tsinghua University, 100084, Beijing, China
| | - Hang Yin
- School of Pharmaceutical Sciences, Tsinghua University, 100084, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Frontier Research Center for Biological Structure, Tsinghua University, 100084, Beijing, China
| | - Yanqing Gong
- Department of Urology, Peking University First Hospital, 100034, Beijing, China
| | - Yu Xia
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Department of Chemistry, Tsinghua University, 100084, Beijing, China.
| |
Collapse
|
6
|
Soudah T, Zoabi A, Margulis K. Desorption electrospray ionization mass spectrometry imaging in discovery and development of novel therapies. MASS SPECTROMETRY REVIEWS 2023; 42:751-778. [PMID: 34642958 DOI: 10.1002/mas.21736] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 09/16/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is one of the least specimen destructive ambient ionization mass spectrometry tissue imaging methods. It enables rapid simultaneous mapping, measurement, and identification of hundreds of molecules from an unmodified tissue sample. Over the years, since its first introduction as an imaging technique in 2005, DESI-MSI has been extensively developed as a tool for separating tissue regions of various histopathologic classes for diagnostic applications. Recently, DESI-MSI has also emerged as a versatile technique that enables drug discovery and can guide the efficient development of drug delivery systems. For example, it has been increasingly employed for uncovering unique patterns of in vivo drug distribution, the discovery of potentially treatable biochemical pathways, revealing novel druggable targets, predicting therapeutic sensitivity of diseased tissues, and identifying early tissue response to pharmacological treatment. These and other recent advances in implementing DESI-MSI as the tool for the development of novel therapies are highlighted in this review.
Collapse
Affiliation(s)
- Terese Soudah
- The Faculty of Medicine, The School of Pharmacy, The Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Amani Zoabi
- The Faculty of Medicine, The School of Pharmacy, The Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Katherine Margulis
- The Faculty of Medicine, The School of Pharmacy, The Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| |
Collapse
|
7
|
Ossoliński K, Ruman T, Ossoliński T, Ossolińska A, Arendowski A, Kołodziej A, Płaza-Altamer A, Nizioł J. Monoisotopic silver nanoparticles-based mass spectrometry imaging of human bladder cancer tissue: Biomarker discovery. Adv Med Sci 2022; 68:38-45. [PMID: 36566601 DOI: 10.1016/j.advms.2022.12.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 09/05/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Bladder cancer (BC) is the 10th most common form of cancer worldwide and the 2nd most common cancer of the urinary tract after prostate cancer, taking into account both incidence and prevalence. MATERIALS/METHODS Tissues from patients with BC and also tissue extracts were analyzed by laser desorption/ionization mass spectrometry imaging (LDI-MSI) with monoisotopic silver-109 nanoparticles-enhanced target (109AgNPET). RESULTS Univariate and multivariate statistical analyses revealed 10 metabolites that differentiated between tumor and normal tissues from six patients with diagnosed BC. Selected metabolites are discussed in detail in relation to their mass spectrometry (MS) imaging results. The pathway analysis enabled us to link these compounds with 17 metabolic pathways. CONCLUSIONS According to receiver operating characteristic (ROC) analysis of biomarkers, 10 known metabolites were identified as the new potential biomarkers with areas under the curve (AUC) higher than >0.99. In both univariate and multivariate analysis, it was predicted that these compounds could serve as useful discriminators of cancerous versus normal tissue in patients diagnosed with BC.
Collapse
Affiliation(s)
| | - Tomasz Ruman
- Rzeszów University of Technology, Faculty of Chemistry, Rzeszów, Poland
| | | | - Anna Ossolińska
- Department of Urology, John Paul II Hospital, Kolbuszowa, Poland
| | - Adrian Arendowski
- Rzeszów University of Technology, Faculty of Chemistry, Rzeszów, Poland
| | - Artur Kołodziej
- Rzeszów University of Technology, Faculty of Chemistry, Rzeszów, Poland; Doctoral School of Engineering and Technical Sciences at the Rzeszów University of Technology, Rzeszów, Poland
| | - Aneta Płaza-Altamer
- Rzeszów University of Technology, Faculty of Chemistry, Rzeszów, Poland; Doctoral School of Engineering and Technical Sciences at the Rzeszów University of Technology, Rzeszów, Poland
| | - Joanna Nizioł
- Rzeszów University of Technology, Faculty of Chemistry, Rzeszów, Poland.
| |
Collapse
|
8
|
Hu H, Laskin J. Emerging Computational Methods in Mass Spectrometry Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203339. [PMID: 36253139 PMCID: PMC9731724 DOI: 10.1002/advs.202203339] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/17/2022] [Indexed: 05/10/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
Collapse
Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| |
Collapse
|
9
|
Ramos-Martín F, D'Amelio N. Biomembrane lipids: When physics and chemistry join to shape biological activity. Biochimie 2022; 203:118-138. [PMID: 35926681 DOI: 10.1016/j.biochi.2022.07.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/13/2022] [Accepted: 07/21/2022] [Indexed: 11/02/2022]
Abstract
Biomembranes constitute the first lines of defense of cells. While small molecules can often permeate cell walls in bacteria and plants, they are generally unable to penetrate the barrier constituted by the double layer of phospholipids, unless specific receptors or channels are present. Antimicrobial or cell-penetrating peptides are in fact highly specialized molecules able to bypass this barrier and even discriminate among different cell types. This capacity is made possible by the intrinsic properties of its phospholipids, their distribution between the internal and external leaflet, and their ability to mutually interact, modulating the membrane fluidity and the exposition of key headgroups. Although common phospholipids can be found in the membranes of most organisms, some are characteristic of specific cell types. Here, we review the properties of the most common lipids and describe how they interact with each other in biomembrane. We then discuss how their assembly in bilayers determines some key physical-chemical properties such as permeability, potential and phase status. Finally, we describe how the exposition of specific phospholipids determines the recognition of cell types by membrane-targeting molecules.
Collapse
Affiliation(s)
- Francisco Ramos-Martín
- Unité de Génie Enzymatique et Cellulaire UMR 7025 CNRS, Université de Picardie Jules Verne, Amiens, 80039, France.
| | - Nicola D'Amelio
- Unité de Génie Enzymatique et Cellulaire UMR 7025 CNRS, Université de Picardie Jules Verne, Amiens, 80039, France.
| |
Collapse
|
10
|
Tian X, Zou Z, Yang Z. Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis. Methods Mol Biol 2022; 2437:253-272. [PMID: 34902154 DOI: 10.1007/978-1-0716-2030-4_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Mass spectrometry imaging (MSI) data generally contains large sizes and high-dimensional structures due to their inherent complex chemical and spatial information. A variety of data analysis methods have been developed to comprehensively analyze the MSI experimental results and extract essential information. Here, we describe the protocols of data preprocessing and emerging methods for data analyses, including multivariate analysis, machine learning, and image fusion, that have been applied to the data generated from the Single-probe MSI technique. These strategies and methods can be potentially applied to handling data produced from other MSI techniques.
Collapse
Affiliation(s)
- Xiang Tian
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA
- Dynamic Omics, Center of Genomics Research (CGR), R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Zhu Zou
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA
| | - Zhibo Yang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA.
| |
Collapse
|
11
|
Fu X, Wang Y, Xia B, Shi P, Zhou Y. Ultrasonic Sputter Desorption Mass Spectrometry Technique for Minimally Invasive Tissue Analysis. Anal Chem 2021; 93:10502-10510. [PMID: 34284576 DOI: 10.1021/acs.analchem.1c01448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Minimally invasive techniques for in vivo tissue analysis are desired by life science and medical research. Herein, a new ionization interface coupled with ultrasonic sputter desorption (USD) was developed for in vitro and in vivo tissue analysis. Sample molecules were effectively sputtered out when the high-frequency ultrasonic probe touched the tissue. Then, the sputtered molecules were collected and ionized by a custom-made heated quartz tube and finally analyzed by mass spectrometry (MS) online. The sample pretreatment of the USD-MS technique was quite simple and required no other steps except for wetting the tissue surface with ethanol to assist molecular extraction. Experimental results demonstrated that the proposed method was suitable for the analysis of different morphologies of tissues (such as liver, brain, kidney, and lung) and performed well in the analysis of liver tumors and paracancerous tissues. Moreover, as the proposed method caused little damage to the tissues during analysis, rats and mice with orthotopic tumors still survived after the experiments. Overall, the newly developed USD-MS technique was an effective tool for minimally invasive tissue analysis and could be used as a new candidate method for in situ and real-time analysis of biological tissues in vitro and in vivo.
Collapse
Affiliation(s)
- Xian Fu
- Chengdu Institute of Biology, Chinese Academy of Sciences, No. 93 South Keyuan Road, Gaoxin Distinct, Chengdu 610041, P. R. China.,Center for Novel Target & Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, P. R. China
| | - Yu Wang
- Chengdu Institute of Biology, Chinese Academy of Sciences, No. 93 South Keyuan Road, Gaoxin Distinct, Chengdu 610041, P. R. China.,University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Bing Xia
- Chengdu Institute of Biology, Chinese Academy of Sciences, No. 93 South Keyuan Road, Gaoxin Distinct, Chengdu 610041, P. R. China
| | - Peiyu Shi
- Chengdu Institute of Biology, Chinese Academy of Sciences, No. 93 South Keyuan Road, Gaoxin Distinct, Chengdu 610041, P. R. China.,University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yan Zhou
- Chengdu Institute of Biology, Chinese Academy of Sciences, No. 93 South Keyuan Road, Gaoxin Distinct, Chengdu 610041, P. R. China
| |
Collapse
|
12
|
Zang Q, Sun C, Chu X, Li L, Gan W, Zhao Z, Song Y, He J, Zhang R, Abliz Z. Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures. Anal Chim Acta 2021; 1155:338342. [PMID: 33766316 DOI: 10.1016/j.aca.2021.338342] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 02/05/2021] [Accepted: 02/16/2021] [Indexed: 12/18/2022]
Abstract
Spatially resolved metabolomics offers unprecedented opportunities for elucidating metabolic mechanisms during cancer progression. It facilitated the discovery of aberrant cellular metabolism with clinical application potential. Here, we developed a novel strategy to discover cancer tissue relevant metabolic signatures by high spatially resolved metabolomics combined with a multicellular tumor spheroid (MCTS) in vitro model. Esophageal cancer MCTS were generated using KYSE-30 human esophageal cancer cells to fully mimic the 3D microenvironment under physiological conditions. Then, the spatial features and temporal variation of metabolites and metabolic pathways in MCTS were accurately mapped by using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with a spatial resolution at ∼12 μm. Metabolites, such as glutamate, tyrosine, inosine and various types of lipids displayed heterogeneous distributions in different microregions inside the MCTS, revealing the metabolic heterogenicity of cancer cells under different proliferative states. Subsequently, through joint analysis of metabolomic data of clinical cancer tissue samples, cancer tissue relevant metabolic signatures in esophageal cancer MCTS were identified, including glutamine metabolism, fatty acid metabolism, de novo synthesis phosphatidylcholine (PC) and phosphatidylethanolamine (PE), etc. In addition, the abnormal expression of the involved metabolic enzymes, i.e., GLS, FASN, CHKA and cPLA2, was further confirmed and showed similar tendencies in esophageal cancer MCTS and cancer tissues. The MALDI-MSI combined with MCTS approach offers molecular insights into cancer metabolism with real-word relevance, which would potentially benefit the biomarker discovery and metabolic mechanism studies.
Collapse
Affiliation(s)
- Qingce Zang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Chenglong Sun
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Xiaoping Chu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Limei Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Wenqiang Gan
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Zitong Zhao
- State Key Laboratory of Molecular Oncology, Cancer Institute, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yongmei Song
- State Key Laboratory of Molecular Oncology, Cancer Institute, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Ruiping Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Zeper Abliz
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China; Center for Imaging and Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing, 100081, China.
| |
Collapse
|
13
|
Guo D, Föll MC, Volkmann V, Enderle-Ammour K, Bronsert P, Schilling O, Vitek O. Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations. Bioinformatics 2021; 36:i300-i308. [PMID: 32657378 PMCID: PMC7355295 DOI: 10.1093/bioinformatics/btaa436] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
MOTIVATION Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance. RESULTS To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states. AVAILABILITY AND IMPLEMENTATION The data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.
Collapse
Affiliation(s)
- Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Veronika Volkmann
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Kathrin Enderle-Ammour
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Peter Bronsert
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Medical Center - University of Freiburg.,German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), 79106 Freiburg, Germany
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| |
Collapse
|
14
|
Mamun A, Islam A, Eto F, Sato T, Kahyo T, Setou M. Mass spectrometry-based phospholipid imaging: methods and findings. Expert Rev Proteomics 2021; 17:843-854. [PMID: 33504247 DOI: 10.1080/14789450.2020.1880897] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Introduction: Imaging is a technique used for direct visualization of the internal structure or distribution of biomolecules of a living system in a two-dimensional or three-dimensional fashion. Phospholipids are important structural components of biological membranes and have been reported to be associated with various human diseases. Therefore, the visualization of phospholipids is crucial to understand the underlying mechanism of cellular and molecular processes in normal and diseased conditions. Areas covered: Mass spectrometry imaging (MSI) has enabled the label-free imaging of individual phospholipids in biological tissues and cells. The commonly used MSI techniques include matrix-assisted laser desorption ionization-MSI (MALDI-MSI), desorption electrospray ionization-MSI (DESI-MSI), and secondary ion mass spectrometry (SIMS) imaging. This special report described those methods, summarized the findings, and discussed the future development for the imaging of phospholipids. Expert opinion: Phospholipids imaging in complex biological samples has been significantly benefited from the development of MSI methods. In MALDI-MSI, novel matrix that produces homogenous crystals exclusively with polar lipids is important for phospholipids imaging with greater efficiency and higher spatial resolution. DESI-MSI has the potential of live imaging of the biological surface while SIMS is expected to image at the subcellular level in the near future.
Collapse
Affiliation(s)
- Al Mamun
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine , Hamamatsu, Shizuoka, Japan
| | - Ariful Islam
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine , Hamamatsu, Shizuoka, Japan
| | - Fumihiro Eto
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine , Hamamatsu, Shizuoka, Japan
| | - Tomohito Sato
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine , Hamamatsu, Shizuoka, Japan
| | - Tomoaki Kahyo
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine , Hamamatsu, Shizuoka, Japan
| | - Mitsutoshi Setou
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine , Hamamatsu, Shizuoka, Japan.,International Mass Imaging Center, Hamamatsu University School of Medicine , Hamamatsu, Shizuoka, Japan.,Department of Systems Molecular Anatomy, Institute for Medical Photonics Research, Preeminent Medical Photonics Education & Research Center , Hamamatsu, Shizuoka, Japan
| |
Collapse
|
15
|
Shahid M, Yeon A, Kim J. Metabolomic and lipidomic approaches to identify biomarkers for bladder cancer and interstitial cystitis (Review). Mol Med Rep 2020; 22:5003-5011. [PMID: 33174036 PMCID: PMC7646957 DOI: 10.3892/mmr.2020.11627] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 09/18/2020] [Indexed: 12/28/2022] Open
Abstract
The discovery, introduction and clinical use of prognostic and diagnostic biomarkers has significantly improved outcomes for patients with various illnesses, including bladder cancer (BC) and other bladder-related diseases, such as benign bladder dysfunction and interstitial cystitis (IC). Several sensitive and noninvasive clinically relevant biomarkers for BC and IC have been identified. Metabolomic- and lipidomic-based biomarkers have notable clinical potential in improving treatment outcomes for patients with cancer; however, there are also some noted limitations. This review article provides a short and concise summary of the literature on metabolomic and lipidomic biomarkers for BC and IC, focusing on the possible clinical utility of profiling metabolic alterations in BC and IC.
Collapse
Affiliation(s)
- Muhammad Shahid
- Department of Surgery, Cedars‑Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Austin Yeon
- Department of Surgery, Cedars‑Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jayoung Kim
- Department of Surgery, Cedars‑Sinai Medical Center, Los Angeles, CA 90048, USA
| |
Collapse
|
16
|
Butler LM, Perone Y, Dehairs J, Lupien LE, de Laat V, Talebi A, Loda M, Kinlaw WB, Swinnen JV. Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention. Adv Drug Deliv Rev 2020; 159:245-293. [PMID: 32711004 PMCID: PMC7736102 DOI: 10.1016/j.addr.2020.07.013] [Citation(s) in RCA: 303] [Impact Index Per Article: 75.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/02/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023]
Abstract
With the advent of effective tools to study lipids, including mass spectrometry-based lipidomics, lipids are emerging as central players in cancer biology. Lipids function as essential building blocks for membranes, serve as fuel to drive energy-demanding processes and play a key role as signaling molecules and as regulators of numerous cellular functions. Not unexpectedly, cancer cells, as well as other cell types in the tumor microenvironment, exploit various ways to acquire lipids and extensively rewire their metabolism as part of a plastic and context-dependent metabolic reprogramming that is driven by both oncogenic and environmental cues. The resulting changes in the fate and composition of lipids help cancer cells to thrive in a changing microenvironment by supporting key oncogenic functions and cancer hallmarks, including cellular energetics, promoting feedforward oncogenic signaling, resisting oxidative and other stresses, regulating intercellular communication and immune responses. Supported by the close connection between altered lipid metabolism and the pathogenic process, specific lipid profiles are emerging as unique disease biomarkers, with diagnostic, prognostic and predictive potential. Multiple preclinical studies illustrate the translational promise of exploiting lipid metabolism in cancer, and critically, have shown context dependent actionable vulnerabilities that can be rationally targeted, particularly in combinatorial approaches. Moreover, lipids themselves can be used as membrane disrupting agents or as key components of nanocarriers of various therapeutics. With a number of preclinical compounds and strategies that are approaching clinical trials, we are at the doorstep of exploiting a hitherto underappreciated hallmark of cancer and promising target in the oncologist's strategy to combat cancer.
Collapse
Affiliation(s)
- Lisa M Butler
- Adelaide Medical School and Freemasons Foundation Centre for Men's Health, University of Adelaide, Adelaide, SA 5005, Australia; South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
| | - Ylenia Perone
- Department of Surgery and Cancer, Imperial College London, Imperial Centre for Translational and Experimental Medicine, London, UK
| | - Jonas Dehairs
- Laboratory of Lipid Metabolism and Cancer, KU Leuven Cancer Institute, 3000 Leuven, Belgium
| | - Leslie E Lupien
- Program in Experimental and Molecular Medicine, Geisel School of Medicine at Dartmouth, 1 Medical Center Drive, Lebanon, NH 037560, USA
| | - Vincent de Laat
- Laboratory of Lipid Metabolism and Cancer, KU Leuven Cancer Institute, 3000 Leuven, Belgium
| | - Ali Talebi
- Laboratory of Lipid Metabolism and Cancer, KU Leuven Cancer Institute, 3000 Leuven, Belgium
| | - Massimo Loda
- Pathology and Laboratory Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - William B Kinlaw
- The Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, 1 Medical Center Drive, Lebanon, NH 03756, USA
| | - Johannes V Swinnen
- Laboratory of Lipid Metabolism and Cancer, KU Leuven Cancer Institute, 3000 Leuven, Belgium.
| |
Collapse
|
17
|
Verbeeck N, Caprioli RM, Van de Plas R. Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. MASS SPECTROMETRY REVIEWS 2020; 39:245-291. [PMID: 31602691 PMCID: PMC7187435 DOI: 10.1002/mas.21602] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/27/2018] [Indexed: 05/20/2023]
Abstract
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.
Collapse
Affiliation(s)
- Nico Verbeeck
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Aspect Analytics NVGenkBelgium
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT)KU LeuvenLeuvenBelgium
| | - Richard M. Caprioli
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
- Department of ChemistryVanderbilt UniversityNashvilleTN
- Department of PharmacologyVanderbilt UniversityNashvilleTN
- Department of MedicineVanderbilt UniversityNashvilleTN
| | - Raf Van de Plas
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
| |
Collapse
|
18
|
Knapp DW, Dhawan D, Ramos-Vara JA, Ratliff TL, Cresswell GM, Utturkar S, Sommer BC, Fulkerson CM, Hahn NM. Naturally-Occurring Invasive Urothelial Carcinoma in Dogs, a Unique Model to Drive Advances in Managing Muscle Invasive Bladder Cancer in Humans. Front Oncol 2020; 9:1493. [PMID: 32039002 PMCID: PMC6985458 DOI: 10.3389/fonc.2019.01493] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 12/11/2019] [Indexed: 12/11/2022] Open
Abstract
There is a great need to improve the outlook for people facing urinary bladder cancer, especially for patients with invasive urothelial carcinoma (InvUC) which is lethal in 50% of cases. Improved outcomes for patients with InvUC could come from advances on several fronts including emerging immunotherapies, targeted therapies, and new drug combinations; selection of patients most likely to respond to a given treatment based on molecular subtypes, immune signatures, and other characteristics; and prevention, early detection, and early intervention. Progress on all of these fronts will require clinically relevant animal models for translational research. The animal model(s) should possess key features that drive success or failure of cancer drugs in humans including tumor heterogeneity, genetic-epigenetic crosstalk, immune cell responsiveness, invasive and metastatic behavior, and molecular subtypes (e.g., luminal, basal). Experimental animal models, while essential in bladder cancer research, do not possess these collective features to accurately predict outcomes in humans. These key features, however, are present in naturally-occurring InvUC in pet dogs. Canine InvUC closely mimics muscle-invasive bladder cancer in humans in cellular and molecular features, molecular subtypes, immune response patterns, biological behavior (sites and frequency of metastasis), and response to therapy. Thus, dogs can offer a highly relevant animal model to complement other models in research for new therapies for bladder cancer. Clinical treatment trials in pet dogs with InvUC are considered a win-win-win scenario; the individual dog benefits from effective treatment, the results are expected to help other dogs, and the findings are expected to translate to better treatment outcomes in humans. In addition, the high breed-associated risk for InvUC in dogs (e.g., 20-fold increased risk in Scottish Terriers) offers an unparalleled opportunity to test new strategies in primary prevention, early detection, and early intervention. This review will provide an overview of canine InvUC, summarize the similarities (and differences) between canine and human InvUC, and provide evidence for the expanding value of this canine model in bladder cancer research.
Collapse
Affiliation(s)
- Deborah W Knapp
- Department of Veterinary Clinical Sciences, Purdue University, West Lafayette, IN, United States.,Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, United States
| | - Deepika Dhawan
- Department of Veterinary Clinical Sciences, Purdue University, West Lafayette, IN, United States
| | - José A Ramos-Vara
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, United States.,Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, United States
| | - Timothy L Ratliff
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, United States.,Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, United States
| | - Gregory M Cresswell
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, United States
| | - Sagar Utturkar
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, United States
| | - Breann C Sommer
- Department of Veterinary Clinical Sciences, Purdue University, West Lafayette, IN, United States
| | - Christopher M Fulkerson
- Department of Veterinary Clinical Sciences, Purdue University, West Lafayette, IN, United States.,Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, United States
| | - Noah M Hahn
- Department of Oncology and Urology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| |
Collapse
|
19
|
Wolrab D, Jirásko R, Chocholoušková M, Peterka O, Holčapek M. Oncolipidomics: Mass spectrometric quantitation of lipids in cancer research. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.04.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
|
20
|
Manzi M, Riquelme G, Zabalegui N, Monge ME. Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics. J Pharm Biomed Anal 2019; 178:112905. [PMID: 31707200 DOI: 10.1016/j.jpba.2019.112905] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 09/27/2019] [Accepted: 10/01/2019] [Indexed: 12/24/2022]
Abstract
The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.
Collapse
Affiliation(s)
- Malena Manzi
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina; Departamento de Química Biológica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Junín 956, C1113AAD, Ciudad de Buenos Aires, Argentina
| | - Gabriel Riquelme
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina; Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina
| | - Nicolás Zabalegui
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina; Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina.
| |
Collapse
|
21
|
Tian X, Zhang G, Zou Z, Yang Z. Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning. Anal Chem 2019; 91:5802-5809. [PMID: 30951294 PMCID: PMC6573030 DOI: 10.1021/acs.analchem.9b00026] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Multicellular spheroids (hereinafter referred to as spheroids) are 3D biological models. The metabolomic profiles inside spheroids provide crucial information reflecting the molecular phenotypes and microenvironment of cells. To study the influence of an anticancer drug on the spatially resolved metabolites, spheroids were cultured using HCT-116 colorectal cancer cells, treated with the anticancer drug Irinotecan under a series of time- and concentration-dependent conditions. The Single-probe mass spectrometry imaging (MSI) technique was utilized to conduct the experiments. The MSI data were analyzed using advanced data analysis methods to efficiently extract metabolomic information. Multivariate curve resolution alternating least square (MCR-ALS) was used to decompose each MS image into different components with grouped species. To improve the efficiency of data analysis, both supervised (Random Forest) and unsupervised (cluster large applications (CLARA)) machine learning (ML) methods were employed to cluster MS images according to their metabolomic features. Our results indicate that anticancer drug significantly affected the abundances of a variety of metabolites in different regions of spheroids. This integrated experiment and data analysis approach can facilitate the studies of metabolites in different types of 3D tumor models and tissues and potentially benefit the drug discovery, therapeutic resistance, and other biological research fields.
Collapse
Affiliation(s)
- Xiang Tian
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Genwei Zhang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Zhu Zou
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Zhibo Yang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| |
Collapse
|
22
|
Tamura K, Horikawa M, Sato S, Miyake H, Setou M. Discovery of lipid biomarkers correlated with disease progression in clear cell renal cell carcinoma using desorption electrospray ionization imaging mass spectrometry. Oncotarget 2019; 10:1688-1703. [PMID: 30899441 PMCID: PMC6422196 DOI: 10.18632/oncotarget.26706] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Accepted: 02/09/2019] [Indexed: 12/24/2022] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) often results in recurrence or metastasis, and there are only a few clinically effective biomarkers for early diagnosis and personalized therapy. Metabolic changes have been widely studied using mass spectrometry (MS) of tissue lysates to identify novel biomarkers. Our objective was to identify lipid biomarkers that can predict disease progression in ccRCC by a tissue-based approach. We retrospectively investigated lipid molecules in cancerous tissues and normal renal cortex tissues obtained from patients with ccRCC (n = 47) using desorption electrospray ionization imaging mass spectrometry (DESI-IMS). We selected eight candidate lipid biomarkers showing higher signal intensity in cancerous than in normal tissues, with a clear distinction of the tissue type based on the images. Of these candidates, low maximum intensity ratio (cancerous/normal) values of ions of oleic acid, m/z 389.2, and 391.3 significantly correlated with shorter progression-free survival compared with high maximum intensity ratio values (P = 0.011, P = 0.022, and P < 0.001, respectively). This study identified novel lipid molecules contributing to the prediction of disease progression in ccRCC using DESI-IMS. Our findings on lipid storage may provide a new diagnostic or therapeutic strategy for targeting cancer cell metabolism.
Collapse
Affiliation(s)
- Keita Tamura
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
- Department of Urology, Hamamatsu University School of Medicine, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Makoto Horikawa
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Shumpei Sato
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Hideaki Miyake
- Department of Urology, Hamamatsu University School of Medicine, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Mitsutoshi Setou
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
- Preeminent Medical Photonics Education and Research Center, Hamamatsu, Shizuoka, Japan
- Department of Anatomy, The University of Hong Kong, Hong Kong, China
| |
Collapse
|
23
|
Sans M, Zhang J, Lin JQ, Feider CL, Giese N, Breen MT, Sebastian K, Liu J, Sood AK, Eberlin LS. Performance of the MasSpec Pen for Rapid Diagnosis of Ovarian Cancer. Clin Chem 2019; 65:674-683. [PMID: 30770374 DOI: 10.1373/clinchem.2018.299289] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/22/2019] [Indexed: 11/06/2022]
Abstract
BACKGROUND Accurate tissue diagnosis during ovarian cancer surgery is critical to maximize cancer excision and define treatment options. Yet, current methods for intraoperative tissue evaluation can be time intensive and subjective. We have developed a handheld and biocompatible device coupled to a mass spectrometer, the MasSpec Pen, which uses a discrete water droplet for molecular extraction and rapid tissue diagnosis. Here we evaluated the performance of this technology for ovarian cancer diagnosis across different sample sets, tissue types, and mass spectrometry systems. METHODS MasSpec Pen analyses were performed on 192 ovarian, fallopian tube, and peritoneum tissue samples. Samples were evaluated by expert pathologists to confirm diagnosis. Performance using an Orbitrap and a linear ion trap mass spectrometer was tested. Statistical models were generated using machine learning and evaluated using validation and test sets. RESULTS High performance for high-grade serous carcinoma (n = 131; clinical sensitivity, 96.7%; specificity, 95.7%) and overall cancer (n = 138; clinical sensitivity, 94.0%; specificity, 94.4%) diagnoses was achieved using Orbitrap data. Variations in the mass spectra from normal tissue, low-grade, and high-grade serous ovarian cancers were observed. Discrimination between cancer and fallopian tube or peritoneum tissues was also achieved with accuracies of 92.6% and 87.9%, respectively, and 100% clinical specificity for both. Using ion trap data, excellent results for high-grade serous cancer vs normal ovarian differentiation (n = 40; clinical sensitivity, 100%; specificity, 100%) were obtained. CONCLUSIONS The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.
Collapse
Affiliation(s)
- Marta Sans
- Department of Chemistry, The University of Texas at Austin, Austin, TX
| | - Jialing Zhang
- Department of Chemistry, The University of Texas at Austin, Austin, TX
| | - John Q Lin
- Department of Chemistry, The University of Texas at Austin, Austin, TX
| | - Clara L Feider
- Department of Chemistry, The University of Texas at Austin, Austin, TX
| | - Noah Giese
- Department of Chemistry, The University of Texas at Austin, Austin, TX
| | - Michael T Breen
- Department of Women's Health, Dell Medical School, The University of Texas at Austin, Austin, TX
| | - Katherine Sebastian
- Department of Internal Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX
| | - Jinsong Liu
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Anil K Sood
- Department of Gynecologic Oncology and Reproductive Medicine, and the Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Livia S Eberlin
- Department of Chemistry, The University of Texas at Austin, Austin, TX;
| |
Collapse
|
24
|
Zou R, Cao W, Chong L, Hua W, Xu H, Mao Y, Page J, Shi R, Xia Y, Hu TY, Zhang W, Ouyang Z. Point-of-Care Tissue Analysis Using Miniature Mass Spectrometer. Anal Chem 2018; 91:1157-1163. [PMID: 30525456 DOI: 10.1021/acs.analchem.8b04935] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The combination of direct sampling ionization and miniature mass spectrometer presents a promising technical pathway of point-of-care analysis in clinical applications. In this work, a miniature mass spectrometry system was used for analysis of tissue samples. Direct tissue sampling coupled with extraction spray ionization was used with a home-built miniature mass spectrometer, Mini 12. Lipid species in tissue samples were well profiled in rat brain, kidney, and liver in a couple of minutes. By incorporating a photochemical (Paternò-Büchi) reaction, fast identification of lipid C═C location was realized. Relative quantitation of the lipid C═C isomer was performed by calculating the intensity ratio C═C diagnostic product ions, by which FA 18:1 (Δ9)/FA 18:1 (Δ11) was found to change significantly in mouse cancerous breast tissue samples. Accumulation of 2-hydroxylglutarate in human glioma samples, not in normal brains, can also be easily identified for rapid diagnosis.
Collapse
Affiliation(s)
- Ran Zou
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument , Tsinghua University , Beijing 100084 , China.,Weldon School of Biomedical Engineering , Purdue University , West Lafayette , Indiana 47907 , United States
| | - Wenbo Cao
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument , Tsinghua University , Beijing 100084 , China
| | - Leelyn Chong
- Weldon School of Biomedical Engineering , Purdue University , West Lafayette , Indiana 47907 , United States.,Department of Chemistry , Purdue University , West Lafayette , Indiana 47907 , United States
| | - Wei Hua
- Department of Neurosurgery, Huashan Hospital , Fudan University , Shanghai 200040 , China
| | - Hao Xu
- Department of Neurosurgery, Huashan Hospital , Fudan University , Shanghai 200040 , China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital , Fudan University , Shanghai 200040 , China
| | - Jessica Page
- Department of Basic Medical Sciences, College of Veterinary Medicine , Purdue University , West Lafayette , Indiana 47907 , United States
| | - Riyi Shi
- Department of Basic Medical Sciences, College of Veterinary Medicine , Purdue University , West Lafayette , Indiana 47907 , United States
| | - Yu Xia
- Department of Chemistry , Purdue University , West Lafayette , Indiana 47907 , United States.,Department of Chemistry , Tsinghua University , Beijing 100084 , China
| | - Tony Y Hu
- The Biodesign Institute , Arizona State University , Tempe , Arizona 85287 , United States
| | - Wenpeng Zhang
- Department of Chemistry , Purdue University , West Lafayette , Indiana 47907 , United States
| | - Zheng Ouyang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument , Tsinghua University , Beijing 100084 , China.,Weldon School of Biomedical Engineering , Purdue University , West Lafayette , Indiana 47907 , United States.,Department of Chemistry , Purdue University , West Lafayette , Indiana 47907 , United States
| |
Collapse
|
25
|
Woolman M, Tata A, Dara D, Meens J, D'Arcangelo E, Perez CJ, Saiyara Prova S, Bluemke E, Ginsberg HJ, Ifa D, McGuigan A, Ailles L, Zarrine-Afsar A. Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration. Analyst 2018; 142:3250-3260. [PMID: 28799592 DOI: 10.1039/c7an00830a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Squamous cell carcinomas constitute a major class of head & neck cancers, where the tumour stroma ratio (TSR) carries prognostic information. Patients affected by stroma-rich tumours exhibit a poor prognosis and a higher chance of relapse. As such, there is a need for a technology platform that allows rapid determination of the tumour stroma ratio. In this work, we provide a proof-of-principle demonstration that Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) can be used to determine tumour stroma ratios. Slices from three independent mouse xenograft tumours from the human FaDu cell line were subjected to DESI-MS imaging, staining and detailed analysis using digital pathology methods. Using multivariate statistical methods we compared the MS profiles with those of isolated stromal cells. We found that m/z 773.53 [PG(18:1)(18:1) - H]-, m/z 835.53 [PI(34:1) - H]- and m/z 863.56 [PI(18:1)(18:0) - H]- are biomarker ions that can distinguish FaDu cancer from cancer associated fibroblast (CAF) cells. A comparison with DESI-MS analysis of controlled mixtures of the CAF and FaDu cells showed that the abundance of the biomarker ions above can be used to determine, with an error margin of close to 5% compared with quantitative pathology estimates, TSR values. This proof-of-principle demonstration is encouraging and must be further validated using human samples and a larger sample base. At maturity, DESI-MS thus may become a stand-alone molecular pathology tool providing an alternative rapid cancer assessment without the need for time-consuming staining and microscopy methods, potentially further conserving human resources.
Collapse
Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
26
|
Al-Khshemawee H, Du X, Agarwal M, Yang JO, Ren YL. Application of Direct Immersion Solid-Phase Microextraction (DI-SPME) for Understanding Biological Changes of Mediterranean Fruit Fly ( Ceratitis capitata) During Mating Procedures. Molecules 2018; 23:E2951. [PMID: 30424544 PMCID: PMC6278405 DOI: 10.3390/molecules23112951] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 11/07/2018] [Accepted: 11/09/2018] [Indexed: 11/22/2022] Open
Abstract
Samples from three different mating stages (before, during and after mating) of the Mediterranean fruit fly Ceratitis capitata were used in this experiment. Samples obtained from whole insects were subjected to extraction with the two mixtures of solvents (acetonitrile/water (A) and methanol/acetonitrile/water (B)) and a comparative study of the extractions using the different solvents was performed. Direct immersion-solid phase microextraction (DI-SPME) was employed, followed by gas chromatographic-mass spectrometry analyses (GC/MS) for the collection, separation and identification of compounds. The method was validated by testing its sensitivity, linearity and reproducibility. The main compounds identified in the three different mating stages were ethyl glycolate, α-farnesene, decanoic acid octyl ester, 2,6,10,15-tetramethylheptadecane, 11-tricosene, 9,12-(Z,Z)-octadecadienoic acid, methyl stearate, 9-(Z)-tricosene, 9,11-didehydro-lumisterol acetate; 1,54-dibromotetrapentacontane, 9-(Z)-hexadecenoic acid hexadecyl ester, 9-(E)-octadecenoic acid and 9-(Z)-hexadecenoic acid octadecyl ester. The novel findings indicated that compound compositions were not significantly different before and during mating. However, new chemical compounds were generated after mating, such as 1-iodododecane, 9-(Z)-tricosene and 11,13-dimethyl-12-tetradecen-1-acetate which were extracted with both (A) and (B) and dodecanoic acid, (Z)-oleic acid, octadecanoic acid and hentriacontane which were extracted with (A) and ethyl glycolate, 9-hexadecenoic acid hexadecyl ester, palmitoleic acid and 9-(E)-octadecenoic acid, which were extracted with solvent (B). This study has demonstrated that DI-SPME is useful in quantitative insect metabolomics by determining changes in the metabolic compounds in response to mating periods. DI-SPME chemical extraction technology might offer analysis of metabolites that could potentially enhance our understanding on the evolution of the medfly.
Collapse
Affiliation(s)
- Hasan Al-Khshemawee
- School of Veterinary and Life Science, Murdoch University, 90 South St., Murdoch, WA 6150, Australia.
- College of Agriculture, Wasit University, Wasit 120, Iraq.
| | - Xin Du
- School of Veterinary and Life Science, Murdoch University, 90 South St., Murdoch, WA 6150, Australia.
| | - Manjree Agarwal
- School of Veterinary and Life Science, Murdoch University, 90 South St., Murdoch, WA 6150, Australia.
| | - Jeong Oh Yang
- Plant Quarantine Technology Centre, Animal and Plant Quarantine Agency (APQA), Gimcheon 39660, Korea.
| | - Yong Lin Ren
- School of Veterinary and Life Science, Murdoch University, 90 South St., Murdoch, WA 6150, Australia.
| |
Collapse
|
27
|
Banerjee S. Ambient ionization mass spectrometry imaging for disease diagnosis: Excitements and challenges. J Biosci 2018; 43:731-738. [PMID: 30207318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Tissue analysis in histology is extremely important and also considered to be a gold standard to diagnose and prognosticate several diseases including cancer. Intraoperative evaluation of surgical margin of tumor also relies on frozen section histopathology, which is time consuming, challenging and often subjective. Recent development in the ambient ionization mass spectrometry imaging (MSI) technique has enabled us to simultaneously visualize hundreds to thousands of molecules (ion images) in the biopsy specimen, which are strikingly different and more powerful than the single optical tissue image analysis in conventional histopathology. This paper will highlight the emergence of the desorption electrospray ionization MSI (DESI-MSI) technique, which is label-free, requires minimal or no sample preparation and operates under ambient conditions. DESI-MSI can record ion images of lipid/metabolite distributions on biopsy specimens, providing a wealth of diagnostic information based on differential distributions of these molecular species in healthy and unhealthy tissues. Remarkable success of this technology in rapidly evaluating the cancer margin intraoperatively with very high accuracy also promises to bring this imaging technique from bench to bedside.
Collapse
Affiliation(s)
- Shibdas Banerjee
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Karakambadi Road, Tirupati 517 507, India,
| |
Collapse
|
28
|
Banerjee S. Ambient ionization mass spectrometry imaging for disease diagnosis: Excitements and challenges. J Biosci 2018. [DOI: 10.1007/s12038-018-9785-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
29
|
D'Hue CA, Dhawan D, Peat T, Ramos-Vara J, Jarmusch A, Knapp DW, Cooks RG. Fatty Acid Patterns Detected By Ambient Ionization Mass Spectrometry in Canine Invasive Urothelial Carcinoma From Dogs of Different Breeds. Bladder Cancer 2018; 4:283-291. [PMID: 30112439 PMCID: PMC6087441 DOI: 10.3233/blc-170125] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Background: In early work ambient ionization mass spectrometry (MS) revealed lipid patterns distinguishing muscle invasive bladder cancer (invasive urothelial carcinoma, InvUC) from normal urothelium. A new ambient ionization MS approach, touch spray MS (TS-MS) can rapidly generate mass spectra in real time, potentially in a point-of-care setting. A tissue sample removed from a patient is touched by a probe, and mass spectra generated within seconds. Objective: To validate TS-MS methods using specimens from naturally-occurring InvUC in dogs where the cancer closely mimics the human condition, and to demonstrate proof-of-concept that TS-MS can elucidate lipid patterns distinguishing InvUC from normal urothelium. Methods: Samples of normal urothelium and InvUC from dogs of several breeds were analyzed by TS-MS with correlative histopathology across each sample. Results were compared to those obtained with desorption electrospray ionization mass spectrometry (DESI-MS), a more traditional method. Data were analyzed by Principal Component Analysis and Linear Discriminant Analysis. Results: Lipid patterns identified by TS-MS, as well as by DESI-MS, differed between InvUC and normal urothelium with m/z 281.5 (oleic acid) and m/z 563.5 (oleic acid dimer) substantially contributing to the differences. Using histologic diagnosis as the gold standard, TS-MS had a global prediction rate of 93%. Conclusions: TS-MS can be used to identify lipid patterns that differentiate canine InvUC from normal urothelium. Optimization of TS-MS could lead to a point-of-care approach to distinguish cancer from normal in ex vivo tissues in real time, and to define biochemical processes leading to cancer development and progression.
Collapse
Affiliation(s)
- Cedric A D'Hue
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Deepika Dhawan
- Department of Veterinary Clinical Sciences, Purdue University, West Lafayette, IN, USA
| | - Tyler Peat
- Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, USA
| | - José Ramos-Vara
- Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, USA
| | - Alan Jarmusch
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Deborah W Knapp
- Department of Veterinary Clinical Sciences, Purdue University, West Lafayette, IN, USA.,Purdue University Center for Cancer Research, West Lafayette, IN, USA
| | - R Graham Cooks
- Department of Chemistry, Purdue University, West Lafayette, IN, USA.,Purdue University Center for Cancer Research, West Lafayette, IN, USA
| |
Collapse
|
30
|
Cooks RG, Yan X. Mass Spectrometry for Synthesis and Analysis. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2018; 11:1-28. [PMID: 29894228 DOI: 10.1146/annurev-anchem-061417-125820] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Mass spectrometry is the science and technology of ions. As such, it is concerned with generating ions, measuring their properties, following their reactions, isolating them, and using them to build and transform materials. Instrumentation is an essential element of these activities, and analytical applications are one driving force. Work from the Aston Laboratories at Purdue University's Department of Chemistry is described here, with an emphasis on accelerated reactions of ions in solution and small-scale synthesis; ion/surface collision processes, including surface-induced dissociation (SID) and ion soft landing; and applications to tissue imaging. Our special interest in chirality and the chemistry behind the origins of life is also featured together with the exciting area of tissue diagnostics.
Collapse
Affiliation(s)
- R Graham Cooks
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47906, USA;
| | - Xin Yan
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47906, USA;
- Current affiliation: Department of Chemistry, Stanford University, Stanford, California 94305, USA
| |
Collapse
|
31
|
Cahill JF, Kertesz V, Porta T, LeBlanc JCY, Heeren RMA, Van Berkel GJ. Solvent effects on differentiation of mouse brain tissue using laser microdissection 'cut and drop' sampling with direct mass spectral analysis. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2018; 32:414-422. [PMID: 29297944 DOI: 10.1002/rcm.8053] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 12/07/2017] [Accepted: 12/14/2017] [Indexed: 05/12/2023]
Abstract
RATIONALE Laser microdissection-liquid vortex capture/electrospray ionization mass spectrometry (LMD-LVC/ESI-MS) has potential for on-line classification of tissue but an investigation into what analytical conditions provide best spectral differentiation has not been conducted. The effects of solvent, ionization polarity, and spectral acquisition parameters on differentiation of mouse brain tissue regions are described. METHODS Individual 40 × 40 μm microdissections from cortex, white, grey, granular, and nucleus regions of mouse brain tissue were analyzed using different capture/ESI solvents, in positive and negative ion mode ESI, using time-of-flight (TOF)-MS and sequential window acquisitions of all theoretical spectra (SWATH)-MS (a permutation of tandem-MS), and combinations thereof. Principal component analysis-linear discriminant analysis (PCA-LDA), applied to each mass spectral dataset, was used to determine the accuracy of differentiation of mouse brain tissue regions. RESULTS Mass spectral differences associated with capture/ESI solvent composition manifested as altered relative distributions of ions rather than the presence or absence of unique ions. In negative ion mode ESI, 80/20 (v/v) methanol/water yielded spectra with low signal/noise ratios relative to other solvents. PCA-LDA models acquired using 90/10 (v/v) methanol/chloroform differentiated tissue regions with 100% accuracy while data collected using methanol misclassified some samples. The combination of SWATH-MS and TOF-MS data improved differentiation accuracy. CONCLUSIONS Combined TOF-MS and SWATH-MS data differentiated white, grey, granular, and nucleus mouse tissue regions with greater accuracy than when solely using TOF-MS data. Using 90/10 (v/v) methanol/chloroform, tissue regions were perfectly differentiated. These results will guide future studies looking to utilize the potential of LMD-LVC/ESI-MS for tissue and disease differentiation.
Collapse
Affiliation(s)
- John F Cahill
- Mass Spectrometry and Laser Spectroscopy Group, Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6131, USA
| | - Vilmos Kertesz
- Mass Spectrometry and Laser Spectroscopy Group, Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6131, USA
| | - Tiffany Porta
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229, ER, Maastricht, The Netherlands
| | | | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229, ER, Maastricht, The Netherlands
| | - Gary J Van Berkel
- Mass Spectrometry and Laser Spectroscopy Group, Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6131, USA
| |
Collapse
|
32
|
Bandu R, Mok HJ, Kim KP. Phospholipids as cancer biomarkers: Mass spectrometry-based analysis. MASS SPECTROMETRY REVIEWS 2018; 37:107-138. [PMID: 27276657 DOI: 10.1002/mas.21510] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 05/19/2016] [Indexed: 05/02/2023]
Abstract
Lipids, particularly phospholipids (PLs), are key components of cellular membrane. PLs play important and diverse roles in cells such as chemical-energy storage, cellular signaling, cell membranes, and cell-cell interactions in tissues. All these cellular processes are pertinent to cells that undergo transformation, cancer progression, and metastasis. Thus, there is a strong possibility that some classes of PLs are expected to present in cancer cells and tissues in cellular physiology. The mass spectrometric soft-ionization techniques, electrospray ionization (ESI), and matrix-assisted laser desorption/ionization (MALDI) are well-established in the proteomics field, have been used for lipidomic analysis in cancer research. This review focused on the applications of mass spectrometry (MS) mainly on ESI-MS and MALDI-MS in the structural characterization, molecular composition and key roles of various PLs present in cancer cells, tissues, blood, and urine, and on their importance for cancer-related problems as well as challenges for development of novel PL-based biomarkers. The profiling of PLs helps to rationalize their functions in biological systems, and will also provide diagnostic information to elucidate mechanisms behind the control of cancer, diabetes, and neurodegenerative diseases. The investigation of cellular PLs with MS methods suggests new insights on various cancer diseases and clinical applications in the drug discovery and development of biomarkers for various PL-related different cancer diseases. PL profiling in tissues, cells and body fluids also reflect the general condition of the whole organism and can indicate the existence of cancer and other diseases. PL profiling with MS opens new prospects to assess alterations of PLs in cancer, screening specific biomarkers and provide a basis for the development of novel therapeutic strategies. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:107-138, 2018.
Collapse
Affiliation(s)
- Raju Bandu
- Department of Applied Chemistry, College of Applied Science, Kyung Hee University, Yong-in City, 446-701, Korea
| | - Hyuck Jun Mok
- Department of Applied Chemistry, College of Applied Science, Kyung Hee University, Yong-in City, 446-701, Korea
| | - Kwang Pyo Kim
- Department of Applied Chemistry, College of Applied Science, Kyung Hee University, Yong-in City, 446-701, Korea
| |
Collapse
|
33
|
Tian X, Zhang G, Shao Y, Yang Z. Towards enhanced metabolomic data analysis of mass spectrometry image: Multivariate Curve Resolution and Machine Learning. Anal Chim Acta 2018; 1037:211-219. [PMID: 30292295 DOI: 10.1016/j.aca.2018.02.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 02/08/2018] [Accepted: 02/10/2018] [Indexed: 12/12/2022]
Abstract
Large amounts of data are generally produced from mass spectrometry imaging (MSI) experiments in obtaining the molecular and spatial information of biological samples. Traditionally, MS images are constructed using manually selected ions, and it is very challenging to comprehensively analyze MSI results due to their large data sizes and highly complex data structures. To overcome these barriers, it is obligatory to develop advanced data analysis approaches to handle the increasingly large MSI data. In the current study, we focused on the method development of using Multivariate Curve Resolution (MCR) and Machine Learning (ML) approaches. We aimed to effectively extract the essential information present in the large and complex MSI data and enhance the metabolomic data analysis of biological tissues. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm was used to obtain major patterns of spatial distribution and grouped metabolites with the same spatial distribution patterns. In addition, both supervised and unsupervised ML methods were established to analyze the MSI data. In the supervised ML approach, Random Forest method was selected, and the model was trained using the selected datasets based on the distribution pattern obtained from MCR-ALS analyses. In the unsupervised ML approach, both DBSCAN (Density-based Spatial Clustering of Applications with Noise) and CLARA (Clustering Large Applications) were applied to cluster the MSI datasets. It is worth noting that similar patterns of spatial distribution were discovered through MSI data analysis using MCR-ALS, supervised ML, and unsupervised ML. Our protocols of data analysis can be applied to process the data acquired using many other types of MSI techniques, and to extract the overall features present in MSI results that are intractable using traditional data analysis approaches.
Collapse
Affiliation(s)
- Xiang Tian
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA
| | - Genwei Zhang
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
| | - Zhibo Yang
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
| |
Collapse
|
34
|
Sans M, Feider CL, Eberlin LS. Advances in mass spectrometry imaging coupled to ion mobility spectrometry for enhanced imaging of biological tissues. Curr Opin Chem Biol 2018; 42:138-146. [PMID: 29275246 PMCID: PMC5828985 DOI: 10.1016/j.cbpa.2017.12.005] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 12/04/2017] [Accepted: 12/11/2017] [Indexed: 11/20/2022]
Abstract
Tissues present complex biochemical and morphological composition associated with their various cell types and physiological functions. Mass spectrometry (MS) imaging technologies are powerful tools to investigate the molecular information from biological tissue samples and visualize their complex spatial distributions. Coupling of gas-phase ion mobility spectrometry (IMS) technologies to MS imaging has been increasingly explored to improve performance for biological tissue imaging. This approach allows improved detection of low abundance ions and separation of isobaric molecular species, thus resulting in more accurate determination of the spatial distribution of molecular ions. In this review, we highlight recent advances in the field focusing on promising applications of these technologies for metabolite, lipid and protein tissue imaging.
Collapse
Affiliation(s)
- Marta Sans
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States
| | - Clara L Feider
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States
| | - Livia S Eberlin
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States.
| |
Collapse
|
35
|
D’Hue C, Moore M, Summerlin DJ, Jarmusch A, Alfaro C, Mantravadi A, Bewley A, Farwell DG, Cooks RG. Feasibility of desorption electrospray ionization mass spectrometry for diagnosis of oral tongue squamous cell carcinoma. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2018; 32:133-141. [PMID: 29078250 PMCID: PMC5757369 DOI: 10.1002/rcm.8019] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 10/13/2017] [Accepted: 10/13/2017] [Indexed: 05/15/2023]
Abstract
RATIONALE Desorption electrospray ionization mass spectrometry (DESI-MS) has demonstrated utility in differentiating tumor from adjacent normal tissue in both urologic and neurosurgical specimens. We sought to evaluate if this technique had similar accuracy in differentiating oral tongue squamous cell carcinoma (SCC) from adjacent normal epithelium due to current issues with late diagnosis of SCC in advanced stages. METHODS Fresh frozen samples of SCC and adjacent normal tissue were obtained by surgical resection. Resections were analyzed using DESI-MS sometimes by a blinded technologist. Normative spectra were obtained for separate regions containing SCC or adjacent normal epithelium. Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) of spectra were used to predict SCC versus normal tongue epithelium. Predictions were compared with pathology to assess accuracy in differentiating oral SCC from adjacent normal tissue. RESULTS Initial PCA score and loading plots showed clear separation of SCC and normal epithelial tissue using DESI-MS. PCA-LDA resulted in accuracy rates of 95% for SCC versus normal and 93% for SCC, adjacent normal and normal. Additional samples were blindly analyzed with PCA-LDA pixel-by-pixel predicted classifications as SCC or normal tongue epithelial tissue and compared against histopathology. The m/z 700-900 prediction model showed a 91% accuracy rate. CONCLUSIONS DESI-MS accurately differentiated oral SCC from adjacent normal epithelium. Classification of all typical tissue types and pixel predictions with additional classifications should increase confidence in the validation model.
Collapse
Affiliation(s)
- Cedric D’Hue
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907-2084
| | - Michael Moore
- University of California at Davis, Comprehensive Cancer Center, Department of Otolaryngology-Head and Neck Surgery, 2521 Stockton Blvd., Suite 7200, Sacramento, CA 95817
| | - Don-John Summerlin
- Indiana University Health Pathology Laboratory, 350 West 11th Street, Room 4022, Indianapolis, IN 46202-4108
| | - Alan Jarmusch
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907-2084
| | - Clint Alfaro
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907-2084
| | - Avinash Mantravadi
- Indiana University Department of Otolaryngology-Head and Neck Surgery, 550 N. University Blvd. Rm 3170, Indianapolis, IN 46202
| | - Arnaud Bewley
- University of California at Davis, Comprehensive Cancer Center, Department of Otolaryngology-Head and Neck Surgery, 2521 Stockton Blvd., Suite 7200, Sacramento, CA 95817
| | - D. Gregory Farwell
- University of California at Davis, Comprehensive Cancer Center, Department of Otolaryngology-Head and Neck Surgery, 2521 Stockton Blvd., Suite 7200, Sacramento, CA 95817
| | - R. Graham Cooks
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907-2084
| |
Collapse
|
36
|
Woolman M, Ferry I, Kuzan-Fischer CM, Wu M, Zou J, Kiyota T, Isik S, Dara D, Aman A, Das S, Taylor MD, Rutka JT, Ginsberg HJ, Zarrine-Afsar A. Rapid determination of medulloblastoma subgroup affiliation with mass spectrometry using a handheld picosecond infrared laser desorption probe. Chem Sci 2017; 8:6508-6519. [PMID: 28989676 PMCID: PMC5628578 DOI: 10.1039/c7sc01974b] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 07/21/2017] [Indexed: 12/25/2022] Open
Abstract
Medulloblastoma (MB), the most prevalent malignant childhood brain tumour, consists of at least 4 distinct subgroups each of which possesses a unique survival rate and response to treatment. To rapidly determine MB subgroup affiliation in a manner that would be actionable during surgery, we subjected murine xenograft tumours of two MB subgroups (SHH and Group 3) to Mass Spectrometry (MS) profiling using a handheld Picosecond InfraRed Laser (PIRL) desorption probe and interface developed by our group. This platform provides real time MS profiles of tissue based on laser desorbed lipids and small molecules with only 5-10 seconds of sampling. PIRL-MS analysis of ex vivo MB tumours offered a 98% success rate in subgroup determination, observed over 194 PIRL-MS datasets collected from 19 independent tumours (∼10 repetitions each) utilizing 6 different established MB cell lines. Robustness was verified by a 5%-leave-out-and-remodel test. PIRL ablated tissue material was collected on a filter paper and subjected to high resolution LC-MS to provide ion identity assignments for the m/z values that contribute most to the statistical discrimination between SHH and Group 3 MB. Based on this analysis, rapid classification of MB with PIRL-MS utilizes a variety of fatty acid chains, glycerophosphates, glycerophosphoglycerols and glycerophosphocholines rapidly extracted from the tumours. In this work, we provide evidence that 5-10 seconds of sampling from ex vivo MB tissue with PIRL-MS can allow robust tumour subgroup classification, and have identified several biomarker ions responsible for the statistical discrimination of MB Group 3 and the SHH subgroup. The existing PIRL-MS platform used herein offers capabilities for future in vivo use.
Collapse
Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health , University Health Network , 100 College Street , Toronto , ON M5G 1P5 , Canada .
- Department of Medical Biophysics , University of Toronto , 101 College Street , Toronto , ON M5G 1L7 , Canada
| | - Isabelle Ferry
- Peter Gilgan Centre for Research and Learning , Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
- Arthur and Sonia Labatt Brain Tumor Research Centre , The Hospital for Sick Children , Toronto , ON M5G 1X8 , Canada
- Developmental & Stem Cell Biology Program , The Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
| | - Claudia M Kuzan-Fischer
- Peter Gilgan Centre for Research and Learning , Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
- Arthur and Sonia Labatt Brain Tumor Research Centre , The Hospital for Sick Children , Toronto , ON M5G 1X8 , Canada
- Developmental & Stem Cell Biology Program , The Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
| | - Megan Wu
- Peter Gilgan Centre for Research and Learning , Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
- Arthur and Sonia Labatt Brain Tumor Research Centre , The Hospital for Sick Children , Toronto , ON M5G 1X8 , Canada
- Developmental & Stem Cell Biology Program , The Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
| | - Jing Zou
- Techna Institute for the Advancement of Technology for Health , University Health Network , 100 College Street , Toronto , ON M5G 1P5 , Canada .
| | - Taira Kiyota
- Drug Discovery Program , Ontario Institute for Cancer Research , 661 University Avenue , Toronto , ON M5G 0A3 , Canada
| | - Semra Isik
- Peter Gilgan Centre for Research and Learning , Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
| | - Delaram Dara
- Techna Institute for the Advancement of Technology for Health , University Health Network , 100 College Street , Toronto , ON M5G 1P5 , Canada .
| | - Ahmed Aman
- Drug Discovery Program , Ontario Institute for Cancer Research , 661 University Avenue , Toronto , ON M5G 0A3 , Canada
| | - Sunit Das
- Peter Gilgan Centre for Research and Learning , Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
- Department of Surgery , University of Toronto , 149 College Street , Toronto , ON M5T 1P5 , Canada
- Keenan Research Center for Biomedical Science , The Li Ka Shing Knowledge Institute , St. Michael's Hospital , 30 Bond Street , Toronto , ON M5B 1W8 , Canada
| | - Michael D Taylor
- Peter Gilgan Centre for Research and Learning , Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
- Department of Surgery , University of Toronto , 149 College Street , Toronto , ON M5T 1P5 , Canada
- Arthur and Sonia Labatt Brain Tumor Research Centre , The Hospital for Sick Children , Toronto , ON M5G 1X8 , Canada
- Developmental & Stem Cell Biology Program , The Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
| | - James T Rutka
- Peter Gilgan Centre for Research and Learning , Hospital for Sick Children , 686 Bay Street , Toronto , ON M5G 0A4 , Canada
- Department of Surgery , University of Toronto , 149 College Street , Toronto , ON M5T 1P5 , Canada
- Arthur and Sonia Labatt Brain Tumor Research Centre , The Hospital for Sick Children , Toronto , ON M5G 1X8 , Canada
| | - Howard J Ginsberg
- Techna Institute for the Advancement of Technology for Health , University Health Network , 100 College Street , Toronto , ON M5G 1P5 , Canada .
- Department of Surgery , University of Toronto , 149 College Street , Toronto , ON M5T 1P5 , Canada
- Keenan Research Center for Biomedical Science , The Li Ka Shing Knowledge Institute , St. Michael's Hospital , 30 Bond Street , Toronto , ON M5B 1W8 , Canada
- Institute of Biomaterials and Biomedical Engineering , University of Toronto , 164 College Street , Toronto , ON M5S 3G9 , Canada
| | - Arash Zarrine-Afsar
- Techna Institute for the Advancement of Technology for Health , University Health Network , 100 College Street , Toronto , ON M5G 1P5 , Canada .
- Department of Medical Biophysics , University of Toronto , 101 College Street , Toronto , ON M5G 1L7 , Canada
- Department of Surgery , University of Toronto , 149 College Street , Toronto , ON M5T 1P5 , Canada
- Keenan Research Center for Biomedical Science , The Li Ka Shing Knowledge Institute , St. Michael's Hospital , 30 Bond Street , Toronto , ON M5B 1W8 , Canada
| |
Collapse
|
37
|
Zhang W, Wang X, Xia Y, Ouyang Z. Ambient Ionization and Miniature Mass Spectrometry Systems for Disease Diagnosis and Therapeutic Monitoring. Theranostics 2017; 7:2968-2981. [PMID: 28839457 PMCID: PMC5566099 DOI: 10.7150/thno.19410] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 06/06/2017] [Indexed: 12/26/2022] Open
Abstract
Mass spectrometry has become a powerful tool in the field of biomedicine. The combination of ambient ionization and miniature mass spectrometry systems could most likely fulfill a significant need in medical diagnostics, providing highly specific molecular information in real time for clinical and even point-of-care analysis. In this review, we discuss the recent development of ambient ionization and miniature mass spectrometers as well as their potential in disease diagnosis and therapeutic monitoring, with an emphasis on their capability in analysis of biofluids and tissues. We also speculate the future development of the integrated, miniature MS systems and provide our perspectives on the challenges in technical development as well as possible solutions for path forward.
Collapse
Affiliation(s)
- Wenpeng Zhang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Xiao Wang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Yu Xia
- Department of Chemistry, Tsinghua University, Beijing 10084, China
- Department of Chemistry, Purdue University, West Lafayette, IN 47906, USA
| | - Zheng Ouyang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| |
Collapse
|
38
|
Sans M, Gharpure K, Tibshirani R, Zhang J, Liang L, Liu J, Young JH, Dood RL, Sood AK, Eberlin LS. Metabolic Markers and Statistical Prediction of Serous Ovarian Cancer Aggressiveness by Ambient Ionization Mass Spectrometry Imaging. Cancer Res 2017; 77:2903-2913. [PMID: 28416487 DOI: 10.1158/0008-5472.can-16-3044] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 01/10/2017] [Accepted: 04/05/2017] [Indexed: 02/06/2023]
Abstract
Ovarian high-grade serous carcinoma (HGSC) results in the highest mortality among gynecological cancers, developing rapidly and aggressively. Dissimilarly, serous borderline ovarian tumors (BOT) can progress into low-grade serous carcinomas and have relatively indolent clinical behavior. The underlying biological differences between HGSC and BOT call for accurate diagnostic methodologies and tailored treatment options, and identification of molecular markers of aggressiveness could provide valuable biochemical insights and improve disease management. Here, we used desorption electrospray ionization (DESI) mass spectrometry (MS) to image and chemically characterize the metabolic profiles of HGSC, BOT, and normal ovarian tissue samples. DESI-MS imaging enabled clear visualization of fine papillary branches in serous BOT and allowed for characterization of spatial features of tumor heterogeneity such as adjacent necrosis and stroma in HGSC. Predictive markers of cancer aggressiveness were identified, including various free fatty acids, metabolites, and complex lipids such as ceramides, glycerophosphoglycerols, cardiolipins, and glycerophosphocholines. Classification models built from a total of 89,826 individual pixels, acquired in positive and negative ion modes from 78 different tissue samples, enabled diagnosis and prediction of HGSC and all tumor samples in comparison with normal tissues, with overall agreements of 96.4% and 96.2%, respectively. HGSC and BOT discrimination was achieved with an overall accuracy of 93.0%. Interestingly, our classification model allowed identification of three BOT samples presenting unusual histologic features that could be associated with the development of low-grade carcinomas. Our results suggest DESI-MS as a powerful approach for rapid serous ovarian cancer diagnosis based on altered metabolic signatures. Cancer Res; 77(11); 2903-13. ©2017 AACR.
Collapse
Affiliation(s)
- Marta Sans
- Department of Chemistry, The University of Texas at Austin, Austin, Texas
| | - Kshipra Gharpure
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Robert Tibshirani
- Departments of Biomedical Data Sciences and Statistics, Stanford University, Stanford, California
| | - Jialing Zhang
- Department of Chemistry, The University of Texas at Austin, Austin, Texas
| | - Li Liang
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jinsong Liu
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jonathan H Young
- Department of Chemistry, The University of Texas at Austin, Austin, Texas
| | - Robert L Dood
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Anil K Sood
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas. .,Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Livia S Eberlin
- Department of Chemistry, The University of Texas at Austin, Austin, Texas.
| |
Collapse
|
39
|
Arentz G, Mittal P, Zhang C, Ho YY, Briggs M, Winderbaum L, Hoffmann MK, Hoffmann P. Applications of Mass Spectrometry Imaging to Cancer. Adv Cancer Res 2017; 134:27-66. [PMID: 28110654 DOI: 10.1016/bs.acr.2016.11.002] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Pathologists play an essential role in the diagnosis and prognosis of benign and cancerous tumors. Clinicians provide tissue samples, for example, from a biopsy, which are then processed and thin sections are placed onto glass slides, followed by staining of the tissue with visible dyes. Upon processing and microscopic examination, a pathology report is provided, which relies on the pathologist's interpretation of the phenotypical presentation of the tissue. Targeted analysis of single proteins provide further insight and together with clinical data these results influence clinical decision making. Recent developments in mass spectrometry facilitate the collection of molecular information about such tissue specimens. These relatively new techniques generate label-free mass spectra across tissue sections providing nonbiased, nontargeted molecular information. At each pixel with spatial coordinates (x/y) a mass spectrum is acquired. The acquired mass spectrums can be visualized as intensity maps displaying the distribution of single m/z values of interest. Based on the sample preparation, proteins, peptides, lipids, small molecules, or glycans can be analyzed. The generated intensity maps/images allow new insights into tumor tissues. The technique has the ability to detect and characterize tumor cells and their environment in a spatial context and combined with histological staining, can be used to aid pathologists and clinicians in the diagnosis and management of cancer. Moreover, such data may help classify patients to aid therapy decisions and predict outcomes. The novel complementary mass spectrometry-based methods described in this chapter will contribute to the transformation of pathology services around the world.
Collapse
Affiliation(s)
- G Arentz
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - P Mittal
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - C Zhang
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - Y-Y Ho
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - M Briggs
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia; ARC Centre for Nanoscale BioPhotonics (CNBP), University of Adelaide, Adelaide, SA, Australia
| | - L Winderbaum
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - M K Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - P Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia.
| |
Collapse
|
40
|
Pirro V, Jarmusch AK, Ferreira CR, Cooks RG. Ambient Lipidomic Analysis of Brain Tissue Using Desorption Electrospray Ionization (DESI) Mass Spectrometry. NEUROMETHODS 2017. [DOI: 10.1007/978-1-4939-6946-3_14] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
|
41
|
Abstract
Ambient ionization mass spectrometry was developed as a sample preparation-free alternative to traditional MS-based workflows. Desorption electrospray ionization (DESI)-MS methods were demonstrated to allow the direct analysis of a broad range of samples including unaltered biological tissue specimens. In contrast to this advantageous feature, nowadays DESI-MS is almost exclusively used for sample preparation intensive mass spectrometric imaging (MSI) in the area of cancer research. As an alternative to MALDI, DESI-MSI offers matrix deposition-free experiment with improved signal in the lower (<500m/z) range. DESI-MSI enables the spatial mapping of tumor metabolism and has been broadly demonstrated to offer an alternative to frozen section histology for intraoperative tissue identification and surgical margin assessment. Rapid evaporative ionization mass spectrometry (REIMS) was developed exclusively for the latter purpose by the direct combination of electrosurgical devices and mass spectrometry. In case of the REIMS technology, aerosol particles produced by electrosurgical dissection are subjected to MS analysis, providing spectral information on the structural lipid composition of tissues. REIMS technology was demonstrated to give real-time information on the histological nature of tissues being dissected, deeming it an ideal tool for intraoperative tissue identification including surgical margin control. More recently, the method has also been used for the rapid lipidomic phenotyping of cancer cell lines as it was demonstrated in case of the NCI-60 cell line collection.
Collapse
Affiliation(s)
- Z Takats
- Imperial College London, London, United Kingdom.
| | - N Strittmatter
- Drug Safety and Metabolism, AstraZeneca, Cambridge, United Kingdom
| | | |
Collapse
|
42
|
Huang KT, Ludy S, Calligaris D, Dunn IF, Laws E, Santagata S, Agar NYR. Rapid Mass Spectrometry Imaging to Assess the Biochemical Profile of Pituitary Tissue for Potential Intraoperative Usage. Adv Cancer Res 2016; 134:257-282. [PMID: 28110653 DOI: 10.1016/bs.acr.2016.11.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Pituitary adenomas are relatively common intracranial neoplasms that are frequently treated with surgical resection. Rapid visualization of pituitary tissue remains a challenge as current techniques either produce little to no information on hormone-secreting function or are too slow to practically aid in intraoperative or even perioperative decision-making. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) represents a powerful method by which molecular maps of tissue samples can be created, yielding a two-dimensional representation of the expression patterns of small molecules and proteins from biologic samples. In this chapter, we review the use of MALDI MSI, its application to the characterization of the pituitary gland, and its potential applications for guiding the management of pituitary adenomas.
Collapse
Affiliation(s)
- K T Huang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - S Ludy
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - D Calligaris
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - I F Dunn
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - E Laws
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - S Santagata
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - N Y R Agar
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
| |
Collapse
|
43
|
Dória ML, McKenzie JS, Mroz A, Phelps DL, Speller A, Rosini F, Strittmatter N, Golf O, Veselkov K, Brown R, Ghaem-Maghami S, Takats Z. Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging. Sci Rep 2016; 6:39219. [PMID: 27976698 PMCID: PMC5156945 DOI: 10.1038/srep39219] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 11/21/2016] [Indexed: 01/08/2023] Open
Abstract
Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology.
Collapse
Affiliation(s)
- Maria Luisa Dória
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - James S McKenzie
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Anna Mroz
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - David L Phelps
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Abigail Speller
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Francesca Rosini
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Nicole Strittmatter
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Ottmar Golf
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Kirill Veselkov
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Robert Brown
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Sadaf Ghaem-Maghami
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Zoltan Takats
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| |
Collapse
|
44
|
Fernandes AMAP, Vendramini PH, Galaverna R, Schwab NV, Alberici LC, Augusti R, Castilho RF, Eberlin MN. Direct Visualization of Neurotransmitters in Rat Brain Slices by Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI - MS). JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2016; 27:1944-1951. [PMID: 27704473 DOI: 10.1007/s13361-016-1475-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 06/30/2016] [Accepted: 07/31/2016] [Indexed: 05/12/2023]
Abstract
Mass spectrometry imaging (MSI) of neurotransmitters has so far been mainly performed by matrix-assisted laser desorption/ionization (MALDI) where derivatization reagents, deuterated matrix and/or high resolution, or tandem MS have been applied to circumvent problems with interfering ion peaks from matrix and from isobaric species. We herein describe the application of desorption electrospray ionization mass spectrometry imaging (DESI)-MSI in rat brain coronal and sagittal slices for direct spatial monitoring of neurotransmitters and choline with no need of derivatization reagents and/or deuterated materials. The amino acids γ-aminobutyric (GABA), glutamate, aspartate, serine, as well as acetylcholine, dopamine, and choline were successfully imaged using a commercial DESI source coupled to a hybrid quadrupole-Orbitrap mass spectrometer. The spatial distribution of the analyzed compounds in different brain regions was determined. We conclude that the ambient matrix-free DESI-MSI is suitable for neurotransmitter imaging and could be applied in studies that involve evaluation of imbalances in neurotransmitters levels. Graphical Abstract ᅟ.
Collapse
Affiliation(s)
- Anna Maria A P Fernandes
- Thomson Mass Spectrometry Laboratory, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil.
| | - Pedro H Vendramini
- Thomson Mass Spectrometry Laboratory, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
| | | | - Nicolas V Schwab
- Thomson Mass Spectrometry Laboratory, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
| | - Luciane C Alberici
- Departamento de Física e Química, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil
| | - Rodinei Augusti
- Departamento de Química, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Roger F Castilho
- Departamento de Patologia Clínica, Faculdade de Ciências Médicas, UNICAMP, Campinas, SP, Brazil
| | - Marcos N Eberlin
- Thomson Mass Spectrometry Laboratory, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil.
| |
Collapse
|
45
|
Advances in Lipidomics for Cancer Biomarkers Discovery. Int J Mol Sci 2016; 17:ijms17121992. [PMID: 27916803 PMCID: PMC5187792 DOI: 10.3390/ijms17121992] [Citation(s) in RCA: 123] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Revised: 11/11/2016] [Accepted: 11/22/2016] [Indexed: 12/11/2022] Open
Abstract
Lipids play critical functions in cellular survival, proliferation, interaction and death, since they are involved in chemical-energy storage, cellular signaling, cell membranes, and cell-cell interactions. These cellular processes are strongly related to carcinogenesis pathways, particularly to transformation, progression, and metastasis, suggesting the bioactive lipids are mediators of a number of oncogenic processes. The current review gives a synopsis of a lipidomic approach in tumor characterization; we provide an overview on potential lipid biomarkers in the oncology field and on the principal lipidomic methodologies applied. The novel lipidomic biomarkers are reviewed in an effort to underline their role in diagnosis, in prognostic characterization and in prediction of therapeutic outcomes. A lipidomic investigation through mass spectrometry highlights new insights on molecular mechanisms underlying cancer disease. This new understanding will promote clinical applications in drug discovery and personalized therapy.
Collapse
|
46
|
Bilkey J, Tata A, McKee TD, Porcari AM, Bluemke E, Woolman M, Ventura M, Eberlin MN, Zarrine-Afsar A. Variations in the Abundance of Lipid Biomarker Ions in Mass Spectrometry Images Correlate to Tissue Density. Anal Chem 2016; 88:12099-12107. [PMID: 28193010 DOI: 10.1021/acs.analchem.6b02767] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
While mass spectrometry (MS) imaging is widely used to investigate the molecular composition of ex vivo slices of cancerous tumors, little is known about how variations in the cellular properties of cancer tissue can influence cancer biomarker ion images. To better understand the basis for variations in the abundances of cancer biomarker ions seen in MS images of relatively homogeneous ex vivo tumor samples, sections of snap frozen human breast cancer murine xenografts were subjected to desorption electrospray ionization mass spectrometry (DESI-MS) imaging. Serial sections were then stained with hematoxylin and eosin (H&E) and subjected to detailed morphometric cellular analysis, using a commercial digital pathology platform augmented with custom-tailored image analysis algorithms developed in-house. Gross morphological heterogeneities due to stroma, vasculature, and noncancer cells were mapped in the tumor and found to not correlate with the areas of suppressed cancer biomarker abundance. Instead, the ion abundances of major breast cancer biomarkers were found to correlate with the cytoplasmic area of cancer cells that comprised the tumor tissue. Therefore, detailed cellular analyses can be used to rationalize subtle heterogeneities in ion abundance in MS images, not explained by the presence of gross morphological heterogeneities such as stroma.
Collapse
Affiliation(s)
- Jade Bilkey
- STTARR Innovation Centre, Princess Margaret Cancer Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Alessandra Tata
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Trevor D McKee
- STTARR Innovation Centre, Princess Margaret Cancer Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Andreia M Porcari
- ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of Campinas , Campinas, SP Brazil
| | - Emma Bluemke
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Manuela Ventura
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Marcos N Eberlin
- ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of Campinas , Campinas, SP Brazil
| | - Arash Zarrine-Afsar
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada.,Department of Medical Biophysics, University of Toronto ,101 College Street Suite 15-701, Toronto, Ontario M5G 1L7, Canada.,Department of Surgery, University of Toronto , 149 College Street, Toronto, Ontario M5T-1P5, Canada.,Keenan Research Centre for Biomedical Science, Li Ka-Shing Knowledge Institute, St. Michael's Hospital , 30 Bond Street, Toronto, Ontario M5B-1W8, Canada
| |
Collapse
|
47
|
Rapid Detection of Necrosis in Breast Cancer with Desorption Electrospray Ionization Mass Spectrometry. Sci Rep 2016; 6:35374. [PMID: 27734938 PMCID: PMC5062153 DOI: 10.1038/srep35374] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 09/26/2016] [Indexed: 02/03/2023] Open
Abstract
Identification of necrosis in tumors is of prognostic value in treatment planning, as necrosis is associated with aggressive forms of cancer and unfavourable outcomes. To facilitate rapid detection of necrosis with Mass Spectrometry (MS), we report the lipid MS profile of necrotic breast cancer with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) imaging validated with statistical analysis and correlating pathology. This MS profile is characterized by (1) the presence of the ion of m/z 572.48 [Cer(d34:1) + Cl]− which is a ceramide absent from the viable cancer subregions; (2) the absence of the ion of m/z 391.25 which is present in small abundance only in viable cancer subregions; and (3) a slight increase in the relative intensity of known breast cancer biomarker ions of m/z 281.25 [FA(18:1)-H]− and 303.23 [FA(20:4)-H]−. Necrosis is accompanied by alterations in the tissue optical depolarization rate, allowing tissue polarimetry to guide DESI-MS analysis for rapid MS profiling or targeted MS imaging. This workflow, in combination with the MS profile of necrosis, may permit rapid characterization of necrotic tumors from tissue slices. Further, necrosis-specific biomarker ions are detected in seconds with single MS scans of necrotic tumor tissue smears, which further accelerates the identification workflow by avoiding tissue sectioning and slide preparation.
Collapse
|
48
|
Smolensky D, Rathore K, Cekanova M. Molecular targets in urothelial cancer: detection, treatment, and animal models of bladder cancer. Drug Des Devel Ther 2016; 10:3305-3322. [PMID: 27784990 PMCID: PMC5063594 DOI: 10.2147/dddt.s112113] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Bladder cancer remains one of the most expensive cancers to treat in the United States due to the length of required treatment and degree of recurrence. In order to treat bladder cancer more effectively, targeted therapies are being investigated. In order to use targeted therapy in a patient, it is important to provide a genetic background of the patient. Recent advances in genome sequencing, as well as transcriptome analysis, have identified major pathway components altered in bladder cancer. The purpose of this review is to provide a broad background on bladder cancer, including its causes, diagnosis, stages, treatments, animal models, as well as signaling pathways in bladder cancer. The major focus is given to the PI3K/AKT pathway, p53/pRb signaling pathways, and the histone modification machinery. Because several promising immunological therapies are also emerging in the treatment of bladder cancer, focus is also given on general activation of the immune system for the treatment of bladder cancer.
Collapse
Affiliation(s)
- Dmitriy Smolensky
- Department of Small Animal Clinical Sciences, College of Veterinary Medicine
- UT-ORNL Graduate School of Genome Science and Technology, The University of Tennessee, Knoxville, TN, USA
| | - Kusum Rathore
- Department of Small Animal Clinical Sciences, College of Veterinary Medicine
| | - Maria Cekanova
- Department of Small Animal Clinical Sciences, College of Veterinary Medicine
- UT-ORNL Graduate School of Genome Science and Technology, The University of Tennessee, Knoxville, TN, USA
| |
Collapse
|
49
|
Jarmusch AK, Alfaro CM, Pirro V, Hattab EM, Cohen-Gadol AA, Cooks RG. Differential Lipid Profiles of Normal Human Brain Matter and Gliomas by Positive and Negative Mode Desorption Electrospray Ionization - Mass Spectrometry Imaging. PLoS One 2016; 11:e0163180. [PMID: 27658243 PMCID: PMC5033406 DOI: 10.1371/journal.pone.0163180] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 09/02/2016] [Indexed: 11/18/2022] Open
Abstract
Desorption electrospray ionization—mass spectrometry (DESI-MS) imaging was used to analyze unmodified human brain tissue sections from 39 subjects sequentially in the positive and negative ionization modes. Acquisition of both MS polarities allowed more complete analysis of the human brain tumor lipidome as some phospholipids ionize preferentially in the positive and others in the negative ion mode. Normal brain parenchyma, comprised of grey matter and white matter, was differentiated from glioma using positive and negative ion mode DESI-MS lipid profiles with the aid of principal component analysis along with linear discriminant analysis. Principal component–linear discriminant analyses of the positive mode lipid profiles was able to distinguish grey matter, white matter, and glioma with an average sensitivity of 93.2% and specificity of 96.6%, while the negative mode lipid profiles had an average sensitivity of 94.1% and specificity of 97.4%. The positive and negative mode lipid profiles provided complementary information. Principal component–linear discriminant analysis of the combined positive and negative mode lipid profiles, via data fusion, resulted in approximately the same average sensitivity (94.7%) and specificity (97.6%) of the positive and negative modes when used individually. However, they complemented each other by improving the sensitivity and specificity of all classes (grey matter, white matter, and glioma) beyond 90% when used in combination. Further principal component analysis using the fused data resulted in the subgrouping of glioma into two groups associated with grey and white matter, respectively, a separation not apparent in the principal component analysis scores plots of the separate positive and negative mode data. The interrelationship of tumor cell percentage and the lipid profiles is discussed, and how such a measure could be used to measure residual tumor at surgical margins.
Collapse
Affiliation(s)
- Alan K. Jarmusch
- Department of Chemistry and Center for Analytical Instrument Development, Purdue University, West Lafayette, Indiana, United States of America
| | - Clint M. Alfaro
- Department of Chemistry and Center for Analytical Instrument Development, Purdue University, West Lafayette, Indiana, United States of America
| | - Valentina Pirro
- Department of Chemistry and Center for Analytical Instrument Development, Purdue University, West Lafayette, Indiana, United States of America
| | - Eyas M. Hattab
- Department of Pathology and Laboratory Medicine, University of Louisville School of Medicine, Louisville, Kentucky, United States of America
| | - Aaron A. Cohen-Gadol
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - R. Graham Cooks
- Department of Chemistry and Center for Analytical Instrument Development, Purdue University, West Lafayette, Indiana, United States of America
- * E-mail:
| |
Collapse
|
50
|
Tata A, Gribble A, Ventura M, Ganguly M, Bluemke E, Ginsberg HJ, Jaffray DA, Ifa DR, Vitkin A, Zarrine-Afsar A. Wide-field tissue polarimetry allows efficient localized mass spectrometry imaging of biological tissues. Chem Sci 2016; 7:2162-2169. [PMID: 30155015 PMCID: PMC6090527 DOI: 10.1039/c5sc03782d] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 12/14/2015] [Indexed: 11/21/2022] Open
Abstract
While mass spectrometers can detect chemical signatures within milliseconds of data acquisition time, the non-targeted nature of mass spectrometry imaging (MSI) necessitates probing the entire surface of the sample to reveal molecular composition even if the information is only sought from a sample subsection. This leads to long analysis times. Here, we used polarimetry to identify, within a biological tissue, areas of polarimetric heterogeneity indicative of cancer. We were then able to target our MS analysis using polarimetry results to either the cancer region itself or to the cancer margin. A tandem of polarimetry and Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) enables fast (10 fold compared to non-targeted imaging), and accurate pathology assessment (cancer typification in less than 2 minutes compared to 30 minutes for histopathology) of ex vivo tissue slices, without additional sample preparation. This workflow reduces the overall analysis time of MSI as a research tool.
Collapse
Affiliation(s)
- Alessandra Tata
- Techna Institute for the Advancement of Technology for Health , University Health Network , Toronto , ON M5G-1P5 , Canada .
| | - Adam Gribble
- Department of Medical Biophysics , University of Toronto , 101 College Street Suite 15-701 , Toronto , ON M5G 1L7 , Canada
| | - Manuela Ventura
- Techna Institute for the Advancement of Technology for Health , University Health Network , Toronto , ON M5G-1P5 , Canada .
| | - Milan Ganguly
- STTARR Innovation Centre , Princess Margaret Cancer Centre , 101 College Street , Toronto , ON M5G 1L7 , Canada
| | - Emma Bluemke
- Techna Institute for the Advancement of Technology for Health , University Health Network , Toronto , ON M5G-1P5 , Canada .
- Department of Medical Biophysics , University of Toronto , 101 College Street Suite 15-701 , Toronto , ON M5G 1L7 , Canada
| | - Howard J Ginsberg
- Techna Institute for the Advancement of Technology for Health , University Health Network , Toronto , ON M5G-1P5 , Canada .
- Department of Surgery , University of Toronto , 149 College Street , Toronto , ON M5T-1P5 , Canada
- Keenan Research Centre for Biomedical Science , Li KaShing Knowledge Institute , St. Michael's Hospital , 30 Bond Street , Toronto , ON M5B-1W8 , Canada
| | - David A Jaffray
- Techna Institute for the Advancement of Technology for Health , University Health Network , Toronto , ON M5G-1P5 , Canada .
- Department of Medical Biophysics , University of Toronto , 101 College Street Suite 15-701 , Toronto , ON M5G 1L7 , Canada
| | - Demian R Ifa
- Department of Chemistry , York University , 4700 Keele Street , Toronto , ON M3J-1P3 , Canada
| | - Alex Vitkin
- Department of Medical Biophysics , University of Toronto , 101 College Street Suite 15-701 , Toronto , ON M5G 1L7 , Canada
- Department of Radiation Oncology , University of Toronto , 610 University Avenue , Toronto , Ontario M5G 2M9 , Canada
- Division of Biophysics and Bioimaging , Ontario Cancer Institute , University Health Network , 610 University Ave , Toronto , ON M5G 2M9 , Canada
| | - Arash Zarrine-Afsar
- Techna Institute for the Advancement of Technology for Health , University Health Network , Toronto , ON M5G-1P5 , Canada .
- Department of Medical Biophysics , University of Toronto , 101 College Street Suite 15-701 , Toronto , ON M5G 1L7 , Canada
- Department of Surgery , University of Toronto , 149 College Street , Toronto , ON M5T-1P5 , Canada
- Keenan Research Centre for Biomedical Science , Li KaShing Knowledge Institute , St. Michael's Hospital , 30 Bond Street , Toronto , ON M5B-1W8 , Canada
| |
Collapse
|