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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.
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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
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2
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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.
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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
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3
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Dabija LG, Yousefi-Taemeh M, Duli E, Lemaire M, Ifa DR. Assessment of MALDI matrices for the detection and visualization of phosphatidylinositols and phosphoinositides in mouse kidneys through matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI). Anal Bioanal Chem 2024; 416:1857-1865. [PMID: 38319357 DOI: 10.1007/s00216-024-05184-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/07/2024]
Abstract
Phosphatidylinositols and their phosphorylated derivatives, known as phosphoinositides, are crucial in cellular processes, with their abnormalities linked to various diseases. Thus, identifying and measuring phosphoinositide levels in tissues are crucial for understanding their contributions to cellular processes and disease development. One powerful technique for mapping the spatial distribution of molecules in biological samples is matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). This technique allows for the simultaneous detection and analysis of multiple lipid classes in situ, making it invaluable for unbiased lipidomic studies. However, detecting phosphoinositides with MALDI-MSI is challenging due to their relatively low abundance in tissues and complex matrix effects. Addressing this, our study focused on optimizing matrix selection and thickness for better detection of phosphatidylinositols and their phosphorylated forms in mouse kidney tissues. Various matrices were assessed, including 9AA, DAN, CMBT, and DHA, adjusting their coating to improve ionization efficiency. Our results demonstrate that DAN, DHA, and CMBT matrices produced high-intensity chemical images of phosphatidylinositol distributions within kidney sections. These matrices, particularly DAN, DHA, and CMBT, allowed the identification of even low-abundance phosphoinositides, through tentative identifications. Notably, DAN and DHA served as optimal candidates due to their prominent detection and ability to map a majority of phosphatidylinositol species, while CMBT showed potential detection capability for phosphatidylinositol triphosphate compounds. These findings not only provide valuable insights for future research on the involvement of phosphoinositides in kidney pathophysiology, but also propose the use of the identified optimal matrices, particularly DAN and DHA, as the preferred choices for enhanced detection and mapping of these lipid species in future studies.
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Affiliation(s)
- Laurentiu G Dabija
- Department of Chemistry, Faculty of Science, York University, Toronto, ON, Canada
| | | | - Ergi Duli
- Cell Biology Program, Division of Nephrology, Department of Pediatrics, SickKids Research Institute, The Hospital for Sick Children, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mathieu Lemaire
- Cell Biology Program, Division of Nephrology, Department of Pediatrics, SickKids Research Institute, The Hospital for Sick Children, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Demian R Ifa
- Department of Chemistry, Faculty of Science, York University, Toronto, ON, Canada.
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Wang S, He T, Wang H. Non-targeted metabolomics study for discovery of hepatocellular carcinoma serum diagnostic biomarker. J Pharm Biomed Anal 2024; 239:115869. [PMID: 38064771 DOI: 10.1016/j.jpba.2023.115869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/06/2023] [Accepted: 11/20/2023] [Indexed: 01/05/2024]
Abstract
Hepatocellular carcinoma (HCC) is one of the most prevalent malignant cancers worldwide. Due to the asymptomatic features of HCC at early stages, patients are often diagnosed at advanced stages and missed effective treatment. Thus, there is an urgent need to identify sensitive and specific biomarkers for HCC early diagnosis. In the present study, an ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) approach was used to profile serum metabolites from HCC patients, liver cirrhosis (LC) patients, and normal controls (NC). Univariate and multivariate statistical analyses were performed to obtain the metabolomic differences of the three groups and select significantly changed metabolites that can be used as diagnostic biomarkers. In total, 757 differential metabolites were quantified among the three groups, and pathway enrichment analysis of these metabolites indicated that glycerophospholipid metabolism, pentose and glucuronate interconversions, phenylalanine, tyrosine and tryptophan biosynthesis, and linoleic acid metabolism were the most altered pathways involved in HCC development. Receiver operating characteristic (ROC) curve analysis was performed to select and evaluate the diagnostic biomarker performance. Seven metabolites were identified as potential biomarkers that can differentiate HCC from LC and NC, and LC from NC with the good diagnostic performance of area under the curve (AUC) from 0.890 to 0.990. In summary, our findings provide highly effective biomarker candidates to differentiate HCC from LC and NC, LC, and NC, which shed insight into HCC pathological mechanisms and will be helpful in better understanding and managing HCC.
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Affiliation(s)
- Shufeng Wang
- Keystonobel Biotechnologies and Pharmaceuticals (Beijing) Co., Ltd, Beijing 100176, PR China
| | - Tingting He
- Department of Hepatology Medicine of Traditional Chinese Medicine, the Fifth Medical Center of Chinese PLA General Hospital, Beijing 100039, PR China
| | - Hongxia Wang
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo 315211, PR China; School of Material Science and Chemical Engineering Ningbo University, Ningbo 315211, PR China; Ningbo Zhenhai Institute of Mass Spectrometry, Ningbo 315206, PR China.
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5
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Affiliation(s)
- Nicolás M Morato
- Department of Chemistry and Bindley Bioscience Center, Purdue University, West Lafayette, Indiana 47907, United States
| | - R Graham Cooks
- Department of Chemistry and Bindley Bioscience Center, Purdue University, West Lafayette, Indiana 47907, United States
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Chappel JR, King ME, Fleming J, Eberlin LS, Reif DM, Baker ES. Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics. Anal Chem 2023; 95:12913-12922. [PMID: 37579019 PMCID: PMC10561690 DOI: 10.1021/acs.analchem.3c02389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often assessed and visualized using various supervised and unsupervised statistical approaches. However, these approaches tend to fall short in identifying and concisely visualizing subtle, phenotype-relevant molecular changes. To address these shortcomings, we developed aggregated molecular phenotype (AMP) scores. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores, therefore, allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes. Due to the ensembled approach, AMP scores are able to overcome limitations associated with individual models, leading to high diagnostic accuracy and interpretability. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization MSI. Initial comparisons of cancerous human tissues to their normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.
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Affiliation(s)
- Jessie R Chappel
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Mary E King
- Department of Surgery, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Jonathon Fleming
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Livia S Eberlin
- Department of Surgery, Baylor College of Medicine, Houston, Texas 77030, United States
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, North Carolina 27709, United States
| | - Erin S Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, United States
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Chappel JR, King ME, Fleming J, Eberlin LS, Reif DM, Baker ES. Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.01.543306. [PMID: 37333214 PMCID: PMC10274704 DOI: 10.1101/2023.06.01.543306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.
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Affiliation(s)
- Jessie R. Chappel
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Mary E. King
- Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Jonathon Fleming
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Livia S. Eberlin
- Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - David M. Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Erin S. Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Shankar V, Vijayalakshmi K, Nolley R, Sonn GA, Kao CS, Zhao H, Wen R, Eberlin LS, Tibshirani R, Zare RN, Brooks JD. Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning. JCO Precis Oncol 2023; 7:e2200668. [PMID: 37285559 PMCID: PMC10309512 DOI: 10.1200/po.22.00668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/26/2023] [Accepted: 04/10/2023] [Indexed: 06/09/2023] Open
Abstract
PURPOSE Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. MATERIALS AND METHODS Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. RESULTS From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. CONCLUSION Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.
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Affiliation(s)
- Vishnu Shankar
- Program in Immunology, Stanford University School of Medicine, Stanford, CA
| | | | - Rosalie Nolley
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Geoffrey A. Sonn
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Chia-Sui Kao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA
| | - Hongjuan Zhao
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Ru Wen
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | | | - Robert Tibshirani
- Department of Biomedical Data Science, and Statistics, Stanford University, Stanford, CA
| | | | - James D. Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA
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9
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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.
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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.
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10
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Xiong JL, Ma N. Transcriptomic and Metabolomic Analyses Reveal That Fullerol Improves Drought Tolerance in Brassica napus L. Int J Mol Sci 2022; 23:ijms232315304. [PMID: 36499633 PMCID: PMC9740425 DOI: 10.3390/ijms232315304] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 11/22/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Carbon nanoparticles have potential threats to plant growth and stress tolerance. The polyhydroxy fullerene-fullerol (one of the carbon nanoparticles) could increase biomass accumulation in several plants subjected to drought; however, the underlying molecular and metabolic mechanisms governed by fullerol in improving drought tolerance in Brassica napus remain unclear. In the present study, exogenous fullerol was applied to the leaves of B. napus seedlings under drought conditions. The results of transcriptomic and metabolomic analyses revealed changes in the molecular and metabolic profiles of B. napus. The differentially expressed genes and the differentially accumulated metabolites, induced by drought or fullerol treatment, were mainly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to carbohydrate metabolism (e.g., "carbon metabolism" and "galactose metabolism"), amino acid metabolism (e.g., "biosynthesis of amino acids" and "arginine and proline metabolism"), and secondary metabolite metabolism (e.g., "biosynthesis of secondary metabolites"). For carbohydrate metabolism, the accumulation of oligosaccharides (e.g., sucrose) was decreased, whereas that of monosaccharides (e.g., mannose and myo-inositol) was increased by drought. With regard to amino acid metabolism, under drought stress, the accumulation of amino acids such as phenylalanine and tryptophan decreased, whereas that of glutamate and proline increased. Further, for secondary metabolite metabolism, B. napus subjected to soil drying showed a reduction in phenolics and flavonoids, such as hyperoside and trans-3-coumaric acid. However, the accumulation of carbohydrates was almost unchanged in fullerol-treated B. napus subjected to drought. When exposed to water shortage, the accumulation of amino acids, such as proline, was decreased upon fullerol treatment. However, that of phenolics and flavonoids, such as luteolin and trans-3-coumaric acid, was enhanced. Our findings suggest that fullerol can alleviate the inhibitory effects of drought on phenolics and flavonoids to enhance drought tolerance in B. napus.
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Affiliation(s)
- Jun-Lan Xiong
- Oil Crops Research Institute, Chinese Academy of Agricultural Science, Wuhan 430062, China
- School of Life Science, Lanzhou University, Lanzhou 730000, China
- Correspondence:
| | - Ni Ma
- Oil Crops Research Institute, Chinese Academy of Agricultural Science, Wuhan 430062, China
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11
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Hu H, Laskin J. Emerging Computational Methods in Mass Spectrometry Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203339. [PMID: 36253139 PMCID: PMC9731724 DOI: 10.1002/advs.202203339] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/17/2022] [Indexed: 05/10/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
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Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
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12
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Rapid Evaporative Ionization Mass Spectrometry-Based Lipidomics for Identification of Canine Mammary Pathology. Int J Mol Sci 2022; 23:ijms231810562. [PMID: 36142485 PMCID: PMC9502565 DOI: 10.3390/ijms231810562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/02/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
The present work proposes the use of a fast analytical platform for the mass spectrometric (MS) profiling of canine mammary tissues in their native form for the building of a predictive statistical model. The latter could be used as a novel diagnostic tool for the real-time identification of different cellular alterations in order to improve tissue resection during veterinary surgery, as previously validated in human oncology. Specifically, Rapid Evaporative Ionization Mass Spectrometry (REIMS) coupled with surgical electrocautery (intelligent knife—iKnife) was used to collect MS data from histologically processed mammary samples, classified into healthy, hyperplastic/dysplastic, mastitis and tumors. Differences in the lipid composition enabled tissue discrimination with an accuracy greater than 90%. The recognition capability of REIMS was tested on unknown mammary samples, and all of them were correctly identified with a correctness score of 98–100%. Triglyceride identification was increased in healthy mammary tissues, while the abundance of phospholipids was observed in altered tissues, reflecting morpho-functional changes in cell membranes, and oxidized species were also tentatively identified as discriminant features. The obtained lipidomic profiles represented unique fingerprints of the samples, suggesting that the iKnife technique is capable of differentiating mammary tissues following chemical changes in cellular metabolism.
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13
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Gao SQ, Zhao JH, Guan Y, Tang YS, Li Y, Liu LY. Mass Spectrometry Imaging technology in metabolomics: a systematic review. Biomed Chromatogr 2022:e5494. [PMID: 36044038 DOI: 10.1002/bmc.5494] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/24/2022] [Accepted: 08/28/2022] [Indexed: 11/11/2022]
Abstract
Mass spectrometry imaging (MSI) is a powerful label-free analysis technique that can provide simultaneous spatial distribution of multiple compounds in a single experiment. By combining the sensitive and rapid screening of high-throughput mass spectrometry with spatial chemical information, metabolite analysis and morphological characteristics are presented in a single image. MSI can be used for qualitative and quantitative analysis of metabolic profiles and it can provide visual analysis of spatial distribution information of complex biological and microbial systems. Matrix assisted laser desorption ionization, laser ablation electrospray ionization and desorption electrospray ionization are commonly used in MSI. Here, we summarize and compare these three technologies, as well as the applications and prospects of MSI in metabolomics.
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Affiliation(s)
- Si-Qi Gao
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Jin-Hui Zhao
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Yue Guan
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Ying-Shu Tang
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Ying Li
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Li-Yan Liu
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
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14
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Arendowski A, Ossoliński K, Ossolińska A, Ossoliński T, Nizioł J, Ruman T. Serum and urine analysis with gold nanoparticle-assisted laser desorption/ionization mass spectrometry for renal cell carcinoma metabolic biomarkers discovery. Adv Med Sci 2021; 66:326-335. [PMID: 34273747 DOI: 10.1016/j.advms.2021.07.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 06/02/2021] [Accepted: 07/06/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Renal cell carcinoma (RCC) is a very aggressive and often fatal heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes. MATERIALS/METHODS Serum and urine samples were collected from patients with diagnosed kidney cancer and assessed with gold nanoparticle enhanced target (AuNPET) surface assisted-laser desorption/ionization mass spectrometry (SALDI MS) based metabolomics and statistical analysis. RESULTS A database search allowed providing assignment of signals for the most promising features with a satisfactory value of the area under the curve and accuracy. Four potential biomarkers were found in urine and serum samples to distinguish clear cell renal cell carcinoma (ccRCC) from controls, 4 for the ccRCC with and without metastases, and 6 metabolites to distinguish low and high stages or grades. CONCLUSIONS This pilot study suggests that serum and urine metabolomics based on AuNPET-LDI MS may be useful in distinguishing types, grades and stages of human RCC.
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15
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Yang X, Song X, Zhang X, Shankar V, Wang S, Yang Y, Chen S, Zhang L, Ni Y, Zare RN, Hu Q. In situ DESI-MSI lipidomic profiles of mucosal margin of oral squamous cell carcinoma. EBioMedicine 2021; 70:103529. [PMID: 34391097 PMCID: PMC8374374 DOI: 10.1016/j.ebiom.2021.103529] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 07/15/2021] [Accepted: 07/28/2021] [Indexed: 01/22/2023] Open
Abstract
Background Although there is consensus that the optimal safe margin is ≥ 5mm, obtaining clear margins (≥5 mm) intraoperatively seems to be the major challenge. We applied a molecular diagnostic method at the lipidomic level to determine the safe surgical resection margin of OSCC by desorption electrospray ionisation mass spectrometry imaging (DESI-MSI). Methods By overlaying mass spectrometry images with hematoxylin-eosin staining (H&E) from 18 recruited OSCC participants, the mass spectra of all pixels across the diagnosed tumour and continuous mucosal margin regions were extracted to serve as the training and validation datasets. A Lasso regression model was used to evaluate the test performance. Findings By leave-one-out validation, the Lasso model achieved 88.6% accuracy in distinguishing between tumour and normal regions. To determine the safe surgical resection distance and margin status of OSCC, a set of 14 lipid ions that gradually decreased from tumour to normal tissue was assigned higher weight coefficients in the Lasso model. The safe surgical resection distance of OSCC was measured using the developed 14 lipid ion molecular diagnostic model for clinical reference. The overall accuracy of predicting tumours, positive margins, and negative margins was 92.6%. Interpretation The spatial segmentation results based on our diagnostic model not only clearly delineated the tumour and normal tissue, but also distinguished the different status of surgical margins. Meanwhile, the safe surgical resection margin of OSCC on frozen sections can also be accurately measured using the developed diagnostic model. Funding This study was supported by Nanjing Municipal Key Medical Laboratory Constructional Project Funding (since 2016) and the Centre of Nanjing Clinical Medicine Tumour (since 2014).
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Affiliation(s)
- Xihu Yang
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, 210008, China; Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210000, China.
| | - Xiaowei Song
- Department of Chemistry, Fudan University, Shanghai, 200438, China
| | - Xiaoxin Zhang
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210000, China
| | - Vishnu Shankar
- Department of Chemistry, Stanford University, Stanford, California, 94305, USA
| | - Shuai Wang
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210000, China
| | - Yan Yang
- Department of Oral Pathology, Stomatological hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210000, China
| | - Sheng Chen
- Department of Oral Pathology, Stomatological hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210000, China
| | - Lei Zhang
- Department of Oral Pathology, Stomatological hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210000, China
| | - Yanhong Ni
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210000, China.
| | - Richard N Zare
- Department of Chemistry, Fudan University, Shanghai, 200438, China; Department of Chemistry, Stanford University, Stanford, California, 94305, USA.
| | - Qingang Hu
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210000, China.
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16
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Otsuka Y. Direct Liquid Extraction and Ionization Techniques for Understanding Multimolecular Environments in Biological Systems (Secondary Publication). Mass Spectrom (Tokyo) 2021; 10:A0095. [PMID: 34249586 PMCID: PMC8246329 DOI: 10.5702/massspectrometry.a0095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 04/21/2021] [Indexed: 11/23/2022] Open
Abstract
A combination of direct liquid extraction using a small volume of solvent and electrospray ionization allows the rapid measurement of complex chemical components in biological samples and visualization of their distribution in tissue sections. This review describes the development of such techniques and their application to biological research since the first reports in the early 2000s. An overview of electrospray ionization, ion suppression in samples, and the acceleration of specific chemical reactions in charged droplets is also presented. Potential future applications for visualizing multimolecular environments in biological systems are discussed.
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Affiliation(s)
- Yoichi Otsuka
- Graduate School of Science, Osaka University, 1–1 Machikaneyama-cho, Toyonaka, Osaka 560–0043, Japan
- JST, PRESTO, 4–1–8 Honcho, Kawaguchi, Saitama 332–0012, Japan
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17
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Guo D, Föll MC, Volkmann V, Enderle-Ammour K, Bronsert P, Schilling O, Vitek O. Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations. Bioinformatics 2021; 36:i300-i308. [PMID: 32657378 PMCID: PMC7355295 DOI: 10.1093/bioinformatics/btaa436] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
MOTIVATION Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance. RESULTS To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states. AVAILABILITY AND IMPLEMENTATION The data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.
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Affiliation(s)
- Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Veronika Volkmann
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Kathrin Enderle-Ammour
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Peter Bronsert
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Medical Center - University of Freiburg.,German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), 79106 Freiburg, Germany
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
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18
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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: 3] [Impact Index Per Article: 1.0] [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.
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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
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19
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Laser Ablation Remote-Electrospray Ionisation Mass Spectrometry (LARESI MSI) Imaging-New Method for Detection and Spatial Localization of Metabolites and Mycotoxins Produced by Moulds. Toxins (Basel) 2020; 12:toxins12110720. [PMID: 33217921 PMCID: PMC7698717 DOI: 10.3390/toxins12110720] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/13/2020] [Accepted: 11/15/2020] [Indexed: 01/11/2023] Open
Abstract
To date, no method has been developed to assess the distribution of mycotoxins on the surface of grains, or other plant material, and the depth of their penetration into the interior. The Infrared (IR) Laser Ablation-Remote-Electrospray Ionization (LARESI) platform coupled to a tandem mass spectrometer (MS/MS), measuring in selected reaction monitoring (SRM) mode, was employed for the targeted imaging of selected metabolites of Aspergillus fumigatus, including mycotoxins in biological objects for the first time. This methodology allowed for the localisation of grain metabolites and fungal metabolites of grain infected by this mould. The distribution of metabolites in spelt grain was differentiated: fumigaclavine C, fumitremorgin C, and fumiquinazoline D were located mainly in the embryo, brevianamide F in the seed coat, and fumagillin in the endosperm. The LARESI mass spectrometry imaging method can be used in the future for the metabolomic analysis of mould metabolites in various plants and agricultural products.
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20
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Möginger U, Marcussen N, Jensen ON. Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections. Oncotarget 2020; 11:3998-4015. [PMID: 33216824 PMCID: PMC7646834 DOI: 10.18632/oncotarget.27787] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 10/10/2020] [Indexed: 12/24/2022] Open
Abstract
Pathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub) types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections. In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n = 11), clear cell renal cell carcinoma (ccRCC, n = 12) and chromophobe renal cell carcinoma (ChRCC, n = 5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients. This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases.
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Affiliation(s)
- Uwe Möginger
- Department of Biochemistry & Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark.,Present address: Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park, Bagsværd, Denmark
| | - Niels Marcussen
- Institute for Pathology, Odense University Hospital, Odense, Denmark
| | - Ole N Jensen
- Department of Biochemistry & Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark
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21
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Laser-assisted rapid evaporative ionisation mass spectrometry (LA-REIMS) as a metabolomics platform in cervical cancer screening. EBioMedicine 2020; 60:103017. [PMID: 32980699 PMCID: PMC7522750 DOI: 10.1016/j.ebiom.2020.103017] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 08/31/2020] [Accepted: 09/08/2020] [Indexed: 12/12/2022] Open
Abstract
Background The introduction of high-risk human papillomavirus (hrHPV) testing as part of primary cervical screening is anticipated to improve sensitivity, but also the number of women who will screen positive. Reflex cytology is the preferred triage test in most settings but has limitations including moderate diagnostic accuracy, lack of automation, inter-observer variability and the need for clinician-collected sample. Novel, objective and cost-effective approaches are needed. Methods In this study, we assessed the potential use of an automated metabolomic robotic platform, employing the principle of laser-assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) in cervical cancer screening. Findings In a population of 130 women, LA-REIMS achieved 94% sensitivity and 83% specificity (AUC: 91.6%) in distinguishing women testing positive (n = 65) or negative (n = 65) for hrHPV. We performed further analysis according to disease severity with LA-REIMS achieving sensitivity and specificity of 91% and 73% respectively (AUC: 86.7%) in discriminating normal from high-grade pre-invasive disease. Interpretation This automated high-throughput technology holds promise as a low-cost and rapid test for cervical cancer screening and triage. The use of platforms like LA-REIMS has the potential to further improve the accuracy and efficiency of the current national screening programme. Funding Work was funded by the MRC Imperial Confidence in Concept Scheme, Imperial College Healthcare Charity, British Society for Colposcopy and Cervical Pathology, National Research Development and Innovation Office of Hungary, Waters corporation and NIHR BRC.
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22
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Nizioł J, Ossoliński K, Tripet BP, Copié V, Arendowski A, Ruman T. Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based serum metabolomics of kidney cancer. Anal Bioanal Chem 2020; 412:5827-5841. [PMID: 32661677 PMCID: PMC7413895 DOI: 10.1007/s00216-020-02807-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 06/19/2020] [Accepted: 07/06/2020] [Indexed: 12/24/2022]
Abstract
Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite all the efforts made, no serum-specific biomarker is currently used in the clinical management of patients with this tumor. In this study, comprehensive high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR) and silver-109 nanoparticle-enhanced steel target laser desorption/ionization mass spectrometry (109AgNPET LDI MS) approaches were conducted, in conjunction with multivariate data analysis, to discriminate the global serum metabolic profiles of kidney cancer (n = 50) and healthy volunteers (n = 49). Eight potential biomarkers have been identified using 1H NMR metabolomics and nine mass spectral features which differed significantly (p < 0.05) between kidney cancer patients and healthy volunteers, as observed by LDI MS. A partial least squares discriminant analysis (OPLS-DA) model generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from cancerous samples (Q2 > 0.7), area under the receiver operative characteristic curve (ROC) AUC > 0.96. Compared with healthy human serum, kidney cancer serum had higher levels of glucose and lower levels of choline, glycerol, glycine, lactate, leucine, myo-inositol, and 1-methylhistidine. Analysis of differences between these metabolite levels in patients with different types and grades of kidney cancer was undertaken. Our results, derived from the combination of LDI MS and 1H NMR methods, suggest that serum biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer. Graphical abstract.
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Affiliation(s)
- Joanna Nizioł
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave, 35-959, Rzeszów, Poland.
| | - Krzysztof Ossoliński
- Department of Urology, John Paul II Hospital, Grunwaldzka 4 St, 36-100, Kolbuszowa, Poland
| | - Brian P Tripet
- The Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, 59717, USA
| | - Valérie Copié
- The Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, 59717, USA
| | - Adrian Arendowski
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave, 35-959, Rzeszów, Poland
| | - Tomasz Ruman
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave, 35-959, Rzeszów, Poland
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23
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Li N, Nie H, Jiang L, Ruan G, Du F, Liu H. Recent advances of ambient ionization mass spectrometry imaging in clinical research. J Sep Sci 2020; 43:3146-3163. [PMID: 32573988 DOI: 10.1002/jssc.202000273] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 05/03/2020] [Accepted: 05/06/2020] [Indexed: 02/06/2023]
Abstract
The structural information and spatial distribution of molecules in biological tissues are closely related to the potential molecular mechanisms of disease origin, transfer, and classification. Ambient ionization mass spectrometry imaging is an effective tool that provides molecular images while describing in situ information of biomolecules in complex samples, in which ionization occurs at atmospheric pressure with the samples being analyzed in the native state. Ambient ionization mass spectrometry imaging can directly analyze tissue samples at a fairly high resolution to obtain molecules in situ information on the tissue surface to identify pathological features associated with a disease, resulting in the wide applications in pharmacy, food science, botanical research, and especially clinical research. Herein, novel ambient ionization techniques, such as techniques based on spray and solid-liquid extraction, techniques based on plasma desorption, techniques based on laser desorption ablation, and techniques based on acoustic desorption were introduced, and the data processing of ambient ionization mass spectrometry imaging was briefly reviewed. Besides, we also highlight recent applications of this imaging technology in clinical researches and discuss the challenges in this imaging technology and the perspectives on the future of the clinical research.
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Affiliation(s)
- Na Li
- Guangxi Key Laboratory of Electrochemical and Magnetochemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, P. R. China
- College of Chemistry and Molecular Engineering, Peking University, Beijing, P. R. China
| | - Honggang Nie
- College of Chemistry and Molecular Engineering, Peking University, Beijing, P. R. China
| | - Liping Jiang
- Guangxi Key Laboratory of Electrochemical and Magnetochemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, P. R. China
| | - Guihua Ruan
- Guangxi Key Laboratory of Electrochemical and Magnetochemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, P. R. China
| | - Fuyou Du
- Guangxi Key Laboratory of Electrochemical and Magnetochemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, P. R. China
- College of Biological and Environmental Engineering, Changsha University, Changsha, P. R. China
| | - Huwei Liu
- College of Chemistry and Molecular Engineering, Peking University, Beijing, P. R. China
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24
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Vijayalakshmi K, Shankar V, Bain RM, Nolley R, Sonn GA, Kao CS, Zhao H, Tibshirani R, Zare RN, Brooks JD. Identification of diagnostic metabolic signatures in clear cell renal cell carcinoma using mass spectrometry imaging. Int J Cancer 2020; 147:256-265. [PMID: 31863456 PMCID: PMC8571954 DOI: 10.1002/ijc.32843] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 11/14/2019] [Accepted: 12/09/2019] [Indexed: 12/31/2022]
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. Intraoperative frozen section (IFS) analysis is used to confirm the diagnosis during partial nephrectomy. However, surgical margin evaluation using IFS analysis is time consuming and unreliable, leading to relatively low utilization. In our study, we demonstrated the use of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) as a molecular diagnostic and prognostic tool for ccRCC. DESI-MSI was conducted on fresh-frozen 23 normal tumor paired nephrectomy specimens of ccRCC. An independent validation cohort of 17 normal tumor pairs was analyzed. DESI-MSI provides two-dimensional molecular images of tissues with mass spectra representing small metabolites, fatty acids and lipids. These tissues were subjected to histopathologic evaluation. A set of metabolites that distinguish ccRCC from normal kidney were identified by performing least absolute shrinkage and selection operator (Lasso) and log-ratio Lasso analysis. Lasso analysis with leave-one-patient-out cross-validation selected 57 peaks from over 27,000 metabolic features across 37,608 pixels obtained using DESI-MSI of ccRCC and normal tissues. Baseline Lasso of metabolites predicted the class of each tissue to be normal or cancerous tissue with an accuracy of 94 and 76%, respectively. Combining the baseline Lasso with the ratio of glucose to arachidonic acid could potentially reduce scan time and improve accuracy to identify normal (82%) and ccRCC (88%) tissue. DESI-MSI allows rapid detection of metabolites associated with normal and ccRCC with high accuracy. As this technology advances, it could be used for rapid intraoperative assessment of surgical margin status.
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Affiliation(s)
| | - Vishnu Shankar
- Department of Biomedical Data Science, and Statistics, Stanford University, Stanford, California 94305 USA
| | - Ryan M. Bain
- Department of Chemistry, Stanford University, Stanford, California 94305 USA
- Present address: Dow Chemical Co. Midland, Michigan 48674 USA
| | - Rosalie Nolley
- Department of Urology, Stanford University, Stanford, California 94305 USA
| | - Geoffrey A. Sonn
- Department of Urology, Stanford University, Stanford, California 94305 USA
| | - Chia-Sui Kao
- Department of Pathology, Stanford University, Stanford, California 94305 USA
| | - Hongjuan Zhao
- Department of Urology, Stanford University, Stanford, California 94305 USA
| | - Robert Tibshirani
- Department of Biomedical Data Science, and Statistics, Stanford University, Stanford, California 94305 USA
| | - Richard N. Zare
- Department of Chemistry, Stanford University, Stanford, California 94305 USA
| | - James D. Brooks
- Department of Urology, Stanford University, Stanford, California 94305 USA
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25
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Distribution and clinical relevance of phospholipids in hepatocellular carcinoma. Hepatol Int 2020; 14:544-555. [PMID: 32504407 PMCID: PMC7366576 DOI: 10.1007/s12072-020-10056-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 03/21/2020] [Indexed: 01/10/2023]
Abstract
Background Hepatocellular carcinoma (HCC) is the most common liver cancer and featured with prominent disparity in incidence and mortality rate between male and female. It remains unclear whether alterations of phospholipids (PL) in hepatic tissues contribute to the pathogenesis, progression, and disparity of HCC. Methods Using electrospray ionization mass spectrometry (ESI–MS), PL profiles including 320 individual phospholipid species in 13 PL classes were determined in paired samples from HCC and adjacent benign hepatic tissues (BHT). Results (1) Concentrations of PLs in most of individual species, in subgroups and in total were decreased in HCC than in BHT in all studied population; (2) the number of individual PL species significantly different between HCC and BHT, and the number of PLs in six subgroups and in total decreased in HCC were more in male population than in female population; (3) panels of PL parameters (more in male population than in female population) were identified as biomarkers in differentiation of HCC from BHT, and in the prediction of pathological grade and clinical stage of HCC with high sensitivity, specificity, and accuracy. Conclusion It is concluded that alterations of PLs in hepatic tissues play important roles in pathogenesis, progression, and gender disparity of HCC. Electronic supplementary material The online version of this article (10.1007/s12072-020-10056-8) contains supplementary material, which is available to authorized users.
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26
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Nizioł J, Sunner J, Beech I, Ossoliński K, Ossolińska A, Ossoliński T, Płaza A, Ruman T. Localization of Metabolites of Human Kidney Tissue with Infrared Laser-Based Selected Reaction Monitoring Mass Spectrometry Imaging and Silver-109 Nanoparticle-Based Surface Assisted Laser Desorption/Ionization Mass Spectrometry Imaging. Anal Chem 2020; 92:4251-4258. [PMID: 32083846 PMCID: PMC7497619 DOI: 10.1021/acs.analchem.9b04580] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
![]()
Infrared (IR) laser
ablation-remote-electrospray ionization (LARESI)
platform coupled to a tandem mass spectrometer (MS/MS) operated in
selected reaction monitoring (SRM) or multiple reaction monitoring
(MRM) modes was developed and employed for imaging of target metabolites
in human kidney cancer tissue. SRM or MRM modes were employed to avoid
artifacts that are present in full scan MS mode. Four tissue samples
containing both cancerous and noncancerous regions, obtained from
three patients with renal cell carcinoma (RCC), were imaged. Sixteen
endogenous metabolites that were reported in the literature as varying
in abundance between cancerous and noncancerous areas in various human
tissues were selected for analysis. Target metabolites comprised ten
amino acids, four nucleosides and nucleobases, lactate, and vitamin
E. For comparison purposes, images of the same metabolites were obtained
with ultraviolet (UV) desorption/ionization mass spectrometry imaging
(UV-LDI-MSI) using monoisotopic silver-109 nanoparticle-enhanced target
(109AgNPET) in full-scan MS mode. The acquired MS images
revealed differences in abundances of selected metabolites between
cancerous and noncancerous regions of the kidney tissue. Importantly,
the two imaging methods offered similar results. This study demonstrates
the applicability of the novel ambient LARESI SRM/MRM MSI method to
both investigating and discovering cancer biomarkers in human tissue.
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Affiliation(s)
- Joanna Nizioł
- Rzeszów University of Technology, Faculty of Chemistry, 6 Powstańców Warszawy Ave., Rzeszów, 35-959, Poland
| | - Jan Sunner
- Center for Biofilm Engineering, Montana State University, 366 Barnard Hall, Bozeman, Montana 59717-3980, United States
| | - Iwona Beech
- Center for Biofilm Engineering, Montana State University, 366 Barnard Hall, Bozeman, Montana 59717-3980, United States
| | - Krzysztof Ossoliński
- Department of Urology, John Paul II Hospital, Grunwaldzka 4 St., Kolbuszowa, 36-100, Poland
| | - Anna Ossolińska
- Department of Urology, John Paul II Hospital, Grunwaldzka 4 St., Kolbuszowa, 36-100, Poland
| | - Tadeusz Ossoliński
- Department of Urology, John Paul II Hospital, Grunwaldzka 4 St., Kolbuszowa, 36-100, Poland
| | - Aneta Płaza
- Doctoral School of Engineering and Technical Sciences at the Rzeszów University of Technology, 8 Powstańców Warszawy Ave., Rzeszów, 35-959, Poland
| | - Tomasz Ruman
- Rzeszów University of Technology, Faculty of Chemistry, 6 Powstańców Warszawy Ave., Rzeszów, 35-959, Poland
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Zhang J, Li SQ, Lin JQ, Yu W, Eberlin LS. Mass Spectrometry Imaging Enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues. Cancer Res 2020; 80:689-698. [PMID: 31843980 PMCID: PMC7024663 DOI: 10.1158/0008-5472.can-19-2522] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 11/02/2019] [Accepted: 12/10/2019] [Indexed: 01/09/2023]
Abstract
Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor. SIGNIFICANCE: Metabolic data acquired by mass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumors and has the potential to be used in the clinic to improve treatment of patients.
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Affiliation(s)
- Jialing Zhang
- Department of Chemistry, The University of Texas at Austin, Austin, Texas
| | - Shirley Q Li
- Department of Chemistry, The University of Texas at Austin, Austin, Texas
| | - John Q Lin
- Department of Chemistry, The University of Texas at Austin, Austin, Texas
| | - Wendong Yu
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas.
| | - Livia S Eberlin
- Department of Chemistry, The University of Texas at Austin, Austin, Texas.
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28
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Cordeiro FB, Jarmusch AK, León M, Ferreira CR, Pirro V, Eberlin LS, Hallett J, Miglino MA, Cooks RG. Mammalian ovarian lipid distributions by desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Anal Bioanal Chem 2020; 412:1251-1262. [PMID: 31953714 DOI: 10.1007/s00216-019-02352-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 11/27/2019] [Accepted: 12/11/2019] [Indexed: 02/03/2023]
Abstract
Merging optical images of tissue sections with the spatial distributions of molecules seen by imaging mass spectrometry is a powerful approach to better understand the metabolic roles of the mapped molecules. Here, we use histologically friendly desorption electrospray ionization-mass spectrometry (DESI-MS) to map the lipid distribution in tissue sections of ovaries from cows (N = 8), sows (N = 3), and mice (N = 12). Morphologically friendly DESI-MS imaging allows the same sections to be examined for morphological information. Independent of the species, ovarian follicles, corpora lutea, and stroma could be differentiated by principal component analysis, showing that lipid profiles are well conserved among species. As examples of specific findings, arachidonic acid and the phosphatidylinositol PI(38:4), were both found concentrated in the follicles and corpora lutea, structures that promoted ovulation and implantation, respectively. Adrenic acid was spatially located in the corpora lutea, suggesting the importance of this fatty acid in the ovary luteal phase. In summary, lipid information captured by DESI-MS imaging could be related to ovarian structures and data were all conserved among cows, sows, and mice. Further application of DESI-MS imaging to either physiological or pathophysiological models of reproductive conditions will likely expand knowledge of the roles of specific lipids and pathways in ovarian activity and mammalian fertility. Graphical abstract Desorption electrospray ionization-mass spectrometry (DESI-MS) is performed directly from frozen ovarian tissue sections placed onto glass slides. Because the desorption and ionization process of small molecules is so gentle, the tissue architecture is preserved. The sample can then be stained and tissue morphology information can be overlaid with the chemical information obtained by DESI-MS.
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Affiliation(s)
- Fernanda Bertuccez Cordeiro
- Laboratorio para Investigaciones Biomédicas, Facultad de Ciencias de la Vida, Escuela Superior Politécnica del Litoral, ESPOL, 090112, Guayaquil, Ecuador
| | - Alan K Jarmusch
- Department of Chemistry and Center for Analytical Instrumentation Development (CAID), Purdue University, West Lafayette, IN, 47907, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences and Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Marisol León
- Surgery Department, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, 05508-270, Brazil
| | - Christina Ramires Ferreira
- Department of Chemistry and Center for Analytical Instrumentation Development (CAID), Purdue University, West Lafayette, IN, 47907, USA.
- Bindley Bioscience Center, Purdue University, West Lafayette, IN, 47907-1393, USA.
| | - Valentina Pirro
- Department of Chemistry and Center for Analytical Instrumentation Development (CAID), Purdue University, West Lafayette, IN, 47907, USA
| | - Livia S Eberlin
- Department of Chemistry, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Judy Hallett
- Purdue Center for Cancer Research Transgenic Mouse Core Facility, Purdue University, 201 S. University Street, West Lafayette, IN, 47907, USA
| | - Maria Angelica Miglino
- Surgery Department, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, 05508-270, Brazil
| | - Robert Graham Cooks
- Department of Chemistry and Center for Analytical Instrumentation Development (CAID), Purdue University, West Lafayette, IN, 47907, USA
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29
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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]
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30
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Pérez-Guaita D, Quintás G, Kuligowski J. Discriminant analysis and feature selection in mass spectrometry imaging using constrained repeated random sampling - Cross validation (CORRS-CV). Anal Chim Acta 2019; 1097:30-36. [PMID: 31910967 DOI: 10.1016/j.aca.2019.10.039] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 10/16/2019] [Accepted: 10/18/2019] [Indexed: 12/20/2022]
Abstract
The identification of biomarkers through Mass spectrometry imaging (MSI) is gaining popularity in the clinical field. However, considering the complexity of spectral and spatial variables faced, data mining of the hyperspectral images can be troublesome. The discovery of markers generally depends on the creation of classification models which should be validated to ensure the statistical significance of the discriminants m/z detected. Internal validation using resampling methods such as cross validation (CV) are widely used for model selection, the estimation of its generalization performance and biomarker discovery when sample sizes are limited and an independent test set is not available. Here, we introduce for first time the use of Constrained Repeated Random Subsampling CV (CORRS-CV) on multi-images for the validation of classification models on MSI. Although several aspects must be taken into account (e.g. image size, CORRS-CV∂value, the similarity across spatially close pixels, the total computation time), CORRS-CV provides more accurate estimates of the model performance than k-fold CV using of biological replicates to define the data split when the number of biological replicates is scarce and holding images back for testing is a waste of valuable information. Besides, the combined use of CORRS-CV and rank products increases the robustness of the selection of discriminant features as candidate biomarkers which is an important issue due to the increased biological, environmental and technical variabilities when analysing multiple images, especially from human tissues collected in clinical studies.
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Affiliation(s)
| | - Guillermo Quintás
- Health & Biomedicine, LEITAT Technological Center, Barcelona, Spain; Unidad Analítica, Health Research Institute Hospital La Fe, Valencia, Spain.
| | - Julia Kuligowski
- Neonatal Research Unit, Health Research Institute Hospital La Fe, Valencia, Spain
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31
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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.
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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.
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32
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León M, Ferreira CR, Eberlin LS, Jarmusch AK, Pirro V, Rodrigues ACB, Favaron PO, Miglino MA, Cooks RG. Metabolites and Lipids Associated with Fetal Swine Anatomy via Desorption Electrospray Ionization - Mass Spectrometry Imaging. Sci Rep 2019; 9:7247. [PMID: 31076607 PMCID: PMC6510765 DOI: 10.1038/s41598-019-43698-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 04/27/2019] [Indexed: 12/11/2022] Open
Abstract
Chemical imaging by mass spectrometry (MS) has been largely used to study diseases in animals and humans, especially cancer; however, this technology has been minimally explored to study the complex chemical changes associated with fetal development. In this work, we report the histologically-compatible chemical imaging of small molecules by desorption electrospray ionization (DESI) - MS of a complete swine fetus at 50 days of gestation. Tissue morphology was unperturbed by morphologically-friendly DESI-MS analysis while allowing detection of a wide range of small molecules. We observed organ-dependent localization of lipids, e.g. a large diversity of phosphatidylserine lipids in brain compared to other organs, as well as metabolites such as N-acetyl-aspartic acid in the developing nervous system and N-acetyl-L-glutamine in the heart. Some lipids abundant in the lungs, such as PC(32:0) and PS(40:6), were similar to surfactant composition reported previously. Sulfatides were highly concentrated in the fetus liver, while hexoses were barely detected at this organ but were abundant in lung and heart. The chemical information on small molecules recorded via DESI-MS imaging coupled with traditional anatomical evaluation is a powerful source of bioanalytical information which reveals the chemical changes associated with embryonic and fetal development that, when disturbed, causes congenital diseases such as spina bifida and cleft palate.
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Affiliation(s)
- Marisol León
- Surgery Department, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, Brazil
| | - Christina R Ferreira
- Department of Chemistry and Center for Analytical Instrumentation Development, Purdue University, West Lafayette, IN, 47907, United States
| | - Livia S Eberlin
- Department of Chemistry, The University of Texas at Austin, Austin, TX, 78712, United States
| | - Alan K Jarmusch
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, United States
| | - Valentina Pirro
- Department of Chemistry and Center for Analytical Instrumentation Development, Purdue University, West Lafayette, IN, 47907, United States
| | - Ana Clara Bastos Rodrigues
- Surgery Department, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, Brazil
| | | | - Maria Angelica Miglino
- Surgery Department, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, Brazil
| | - R Graham Cooks
- Department of Chemistry and Center for Analytical Instrumentation Development, Purdue University, West Lafayette, IN, 47907, United States.
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33
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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.
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Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada
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34
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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]
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35
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Yu CQ, Chen JP, Zhong YM, Zhong XL, Tang CP, Yang Y, Lin HQ. Metabolomic profiling of rat urine after oral administration of the prescription antipyretic Hao Jia Xu Re Qing Granules by UPLC/Q-TOF-MS. Biomed Chromatogr 2018; 32:e4332. [PMID: 29981286 DOI: 10.1002/bmc.4332] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 06/21/2018] [Accepted: 06/28/2018] [Indexed: 11/09/2022]
Abstract
Hao Jia Xu Re Qing Granules (HJ), is an effective clinically used antipyretic based on traditional Chinese medicine. Although its antipyretic therapeutic effectiveness is obvious, its therapeutic mechanism has not been comprehensively explored yet. In this research, we first identified potential biomarkers which may be relevant for the antipyretic effect of HJ based on urine metabolomics using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). A rat model of fever was established using the yeast-induced febrile response. Total-ion-current metabolic profiles of different groups were acquired and the data were processed by multivariate statistical analysis-partial least-squares discriminant analysis. As envisioned, the results revealed changes of urine metabolites related to the antipyretic effect. Fourteen potential biomarkers were selected from the urine samples based on the results of Student's t-test, "shrinkage t", variable importance in projection and partial least-squares discriminant analysis. N-Acetylleucine, kynurenic acid, indole-3-ethanol, nicotinuric acid, pantothenic acid and tryptophan were the most significant biomarkers found in the urine samples, and may be crucially related to the antipyretic effect of HJ. Consequently, we propose the hypothesis that the significant antipyretic effect the HJ may be related to the inhibition of tryptophan metabolism. This research thus provides strong theoretical support and further direction to explain the antipyretic mechanism of HJ, laying the foundation for future studies.
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Affiliation(s)
- Chu-Qin Yu
- Guangdong Provincial Key Laboratory of Advanced Drug Delivery Systems, Guangdong Pharmaceutical University, Guangzhou, China.,School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, China
| | - Jian-Ping Chen
- The First Hospital Affiliated to Sun Yat-sen University, Guangzhou, P.R. China
| | - Yan-Mei Zhong
- Central Laboratory, Guangdong Pharmaceutical University, Guangzhou, China
| | - Xun-Long Zhong
- Department of Pharmacy, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, P.R. China
| | - Chun-Ping Tang
- Guangdong Provincial Key Laboratory of Advanced Drug Delivery Systems, Guangdong Pharmaceutical University, Guangzhou, China
| | - Yi Yang
- Guangdong Provincial Key Laboratory of Advanced Drug Delivery Systems, Guangdong Pharmaceutical University, Guangzhou, China.,School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, China
| | - Hua-Qing Lin
- Guangdong Provincial Key Laboratory of Advanced Drug Delivery Systems, Guangdong Pharmaceutical University, Guangzhou, China.,School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, China
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Hájek R, Lísa M, Khalikova M, Jirásko R, Cífková E, Študent V, Vrána D, Opálka L, Vávrová K, Matzenauer M, Melichar B, Holčapek M. HILIC/ESI-MS determination of gangliosides and other polar lipid classes in renal cell carcinoma and surrounding normal tissues. Anal Bioanal Chem 2018; 410:6585-6594. [PMID: 30054694 DOI: 10.1007/s00216-018-1263-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 07/02/2018] [Accepted: 07/11/2018] [Indexed: 02/07/2023]
Abstract
Negative-ion hydrophilic liquid chromatography-electrospray ionization mass spectrometry (HILIC/ESI-MS) method has been optimized for the quantitative analysis of ganglioside (GM3) and other polar lipid classes, such as sulfohexosylceramides (SulfoHexCer), sulfodihexosylceramides (SulfoHex2Cer), phosphatidylglycerols (PG), phosphatidylinositols (PI), lysophosphatidylinositols (LPI), and phosphatidylserines (PS). The method is fully validated for the quantitation of the studied lipids in kidney normal and tumor tissues of renal cell carcinoma (RCC) patients based on the lipid class separation and the coelution of lipid class internal standard with the species from the same lipid class. The raw data are semi-automatically processed using our software LipidQuant and statistically evaluated using multivariate data analysis (MDA) methods, which allows the complete differentiation of both groups with 100% specificity and sensitivity. In total, 21 GM3, 28 SulfoHexCer, 26 SulfoHex2Cer, 10 PG, 19 PI, 4 LPI, and 7 PS are determined in the aqueous phase of lipidomic extracts from kidney tumor tissue samples and surrounding normal tissue samples of 20 RCC patients. S-plots allow the identification of most upregulated (PI 40:5, PI 40:4, GM3 34:1, and GM3 42:2) and most downregulated (PI 32:0, PI 34:0, PS 36:4, and LPI 16:0) lipids, which are primarily responsible for the differentiation of tumor and normal groups. Another confirmation of most dysregulated lipids is performed by the calculation of fold changes together with T and p values to highlight their statistical significance. The comparison of HILIC/ESI-MS data and matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) data confirms that lipid dysregulation patterns are similar for both methods. Graphical abstract ᅟ.
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Affiliation(s)
- Roman Hájek
- Faculty of Chemical Technology, Department of Analytical Chemistry, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Miroslav Lísa
- Faculty of Chemical Technology, Department of Analytical Chemistry, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Maria Khalikova
- Faculty of Chemical Technology, Department of Analytical Chemistry, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Robert Jirásko
- Faculty of Chemical Technology, Department of Analytical Chemistry, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Eva Cífková
- Faculty of Chemical Technology, Department of Analytical Chemistry, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Vladimír Študent
- Department of Urology, Faculty of Medicine and Dentistry, Palacký University and University Hospital, I.P. Pavlova 6, 775 20, Olomouc, Czech Republic
| | - David Vrána
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University and University Hospital, I.P. Pavlova 6, 775 20, Olomouc, Czech Republic
| | - Lukáš Opálka
- Faculty of Pharmacy Hradec Králové, Department of Organic and Bioorganic Chemistry, Charles University, Akademika Heyrovského 1203, 500 05, Hradec Králové, Czech Republic
| | - Kateřina Vávrová
- Faculty of Pharmacy Hradec Králové, Department of Organic and Bioorganic Chemistry, Charles University, Akademika Heyrovského 1203, 500 05, Hradec Králové, Czech Republic
| | - Marcel Matzenauer
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University and University Hospital, I.P. Pavlova 6, 775 20, Olomouc, Czech Republic
| | - Bohuslav Melichar
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University and University Hospital, I.P. Pavlova 6, 775 20, Olomouc, Czech Republic
| | - Michal Holčapek
- Faculty of Chemical Technology, Department of Analytical Chemistry, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic.
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37
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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.
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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
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38
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Nizioł J, Bonifay V, Ossoliński K, Ossoliński T, Ossolińska A, Sunner J, Beech I, Arendowski A, Ruman T. Metabolomic study of human tissue and urine in clear cell renal carcinoma by LC-HRMS and PLS-DA. Anal Bioanal Chem 2018; 410:3859-3869. [PMID: 29658093 PMCID: PMC5956006 DOI: 10.1007/s00216-018-1059-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 03/01/2018] [Accepted: 04/03/2018] [Indexed: 12/14/2022]
Abstract
Renal cell carcinoma (RCC) is the most prevalent and lethal malignancy of the kidney. Despite all the efforts made, no tissue biomarker is currently used in the clinical management of patients with kidney cancer. A search for possible biomarkers in urine for clear cell renal cell carcinoma (ccRCC) has been conducted. Non-targeted metabolomic analyses were performed on paired samples of surgically removed renal cancer and normal tissue, as well as on urine samples. Extracts were analyzed by liquid chromatography/high-resolution mass spectrometry (LC-HRMS). Hydroxybutyrylcarnitine, decanoylcarnitine, propanoylcarnitine, carnitine, dodecanoylcarnitine, and norepinephrine sulfate were found in much higher concentrations in both cancer tissues (compared with the paired normal tissue) and in urine of cancer patients (compared with control urine). In contrast, riboflavin and acetylaspartylglutamate (NAAG) were present at significantly higher concentrations both in normal kidney tissue as well as in urine samples of healthy persons. This preliminary study resulted in the identification of several compounds that may be considered potential clear cell renal carcinoma biomarkers. Graphical abstract PLS-DA plot based on LC-MS data for normal and cancer human tissue samples. The aim of this work was the identification of up- and downregulated compounds that could potentially serve as renal cancer biomarkers.
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Affiliation(s)
- Joanna Nizioł
- Faculty of Chemistry, Rzeszow University of Technology, 35-959, Rzeszow, Poland.
| | - Vincent Bonifay
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA
| | - Krzysztof Ossoliński
- Department of General Surgery and Urology, John Paul II Hospital, Grunwaldzka 4 St., 36-100, Kolbuszowa, Poland
| | - Tadeusz Ossoliński
- Department of General Surgery and Urology, John Paul II Hospital, Grunwaldzka 4 St., 36-100, Kolbuszowa, Poland
| | - Anna Ossolińska
- Department of General Surgery and Urology, John Paul II Hospital, Grunwaldzka 4 St., 36-100, Kolbuszowa, Poland
| | - Jan Sunner
- Department of Chemistry, Montana State University, 103 Chemistry and Biochemistry Building, Bozeman, MT, 59717, USA
| | - Iwona Beech
- Center of Biofilm Engineering, Montana State University, 366 Barnard Hall, Bozeman, MT, 59717, USA
| | - Adrian Arendowski
- Faculty of Chemistry, Rzeszow University of Technology, 35-959, Rzeszow, Poland
| | - Tomasz Ruman
- Faculty of Chemistry, Rzeszow University of Technology, 35-959, Rzeszow, Poland
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39
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Fox SA, Farah CS. Mass spectrometry in the palm of your hand: Future applications of in vivo tissue analysis. Oral Dis 2018; 25:639-642. [PMID: 29782691 DOI: 10.1111/odi.12897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 03/29/2018] [Indexed: 11/30/2022]
Affiliation(s)
- Simon A Fox
- Australian Centre for Oral Oncology Research and Education, UWA Dental School, University of Western Australia, Nedlands, WA, Australia
| | - Camile S Farah
- Australian Centre for Oral Oncology Research and Education, UWA Dental School, University of Western Australia, Nedlands, WA, Australia
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40
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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.
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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
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41
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Study on Tissue Distribution of A Variety of Endogenous Metabolites By Air Flow Assisted Ionization-Ultra High Resolution Mass Spectrometry Imaging. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2018. [DOI: 10.1016/s1872-2040(17)61074-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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42
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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.
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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
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43
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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.
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Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health , University Health Network , 100 College Street , Toronto , ON M5G 1P5 , Canada .
- 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
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Abstract
Since the introduction of desorption electrospray ionization (DESI) mass spectrometry (MS), ambient MS methods have seen increased use in a variety of fields from health to food science. Increasing its popularity in metabolomics, ambient MS offers limited sample preparation, rapid and direct analysis of liquids, solids, and gases, in situ and in vivo analysis, and imaging. The metabolome consists of a constantly changing collection of small (<1.5 kDa) molecules. These include endogenous molecules that are part of primary metabolism pathways, secondary metabolites with specific functions such as signaling, chemicals incorporated in the diet or resulting from environmental exposures, and metabolites associated with the microbiome. Characterization of the responsive changes of this molecule cohort is the principal goal of any metabolomics study. With adjustments to experimental parameters, metabolites with a range of chemical and physical properties can be selectively desorbed and ionized and subsequently analyzed with increased speed and sensitivity. This review covers the broad applications of a variety of ambient MS techniques in four primary fields in which metabolomics is commonly employed.
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Affiliation(s)
- Chaevien S. Clendinen
- School of Chemistry and Biochemistry & Petit Institute for Bioengineering & Bioscience (IBB), Georgia Institute of Technology, 901 Atlantic Drive NW. Atlanta, GA
| | - 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
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry & Petit Institute for Bioengineering & Bioscience (IBB), Georgia Institute of Technology, 901 Atlantic Drive NW. Atlanta, GA
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45
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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.
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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
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46
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Pirro V, Guffey SC, Sepúlveda MS, Mahapatra CT, Ferreira CR, Jarmusch AK, Cooks RG. Lipid dynamics in zebrafish embryonic development observed by DESI-MS imaging and nanoelectrospray-MS. MOLECULAR BIOSYSTEMS 2017; 12:2069-79. [PMID: 27120110 DOI: 10.1039/c6mb00168h] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The zebrafish Danio rerio is a model vertebrate organism for understanding biological mechanisms. Recent studies have explored using zebrafish as a model for lipid-related diseases, for in vivo fish bioassays, and for embryonic toxicity experiments. Mass spectrometry (MS) and MS imaging are established tools for lipid profiling and spatial mapping of biomolecules and offer rapid, sensitive, and simple analytical protocols for zebrafish analysis. When ambient ionization techniques are used, ions are generated in native environmental conditions, requiring neither sample preparation nor separation of molecules prior to MS. We used two direct MS techniques to describe the dynamics of the lipid profile during zebrafish embryonic development from 0 to 96 hours post-fertilization and to explore these analytical approaches as molecular diagnostic assays. Desorption electrospray ionization (DESI) MS imaging followed by nanoelectrospray (nESI) MS and tandem MS (MS/MS) were used in positive and negative ion modes, allowing the detection of a large variety of phosphatidylglycerols, phosphatidylcholines, phosphatidylinositols, free fatty acids, triacylglycerols, ubiquinone, squalene, and other lipids, and revealed information on the spatial distributions of lipids within the embryo and on lipid molecular structure. Differences were observed in the relative ion abundances of free fatty acids, triacylglycerols, and ubiquinone - essentially localized to the yolk - across developmental stages, whereas no relevant differences were found in the distribution of complex membrane glycerophospholipids, indicating conserved lipid constitution. Embryos exposed to trichloroethylene for 72 hours exhibited an altered lipid profile, indicating the potential utility of this technique for testing the effects of environmental contaminants.
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Affiliation(s)
- V Pirro
- Department of Chemistry and Center for Analytical Instrumentation Development, Purdue University, West Lafayette, IN 47907, USA
| | - S C Guffey
- Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA.
| | - M S Sepúlveda
- Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA.
| | - C T Mahapatra
- Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA.
| | - C R Ferreira
- Department of Chemistry and Center for Analytical Instrumentation Development, Purdue University, West Lafayette, IN 47907, USA and Metabolite Profiling Facility, Bindley Bioscience Center, Purdue University, West Lafayette, IN, USA
| | - A K Jarmusch
- Department of Chemistry and Center for Analytical Instrumentation Development, Purdue University, West Lafayette, IN 47907, USA
| | - R G Cooks
- Department of Chemistry and Center for Analytical Instrumentation Development, Purdue University, West Lafayette, IN 47907, USA
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47
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Goracci L, Tortorella S, Tiberi P, Pellegrino RM, Di Veroli A, Valeri A, Cruciani G. Lipostar, a Comprehensive Platform-Neutral Cheminformatics Tool for Lipidomics. Anal Chem 2017; 89:6257-6264. [DOI: 10.1021/acs.analchem.7b01259] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Laura Goracci
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Sara Tortorella
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Paolo Tiberi
- Molecular Discovery Ltd., Centennial
Park, Borehamwood, Hertfordshire, United Kingdom
| | - Roberto Maria Pellegrino
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Alessandra Di Veroli
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Aurora Valeri
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Gabriele Cruciani
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
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48
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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.
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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.
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Zhang Z, He M, Liu L, Xiong X, Fang X, Xu W. Electro-kinetic assisted electrospray ionization for enhanced complex sample analysis. Talanta 2017; 164:45-51. [DOI: 10.1016/j.talanta.2016.11.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 11/14/2016] [Accepted: 11/14/2016] [Indexed: 02/06/2023]
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Leuthold P, Schaeffeler E, Winter S, Büttner F, Hofmann U, Mürdter TE, Rausch S, Sonntag D, Wahrheit J, Fend F, Hennenlotter J, Bedke J, Schwab M, Haag M. Comprehensive Metabolomic and Lipidomic Profiling of Human Kidney Tissue: A Platform Comparison. J Proteome Res 2017; 16:933-944. [DOI: 10.1021/acs.jproteome.6b00875] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Patrick Leuthold
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
| | - Stefan Winter
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
| | - Florian Büttner
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
| | - Thomas E. Mürdter
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
| | - Steffen Rausch
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
- Department
of Urology, University Hospital Tübingen, 72076 Tübingen, Germany
| | | | | | - Falko Fend
- Institute
of Pathology and Neuropathology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Jörg Hennenlotter
- Department
of Urology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Jens Bedke
- Department
of Urology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
- Department
of Clinical Pharmacology, University Hospital Tübingen, 72076 Tübingen, Germany
- Department
of Pharmacy and Biochemistry, University of Tübingen, 72076 Tübingen, Germany
| | - Mathias Haag
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
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