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Paglia G, Smith AJ, Astarita G. Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics. MASS SPECTROMETRY REVIEWS 2022; 41:722-765. [PMID: 33522625 DOI: 10.1002/mas.21686] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/17/2021] [Accepted: 01/17/2021] [Indexed: 06/12/2023]
Abstract
Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.
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Affiliation(s)
- Giuseppe Paglia
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Andrew J Smith
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Giuseppe Astarita
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
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Maiju L, Anna A, Artturi V, Teemu T, Anton K, Markus K, Antti V, Antti R, Niku O. Laser desorption tissue imaging with Differential Mobility Spectrometry. Exp Mol Pathol 2022; 125:104759. [PMID: 35337806 DOI: 10.1016/j.yexmp.2022.104759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 02/27/2022] [Accepted: 03/19/2022] [Indexed: 11/04/2022]
Abstract
Pathological gross examination of breast carcinoma samples is sometimes laborious. A tissue pre-mapping method could indicate neoplastic areas to the pathologist and enable focused sampling. Differential Mobility Spectrometry (DMS) is a rapid and affordable technology for complex gas mixture analysis. We present an automated tissue laser analysis system for imaging approaches (iATLAS), which utilizes a computer-controlled laser evaporator unit coupled with a DMS gas analyzer. The system is demonstrated in the classification of porcine tissue samples and three human breast carcinomas. Tissue samples from eighteen landrace pigs were classified with the system based on a pre-designed matrix (spatial resolution 1-3 mm). The smoke samples were analyzed with DMS, and tissue classification was performed with several machine learning approaches. Porcine skeletal muscle (n = 1030), adipose tissue (n = 1329), normal breast tissue (n = 258), bone (n = 680), and liver (n = 264) were identified with 86% cross-validation (CV) accuracy with a convolutional neural network (CNN) model. Further, a panel tissue that comprised all five tissue types was applied as an independent validation dataset. In this test, 82% classification accuracy with CNN was achieved. An analogous procedure was applied to demonstrate the feasibility of iATLAS in breast cancer imaging according to 1) macroscopically and 2) microscopically annotated data with 10-fold CV and SVM (radial kernel). We reached a classification accuracy of 94%, specificity of 94%, and sensitivity of 93% with the macroscopically annotated data from three breast cancer specimens. The microscopic annotation was applicable to two specimens. For the first specimen, the classification accuracy was 84% (specificity 88% and sensitivity 77%). For the second, the classification accuracy was 72% (specificity 88% and sensitivity 24%). This study presents a promising method for automated tissue imaging in an animal model and lays foundation for breast cancer imaging.
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Affiliation(s)
- Lepomäki Maiju
- Surgery, Faculty of Medicine and Health Technology, Tampere University, Kauppi Campus, Arvo Building, Arvo Ylpön katu 34, 33520 Tampere, Finland; Department of Pathology, Fimlab Laboratories, Arvo Ylpön katu 4, FI-33520 Tampere, Finland.
| | - Anttalainen Anna
- Olfactomics Ltd, Kampusareena, Korkeakoulunkatu 7, FI-33720 Tampere, Finland; Sensor Technology and Biomeasurements, Faculty of Medicine and Health Technology, Tampere University, Hervanta Campus, Sähkötalo Building, Korkeakoulunkatu 3, FI-33720 Tampere, Finland
| | - Vuorinen Artturi
- Sensor Technology and Biomeasurements, Faculty of Medicine and Health Technology, Tampere University, Hervanta Campus, Sähkötalo Building, Korkeakoulunkatu 3, FI-33720 Tampere, Finland
| | - Tolonen Teemu
- Department of Pathology, Fimlab Laboratories, Arvo Ylpön katu 4, FI-33520 Tampere, Finland
| | - Kontunen Anton
- Olfactomics Ltd, Kampusareena, Korkeakoulunkatu 7, FI-33720 Tampere, Finland; Sensor Technology and Biomeasurements, Faculty of Medicine and Health Technology, Tampere University, Hervanta Campus, Sähkötalo Building, Korkeakoulunkatu 3, FI-33720 Tampere, Finland
| | - Karjalainen Markus
- Olfactomics Ltd, Kampusareena, Korkeakoulunkatu 7, FI-33720 Tampere, Finland; Sensor Technology and Biomeasurements, Faculty of Medicine and Health Technology, Tampere University, Hervanta Campus, Sähkötalo Building, Korkeakoulunkatu 3, FI-33720 Tampere, Finland
| | - Vehkaoja Antti
- Sensor Technology and Biomeasurements, Faculty of Medicine and Health Technology, Tampere University, Hervanta Campus, Sähkötalo Building, Korkeakoulunkatu 3, FI-33720 Tampere, Finland
| | - Roine Antti
- Surgery, Faculty of Medicine and Health Technology, Tampere University, Kauppi Campus, Arvo Building, Arvo Ylpön katu 34, 33520 Tampere, Finland; Olfactomics Ltd, Kampusareena, Korkeakoulunkatu 7, FI-33720 Tampere, Finland
| | - Oksala Niku
- Surgery, Faculty of Medicine and Health Technology, Tampere University, Kauppi Campus, Arvo Building, Arvo Ylpön katu 34, 33520 Tampere, Finland; Olfactomics Ltd, Kampusareena, Korkeakoulunkatu 7, FI-33720 Tampere, Finland; Vascular Centre, Tampere University Hospital, Central Hospital, P.O. Box 2000, FI-33521 Tampere, Finland
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Borchman D. Lipid conformational order and the etiology of cataract and dry eye. J Lipid Res 2021; 62:100039. [PMID: 32554545 PMCID: PMC7910524 DOI: 10.1194/jlr.tr120000874] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/15/2020] [Indexed: 12/12/2022] Open
Abstract
Lens and tear film lipids are as unique as the systems they reside in. The major lipid of the human lens is dihydrosphingomylein, found in quantity only in the lens. The lens contains a cholesterol to phospholipid molar ratio as high as 10:1, more than anywhere else in the body. Lens lipids contribute to maintaining lens clarity, and alterations in lens lipid composition due to age are likely to contribute to cataract. Lens lipid composition reflects adaptations to the unique characteristics of the lens: no turnover of lens lipids or proteins; the lowest amount of oxygen of any tissue; and contains almost no intracellular organelles. The tear film lipid layer (TFLL) is also unique. The TFLL is a thin (100 nm) layer of lipid on the surface of tears covering the cornea that contributes to tear film stability. The major lipids of the TFLL are wax esters and cholesterol esters that are not found in the lens. The hydrocarbon chains associated with the esters are longer than those found anywhere else in the body (as long as 32 carbons), and many are branched. Changes in the composition and structure of the 30,000 different moieties of TFLL contribute to the instability of tears. The focus of the current review is how spectroscopy has been used to elucidate the relationships between lipid composition, conformational order and function, and the etiology of cataract and dry eye.
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Affiliation(s)
- Douglas Borchman
- Department of Ophthalmology and Visual Sciences, University of Louisville, Louisville, KY 40202.
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Teclemariam ET, Pergande MR, Cologna SM. Considerations for mass spectrometry-based multi-omic analysis of clinical samples. Expert Rev Proteomics 2020; 17:99-107. [PMID: 31996049 DOI: 10.1080/14789450.2020.1724540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Introduction: The role of mass spectrometry in biomolecule analysis has become paramount over the last several decades ranging in the analysis across model systems and human specimens. Accordingly, the presence of mass spectrometers in clinical laboratories has also expanded alongside the number of researchers investigating the protein, lipid, and metabolite composition of an array of biospecimens. With this increase in the number of omic investigations, it is important to consider the entire experimental strategy from sample collection and storage, data collection and analysis.Areas covered: In this short review, we outline considerations for working with clinical (e.g. human) specimens including blood, urine, and cerebrospinal fluid, with emphasis on sample handling, profiling composition, targeted measurements and relevance to disease. Discussions of integrated genomic or transcriptomic datasets are not included. A brief commentary is also provided regarding new technologies with clinical relevance.Expert opinion: The role of mass spectrometry to investigate clinically related specimens is on the rise and the ability to integrate multiple omics datasets from mass spectrometry measurements will be crucial to further understanding human health and disease.
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Affiliation(s)
- Esei T Teclemariam
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA
| | - Melissa R Pergande
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA.,Laboratory of Integrated Neuroscience, University of Illinois at Chicago, Chicago, IL, USA
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