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Lausecker F, Lennon R, Randles MJ. The kidney matrisome in health, aging, and disease. Kidney Int 2022; 102:1000-1012. [PMID: 35870643 DOI: 10.1016/j.kint.2022.06.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/15/2022] [Accepted: 06/24/2022] [Indexed: 02/06/2023]
Abstract
Dysregulated extracellular matrix is the hallmark of fibrosis, and it has a profound impact on kidney function in disease. Furthermore, perturbation of matrix homeostasis is a feature of aging and is associated with declining kidney function. Understanding these dynamic processes, in the hope of developing therapies to combat matrix dysregulation, requires the integration of data acquired by both well-established and novel technologies. Owing to its complexity, the extracellular proteome, or matrisome, still holds many secrets and has great potential for the identification of clinical biomarkers and drug targets. The molecular resolution of matrix composition during aging and disease has been illuminated by cutting-edge mass spectrometry-based proteomics in recent years, but there remain key questions about the mechanisms that drive altered matrix composition. Basement membrane components are particularly important in the context of kidney function; and data from proteomic studies suggest that switches between basement membrane and interstitial matrix proteins are likely to contribute to organ dysfunction during aging and disease. Understanding the impact of such changes on physical properties of the matrix, and the subsequent cellular response to altered stiffness and viscoelasticity, is of critical importance. Likewise, the comparison of proteomic data sets from multiple organs is required to identify common matrix biomarkers and shared pathways for therapeutic intervention. Coupled with single-cell transcriptomics, there is the potential to identify the cellular origin of matrix changes, which could enable cell-targeted therapy. This review provides a contemporary perspective of the complex kidney matrisome and draws comparison to altered matrix in heart and liver disease.
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Affiliation(s)
- Franziska Lausecker
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals National Health Service (NHS) Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Michael J Randles
- Chester Medical School, Faculty of Medicine and Life Sciences, University of Chester, Chester, UK.
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2
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Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models. Biotechnol Adv 2021; 55:107883. [PMID: 34875362 DOI: 10.1016/j.biotechadv.2021.107883] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/22/2021] [Accepted: 11/30/2021] [Indexed: 02/07/2023]
Abstract
Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.
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3
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Wang K, Donnarumma F, Pettit ME, Szot CW, Solouki T, Murray KK. MALDI imaging directed laser ablation tissue microsampling for data independent acquisition proteomics. JOURNAL OF MASS SPECTROMETRY : JMS 2020; 55:e4475. [PMID: 31726477 DOI: 10.1002/jms.4475] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 10/25/2019] [Accepted: 11/07/2019] [Indexed: 06/10/2023]
Abstract
A multimodal workflow for mass spectrometry imaging was developed that combines MALDI imaging with protein identification and quantification by liquid chromatography tandem mass spectrometry (LC-MS/MS). Thin tissue sections were analyzed by MALDI imaging, and the regions of interest (ROI) were identified using a smoothing and edge detection procedure. A midinfrared laser at 3-μm wavelength was used to remove the ROI from the brain tissue section after MALDI mass spectrometry imaging (MALDI MSI). The captured material was processed using a single-pot solid-phase-enhanced sample preparation (SP3) method and analyzed by LC-MS/MS using ion mobility (IM) enhanced data independent acquisition (DIA) to identify and quantify proteins; more than 600 proteins were identified. Using a modified database that included isoform and the post-translational modifications chain, loss of the initial methionine, and acetylation, 14 MALDI MSI peaks were identified. Comparison of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the identified proteins was achieved through an evolutionary relationships classification system.
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Affiliation(s)
- Kelin Wang
- Department of Chemistry, Louisiana State University, Baton Rouge, LA, 70803, United States
| | - Fabrizio Donnarumma
- Department of Chemistry, Louisiana State University, Baton Rouge, LA, 70803, United States
| | - Michael E Pettit
- Department of Chemistry and Biochemistry, Baylor University, Waco, TX, 76706, United States
| | - Carson W Szot
- Department of Chemistry, Louisiana State University, Baton Rouge, LA, 70803, United States
| | - Touradj Solouki
- Department of Chemistry and Biochemistry, Baylor University, Waco, TX, 76706, United States
| | - Kermit K Murray
- Department of Chemistry, Louisiana State University, Baton Rouge, LA, 70803, United States
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Piehowski PD, Zhu Y, Bramer LM, Stratton KG, Zhao R, Orton DJ, Moore RJ, Yuan J, Mitchell HD, Gao Y, Webb-Robertson BJM, Dey SK, Kelly RT, Burnum-Johnson KE. Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. Nat Commun 2020; 11:8. [PMID: 31911630 PMCID: PMC6946663 DOI: 10.1038/s41467-019-13858-z] [Citation(s) in RCA: 141] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 11/20/2019] [Indexed: 12/19/2022] Open
Abstract
Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation. Imaging mass spectrometry is a powerful emerging tool for mapping the spatial distribution of biomolecules across tissue surfaces. Here the authors showcase an automated technology for deep proteome imaging that utilizes ultrasensitive microfluidics and a mass spectrometry workflow to analyze tissue voxels, generating quantitative cell-type-specific images.
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Affiliation(s)
- Paul D Piehowski
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ying Zhu
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Lisa M Bramer
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Kelly G Stratton
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Rui Zhao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Daniel J Orton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Jia Yuan
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Hugh D Mitchell
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Sudhansu K Dey
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Ryan T Kelly
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA. .,Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.
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Föll MC, Moritz L, Wollmann T, Stillger MN, Vockert N, Werner M, Bronsert P, Rohr K, Grüning BA, Schilling O. Accessible and reproducible mass spectrometry imaging data analysis in Galaxy. Gigascience 2019; 8:giz143. [PMID: 31816088 PMCID: PMC6901077 DOI: 10.1093/gigascience/giz143] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 09/10/2019] [Accepted: 11/10/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. FINDINGS We have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Furthermore, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research. CONCLUSION The Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access, together with high levels of reproducibility and transparency.
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Affiliation(s)
- Melanie Christine Föll
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Biology, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
| | - Lennart Moritz
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
| | - Thomas Wollmann
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Maren Nicole Stillger
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Biology, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
- Institute of Molecular Medicine and Cell Research, Faculty of Medicine, University of Freiburg, Stefan-Meier-Straße 17, 79104 Freiburg, Germany
| | - Niklas Vockert
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Martin Werner
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
| | - Peter Bronsert
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
| | - Karl Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Björn Andreas Grüning
- Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Oliver Schilling
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
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Quantitative Mass Spectrometry Imaging Reveals Mutation Status-independent Lack of Imatinib in Liver Metastases of Gastrointestinal Stromal Tumors. Sci Rep 2019; 9:10698. [PMID: 31337874 PMCID: PMC6650609 DOI: 10.1038/s41598-019-47089-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 07/09/2019] [Indexed: 01/08/2023] Open
Abstract
Mass spectrometry imaging (MSI) is an enabling technology for label-free drug disposition studies at high spatial resolution in life science- and pharmaceutical research. We present the first extensive clinical matrix-assisted laser desorption/ionization (MALDI) quantitative mass spectrometry imaging (qMSI) study of drug uptake and distribution in clinical specimen, analyzing 56 specimens of tumor and corresponding non-tumor tissues from 27 imatinib-treated patients with the biopsy-proven rare disease gastrointestinal stromal tumors (GIST). For validation, we compared MALDI-TOF-qMSI with conventional UPLC-ESI-QTOF-MS-based quantification from tissue extracts and with ultra-high resolution MALDI-FTICR-qMSI. We introduced a novel generalized nonlinear calibration model of drug quantities based on computational evaluation of drug-containing areas that enabled better data fitting and assessment of the inherent method nonlinearities. Imatinib tissue spatial maps revealed striking inefficiency in drug penetration into GIST liver metastases even though the corresponding healthy liver tissues in the vicinity showed abundant imatinib levels beyond the limit of quantification (LOQ), thus providing evidence for secondary drug resistance independent of mutation status. Taken together, these findings underscore the important application of MALDI-qMSI in studying the spatial distribution of molecularly targeted therapeutics in oncology, namely to serve as orthogonal post-surgical approach to evaluate the contribution of anticancer drug disposition to resistance against treatment.
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Peng L, Cantor DI, Huang C, Wang K, Baker MS, Nice EC. Tissue and plasma proteomics for early stage cancer detection. Mol Omics 2018; 14:405-423. [PMID: 30251724 DOI: 10.1039/c8mo00126j] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The pursuit of novel and effective biomarkers is essential in the struggle against cancer, which is a leading cause of mortality worldwide. Here we discuss the relative advantages and disadvantages of the most frequently used proteomics techniques, concentrating on the latest advances and application of tissue and plasma proteomics for novel cancer biomarker discovery.
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Affiliation(s)
- Liyuan Peng
- Dept of Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy
- Chengdu
- P. R. China
| | - David I. Cantor
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Macquarie University
- New South Wales
- Australia
| | - Canhua Huang
- Dept of Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy
- Chengdu
- P. R. China
| | - Kui Wang
- Dept of Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy
- Chengdu
- P. R. China
| | - Mark S. Baker
- Department of Biomedical Sciences, Faculty of Medicine & Health Sciences, Macquarie University
- Australia
| | - Edouard C. Nice
- Department of Biochemistry and Molecular Biology, Monash University
- Clayton
- Australia
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Quanico J, Franck J, Wisztorski M, Salzet M, Fournier I. Integrated mass spectrometry imaging and omics workflows on the same tissue section using grid-aided, parafilm-assisted microdissection. Biochim Biophys Acta Gen Subj 2017; 1861:1702-1714. [PMID: 28300637 DOI: 10.1016/j.bbagen.2017.03.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2016] [Revised: 03/08/2017] [Accepted: 03/10/2017] [Indexed: 01/03/2023]
Abstract
BACKGROUND In spite of the number of applications describing the use of MALDI MSI, one of its major drawbacks is the limited capability of identifying multiple compound classes directly on the same tissue section. METHODS We demonstrate the use of grid-aided, parafilm-assisted microdissection to perform MALDI MS imaging and shotgun proteomics and metabolomics in a combined workflow and using only a single tissue section. The grid is generated by microspotting acid dye 25 using a piezoelectric microspotter, and this grid was used as a guide to locate regions of interest and as an aid during manual microdissection. Subjecting the dissected pieces to the modified Folch method allows to separate the metabolites from proteins. The proteins can then be subjected to digestion under controlled conditions to improve protein identification yields. RESULTS The proof of concept experiment on rat brain generated 162 and 140 metabolite assignments from three ROIs (cerebellum, hippocampus and midbrain/hypothalamus) in positive and negative modes, respectively, and 890, 1303 and 1059 unique proteins. Integrated metabolite and protein overrepresentation analysis identified pathways associated with the biological functions of each ROI, most of which were not identified when looking at the protein and metabolite lists individually. CONCLUSIONS This combined MALDI MS imaging and multi-omics approach further extends the amount of information that can be generated from single tissue sections. GENERAL SIGNIFICANCE To the best of our knowledge, this is the first report combining both imaging and multi-omics analyses in the same workflow and on the same tissue section.
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Affiliation(s)
- Jusal Quanico
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France
| | - Julien Franck
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France
| | - Maxence Wisztorski
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France
| | - Michel Salzet
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France
| | - Isabelle Fournier
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France.
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Quanico J, Franck J, Wisztorski M, Salzet M, Fournier I. Progress and Potential of Imaging Mass Spectrometry Applied to Biomarker Discovery. Methods Mol Biol 2017; 1598:21-43. [PMID: 28508356 DOI: 10.1007/978-1-4939-6952-4_2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Mapping provides a direct means to assess the impact of protein biomarkers and puts into context their relevance in the type of cancer being examined. To this end, mass spectrometry imaging (MSI) was developed to provide the needed spatial information which is missing in traditional liquid-based mass spectrometric proteomics approaches. Aptly described as a "molecular histology" technique, MSI gives an additional dimension in characterizing tumor biopsies, allowing for mapping of hundreds of molecules in a single analysis. A decade of developments focused on improving and standardizing MSI so that the technique can be translated into the clinical setting. This review describes the progress made in addressing the technological development that allows to bridge local protein detection by MSI to its identification and to illustrate its potential in studying various aspects of cancer biomarker discovery.
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Affiliation(s)
- Jusal Quanico
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France
| | - Julien Franck
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France
| | - Maxence Wisztorski
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France
| | - Michel Salzet
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France
| | - Isabelle Fournier
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France.
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Huang Z, Ma L, Huang C, Li Q, Nice EC. Proteomic profiling of human plasma for cancer biomarker discovery. Proteomics 2016; 17. [PMID: 27550791 DOI: 10.1002/pmic.201600240] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 08/03/2016] [Accepted: 08/18/2016] [Indexed: 02/05/2023]
Affiliation(s)
- Zhao Huang
- Key Laboratory of Tropical Diseases and Translational Medicine of Ministry of Education & Department of Neurology; The Affiliated Hospital of Hainan Medical College; Haikou P. R. China
- Criminal police detachment of Guang'an City Public Security Bureau; P. R. China
| | - Linguang Ma
- Criminal police detachment of Guang'an City Public Security Bureau; P. R. China
| | - Canhua Huang
- State Key Laboratory for Biotherapy and Cancer Center; West China Hospital; Sichuan University, and Collaborative Innovation Center of Biotherapy; Chengdu P. R. China
| | - Qifu Li
- Key Laboratory of Tropical Diseases and Translational Medicine of Ministry of Education & Department of Neurology; The Affiliated Hospital of Hainan Medical College; Haikou P. R. China
| | - Edouard C. Nice
- Department of Biochemistry and Molecular Biology; Monash University; Clayton Australia
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