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Piga I, Magni F, Smith A. The journey towards clinical adoption of MALDI-MS-based imaging proteomics: from current challenges to future expectations. FEBS Lett 2024; 598:621-634. [PMID: 38140823 DOI: 10.1002/1873-3468.14795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/06/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023]
Abstract
Among the spatial omics techniques available, mass spectrometry imaging (MSI) represents one of the most promising owing to its capability to map the distribution of hundreds of peptides and proteins, as well as other classes of biomolecules, within a complex sample background in a multiplexed and relatively high-throughput manner. In particular, matrix-assisted laser desorption/ionisation (MALDI-MSI) has come to the fore and established itself as the most widely used technique in clinical research. However, the march of this technique towards clinical utility has been hindered by issues related to method reproducibility, appropriate biocomputational tools, and data storage. Notwithstanding these challenges, significant progress has been achieved in recent years regarding multiple facets of the technology and has rendered it more suitable for a possible clinical role. As such, there is now more robust and extensive evidence to suggest that the technology has the potential to support clinical decision-making processes under appropriate circumstances. In this review, we will discuss some of the recent developments that have facilitated this progress and outline some of the more promising clinical proteomics applications which have been developed with a clear goal towards implementation in mind.
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Affiliation(s)
- Isabella Piga
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro, Italy
| | - Fulvio Magni
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro, Italy
| | - Andrew Smith
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro, Italy
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2
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Chung HH, Huang P, Chen CL, Lee C, Hsu CC. Next-generation pathology practices with mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2023; 42:2446-2465. [PMID: 35815718 DOI: 10.1002/mas.21795] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.
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Affiliation(s)
- Hsin-Hsiang Chung
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Penghsuan Huang
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chih-Lin Chen
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chuping Lee
- Department of Chemistry, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Cheng-Chih Hsu
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
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3
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Mao X, Huang L, Li T, Abliz Z, He J, Chen J. Identification of Diagnostic Metabolic Signatures in Thyroid Tumors Using Mass Spectrometry Imaging. Molecules 2023; 28:5791. [PMID: 37570761 PMCID: PMC10421042 DOI: 10.3390/molecules28155791] [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: 05/30/2023] [Revised: 07/26/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
"Gray zone" thyroid follicular tumors are difficult to diagnose, especially when distinguishing between benign follicular thyroid adenoma (FTA) and malignant carcinoma (FTC). Thus, proper classification of thyroid follicular diseases may improve clinical prognosis. In this study, the diagnostic performance of metabolite enzymes was evaluated using imaging mass spectrometry to distinguish FTA from FTC and determine the association between metabolite enzyme expression with thyroid follicular borderline tumor diagnosis. Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFAIDESI-MSI) was used to build a classification model for thyroid follicular tumor characteristics among 24 samples. We analyzed metabolic enzyme marker expression in an independent validation set of 133 cases and further evaluated the potential biological behavior of 19 thyroid borderline lesions. Phospholipids and fatty acids (FAs) were more abundant in FTA than FTC (p < 0.001). The metabolic enzyme panel, which included FA synthase and Ca2+-independent PLA2, was further validated in follicular thyroid tumors. The marker combination showed optimal performance in the validation group (area under the ROC, sensitivity, and specificity: 73.6%, 82.1%, and 60.6%, respectively). The findings indicate that AFAIDESI-MSI, in combination with low metabolic enzyme expression, could play a role in the diagnosis of thyroid follicular borderline tumors for strict follow-up.
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Affiliation(s)
- Xinxin Mao
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China;
| | - Luojiao Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; (L.H.); (T.L.); (Z.A.)
| | - Tiegang Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; (L.H.); (T.L.); (Z.A.)
| | - Zeper Abliz
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; (L.H.); (T.L.); (Z.A.)
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; (L.H.); (T.L.); (Z.A.)
| | - Jie Chen
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China;
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King ME, Lin M, Spradlin M, Eberlin LS. Advances and Emerging Medical Applications of Direct Mass Spectrometry Technologies for Tissue Analysis. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:1-25. [PMID: 36944233 DOI: 10.1146/annurev-anchem-061020-015544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis.
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Affiliation(s)
- Mary E King
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA;
- Department of Surgery, Baylor College of Medicine, Houston, Texas, USA;
| | - Monica Lin
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA;
| | - Meredith Spradlin
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA;
| | - Livia S Eberlin
- Department of Surgery, Baylor College of Medicine, Houston, Texas, USA;
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5
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Kanter F, Lellmann J, Thiele H, Kalloger S, Schaeffer DF, Wellmann A, Klein O. Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks. Cancers (Basel) 2023; 15:cancers15030686. [PMID: 36765644 PMCID: PMC9913229 DOI: 10.3390/cancers15030686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 01/25/2023] Open
Abstract
Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.
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Affiliation(s)
- Frederic Kanter
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
| | - Jan Lellmann
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
- Correspondence: (J.L.); (O.K.)
| | - Herbert Thiele
- Fraunhofer Institute for Digital Medicine MEVIS, 23562 Luebeck, Germany
| | - Steve Kalloger
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David F. Schaeffer
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Pancreas Centre BC, Vancouver, BC V5Z 1G1, Canada
- Division of Anatomic Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada
| | - Axel Wellmann
- Institute of Pathology, Wittinger Strasse 14, 29223 Celle, Germany
| | - Oliver Klein
- BIH Center for Regenerative Therapies, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
- Correspondence: (J.L.); (O.K.)
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Baltzer AW, Casadonte R, Korff A, Baltzer LM, Kriegsmann K, Kriegsmann M, Kriegsmann J. Biological injection therapy with leukocyte-poor platelet-rich plasma induces cellular alterations, enhancement of lubricin, and inflammatory downregulation in vivo in human knees: A controlled, prospective human clinical trial based on mass spectrometry imaging analysis. Front Surg 2023; 10:1169112. [PMID: 37151865 PMCID: PMC10160617 DOI: 10.3389/fsurg.2023.1169112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 03/27/2023] [Indexed: 05/09/2023] Open
Abstract
Objective To investigate the in vivo biological effects of leukocyte-poor platelet-rich plasma (LpPRP) treatment in human synovial layer to establish the cellular basis for a prolonged clinical improvement. Methods Synovial tissues (n = 367) were prospectively collected from patients undergoing arthroscopic surgery. Autologous-conditioned plasma, LpPRP, was injected into the knees of 163 patients 1-7 days before surgery to reduce operative trauma and inflammation, and to induce the onset of regeneration. A total of 204 patients did not receive any injection. All samples were analyzed by mass spectrometry imaging. Data analysis was evaluated by clustering, classification, and investigation of predictive peptides. Peptide identification was done by tandem mass spectrometry and database matching. Results Data analysis revealed two major clusters belonging to LpPRP-treated (LpPRP-1) and untreated (LpPRP-0) patients. Classification analysis showed a discrimination accuracy of 82%-90%. We identified discriminating peptides for CD45 and CD29 receptors (receptor-type tyrosine-protein phosphatase C and integrin beta 1), indicating an enhancement of musculoskeletal stem cells, as well as an enhancement of lubricin, collagen alpha-1-(I) chain, and interleukin-receptor-17-E, dampening the inflammatory reaction in the LpPRP-1 group following LpPRP injection. Conclusions We could demonstrate for the first time that injection therapy using "autologic-conditioned biologics" may lead to cellular changes in the synovial membrane that might explain the reported prolonged beneficial clinical effects. Here, we show in vivo cellular changes, possibly based on muscular skeletal stem cell alterations, in the synovial layer. The gliding capacities of joints might be improved by enhancing of lubricin, anti-inflammation by activation of interleukin-17 receptor E, and reduction of the inflammatory process by blocking interleukin-17.
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Affiliation(s)
- Axel W. Baltzer
- Center for Molecular Orthopaedics, MVZ Ortho Koenigsallee, Düsseldorf, Germany
- Correspondence: Axel W. Baltzer
| | - Rita Casadonte
- Imaging Mass Spectrometry, Proteopath GmbH, Trier, Germany
| | - Alexei Korff
- Center for Molecular Orthopaedics, MVZ Ortho Koenigsallee, Düsseldorf, Germany
| | | | - Katharina Kriegsmann
- Department for Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
| | - Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
- Germany Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), Heidelberg, Germany
| | - Jörg Kriegsmann
- Imaging Mass Spectrometry, Proteopath GmbH, Trier, Germany
- MVZ-Zentrum für Histologie, Zytologie und Molekulare Diagnostik, Trier, Germany
- Department of Medicine, Faculty of Medicine/Dentistry, Danube Private University, Krems, Austria
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7
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Urbiola-Salvador V, Miroszewska D, Jabłońska A, Qureshi T, Chen Z. Proteomics approaches to characterize the immune responses in cancer. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2022; 1869:119266. [PMID: 35390423 DOI: 10.1016/j.bbamcr.2022.119266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 03/01/2022] [Accepted: 03/28/2022] [Indexed: 06/14/2023]
Abstract
Despite the dynamic development of cancer research, annually millions of people die of cancer. The human immune system is the major 'guard' against tumor development. Unfortunately, cancer cells have the ability to evade the immune system and continue to grow. The proper understanding of the intricate immune response in tumorigenesis remains the holy grail of cancer immunology and designing effective immunotherapy. To decode the immune responses in cancer, in recent years, proteomics studies have received considerable attention. Proteomics studies focus on the detection and quantification of proteins, which are the effectors of biological functions, and as such, are proven to reflect the cell state more accurately, in comparison to genomic or transcriptomic studies. In this review, we discuss the proteomics studies applied to characterize the immune responses in cancer and tumor immune microenvironment heterogeneity. Further, we describe emerging single-cell proteomics approaches that have the potential to be applied in cancer immunity studies.
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Affiliation(s)
- Víctor Urbiola-Salvador
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Poland.
| | - Dominika Miroszewska
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Poland.
| | - Agnieszka Jabłońska
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Poland.
| | - Talha Qureshi
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
| | - Zhi Chen
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Poland; Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
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8
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Bollwein C, Gonҫalves JPL, Utpatel K, Weichert W, Schwamborn K. MALDI Mass Spectrometry Imaging for the Distinction of Adenocarcinomas of the Pancreas and Biliary Tree. Molecules 2022; 27:3464. [PMID: 35684402 PMCID: PMC9182561 DOI: 10.3390/molecules27113464] [Citation(s) in RCA: 6] [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: 04/14/2022] [Revised: 05/14/2022] [Accepted: 05/23/2022] [Indexed: 02/04/2023] Open
Abstract
Pancreatic ductal adenocarcinoma and cholangiocarcinoma constitute two aggressive tumor types that originate from the epithelial lining of the excretory ducts of the pancreatobiliary tract. Given their close histomorphological resemblance, a correct diagnosis can be challenging and almost impossible without clinical information. In this study, we investigated whether mass spectrometric peptide features could be employed to distinguish pancreatic ductal adenocarcinoma from cholangiocarcinoma. Three tissue microarrays of formalin-fixed and paraffin-embedded material (FFPE) comprising 41 cases of pancreatic ductal adenocarcinoma and 41 cases of cholangiocarcinoma were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). The derived peptide features and respective intensities were used to build different supervised classification algorithms: gradient boosting (GB), support vector machine (SVM), and k-nearest neighbors (KNN). On a pixel-by-pixel level, a classification accuracy of up to 95% could be achieved. The tentative identification of discriminative tryptic peptide signatures revealed proteins that are involved in the epigenetic regulation of the genome and tumor microenvironment. Despite their histomorphological similarities, mass spectrometry imaging represents an efficient and reliable approach for the distinction of PDAC from CC, offering a promising complementary or alternative approach to the existing tools used in diagnostics such as immunohistochemistry.
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Affiliation(s)
- Christine Bollwein
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany; (J.P.L.G.); (W.W.); (K.S.)
| | - Juliana Pereira Lopes Gonҫalves
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany; (J.P.L.G.); (W.W.); (K.S.)
| | - Kirsten Utpatel
- Institute of Pathology, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany;
| | - Wilko Weichert
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany; (J.P.L.G.); (W.W.); (K.S.)
| | - Kristina Schwamborn
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany; (J.P.L.G.); (W.W.); (K.S.)
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9
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Casadonte R, Kriegsmann M, Kriegsmann K, Hauk I, Meliß RR, Müller CSL, Kriegsmann J. Imaging Mass Spectrometry-Based Proteomic Analysis to Differentiate Melanocytic Nevi and Malignant Melanoma. Cancers (Basel) 2021; 13:3197. [PMID: 34206844 PMCID: PMC8267712 DOI: 10.3390/cancers13133197] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 12/15/2022] Open
Abstract
The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, no definitive diagnosis can be made. We studied both lesions by imaging mass spectrometry (IMS) in a large cohort (n = 203) to determine a different proteomic profile between cutaneous melanomas and melanocytic nevi. Sample preparation and instrument setting were tested to obtain optimal results in term of data quality and reproducibility. A proteomic signature was found by linear discriminant analysis to discern malignant melanoma from benign nevus (n = 113) with an overall accuracy of >98%. The prediction model was tested in an independent set (n = 90) reaching an overall accuracy of 93% in classifying melanoma from nevi. Statistical analysis of the IMS data revealed mass-to-charge ratio (m/z) peaks which varied significantly (Area under the receiver operating characteristic curve > 0.7) between the two tissue types. To our knowledge, this is the largest IMS study of cutaneous melanoma and nevi performed up to now. Our findings clearly show that discrimination of melanocytic nevi from melanoma is possible by IMS.
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Affiliation(s)
| | - Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany;
| | - Katharina Kriegsmann
- Department of Hematology Oncology and Rheumatology, University Hospital Heidelberg, 69120 Heidelberg, Germany;
| | - Isabella Hauk
- Faculty of Medicine/Dentistry, Danube Private University, 3500 Krems-Stein, Austria;
| | - Rolf R. Meliß
- Institute für Dermatopathologie, 30519 Hannover, Germany;
| | - Cornelia S. L. Müller
- MVZ für Histologie, Zytologie und Molekulare Diagnostik Trier, 54296 Trier, Germany;
| | - Jörg Kriegsmann
- Proteopath GmbH, 54926 Trier, Germany; or
- Faculty of Medicine/Dentistry, Danube Private University, 3500 Krems-Stein, Austria;
- MVZ für Histologie, Zytologie und Molekulare Diagnostik Trier, 54296 Trier, Germany;
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Basu SS, Agar NYR. Bringing Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging to the Clinics. Clin Lab Med 2021; 41:309-324. [PMID: 34020766 DOI: 10.1016/j.cll.2021.03.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is an emerging analytical technique that promises to change tissue-based diagnostics. This article provides a brief introduction to MALDI MSI as well as clinical diagnostic workflows and opportunities to apply this powerful approach. It describes various MALDI MSI applications, from more clinically mature applications such as cancer to emerging applications such as infectious diseases and drug distribution. In addition, it discusses the analytical considerations that need to be considered when bringing these approaches to different diagnostic problems and settings.
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Affiliation(s)
- Sankha S Basu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.
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11
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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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12
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Mass Spectrometry Imaging for Reliable and Fast Classification of Non-Small Cell Lung Cancer Subtypes. Cancers (Basel) 2020; 12:cancers12092704. [PMID: 32967325 PMCID: PMC7564257 DOI: 10.3390/cancers12092704] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 08/25/2020] [Accepted: 09/16/2020] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Diagnostic subtyping of non-small cell lung cancer is paramount for therapy stratification. Our study shows that the subtyping into pulmonary adenocarcinoma and pulmonary squamous cell carcinoma by mass spectrometry imaging is rapid and accurate using limited tissue material. Abstract Subtyping of non-small cell lung cancer (NSCLC) is paramount for therapy stratification. In this study, we analyzed the largest NSCLC cohort by mass spectrometry imaging (MSI) to date. We sought to test different classification algorithms and to validate results obtained in smaller patient cohorts. Tissue microarrays (TMAs) from including adenocarcinoma (ADC, n = 499) and squamous cell carcinoma (SqCC, n = 440), were analyzed. Linear discriminant analysis, support vector machine, and random forest (RF) were applied using samples randomly assigned for training (66%) and validation (33%). The m/z species most relevant for the classification were identified by on-tissue tandem mass spectrometry and validated by immunohistochemistry (IHC). Measurements from multiple TMAs were comparable using standardized protocols. RF yielded the best classification results. The classification accuracy decreased after including less than six of the most relevant m/z species. The sensitivity and specificity of MSI in the validation cohort were 92.9% and 89.3%, comparable to IHC. The most important protein for the discrimination of both tumors was cytokeratin 5. We investigated the largest NSCLC cohort by MSI to date and found that the classification of NSCLC into ADC and SqCC is possible with high accuracy using a limited set of m/z species.
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13
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Kriegsmann M, Casadonte R, Maurer N, Stoehr C, Erlmeier F, Moch H, Junker K, Zgorzelski C, Weichert W, Schwamborn K, Deininger SO, Gaida M, Mechtersheimer G, Stenzinger A, Schirmacher P, Hartmann A, Kriegsmann J, Kriegsmann K. Mass Spectrometry Imaging Differentiates Chromophobe Renal Cell Carcinoma and Renal Oncocytoma with High Accuracy. J Cancer 2020; 11:6081-6089. [PMID: 32922548 PMCID: PMC7477404 DOI: 10.7150/jca.47698] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 07/29/2020] [Indexed: 12/18/2022] Open
Abstract
Background: While subtyping of the majority of malignant chromophobe renal cell carcinoma (cRCC) and benign renal oncocytoma (rO) is possible on morphology alone, additional histochemical, immunohistochemical or molecular investigations are required in a subset of cases. As currently used histochemical and immunohistological stains as well as genetic aberrations show considerable overlap in both tumors, additional techniques are required for differential diagnostics. Mass spectrometry imaging (MSI) combining the detection of multiple peptides with information about their localization in tissue may be a suitable technology to overcome this diagnostic challenge. Patients and Methods: Formalin-fixed paraffin embedded (FFPE) tissue specimens from cRCC (n=71) and rO (n=64) were analyzed by MSI. Data were classified by linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) algorithm with internal cross validation and visualized by t-distributed stochastic neighbor embedding (t-SNE). Most important variables for classification were identified and the classification algorithm was optimized. Results: Applying different machine learning algorithms on all m/z peaks, classification accuracy between cRCC and rO was 85%, 82%, 84%, 77% and 64% for RF, SVM, KNN, CART and LDA. Under the assumption that a reduction of m/z peaks would lead to improved classification accuracy, m/z peaks were ranked based on their variable importance. Reduction to six most important m/z peaks resulted in improved accuracy of 89%, 85%, 85% and 85% for RF, SVM, KNN, and LDA and remained at the level of 77% for CART. t-SNE showed clear separation of cRCC and rO after algorithm improvement. Conclusion: In summary, we acquired MSI data on FFPE tissue specimens of cRCC and rO, performed classification and detected most relevant biomarkers for the differential diagnosis of both diseases. MSI data might be a useful adjunct method in the differential diagnosis of cRCC and rO.
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Affiliation(s)
- Mark Kriegsmann
- Institute of Pathology, Heidelberg University, Heidelberg, Germany
| | | | - Nadine Maurer
- Institute of Pathology, University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Christine Stoehr
- Institute of Pathology, University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Franziska Erlmeier
- Institute of Pathology, University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Holger Moch
- Institute of Pathology and Molecular Pathology, University Hospital Zurich, Switzerland
| | - Klaus Junker
- Department of Urology and Pediatric Urology, University of Saarland, Homburg/Saar, Germany
| | | | | | | | | | | | | | | | | | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Joerg Kriegsmann
- Proteopath Trier, Trier, Germany.,Centre for Histology, Cytology and molecular Diagnostics Trier, Trier, Germany.,Danube Private University, Krems, Austria
| | - Katharina Kriegsmann
- Department Hematology, Oncology and Rheumatology, Heidelberg University, Heidelberg, Germany
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14
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Ščupáková K, Balluff B, Tressler C, Adelaja T, Heeren RM, Glunde K, Ertaylan G. Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges. Clin Chem Lab Med 2020; 58:914-929. [PMID: 31665113 PMCID: PMC9867918 DOI: 10.1515/cclm-2019-0858] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 10/07/2019] [Indexed: 02/07/2023]
Abstract
Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.
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Affiliation(s)
- Klára Ščupáková
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Benjamin Balluff
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Caitlin Tressler
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tobi Adelaja
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ron M.A. Heeren
- Corresponding author: Ron M.A. Heeren, Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands,
| | - Kristine Glunde
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; and The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gökhan Ertaylan
- Unit Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
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15
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Implementation of MALDI Mass Spectrometry Imaging in Cancer Proteomics Research: Applications and Challenges. J Pers Med 2020; 10:jpm10020054. [PMID: 32580362 PMCID: PMC7354689 DOI: 10.3390/jpm10020054] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/12/2020] [Accepted: 06/19/2020] [Indexed: 02/07/2023] Open
Abstract
Studying the proteome–the entire set of proteins in cells, tissues, organs and body fluids—is of great relevance in cancer research, as differential forms of proteins are expressed in response to specific intrinsic and extrinsic signals. Discovering protein signatures/pathways responsible for cancer transformation may lead to a better understanding of tumor biology and to a more effective diagnosis, prognosis, recurrence and response to therapy. Moreover, proteins can act as a biomarker or potential drug targets. Hence, it is of major importance to implement proteomic, particularly mass spectrometric, approaches in cancer research, to provide new crucial insights into tumor biology. Recently, mass spectrometry imaging (MSI) approaches were implemented in cancer research, to provide individual molecular characteristics of each individual tumor while retaining molecular spatial distribution, essential in the context of personalized disease management and medicine.
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Giusti L, Angeloni C, Lucacchini A. Update on proteomic studies of formalin-fixed paraffin-embedded tissues. Expert Rev Proteomics 2019; 16:513-520. [DOI: 10.1080/14789450.2019.1615452] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Laura Giusti
- School of Pharmacy, University of Camerino, Camerino, Italy
| | | | - Antonio Lucacchini
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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17
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Longuespée R, Casadonte R, Schwamborn K, Kriegsmann M. Proteomics in Pathology: The Special Issue. Proteomics Clin Appl 2019; 13:e1800167. [PMID: 30730117 DOI: 10.1002/prca.201800167] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Rémi Longuespée
- Institute of Pathology, University of Heidelberg, 69120, Heidelberg, Germany
| | | | - Kristina Schwamborn
- Institute of Pathology, Technical University of Munich, 81675, Munich, Germany
| | - Mark Kriegsmann
- Institute of Pathology, University of Heidelberg, 69120, Heidelberg, Germany
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