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Tasca P, van den Berg BM, Rabelink TJ, Wang G, Heijs B, van Kooten C, de Vries APJ, Kers J. Application of spatial-omics to the classification of kidney biopsy samples in transplantation. Nat Rev Nephrol 2024:10.1038/s41581-024-00861-x. [PMID: 38965417 DOI: 10.1038/s41581-024-00861-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2024] [Indexed: 07/06/2024]
Abstract
Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.
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Affiliation(s)
- Paola Tasca
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - Bernard M van den Berg
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Ton J Rabelink
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (Renew), Leiden University Medical Center, Leiden, the Netherlands
| | - Gangqi Wang
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (Renew), Leiden University Medical Center, Leiden, the Netherlands
| | - Bram Heijs
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Cees van Kooten
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Aiko P J de Vries
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands.
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands.
| | - Jesper Kers
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
- Center for Analytical Sciences Amsterdam, Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, the Netherlands
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Steven RT, Burton A, Taylor AJ, Robinson KN, Dexter A, Nikula CJ, Bunch J. Evaluation of Inlet Temperature with Three Sprayer Designs for Desorption Electrospray Ionization Mass Spectrometry Tissue Analysis. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:224-233. [PMID: 38181191 DOI: 10.1021/jasms.3c00332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2024]
Abstract
Mass spectrometry imaging (MSI) allows for the spatially resolved detection of endogenous and exogenous molecules and atoms in biological samples, typically prepared as thin tissue sections. Desorption electrospray ionization (DESI) is one of the most commonly utilized MSI modalities in preclinical research. DESI ion source technology is still rapidly evolving, with new sprayer designs and heated inlet capillaries having recently been incorporated in commercially available systems. In this study, three iterations of DESI sprayer designs are evaluated: (1) the first, and until recently only, commercially available Waters sprayer; (2) a developmental desorption electro-flow focusing ionization (DEFFI)-type sprayer; and (3) a prototype of the newly released Waters commercial sprayer. A heated inlet capillary is also employed, allowing for controlled inlet temperatures up to 500 °C. These three sprayers are evaluated by comparative tissue imaging analyses of murine testes across this temperature range. Single ion intensity versus temperature trends are evaluated as exemplar cases for putatively identified species of interest, such as lactate and glutamine. A range of trends are observed, where intensities follow either increasing, decreasing, bell-shaped, or other trends with temperature. Data for all sprayers show approximately similar trends for the ions studied, with the commercial prototype sprayer (sprayer version 3) matching or outperforming the other sprayers for the ions investigated. Finally, the mass spectra acquired using sprayer version 3 are evaluated by uniform manifold approximation and projection (UMAP) and k-means clustering. This approach is shown to provide valuable insight that is complementary to the presented univariate evaluation for reviewing the parameter space in this study. Full spectral temperature optimization data are provided as supporting data to enable other researchers to design experiments that are optimal for specific ions.
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Affiliation(s)
- Rory T Steven
- National Physical Laboratory Teddington TW11 0LW, U.K
| | - Amy Burton
- National Physical Laboratory Teddington TW11 0LW, U.K
| | - Adam J Taylor
- National Physical Laboratory Teddington TW11 0LW, U.K
| | | | - Alex Dexter
- National Physical Laboratory Teddington TW11 0LW, U.K
| | | | - Josephine Bunch
- National Physical Laboratory Teddington TW11 0LW, U.K
- Imperial College London, Department of Metabolism, Digestion and Reproduction, Sir Alexander Fleming Building, South Kensington Campus, London SW7 2AZ, U.K
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3
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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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4
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Desaire H, Go EP, Hua D. Advances, obstacles, and opportunities for machine learning in proteomics. CELL REPORTS. PHYSICAL SCIENCE 2022; 3:101069. [PMID: 36381226 PMCID: PMC9648337 DOI: 10.1016/j.xcrp.2022.101069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.
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Affiliation(s)
- Heather Desaire
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
| | - Eden P. Go
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
| | - David Hua
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
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5
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Pertzborn D, Arolt C, Ernst G, Lechtenfeld OJ, Kaesler J, Pelzel D, Guntinas-Lichius O, von Eggeling F, Hoffmann F. Multi-Class Cancer Subtyping in Salivary Gland Carcinomas with MALDI Imaging and Deep Learning. Cancers (Basel) 2022; 14:4342. [PMID: 36077876 PMCID: PMC9454426 DOI: 10.3390/cancers14174342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/18/2022] [Accepted: 09/03/2022] [Indexed: 11/16/2022] Open
Abstract
Salivary gland carcinomas (SGC) are a heterogeneous group of tumors. The prognosis varies strongly according to its type, and even the distinction between benign and malign tumor is challenging. Adenoid cystic carcinoma (AdCy) is one subgroup of SGCs that is prone to late metastasis. This makes accurate tumor subtyping an important task. Matrix-assisted laser desorption/ionization (MALDI) imaging is a label-free technique capable of providing spatially resolved information about the abundance of biomolecules according to their mass-to-charge ratio. We analyzed tissue micro arrays (TMAs) of 25 patients (including six different SGC subtypes and a healthy control group of six patients) with high mass resolution MALDI imaging using a 12-Tesla magnetic resonance mass spectrometer. The high mass resolution allowed us to accurately detect single masses, with strong contributions to each class prediction. To address the added complexity created by the high mass resolution and multiple classes, we propose a deep-learning model. We showed that our deep-learning model provides a per-class classification accuracy of greater than 80% with little preprocessing. Based on this classification, we employed methods of explainable artificial intelligence (AI) to gain further insights into the spectrometric features of AdCys.
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Affiliation(s)
- David Pertzborn
- Innovative Biophotonics & MALDI Imaging, ENT Department, Jena University Hospital, 07747 Jena, Germany
| | - Christoph Arolt
- Institute of Pathology, Medical Faculty, University of Cologne, 50937 Cologne, Germany
| | - Günther Ernst
- Innovative Biophotonics & MALDI Imaging, ENT Department, Jena University Hospital, 07747 Jena, Germany
| | - Oliver J. Lechtenfeld
- Department of Analytical Chemistry, Research Group BioGeoOmics, Helmholtz Centre for Environmental Research—UFZ, 04318 Leipzig, Germany
- ProVIS−Centre for Chemical Microscopy, Helmholtz Centre for Environmental Research—UFZ, 04318 Leipzig, Germany
| | - Jan Kaesler
- Department of Analytical Chemistry, Research Group BioGeoOmics, Helmholtz Centre for Environmental Research—UFZ, 04318 Leipzig, Germany
| | - Daniela Pelzel
- Innovative Biophotonics & MALDI Imaging, ENT Department, Jena University Hospital, 07747 Jena, Germany
| | - Orlando Guntinas-Lichius
- Innovative Biophotonics & MALDI Imaging, ENT Department, Jena University Hospital, 07747 Jena, Germany
| | - Ferdinand von Eggeling
- Innovative Biophotonics & MALDI Imaging, ENT Department, Jena University Hospital, 07747 Jena, Germany
| | - Franziska Hoffmann
- Innovative Biophotonics & MALDI Imaging, ENT Department, Jena University Hospital, 07747 Jena, Germany
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Erlmeier F, Sun N, Shen J, Feuchtinger A, Buck A, Prade VM, Kunzke T, Schraml P, Moch H, Autenrieth M, Weichert W, Hartmann A, Walch A. MALDI Mass Spectrometry Imaging-Prognostic Pathways and Metabolites for Renal Cell Carcinomas. Cancers (Basel) 2022; 14:cancers14071763. [PMID: 35406537 PMCID: PMC8996951 DOI: 10.3390/cancers14071763] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/24/2022] [Accepted: 03/24/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Nevertheless, prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by high mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) for prognostic biomarker detection. This is a suitable method for biomarker detection for several tumor entities. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes. Abstract High mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a suitable method for biomarker detection for several tumor entities. Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by MALDI for prognostic biomarker detection. MALDI-Fourier-transform ion cyclotron resonance (FT-ICR)-MSI analysis was performed for renal carcinoma tissue sections from 782 patients. SPACiAL pipeline was integrated for automated co-registration of histological and molecular features. Kaplan–Meier analyses with overall survival as endpoint were executed to determine the metabolic features associated with clinical outcome. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes.
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Affiliation(s)
- Franziska Erlmeier
- Institute of Pathology, University Hospital Erlangen-Nuremberg, 91054 Erlangen, Germany;
- Correspondence: (F.E.); (N.S.)
| | - Na Sun
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
- Correspondence: (F.E.); (N.S.)
| | - Jian Shen
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Achim Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Verena M. Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University Hospital Zurich, 8091 Zurich, Switzerland; (P.S.); (H.M.)
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University Hospital Zurich, 8091 Zurich, Switzerland; (P.S.); (H.M.)
| | - Michael Autenrieth
- Department of Urology, Rechts der Isar Medical Center, Technical University of Munich, 81675 Munich, Germany;
| | - Wilko Weichert
- Institute of Pathology, Technical University Munich, 81675 Munich, Germany;
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen-Nuremberg, 91054 Erlangen, Germany;
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
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Prade VM, Sun N, Shen J, Feuchtinger A, Kunzke T, Buck A, Schraml P, Moch H, Schwamborn K, Autenrieth M, Gschwend JE, Erlmeier F, Hartmann A, Walch A. The synergism of spatial metabolomics and morphometry improves machine learning‐based renal tumour subtype classification. Clin Transl Med 2022; 12:e666. [PMID: 35184396 PMCID: PMC8858620 DOI: 10.1002/ctm2.666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/11/2021] [Accepted: 11/17/2021] [Indexed: 12/14/2022] Open
Affiliation(s)
- Verena M. Prade
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Na Sun
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Jian Shen
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Annette Feuchtinger
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Thomas Kunzke
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Achim Buck
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Peter Schraml
- Institute of Pathology and Molecular Pathology University Hospital Zurich Zurich Switzerland
| | - Holger Moch
- Institute of Pathology and Molecular Pathology University Hospital Zurich Zurich Switzerland
| | | | | | | | - Franziska Erlmeier
- Institute of Pathology, University Hospital Erlangen Friedrich‐Alexander‐University Erlangen‐Nürnberg Erlangen Germany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN) Erlangen Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen Friedrich‐Alexander‐University Erlangen‐Nürnberg Erlangen Germany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN) Erlangen Germany
| | - Axel Walch
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
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Davidovic LM, Cumic J, Dugalic S, Vicentic S, Sevarac Z, Petroianu G, Corridon P, Pantic I. Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2022; 28:265-271. [PMID: 34937605 DOI: 10.1017/s1431927621013878] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.
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Affiliation(s)
| | - Jelena Cumic
- University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Dr. Koste Todorovica 8, RS-11129 Belgrade, Serbia
| | - Stefan Dugalic
- University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Dr. Koste Todorovica 8, RS-11129 Belgrade, Serbia
| | - Sreten Vicentic
- University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Clinic of Psychiatry, Pasterova 2, RS-11000 Belgrade, Serbia
| | - Zoran Sevarac
- University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154, RS-11000 Belgrade, Serbia
| | - Georg Petroianu
- Department of Pharmacology & Therapeutics, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE
| | - Peter Corridon
- Department of Immunology and Physiology, College of Medicine and Health Sciences; Biomedical Engineering, Healthcare Engineering Innovation Center; Center for Biotechnology; Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE
| | - Igor Pantic
- University of Belgrade, Faculty of Medicine, Department of Medical Physiology, Laboratory for Cellular Physiology, Visegradska 26/II, RS-11129 Belgrade, Serbia
- University of Haifa, 199 Abba Hushi Blvd. Mount Carmel, HaifaIL-3498838, Israel
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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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