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Zirem Y, Ledoux L, Roussel L, Maurage CA, Tirilly P, Le Rhun É, Meresse B, Yagnik G, Lim MJ, Rothschild KJ, Duhamel M, Salzet M, Fournier I. Real-time glioblastoma tumor microenvironment assessment by SpiderMass for improved patient management. Cell Rep Med 2024; 5:101482. [PMID: 38552622 PMCID: PMC11031375 DOI: 10.1016/j.xcrm.2024.101482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/15/2024] [Accepted: 03/01/2024] [Indexed: 04/19/2024]
Abstract
Glioblastoma is a highly heterogeneous and infiltrative form of brain cancer associated with a poor outcome and limited therapeutic effectiveness. The extent of the surgery is related to survival. Reaching an accurate diagnosis and prognosis assessment by the time of the initial surgery is therefore paramount in the management of glioblastoma. To this end, we are studying the performance of SpiderMass, an ambient ionization mass spectrometry technology that can be used in vivo without invasiveness, coupled to our recently established artificial intelligence pipeline. We demonstrate that we can both stratify isocitrate dehydrogenase (IDH)-wild-type glioblastoma patients into molecular sub-groups and achieve an accurate diagnosis with over 90% accuracy after cross-validation. Interestingly, the developed method offers the same accuracy for prognosis. In addition, we are testing the potential of an immunoscoring strategy based on SpiderMass fingerprints, showing the association between prognosis and immune cell infiltration, to predict patient outcome.
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Affiliation(s)
- Yanis Zirem
- Université de Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, 59000 Lille, France
| | - Léa Ledoux
- Université de Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, 59000 Lille, France
| | - Lucas Roussel
- Université de Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, 59000 Lille, France
| | | | - Pierre Tirilly
- Université de Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, 59000 Lille, France
| | - Émilie Le Rhun
- Université de Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, 59000 Lille, France; Departments of Neurosurgery and Neurology, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Bertrand Meresse
- Université de Lille, Inserm, CHU Lille, U1286 - INFINITE - Institute for Translational Research in Inflammation, 59000 Lille, France
| | | | | | - Kenneth J Rothschild
- AmberGen, Inc., Billerica, MA, USA; Department of Physics and Photonics Center, Boston University, Boston, MA, USA
| | - Marie Duhamel
- Université de Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, 59000 Lille, France
| | - Michel Salzet
- Université de Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, 59000 Lille, France; Institut Universitaire de France (IUF), Paris, France.
| | - Isabelle Fournier
- Université de Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, 59000 Lille, France; Institut Universitaire de France (IUF), Paris, France.
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Akbari B, Huber BR, Sherman JH. Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques. Crit Rev Anal Chem 2023:1-30. [PMID: 37847593 DOI: 10.1080/10408347.2023.2266838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
Multimodal imaging (MMI) has emerged as a powerful tool in clinical research, combining different imaging modes to acquire comprehensive information and enabling scientists and surgeons to study tissue identification, localization, metabolic activity, and molecular discovery, thus aiding in disease progression analysis. While multimodal instruments are gaining popularity, challenges such as non-standardized characteristics, custom software, inadequate commercial support, and integration issues with other instruments need to be addressed. The field of multimodal imaging or multiplexed imaging allows for simultaneous signal reproduction from multiple imaging strategies. Intraoperatively, MMI can be integrated into frameless stereotactic surgery. Recent developments in medical imaging modalities such as magnetic resonance imaging (MRI), and Positron Emission Topography (PET) have brought new perspectives to multimodal imaging, enabling early cancer detection, molecular tracking, and real-time progression monitoring. Despite the evidence supporting the role of MMI in surgical decision-making, there is a need for comprehensive studies to validate and perform integration at the intersection of multiple imaging technologies. They were integrating mass spectrometry-based technologies (e.g., imaging mass spectrometry (IMS), imaging mass cytometry (IMC), and Ion mobility mass spectrometry ((IM-IM) with medical imaging modalities, offering promising avenues for molecular discovery and clinical applications. This review emphasizes the potential of multi-omics approaches in tissue mapping using MMI integrated into desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI), allowing for sequential analyses of the same section. By addressing existing knowledge gaps, this review encourages future research endeavors toward multi-omics approaches, providing a roadmap for future research and enhancing the value of MMI in molecular pathology for diagnosis.
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Affiliation(s)
- Behnaz Akbari
- Department of Chemistry, Purdue University, West Lafayette, Indiana, USA
| | - Bertrand Russell Huber
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- US Department of Veteran Affairs, VA Boston Healthcare System, Boston, Massachusetts USA
- US Department of Veterans Affairs, National Center for PTSD, Boston, Massachusetts USA
| | - Janet Hope Sherman
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
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Wang Z, Zhu H, Xiong W. Advances in mass spectrometry-based multi-scale metabolomic methodologies and their applications in biological and clinical investigations. Sci Bull (Beijing) 2023; 68:2268-2284. [PMID: 37666722 DOI: 10.1016/j.scib.2023.08.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/25/2023] [Accepted: 08/22/2023] [Indexed: 09/06/2023]
Abstract
Metabolomics is a nascent field of inquiry that emerged in the late 20th century. It encompasses the comprehensive profiling of metabolites across a spectrum of organisms, ranging from bacteria and cells to tissues. The rapid evolution of analytical methods and data analysis has greatly accelerated progress in this dynamic discipline over recent decades. Sophisticated techniques such as liquid chromatograph mass spectrometry (MS), gas chromatograph MS, capillary electrophoresis MS, and nuclear magnetic resonance serve as the cornerstone of metabolomic analysis. Building upon these methods, a plethora of modifications and combinations have emerged to propel the advancement of metabolomics. Despite this progress, scrutinizing metabolism at the single-cell or single-organelle level remains an arduous task over the decades. Some of the most thrilling advancements, such as single-cell and single-organelle metabolic profiling techniques, offer profound insights into the intricate mechanisms within cells and organelles. This allows for a comprehensive study of metabolic heterogeneity and its pivotal role in multiple biological processes. The progress made in MS imaging has enabled high-resolution in situ metabolic profiling of tissue sections and even individual cells. Spatial reconstruction techniques enable the direct representation of metabolic distribution and alteration in three-dimensional space. The application of novel metabolomic techniques has led to significant breakthroughs in biological and clinical studies, including the discovery of novel metabolic pathways, determination of cell fate in differentiation, anti-aging intervention through modulating metabolism, metabolomics-based clinicopathologic analysis, and surgical decision-making based on on-site intraoperative metabolic analysis. This review presents a comprehensive overview of both conventional and innovative metabolomic techniques, highlighting their applications in groundbreaking biological and clinical studies.
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Affiliation(s)
- Ziyi Wang
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Hongying Zhu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China; Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; CAS Key Laboratory of Brain Function and Disease, Hefei 230026, China; Anhui Province Key Laboratory of Biomedical Aging Research, Hefei 230026, China.
| | - Wei Xiong
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China; Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; CAS Key Laboratory of Brain Function and Disease, Hefei 230026, China; Anhui Province Key Laboratory of Biomedical Aging Research, Hefei 230026, China.
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4
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Zhai Y, Fu X, Xu W. Miniature mass spectrometers and their potential for clinical point-of-care analysis. MASS SPECTROMETRY REVIEWS 2023. [PMID: 37610153 DOI: 10.1002/mas.21867] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/04/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023]
Abstract
Mass spectrometry (MS) has become a powerful technique for clinical applications with high sensitivity and specificity. Different from conventional MS diagnosis in laboratory, point-of-care (POC) analyses in clinics require mass spectrometers and analytical procedures to be friendly for novice users and applicable for on-site clinical diagnosis. The recent decades have seen the progress in the development of miniature mass spectrometers, providing a promising solution for clinical POC applications. In this review, we report recent advances of miniature mass spectrometers and their exploration in clinical applications, mainly including the rapid analysis of illegal drugs, on-site monitoring of therapeutic drugs, and detection of biomarkers. With improved analytical performance, miniature mass spectrometers are also expected to apply to more and more clinical applications. Some promising POC analyses that can be performed by miniature mass spectrometers in the future are discussed. Lastly, we also provide our perspectives on the challenges in technical development of miniature mass spectrometers for clinical POC analysis.
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Affiliation(s)
- Yanbing Zhai
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Xinyan Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Wei Xu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
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Shankar V, Vijayalakshmi K, Nolley R, Sonn GA, Kao CS, Zhao H, Wen R, Eberlin LS, Tibshirani R, Zare RN, Brooks JD. Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning. JCO Precis Oncol 2023; 7:e2200668. [PMID: 37285559 PMCID: PMC10309512 DOI: 10.1200/po.22.00668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/26/2023] [Accepted: 04/10/2023] [Indexed: 06/09/2023] Open
Abstract
PURPOSE Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. MATERIALS AND METHODS Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. RESULTS From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. CONCLUSION Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.
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Affiliation(s)
- Vishnu Shankar
- Program in Immunology, Stanford University School of Medicine, Stanford, CA
| | | | - Rosalie Nolley
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Geoffrey A. Sonn
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Chia-Sui Kao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA
| | - Hongjuan Zhao
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Ru Wen
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | | | - Robert Tibshirani
- Department of Biomedical Data Science, and Statistics, Stanford University, Stanford, CA
| | | | - James D. Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA
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6
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Rončević A, Koruga N, Soldo Koruga A, Rončević R, Rotim T, Šimundić T, Kretić D, Perić M, Turk T, Štimac D. Personalized Treatment of Glioblastoma: Current State and Future Perspective. Biomedicines 2023; 11:1579. [PMID: 37371674 DOI: 10.3390/biomedicines11061579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/24/2023] [Accepted: 05/27/2023] [Indexed: 06/29/2023] Open
Abstract
Glioblastoma (GBM) is the most aggressive glial tumor of the central nervous system. Despite intense scientific efforts, patients diagnosed with GBM and treated with the current standard of care have a median survival of only 15 months. Patients are initially treated by a neurosurgeon with the goal of maximal safe resection of the tumor. Obtaining tissue samples during surgery is indispensable for the diagnosis of GBM. Technological improvements, such as navigation systems and intraoperative monitoring, significantly advanced the possibility of safe gross tumor resection. Usually within six weeks after the surgery, concomitant radiotherapy and chemotherapy with temozolomide are initiated. However, current radiotherapy regimens are based on population-level studies and could also be improved. Implementing artificial intelligence in radiotherapy planning might be used to individualize treatment plans. Furthermore, detailed genetic and molecular markers of the tumor could provide patient-tailored immunochemotherapy. In this article, we review current standard of care and possibilities of personalizing these treatments. Additionally, we discuss novel individualized therapeutic options with encouraging results. Due to inherent heterogeneity of GBM, applying patient-tailored treatment could significantly prolong survival of these patients.
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Affiliation(s)
- Alen Rončević
- Department of Neurosurgery, University Hospital Center Osijek, 31000 Osijek, Croatia
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Nenad Koruga
- Department of Neurosurgery, University Hospital Center Osijek, 31000 Osijek, Croatia
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Anamarija Soldo Koruga
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Neurology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Robert Rončević
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Tatjana Rotim
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Tihana Šimundić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Nephrology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Domagoj Kretić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Marija Perić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Cytology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Tajana Turk
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Damir Štimac
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Radiology, National Memorial Hospital Vukovar, 32000 Vukovar, Croatia
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Rončević A, Koruga N, Soldo Koruga A, Debeljak Ž, Rončević R, Turk T, Kretić D, Rotim T, Krivdić Dupan Z, Troha D, Perić M, Šimundić T. MALDI Imaging Mass Spectrometry of High-Grade Gliomas: A Review of Recent Progress and Future Perspective. Curr Issues Mol Biol 2023; 45:838-851. [PMID: 36826000 PMCID: PMC9955680 DOI: 10.3390/cimb45020055] [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: 11/18/2022] [Revised: 12/22/2022] [Accepted: 01/14/2023] [Indexed: 01/20/2023] Open
Abstract
Glioblastoma (GBM) is the most common malignancy of the brain with a relatively short median survival and high mortality. Advanced age, high socioeconomic status, exposure to ionizing radiation, and other factors have been correlated with an increased incidence of GBM, while female sex hormones, history of allergies, and frequent use of specific drugs might exert protective effects against this disease. However, none of these explain the pathogenesis of GBM. The most recent WHO classification of CNS tumors classifies neoplasms based on their histopathological and molecular characteristics. Modern laboratory techniques, such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, enable the comprehensive metabolic analysis of the tissue sample. MALDI imaging is able to characterize the spatial distribution of a wide array of biomolecules in a sample, in combination with histological features, without sacrificing the tissue integrity. In this review, we first provide an overview of GBM epidemiology, risk, and protective factors, as well as the recent WHO classification of CNS tumors. We then provide an overview of mass spectrometry workflow, with a focus on MALDI imaging, and recent advances in cancer research. Finally, we conclude the review with studies of GBM that utilized MALDI imaging and offer our perspective on future research.
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Affiliation(s)
- Alen Rončević
- Department of Neurosurgery, University Hospital Center Osijek, 31000 Osijek, Croatia
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Correspondence: ; Tel.: +385-98-169-8481
| | - Nenad Koruga
- Department of Neurosurgery, University Hospital Center Osijek, 31000 Osijek, Croatia
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Anamarija Soldo Koruga
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Neurology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Željko Debeljak
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Clinical Institute of Laboratory Diagnostics, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Robert Rončević
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Tajana Turk
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Domagoj Kretić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Tatjana Rotim
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Zdravka Krivdić Dupan
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Damir Troha
- Department of Radiology, Vinkovci General Hospital, 31000 Osijek, Croatia
| | - Marija Perić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Clinical Cytology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Tihana Šimundić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Nephrology, University Hospital Center Osijek, 31000 Osijek, Croatia
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Haddad AF, Aghi MK, Butowski N. Novel intraoperative strategies for enhancing tumor control: Future directions. Neuro Oncol 2022; 24:S25-S32. [PMID: 36322096 PMCID: PMC9629473 DOI: 10.1093/neuonc/noac090] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2023] Open
Abstract
Maximal safe surgical resection plays a key role in the care of patients with gliomas. A range of technologies have been developed to aid surgeons in distinguishing tumor from normal tissue, with the goal of increasing tumor resection and limiting postoperative neurological deficits. Technologies that are currently being investigated to aid in improving tumor control include intraoperative imaging modalities, fluorescent tumor makers, intraoperative cell and molecular profiling of tumors, improved microscopic imaging, intraoperative mapping, augmented and virtual reality, intraoperative drug and radiation delivery, and ablative technologies. In this review, we summarize the aforementioned advancements in neurosurgical oncology and implications for improving patient outcomes.
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Affiliation(s)
- Alexander F Haddad
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Manish K Aghi
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Nicholas Butowski
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
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9
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O’Neill KC, Liapis E, Harris BT, Perlin DS, Carter CL. Mass spectrometry imaging discriminates glioblastoma tumor cell subpopulations and different microvascular formations based on their lipid profiles. Sci Rep 2022; 12:17069. [PMID: 36224354 PMCID: PMC9556690 DOI: 10.1038/s41598-022-22093-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 10/10/2022] [Indexed: 12/30/2022] Open
Abstract
Glioblastoma is a prevalent malignant brain tumor and despite clinical intervention, tumor recurrence is frequent and usually fatal. Genomic investigations have provided a greater understanding of molecular heterogeneity in glioblastoma, yet there are still no curative treatments, and the prognosis has remained unchanged. The aggressive nature of glioblastoma is attributed to the heterogeneity in tumor cell subpopulations and aberrant microvascular proliferation. Ganglioside-directed immunotherapy and membrane lipid therapy have shown efficacy in the treatment of glioblastoma. To truly harness these novel therapeutics and develop a regimen that improves clinical outcome, a greater understanding of the altered lipidomic profiles within the glioblastoma tumor microenvironment is urgently needed. In this work, high resolution mass spectrometry imaging was utilized to investigate lipid heterogeneity in human glioblastoma samples. Data presented offers the first insight into the histology-specific accumulation of lipids involved in cell metabolism and signaling. Cardiolipins, phosphatidylinositol, ceramide-1-phosphate, and gangliosides, including the glioblastoma stem cell marker, GD3, were shown to differentially accumulate in tumor and endothelial cell subpopulations. Conversely, a reduction in sphingomyelins and sulfatides were detected in tumor cell regions. Cellular accumulation for each lipid class was dependent upon their fatty acid residue composition, highlighting the importance of understanding lipid structure-function relationships. Discriminating ions were identified and correlated to histopathology and Ki67 proliferation index. These results identified multiple lipids within the glioblastoma microenvironment that warrant further investigation for the development of predictive biomarkers and lipid-based therapeutics.
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Affiliation(s)
- Kelly C. O’Neill
- grid.429392.70000 0004 6010 5947Center for Discovery and Innovation, Hackensack Meridian Health, 111 Ideation Way, Nutley, NJ 07110 USA
| | - Evangelos Liapis
- grid.429392.70000 0004 6010 5947Center for Discovery and Innovation, Hackensack Meridian Health, 111 Ideation Way, Nutley, NJ 07110 USA
| | - Brent T. Harris
- grid.411667.30000 0001 2186 0438Departments of Neurology and Pathology, Georgetown University Medical Center, Washington, D.C. 20007 USA
| | - David S. Perlin
- grid.429392.70000 0004 6010 5947Center for Discovery and Innovation, Hackensack Meridian Health, 111 Ideation Way, Nutley, NJ 07110 USA ,grid.429392.70000 0004 6010 5947Department of Medical Sciences, Hackensack Meridian School of Medicine, Nutley, NJ 07110 USA
| | - Claire L. Carter
- grid.429392.70000 0004 6010 5947Center for Discovery and Innovation, Hackensack Meridian Health, 111 Ideation Way, Nutley, NJ 07110 USA ,grid.429392.70000 0004 6010 5947Department of Pathology, Hackensack Meridian School of Medicine, Nutley, NJ 07110 USA
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10
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Shi L, Habib A, Bi L, Hong H, Begum R, Wen L. Ambient Ionization Mass Spectrometry: Application and Prospective. Crit Rev Anal Chem 2022:1-50. [PMID: 36206159 DOI: 10.1080/10408347.2022.2124840] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
Abstract
Mass spectrometry (MS) is a formidable analytical tool for the analysis of non-polar to polar compounds individually and/or from mixtures, providing information on the molecular weights and chemical structures of the analytes. During the last more than one-decade, ambient ionization mass spectrometry (AIMS) has developed quickly, producing a wide range of platforms and proving scientific improvements in a variety of domains, from biological imaging to quick quality control. These methods have made it possible to detect target analytes in real time without sample preparation in an open environment, and they can be connected to any MS system with an atmospheric pressure interface. They also have the ability to analyze explosives, illicit drugs, disease diagnostics, drugs in biological samples, adulterants in food and agricultural products, reaction progress, and environmental monitoring. The development of novel ambient ionization techniques, such as probe electrospray ionization, paper spray ionization, and fiber spray ionization, employed even at picolitre to femtolitre solution levels to provide femtogram to attogram levels of the target analytes. The special characteristic of this ambient ion source, which has been extensively used, is the noninvasive property of PESI of examination of biological real samples. The results in the current review supports the idea that AIMS has emerged as a pioneer in MS-based approaches and that methods will continue to be developed along with improvements to existing ones in the near future.
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Affiliation(s)
- Lulu Shi
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
| | - Ahsan Habib
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
- Department of Chemistry, University of Dhaka, Dhaka, Bangladesh
| | - Lei Bi
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
| | - Huanhuan Hong
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
| | - Rockshana Begum
- Department of Chemistry, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Luhong Wen
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
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11
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Paglia G, Smith AJ, Astarita G. Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics. MASS SPECTROMETRY REVIEWS 2022; 41:722-765. [PMID: 33522625 DOI: 10.1002/mas.21686] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/17/2021] [Accepted: 01/17/2021] [Indexed: 06/12/2023]
Abstract
Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.
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Affiliation(s)
- Giuseppe Paglia
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Andrew J Smith
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Giuseppe Astarita
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
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12
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Ehrlich J, Jamzad A, Asselin M, Rodgers JR, Kaufmann M, Haidegger T, Rudan J, Mousavi P, Fichtinger G, Ungi T. Sensor-Based Automated Detection of Electrosurgical Cautery States. SENSORS (BASEL, SWITZERLAND) 2022; 22:5808. [PMID: 35957364 PMCID: PMC9371045 DOI: 10.3390/s22155808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/30/2022] [Accepted: 08/01/2022] [Indexed: 02/04/2023]
Abstract
In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the energy event. By continuously tracking the electrosurgical tools' location using a navigation system, energy events can help determine locations of sensor-classified tissues. Our objective was to detect the energy event and determine the settings of electrosurgical cautery-robustly and automatically based on sensor data. This study aims to demonstrate the feasibility of using the cautery state to detect surgical incisions, without disrupting the surgical workflow. We detected current changes in the wires of the cautery device and grounding pad using non-invasive current sensors and an oscilloscope. An open-source software was implemented to apply machine learning on sensor data to detect energy events and cautery settings. Our methods classified each cautery state at an average accuracy of 95.56% across different tissue types and energy level parameters altered by surgeons during an operation. Our results demonstrate the feasibility of automatically identifying energy events during surgical incisions, which could be an important safety feature in robotic and computer-integrated surgery. This study provides a key step towards locating tissue classifications during breast cancer operations and reducing the rate of positive margins.
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Affiliation(s)
- Josh Ehrlich
- School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada; (J.E.); (A.J.); (M.A.); (J.R.R.); (P.M.); (G.F.)
| | - Amoon Jamzad
- School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada; (J.E.); (A.J.); (M.A.); (J.R.R.); (P.M.); (G.F.)
| | - Mark Asselin
- School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada; (J.E.); (A.J.); (M.A.); (J.R.R.); (P.M.); (G.F.)
| | - Jessica Robin Rodgers
- School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada; (J.E.); (A.J.); (M.A.); (J.R.R.); (P.M.); (G.F.)
| | - Martin Kaufmann
- Department of Surgery, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada; (M.K.); (J.R.)
| | - Tamas Haidegger
- University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
| | - John Rudan
- Department of Surgery, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada; (M.K.); (J.R.)
| | - Parvin Mousavi
- School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada; (J.E.); (A.J.); (M.A.); (J.R.R.); (P.M.); (G.F.)
| | - Gabor Fichtinger
- School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada; (J.E.); (A.J.); (M.A.); (J.R.R.); (P.M.); (G.F.)
| | - Tamas Ungi
- School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada; (J.E.); (A.J.); (M.A.); (J.R.R.); (P.M.); (G.F.)
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13
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He MJ, Pu W, Wang X, Zhang W, Tang D, Dai Y. Comparing DESI-MSI and MALDI-MSI Mediated Spatial Metabolomics and Their Applications in Cancer Studies. Front Oncol 2022; 12:891018. [PMID: 35924152 PMCID: PMC9340374 DOI: 10.3389/fonc.2022.891018] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/20/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.
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Affiliation(s)
- Michelle Junyi He
- Department of Biology, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, China
| | - Wenjun Pu
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, China
| | - Xi Wang
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, China
| | - Wei Zhang
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, China
| | - Donge Tang
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, China
| | - Yong Dai
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen Engineering Research Center of Autoimmune Disease, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, China
- Guangxi Key Laboratory of Metabolic Disease Research, Central Laboratory of Guilin, 924st Hospital, Guilin, China
- *Correspondence: Yong Dai,
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14
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Subasinghe SAAS, Pautler RG, Samee MAH, Yustein JT, Allen MJ. Dual-Mode Tumor Imaging Using Probes That Are Responsive to Hypoxia-Induced Pathological Conditions. BIOSENSORS 2022; 12:bios12070478. [PMID: 35884281 PMCID: PMC9313010 DOI: 10.3390/bios12070478] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/22/2022] [Accepted: 06/26/2022] [Indexed: 05/02/2023]
Abstract
Hypoxia in solid tumors is associated with poor prognosis, increased aggressiveness, and strong resistance to therapeutics, making accurate monitoring of hypoxia important. Several imaging modalities have been used to study hypoxia, but each modality has inherent limitations. The use of a second modality can compensate for the limitations and validate the results of any single imaging modality. In this review, we describe dual-mode imaging systems for the detection of hypoxia that have been reported since the start of the 21st century. First, we provide a brief overview of the hallmarks of hypoxia used for imaging and the imaging modalities used to detect hypoxia, including optical imaging, ultrasound imaging, photoacoustic imaging, single-photon emission tomography, X-ray computed tomography, positron emission tomography, Cerenkov radiation energy transfer imaging, magnetic resonance imaging, electron paramagnetic resonance imaging, magnetic particle imaging, and surface-enhanced Raman spectroscopy, and mass spectrometric imaging. These overviews are followed by examples of hypoxia-relevant imaging using a mixture of probes for complementary single-mode imaging techniques. Then, we describe dual-mode molecular switches that are responsive in multiple imaging modalities to at least one hypoxia-induced pathological change. Finally, we offer future perspectives toward dual-mode imaging of hypoxia and hypoxia-induced pathophysiological changes in tumor microenvironments.
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Affiliation(s)
| | - Robia G. Pautler
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA; (R.G.P.); (M.A.H.S.)
| | - Md. Abul Hassan Samee
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA; (R.G.P.); (M.A.H.S.)
| | - Jason T. Yustein
- Integrative Molecular and Biomedical Sciences and the Department of Pediatrics in the Texas Children’s Cancer and Hematology Centers and The Faris D. Virani Ewing Sarcoma Center, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Matthew J. Allen
- Department of Chemistry, Wayne State University, 5101 Cass Avenue, Detroit, MI 48202, USA;
- Correspondence:
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15
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Bernstock JD, Gary SE, Klinger N, Valdes PA, Ibn Essayed W, Olsen HE, Chagoya G, Elsayed G, Yamashita D, Schuss P, Gessler FA, Peruzzi PP, Bag A, Friedman GK. Standard clinical approaches and emerging modalities for glioblastoma imaging. Neurooncol Adv 2022; 4:vdac080. [PMID: 35821676 PMCID: PMC9268747 DOI: 10.1093/noajnl/vdac080] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.
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Affiliation(s)
- Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Sam E Gary
- Medical Scientist Training Program, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Neil Klinger
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Pablo A Valdes
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Walid Ibn Essayed
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Hannah E Olsen
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Gustavo Chagoya
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Galal Elsayed
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Daisuke Yamashita
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Patrick Schuss
- Department of Neurosurgery, Unfallkrankenhaus Berlin , Berlin, Germany
| | | | - Pier Paolo Peruzzi
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Asim Bag
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital , Memphis, TN USA
| | - Gregory K Friedman
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, University of Alabama at Birmingham , Birmingham, AL, USA
- Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham , AL, USA
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16
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Haddad AF, Young JS, Morshed RA, Berger MS. FLAIRectomy: Resecting beyond the Contrast Margin for Glioblastoma. Brain Sci 2022; 12:brainsci12050544. [PMID: 35624931 PMCID: PMC9139350 DOI: 10.3390/brainsci12050544] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/21/2022] [Accepted: 04/21/2022] [Indexed: 12/11/2022] Open
Abstract
The standard of care for isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) is maximal resection followed by chemotherapy and radiation. Studies investigating the resection of GBM have primarily focused on the contrast enhancing portion of the tumor on magnetic resonance imaging. Histopathological studies, however, have demonstrated tumor infiltration within peri-tumoral fluid-attenuated inversion recovery (FLAIR) abnormalities, which is often not resected. The histopathology of FLAIR and local recurrence patterns of GBM have prompted interest in the resection of peri-tumoral FLAIR, or FLAIRectomy. To this point, recent studies have suggested a significant survival benefit associated with safe peri-tumoral FLAIR resection. In this review, we discuss the evidence surrounding the composition of peri-tumoral FLAIR, outcomes associated with FLAIRectomy, future directions of the field, and potential implications for patients.
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17
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Versatile Mass Spectrometry-Based Intraoperative Diagnosis of Liver Tumor in a Multiethnic Cohort. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Currently used techniques for intraoperative assessment of tumor resection margins are time-consuming and laborious and, more importantly, lack specificity. Moreover, pathological diagnosis during surgery does not often give a clear outcome. Recent advances in mass spectrometry (MS) and instrumentation have made it possible to obtain detailed molecular information from tissue specimens in real-time, with minimal sample pre-treatment. Probe Electro Spray Ionization MS (PESI-MS), combined with artificial intelligence (AI), has demonstrated its effectiveness in distinguishing liver cancer tissues from healthy tissues in a large Italian population group. As the MS profile can reflect the patient’s ethnicity, dietary habits, or particular operating room procedures, the AI algorithm must be well trained to distinguish different groups. We used a large dataset composed of liver tumor and healthy specimens, from the Italian and Japanese populations, to develop a versatile algorithm free from ethnic bias. The system can classify tissues with discrepancies <5% from the pathologist’s diagnosis. These results demonstrate the potential of the PESI-MS system to distinguish tumor from surrounding non-tumor tissues in patients, with minimal bias from race/ethnicity or etiological characteristics or operating room procedures.
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18
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Van Hese L, De Vleeschouwer S, Theys T, Larivière E, Solie L, Sciot R, Siegel TP, Rex S, Heeren RM, Cuypers E. Towards real-time intraoperative tissue interrogation for REIMS-guided glioma surgery. J Mass Spectrom Adv Clin Lab 2022; 24:80-89. [PMID: 35572786 PMCID: PMC9095887 DOI: 10.1016/j.jmsacl.2022.04.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 04/27/2022] [Accepted: 04/27/2022] [Indexed: 11/17/2022] Open
Abstract
REIMS can differentiate glioblastoma from normal brain with 99.2% sensitivity. Starting from 5% glioblastoma, REIMS showed a 100% correct classification rate. Low-grade gliomas can be identified with a 97.5% sensitivity.
Introduction Objectives Methods Results Conclusion
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Affiliation(s)
- Laura Van Hese
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Anaesthesiology, UZ Leuven; Department of Cardiovascular Sciences, KU Leuven, 3000 Leuven, Belgium
| | - Steven De Vleeschouwer
- Department of Neurosurgery, Laboratory for Experimental Neurosurgery and Neuroanatomy, UZ Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Tom Theys
- Department of Neurosurgery, Laboratory for Experimental Neurosurgery and Neuroanatomy, UZ Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Emma Larivière
- Department of Neurosurgery, Laboratory for Experimental Neurosurgery and Neuroanatomy, UZ Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Lien Solie
- Department of Neurosurgery, Laboratory for Experimental Neurosurgery and Neuroanatomy, UZ Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Raf Sciot
- Department of Pathology, University Hospitals Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Steffen Rex
- Department of Anaesthesiology, UZ Leuven; Department of Cardiovascular Sciences, KU Leuven, 3000 Leuven, Belgium
| | - Ron M.A. Heeren
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Eva Cuypers
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
- Corresponding author at: M4I Institute, Division of Imaging Mass Spectrometry, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands.
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19
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Mass spectrometry imaging: the future is now. Bioanalysis 2022; 14:383-386. [PMID: 35249371 DOI: 10.4155/bio-2021-0257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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20
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Santilli AML, Ren K, Oleschuk R, Kaufmann M, Rudan J, Fichtinger G, Mousavi P. Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review. IEEE Trans Biomed Eng 2022; 69:2220-2232. [PMID: 34982670 DOI: 10.1109/tbme.2021.3139992] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeons ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines. METHODS We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies. RESULTS Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively. CONCLUSION Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible. SIGNIFICANCE By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.
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21
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Shedlock CJ, Stumpo KA. Data parsing in mass spectrometry imaging using R Studio and Cardinal: A tutorial. J Mass Spectrom Adv Clin Lab 2022; 23:58-70. [PMID: 35072143 PMCID: PMC8762469 DOI: 10.1016/j.jmsacl.2021.12.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 12/14/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Mass spectrometry imaging (MSI) has emerged as a rapidly expanding field in the MS community. The analysis of large spectral data is further complicated by the added spatial dimension of MSI. A plethora of resources exist for expert users to begin parsing MSI data in R, but there is a critical lack of guidance for absolute beginners. This tutorial is designed to serve as a one-stop guide to start using R with MSI data and describe the possibilities that data science can bring to MSI analysis.
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Key Words
- AuNP, gold nanoparticle
- Cardinal
- DESI, desorption electrospray ioniziation
- Data validation
- IACUC, Institutional Animal Care and Use Committee
- ITO, indium tin oxide
- MSI, mass spectrometry imaging
- Mass spectrometry imaging
- PCA, principal component analysis
- R Studio
- RAM, random access memory
- RMS, root mean squared
- SNR, signal to noise ratio
- SSC, spatial shrunken centroid
- SSD, solid state drive
- TIC, total ion current
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Affiliation(s)
- Cameron J. Shedlock
- Department of Chemistry, University of Scranton, Scranton, PA 18510, United States
| | - Katherine A. Stumpo
- Department of Chemistry, University of Scranton, Scranton, PA 18510, United States
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Bruker Scientific, Billerica, MA 01821, United States
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22
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Katz L, Tata A, Woolman M, Zarrine-Afsar A. Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns. Metabolites 2021; 11:metabo11100660. [PMID: 34677375 PMCID: PMC8537725 DOI: 10.3390/metabo11100660] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/18/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023] Open
Abstract
Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.
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Affiliation(s)
- Lauren Katz
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7, Canada; (L.K.); (M.W.)
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada
| | - Alessandra Tata
- Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico delle Venezie, Viale Fiume 78, 36100 Vicenza, Italy;
| | - Michael Woolman
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7, Canada; (L.K.); (M.W.)
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada
| | - Arash Zarrine-Afsar
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7, Canada; (L.K.); (M.W.)
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada
- Department of Surgery, University of Toronto, 149 College Street, Toronto, ON M5T 1P5, Canada
- Keenan Research Center for Biomedical Science & the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, ON M5B 1W8, Canada
- Correspondence: ; Tel.: +1-416-581-8473
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23
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Ogrinc N, Saudemont P, Takats Z, Salzet M, Fournier I. Cancer Surgery 2.0: Guidance by Real-Time Molecular Technologies. Trends Mol Med 2021; 27:602-615. [PMID: 33965341 DOI: 10.1016/j.molmed.2021.04.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 03/30/2021] [Accepted: 04/02/2021] [Indexed: 12/14/2022]
Abstract
In vivo cancer margin delineation during surgery remains a major challenge. Despite the availability of several image guidance techniques and intraoperative assessment, clear surgical margins and debulking efficiency remain scarce. For this reason, there is particular interest in developing rapid intraoperative tools with high sensitivity and specificity to help guide cancer surgery in vivo. Recently, several emerging technologies including intraoperative mass spectrometry have paved the way for molecular guidance in a clinical setting. We evaluate these techniques and assess their relevance for intraoperative surgical guidance and how they can transform the future of molecular cancer surgery, diagnostics, patient management and care.
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Affiliation(s)
- Nina Ogrinc
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France
| | - Philippe Saudemont
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France
| | - Zoltan Takats
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France
| | - Michel Salzet
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France; Institut Universitaire de France (IUF), Paris, France.
| | - Isabelle Fournier
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France; Institut Universitaire de France (IUF), Paris, France.
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24
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Woolman M, Katz L, Tata A, Basu SS, Zarrine-Afsar A. Breaking Through the Barrier: Regulatory Considerations Relevant to Ambient Mass Spectrometry at the Bedside. Clin Lab Med 2021; 41:221-246. [PMID: 34020761 DOI: 10.1016/j.cll.2021.03.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Rapid characterization of tissue disorder using ambient mass spectrometry (MS) techniques, requiring little to no preanalytical preparations of sampled tissues, has been shown using a variety of ion sources and with many disease classes. A brief overview of ambient MS in clinical applications, the state of the art in regulatory affairs, and recommendations to facilitate adoption for use at the bedside are presented. Unique challenges in the validation of untargeted MS methods and additional safety and compliance requirements for deployment within a clinical setting are further discussed. Development of a harmonized validation strategy for ambient MS methods is emphasized.
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Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, Ontario M5G 1P5, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Lauren Katz
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, Ontario M5G 1P5, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Alessandra Tata
- Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico delle Venezie, Viale Fiume 78, 36100 Vicenza, Italy
| | - Sankha S Basu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Arash Zarrine-Afsar
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, Ontario M5G 1P5, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada; Department of Surgery, University of Toronto, 149 College Street, Toronto, Ontario M5T 1P5, Canada; Keenan Research Center for Biomedical Science & the Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada.
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25
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Suzuki K, Yoshimura K, Kawataki T, Hanihara M, Takeda S, Kinouchi H. Prediction of Pathological and Radiological Nature of Glioma by Mass Spectrometry Combined With Machine Learning. NEUROSURGERY OPEN 2021. [DOI: 10.1093/neuopn/okaa026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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26
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Vaysse PM, Grabsch HI, van den Hout MFCM, Bemelmans MHA, Heeren RMA, Olde Damink SWM, Porta Siegel T. Real-time lipid patterns to classify viable and necrotic liver tumors. J Transl Med 2021; 101:381-395. [PMID: 33483597 DOI: 10.1038/s41374-020-00526-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 11/30/2020] [Accepted: 12/02/2020] [Indexed: 12/14/2022] Open
Abstract
Real-time tissue classifiers based on molecular patterns are emerging tools for fast tumor diagnosis. Here, we used rapid evaporative ionization mass spectrometry (REIMS) and multivariate statistical analysis (principal component analysis-linear discriminant analysis) to classify tissues with subsequent comparison to gold standard histopathology. We explored whether REIMS lipid patterns can identify human liver tumors and improve the rapid characterization of their underlying metabolic features. REIMS-based classification of liver parenchyma (LP), hepatocellular carcinoma (HCC), and metastatic adenocarcinoma (MAC) reached an accuracy of 98.3%. Lipid patterns of LP were more similar to those of HCC than to those of MAC and allowed clear distinction between primary and metastatic liver tumors. HCC lipid patterns were more heterogeneous than those of MAC, which is consistent with the variation seen in the histopathological phenotype. A common ceramide pattern discriminated necrotic from viable tumor in MAC with 92.9% accuracy and in other human tumors. Targeted analysis of ceramide and related sphingolipid mass features in necrotic tissues may provide a new classification of tumor cell death based on metabolic shifts. Real-time lipid patterns may have a role in future clinical decision-making in cancer precision medicine.
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Affiliation(s)
- Pierre-Maxence Vaysse
- Maastricht MultiModal Molecular Imaging Institute (M4i), University of Maastricht, Maastricht, The Netherlands
- Department of Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Otorhinolaryngology, Head & Neck Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Heike I Grabsch
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
| | - Mari F C M van den Hout
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Marc H A Bemelmans
- Department of Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging Institute (M4i), University of Maastricht, Maastricht, The Netherlands
| | - Steven W M Olde Damink
- Department of Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of General, Visceral and Transplantation Surgery, RWTH University Hospital Aachen, Aachen, Germany
- NUTRIM School of Nutrition and Translational Research in Metabolism Faculty of Health, University of Maastricht, Maastricht, The Netherlands
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging Institute (M4i), University of Maastricht, Maastricht, The Netherlands.
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27
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Giordano S, Takeda S, Donadon M, Saiki H, Brunelli L, Pastorelli R, Cimino M, Soldani C, Franceschini B, Di Tommaso L, Lleo A, Yoshimura K, Nakajima H, Torzilli G, Davoli E. Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence. Liver Int 2020; 40:3117-3124. [PMID: 32662575 PMCID: PMC7754124 DOI: 10.1111/liv.14604] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 06/17/2020] [Accepted: 07/09/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND AIMS Complete surgical resection with negative margin is one of the pillars in treatment of liver tumours. However, current techniques for intra-operative assessment of tumour resection margins are time-consuming and empirical. Mass spectrometry (MS) combined with artificial intelligence (AI) is useful for classifying tissues and provides valuable prognostic information. The aim of this study was to develop a MS-based system for rapid and objective liver cancer identification and classification. METHODS A large dataset derived from 222 patients with hepatocellular carcinoma (HCC, 117 tumours and 105 non-tumours) and 96 patients with mass-forming cholangiocarcinoma (MFCCC, 50 tumours and 46 non-tumours) were analysed by Probe Electrospray Ionization (PESI) MS. AI by means of support vector machine (SVM) and random forest (RF) algorithms was employed. For each classifier, sensitivity, specificity and accuracy were calculated. RESULTS The overall diagnostic accuracy exceeded 94% in both the AI algorithms. For identification of HCC vs non-tumour tissue, RF was the best, with 98.2% accuracy, 97.4% sensitivity and 99% specificity. For MFCCC vs non-tumour tissue, both algorithms gave 99.0% accuracy, 98% sensitivity and 100% specificity. CONCLUSIONS The herein reported MS-based system, combined with AI, permits liver cancer identification with high accuracy. Its bench-top size, minimal sample preparation and short working time are the main advantages. From diagnostics to therapeutics, it has the potential to influence the decision-making process in real-time with the ultimate aim of improving cancer patient cure.
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Affiliation(s)
- Silvia Giordano
- Mass Spectrometry LaboratoryEnvironmental Health Sciences DepartmentIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly,Present address:
Shimadzu Italia SrlMilanItaly
| | - Sen Takeda
- Department of Anatomy and Cell BiologyUniversity of Yamanashi Faculty of MedicineChuoJapan
| | - Matteo Donadon
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly,Laboratory of Hepatobiliary ImmunopathologyHumanitas Clinical and Research Center – IRCCSMilanItaly
| | | | - Laura Brunelli
- Mass Spectrometry LaboratoryEnvironmental Health Sciences DepartmentIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly
| | - Roberta Pastorelli
- Mass Spectrometry LaboratoryEnvironmental Health Sciences DepartmentIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly
| | - Matteo Cimino
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly,Laboratory of Hepatobiliary ImmunopathologyHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Cristiana Soldani
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Barbara Franceschini
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Luca Di Tommaso
- Department of PathologyHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Ana Lleo
- Laboratory of Hepatobiliary ImmunopathologyHumanitas Clinical and Research Center – IRCCSMilanItaly,Department of Internal MedicineHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Kentaro Yoshimura
- Department of Anatomy and Cell BiologyUniversity of Yamanashi Faculty of MedicineChuoJapan
| | | | - Guido Torzilli
- Department of Hepatobiliary and General SurgeryHumanitas UniversityHumanitas Clinical and Research Center – IRCCSMilanItaly,Laboratory of Hepatobiliary ImmunopathologyHumanitas Clinical and Research Center – IRCCSMilanItaly
| | - Enrico Davoli
- Mass Spectrometry LaboratoryEnvironmental Health Sciences DepartmentIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly
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28
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Prasad M, Postma G, Franceschi P, Morosi L, Giordano S, Falcetta F, Giavazzi R, Davoli E, Buydens LMC, Jansen J. A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data. Gigascience 2020; 9:6006351. [PMID: 33241286 PMCID: PMC7688471 DOI: 10.1093/gigascience/giaa131] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/28/2020] [Accepted: 11/01/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection. RESULTS The number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method. CONCLUSIONS In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.
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Affiliation(s)
- Mridula Prasad
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands.,Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010 San Michele all' Adige, Italy
| | - Geert Postma
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
| | - Pietro Franceschi
- Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010 San Michele all' Adige, Italy
| | - Lavinia Morosi
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Silvia Giordano
- Mass Spectrometry Laboratory, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Francesca Falcetta
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Raffaella Giavazzi
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Enrico Davoli
- Mass Spectrometry Laboratory, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Lutgarde M C Buydens
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
| | - Jeroen Jansen
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
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29
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Yang H, Chandler CE, Jackson SN, Woods AS, Goodlett DR, Ernst RK, Scott AJ. On-Tissue Derivatization of Lipopolysaccharide for Detection of Lipid A Using MALDI-MSI. Anal Chem 2020; 92:13667-13671. [PMID: 32902263 DOI: 10.1021/acs.analchem.0c02566] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
We developed a method to directly detect and map the Gram-negative bacterial virulence factor lipid A derived from lipopolysaccharide (LPS) by coupling acid hydrolysis with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). As the structure of lipid A (endotoxin) determines the innate immune outcome during infection, the ability to map its location within an infected organ or animal is needed to understand localized inflammatory responses that results during host-pathogen interactions. We previously demonstrated detection of free lipid A from infected tissue; however detection of lipid A derived from intact (smooth) LPS from host-pathogen MSI studies, proved elusive. Here, we detected LPS-derived lipid A from the Gram-negative pathogens, Escherichia coli (Ec, m/z 1797) and Pseudomonas aeruginosa (Pa, m/z 1446) using on-tissue acid hydrolysis to cleave the glycosidic linkage between the polysaccharide (core and O-antigen) and lipid A moieties of LPS. Using accurate mass methods, the ion corresponding to the major Ec and Pa lipid A species (m/z 1797 and 1446, respectively) were unambiguously discriminated from complex tissue substrates. Further, we evaluated potential delocalization and signal loss of other tissue lipids and found no evidence for either, making this LPS-to-Lipid A-MSI (LLA-MSI) method, compatible with simultaneous host-pathogen lipid imaging following acid hydrolysis. This spatially sensitive technique is the first step in mapping host-influenced de novo lipid A modifications, such as those associated with antimicrobial resistance phenotypes, during Gram-negative bacterial infection and will advance our understanding of the host-pathogen interface.
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Affiliation(s)
- Hyojik Yang
- Department of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, Maryland 21201, United States
| | - Courtney E Chandler
- Department of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, Maryland 21201, United States
| | - Shelley N Jackson
- Structural Biology Core, NIDA IRP, NIH, 333 Cassell Drive, Room 1120, Baltimore, Maryland 21224, United States
| | - Amina S Woods
- Structural Biology Core, NIDA IRP, NIH, 333 Cassell Drive, Room 1120, Baltimore, Maryland 21224, United States.,Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine. Baltimore, Maryland 21205, United States
| | - David R Goodlett
- Department of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, Maryland 21201, United States.,University of Gdansk, International Centre for Cancer Vaccine Science, Gdansk, Poland
| | - Robert K Ernst
- Department of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, Maryland 21201, United States
| | - Alison J Scott
- Department of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, Maryland 21201, United States.,Maastricht Multimodal Molecular Imaging (M4I) Institute, Maastricht University, Maastricht, Netherlands
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30
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Goodwin RJA, Takats Z, Bunch J. A Critical and Concise Review of Mass Spectrometry Applied to Imaging in Drug Discovery. SLAS DISCOVERY 2020; 25:963-976. [PMID: 32713279 DOI: 10.1177/2472555220941843] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
During the past decade, mass spectrometry imaging (MSI) has become a robust and versatile methodology to support modern pharmaceutical research and development. The technologies provide data on the biodistribution, metabolism, and delivery of drugs in tissues, while also providing molecular maps of endogenous metabolites, lipids, and proteins. This allows researchers to make both pharmacokinetic and pharmacodynamic measurements at cellular resolution in tissue sections or clinical biopsies. Despite drug imaging within samples now playing a vital role within research and development (R&D) in leading pharmaceutical companies, however, the challenges in turning compounds into medicines continue to evolve as rapidly as the technologies used to discover them. The increasing cost of development of new and emerging therapeutic modalities, along with the associated risks of late-stage program attrition, means there is still an unmet need in our ability to address an increasing array of challenging bioanalytical questions within drug discovery. We require new capabilities and strategies of integrated imaging to provide context for fundamental disease-related biological questions that can also offer insights into specific project challenges. Integrated molecular imaging and advanced image analysis have the opportunity to provide a world-class capability that can be deployed on projects in which we cannot answer the question with our battery of established assays. Therefore, here we will provide an updated concise review of the use of MSI for drug discovery; we will also critically consider what is required to embed MSI into a wider evolving R&D landscape and allow long-lasting impact in the industry.
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Affiliation(s)
- Richard J A Goodwin
- Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.,Institute of Infection, Immunity, and Inflammation, College of Medical, Veterinary, and Life Sciences, University of Glasgow, UK
| | - Zoltan Takats
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.,The Rosalind Franklin Institute, Oxfordshire, UK
| | - Josephine Bunch
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.,The Rosalind Franklin Institute, Oxfordshire, UK.,National Physical Laboratory, Teddington, London, UK
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31
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Woolman M, Qiu J, Kuzan-Fischer CM, Ferry I, Dara D, Katz L, Daud F, Wu M, Ventura M, Bernards N, Chan H, Fricke I, Zaidi M, Wouters BG, Rutka JT, Das S, Irish J, Weersink R, Ginsberg HJ, Jaffray DA, Zarrine-Afsar A. In situ tissue pathology from spatially encoded mass spectrometry classifiers visualized in real time through augmented reality. Chem Sci 2020; 11:8723-8735. [PMID: 34123126 PMCID: PMC8163395 DOI: 10.1039/d0sc02241a] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Integration between a hand-held mass spectrometry desorption probe based on picosecond infrared laser technology (PIRL-MS) and an optical surgical tracking system demonstrates in situ tissue pathology from point-sampled mass spectrometry data. Spatially encoded pathology classifications are displayed at the site of laser sampling as color-coded pixels in an augmented reality video feed of the surgical field of view. This is enabled by two-way communication between surgical navigation and mass spectrometry data analysis platforms through a custom-built interface. Performance of the system was evaluated using murine models of human cancers sampled in situ in the presence of body fluids with a technical pixel error of 1.0 ± 0.2 mm, suggesting a 84% or 92% (excluding one outlier) cancer type classification rate across different molecular models that distinguish cell-lines of each class of breast, brain, head and neck murine models. Further, through end-point immunohistochemical staining for DNA damage, cell death and neuronal viability, spatially encoded PIRL-MS sampling is shown to produce classifiable mass spectral data from living murine brain tissue, with levels of neuronal damage that are comparable to those induced by a surgical scalpel. This highlights the potential of spatially encoded PIRL-MS analysis for in vivo use during neurosurgical applications of cancer type determination or point-sampling in vivo tissue during tumor bed examination to assess cancer removal. The interface developed herein for the analysis and the display of spatially encoded PIRL-MS data can be adapted to other hand-held mass spectrometry analysis probes currently available. Integration between a hand-held mass spectrometry desorption probe based on picosecond infrared laser technology (PIRL-MS) and an optical surgical tracking system demonstrates in situ tissue pathology from point-sampled mass spectrometry data.![]()
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Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473.,Department of Medical Biophysics, University of Toronto 101 College Street Toronto ON M5G 1L7 Canada
| | - Jimmy Qiu
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473
| | - Claudia M Kuzan-Fischer
- Peter Gilgan Centre for Research and Learning, Hospital for Sick Children 686 Bay Street Toronto ON M5G 0A4 Canada.,Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children Toronto ON M5G 1X8 Canada
| | - Isabelle Ferry
- Peter Gilgan Centre for Research and Learning, Hospital for Sick Children 686 Bay Street Toronto ON M5G 0A4 Canada.,Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children Toronto ON M5G 1X8 Canada
| | - Delaram Dara
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473
| | - Lauren Katz
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473.,Department of Medical Biophysics, University of Toronto 101 College Street Toronto ON M5G 1L7 Canada
| | - Fowad Daud
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473.,Department of Medical Biophysics, University of Toronto 101 College Street Toronto ON M5G 1L7 Canada
| | - Megan Wu
- Peter Gilgan Centre for Research and Learning, Hospital for Sick Children 686 Bay Street Toronto ON M5G 0A4 Canada
| | - Manuela Ventura
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473
| | - Nicholas Bernards
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473
| | - Harley Chan
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473
| | - Inga Fricke
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473
| | - Mark Zaidi
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473
| | - Brad G Wouters
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473.,Department of Medical Biophysics, University of Toronto 101 College Street Toronto ON M5G 1L7 Canada
| | - James T Rutka
- Peter Gilgan Centre for Research and Learning, Hospital for Sick Children 686 Bay Street Toronto ON M5G 0A4 Canada.,Department of Surgery, University of Toronto 149 College Street Toronto ON M5T 1P5 Canada.,Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children Toronto ON M5G 1X8 Canada
| | - Sunit Das
- Peter Gilgan Centre for Research and Learning, Hospital for Sick Children 686 Bay Street Toronto ON M5G 0A4 Canada.,Department of Surgery, University of Toronto 149 College Street Toronto ON M5T 1P5 Canada.,Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children Toronto ON M5G 1X8 Canada
| | - Jonathan Irish
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473
| | - Robert Weersink
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473
| | - Howard J Ginsberg
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473.,Department of Surgery, University of Toronto 149 College Street Toronto ON M5T 1P5 Canada.,Keenan Research Center for Biomedical Science, The Li Ka Shing Knowledge Institute, St. Michael's Hospital 30 Bond Street Toronto ON M5B 1W8 Canada
| | - David A Jaffray
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473.,Department of Medical Biophysics, University of Toronto 101 College Street Toronto ON M5G 1L7 Canada
| | - Arash Zarrine-Afsar
- Techna Institute for the Advancement of Technology for Health, University Health Network 100 College Street, Room 7-207, MaRS Building, Princess Margaret Cancer Research Tower, 7th floor (STTARR) Toronto ON M5G 1P5 Canada +1-416-581-8473.,Department of Medical Biophysics, University of Toronto 101 College Street Toronto ON M5G 1L7 Canada.,Department of Surgery, University of Toronto 149 College Street Toronto ON M5T 1P5 Canada.,Keenan Research Center for Biomedical Science, The Li Ka Shing Knowledge Institute, St. Michael's Hospital 30 Bond Street Toronto ON M5B 1W8 Canada
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32
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Holmes DT, Romney MG, Angel P, DeMarco ML. Proteomic applications in pathology and laboratory medicine: Present state and future prospects. Clin Biochem 2020; 82:12-20. [PMID: 32442429 DOI: 10.1016/j.clinbiochem.2020.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 05/13/2020] [Accepted: 05/13/2020] [Indexed: 12/11/2022]
Abstract
Clinical mass spectrometry applications have traditionally focused on small molecules, particularly in the areas of therapeutic drug monitoring, toxicology, and measurement of endogenous and exogenous steroids. More recently, the use of matrix assisted laser desorption/ionization time of flight mass spectrometry for the identification of microbial pathogens has been widely implemented. Following this evolution, there has been an expanding role for the measurement of peptides and proteins in pathology and laboratory medicine. This review explores the current state of protein measurement by clinical mass spectrometry and the analytical strategies employed, as well as emerging applications in clinical chemistry, clinical microbiology and anatomical pathology.
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Affiliation(s)
- Daniel T Holmes
- Department of Pathology and Laboratory Medicine, St. Paul's Hospital, 1081 Burrard St, Vancouver, BC V6Z 1Y6, Canada; University of British Columbia Department of Pathology and Laboratory Medicine, Vancouver, BC V6T 2B5 Canada.
| | - Marc G Romney
- Department of Pathology and Laboratory Medicine, St. Paul's Hospital, 1081 Burrard St, Vancouver, BC V6Z 1Y6, Canada; University of British Columbia Department of Pathology and Laboratory Medicine, Vancouver, BC V6T 2B5 Canada.
| | - Peggi Angel
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charelston, SC 29425 Canada.
| | - Mari L DeMarco
- Department of Pathology and Laboratory Medicine, St. Paul's Hospital, 1081 Burrard St, Vancouver, BC V6Z 1Y6, Canada; University of British Columbia Department of Pathology and Laboratory Medicine, Vancouver, BC V6T 2B5 Canada.
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33
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Gouw AM, Margulis K, Liu NS, Raman SJ, Mancuso A, Toal GG, Tong L, Mosley A, Hsieh AL, Sullivan DK, Stine ZE, Altman BJ, Schulze A, Dang CV, Zare RN, Felsher DW. The MYC Oncogene Cooperates with Sterol-Regulated Element-Binding Protein to Regulate Lipogenesis Essential for Neoplastic Growth. Cell Metab 2019; 30:556-572.e5. [PMID: 31447321 PMCID: PMC6911354 DOI: 10.1016/j.cmet.2019.07.012] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 09/24/2018] [Accepted: 07/24/2019] [Indexed: 12/14/2022]
Abstract
Lipid metabolism is frequently perturbed in cancers, but the underlying mechanism is unclear. We present comprehensive evidence that oncogene MYC, in collaboration with transcription factor sterol-regulated element-binding protein (SREBP1), regulates lipogenesis to promote tumorigenesis. We used human and mouse tumor-derived cell lines, tumor xenografts, and four conditional transgenic mouse models of MYC-induced tumors to show that MYC regulates lipogenesis genes, enzymes, and metabolites. We found that MYC induces SREBP1, and they collaborate to activate fatty acid (FA) synthesis and drive FA chain elongation from glucose and glutamine. Further, by employing desorption electrospray ionization mass spectrometry imaging (DESI-MSI), we observed in vivo lipidomic changes upon MYC induction across different cancers, for example, a global increase in glycerophosphoglycerols. After inhibition of FA synthesis, tumorigenesis was blocked, and tumors regressed in both xenograft and primary transgenic mouse models, revealing the vulnerability of MYC-induced tumors to the inhibition of lipogenesis.
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Affiliation(s)
- Arvin M Gouw
- Division of Oncology, Departments of Medicine and Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Natalie S Liu
- Division of Oncology, Departments of Medicine and Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sudha J Raman
- Department of Biochemistry and Molecular Biology, Wurzburg University, Wurzburg, Germany
| | - Anthony Mancuso
- Department of Medicine, Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Georgia G Toal
- Division of Oncology, Departments of Medicine and Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ling Tong
- Division of Oncology, Departments of Medicine and Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Adriane Mosley
- Division of Oncology, Departments of Medicine and Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Annie L Hsieh
- Department of Medicine, Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Delaney K Sullivan
- Division of Oncology, Departments of Medicine and Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Zachary E Stine
- Department of Medicine, Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian J Altman
- Department of Medicine, Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Almut Schulze
- Department of Biochemistry and Molecular Biology, Wurzburg University, Wurzburg, Germany
| | - Chi V Dang
- Department of Medicine, Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Ludwig Institute for Cancer Research, New York, NY 10017, USA; The Wistar Institute, Philadelphia, PA 19104, USA.
| | - Richard N Zare
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA; Ludwig Institute for Cancer Research, New York, NY 10017, USA.
| | - Dean W Felsher
- Division of Oncology, Departments of Medicine and Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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34
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Current and Future Trends on Diagnosis and Prognosis of Glioblastoma: From Molecular Biology to Proteomics. Cells 2019; 8:cells8080863. [PMID: 31405017 PMCID: PMC6721640 DOI: 10.3390/cells8080863] [Citation(s) in RCA: 137] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/02/2019] [Accepted: 08/06/2019] [Indexed: 02/07/2023] Open
Abstract
Glioblastoma multiforme is the most aggressive malignant tumor of the central nervous system. Due to the absence of effective pharmacological and surgical treatments, the identification of early diagnostic and prognostic biomarkers is of key importance to improve the survival rate of patients and to develop new personalized treatments. On these bases, the aim of this review article is to summarize the current knowledge regarding the application of molecular biology and proteomics techniques for the identification of novel biomarkers through the analysis of different biological samples obtained from glioblastoma patients, including DNA, microRNAs, proteins, small molecules, circulating tumor cells, extracellular vesicles, etc. Both benefits and pitfalls of molecular biology and proteomics analyses are discussed, including the different mass spectrometry-based analytical techniques, highlighting how these investigation strategies are powerful tools to study the biology of glioblastoma, as well as to develop advanced methods for the management of this pathology.
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35
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Randall EC, Zadra G, Chetta P, Lopez BGC, Syamala S, Basu SS, Agar JN, Loda M, Tempany CM, Fennessy FM, Agar NYR. Molecular Characterization of Prostate Cancer with Associated Gleason Score Using Mass Spectrometry Imaging. Mol Cancer Res 2019; 17:1155-1165. [PMID: 30745465 PMCID: PMC6497547 DOI: 10.1158/1541-7786.mcr-18-1057] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 12/19/2018] [Accepted: 02/06/2019] [Indexed: 12/31/2022]
Abstract
Diagnosis of prostate cancer is based on histologic evaluation of tumor architecture using a system known as the "Gleason score." This diagnostic paradigm, while the standard of care, is time-consuming, shows intraobserver variability, and provides no information about the altered metabolic pathways, which result in altered tissue architecture. Characterization of the molecular composition of prostate cancer and how it changes with respect to the Gleason score (GS) could enable a more objective and faster diagnosis. It may also aid in our understanding of disease onset and progression. In this work, we present mass spectrometry imaging for identification and mapping of lipids and metabolites in prostate tissue from patients with known prostate cancer with GS from 6 to 9. A gradient of changes in the intensity of various lipids was observed, which correlated with increasing GS. Interestingly, these changes were identified in both regions of high tumor cell density, and in regions of tissue that appeared histologically benign, possibly suggestive of precancerous metabolomic changes. A total of 31 lipids, including several phosphatidylcholines, phosphatidic acids, phosphatidylserines, phosphatidylinositols, and cardiolipins were detected with higher intensity in GS (4+3) compared with GS (3+4), suggesting they may be markers of prostate cancer aggression. Results obtained through mass spectrometry imaging studies were subsequently correlated with a fast, ambient mass spectrometry method for potential use as a clinical tool to support image-guided prostate biopsy. IMPLICATIONS: In this study, we suggest that metabolomic differences between prostate cancers with different Gleason scores can be detected by mass spectrometry imaging.
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Affiliation(s)
- Elizabeth C Randall
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Giorgia Zadra
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Paolo Chetta
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
- University of Milan, Milan, Italy
| | - Begona G C Lopez
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sudeepa Syamala
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Sankha S Basu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jeffrey N Agar
- Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts
| | - Massimo Loda
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Clare M Tempany
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Nathalie Y R Agar
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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36
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Abdelmoula WM, Regan MS, Lopez BGC, Randall EC, Lawler S, Mladek AC, Nowicki MO, Marin BM, Agar JN, Swanson KR, Kapur T, Sarkaria JN, Wells W, Agar NYR. Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data. Anal Chem 2019; 91:6206-6216. [PMID: 30932478 DOI: 10.1021/acs.analchem.9b00854] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Multimodal integration between mass spectrometry imaging (MSI) and radiology-established modalities such as magnetic resonance imaging (MRI) would allow the investigations of key questions in complex biological systems such as the central nervous system. Such integration would provide complementary multiscale data to bridge the gap between molecular and anatomical phenotypes, potentially revealing new insights into molecular mechanisms underlying anatomical pathologies presented on MRI. Automatic coregistration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and nonlinear tissue deformation. Here, we present a new computational approach based on stochastic neighbor embedding to nonlinearly align 3D MSI to MRI data, identify and reconstruct biologically relevant molecular patterns in 3D, and fuse the MSI datacube to the MRI space. We demonstrate our method using multimodal high-spectral resolution matrix-assisted laser desorption ionization (MALDI) 9.4 T MSI and 7 T in vivo MRI data, acquired from a patient-derived, xenograft mouse brain model of glioblastoma following administration of the EGFR inhibitor drug of Erlotinib. Results show the distribution of some identified molecular ions of the EGFR inhibitor erlotinib, a phosphatidylcholine lipid, and cholesterol, which were reconstructed in 3D and mapped to the MRI space. The registration quality was evaluated on two normal mouse brains using the Dice coefficient for the regions of brainstem, hippocampus, and cortex. The method is generic and can therefore be applied to hyperspectral images from different mass spectrometers and integrated with other established in vivo imaging modalities such as computed tomography (CT) and positron emission tomography (PET).
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Affiliation(s)
- Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Michael S Regan
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Begona G C Lopez
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Elizabeth C Randall
- Department of Radiology, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Sean Lawler
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Ann C Mladek
- Department of Radiation Oncology , Mayo Clinic , 200 First Street SW , Rochester , Minnesota 55902 , United States
| | - Michal O Nowicki
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Bianca M Marin
- Department of Radiation Oncology , Mayo Clinic , 200 First Street SW , Rochester , Minnesota 55902 , United States
| | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology , Northeastern University , 412 TF (140 The Fenway) , Boston , Massachusetts 02111 , United States
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Department of Neurosurgery , Mayo Clinic , 5777 East Mayo Boulevard , Phoenix , Arizona 85054 , United States
| | - Tina Kapur
- Department of Radiology, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States
| | - Jann N Sarkaria
- Department of Radiation Oncology , Mayo Clinic , 200 First Street SW , Rochester , Minnesota 55902 , United States
| | - William Wells
- Department of Radiology, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States.,Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , Cambridge , Massachusetts 02139 , United States
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States.,Department of Radiology, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States.,Department of Cancer Biology, Dana-Farber Cancer Institute , Harvard Medical School , Boston , Massachusetts 02115 , United States
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37
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Mass Spectrometry Imaging and Integration with Other Imaging Modalities for Greater Molecular Understanding of Biological Tissues. Mol Imaging Biol 2019; 20:888-901. [PMID: 30167993 PMCID: PMC6244545 DOI: 10.1007/s11307-018-1267-y] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Over the last two decades, mass spectrometry imaging (MSI) has been increasingly employed to investigate the spatial distribution of a wide variety of molecules in complex biological samples. MSI has demonstrated its potential in numerous applications from drug discovery, disease state evaluation through proteomic and/or metabolomic studies. Significant technological and methodological advancements have addressed natural limitations of the techniques, i.e., increased spatial resolution, increased detection sensitivity especially for large molecules, higher throughput analysis and data management. One of the next major evolutions of MSI is linked to the introduction of imaging mass cytometry (IMC). IMC is a multiplexed method for tissue phenotyping, imaging signalling pathway or cell marker assessment, at sub-cellular resolution (1 μm). It uses MSI to simultaneously detect and quantify up to 30 different antibodies within a tissue section. The combination of MSI with other molecular imaging techniques can also provide highly relevant complementary information to explore new scientific fields. Traditionally, classical histology (especially haematoxylin and eosin–stained sections) is overlaid with molecular profiles obtained by MSI. Thus, MSI-based molecular histology provides a snapshot of a tissue microenvironment and enables the correlation of drugs, metabolites, lipids, peptides or proteins with histological/pathological features or tissue substructures. Recently, many examples combining MSI with other imaging modalities such as fluorescence, confocal Raman spectroscopy and MRI have emerged. For instance, brain pathophysiology has been studied using both MRI and MSI, establishing correlations between in and ex vivo molecular imaging techniques. Endogenous metabolite and small peptide modulation were evaluated depending on disease state. Here, we review advanced ‘hot topics’ in MSI development and explore the combination of MSI with established molecular imaging techniques to improve our understanding of biological and pathophysiological processes.
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38
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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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39
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Woolman M, Zarrine-Afsar A. Platforms for rapid cancer characterization by ambient mass spectrometry: advancements, challenges and opportunities for improvement towards intrasurgical use. Analyst 2019; 143:2717-2722. [PMID: 29786708 DOI: 10.1039/c8an00310f] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Ambient Mass Spectrometry (MS) analysis is widely used to characterize biological and non-biological samples. Advancements that allow rapid analysis of samples by ambient methods such as Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) and Rapid Evaporative Ionization Mass Spectrometry (REIMS) are discussed. A short, non-comprehensive overview of ambient MS is provided that only contains example applications due to space limitations. A spatially encoded mass spectrometry analysis concept to plan cancer resection is introduced. The application of minimally destructive tissue ablation probes to survey the surgical field for sites of pathology using on-line analysis methods is discussed. The technological challenges that must be overcome for ambient MS to become a robust method for intrasurgical pathology assessments are reviewed.
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Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada.
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40
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A high-performance bio-tissue imaging method using air flow-assisted desorption electrospray ionization coupled with a high-resolution mass spectrometer. CHINESE CHEM LETT 2019. [DOI: 10.1016/j.cclet.2018.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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41
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Zhvansky ES, Pekov SI, Sorokin AA, Shurkhay VA, Eliferov VA, Potapov AA, Nikolaev EN, Popov IA. Metrics for evaluating the stability and reproducibility of mass spectra. Sci Rep 2019; 9:914. [PMID: 30696886 PMCID: PMC6351633 DOI: 10.1038/s41598-018-37560-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 12/06/2018] [Indexed: 11/15/2022] Open
Abstract
In this work, we demonstrate a new approach for assessing the stability and reproducibility of mass spectra obtained via ambient ionization methods. This method is suitable for both comparing experiments during which only one mass spectrum is measured and for evaluating the internal homogeneity of mass spectra collected over a period of time. The approach uses Pearson’s r coefficient and the cosine measure to compare the spectra. It is based on the visualization of dissimilarities between measurements, thus leading to the analysis of dissimilarity patterns. The cosine measure and correlations are compared to obtain better metrics for spectra homogeneity. The method filters out unreliable scans to prevent the analyzed sample from being wrongly characterized. The applicability of the method is demonstrated on a set of brain tumor samples. The developed method could be employed in neurosurgical applications, where mass spectrometry is used to monitor the intraoperative tumor border.
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Affiliation(s)
- E S Zhvansky
- Moscow Institute of Physics and Technology, Dolgoprudnyy, Moscow Region, Moscow, Russian Federation
| | - S I Pekov
- Moscow Institute of Physics and Technology, Dolgoprudnyy, Moscow Region, Moscow, Russian Federation
| | - A A Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudnyy, Moscow Region, Moscow, Russian Federation
| | - V A Shurkhay
- Federal State Autonomous Institution «N.N. Burdenko National Scientific and Practical Center for Neurosurgery» of the Ministry of Healthcare of the Russian Federation, Moscow, Russian Federation
| | - V A Eliferov
- Moscow Institute of Physics and Technology, Dolgoprudnyy, Moscow Region, Moscow, Russian Federation
| | - A A Potapov
- Federal State Autonomous Institution «N.N. Burdenko National Scientific and Practical Center for Neurosurgery» of the Ministry of Healthcare of the Russian Federation, Moscow, Russian Federation
| | - E N Nikolaev
- Skolkovo Institute of Science and Technology, Moscow, Russian Federation.
| | - I A Popov
- Moscow Institute of Physics and Technology, Dolgoprudnyy, Moscow Region, Moscow, Russian Federation
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42
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Assessment of Metabolic Signature for Cancer Diagnosis Using Desorption Electrospray Ionization Mass Spectrometric Imaging. Methods Mol Biol 2019; 1928:275-297. [PMID: 30725461 DOI: 10.1007/978-1-4939-9027-6_15] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Metabolic reprogramming is a hallmark of tumor development. A technique that can map this complex biochemical shift by taking a snapshot of various metabolites in a tissue specimen (biopsy) is of high utility in the context of cancer diagnosis. Desorption electrospray ionization mass spectrometric imaging (DESI-MSI) is such a powerful and emerging analytical technique to simultaneously visualize the distributions of hundreds of metabolites, lipids, and other small molecules in the biological tissue. In DESI-MSI, a fine spray of high-velocity charged microdroplets rapidly extracts molecular species from the tissue surface and subsequently transfers them to the mass spectrometer, while the sample is continuously moved in two dimensions under the impinging spray of microdroplets. This allows a detailed multiplex molecular mapping of the tissue. DESI-MSI enables simultaneous examination of hundreds of putative metabolic biomarkers, an approach that lends much more predictive power than simply evaluating one or a few candidate biomarkers. The speed, versatility, lack of complicated sample preparation, and operation at ambient conditions make DESI-MSI extremely promising as a rapid diagnostic and prognostic tool.
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43
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Stammes MA, Bugby SL, Porta T, Pierzchalski K, Devling T, Otto C, Dijkstra J, Vahrmeijer AL, de Geus-Oei LF, Mieog JSD. Modalities for image- and molecular-guided cancer surgery. Br J Surg 2018; 105:e69-e83. [PMID: 29341161 DOI: 10.1002/bjs.10789] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 10/25/2017] [Accepted: 11/05/2017] [Indexed: 12/19/2022]
Abstract
BACKGROUND Surgery is the cornerstone of treatment for many solid tumours. A wide variety of imaging modalities are available before surgery for staging, although surgeons still rely primarily on visual and haptic cues in the operating environment. Image and molecular guidance might improve the adequacy of resection through enhanced tumour definition and detection of aberrant deposits. Intraoperative modalities available for image- and molecular-guided cancer surgery are reviewed here. METHODS Intraoperative cancer detection techniques were identified through a systematic literature search, with selection of peer-reviewed publications from January 2012 to January 2017. Modalities were reviewed, described and compared according to 25 predefined characteristics. To summarize the data in a comparable way, a three-point rating scale was applied to quantitative characteristics. RESULTS The search identified ten image- and molecular-guided surgery techniques, which can be divided into four groups: conventional, optical, nuclear and endogenous reflectance modalities. Conventional techniques are the most well known imaging modalities, but unfortunately have the drawback of a defined resolution and long acquisition time. Optical imaging is a real-time modality; however, the penetration depth is limited. Nuclear modalities have excellent penetration depth, but their intraoperative use is limited by the use of radioactivity. Endogenous reflectance modalities provide high resolution, although with a narrow field of view. CONCLUSION Each modality has its strengths and weaknesses; no single technique will be suitable for all surgical procedures. Strict selection of modalities per cancer type and surgical requirements is required as well as combining techniques to find the optimal balance.
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Affiliation(s)
- M A Stammes
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Percuros, Enschede, The Netherlands
| | - S L Bugby
- Space Research Centre, Department of Physics and Astronomy, University of Leicester, Leicester, UK
| | - T Porta
- Maastricht MultiModal Molecular Imaging Institute (M4I), Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, The Netherlands
| | - K Pierzchalski
- Maastricht MultiModal Molecular Imaging Institute (M4I), Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, The Netherlands
| | | | - C Otto
- Medical Cell Bio Physics, University of Twente, Enschede, The Netherlands
| | - J Dijkstra
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - A L Vahrmeijer
- Department of Surgery, Leiden University Medical Centre, Leiden, The Netherlands
| | - L-F de Geus-Oei
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Biomedical Photonic Imaging Group, MIRA Institute, University of Twente, Enschede, The Netherlands
| | - J S D Mieog
- Department of Surgery, Leiden University Medical Centre, Leiden, The Netherlands
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44
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Woolman M, Tata A, Dara D, Meens J, D'Arcangelo E, Perez CJ, Saiyara Prova S, Bluemke E, Ginsberg HJ, Ifa D, McGuigan A, Ailles L, Zarrine-Afsar A. Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration. Analyst 2018; 142:3250-3260. [PMID: 28799592 DOI: 10.1039/c7an00830a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Squamous cell carcinomas constitute a major class of head & neck cancers, where the tumour stroma ratio (TSR) carries prognostic information. Patients affected by stroma-rich tumours exhibit a poor prognosis and a higher chance of relapse. As such, there is a need for a technology platform that allows rapid determination of the tumour stroma ratio. In this work, we provide a proof-of-principle demonstration that Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) can be used to determine tumour stroma ratios. Slices from three independent mouse xenograft tumours from the human FaDu cell line were subjected to DESI-MS imaging, staining and detailed analysis using digital pathology methods. Using multivariate statistical methods we compared the MS profiles with those of isolated stromal cells. We found that m/z 773.53 [PG(18:1)(18:1) - H]-, m/z 835.53 [PI(34:1) - H]- and m/z 863.56 [PI(18:1)(18:0) - H]- are biomarker ions that can distinguish FaDu cancer from cancer associated fibroblast (CAF) cells. A comparison with DESI-MS analysis of controlled mixtures of the CAF and FaDu cells showed that the abundance of the biomarker ions above can be used to determine, with an error margin of close to 5% compared with quantitative pathology estimates, TSR values. This proof-of-principle demonstration is encouraging and must be further validated using human samples and a larger sample base. At maturity, DESI-MS thus may become a stand-alone molecular pathology tool providing an alternative rapid cancer assessment without the need for time-consuming staining and microscopy methods, potentially further conserving human resources.
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Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada
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45
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Margulis K, Zhou Z, Fang Q, Sievers RE, Lee RJ, Zare RN. Combining Desorption Electrospray Ionization Mass Spectrometry Imaging and Machine Learning for Molecular Recognition of Myocardial Infarction. Anal Chem 2018; 90:12198-12206. [PMID: 30188683 DOI: 10.1021/acs.analchem.8b03410] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Lipid profile changes in heart muscle have been previously linked to cardiac ischemia and myocardial infarction, but the spatial distribution of lipids and metabolites in ischemic heart remains to be fully investigated. We performed desorption electrospray ionization mass spectrometry imaging of hearts from in vivo myocardial infarction mouse models. In these mice, myocardial ischemia was induced by blood supply restriction via a permanent ligation of left anterior descending coronary artery. We showed that applying the machine learning algorithm of gradient boosting tree ensemble to the ambient mass spectrometry imaging data allows us to distinguish segments of infarcted myocardium from normally perfused hearts on a pixel by pixel basis. The machine learning algorithm selected 62 molecular ion peaks important for classification of each 200 μm-diameter pixel of the cardiac tissue map as normally perfused or ischemic. This approach achieved very high average accuracy (97.4%), recall (95.8%), and precision (96.8%) at a spatial resolution of ∼200 μm. In addition, we determined the chemical identity of 27 species, mostly small metabolites and lipids, selected by the algorithm as the most significant for cardiac pathology classification. This molecular signature of myocardial infarction may provide new mechanistic insights into cardiac ischemia, assist with infarct size assessment, and point toward novel therapeutic interventions.
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Affiliation(s)
- Katherine Margulis
- Department of Chemistry , Stanford University , Stanford , California 94305 , United States
| | - Zhenpeng Zhou
- Department of Chemistry , Stanford University , Stanford , California 94305 , United States
| | - Qizhi Fang
- Cardiovascular Research Institute and Department of Medicine , University of California San Francisco , San Francisco , California 94131 , United States
| | - Richard E Sievers
- Cardiovascular Research Institute and Department of Medicine , University of California San Francisco , San Francisco , California 94131 , United States
| | - Randall J Lee
- Cardiovascular Research Institute and Department of Medicine , University of California San Francisco , San Francisco , California 94131 , United States
| | - Richard N Zare
- Department of Chemistry , Stanford University , Stanford , California 94305 , United States
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Banerjee S. Ambient ionization mass spectrometry imaging for disease diagnosis: Excitements and challenges. J Biosci 2018; 43:731-738. [PMID: 30207318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Tissue analysis in histology is extremely important and also considered to be a gold standard to diagnose and prognosticate several diseases including cancer. Intraoperative evaluation of surgical margin of tumor also relies on frozen section histopathology, which is time consuming, challenging and often subjective. Recent development in the ambient ionization mass spectrometry imaging (MSI) technique has enabled us to simultaneously visualize hundreds to thousands of molecules (ion images) in the biopsy specimen, which are strikingly different and more powerful than the single optical tissue image analysis in conventional histopathology. This paper will highlight the emergence of the desorption electrospray ionization MSI (DESI-MSI) technique, which is label-free, requires minimal or no sample preparation and operates under ambient conditions. DESI-MSI can record ion images of lipid/metabolite distributions on biopsy specimens, providing a wealth of diagnostic information based on differential distributions of these molecular species in healthy and unhealthy tissues. Remarkable success of this technology in rapidly evaluating the cancer margin intraoperatively with very high accuracy also promises to bring this imaging technique from bench to bedside.
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Affiliation(s)
- Shibdas Banerjee
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Karakambadi Road, Tirupati 517 507, India,
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Rapid discrimination of pediatric brain tumors by mass spectrometry imaging. J Neurooncol 2018; 140:269-279. [PMID: 30128689 PMCID: PMC6244779 DOI: 10.1007/s11060-018-2978-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 08/11/2018] [Indexed: 01/10/2023]
Abstract
Purpose Medulloblastoma, the most common primary pediatric malignant brain tumor, originates in the posterior fossa of the brain. Pineoblastoma, which originates within the pineal gland, is a rarer malignancy that also presents in the pediatric population. Medulloblastoma and pineoblastoma exhibit overlapping clinical features and have similar histopathological characteristics. Histopathological similarities confound rapid diagnoses of these two tumor types. We have conducted a pilot feasibility study analyzing the molecular profile of archived frozen human tumor specimens using mass spectrometry imaging (MSI) to identify potential biomarkers capable of classifying and distinguishing between medulloblastoma and pineoblastoma. Methods We performed matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry imaging on eight medulloblastoma biopsy specimens and three pineoblastoma biopsy specimens. Multivariate statistical analyses were performed on the MSI dataset to generate classifiers that distinguish the two tumor types. Lastly, the molecules that were discriminative of tumor type were queried against the Lipid Maps database and identified. Results In this pilot study we show that medulloblastoma and pineoblastoma can be discriminated using molecular profiles determined by MSI. The highest-ranking discriminating classifiers of medulloblastoma and pineoblastoma were glycerophosphoglycerols and sphingolipids, respectively. Conclusion We demonstrate proof-of-concept that medulloblastoma and pineoblastoma can be rapidly distinguished by using MSI lipid profiles. We identified biomarker candidates capable of distinguishing these two histopathologically similar tumor types. This work expands the current molecular knowledge of medulloblastoma and pineoblastoma by characterizing their lipidomic profiles, which may be useful for developing novel diagnostic, prognostic and therapeutic strategies. Electronic supplementary material The online version of this article (10.1007/s11060-018-2978-2) contains supplementary material, which is available to authorized users.
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Banerjee S. Ambient ionization mass spectrometry imaging for disease diagnosis: Excitements and challenges. J Biosci 2018. [DOI: 10.1007/s12038-018-9785-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Mascagni P, Longo F, Barberio M, Seeliger B, Agnus V, Saccomandi P, Hostettler A, Marescaux J, Diana M. New intraoperative imaging technologies: Innovating the surgeon’s eye toward surgical precision. J Surg Oncol 2018; 118:265-282. [DOI: 10.1002/jso.25148] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 06/04/2018] [Indexed: 12/13/2022]
Affiliation(s)
- Pietro Mascagni
- IHU-Strasbourg; Institute of Image-Guided Surgery; Strasbourg France
| | - Fabio Longo
- IHU-Strasbourg; Institute of Image-Guided Surgery; Strasbourg France
| | - Manuel Barberio
- IHU-Strasbourg; Institute of Image-Guided Surgery; Strasbourg France
| | - Barbara Seeliger
- IHU-Strasbourg; Institute of Image-Guided Surgery; Strasbourg France
| | - Vincent Agnus
- IRCAD, Research Institute against Digestive Cancer; Strasbourg France
| | - Paola Saccomandi
- IHU-Strasbourg; Institute of Image-Guided Surgery; Strasbourg France
| | | | - Jacques Marescaux
- IHU-Strasbourg; Institute of Image-Guided Surgery; Strasbourg France
- IRCAD, Research Institute against Digestive Cancer; Strasbourg France
| | - Michele Diana
- IHU-Strasbourg; Institute of Image-Guided Surgery; Strasbourg France
- IRCAD, Research Institute against Digestive Cancer; Strasbourg France
- Department of General, Digestive and Endocrine Surgery; University of Strasbourg; Strasbourg France
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Distinguishing malignant from benign microscopic skin lesions using desorption electrospray ionization mass spectrometry imaging. Proc Natl Acad Sci U S A 2018; 115:6347-6352. [PMID: 29866838 DOI: 10.1073/pnas.1803733115] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Detection of microscopic skin lesions presents a considerable challenge in diagnosing early-stage malignancies as well as in residual tumor interrogation after surgical intervention. In this study, we established the capability of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to distinguish between micrometer-sized tumor aggregates of basal cell carcinoma (BCC), a common skin cancer, and normal human skin. We analyzed 86 human specimens collected during Mohs micrographic surgery for BCC to cross-examine spatial distributions of numerous lipids and metabolites in BCC aggregates versus adjacent skin. Statistical analysis using the least absolute shrinkage and selection operation (Lasso) was employed to categorize each 200-µm-diameter picture element (pixel) of investigated skin tissue map as BCC or normal. Lasso identified 24 molecular ion signals, which are significant for pixel classification. These ion signals included lipids observed at m/z 200-1,200 and Krebs cycle metabolites observed at m/z < 200. Based on these features, Lasso yielded an overall 94.1% diagnostic accuracy pixel by pixel of the skin map compared with histopathological evaluation. We suggest that DESI-MSI/Lasso analysis can be employed as a complementary technique for delineation of microscopic skin tumors.
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