1
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Berrell N, Sadeghirad H, Blick T, Bidgood C, Leggatt GR, O'Byrne K, Kulasinghe A. Metabolomics at the tumor microenvironment interface: Decoding cellular conversations. Med Res Rev 2024; 44:1121-1146. [PMID: 38146814 DOI: 10.1002/med.22010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/08/2023] [Accepted: 12/07/2023] [Indexed: 12/27/2023]
Abstract
Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.
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Affiliation(s)
- Naomi Berrell
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Habib Sadeghirad
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Tony Blick
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Charles Bidgood
- APCRC-Q, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Graham R Leggatt
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Ken O'Byrne
- Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
| | - Arutha Kulasinghe
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
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2
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Duncan KD, Pětrošová H, Lum JJ, Goodlett DR. Mass spectrometry imaging methods for visualizing tumor heterogeneity. Curr Opin Biotechnol 2024; 86:103068. [PMID: 38310648 DOI: 10.1016/j.copbio.2024.103068] [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: 08/28/2023] [Revised: 01/09/2024] [Accepted: 01/09/2024] [Indexed: 02/06/2024]
Abstract
Profiling spatial distributions of lipids, metabolites, and proteins in tumors can reveal unique cellular microenvironments and provide molecular evidence for cancer cell dysfunction and proliferation. Mass spectrometry imaging (MSI) is a label-free technique that can be used to map biomolecules in tumors in situ. Here, we discuss current progress in applying MSI to uncover molecular heterogeneity in tumors. First, the analytical strategies to profile small molecules and proteins are outlined, and current methods for multimodal imaging to maximize biological information are highlighted. Second, we present and summarize biological insights obtained by MSI of tumor tissue. Finally, we discuss important considerations for designing MSI experiments and several current analytical challenges.
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Affiliation(s)
- Kyle D Duncan
- Department of Chemistry, Vancouver Island University, Nanaimo, British Columbia, Canada; Department of Chemistry, University of Victoria, Victoria, British Columbia, Canada.
| | - Helena Pětrošová
- University of Victoria Genome British Columbia Proteomics Center, University of Victoria, Victoria, British Columbia, Canada; Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada.
| | - Julian J Lum
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada; Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, British Columbia, Canada
| | - David R Goodlett
- University of Victoria Genome British Columbia Proteomics Center, University of Victoria, Victoria, British Columbia, Canada; Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada
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3
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Wang J, Sun N, Kunzke T, Shen J, Feuchtinger A, Wang Q, Meixner R, Gleut RL, Haffner I, Luber B, Lordick F, Walch A. Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer. Br J Cancer 2024; 130:1036-1045. [PMID: 38267634 PMCID: PMC10951255 DOI: 10.1038/s41416-023-02559-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 12/06/2023] [Accepted: 12/14/2023] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear. METHODS Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K-means clustering algorithm. Simpson's diversity index was applied to compare the metabolic heterogeneity level of individual patients. RESULTS Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab (p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance. CONCLUSIONS This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy. CLINICAL TRIAL REGISTRATION The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).
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Affiliation(s)
- Jun Wang
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Na Sun
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Jian Shen
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Qian Wang
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Raphael Meixner
- Core Facility Statistical Consulting, Helmholtz Zentrum München, 85764, Neuherberg, Germany
| | - Ronan Le Gleut
- Core Facility Statistical Consulting, Helmholtz Zentrum München, 85764, Neuherberg, Germany
| | - Ivonne Haffner
- University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, Germany
| | - Birgit Luber
- Technische Universität München, Fakultät für Medizin, Klinikum rechts der Isar, Institut für Allgemeine Pathologie und Pathologische Anatomie, München, Germany
| | - Florian Lordick
- University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, Germany
- Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, Leipzig University Medical Center, Leipzig, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
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4
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Ma X, Fernández FM. Advances in mass spectrometry imaging for spatial cancer metabolomics. MASS SPECTROMETRY REVIEWS 2024; 43:235-268. [PMID: 36065601 PMCID: PMC9986357 DOI: 10.1002/mas.21804] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 05/09/2023]
Abstract
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.
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Affiliation(s)
- Xin Ma
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
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5
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de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
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Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
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6
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Stillger MN, Li MJ, Hönscheid P, von Neubeck C, Föll MC. Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma. Proteomics 2024:e2300001. [PMID: 38402423 DOI: 10.1002/pmic.202300001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 02/10/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.
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Affiliation(s)
- Maren Nicole Stillger
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Mujia Jenny Li
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Institute for Pharmaceutical Sciences, University of Freiburg, Freiburg, Germany
| | - Pia Hönscheid
- Institute of Pathology, Faculty of Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases, Partner Site Dresden, German Cancer Research Center Heidelberg, Dresden, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cläre von Neubeck
- Department of Particle Therapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
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7
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Krestensen KK, Heeren RMA, Balluff B. State-of-the-art mass spectrometry imaging applications in biomedical research. Analyst 2023; 148:6161-6187. [PMID: 37947390 DOI: 10.1039/d3an01495a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.
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Affiliation(s)
- Kasper K Krestensen
- The Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Ron M A Heeren
- The Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Benjamin Balluff
- The Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, 6229 ER Maastricht, The Netherlands.
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8
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Wang J, Sun N, Kunzke T, Shen J, Zens P, Prade VM, Feuchtinger A, Berezowska S, Walch A. Spatial metabolomics identifies distinct tumor-specific and stroma-specific subtypes in patients with lung squamous cell carcinoma. NPJ Precis Oncol 2023; 7:114. [PMID: 37919427 PMCID: PMC10622419 DOI: 10.1038/s41698-023-00434-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 08/08/2023] [Indexed: 11/04/2023] Open
Abstract
Molecular subtyping of lung squamous cell carcinoma (LUSC) has been performed at the genomic, transcriptomic, and proteomic level. However, LUSC stratification based on tissue metabolomics is still lacking. Combining high-mass-resolution imaging mass spectrometry with consensus clustering, four tumor- and four stroma-specific subtypes with distinct metabolite patterns were identified in 330 LUSC patients. The first tumor subtype T1 negatively correlated with DNA damage and immunological features including CD3, CD8, and PD-L1. The same features positively correlated with the tumor subtype T2. Tumor subtype T4 was associated with high PD-L1 expression. Compared with the status of subtypes T1 and T4, patients with subtype T3 had improved prognosis, and T3 was an independent prognostic factor with regard to UICC stage. Similarly, stroma subtypes were linked to distinct immunological features and metabolic pathways. Stroma subtype S4 had a better prognosis than S2. Subsequently, analyses based on an independent LUSC cohort treated by neoadjuvant therapy revealed that the S2 stroma subtype was associated with chemotherapy resistance. Clinically relevant patient subtypes as determined by tissue-based spatial metabolomics are a valuable addition to existing molecular classification systems. Metabolic differences among the subtypes and their associations with immunological features may contribute to the improvement of personalized therapy.
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Affiliation(s)
- Jun Wang
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Na Sun
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Jian Shen
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Philipp Zens
- Institute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Mittelstrasse 43, Bern, 3012, Switzerland
| | - Verena M Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Sabina Berezowska
- Institute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland.
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, 1011, Switzerland.
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany.
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9
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Chung HH, Huang P, Chen CL, Lee C, Hsu CC. Next-generation pathology practices with mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2023; 42:2446-2465. [PMID: 35815718 DOI: 10.1002/mas.21795] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.
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Affiliation(s)
- Hsin-Hsiang Chung
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Penghsuan Huang
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chih-Lin Chen
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chuping Lee
- Department of Chemistry, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Cheng-Chih Hsu
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
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10
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Bourceau P, Geier B, Suerdieck V, Bien T, Soltwisch J, Dreisewerd K, Liebeke M. Visualization of metabolites and microbes at high spatial resolution using MALDI mass spectrometry imaging and in situ fluorescence labeling. Nat Protoc 2023; 18:3050-3079. [PMID: 37674095 DOI: 10.1038/s41596-023-00864-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/31/2023] [Indexed: 09/08/2023]
Abstract
Label-free molecular imaging techniques such as matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enable the direct and simultaneous mapping of hundreds of different metabolites in thin sections of biological tissues. However, in host-microbe interactions it remains challenging to localize microbes and to assign metabolites to the host versus members of the microbiome. We therefore developed a correlative imaging approach combining MALDI-MSI with fluorescence in situ hybridization (FISH) on the same section to identify and localize microbial cells. Here, we detail metaFISH as a robust and easy method for assigning the spatial distribution of metabolites to microbiome members based on imaging of nucleic acid probes, down to single-cell resolution. We describe the steps required for tissue preparation, on-tissue hybridization, fluorescence microscopy, data integration into a correlative image dataset, matrix application and MSI data acquisition. Using metaFISH, we map hundreds of metabolites and several microbial species to the micrometer scale on a single tissue section. For example, intra- and extracellular bacteria, host cells and their associated metabolites can be localized in animal tissues, revealing their complex metabolic interactions. We explain how we identify low-abundance bacterial infection sites as regions of interest for high-resolution MSI analysis, guiding the user to a trade-off between metabolite signal intensities and fluorescence signals. MetaFISH is suitable for a broad range of users from environmental microbiologists to clinical scientists. The protocol requires ~2 work days.
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Affiliation(s)
- Patric Bourceau
- Max Planck Institute for Marine Microbiology, Bremen, Germany
- MARUM - Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
| | - Benedikt Geier
- Max Planck Institute for Marine Microbiology, Bremen, Germany
- Stanford University School of Medicine, Stanford, CA, USA
| | | | - Tanja Bien
- Institute of Hygiene, University of Münster, Münster, Germany
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Jens Soltwisch
- Institute of Hygiene, University of Münster, Münster, Germany
| | | | - Manuel Liebeke
- Max Planck Institute for Marine Microbiology, Bremen, Germany.
- Institute of Human Nutrition and Food Sciences, University of Kiel, Kiel, Germany.
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11
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Shen J, Sun N, Wang J, Zens P, Kunzke T, Buck A, Prade VM, Wang Q, Feuchtinger A, Hu R, Berezowska S, Walch A. Patterns of Carbon-Bound Exogenous Compounds Impact Disease Pathophysiology in Lung Cancer Subtypes in Different Ways. ACS NANO 2023; 17:16396-16411. [PMID: 37639684 PMCID: PMC10510585 DOI: 10.1021/acsnano.2c11161] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 08/23/2023] [Indexed: 08/31/2023]
Abstract
Carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAHs), tobacco-specific nitrosamines, aromatic amines, and organohalogens, are known to affect both tumor characteristics and patient outcomes in lung squamous cell carcinoma (LUSC); however, the roles of these compounds in lung adenocarcinoma (LUAD) remain unclear. We analyzed 11 carbon-bound exogenous compounds in LUAD and LUSC samples using in situ high mass-resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging and performed a cluster analysis to compare the patterns of carbon-bound exogenous compounds between these two lung cancer subtypes. Correlation analyses were conducted to investigate associations among exogenous compounds, endogenous metabolites, and clinical data, including patient survival outcomes and smoking behaviors. Additionally, we examined differences in exogenous compound patterns between normal and tumor tissues. Our analyses revealed that PAHs, aromatic amines, and organohalogens were more abundant in LUAD than in LUSC, whereas the tobacco-specific nitrosamine nicotine-derived nitrosamine ketone was more abundant in LUSC. Patients with LUAD and LUSC could be separated according to carbon-bound exogenous compound patterns detected in the tumor compartment. The same compounds had differential impacts on patient outcomes, depending on the cancer subtype. Correlation and network analyses indicated substantial differences between LUAD and LUSC metabolomes, associated with substantial differences in the patterns of the carbon-bound exogenous compounds. These data suggest that the contributions of these carcinogenic compounds to cancer biology may differ according to the cancer subtypes.
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Affiliation(s)
- Jian Shen
- Research
Unit Analytical Pathology, Helmholtz Zentrum
München − German Research Center for Environmental Health, Neuherberg 85764, Germany
- Nanxishan
Hospital of Guangxi Zhuang Autonomous Region, Institute of Pathology, Guilin 541002, People’s Republic of China
| | - Na Sun
- Research
Unit Analytical Pathology, Helmholtz Zentrum
München − German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Jun Wang
- Research
Unit Analytical Pathology, Helmholtz Zentrum
München − German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Philipp Zens
- Institute
of Tissue Medicine and Pathology, University
of Bern, Murtenstrasse 31, Bern 3008, Switzerland
- Graduate
School for Health Sciences, University of
Bern, Mittelstrasse 43, Bern 3012, Switzerland
| | - Thomas Kunzke
- Research
Unit Analytical Pathology, Helmholtz Zentrum
München − German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Achim Buck
- Research
Unit Analytical Pathology, Helmholtz Zentrum
München − German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Verena M. Prade
- Research
Unit Analytical Pathology, Helmholtz Zentrum
München − German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Qian Wang
- Research
Unit Analytical Pathology, Helmholtz Zentrum
München − German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Annette Feuchtinger
- Research
Unit Analytical Pathology, Helmholtz Zentrum
München − German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Ronggui Hu
- Center
for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200030, People’s
Republic of China
| | - Sabina Berezowska
- Institute
of Tissue Medicine and Pathology, University
of Bern, Murtenstrasse 31, Bern 3008, Switzerland
- Department
of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland
| | - Axel Walch
- Research
Unit Analytical Pathology, Helmholtz Zentrum
München − German Research Center for Environmental Health, Neuherberg 85764, Germany
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12
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Wang Q, Sun N, Meixner R, Le Gleut R, Kunzke T, Feuchtinger A, Wang J, Shen J, Kircher S, Dischinger U, Weigand I, Beuschlein F, Fassnacht M, Kroiss M, Walch A. Metabolic heterogeneity in adrenocortical carcinoma impacts patient outcomes. JCI Insight 2023; 8:e167007. [PMID: 37606037 PMCID: PMC10543722 DOI: 10.1172/jci.insight.167007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 07/06/2023] [Indexed: 08/23/2023] Open
Abstract
Spatially resolved metabolomics enables the investigation of tumoral metabolites in situ. Inter- and intratumor heterogeneity are key factors associated with patient outcomes. Adrenocortical carcinoma (ACC) is an exceedingly rare tumor associated with poor survival. Its clinical prognosis is highly variable, but the contributions of tumor metabolic heterogeneity have not been investigated thus far to our knowledge. An in-depth understanding of tumor heterogeneity requires molecular feature-based identification of tumor subpopulations associated with tumor aggressiveness. Here, using spatial metabolomics by high-mass resolution MALDI Fourier transform ion cyclotron resonance mass spectrometry imaging, we assessed metabolic heterogeneity by de novo discovery of metabolic subpopulations and Simpson's diversity index. After identification of tumor subpopulations in 72 patients with ACC, we additionally performed a comparison with 25 tissue sections of normal adrenal cortex to identify their common and unique metabolic subpopulations. We observed variability of ACC tumor heterogeneity and correlation of high metabolic heterogeneity with worse clinical outcome. Moreover, we identified tumor subpopulations that served as independent prognostic factors and, furthermore, discovered 4 associated anticancer drug action pathways. Our research may facilitate comprehensive understanding of the biological implications of tumor subpopulations in ACC and showed that metabolic heterogeneity might impact chemotherapy.
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Affiliation(s)
- Qian Wang
- Research Unit Analytical Pathology and
| | - Na Sun
- Research Unit Analytical Pathology and
| | - Raphael Meixner
- Core Facility Statistical Consulting, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt, Neuherberg, Germany
| | - Ronan Le Gleut
- Core Facility Statistical Consulting, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt, Neuherberg, Germany
| | | | | | - Jun Wang
- Research Unit Analytical Pathology and
| | - Jian Shen
- Research Unit Analytical Pathology and
| | | | - Ulrich Dischinger
- Division of Endocrinology and Diabetes, Department of Internal Medicine, University Hospital of Wuerzburg, Wuerzburg, Germany
| | - Isabel Weigand
- Division of Endocrinology and Diabetes, Department of Internal Medicine, University Hospital of Wuerzburg, Wuerzburg, Germany
- Department of Internal Medicine IV, LMU Hospital, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
| | - Felix Beuschlein
- Department of Internal Medicine IV, LMU Hospital, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
- Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich (USZ) and University of Zurich (UZH), Zurich, Switzerland
| | - Martin Fassnacht
- Division of Endocrinology and Diabetes, Department of Internal Medicine, University Hospital of Wuerzburg, Wuerzburg, Germany
- Comprehensive Cancer Center Mainfranken, University Hospital of Wuerzburg, Wuerzburg, Germany
| | - Matthias Kroiss
- Division of Endocrinology and Diabetes, Department of Internal Medicine, University Hospital of Wuerzburg, Wuerzburg, Germany
- Department of Internal Medicine IV, LMU Hospital, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
- Comprehensive Cancer Center Mainfranken, University Hospital of Wuerzburg, Wuerzburg, Germany
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13
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Zhang Z, Bao C, Jiang L, Wang S, Wang K, Lu C, Fang H. When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective. Front Oncol 2023; 12:1054233. [PMID: 36686803 PMCID: PMC9854130 DOI: 10.3389/fonc.2022.1054233] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/20/2022] [Indexed: 01/07/2023] Open
Abstract
Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.
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Affiliation(s)
- Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Jiang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kankan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Hai Fang,
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14
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Ackermann M, Kamp JC, Werlein C, Walsh CL, Stark H, Prade V, Surabattula R, Wagner WL, Disney C, Bodey AJ, Illig T, Leeming DJ, Karsdal MA, Tzankov A, Boor P, Kühnel MP, Länger FP, Verleden SE, Kvasnicka HM, Kreipe HH, Haverich A, Black SM, Walch A, Tafforeau P, Lee PD, Hoeper MM, Welte T, Seeliger B, David S, Schuppan D, Mentzer SJ, Jonigk DD. The fatal trajectory of pulmonary COVID-19 is driven by lobular ischemia and fibrotic remodelling. EBioMedicine 2022; 85:104296. [PMID: 36206625 PMCID: PMC9535314 DOI: 10.1016/j.ebiom.2022.104296] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND COVID-19 is characterized by a heterogeneous clinical presentation, ranging from mild symptoms to severe courses of disease. 9-20% of hospitalized patients with severe lung disease die from COVID-19 and a substantial number of survivors develop long-COVID. Our objective was to provide comprehensive insights into the pathophysiology of severe COVID-19 and to identify liquid biomarkers for disease severity and therapy response. METHODS We studied a total of 85 lungs (n = 31 COVID autopsy samples; n = 7 influenza A autopsy samples; n = 18 interstitial lung disease explants; n = 24 healthy controls) using the highest resolution Synchrotron radiation-based hierarchical phase-contrast tomography, scanning electron microscopy of microvascular corrosion casts, immunohistochemistry, matrix-assisted laser desorption ionization mass spectrometry imaging, and analysis of mRNA expression and biological pathways. Plasma samples from all disease groups were used for liquid biomarker determination using ELISA. The anatomic/molecular data were analyzed as a function of patients' hospitalization time. FINDINGS The observed patchy/mosaic appearance of COVID-19 in conventional lung imaging resulted from microvascular occlusion and secondary lobular ischemia. The length of hospitalization was associated with increased intussusceptive angiogenesis. This was associated with enhanced angiogenic, and fibrotic gene expression demonstrated by molecular profiling and metabolomic analysis. Increased plasma fibrosis markers correlated with their pulmonary tissue transcript levels and predicted disease severity. Plasma analysis confirmed distinct fibrosis biomarkers (TSP2, GDF15, IGFBP7, Pro-C3) that predicted the fatal trajectory in COVID-19. INTERPRETATION Pulmonary severe COVID-19 is a consequence of secondary lobular microischemia and fibrotic remodelling, resulting in a distinctive form of fibrotic interstitial lung disease that contributes to long-COVID. FUNDING This project was made possible by a number of funders. The full list can be found within the Declaration of interests / Acknowledgements section at the end of the manuscript.
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Affiliation(s)
- Maximilian Ackermann
- Institute of Pathology and Molecular Pathology, Helios University Clinic Wuppertal, University of Witten/Herdecke, Germany
- Institute of Functional and Clinical Anatomy, University Medical Center of the Johannes Gutenberg-University Mainz, Germany
| | - Jan C. Kamp
- Department of Respiratory Medicine, Hannover Medical School, Hannover, Germany
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
| | - Christopher Werlein
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
- Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Claire L. Walsh
- Centre for Advanced Biomedical Imaging, University College London, UK
| | - Helge Stark
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
- Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Verena Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Rambabu Surabattula
- Institute of Translational Immunology and Research Center for Immune Therapy, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Willi L. Wagner
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
- Member of the German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC), Heidelberg, Germany
| | - Catherine Disney
- Department of Mechanical Engineering, University College London, UK
| | | | - Thomas Illig
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover Medical School, Germany
| | - Diana J. Leeming
- Hannover Unified Biobank, Hannover Medical School, Hannover Medical School, Germany
| | | | - Alexandar Tzankov
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Mark P. Kühnel
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
- Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Florian P. Länger
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
- Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Stijn E. Verleden
- Department of Thoracic Surgery, University Hospital Antwerp Edegem, Belgium
| | - Hans M. Kvasnicka
- Institute of Pathology and Molecular Pathology, Helios University Clinic Wuppertal, University of Witten/Herdecke, Germany
| | - Hans H. Kreipe
- Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Axel Haverich
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
- Department of Cardiothoracic, Transplantation, and Vascular Surgery, Hannover Medical School, Germany
| | - Stephen M. Black
- Department of Cellular Biology and Pharmacology, Center for Translational Research, Florida International University, USA
| | - Axel Walch
- Nordic Bioscience Biomarkers and Research, Herlev, Denmark
| | - Paul Tafforeau
- European Synchrotron Radiation Facility, Grenoble, France
| | - Peter D. Lee
- Hannover Unified Biobank, Hannover Medical School, Hannover Medical School, Germany
| | - Marius M. Hoeper
- Department of Respiratory Medicine, Hannover Medical School, Hannover, Germany
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
| | - Tobias Welte
- Department of Respiratory Medicine, Hannover Medical School, Hannover, Germany
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
| | - Benjamin Seeliger
- Department of Respiratory Medicine, Hannover Medical School, Hannover, Germany
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
| | - Sascha David
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Detlef Schuppan
- Institute of Translational Immunology and Research Center for Immune Therapy, University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Steven J. Mentzer
- Laboratory of Adaptive and Regenerative Biology, Harvard Medical School, Brigham & Women's Hospital, Boston, United States
| | - Danny D. Jonigk
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
- Institute of Pathology, Hannover Medical School, Hannover, Germany
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15
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Buck A, Prade VM, Kunzke T, Erben RG, Walch A. Spatial metabolomics reveals upregulation of several pyrophosphate-producing pathways in cortical bone of Hyp mice. JCI Insight 2022; 7:162138. [DOI: 10.1172/jci.insight.162138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022] Open
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16
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Denti V, Capitoli G, Piga I, Clerici F, Pagani L, Criscuolo L, Bindi G, Principi L, Chinello C, Paglia G, Magni F, Smith A. Spatial Multiomics of Lipids, N-Glycans, and Tryptic Peptides on a Single FFPE Tissue Section. J Proteome Res 2022; 21:2798-2809. [PMID: 36259755 PMCID: PMC9639202 DOI: 10.1021/acs.jproteome.2c00601] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Mass spectrometry
imaging (MSI) is an emerging technology
that
is capable of mapping various biomolecules within their native spatial
context, and performing spatial multiomics on formalin-fixed paraffin-embedded
(FFPE) tissues may further increase the molecular characterization
of pathological states. Here we present a novel workflow which enables
the sequential MSI of lipids, N-glycans, and tryptic peptides on a
single FFPE tissue section and highlight the enhanced molecular characterization
that is offered by combining the multiple spatial omics data sets.
In murine brain and clear cell renal cell carcinoma (ccRCC) tissue,
the three molecular levels provided complementary information and
characterized different histological regions. Moreover, when the spatial
omics data was integrated, the different histopathological regions
of the ccRCC tissue could be better discriminated with respect to
the imaging data set of any single omics class. Taken together, these
promising findings demonstrate the capability to more comprehensively
map the molecular complexity within pathological tissue.
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Affiliation(s)
- Vanna Denti
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Giulia Capitoli
- Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy
| | - Isabella Piga
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Francesca Clerici
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Lisa Pagani
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Lucrezia Criscuolo
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Greta Bindi
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Lucrezia Principi
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Clizia Chinello
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Giuseppe Paglia
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Fulvio Magni
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Andrew Smith
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy
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17
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Bourceau P, Michellod D, Geier B, Liebeke M. Spatial metabolomics shows contrasting phosphonolipid distributions in tissues of marine bivalves. PEERJ ANALYTICAL CHEMISTRY 2022. [DOI: 10.7717/peerj-achem.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Lipids are an integral part of cellular membranes that allow cells to alter stiffness, permeability, and curvature. Among the diversity of lipids, phosphonolipids uniquely contain a phosphonate bond between carbon and phosphorous. Despite this distinctive biochemical characteristic, few studies have explored the biological role of phosphonolipids, although a protective function has been inferred based on chemical and biological stability. We analyzed two species of marine mollusks, the blue mussel Mytilus edulis and pacific oyster Crassostrea gigas, and determined the diversity of phosphonolipids and their distribution in different organs. High-resolution spatial metabolomics revealed that the lipidome varies significantly between tissues within one organ. Despite their chemical similarity, we observed a high heterogeneity of phosphonolipid distributions that originated from minor structural differences. Some phosphonolipids are ubiquitously distributed, while others are present almost exclusively in the layer of ciliated epithelial cells. This distinct localization of certain phosphonolipids in tissues exposed to the environment could support the hypothesis of a protective function in mollusks. This study highlights that the tissue specific distribution of an individual metabolite can be a valuable tool for inferring its function and guiding functional analyses.
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Affiliation(s)
- Patric Bourceau
- Max Planck Institute for Marine Microbiology, Bremen, Germany
- MARUM—Center for Marine Environmental Sciences of the University of Bremen, Bremen, Germany
| | - Dolma Michellod
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Benedikt Geier
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Manuel Liebeke
- Max Planck Institute for Marine Microbiology, Bremen, Germany
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18
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Shen J, Sun N, Zens P, Kunzke T, Buck A, Prade VM, Wang J, Wang Q, Hu R, Feuchtinger A, Berezowska S, Walch A. Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients. Cancer Commun (Lond) 2022; 42:517-535. [PMID: 35593195 PMCID: PMC9198346 DOI: 10.1002/cac2.12310] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 02/18/2022] [Accepted: 05/10/2022] [Indexed: 11/23/2022] Open
Abstract
Background The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non‐small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read‐out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning. Methods Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high‐resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy‐naïve patients with NSCLC (n = 85). Results The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716–8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004–4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445–1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977–1.550) were not independent prognostic factors. Using Kaplan‐Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non‐responders (P < 0.001). Conclusions Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology‐based assessment of tumor response.
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Affiliation(s)
- Jian Shen
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Na Sun
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Philipp Zens
- Institute of Pathology, University of Bern, Bern, 3008, Switzerland.,Graduate School for Health Sciences, University of Bern, Mittelstrasse 43, Bern, 3012, Switzerland
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Achim Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Verena M Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Jun Wang
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Qian Wang
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Ronggui Hu
- Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, P. R. China
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Sabina Berezowska
- Institute of Pathology, University of Bern, Bern, 3008, Switzerland.,Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, 1011, Switzerland
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
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19
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Tuck M, Grélard F, Blanc L, Desbenoit N. MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives. Front Chem 2022; 10:904688. [PMID: 35615316 PMCID: PMC9124797 DOI: 10.3389/fchem.2022.904688] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/14/2022] [Indexed: 01/22/2023] Open
Abstract
Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI.
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20
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Wang J, Kunzke T, Prade VM, Shen J, Buck A, Feuchtinger A, Haffner I, Luber B, Liu DHW, Langer R, Lordick F, Sun N, Walch A. Spatial metabolomics identifies distinct tumor-specific subtypes in gastric cancer patients. Clin Cancer Res 2022; 28:2865-2877. [PMID: 35395077 DOI: 10.1158/1078-0432.ccr-21-4383] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/01/2022] [Accepted: 04/06/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE Current systems of gastric cancer (GC) molecular classification include genomic, molecular, and morphological features. GC classification based on tissue metabolomics remains lacking. This study aimed to define metabolically distinct GC subtypes and identify their clinicopathological and molecular characteristics. EXPERIMENTAL DESIGN Spatial metabolomics by high mass resolution imaging mass spectrometry was performed in 362 GC patients. K-means clustering was used to define tumor and stroma-related subtypes based on tissue metabolites. The identified subtypes were linked with clinicopathological characteristics, molecular features, and metabolic signatures. Responses to trastuzumab treatment were investigated across the subtypes by introducing an independent patient cohort with HER2-positive GC from a multicenter observational study. RESULTS Three tumor- and three stroma-specific subtypes with distinct tissue metabolite patterns were identified. Tumor-specific subtype T1(HER2+MIB+CD3+) positively correlated with HER2, MIB1, DEFA-1, CD3, CD8, FOXP3, but negatively correlated with MMR. Tumor-specific subtype T2(HER2-MIB-CD3-) negatively correlated with HER2, MIB1, CD3, FOXP3, but positively correlated with MMR. Tumor-specific subtype T3(pEGFR+) positively correlated with pEGFR. Patients with tumor subtype T1(HER2+MIB+CD3+) had elevated nucleotide levels, enhanced DNA metabolism, and a better prognosis than T2(HER2-MIB-CD3-) and T3(pEGFR+). An independent validation cohort confirmed that the T1 subtype benefited from trastuzumab therapy. Stroma-specific subtypes had no association with clinicopathological characteristics, however linked to distinct metabolic pathways and molecular features. CONCLUSIONS Patient subtypes derived by tissue-based spatial metabolomics are a valuable addition to existing GC molecular classification systems. Metabolic differences between the subtypes and their associations with molecular features could provide a valuable tool to aid in selecting specific treatment approaches.
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Affiliation(s)
- Jun Wang
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Verena M Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Jian Shen
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Achim Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Ivonne Haffner
- University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, Germany
| | - Birgit Luber
- Technische Universität München, Fakultät für Medizin, Klinikum rechts der Isar, Institut für Allgemeine Pathologie und Pathologische Anatomie, München, Germany
| | - Drolaiz H W Liu
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, the Netherlands
- Institute of Clinical Pathology and Molecular Pathology, Kepler University Hospital and Johannes Kepler University, Linz, Austria
| | - Rupert Langer
- Institute of Clinical Pathology and Molecular Pathology, Kepler University Hospital and Johannes Kepler University, Linz, Austria
| | - Florian Lordick
- University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, Germany
- Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, Leipzig University Medical Center, Leipzig, Germany
| | - Na Sun
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
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21
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Wang Q, Sun N, Kunzke T, Buck A, Shen J, Prade VM, Stöckl B, Wang J, Feuchtinger A, Walch A. A simple preparation step to remove excess liquid lipids in white adipose tissue enabling improved detection of metabolites via MALDI-FTICR imaging MS. Histochem Cell Biol 2022; 157:595-605. [PMID: 35391562 PMCID: PMC9114030 DOI: 10.1007/s00418-022-02088-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/09/2022] [Indexed: 11/10/2022]
Abstract
Matrix-assisted laser desorption ionization (MALDI) Fourier transform ion cyclotron resonance (FTICR) imaging mass spectrometry (MS) is a powerful technology used to analyze metabolites in various tissues. However, it faces significant challenges in studying adipose tissues. Poor matrix distribution and crystallization caused by excess liquid lipids on the surface of tissue sections hamper m/z species detection, an adverse effect that particularly presents in lipid-rich white adipose tissue (WAT). In this study, we integrated a simple and low-cost preparation step into the existing MALDI-FTICR imaging MS pipeline. The new method—referred to as filter paper application—is characterized by an easy sample handling and high reproducibility. The aforementioned filter paper is placed onto the tissue prior to matrix application in order to remove the layer of excess liquid lipids. Consequently, MALDI-FTICR imaging MS detection was significantly improved, resulting in a higher number of detected m/z species and higher ion intensities. After analyzing various durations of filter paper application, 30 s was found to be optimal, resulting in the detection of more than 3700 m/z species. Apart from the most common lipids found in WAT, other molecules involved in various metabolic pathways were detected, including nucleotides, carbohydrates, and amino acids. Our study is the first to propose a solution to a specific limitation of MALDI-FTICR imaging MS in investigating lipid-rich WAT. The filter paper approach can be performed quickly and is particularly effective for achieving uniform matrix distribution on fresh frozen WAT while maintaining tissue integrity. It thus helps to gain insight into the metabolism in WAT.
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Affiliation(s)
- Qian Wang
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Na Sun
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Achim Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Jian Shen
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Verena M Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Barbara Stöckl
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Jun Wang
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany.
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22
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Erlmeier F, Sun N, Shen J, Feuchtinger A, Buck A, Prade VM, Kunzke T, Schraml P, Moch H, Autenrieth M, Weichert W, Hartmann A, Walch A. MALDI Mass Spectrometry Imaging-Prognostic Pathways and Metabolites for Renal Cell Carcinomas. Cancers (Basel) 2022; 14:cancers14071763. [PMID: 35406537 PMCID: PMC8996951 DOI: 10.3390/cancers14071763] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/24/2022] [Accepted: 03/24/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Nevertheless, prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by high mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) for prognostic biomarker detection. This is a suitable method for biomarker detection for several tumor entities. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes. Abstract High mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a suitable method for biomarker detection for several tumor entities. Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by MALDI for prognostic biomarker detection. MALDI-Fourier-transform ion cyclotron resonance (FT-ICR)-MSI analysis was performed for renal carcinoma tissue sections from 782 patients. SPACiAL pipeline was integrated for automated co-registration of histological and molecular features. Kaplan–Meier analyses with overall survival as endpoint were executed to determine the metabolic features associated with clinical outcome. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes.
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Affiliation(s)
- Franziska Erlmeier
- Institute of Pathology, University Hospital Erlangen-Nuremberg, 91054 Erlangen, Germany;
- Correspondence: (F.E.); (N.S.)
| | - Na Sun
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
- Correspondence: (F.E.); (N.S.)
| | - Jian Shen
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Achim Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Verena M. Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University Hospital Zurich, 8091 Zurich, Switzerland; (P.S.); (H.M.)
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University Hospital Zurich, 8091 Zurich, Switzerland; (P.S.); (H.M.)
| | - Michael Autenrieth
- Department of Urology, Rechts der Isar Medical Center, Technical University of Munich, 81675 Munich, Germany;
| | - Wilko Weichert
- Institute of Pathology, Technical University Munich, 81675 Munich, Germany;
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen-Nuremberg, 91054 Erlangen, Germany;
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
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23
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Prade VM, Sun N, Shen J, Feuchtinger A, Kunzke T, Buck A, Schraml P, Moch H, Schwamborn K, Autenrieth M, Gschwend JE, Erlmeier F, Hartmann A, Walch A. The synergism of spatial metabolomics and morphometry improves machine learning‐based renal tumour subtype classification. Clin Transl Med 2022; 12:e666. [PMID: 35184396 PMCID: PMC8858620 DOI: 10.1002/ctm2.666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/11/2021] [Accepted: 11/17/2021] [Indexed: 12/14/2022] Open
Affiliation(s)
- Verena M. Prade
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Na Sun
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Jian Shen
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Annette Feuchtinger
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Thomas Kunzke
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Achim Buck
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Peter Schraml
- Institute of Pathology and Molecular Pathology University Hospital Zurich Zurich Switzerland
| | - Holger Moch
- Institute of Pathology and Molecular Pathology University Hospital Zurich Zurich Switzerland
| | | | | | | | - Franziska Erlmeier
- Institute of Pathology, University Hospital Erlangen Friedrich‐Alexander‐University Erlangen‐Nürnberg Erlangen Germany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN) Erlangen Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen Friedrich‐Alexander‐University Erlangen‐Nürnberg Erlangen Germany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN) Erlangen Germany
| | - Axel Walch
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
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24
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Sato S, Dyar KA, Treebak JT, Jepsen SL, Ehrlich AM, Ashcroft SP, Trost K, Kunzke T, Prade VM, Small L, Basse AL, Schönke M, Chen S, Samad M, Baldi P, Barrès R, Walch A, Moritz T, Holst JJ, Lutter D, Zierath JR, Sassone-Corsi P. Atlas of exercise metabolism reveals time-dependent signatures of metabolic homeostasis. Cell Metab 2022; 34:329-345.e8. [PMID: 35030324 DOI: 10.1016/j.cmet.2021.12.016] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/22/2021] [Accepted: 12/15/2021] [Indexed: 12/12/2022]
Abstract
Tissue sensitivity and response to exercise vary according to the time of day and alignment of circadian clocks, but the optimal exercise time to elicit a desired metabolic outcome is not fully defined. To understand how tissues independently and collectively respond to timed exercise, we applied a systems biology approach. We mapped and compared global metabolite responses of seven different mouse tissues and serum after an acute exercise bout performed at different times of the day. Comparative analyses of intra- and inter-tissue metabolite dynamics, including temporal profiling and blood sampling across liver and hindlimb muscles, uncovered an unbiased view of local and systemic metabolic responses to exercise unique to time of day. This comprehensive atlas of exercise metabolism provides clarity and physiological context regarding the production and distribution of canonical and novel time-dependent exerkine metabolites, such as 2-hydroxybutyrate (2-HB), and reveals insight into the health-promoting benefits of exercise on metabolism.
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Affiliation(s)
- Shogo Sato
- Center for Epigenetics and Metabolism, INSERM U1233, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Kenneth A Dyar
- Metabolic Physiology, Institute for Diabetes and Cancer, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Jonas T Treebak
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sara L Jepsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Biomedical Sciences, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
| | - Amy M Ehrlich
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephen P Ashcroft
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kajetan Trost
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Verena M Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lewin Small
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Astrid Linde Basse
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Milena Schönke
- Department of Molecular Medicine and Surgery, Integrative Physiology, Karolinska Institutet, Stockholm, Sweden
| | - Siwei Chen
- Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA, USA
| | - Muntaha Samad
- Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA, USA
| | - Pierre Baldi
- Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA, USA
| | - Romain Barrès
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Moritz
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jens J Holst
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Biomedical Sciences, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
| | - Dominik Lutter
- German Center for Diabetes Research, Neuherberg, Germany; Computational Discovery Research, Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Juleen R Zierath
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Molecular Medicine and Surgery, Integrative Physiology, Karolinska Institutet, Stockholm, Sweden; Department of Physiology and Pharmacology, Integrative Physiology, Karolinska Institutet, Stockholm, Sweden.
| | - Paolo Sassone-Corsi
- Center for Epigenetics and Metabolism, INSERM U1233, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, USA
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25
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Balluff B, Heeren RM, Race AM. An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities. J Mass Spectrom Adv Clin Lab 2022; 23:26-38. [PMID: 35156074 PMCID: PMC8821033 DOI: 10.1016/j.jmsacl.2021.12.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 01/25/2023] Open
Abstract
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. Integration with other imaging modalities is essential in clinical MSI. Image integration is performed by image registration techniques. Technical potential of image registration in MSI has not been fully exploited. Roadmap proposed to improve registration accuracy.
Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.
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26
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Buck A, Prade VM, Kunzke T, Feuchtinger A, Kröll D, Feith M, Dislich B, Balluff B, Langer R, Walch A. Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients. J Pathol 2021; 256:202-213. [PMID: 34719782 DOI: 10.1002/path.5828] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/04/2021] [Accepted: 10/28/2021] [Indexed: 11/11/2022]
Abstract
The response to neoadjuvant therapy can vary widely between individual patients. Histopathological tumor regression grading (TRG) is a strong factor for treatment response and survival prognosis of esophageal adenocarcinoma (EAC) patients following neoadjuvant treatment and surgery. However, TRG systems are usually based on the estimation of residual tumor but do not consider stromal or metabolic changes after treatment. Spatial metabolomics analysis is a powerful tool for molecular tissue phenotyping but has not been used so far in the context of neoadjuvant treatment of esophageal cancer. We used imaging mass spectrometry to assess the potential of spatial metabolomics on tumor and stroma tissue for evaluating therapy response of neoadjuvant-treated EAC patients. With an accuracy of 89.7%, the binary classifier trained on spatial tumor metabolite data proved to be superior for stratifying patients when compared to histopathological response assessment which had an accuracy of 70.5%. Sensitivities and specificities for the poor and favorable survival patient groups ranged from 84.9 to 93.3% using the metabolic classifier and from 62.2 to 78.1% using TRG. The tumor classifier was the only significant prognostic factor (HR 3.38, 95% CI = 1.40-8.12, P = 0.007) when adjusted for clinicopathological parameters such as TRG (HR 1.01, 95% CI = 0.67-1.53, P = 0.968) or stromal classifier (HR 1.856, 95% CI = 0.81-4.25, P = 0.143). The classifier even allowed to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment has been figured out through network analysis. In summary, metabolic response evaluation outperformed histopathological response evaluation in our study with regard to prognostic stratification. This finding indicates that the metabolic constitution of tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Achim Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Verena M Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Dino Kröll
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, 3008, Bern, Switzerland.,Department of Surgery, Campus Charité Mitte
- Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Marcus Feith
- Department of Surgery, Klinikum rechts der Isar, TUM School of Medicine, 81675, Munich, Germany
| | - Bastian Dislich
- Institute of Pathology, University of Bern, Bern, Switzerland
| | - Benjamin Balluff
- Maastricht Multimodal Molecular Imaging Institute (M4i), Maastricht University, Maastricht, The Netherlands
| | - Rupert Langer
- Institute of Pathology, University of Bern, Bern, Switzerland.,Institute of Clinical Pathology and Molecular Pathology, Kepler University Hospital and Johannes Kepler University, Linz, Austria
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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27
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Kunzke T, Prade VM, Buck A, Sun N, Feuchtinger A, Matzka M, Fernandez IE, Wuyts W, Ackermann M, Jonigk D, Aichler M, Schmid RA, Eickelberg O, Berezowska S, Walch A. Patterns of carbon-bound exogenous compounds in lung cancer patients and association with disease pathophysiology. Cancer Res 2021; 81:5862-5875. [PMID: 34666994 DOI: 10.1158/0008-5472.can-21-1175] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/30/2021] [Accepted: 10/14/2021] [Indexed: 11/16/2022]
Abstract
Asymptomatic anthracosis is the accumulation of black carbon particles in adult human lungs. It is a common occurrence, but the pathophysiological significance of anthracosis is debatable. Using in situ high mass resolution matrix-assisted laser desorption/ionization (MALDI) fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging analysis, we discovered noxious carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAHs), tobacco-specific nitrosamines, or aromatic amines, in a series of 330 lung cancer patients in highly variable and unique patterns. The characteristic nature of carbon-bound exogenous compound had a strong association with patient outcome, tumor progression, the tumor immune microenvironment, PD-L1 expression, and DNA damage. Spatial correlation network analyses revealed substantial differences in the metabolome of tumor cells compared to tumor stroma depending on carbon-bound exogenous compounds. Overall, the bioactive pool of exogenous compounds is associated with several changes in lung cancer pathophysiology and correlates with patient outcome. Given the high prevalence of anthracosis in the lungs of adult humans, future work should investigate the role of carbon-bound exogenous compounds in lung carcinogenesis and lung cancer therapy.
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Affiliation(s)
- Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Center Munich - German Research Center for Environmental Health
| | - Verena M Prade
- Research Unit Analytical Pathology, Helmholtz Center Munich - German Research Center for Environmental Health
| | - Achim Buck
- Research Unit Analytical Pathology, Helmholtz Center Munich - German Research Center for Environmental Health
| | - Na Sun
- Research Unit Analytical Pathology, Helmholtz Center Munich - German Research Center for Environmental Health
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Center Munich - German Research Center for Environmental Health
| | - Marco Matzka
- Research Unit Analytical Pathology, Helmholtz Center Munich - German Research Center for Environmental Health
| | | | | | | | | | | | | | | | - Sabina Berezowska
- Deparment of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Center Munich - German Research Center for Environmental Health
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28
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Angel PM, Rujchanarong D, Pippin S, Spruill L, Drake R. Mass Spectrometry Imaging of Fibroblasts: Promise and Challenge. Expert Rev Proteomics 2021; 18:423-436. [PMID: 34129411 PMCID: PMC8717608 DOI: 10.1080/14789450.2021.1941893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/09/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Fibroblasts maintain tissue and organ homeostasis through output of extracellular matrix that affects nearby cell signaling within the stroma. Altered fibroblast signaling contributes to many disease states and extracellular matrix secreted by fibroblasts has been used to stratify patient by outcome, recurrence, and therapeutic resistance. Recent advances in imaging mass spectrometry allow access to single cell fibroblasts and their ECM niche within clinically relevant tissue samples. AREAS COVERED We review biological and technical challenges as well as new solutions to proteomic access of fibroblast expression within the complex tissue microenvironment. Review topics cover conventional proteomic methods for single fibroblast analysis and current approaches to accessing single fibroblast proteomes by imaging mass spectrometry approaches. Strategies to target and evaluate the single cell stroma proteome on the basis of cell signaling are presented. EXPERT OPINION The promise of defining proteomic signatures from fibroblasts and their extracellular matrix niches is the discovery of new disease markers and the ability to refine therapeutic treatments. Several imaging mass spectrometry approaches exist to define the fibroblast in the setting of pathological changes from clinically acquired samples. Continued technology advances are needed to access and understand the stromal proteome and apply testing to the clinic.
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Affiliation(s)
- Peggi M. Angel
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| | - Denys Rujchanarong
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| | - Sarah Pippin
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| | - Laura Spruill
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC
| | - Richard Drake
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
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Strittmatter N, England RM, Race AM, Sutton D, Moss JI, Maglennon G, Ling S, Wong E, Rose J, Purvis I, Macdonald R, Barry ST, Ashford MB, Goodwin RJA. Method to Investigate the Distribution of Water-Soluble Drug-Delivery Systems in Fresh Frozen Tissues Using Imaging Mass Cytometry. Anal Chem 2021; 93:3742-3749. [PMID: 33606520 DOI: 10.1021/acs.analchem.0c03908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Imaging mass cytometry (IMC) offers the opportunity to image metal- and heavy halogen-containing xenobiotics in a highly multiplexed experiment with other immunochemistry-based reagents to distinguish uptake into different tissue structures or cell types. However, in practice, many xenobiotics are not amenable to this analysis, as any compound which is not bound to the tissue matrix will delocalize during aqueous sample-processing steps required for IMC analysis. Here, we present a strategy to perform IMC experiments on a water-soluble polysarcosine-modified dendrimer drug-delivery system (S-Dends). This strategy involves two consecutive imaging acquisitions on the same tissue section using the same instrumental platform, an initial laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MSI) experiment followed by tissue staining and a standard IMC experiment. We demonstrated that settings can be found for the initial ablation step that leave sufficient residual tissue for subsequent antibody staining and visualization. This workflow results in lateral resolution for the S-Dends of 2 μm followed by imaging of metal-tagged antibodies at 1 μm.
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Affiliation(s)
- Nicole Strittmatter
- Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Richard M England
- Advanced Drug Delivery, Pharmaceutical Sciences, R&D BioPharmaceuticals, AstraZeneca, Macclesfield SK10 2NA, U.K
| | - Alan M Race
- Institute of Medical Bioinformatics and Biostatistics, Philipps University of Marburg, Marburg 35037, Germany
| | - Daniel Sutton
- Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Jennifer I Moss
- Bioscience, Discovery, Oncology R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Gareth Maglennon
- Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Stephanie Ling
- Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Edmond Wong
- Antibody Discovery and Protein Engineering, R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Jonathan Rose
- Animal Sciences and Technologies, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Ian Purvis
- Animal Sciences and Technologies, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Ruth Macdonald
- Animal Sciences and Technologies, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Simon T Barry
- Bioscience, Discovery, Oncology R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Marianne B Ashford
- Advanced Drug Delivery, Pharmaceutical Sciences, R&D BioPharmaceuticals, AstraZeneca, Macclesfield SK10 2NA, U.K
| | - Richard J A Goodwin
- Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K.,Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, U.K
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30
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Neumann EK, Djambazova KV, Caprioli RM, Spraggins JM. Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:2401-2415. [PMID: 32886506 PMCID: PMC9278956 DOI: 10.1021/jasms.0c00232] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.
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Affiliation(s)
- Elizabeth K Neumann
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, Tennessee 37205, United States
- Mass Spectrometry Research Center, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
| | - Katerina V Djambazova
- Mass Spectrometry Research Center, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, Tennessee 37235, United States
| | - Richard M Caprioli
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, Tennessee 37205, United States
- Mass Spectrometry Research Center, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, Tennessee 37235, United States
- Department of Pharmacology, Vanderbilt University, 2220 Pierce Avenue, Nashville, Tennessee 37232, United States
- Department of Medicine, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
| | - Jeffrey M Spraggins
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, Tennessee 37205, United States
- Mass Spectrometry Research Center, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, Tennessee 37235, United States
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31
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Metabolic Constrains Rule Metastasis Progression. Cells 2020; 9:cells9092081. [PMID: 32932943 PMCID: PMC7563739 DOI: 10.3390/cells9092081] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/08/2020] [Accepted: 09/10/2020] [Indexed: 02/06/2023] Open
Abstract
Metastasis formation accounts for the majority of tumor-associated deaths and consists of different steps, each of them being characterized by a distinctive adaptive phenotype of the cancer cells. Metabolic reprogramming represents one of the main adaptive phenotypes exploited by cancer cells during all the main steps of tumor and metastatic progression. In particular, the metabolism of cancer cells evolves profoundly through all the main phases of metastasis formation, namely the metastatic dissemination, the metastatic colonization of distant organs, the metastatic dormancy, and ultimately the outgrowth into macroscopic lesions. However, the metabolic reprogramming of metastasizing cancer cells has only recently become the subject of intense study. From a clinical point of view, the latter steps of the metastatic process are very important, because patients often undergo surgical removal of the primary tumor when cancer cells have already left the primary tumor site, even though distant metastases are not clinically detectable yet. In this scenario, to precisely elucidate if and how metabolic reprogramming drives acquisition of cancer-specific adaptive phenotypes might pave the way to new therapeutic strategies by combining chemotherapy with metabolic drugs for better cancer eradication. In this review we discuss the latest evidence that claim the importance of metabolic adaptation for cancer progression.
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