1
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Huang L, Kim Y, Balluff B, Cillero-Pastor B. Quality Control Standards for Batch Effect Evaluation and Correction in Mass Spectrometry Imaging. Anal Chem 2025; 97:10919-10928. [PMID: 40354550 PMCID: PMC12120824 DOI: 10.1021/acs.analchem.5c02020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2025] [Revised: 05/05/2025] [Accepted: 05/05/2025] [Indexed: 05/14/2025]
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) allows spatial molecular profiling. Despite many successful applications, an appropriate control of technical variations is still lacking for result reproducibility assessment and for maximizing the MSI data quality. To address this, we introduce a novel quality control standard (QCS) design and data analysis pipeline accounting for variability due to sample preparation and instrument performance. Firstly, we created a tissue mimicking QCS consisting of propranolol in a gelatin matrix. We showed that this QCS mimics ion suppression of propranolol in the tissue. Next, a three-day batch experiment demonstrated the QCS's performance to longitudinal technical variations, establishing it as an effective indicator of batch effects. Then three computational approaches for batch effect correction were applied for the first time to MALDI-MSI data, leading to a significant reduction of QCS variation and to improved sample clustering by using multivariate principal component analysis. Altogether, we offer the designed QCS in combination with a data correction pipeline for MALDI-MSI users for batch effect evaluation and correction.
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Affiliation(s)
- Luojiao Huang
- Cell
Biology-Inspired Tissue Engineering, Institute for Technology-Inspired
Regenerative Medicine, Maastricht University, 6229ERMaastricht, Netherlands
| | - Yaejin Kim
- Cell
Biology-Inspired Tissue Engineering, Institute for Technology-Inspired
Regenerative Medicine, Maastricht University, 6229ERMaastricht, Netherlands
| | - Benjamin Balluff
- Maastricht
MultiModal Molecular Imaging Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229ERMaastricht, Netherlands
| | - Berta Cillero-Pastor
- Cell
Biology-Inspired Tissue Engineering, Institute for Technology-Inspired
Regenerative Medicine, Maastricht University, 6229ERMaastricht, Netherlands
- Maastricht
MultiModal Molecular Imaging Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229ERMaastricht, Netherlands
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2
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Yang X, Shi M, Hong M, Hui Z, Pan J, Xiu G, Zhou L. In situ quantification of fungicide residue on wheat leaf surfaces using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging technology. Food Chem X 2025; 25:102162. [PMID: 39885919 PMCID: PMC11780123 DOI: 10.1016/j.fochx.2025.102162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 01/02/2025] [Accepted: 01/04/2025] [Indexed: 02/01/2025] Open
Abstract
To overcome the time-consuming off-site limitations in conventional pesticide detection, this contribution presents an in situ quantitative analysis detection strategy for pesticides on leaf surfaces using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging technology. Taking fungicide metrafenone as a representative, we initially screened seven commonly used matrices, and identifying α-cyano-4-hydroxycinnamic acid as the most effective one in positive mode. Subsequently, coating the matrix using sublimation spraying method resulted in the highest mass intensity. The optimal combination of operation parameters were established with imaging step size of 30 μm × 30 μm, laser diameter of 75 μm, and laser energy of 4.01 μJ/pulse. Under these conditions, a lowest detection limit of 0.6 ng/mm2 was reached for metrafenone, demonstrating extremely high sensitivity and strong repeatability. This method exhibits great potential for the analysis of various pesticides, including triadimefon and tebuconazole, thereby offering innovative approaches to the analysis of pesticide residues.
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Affiliation(s)
- Xuerui Yang
- State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
- Shanghai Environmental Protection Key Laboratory for Environmental Standard and Risk Management of Chemical Pollutants, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Mengyao Shi
- State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Minghui Hong
- Nanjing Institute of Environmental Science, Ministry of Environmental Protection of China, Nanjing 210042, China
| | - Zhixin Hui
- State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Jiaqi Pan
- State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Guangli Xiu
- State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
- Shanghai Environmental Protection Key Laboratory for Environmental Standard and Risk Management of Chemical Pollutants, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Lei Zhou
- State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
- Shanghai Environmental Protection Key Laboratory for Environmental Standard and Risk Management of Chemical Pollutants, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
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3
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Xiao Y, Li Y, Zhao H. Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective. Mol Cancer 2024; 23:202. [PMID: 39294747 PMCID: PMC11409752 DOI: 10.1186/s12943-024-02113-9] [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: 07/03/2024] [Accepted: 09/05/2024] [Indexed: 09/21/2024] Open
Abstract
Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations.
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Affiliation(s)
- Yang Xiao
- Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400044, China
| | - Yongsheng Li
- Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400044, China.
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China.
| | - Huakan Zhao
- Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400044, China.
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China.
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4
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Zhang H, Lu KH, Ebbini M, Huang P, Lu H, Li L. Mass spectrometry imaging for spatially resolved multi-omics molecular mapping. NPJ IMAGING 2024; 2:20. [PMID: 39036554 PMCID: PMC11254763 DOI: 10.1038/s44303-024-00025-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024]
Abstract
The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future.
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Affiliation(s)
- Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Kelly H. Lu
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Malik Ebbini
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Penghsuan Huang
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
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5
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Zhao Y, Hadavi D, Dijkgraaf I, Honing M. Coupling of surface plasmon resonance and mass spectrometry for molecular interaction studies in drug discovery. Drug Discov Today 2024; 29:104027. [PMID: 38762085 DOI: 10.1016/j.drudis.2024.104027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/01/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
Various analytical technologies have been developed for the study of target-ligand interactions. The combination of these technologies gives pivotal information on the binding mechanism, kinetics, affinity, residence time, and changes in molecular structures. Mass spectrometry (MS) offers structural information, enabling the identification and quantification of target-ligand interactions. Surface plasmon resonance (SPR) provides kinetic information on target-ligand interaction in real time. The coupling of MS and SPR complements each other in the studies of target-ligand interactions. Over the last two decades, the capabilities and added values of SPR-MS have been reported. This review summarizes and highlights the benefits, applications, and potential for further research of the SPR-MS approach.
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Affiliation(s)
- Yuandi Zhao
- Maastricht Multimodal Molecular Imaging (M4i) Institute, Maastricht University, Maastricht, the Netherlands
| | - Darya Hadavi
- Maastricht Multimodal Molecular Imaging (M4i) Institute, Maastricht University, Maastricht, the Netherlands.
| | - Ingrid Dijkgraaf
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, MUMC+, The Netherlands
| | - Maarten Honing
- Maastricht Multimodal Molecular Imaging (M4i) Institute, Maastricht University, Maastricht, the Netherlands
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6
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Enzlein T, Lashley T, Sammour DA, Hopf C, Chávez-Gutiérrez L. Integrative Single-Plaque Analysis Reveals Signature Aβ and Lipid Profiles in the Alzheimer's Brain. Anal Chem 2024; 96:9799-9807. [PMID: 38830618 PMCID: PMC11190877 DOI: 10.1021/acs.analchem.3c05557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 06/05/2024]
Abstract
Cerebral accumulation of amyloid-β (Aβ) initiates molecular and cellular cascades that lead to Alzheimer's disease (AD). However, amyloid deposition does not invariably lead to dementia. Amyloid-positive but cognitively unaffected (AP-CU) individuals present widespread amyloid pathology, suggesting that molecular signatures more complex than the total amyloid burden are required to better differentiate AD from AP-CU cases. Motivated by the essential role of Aβ and the key lipid involvement in AD pathogenesis, we applied multimodal mass spectrometry imaging (MSI) and machine learning (ML) to investigate amyloid plaque heterogeneity, regarding Aβ and lipid composition, in AP-CU versus AD brain samples at the single-plaque level. Instead of focusing on a population mean, our analytical approach allowed the investigation of large populations of plaques at the single-plaque level. We found that different (sub)populations of amyloid plaques, differing in Aβ and lipid composition, coexist in the brain samples studied. The integration of MSI data with ML-based feature extraction further revealed that plaque-associated gangliosides GM2 and GM1, as well as Aβ1-38, but not Aβ1-42, are relevant differentiators between the investigated pathologies. The pinpointed differences may guide further fundamental research investigating the role of amyloid plaque heterogeneity in AD pathogenesis/progression and may provide molecular clues for further development of emerging immunotherapies to effectively target toxic amyloid assemblies in AD therapy. Our study exemplifies how an integrative analytical strategy facilitates the unraveling of complex biochemical phenomena, advancing our understanding of AD from an analytical perspective and offering potential avenues for the refinement of diagnostic tools.
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Affiliation(s)
- Thomas Enzlein
- Center
for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany
- KU
Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium
- Department
of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Tammaryn Lashley
- Department
of Neurodegenerative Diseases, UCL Queen
Square Institute of Neurology, London WC1N 3BG, U.K.
| | - Denis Abu Sammour
- Center
for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany
| | - Carsten Hopf
- Center
for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany
- Mannheim
Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Heidelberg 69120, Germany
- Medical Faculty, Heidelberg University, Heidelberg 69120, Germany
| | - Lucía Chávez-Gutiérrez
- KU
Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium
- Department
of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
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7
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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; 24:e2300001. [PMID: 38402423 DOI: 10.1002/pmic.202300001] [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: 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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8
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Pečinka L, Moráň L, Kovačovicová P, Meloni F, Havel J, Pivetta T, Vaňhara P. Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing. Heliyon 2024; 10:e29936. [PMID: 38707401 PMCID: PMC11066331 DOI: 10.1016/j.heliyon.2024.e29936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Intact (whole) cell MALDI TOF mass spectrometry is a commonly used tool in clinical microbiology for several decades. Recently it was introduced to analysis of eukaryotic cells, including cancer and stem cells. Besides targeted metabolomic and proteomic applications, the intact cell MALDI TOF mass spectrometry provides a sufficient sensitivity and specificity to discriminate cell types, isogenous cell lines or even the metabolic states. This makes the intact cell MALDI TOF mass spectrometry a promising tool for quality control in advanced cell cultures with a potential to reveal batch-to-batch variation, aberrant clones, or unwanted shifts in cell phenotype. However, cellular alterations induced by change in expression of a single gene has not been addressed by intact cell mass spectrometry yet. In this work we used a well-characterized human ovarian cancer cell line SKOV3 with silenced expression of a tumor suppressor candidate 3 gene (TUSC3). TUSC3 is involved in co-translational N-glycosylation of proteins with well-known global impact on cell phenotype. Altogether, this experimental design represents a highly suitable model for optimization of intact cell mass spectrometry and analysis of spectral data. Here we investigated five machine learning algorithms (k-nearest neighbors, decision tree, random forest, partial least squares discrimination, and artificial neural network) and optimized their performance either in pure populations or in two-component mixtures composed of cells with normal or silenced expression of TUSC3. All five algorithms reached accuracy over 90 % and were able to reveal even subtle changes in mass spectra corresponding to alterations of TUSC3 expression. In summary, we demonstrate that spectral fingerprints generated by intact cell MALDI-TOF mass spectrometry coupled to a machine learning classifier can reveal minute changes induced by alteration of a single gene, and therefore contribute to the portfolio of quality control applications in routine cell and tissue cultures.
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Affiliation(s)
- Lukáš Pečinka
- Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Czech Republic
| | - Lukáš Moráň
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Research Centre for Applied Molecular Oncology (RECAMO), Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Petra Kovačovicová
- International Clinical Research Center, St. Anne's University Hospital Brno, Czech Republic
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Francesca Meloni
- Chemical and Geological Sciences Department, University of Cagliari, Cittadella Universitaria, Monserrato, Italy
| | - Josef Havel
- Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Czech Republic
| | - Tiziana Pivetta
- Chemical and Geological Sciences Department, University of Cagliari, Cittadella Universitaria, Monserrato, Italy
| | - Petr Vaňhara
- International Clinical Research Center, St. Anne's University Hospital Brno, Czech Republic
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
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9
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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 PMCID: PMC11520788 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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10
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Cordes J, Enzlein T, Hopf C, Wolf I. pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning. Bioinformatics 2024; 40:btae133. [PMID: 38445753 PMCID: PMC10948279 DOI: 10.1093/bioinformatics/btae133] [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: 11/15/2022] [Revised: 11/20/2023] [Accepted: 03/04/2024] [Indexed: 03/07/2024] Open
Abstract
SUMMARY Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with a focus on memory-efficient handling, processing and convenient data-access for DL applications. pyM2aia provides interfaces to its parent application M2aia, which offers interactive capabilities for exploring and annotating MSI data in imzML format. pyM2aia utilizes the image input and output routines, data formats, and processing functions of M2aia, ensures data interchangeability, and enables the writing of readable and easy-to-maintain DL pipelines by providing batch generators for typical MSI data access strategies. We showcase the package in several examples, including imzML metadata parsing, signal processing, ion-image generation, and, in particular, DL model training and inference for spectrum-wise approaches, ion-image-based approaches, and approaches that use spectral and spatial information simultaneously. AVAILABILITY AND IMPLEMENTATION Python package, code and examples are available at (https://m2aia.github.io/m2aia).
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Affiliation(s)
- Jonas Cordes
- Faculty of Computer Science, Mannheim University of Applied Sciences, Mannheim 68163, Germany
- Medical Faculty Mannheim, Heidelberg University, Mannheim 68167, Germany
| | - Thomas Enzlein
- Center for Mass Spectrometry and Optical Spectroscopy, Mannheim University of Applied Sciences, Mannheim 68163, Germany
| | - Carsten Hopf
- Medical Faculty Mannheim, Heidelberg University, Mannheim 68167, Germany
- Center for Mass Spectrometry and Optical Spectroscopy, Mannheim University of Applied Sciences, Mannheim 68163, Germany
- Medical Faculty, Heidelberg University, Heidelberg 69120, Germany
| | - Ivo Wolf
- Faculty of Computer Science, Mannheim University of Applied Sciences, Mannheim 68163, Germany
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11
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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: 57] [Impact Index Per Article: 57.0] [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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12
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Jha D, Blennow K, Zetterberg H, Savas JN, Hanrieder J. Spatial neurolipidomics-MALDI mass spectrometry imaging of lipids in brain pathologies. JOURNAL OF MASS SPECTROMETRY : JMS 2024; 59:e5008. [PMID: 38445816 PMCID: PMC12013527 DOI: 10.1002/jms.5008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/03/2024] [Accepted: 01/25/2024] [Indexed: 03/07/2024]
Abstract
Given the complexity of nervous tissues, understanding neurochemical pathophysiology puts high demands on bioanalytical techniques with respect to specificity and sensitivity. Mass spectrometry imaging (MSI) has evolved to become an important, biochemical imaging technology for spatial biology in biological and translational research. The technique facilitates comprehensive, sensitive elucidation of the spatial distribution patterns of drugs, lipids, peptides, and small proteins in situ. Matrix-assisted laser desorption ionization (MALDI)-based MSI is the dominating modality due to its broad applicability and fair compromise of selectivity, sensitivity price, throughput, and ease of use. This is particularly relevant for the analysis of spatial lipid patterns, where no other comparable spatial profiling tools are available. Understanding spatial lipid biology in nervous tissue is therefore a key and emerging application area of MSI research. The aim of this review is to give a concise guide through the MSI workflow for lipid imaging in central nervous system (CNS) tissues and essential parameters to consider while developing and optimizing MSI assays. Further, this review provides a broad overview of key developments and applications of MALDI MSI-based spatial neurolipidomics to map lipid dynamics in neuronal structures, ultimately contributing to a better understanding of neurodegenerative disease pathology.
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Affiliation(s)
- Durga Jha
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, SE-431 80 Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, SE-431 80 Mölndal, Sweden
- Clinical Neurochemistry Lab, Sahlgrenska University Hospital, Mölndal, Sweden
- Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, P.R. China
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, SE-431 80 Mölndal, Sweden
- Clinical Neurochemistry Lab, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, UK
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Jeffrey N. Savas
- Department of Neurology, Northwestern University Feinberg School of Medicine, Northwestern University, Chicago IL, 60611, USA
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, SE-431 80 Mölndal, Sweden
- Clinical Neurochemistry Lab, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, United Kingdom
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13
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Pitchai A, Buhman K, Shannahan JH. Lipid mediators of inhalation exposure-induced pulmonary toxicity and inflammation. Inhal Toxicol 2024; 36:57-74. [PMID: 38422051 PMCID: PMC11022128 DOI: 10.1080/08958378.2024.2318389] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 02/07/2024] [Indexed: 03/02/2024]
Abstract
Many inhalation exposures induce pulmonary inflammation contributing to disease progression. Inflammatory processes are actively regulated via mediators including bioactive lipids. Bioactive lipids are potent signaling molecules involved in both pro-inflammatory and resolution processes through receptor interactions. The formation and clearance of lipid signaling mediators are controlled by multiple metabolic enzymes. An imbalance of these lipids can result in exacerbated and sustained inflammatory processes which may result in pulmonary damage and disease. Dysregulation of pulmonary bioactive lipids contribute to inflammation and pulmonary toxicity following exposures. For example, inhalation of cigarette smoke induces activation of pro-inflammatory bioactive lipids such as sphingolipids, and ceramides contributing to chronic obstructive pulmonary disease. Additionally, exposure to silver nanoparticles causes dysregulation of inflammatory resolution lipids. As inflammation is a common consequence resulting from inhaled exposures and a component of numerous diseases it represents a broadly applicable target for therapeutic intervention. With new appreciation for bioactive lipids, technological advances to reliably identify and quantify lipids have occurred. In this review, we will summarize, integrate, and discuss findings from recent studies investigating the impact of inhaled exposures on pro-inflammatory and resolution lipids within the lung and their contribution to disease. Throughout the review current knowledge gaps in our understanding of bioactive lipids and their contribution to pulmonary effects of inhaled exposures will be presented. New methods being employed to detect and quantify disruption of pulmonary lipid levels following inhalation exposures will be highlighted. Lastly, we will describe how lipid dysregulation could potentially be addressed by therapeutic strategies to address inflammation.
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Affiliation(s)
- Arjun Pitchai
- School of Health Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN, United States
| | - Kimberly Buhman
- Department of Nutrition, College of Health and Human Sciences, Purdue University, West Lafayette, IN, United States
| | - Jonathan H. Shannahan
- School of Health Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN, United States
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14
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Rajbhandari P, Neelakantan TV, Hosny N, Stockwell BR. Spatial pharmacology using mass spectrometry imaging. Trends Pharmacol Sci 2024; 45:67-80. [PMID: 38103980 PMCID: PMC10842749 DOI: 10.1016/j.tips.2023.11.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/07/2023] [Accepted: 11/11/2023] [Indexed: 12/19/2023]
Abstract
The emerging and powerful field of spatial pharmacology can map the spatial distribution of drugs and their metabolites, as well as their effects on endogenous biomolecules including metabolites, lipids, proteins, peptides, and glycans, without the need for labeling. This is enabled by mass spectrometry imaging (MSI) that provides previously inaccessible information in diverse phases of drug discovery and development. We provide a perspective on how MSI technologies and computational tools can be implemented to reveal quantitative spatial drug pharmacokinetics and toxicology, tissue subtyping, and associated biomarkers. We also highlight the emerging potential of comprehensive spatial pharmacology through integration of multimodal MSI data with other spatial technologies. Finally, we describe how to overcome challenges including improving reproducibility and compound annotation to generate robust conclusions that will improve drug discovery and development processes.
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Affiliation(s)
- Presha Rajbhandari
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | | | - Noreen Hosny
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA; Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Brent R Stockwell
- Department of Biological Sciences, Columbia University, New York, NY, USA; Department of Chemistry, Columbia University, New York, NY, USA; Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA; Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA.
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15
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Moore JL, Patterson NH, Norris JL, Caprioli RM. Prospective on Imaging Mass Spectrometry in Clinical Diagnostics. Mol Cell Proteomics 2023; 22:100576. [PMID: 37209813 PMCID: PMC10545939 DOI: 10.1016/j.mcpro.2023.100576] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/10/2023] [Accepted: 05/12/2023] [Indexed: 05/22/2023] Open
Abstract
Imaging mass spectrometry (IMS) is a molecular technology utilized for spatially driven research, providing molecular maps from tissue sections. This article reviews matrix-assisted laser desorption ionization (MALDI) IMS and its progress as a primary tool in the clinical laboratory. MALDI mass spectrometry has been used to classify bacteria and perform other bulk analyses for plate-based assays for many years. However, the clinical application of spatial data within a tissue biopsy for diagnoses and prognoses is still an emerging opportunity in molecular diagnostics. This work considers spatially driven mass spectrometry approaches for clinical diagnostics and addresses aspects of new imaging-based assays that include analyte selection, quality control/assurance metrics, data reproducibility, data classification, and data scoring. It is necessary to implement these tasks for the rigorous translation of IMS to the clinical laboratory; however, this requires detailed standardized protocols for introducing IMS into the clinical laboratory to deliver reliable and reproducible results that inform and guide patient care.
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Affiliation(s)
| | - Nathan Heath Patterson
- Frontier Diagnostics, Nashville, Tennessee, USA; Vanderbilt University Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Jeremy L Norris
- Frontier Diagnostics, Nashville, Tennessee, USA; Vanderbilt University Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Richard M Caprioli
- Frontier Diagnostics, Nashville, Tennessee, USA; Vanderbilt University Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Departments of Biochemistry, Pharmacology, Chemistry, and Medicine, Vanderbilt University, Nashville, Tennessee, USA.
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16
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Park YM, Meyer MR, Müller R, Herrmann J. Optimization of Mass Spectrometry Imaging for Drug Metabolism and Distribution Studies in the Zebrafish Larvae Model: A Case Study with the Opioid Antagonist Naloxone. Int J Mol Sci 2023; 24:10076. [PMID: 37373226 DOI: 10.3390/ijms241210076] [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: 04/29/2023] [Revised: 06/04/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
Zebrafish (ZF; Danio rerio) larvae have emerged as a promising in vivo model in drug metabolism studies. Here, we set out to ready this model for integrated mass spectrometry imaging (MSI) to comprehensively study the spatial distribution of drugs and their metabolites inside ZF larvae. In our pilot study with the overall goal to improve MSI protocols for ZF larvae, we investigated the metabolism of the opioid antagonist naloxone. We confirmed that the metabolic modification of naloxone is in high accordance with metabolites detected in HepaRG cells, human biosamples, and other in vivo models. In particular, all three major human metabolites were detected at high abundance in the ZF larvae model. Next, the in vivo distribution of naloxone was investigated in three body sections of ZF larvae using LC-HRMS/MS showing that the opioid antagonist is mainly present in the head and body sections, as suspected from published human pharmacological data. Having optimized sample preparation procedures for MSI (i.e., embedding layer composition, cryosectioning, and matrix composition and spraying), we were able to record MS images of naloxone and its metabolites in ZF larvae, providing highly informative distributional images. In conclusion, we demonstrate that all major ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters, as part of in vivo pharmacokinetic studies, can be assessed in a simple and cost-effective ZF larvae model. Our established protocols for ZF larvae using naloxone are broadly applicable, particularly for MSI sample preparation, to various types of compounds, and they will help to predict and understand human metabolism and pharmacokinetics.
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Affiliation(s)
- Yu Mi Park
- Helmholtz Centre for Infection Research, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Campus E8 1, Saarland University, 66123 Saarbrücken, Germany
- Environmental Safety Group, Korea Institute of Science and Technology (KIST) Europe, 66123 Saarbrücken, Germany
- Department of Pharmacy, Saarland University, 66123 Saarbrücken, Germany
| | - Markus R Meyer
- Center for Molecular Signaling (PZMS), Institute of Experimental and Clinical Pharmacology and Toxicology, Department of Experimental and Clinical Toxicology, Saarland University, 66421 Homburg, Germany
| | - Rolf Müller
- Helmholtz Centre for Infection Research, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Campus E8 1, Saarland University, 66123 Saarbrücken, Germany
- Department of Pharmacy, Saarland University, 66123 Saarbrücken, Germany
- German Center for Infection Research (DZIF), 38124 Braunschweig, Germany
| | - Jennifer Herrmann
- Helmholtz Centre for Infection Research, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Campus E8 1, Saarland University, 66123 Saarbrücken, Germany
- German Center for Infection Research (DZIF), 38124 Braunschweig, Germany
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17
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Tzvetkov J, Stephen LA, Dillon S, Millan JL, Roelofs AJ, De Bari C, Farquharson C, Larson T, Genever P. Spatial Lipidomic Profiling of Mouse Joint Tissue Demonstrates the Essential Role of PHOSPHO1 in Growth Plate Homeostasis. J Bone Miner Res 2023; 38:792-807. [PMID: 36824055 PMCID: PMC10946796 DOI: 10.1002/jbmr.4796] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 01/19/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023]
Abstract
Lipids play a crucial role in signaling and metabolism, regulating the development and maintenance of the skeleton. Membrane lipids have been hypothesized to act as intermediates upstream of orphan phosphatase 1 (PHOSPHO1), a major contributor to phosphate generation required for bone mineralization. Here, we spatially resolve the lipid atlas of the healthy mouse knee and demonstrate the effects of PHOSPHO1 ablation on the growth plate lipidome. Lipids spanning 17 subclasses were mapped across the knee joints of healthy juvenile and adult mice using matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS), with annotation supported by shotgun lipidomics. Multivariate analysis identified 96 and 80 lipid ions with differential abundances across joint tissues in juvenile and adult mice, respectively. In both ages, marrow was enriched in phospholipid platelet activating factors (PAFs) and related metabolites, cortical bone had a low lipid content, whereas lysophospholipids were strikingly enriched in the growth plate, an active site of mineralization and PHOSPHO1 activity. Spatially-resolved profiling of PHOSPHO1-knockout (KO) mice across the resting, proliferating, and hypertrophic growth plate zones revealed 272, 306, and 296 significantly upregulated, and 155, 220, and 190 significantly downregulated features, respectively, relative to wild-type (WT) controls. Of note, phosphatidylcholine, lysophosphatidylcholine, sphingomyelin, lysophosphatidylethanolamine, and phosphatidylethanolamine derived lipid ions were upregulated in PHOSPHO1-KO versus WT. Our imaging pipeline has established a spatially-resolved lipid signature of joint tissues and has demonstrated that PHOSPHO1 ablation significantly alters the growth plate lipidome, highlighting an essential role of the PHOSPHO1-mediated membrane phospholipid metabolism in lipid and bone homeostasis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Jordan Tzvetkov
- York Biomedical Research Institute and Department of BiologyUniversity of YorkYorkUK
| | | | - Scott Dillon
- Wellcome‐Medical Research Council (MRC) Cambridge Stem Cell InstituteUniversity of CambridgeCambridgeUK
| | - Jose Luis Millan
- Sanford Burnham Prebys, Medical Discovery InstituteLa JollaCAUSA
| | - Anke J. Roelofs
- Centre for Arthritis and Musculoskeletal HealthUniversity of AberdeenAberdeenUK
| | - Cosimo De Bari
- Centre for Arthritis and Musculoskeletal HealthUniversity of AberdeenAberdeenUK
| | | | - Tony Larson
- York Biomedical Research Institute and Department of BiologyUniversity of YorkYorkUK
| | - Paul Genever
- York Biomedical Research Institute and Department of BiologyUniversity of YorkYorkUK
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18
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Abu Sammour D, Cairns JL, Boskamp T, Marsching C, Kessler T, Ramallo Guevara C, Panitz V, Sadik A, Cordes J, Schmidt S, Mohammed SA, Rittel MF, Friedrich M, Platten M, Wolf I, von Deimling A, Opitz CA, Wick W, Hopf C. Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging. Nat Commun 2023; 14:1823. [PMID: 37005414 PMCID: PMC10067847 DOI: 10.1038/s41467-023-37394-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 03/13/2023] [Indexed: 04/04/2023] Open
Abstract
Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR ( https://github.com/CeMOS-Mannheim/moleculaR ) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.
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Affiliation(s)
- Denis Abu Sammour
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - James L Cairns
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Tobias Boskamp
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
- Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Christian Marsching
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Tobias Kessler
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Carina Ramallo Guevara
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Verena Panitz
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology and National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Ahmed Sadik
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Bioscience, Heidelberg University, Heidelberg, Germany
| | - Jonas Cordes
- Faculty of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Stefan Schmidt
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Shad A Mohammed
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Miriam F Rittel
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirco Friedrich
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- DKTK Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Platten
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- DKTK Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ivo Wolf
- Faculty of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Andreas von Deimling
- Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
- DKTK Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christiane A Opitz
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology and National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Carsten Hopf
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany.
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.
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19
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Device-Controlled Microcondensation for Spatially Confined On-Tissue Digests in MALDI Imaging of N-Glycans. Pharmaceuticals (Basel) 2022; 15:ph15111356. [PMID: 36355528 PMCID: PMC9698097 DOI: 10.3390/ph15111356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/11/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
Abstract
On-tissue enzymatic digestion is a prerequisite for MALDI mass spectrometry imaging (MSI) and spatialomic analysis of tissue proteins and their N-glycan conjugates. Despite the more widely accepted importance of N-glycans as diagnostic and prognostic biomarkers of many diseases and their potential as pharmacodynamic markers, the crucial sample preparation step, namely on-tissue digestion with enzymes like PNGaseF, is currently mainly carried out by specialized laboratories using home-built incubation arrangements, e.g., petri dishes placed in an incubator. Standardized spatially confined enzyme digests, however, require precise control and possible regulation of humidity and temperature, as high humidity increases the risk of analyte dislocation and low humidity compromises enzyme function. Here, a digestion device that controls humidity by cyclic ventilation and heating of the slide holder and the chamber lid was designed to enable controlled micro-condensation on the slide and to stabilize and monitor the digestion process. The device presented here may help with standardization in MSI. Using sagittal mouse brain sections and xenografted human U87 glioblastoma cells in CD1 nu/nu mouse brain, a device-controlled workflow for MALDI MSI of N-glycans was developed.
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20
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Ali A, Davidson S, Fraenkel E, Gilmore I, Hankemeier T, Kirwan JA, Lane AN, Lanekoff I, Larion M, McCall LI, Murphy M, Sweedler JV, Zhu C. Single cell metabolism: current and future trends. Metabolomics 2022; 18:77. [PMID: 36181583 PMCID: PMC10063251 DOI: 10.1007/s11306-022-01934-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
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Affiliation(s)
- Ahmed Ali
- Leiden Academic Centre for Drug Research, University of Leiden, Gorlaeus Building Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Shawn Davidson
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Ernest Fraenkel
- Department of Biological Engineering and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ian Gilmore
- National Physical Laboratory, Teddington, TW11 0LW, Middlesex, UK
| | - Thomas Hankemeier
- Leiden Academic Centre for Drug Research, University of Leiden, Room number GW4.07, Gorlaeus Building, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Jennifer A Kirwan
- Berlin Institute of Health, Metabolomics Platform, Translational Research Unit of the Charite-Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str 2, 10178, Berlin, Germany
| | - Andrew N Lane
- Department of Toxicology and Cancer Biology, and Center for Environmental and Systems Biochemistry, University of Kentucky, 789 S. Limestone St, Lexington, KY, 40536, USA.
| | - Ingela Lanekoff
- Department of Chemistry-BMC, Uppsala University, Husargatan 3 (576), 751 23, Uppsala, Sweden
| | - Mioara Larion
- Center for Cancer Research, National Cancer Institute, Building 37, Room 1136A, Bethesda, MD, 20892, USA
| | - Laura-Isobel McCall
- Department of Chemistry & Biochemistry, Department of Microbiology and Plant Biology, Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, 101 Stephenson Parkway, room 3750, Norman, OK, 73019-5251, USA
| | - Michael Murphy
- Departments of Biological Engineering, Department of Electrical Engineering, and Computer Science and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, USA
| | - Jonathan V Sweedler
- Department of Chemistry, and the Beckman Institute, University of Illinois Urbana-Champaign, 505 South Mathews Avenue, Urbana, IL, 61801, USA
| | - Caigang Zhu
- Department of Biomedical Engineering, University of Kentucky, Lexington, KY, 40536, USA
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21
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Janßen C, Boskamp T, Hauberg-Lotte L, Behrmann J, Deininger SO, Kriegsmann M, Kriegsmann K, Steinbuß G, Winter H, Muley T, Casadonte R, Kriegsmann J, Maaß P. Robust subtyping of non-small cell lung cancer whole sections through MALDI mass spectrometry imaging. Proteomics Clin Appl 2022; 16:e2100068. [PMID: 35238465 DOI: 10.1002/prca.202100068] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 01/24/2022] [Accepted: 02/28/2022] [Indexed: 12/30/2022]
Abstract
Subtyping of the most common non-small cell lung cancer (NSCLC) tumor types adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) is still a challenge in the clinical routine and a correct diagnosis is crucial for an adequate therapy selection. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has shown potential for NSCLC subtyping but is subject to strong technical variability and has only been applied to tissue samples assembled in tissue microarrays (TMAs). To our knowledge, a successful transfer of a classifier from TMAs to whole sections, which are generated in the standard clinical routine, has not been presented in the literature as of yet. We introduce a classification algorithm using extensive preprocessing and a classifier (either a neural network or a linear discriminant analysis (LDA)) to robustly classify whole sections of ADC and SqCC lung tissue. The classifiers were trained on TMAs and validated and tested on whole sections. Vital for a successful application on whole sections is the extensive preprocessing and the use of whole sections for hyperparameter selection. The classification system with the neural network/LDA results in 99.0%/98.3% test accuracy on spectra level and 100.0%/100.0% test accuracy on whole section level, respectively, and, therefore, provides a powerful tool to support the pathologist's decision making process. The presented method is a step further towards a clinical application of MALDI MSI and artificial intelligence for subtyping of NSCLC tissue sections.
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Affiliation(s)
- Charlotte Janßen
- Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany
| | - Tobias Boskamp
- Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany.,Bruker Daltonics GmbH, Bremen, Germany
| | - Lena Hauberg-Lotte
- Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany
| | - Jens Behrmann
- Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany
| | | | - Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany
| | - Georg Steinbuß
- Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany
| | - Hauke Winter
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Thoracic Surgery, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany
| | - Thomas Muley
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.,Translational Research Unit, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Jörg Kriegsmann
- Proteopath, Trier, Germany.,Center for Histology, Cytology and Molecular Diagnostic, Trier, Germany
| | - Peter Maaß
- Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany
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22
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Deininger SO, Bollwein C, Casadonte R, Wandernoth P, Gonçalves JPL, Kriegsmann K, Kriegsmann M, Boskamp T, Kriegsmann J, Weichert W, Schirmacher P, Ly A, Schwamborn K. Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging. Anal Chem 2022; 94:8194-8201. [PMID: 35658398 DOI: 10.1021/acs.analchem.2c00097] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Many studies have demonstrated that tissue phenotyping (tissue typing) based on mass spectrometric imaging data is possible; however, comprehensive studies assessing variation and classifier transferability are largely lacking. This study evaluated the generalization of tissue classification based on Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometric imaging (MSI) across measurements performed at different sites. Sections of a tissue microarray (TMA) consisting of different formalin-fixed and paraffin-embedded (FFPE) human tissue samples from different tumor entities (leiomyoma, seminoma, mantle cell lymphoma, melanoma, breast cancer, and squamous cell carcinoma of the lung) were prepared and measured by MALDI-MSI at different sites using a standard protocol (SOP). Technical variation was deliberately introduced on two separate measurements via a different sample preparation protocol and a MALDI Time of Flight mass spectrometer that was not tuned to optimal performance. Using standard data preprocessing, a classification accuracy of 91.4% per pixel was achieved for intrasite classifications. When applying a leave-one-site-out cross-validation strategy, accuracy per pixel over sites was 78.6% for the SOP-compliant data sets and as low as 36.1% for the mistuned instrument data set. Data preprocessing designed to remove technical variation while retaining biological information substantially increased classification accuracy for all data sets with SOP-compliant data sets improved to 94.3%. In particular, classification accuracy of the mistuned instrument data set improved to 81.3% and from 67.0% to 87.8% per pixel for the non-SOP-compliant data set. We demonstrate that MALDI-MSI-based tissue classification is possible across sites when applying histological annotation and an optimized data preprocessing pipeline to improve generalization of classifications over technical variation and increasing overall robustness.
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Affiliation(s)
| | - Christine Bollwein
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstrasse 18, 81675 München, Germany
| | - Rita Casadonte
- Proteopath GmbH, Max-Planck-Strasse 17, 54296 Trier, Germany
| | - Petra Wandernoth
- MVZ für Histologie, Zytologie und molekulare Diagnostik Trier GmbH, Max-Planck-Strasse 5, 54296 Trier, Germany
| | | | - Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
| | - Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
| | - Tobias Boskamp
- Bruker Daltonics GmbH & Co KG, Fahrenheitstrasse 4, 28359 Bremen, Germany.,Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
| | - Jörg Kriegsmann
- MVZ für Histologie, Zytologie und molekulare Diagnostik Trier GmbH, Max-Planck-Strasse 5, 54296 Trier, Germany.,Danube Private University (DPU) Faculty of Medicine/Dentistry, Steiner Landstrasse 124, 3500 Krems-Stein, Austria
| | - Wilko Weichert
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstrasse 18, 81675 München, Germany
| | - Peter Schirmacher
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
| | - Alice Ly
- Bruker Daltonics GmbH & Co KG, Fahrenheitstrasse 4, 28359 Bremen, Germany
| | - Kristina Schwamborn
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstrasse 18, 81675 München, Germany
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23
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Buszewska-Forajta M, Rafińska K, Buszewski B. Tissue sample preparations for preclinical research determined by molecular imaging mass spectrometry using MALDI. J Sep Sci 2022; 45:1345-1361. [PMID: 35122386 DOI: 10.1002/jssc.202100578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 11/09/2022]
Abstract
Matrix-assisted laser desorption/ionization - imaging mass spectrometry is an alternative tool, which can be implemented in order to obtain and visualize the "omic" signature of tissue samples. Its application to clinical study enables simultaneous imaging-based morphological observations and mass spectrometry analysis. Application of fully informative material like tissue, allows to obtain the complex and unique profile of analyzed samples. This knowledge leads to diagnose disease, study the mechanism of cancer development, select the potential biomarkers as well as correlating obtained image with prognosis. Nevertheless, it is worth to notice that this method is found to be objective but the result of analysis is mainly influenced by the sample preparation protocol, included collection of biological material, its preservation and processing. However, application of this approach requires a special sample preparation procedure. The main goal of the study is to present the current knowledge on the clinical application of matrix-assisted laser desorption/ionization - imaging mass spectrometry in cancer research, with particular emphasis on the sample preparation step. For this purpose, several protocols based on cryosections and formalin-fixed paraffin embedded tissue were compiled and compared, taking into account the measured metabolites of potential diagnostic importance for a given type of cancer. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Magdalena Buszewska-Forajta
- Institute of Veterinary Medicine, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, Lwowska 1, Toruń, 87-100, Poland
| | - Katarzyna Rafińska
- Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University, 7 Gagarina Str., Torun, 87-100, Poland
| | - Boguslaw Buszewski
- Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University, 7 Gagarina Str., Torun, 87-100, Poland.,Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Torun, 4 Wileńska Str., Torun, 87-100, Poland
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24
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Engel KM, Prabutzki P, Leopold J, Nimptsch A, Lemmnitzer K, Vos DRN, Hopf C, Schiller J. A new update of MALDI-TOF mass spectrometry in lipid research. Prog Lipid Res 2022; 86:101145. [PMID: 34995672 DOI: 10.1016/j.plipres.2021.101145] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/06/2021] [Accepted: 12/29/2021] [Indexed: 01/06/2023]
Abstract
Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS) is an indispensable tool in modern lipid research since it is fast, sensitive, tolerates sample impurities and provides spectra without major analyte fragmentation. We will discuss some methodological aspects, the related ion-forming processes and the MALDI MS characteristics of the different lipid classes (with the focus on glycerophospholipids) and the progress, which was achieved during the last ten years. Particular attention will be given to quantitative aspects of MALDI MS since this is widely considered as the most serious drawback of the method. Although the detailed role of the matrix is not yet completely understood, it will be explicitly shown that the careful choice of the matrix is crucial (besides the careful evaluation of the positive and negative ion mass spectra) in order to be able to detect all lipid classes of interest. Two developments will be highlighted: spatially resolved Imaging MS is nowadays well established and the distribution of lipids in tissues merits increasing interest because lipids are readily detectable and represent ubiquitous compounds. It will also be shown that a combination of MALDI MS with thin-layer chromatography (TLC) enables a fast spatially resolved screening of an entire TLC plate which makes the method competitive with LC/MS.
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Affiliation(s)
- Kathrin M Engel
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - Patricia Prabutzki
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - Jenny Leopold
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - Ariane Nimptsch
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - Katharina Lemmnitzer
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - D R Naomi Vos
- Center for Biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Strasse 10, D-68163 Mannheim, Germany
| | - Carsten Hopf
- Center for Biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Strasse 10, D-68163 Mannheim, Germany
| | - Jürgen Schiller
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany.
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25
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Yu D, Lai P, Yan T, Fang K, Chen L, Zhang S. Quantifying the Matrix Metalloproteinase 2 (MMP2) Spatially in Tissues by Probe via MALDI Imaging Mass Spectrometry. Front Chem 2021; 9:786283. [PMID: 34976953 PMCID: PMC8715900 DOI: 10.3389/fchem.2021.786283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/25/2021] [Indexed: 11/25/2022] Open
Abstract
As a matrix metalloproteinase, the abnormal expression of MMP2 is associated with multiple biological processes, including tissue remodeling and cancer progression. Therefore, spatial analysis of MMP2 protein in tissues can be used as an important approach to evaluate the expression distribution of MMP2 in complex tissue environments, which will help the diagnosis and treatment of various diseases, including tissue or organ injuries. Moreover, this analysis will also help the evaluation of prognoses. However, MMP2 is difficult to be spatially determined by MALDI TOF mass spectrometry due to its large molecular weight (over 72 KD) and low content. Therefore, a new method should be developed to help this detection. Here, we have designed a specific MMP2 probe that closely binds to MMP2 protein in tissue. This probe has a Cl on Tyr at the terminal, which can provide two isotope peaks to help the accuracy quantitative of MMP2 protein. Based on this, we used the probe to determine the spatial expression of MMP2 in the tissues based on MALDI TOF mass spectrometry. This approach may help to study the influence of multifunctional proteases on the degree of malignancy in vivo.
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Affiliation(s)
- Daojiang Yu
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, China
- *Correspondence: Daojiang Yu, ; Shuyu Zhang,
| | - Peng Lai
- Department of Endocrinology, Xuzhou Center Hospital, Xuzhou, China
| | - Tao Yan
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, China
| | - Kai Fang
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, China
| | - Lei Chen
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, China
| | - Shuyu Zhang
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, China
- Department of Oncology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
- *Correspondence: Daojiang Yu, ; Shuyu Zhang,
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26
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Pathmasiri KC, Nguyen TTA, Khamidova N, Cologna SM. Mass spectrometry-based lipid analysis and imaging. CURRENT TOPICS IN MEMBRANES 2021; 88:315-357. [PMID: 34862030 DOI: 10.1016/bs.ctm.2021.10.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mass spectrometry imaging (MSI) is a powerful tool for in situ mapping of analytes across a sample. With growing interest in lipid biochemistry, the ability to perform such mapping without antibodies has opened many opportunities for MSI and lipid analysis. Herein, we discuss the basics of MSI with particular emphasis on MALDI mass spectrometry and lipid analysis. A discussion of critical advancements as well as protocol details are provided to the reader. In addition, strategies for improving the detection of lipids, as well as applications in biomedical research, are presented.
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Affiliation(s)
- Koralege C Pathmasiri
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, United States
| | - Thu T A Nguyen
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, United States
| | - Nigina Khamidova
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, United States
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, United States; Laboratory of Integrated Neuroscience, University of Illinois at Chicago, Chicago, IL, United States.
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27
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Denti V, Andersen MK, Smith A, Bofin AM, Nordborg A, Magni F, Moestue SA, Giampà M. Reproducible Lipid Alterations in Patient-Derived Breast Cancer Xenograft FFPE Tissue Identified with MALDI MSI for Pre-Clinical and Clinical Application. Metabolites 2021; 11:577. [PMID: 34564393 PMCID: PMC8467053 DOI: 10.3390/metabo11090577] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 08/18/2021] [Accepted: 08/24/2021] [Indexed: 12/20/2022] Open
Abstract
The association between lipid metabolism and long-term outcomes is relevant for tumor diagnosis and therapy. Archival material such as formalin-fixed and paraffin embedded (FFPE) tissues is a highly valuable resource for this aim as it is linked to long-term clinical follow-up. Therefore, there is a need to develop robust methodologies able to detect lipids in FFPE material and correlate them with clinical outcomes. In this work, lipidic alterations were investigated in patient-derived xenograft of breast cancer by using a matrix-assisted laser desorption ionization mass spectrometry (MALDI MSI) based workflow that included antigen retrieval as a sample preparation step. We evaluated technical reproducibility, spatial metabolic differentiation within tissue compartments, and treatment response induced by a glutaminase inhibitor (CB-839). This protocol shows a good inter-day robustness (CV = 26 ± 12%). Several lipids could reliably distinguish necrotic and tumor regions across the technical replicates. Moreover, this protocol identified distinct alterations in the tissue lipidome of xenograft treated with glutaminase inhibitors. In conclusion, lipidic alterations in FFPE tissue of breast cancer xenograft observed in this study are a step-forward to a robust and reproducible MALDI-MSI based workflow for pre-clinical and clinical applications.
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Affiliation(s)
- Vanna Denti
- Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20854 Vedano al Lambro, MB, Italy; (V.D.); (A.S.); (F.M.)
| | - Maria K. Andersen
- Department of Circulation and Medical Imaging, NTNU–Norwegian University of Science and Technology, 7491 Trondheim, Norway;
| | - Andrew Smith
- Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20854 Vedano al Lambro, MB, Italy; (V.D.); (A.S.); (F.M.)
| | - Anna Mary Bofin
- Department of Clinical and Molecular Medicine, NTNU–Norwegian University of Science and Technology, 7491 Trondheim, Norway; (A.M.B.); (S.A.M.)
| | - Anna Nordborg
- Department of Biotechnology and Nanomedicine, SINTEF, 7034 Trondheim, Norway;
| | - Fulvio Magni
- Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20854 Vedano al Lambro, MB, Italy; (V.D.); (A.S.); (F.M.)
| | - Siver Andreas Moestue
- Department of Clinical and Molecular Medicine, NTNU–Norwegian University of Science and Technology, 7491 Trondheim, Norway; (A.M.B.); (S.A.M.)
- Department of Pharmacy, Nord University, 8026 Bodø, Norway
| | - Marco Giampà
- Department of Clinical and Molecular Medicine, NTNU–Norwegian University of Science and Technology, 7491 Trondheim, Norway; (A.M.B.); (S.A.M.)
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28
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Boskamp T, Casadonte R, Hauberg-Lotte L, Deininger S, Kriegsmann J, Maass P. Cross-Normalization of MALDI Mass Spectrometry Imaging Data Improves Site-to-Site Reproducibility. Anal Chem 2021; 93:10584-10592. [PMID: 34297545 DOI: 10.1021/acs.analchem.1c01792] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an established tool for the investigation of formalin-fixed paraffin-embedded (FFPE) tissue samples and shows a high potential for applications in clinical research and histopathological tissue classification. However, the applicability of this method to serial clinical and pharmacological studies is often hampered by inevitable technical variation and limited reproducibility. We present a novel spectral cross-normalization algorithm that differs from the existing normalization methods in two aspects: (a) it is based on estimating the full statistical distribution of spectral intensities and (b) it involves applying a non-linear, mass-dependent intensity transformation to align this distribution with a reference distribution. This method is combined with a model-driven resampling step that is specifically designed for data from MALDI imaging of tryptic peptides. This method was performed on two sets of tissue samples: a single human teratoma sample and a collection of five tissue microarrays (TMAs) of breast and ovarian tumor tissue samples (N = 241 patients). The MALDI MSI data was acquired in two labs using multiple protocols, allowing us to investigate different inter-lab and cross-protocol scenarios, thus covering a wide range of technical variations. Our results suggest that the proposed cross-normalization significantly reduces such batch effects not only in inter-sample and inter-lab comparisons but also in cross-protocol scenarios. This demonstrates the feasibility of cross-normalization and joint data analysis even under conditions where preparation and acquisition protocols themselves are subject to variation.
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Affiliation(s)
- Tobias Boskamp
- Bruker Daltonics GmbH & Co. KG, 28359 Bremen, Germany.,Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
| | | | - Lena Hauberg-Lotte
- Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
| | | | - Jörg Kriegsmann
- Proteopath, 54296 Trier, Germany.,Center for Histology, Cytology and Molecular Diagnostic, 54296 Trier, Germany
| | - Peter Maass
- Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
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