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Li CMY, Briggs MT, Lee YR, Tin T, Young C, Pierides J, Kaur G, Drew P, Maddern GJ, Hoffmann P, Klingler-Hoffmann M, Fenix K. Use of tryptic peptide MALDI mass spectrometry imaging to identify the spatial proteomic landscape of colorectal cancer liver metastases. Clin Exp Med 2024; 24:53. [PMID: 38492056 PMCID: PMC10944452 DOI: 10.1007/s10238-024-01311-5] [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: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 03/18/2024]
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. CRC liver metastases (CRLM) are often resistant to conventional treatments, with high rates of recurrence. Therefore, it is crucial to identify biomarkers for CRLM patients that predict cancer progression. This study utilised matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) in combination with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to spatially map the CRLM tumour proteome. CRLM tissue microarrays (TMAs) of 84 patients were analysed using tryptic peptide MALDI-MSI to spatially monitor peptide abundances across CRLM tissues. Abundance of peptides was compared between tumour vs stroma, male vs female and across three groups of patients based on overall survival (0-3 years, 4-6 years, and 7+ years). Peptides were then characterised and matched using LC-MS/MS. A total of 471 potential peptides were identified by MALDI-MSI. Our results show that two unidentified m/z values (1589.876 and 1092.727) had significantly higher intensities in tumours compared to stroma. Ten m/z values were identified to have correlation with biological sex. Survival analysis identified three peptides (Histone H4, Haemoglobin subunit alpha, and Inosine-5'-monophosphate dehydrogenase 2) and two unidentified m/z values (1305.840 and 1661.060) that were significantly higher in patients with shorter survival (0-3 years relative to 4-6 years and 7+ years). This is the first study using MALDI-MSI, combined with LC-MS/MS, on a large cohort of CRLM patients to identify the spatial proteome in this malignancy. Further, we identify several protein candidates that may be suitable for drug targeting or for future prognostic biomarker development.
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Affiliation(s)
- Celine Man Ying Li
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Matthew T Briggs
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | - Yea-Rin Lee
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | - Teresa Tin
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Clifford Young
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | - John Pierides
- SA Pathology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia
| | - Gurjeet Kaur
- Institute for Research in Molecular Medicine, University Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Paul Drew
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Guy J Maddern
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Peter Hoffmann
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | | | - Kevin Fenix
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia.
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia.
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2
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Marshall CR, Farrow MA, Djambazova KV, Spraggins JM. Untangling Alzheimer's disease with spatial multi-omics: a brief review. Front Aging Neurosci 2023; 15:1150512. [PMID: 37533766 PMCID: PMC10390637 DOI: 10.3389/fnagi.2023.1150512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/13/2023] [Indexed: 08/04/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.
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Affiliation(s)
- Cody R. Marshall
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Melissa A. Farrow
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Katerina V. Djambazova
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Jeffrey M. Spraggins
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
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3
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Lohani V, A.R A, Kundu S, Akhter MDQ, Bag S. Single-Cell Proteomics with Spatial Attributes: Tools and Techniques. ACS OMEGA 2023; 8:17499-17510. [PMID: 37251119 PMCID: PMC10210017 DOI: 10.1021/acsomega.3c00795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/12/2023] [Indexed: 05/31/2023]
Abstract
Now-a-days, the single-cell proteomics (SCP) concept is attracting interest, especially in clinical research, because it can identify the proteomic signature specific to diseased cells. This information is very essential when dealing with the progression of certain diseases, such as cancer, diabetes, Alzheimer's, etc. One of the major drawbacks of conventional destructive proteomics is that it gives an average idea about the protein expression profile in the disease condition. During the extraction of the protein from a biopsy or blood sample, proteins may come from both diseased cells and adjacent normal cells or any other cells from the disease environment. Again, SCP along with spatial attributes is utilized to learn about the heterogeneous function of a single protein. Before performing SCP, it is necessary to isolate single cells. This can be done by various techniques, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), microfluidics, manual cell picking/micromanipulation, etc. Among the different approaches for proteomics, mass spectrometry-based proteomics tools are widely used for their high resolution as well as sensitivity. This Review mainly focuses on the mass spectrometry-based approaches for the study of single-cell proteomics.
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Affiliation(s)
- Vartika Lohani
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
- PG Scholar, Department of Pharmacy, Banasthali
Vidyapith, Jaipur, Rajasthan 302001, India
| | - Akhiya A.R
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
- PG Scholar, Department of Computational
Biology and Bioinformatics, University of
Kerala, Thiruvananthapuram, Kerala 695034, India
| | - Soumen Kundu
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
- Academy
of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - MD Quasid Akhter
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
| | - Swarnendu Bag
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
- Academy
of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
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4
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Brožová K, Hantusch B, Kenner L, Kratochwill K. Spatial Proteomics for the Molecular Characterization of Breast Cancer. Proteomes 2023; 11:17. [PMID: 37218922 PMCID: PMC10204503 DOI: 10.3390/proteomes11020017] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/30/2023] [Accepted: 04/23/2023] [Indexed: 05/24/2023] Open
Abstract
Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.
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Affiliation(s)
- Klára Brožová
- Core Facility Proteomics, Medical University of Vienna, 1090 Vienna, Austria
- Department of Pathology, Medical University of Vienna, 1090 Vienna, Austria
- Division of Molecular and Structural Preclinical Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1210 Vienna, Austria
- Unit of Laboratory Animal Pathology, University of Veterinary Medicine, 1090 Vienna, Austria
| | - Brigitte Hantusch
- Department of Pathology, Medical University of Vienna, 1090 Vienna, Austria
| | - Lukas Kenner
- Department of Pathology, Medical University of Vienna, 1090 Vienna, Austria
- Unit of Laboratory Animal Pathology, University of Veterinary Medicine, 1090 Vienna, Austria
- CBmed GmbH—Center for Biomarker Research in Medicine, 8010 Graz, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Klaus Kratochwill
- Core Facility Proteomics, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, 1090 Vienna, Austria
- Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, 1090 Vienna, Austria
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5
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Applications of mass spectroscopy in understanding cancer proteomics. Proteomics 2023. [DOI: 10.1016/b978-0-323-95072-5.00007-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
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6
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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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Isberg OG, Giunchiglia V, McKenzie JS, Takats Z, Jonasson JG, Bodvarsdottir SK, Thorsteinsdottir M, Xiang Y. Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning. Metabolites 2022; 12:455. [PMID: 35629959 PMCID: PMC9143055 DOI: 10.3390/metabo12050455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/10/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.
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Affiliation(s)
- Olof Gerdur Isberg
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
- Faculty of Pharmaceutical Sciences, University of Iceland, Hofsvallagata 53, 107 Reykjavik, Iceland
- Biomedical Center, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland;
| | - Valentina Giunchiglia
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
| | - James S. McKenzie
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
| | - Jon Gunnlaugur Jonasson
- Department of Pathology, Landspitali the National University Hospital, Hringbraut, 101 Reykjavik, Iceland;
- Faculty of Medicine, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland
| | | | - Margret Thorsteinsdottir
- Faculty of Pharmaceutical Sciences, University of Iceland, Hofsvallagata 53, 107 Reykjavik, Iceland
- Biomedical Center, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland;
| | - Yuchen Xiang
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
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8
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Strnad Š, Strnadová V, Sýkora D, Cvačka J, Maletínská L, Vrkoslav V. MALDI Mass Spectrometry Imaging of Lipids on Free-Floating Brain Sections and Immunohistochemically Colocalized Markers of Neurodegeneration. Methods Mol Biol 2022; 2437:229-239. [PMID: 34902152 DOI: 10.1007/978-1-0716-2030-4_16] [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] [Indexed: 06/14/2023]
Abstract
In mass spectrometry imaging (MSI), the essential steps in sample preparation include collection and storage. The most widely used preservation procedure for MSI consists in freezing samples and storing them at temperatures below -80 °C. On the other hand, the most common method for preserving biological samples in clinical practice is their fixation in paraformaldehyde. The storage of free-floating sections is a particular type of the preservation of paraformaldehyde-fixed tissues that is used in immunohistochemistry. This chapter describes the approach of the multimodal imaging of free-floating brain sections using the MSI of lipids and the immunohistochemistry of neurodegeneration markers.
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Affiliation(s)
- Štěpán Strnad
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Veronika Strnadová
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - David Sýkora
- University of Chemistry and Technology Prague, Prague, Czech Republic
| | - Josef Cvačka
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Lenka Maletínská
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Vladimír Vrkoslav
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
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Buchholz R, Krossa S, Andersen MK, Holtkamp M, Sperling M, Karst U, Tessem MB. OUP accepted manuscript. Metallomics 2022; 14:6549564. [PMID: 35294013 PMCID: PMC8963325 DOI: 10.1093/mtomcs/mfac013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/17/2022] [Indexed: 11/29/2022]
Abstract
A rapid and cost-efficient tissue preparation protocol for laser ablation-inductively coupled plasma-mass spectrometry imaging (LA–ICP–MSI) has been developed within this study as an alternative to the current gold standard using fresh-frozen samples or other preparation techniques such as formalin fixation (FFix) and formalin-fixed paraffin-embedding (FFPE). Samples were vacuum dried at room temperature (RT) and stored in sealed vacuum containers for storage and shipping between collaborating parties. We compared our new protocol to established methods using prostate tissue sections investigating typical endogenous elements such as zinc, iron, and phosphorous with LA–ICP–MSI. The new protocol yielded comparable imaging results as fresh-frozen sections. FFPE sections were also tested due to the wide use and availability of FFPE tissue. However, the FFPE protocol and the FFix alone led to massive washout of the target elements on the sections tested in this work. Therefore, our new protocol presents an easy and rapid alternative for tissue preservation with comparable results to fresh-frozen sections for LA–ICP–MSI. It overcomes washout risks of commonly used tissue fixation techniques and does not require expensive and potentially unstable and time-critical shipping of frozen material on dry ice. Additionally, this protocol is likely applicable for several bioimaging approaches, as the dry condition may act comparable to other dehydrating fixatives, such as acetone or methanol, preventing degradation while avoiding washout effects.
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Affiliation(s)
| | | | - Maria K Andersen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Michael Holtkamp
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Michael Sperling
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
- European Virtual Institute for Speciation Analysis (EVISA), Münster, Germany
| | - Uwe Karst
- Correspondence: Institute of Inorganic and Analytical Chemistry, University of Münster, Corrensstr. 48, D-48149 Münster, Germany. E-mail: (Uwe Karst)
| | - May-Britt Tessem
- Correspondence: NTNU, Faculty of Medicine and Health Sciences, Department of Circulation and Medical Imaging, Postboks 8905, 7491 Trondheim, Norway. E-mail: (May-Britt Tessem)
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10
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Isberg OG, Xiang Y, Bodvarsdottir SK, Jonasson JG, Thorsteinsdottir M, Takats Z. The effect of sample age on the metabolic information extracted from formalin-fixed and paraffin embedded tissue samples using desorption electrospray ionization mass spectrometry imaging. J Mass Spectrom Adv Clin Lab 2021; 22:50-55. [PMID: 34939055 PMCID: PMC8662337 DOI: 10.1016/j.jmsacl.2021.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background: Metabolites, especially lipids, have been shown to be promising therapeutic targets. In conjugation with genes and proteins they can be used to identify phenotypes of disease and support the development of targeted treatments. The majority of clinically collected tissue samples are stored in formalin-fixed and paraffin embedded (FFPE) blocks due to their tissue conservation ability and indefinite storage capacity. For metabolic analysis, however, fresh frozen (FF) samples are currently preferred over FFPE samples due to concerns of metabolic information being lost when preparing the samples. With little or no sample preparation, desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) allows for the study of spatial as well as spectral information. Methods: DESI-MSI analysis was performed on FFPE breast cancer tissue microarray samples from 213 patients collected between the years 1935-2013. Logistic regression (LR) models were built to classify samples based on age and FF samples were used for feature validation. Results: LR models developed on the FFPE samples achieved an average classification accuracy of 96% when predicting their age with a 10-year grouping. Closer examination of the metabolic change over time revealed that the mean signal intensities for the lower mass range (100 - 500 m/z) linearly decrease over time, while the mean intensities for the higher mass range (500 - 900 m/z), remained relatively constant. Conclusions: In our samples, which span over 70 years, sample age has a weak yet quantifiable impact on metabolite content in FFPE samples, while the higher mass range is seemingly unaffected. FFPE samples thus provide an alternative avenue for metabolic analysis of lipids.
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Affiliation(s)
- Olof Gerdur Isberg
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, United Kingdom
- Faculty of Pharmaceutical Sciences, University of Iceland, Hofsvallagata 53, 107 Reykjavik, Iceland
- Biomedical Center, University of Iceland, Reykjavik, Iceland
| | - Yuchen Xiang
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, United Kingdom
| | | | - Jon Gunnlaugur Jonasson
- Pathology, Landspitali-National University Hospital, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Margret Thorsteinsdottir
- Faculty of Pharmaceutical Sciences, University of Iceland, Hofsvallagata 53, 107 Reykjavik, Iceland
- Biomedical Center, University of Iceland, Reykjavik, Iceland
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, United Kingdom
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11
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Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging. Cancers (Basel) 2021; 13:cancers13215388. [PMID: 34771551 PMCID: PMC8582378 DOI: 10.3390/cancers13215388] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/15/2021] [Accepted: 10/24/2021] [Indexed: 01/13/2023] Open
Abstract
Simple Summary Classic histopathological examination of tissues remains the mainstay for cancer diagnosis and staging. However, in some cases histopathologic analysis yields ambiguous results, leading to inconclusive disease classification. We set out to explore the diagnostic potential of mass spectrometry-based imaging for tumour classification based on proteomic fingerprints. Combining mass spectrometry with supervised machine learning, we were able to distinguish colorectal tumor from normal tissue with an overall accuracy of 98%. In addition, this approach was able to predict the presence of lymph node metastasis in primary tumour of endometrial cancer with an overall accuracy of 80%. These results highlight the potential of this technology to determine the optimal treatment for cancer patients to reduce morbidity and improve patients’ outcomes. Abstract Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.
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12
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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: 19] [Impact Index Per Article: 6.3] [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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Jain D, Torres R, Celli R, Koelmel J, Charkoftaki G, Vasiliou V. Evolution of the liver biopsy and its future. Transl Gastroenterol Hepatol 2021; 6:20. [PMID: 33824924 PMCID: PMC7829074 DOI: 10.21037/tgh.2020.04.01] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 03/19/2020] [Indexed: 12/12/2022] Open
Abstract
Liver biopsies are commonly used to evaluate a wide variety of medical disorders, including neoplasms and post-transplant complications. However, its use is being impacted by improved clinical diagnosis of disorders, and non-invasive methods for evaluating liver tissue and as a result the indications of a liver biopsy have undergone major changes in the last decade. The evolution of highly effective treatments for some of the common indications for liver biopsy in the last decade (e.g., viral hepatitis B and C) has led to a decline in the number of liver biopsies in recent years. At the same time, the emergence of better technologies for histologic evaluation, tissue content analysis and genomics are among the many new and exciting developments in the field that hold great promise for the future and are going to shape the indications for a liver biopsy in the future. Recent advances in slide scanners now allow creation of "digital/virtual" slides that have image of the entire tissue section present in a slide [whole slide imaging (WSI)]. WSI can now be done very rapidly and at very high resolution, allowing its use in routine clinical practice. In addition, a variety of technologies have been developed in recent years that use different light sources and/or microscopes allowing visualization of tissues in a completely different way. One such technique that is applicable to liver specimens combines multiphoton microscopy (MPM) with advanced clearing and fluorescent stains known as Clearing Histology with MultiPhoton Microscopy (CHiMP). Although it has not yet been extensively validated, the technique has the potential to decrease inefficiency, reduce artifacts, and increase data while being readily integrable into clinical workflows. Another technology that can provide rapid and in-depth characterization of thousands of molecules in a tissue sample, including liver tissues, is matrix assisted laser desorption/ionization (MALDI) mass spectrometry. MALDI has already been applied in a clinical research setting with promising diagnostic and prognostic capabilities, as well as being able to elucidate mechanisms of liver diseases that may be targeted for the development of new therapies. The logical next step in huge data sets obtained from such advanced analysis of liver tissues is the application of machine learning (ML) algorithms and application of artificial intelligence (AI), for automated generation of diagnoses and prognoses. This review discusses the evolving role of liver biopsies in clinical practice over the decades, and describes newer technologies that are likely to have a significant impact on how they will be used in the future.
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Affiliation(s)
- Dhanpat Jain
- Department of Anatomic Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Richard Torres
- Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Romulo Celli
- Department of Anatomic Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Jeremy Koelmel
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Georgia Charkoftaki
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
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14
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Guo L, Hu Z, Zhao C, Xu X, Wang S, Xu J, Dong J, Cai Z. Data Filtering and Its Prioritization in Pipelines for Spatial Segmentation of Mass Spectrometry Imaging. Anal Chem 2021; 93:4788-4793. [PMID: 33683863 DOI: 10.1021/acs.analchem.0c05242] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Mass spectrometry imaging (MSI) could provide vast amounts of data at the temporal-spatial scale in heterogeneous biological specimens, which challenges us to segment accurately suborgans/microregions from complex MSI data. Several pipelines had been proposed for MSI spatial segmentation in the past decade. More importantly, data filtering was found to be an efficient procedure to improve the outcomes of MSI segmentation pipelines. It is not clear, however, how the filtering procedure affects the MSI segmentation. An improved pipeline was established by elaborating the filtering prioritization and filtering algorithm. Lipidomic-characteristic-based MSI data of a whole-body mouse fetus was used to evaluate the established pipeline on localization of the physiological position of suborgans by comparing with three commonly used pipelines and commercial SCiLS Lab software. Two structural measurements were used to quantify the performances of the pipelines including the percentage of abnormal edge pixel (PAEP) and CHAOS. Our results demonstrated that the established pipeline outperformed the other pipelines in visual inspection, spatial consistence, time-cost, and robustness analysis. For example, the dorsal pallium (isocortex) and hippocampal formation (Hpf) regions, midbrain, cerebellum, and brainstem on the mouse brain were annotated and located by the established pipeline. As a generic pipeline, the established pipeline could help with the accurate assessment and screening of drug/chemical-induced targeted organs and exploration of the progression and molecular mechanisms of diseases. The filter-based strategy is expected to become a critical component in the standard operating procedure of MSI data sets.
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Affiliation(s)
- Lei Guo
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Zhenxing Hu
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Chao Zhao
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China.,Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiangnan Xu
- School of Mathematics and Statistics, The University of Sydney, Camperdown Sydney, NSW 2006, Australia
| | - Shujuan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing 102206, China
| | - Jingjing Xu
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Jiyang Dong
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
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15
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Manzo T, Prentice BM, Anderson KG, Raman A, Schalck A, Codreanu GS, Nava Lauson CB, Tiberti S, Raimondi A, Jones MA, Reyzer M, Bates BM, Spraggins JM, Patterson NH, McLean JA, Rai K, Tacchetti C, Tucci S, Wargo JA, Rodighiero S, Clise-Dwyer K, Sherrod SD, Kim M, Navin NE, Caprioli RM, Greenberg PD, Draetta G, Nezi L. Accumulation of long-chain fatty acids in the tumor microenvironment drives dysfunction in intrapancreatic CD8+ T cells. J Exp Med 2021; 217:151833. [PMID: 32491160 PMCID: PMC7398173 DOI: 10.1084/jem.20191920] [Citation(s) in RCA: 144] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 01/14/2020] [Accepted: 03/19/2020] [Indexed: 12/13/2022] Open
Abstract
CD8+ T cells are master effectors of antitumor immunity, and their presence at tumor sites correlates with favorable outcomes. However, metabolic constraints imposed by the tumor microenvironment (TME) can dampen their ability to control tumor progression. We describe lipid accumulation in the TME areas of pancreatic ductal adenocarcinoma (PDA) populated by CD8+ T cells infiltrating both murine and human tumors. In this lipid-rich but otherwise nutrient-poor TME, access to using lipid metabolism becomes particularly valuable for sustaining cell functions. Here, we found that intrapancreatic CD8+ T cells progressively accumulate specific long-chain fatty acids (LCFAs), which, rather than provide a fuel source, impair their mitochondrial function and trigger major transcriptional reprogramming of pathways involved in lipid metabolism, with the subsequent reduction of fatty acid catabolism. In particular, intrapancreatic CD8+ T cells specifically exhibit down-regulation of the very-long-chain acyl-CoA dehydrogenase (VLCAD) enzyme, which exacerbates accumulation of LCFAs and very-long-chain fatty acids (VLCFAs) that mediate lipotoxicity. Metabolic reprogramming of tumor-specific T cells through enforced expression of ACADVL enabled enhanced intratumoral T cell survival and persistence in an engineered mouse model of PDA, overcoming one of the major hurdles to immunotherapy for PDA.
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Affiliation(s)
- Teresa Manzo
- Department of Experimental Oncology, IRCCS European Institute of Oncology, Milano, Italy.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Boone M Prentice
- Department of Biochemistry, Mass Spectrometry Research Center, Department of Chemistry, Department of Pharmacology and Medicine, Vanderbilt University, Nashville, TN
| | - Kristin G Anderson
- Clinical Research Division and Program in Immunology, Fred Hutchinson Cancer Research Center, Seattle, WA.,Departments of Medicine/Oncology and Immunology, University of Washington School of Medicine, Seattle, WA
| | - Ayush Raman
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Aislyn Schalck
- Department of Genetics and Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Carina B Nava Lauson
- Department of Experimental Oncology, IRCCS European Institute of Oncology, Milano, Italy
| | - Silvia Tiberti
- Department of Experimental Oncology, IRCCS European Institute of Oncology, Milano, Italy
| | - Andrea Raimondi
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, San Raffaele Vita-Salute University, Milano, Italy
| | - Marissa A Jones
- Department of Biochemistry, Mass Spectrometry Research Center, Department of Chemistry, Department of Pharmacology and Medicine, Vanderbilt University, Nashville, TN
| | - Michelle Reyzer
- Department of Biochemistry, Mass Spectrometry Research Center, Department of Chemistry, Department of Pharmacology and Medicine, Vanderbilt University, Nashville, TN
| | - Breanna M Bates
- Clinical Research Division and Program in Immunology, Fred Hutchinson Cancer Research Center, Seattle, WA.,Departments of Medicine/Oncology and Immunology, University of Washington School of Medicine, Seattle, WA
| | - Jeffrey M Spraggins
- Department of Biochemistry, Mass Spectrometry Research Center, Department of Chemistry, Department of Pharmacology and Medicine, Vanderbilt University, Nashville, TN
| | - Nathan H Patterson
- Department of Biochemistry, Mass Spectrometry Research Center, Department of Chemistry, Department of Pharmacology and Medicine, Vanderbilt University, Nashville, TN
| | - John A McLean
- Center for Innovative Technology, Vanderbilt University, Nashville, TN
| | - Kunal Rai
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Carlo Tacchetti
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, San Raffaele Vita-Salute University, Milano, Italy
| | - Sara Tucci
- Laboratory of Clinical Biochemistry and Metabolism Center for Pediatrics and Adolescent Medicine, University of Freiburg, Freiburg, Germany
| | - Jennifer A Wargo
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Simona Rodighiero
- Department of Experimental Oncology, IRCCS European Institute of Oncology, Milano, Italy
| | - Karen Clise-Dwyer
- Department of Stem Cell Transplantation, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Stacy D Sherrod
- Center for Innovative Technology, Vanderbilt University, Nashville, TN
| | - Michael Kim
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Nicholas E Navin
- Department of Genetics and Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Richard M Caprioli
- Department of Biochemistry, Mass Spectrometry Research Center, Department of Chemistry, Department of Pharmacology and Medicine, Vanderbilt University, Nashville, TN
| | - Philip D Greenberg
- Clinical Research Division and Program in Immunology, Fred Hutchinson Cancer Research Center, Seattle, WA.,Departments of Medicine/Oncology and Immunology, University of Washington School of Medicine, Seattle, WA
| | - Giulio Draetta
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Luigi Nezi
- Department of Experimental Oncology, IRCCS European Institute of Oncology, Milano, Italy.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
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16
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Chakraborty J, Roy S, Ghosh S. Regulation of decellularized matrix mediated immune response. Biomater Sci 2020; 8:1194-1215. [PMID: 31930231 DOI: 10.1039/c9bm01780a] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The substantially growing gap between suitable donors and patients waiting for new organ transplantation has compelled tissue engineers to look for suitable patient-specific alternatives. Lately, a decellularized extracellular matrix (dECM), obtained primarily from either discarded human tissues/organs or other species, has shown great promise in the constrained availability of high-quality donor tissues. In this review, we have addressed critical gaps and often-ignored aspects of understanding the innate and adaptive immune response to the dECM. Firstly, although most of the studies claim preservation of the ECM ultrastructure, almost all methods employed for decellularization would inevitably cause a certain degree of disruption to the ECM ultrastructure and modulation in secondary conformations, which may elicit a distinct immunogenic response. Secondly, it is still a major challenge to find ways to conserve the native biochemical, structural and biomechanical cues by making a judicious decision regarding the choice of decellularization agents/techniques. We have critically analyzed various decellularization protocols and tried to find answers on various aspects such as whether the secondary structural conformation of dECM proteins would be preserved after decellularization. Thirdly, to keep the dECM ultrastructure as close to the native ECM we have raised the question "How good is good enough?" Even residual cellular antigens or nucleic acid fragments may elicit antigenicity leading to a low-grade immune response. A combinative knowledge of macrophage plasticity in the decellularized tissue and limits of decellularization will help achieve the native ultrastructure. Lastly, we have shifted our focus on the scientific basis of the presently accepted criteria for decellularization, and the effect on immune response concerning the interaction between the decellularized extracellular matrix and macrophages with the subsequent influence of T-cell activation. Amalgamating suitable decellularization approaches, sufficient knowledge of macrophage plasticity and elucidation of molecular pathways together will help fabricate functional immune informed decellularized tissues in vitro that will have substantial implications for efficient clinical translation and prediction for in vivo reprogramming and tissue regeneration.
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Affiliation(s)
- Juhi Chakraborty
- Regenerative Engineering Laboratory, Department of Textile & Fibre Engineering, Indian Institute of Technology Delhi, 110016 India.
| | - Subhadeep Roy
- Regenerative Engineering Laboratory, Department of Textile & Fibre Engineering, Indian Institute of Technology Delhi, 110016 India.
| | - Sourabh Ghosh
- Regenerative Engineering Laboratory, Department of Textile & Fibre Engineering, Indian Institute of Technology Delhi, 110016 India.
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17
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Klein O, Haeckel A, Reimer U, Nebrich G, Schellenberger E. Multiplex enzyme activity imaging by MALDI-IMS of substrate library conversions. Sci Rep 2020; 10:15522. [PMID: 32968143 PMCID: PMC7511933 DOI: 10.1038/s41598-020-72436-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 08/14/2020] [Indexed: 01/05/2023] Open
Abstract
Enzymes are fundamental to biological processes and involved in most pathologies. Here we demonstrate the concept of simultaneously mapping multiple enzyme activities (EA) by applying enzyme substrate libraries to tissue sections and analyzing their conversion by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). To that end, we spray-applied a solution of 20 naturally derived peptides that are known substrates for proteases, kinases, and phosphatases to zinc-fixed paraffin tissue sections of mouse kidneys. After enzyme conversion for 5 to 120 min at 37 °C and matrix application, the tissue sections were imaged by MALDI-IMS. We could image incubation time-dependently 16 of the applied substrates with differing signal intensities and 12 masses of expected products. Utilizing inherent enzyme amplification, EA-IMS can become a powerful tool to locally study multiple, potentially even lowly expressed, enzyme activities, networks, and their pharmaceutical modulation. Differences in the substrate detectability highlight the need for future optimizations.
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Affiliation(s)
- Oliver Klein
- Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Akvile Haeckel
- Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Ulf Reimer
- JPT Peptide Technologies GmbH, Volmerstraße 5, 12489, Berlin, Germany
| | - Grit Nebrich
- Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Eyk Schellenberger
- Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
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18
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Mass Spectrometry Imaging for Reliable and Fast Classification of Non-Small Cell Lung Cancer Subtypes. Cancers (Basel) 2020; 12:cancers12092704. [PMID: 32967325 PMCID: PMC7564257 DOI: 10.3390/cancers12092704] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 08/25/2020] [Accepted: 09/16/2020] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Diagnostic subtyping of non-small cell lung cancer is paramount for therapy stratification. Our study shows that the subtyping into pulmonary adenocarcinoma and pulmonary squamous cell carcinoma by mass spectrometry imaging is rapid and accurate using limited tissue material. Abstract Subtyping of non-small cell lung cancer (NSCLC) is paramount for therapy stratification. In this study, we analyzed the largest NSCLC cohort by mass spectrometry imaging (MSI) to date. We sought to test different classification algorithms and to validate results obtained in smaller patient cohorts. Tissue microarrays (TMAs) from including adenocarcinoma (ADC, n = 499) and squamous cell carcinoma (SqCC, n = 440), were analyzed. Linear discriminant analysis, support vector machine, and random forest (RF) were applied using samples randomly assigned for training (66%) and validation (33%). The m/z species most relevant for the classification were identified by on-tissue tandem mass spectrometry and validated by immunohistochemistry (IHC). Measurements from multiple TMAs were comparable using standardized protocols. RF yielded the best classification results. The classification accuracy decreased after including less than six of the most relevant m/z species. The sensitivity and specificity of MSI in the validation cohort were 92.9% and 89.3%, comparable to IHC. The most important protein for the discrimination of both tumors was cytokeratin 5. We investigated the largest NSCLC cohort by MSI to date and found that the classification of NSCLC into ADC and SqCC is possible with high accuracy using a limited set of m/z species.
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19
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Mass Spectrometry Imaging as a Potential Tool to Investigate Human Osteoarthritis at the Tissue Level. Int J Mol Sci 2020; 21:ijms21176414. [PMID: 32899238 PMCID: PMC7503948 DOI: 10.3390/ijms21176414] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 08/28/2020] [Accepted: 08/31/2020] [Indexed: 12/25/2022] Open
Abstract
Osteoarthritis (OA) is the most common degenerative joint disease, predicted to increase in incidence year by year due to an ageing population. Due to the biological complexity of the disease, OA remains highly heterogeneous. Although much work has been undertaken in the past few years, underlying molecular mechanisms leading to joint tissue structural deterioration are not fully understood, with only few validated markers for disease diagnosis and progression being available. Discovery and quantitation of various OA-specific biomarkers is still largely focused on the bodily fluids which does not appear to be reliable and sensitive enough. However, with the advancement of spatial proteomic techniques, several novel peptides and proteins, as well as N-glycans, can be identified and localised in a reliable and sensitive manner. To summarise the important findings from OA biomarker studies, papers published between 2000 and 2020 were searched via Google Scholar and PubMed. Medical subject heading (MeSH) terms ‘osteoarthritis’, ‘biomarker’, ‘synovial fluid’, ‘serum’, ‘urine’, ’matrix-assisted laser desorption/ionisation’, ‘mass spectrometry imaging’, ‘proteomic’, ‘glycomic’, ‘cartilage’, ‘synovium’ AND ‘subchondral bone’ were selectively used. The literature search was restricted to full-text original research articles and written only in English. Two main areas were reviewed for OA biomarker studies: (1) an overview of disease-specific markers detected from different types of OA bio-samples, and (2) an up-to-date summary of the tissue-specific OA studies that have utilised matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI). Overall, these OA biomarkers could provide clinicians with information for better the diagnosis, and prognosis of individual patients, and ultimately help facilitate the development of disease-modifying treatments.
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20
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Klein O, Fogt F, Hollerbach S, Nebrich G, Boskamp T, Wellmann A. Classification of Inflammatory Bowel Disease from Formalin-Fixed, Paraffin-Embedded Tissue Biopsies via Imaging Mass Spectrometry. Proteomics Clin Appl 2020; 14:e1900131. [PMID: 32691971 DOI: 10.1002/prca.201900131] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 05/25/2020] [Indexed: 01/09/2023]
Abstract
PURPOSE Discrimination between ulcerative colitis (UC) and Crohn's disease (CD) by histologic features alone can be challenging and often leads to inaccurate initial diagnoses in inflammatory bowel disease (IBD) patients. This is mostly due to an overlap of clinical and histologic features. However, exact diagnosis is not only important for patient treatment but it also has a socioeconomic impact. It is therefore important to develop and improve diagnostic tools complementing traditional histomorphological approaches. EXPERIMENTAL DESIGN In this retrospective proof-of-concept study, the utilization of MALDI imaging is explored in combination with multi-variate data analysis methods to classify formalin-fixed, paraffin-embedded (FFPE) colon biopsies from UC (87 biopsies, 14 patients), CD (71 biopsies, 14 patients), and normal colonic (21 biopsies, 14 patients) tissues. RESULTS The proposed method results in an overall balanced accuracy of 85.7% on patient and of 80.4% on sample level, thus demonstrating that the assessment of IBD from FFPE tissue specimens via MALDI imaging is feasible. CONCLUSIONS AND CLINICAL RELEVANCE The results emphasize the high potential of this method to distinguish IBD subtypes in FFPE tissue sections, which is a prerequisite for further investigations in retrospective multicenter studies, as well as for a future implementation into clinical routine.
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Affiliation(s)
- Oliver Klein
- Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany.,Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Franz Fogt
- Penn Presbyterian Medical Center, Hospital of the University of Pennsylvania, 51N 39th Street, Philadelphia, PA, 19104, USA
| | - Stephan Hollerbach
- Department of Gastroenterology, AKH Celle, Siemensplatz 4, 29223, Celle, Germany
| | - Grit Nebrich
- Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany.,Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Tobias Boskamp
- Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359, Bremen, Germany.,SCiLS, Bruker Daltonik GmbH, Fahrenheitstr. 4, 28359, Bremen, Germany
| | - Axel Wellmann
- Institute of Pathology, Wittinger Strasse 14, 29223, Celle, Germany
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21
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Leuschner J, Schmidt M, Fernsel P, Lachmund D, Boskamp T, Maass P. Supervised non-negative matrix factorization methods for MALDI imaging applications. Bioinformatics 2020; 35:1940-1947. [PMID: 30395171 PMCID: PMC6546133 DOI: 10.1093/bioinformatics/bty909] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 05/25/2018] [Accepted: 11/02/2018] [Indexed: 12/18/2022] Open
Abstract
Motivation Non-negative matrix factorization (NMF) is a common tool for obtaining low-rank approximations of non-negative data matrices and has been widely used in machine learning, e.g. for supporting feature extraction in high-dimensional classification tasks. In its classical form, NMF is an unsupervised method, i.e. the class labels of the training data are not used when computing the NMF. However, incorporating the classification labels into the NMF algorithms allows to specifically guide them toward the extraction of data patterns relevant for discriminating the respective classes. This approach is particularly suited for the analysis of mass spectrometry imaging (MSI) data in clinical applications, such as tumor typing and classification, which are among the most challenging tasks in pathology. Thus, we investigate algorithms for extracting tumor-specific spectral patterns from MSI data by NMF methods. Results In this article, we incorporate a priori class labels into the NMF cost functional by adding appropriate supervised penalty terms. Numerical experiments on a MALDI imaging dataset confirm that the novel supervised NMF methods lead to significantly better classification accuracy and stability as compared with other standard approaches. Availability and implementaton https://gitlab.informatik.uni-bremen.de/digipath/Supervised_NMF_Methods_for_MALDI.git Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Johannes Leuschner
- Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Maximilian Schmidt
- Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Pascal Fernsel
- Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Delf Lachmund
- Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Tobias Boskamp
- Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Peter Maass
- Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany
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22
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Strnad Š, PraŽienková V, Holubová M, Sýkora D, Cvačka J, Maletínská L, Železná B, Kuneš J, Vrkoslav V. Mass spectrometry imaging of free-floating brain sections detects pathological lipid distribution in a mouse model of Alzheimer's-like pathology. Analyst 2020; 145:4595-4605. [PMID: 32436545 DOI: 10.1039/d0an00592d] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Mass spectrometry imaging (MSI) is a modern analytical technique capable of monitoring the spatial distribution of compounds within target tissues. Collection and storage are important steps in sample preparation. The recommended and most widely used preservation procedure for MSI is freezing samples in isopentane and storing them at temperatures below -80 °C. On the other hand, the most common and general method for preserving biological samples in clinical practice is fixation in paraformaldehyde. Special types of samples prepared from these fixed tissues that are used for histology and immunohistochemistry are free-floating sections. It would be very beneficial if the latter procedure could also be applicable for the samples intended for subsequent MSI analysis. In the present work, we optimized and evaluated paraformaldehyde-fixed free-floating sections for the analysis of lipids in mouse brains and used the sections for the study of lipid changes in double transgenic APP/PS1 mice, a model of Alzheimer's-like pathology. Moreover, we examined the neuroprotective properties of palm11-PrRP31, an anorexigenic and glucose-lowering analog of prolactin-releasing peptide, and liraglutide, a type 2 diabetes drug. From the free-floating sections, we obtained lipid images without interference or delocalization, and we demonstrated that free-floating sections can be used for the MSI of lipids. In the APP/PS1 mice, we observed a changed distribution of various lipids compared to the controls. The most significant changes in lipids in the brains of APP/PS1 mice compared to wild-type controls were related to gangliosides (GM2 36:1, GM3 36:1) and phosphatidylinositols (PI 38:4, 36:4) in regions where the accumulation of senile plaques occurred. In APP/PS1 mice peripherally treated with palm11-PrRP31 or liraglutide for 2 months, we found that both peptides reduced the amount and space occupied by lipids, which were linked to the senile plaques. These results indicate that palm11-PrRP31 as well as liraglutide might be potentially useful in the treatment of neurodegenerative diseases.
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Affiliation(s)
- Štěpán Strnad
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, 166 10, Prague, Czech Republic.
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23
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Verbeeck N, Caprioli RM, Van de Plas R. Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. MASS SPECTROMETRY REVIEWS 2020; 39:245-291. [PMID: 31602691 PMCID: PMC7187435 DOI: 10.1002/mas.21602] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/27/2018] [Indexed: 05/20/2023]
Abstract
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.
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Affiliation(s)
- Nico Verbeeck
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Aspect Analytics NVGenkBelgium
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT)KU LeuvenLeuvenBelgium
| | - Richard M. Caprioli
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
- Department of ChemistryVanderbilt UniversityNashvilleTN
- Department of PharmacologyVanderbilt UniversityNashvilleTN
- Department of MedicineVanderbilt UniversityNashvilleTN
| | - Raf Van de Plas
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
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24
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Sample preparation of formalin-fixed paraffin-embedded tissue sections for MALDI-mass spectrometry imaging. Anal Bioanal Chem 2020; 412:1263-1275. [PMID: 31989198 PMCID: PMC7021751 DOI: 10.1007/s00216-019-02296-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 11/06/2019] [Accepted: 11/20/2019] [Indexed: 12/11/2022]
Abstract
Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MALDI MSI) has become a powerful tool with a high potential relevance for the analysis of biomolecules in tissue samples in the context of diseases like cancer and cardiovascular or cardiorenal diseases. In recent years, significant progress has been made in the technology of MALDI MSI. However, a more systematic optimization of sample preparation would likely achieve an increase in the molecular information derived from MALDI MSI. Therefore, we have employed a systematic approach to develop, establish and validate an optimized "standard operating protocol" (SOP) for sample preparation in MALDI MSI of formalin-fixed paraffin-embedded (FFPE) tissue sample analyses within this study. The optimized parameters regarding the impact on the resulting signal-to-noise (S/N) ratio were as follows: (i) trypsin concentration, solvents, deposition method, and incubation time; (ii) tissue washing procedures and drying processes; and (iii) spray flow rate, number of layers of trypsin deposition, and grid size. The protocol was evaluated on interday variability and its applicability for analyzing the mouse kidney, aorta, and heart FFPE tissue samples. In conclusion, an optimized SOP for MALDI MSI of FFPE tissue sections was developed to generate high sensitivity, to enhance spatial resolution and reproducibility, and to increase its applicability for various tissue types. This optimized SOP will further increase the molecular information content and intensify the use of MSI in future basic research and diagnostic applications. Graphical Abstract.
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25
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Bockmayr T, Erdmann G, Treue D, Jurmeister P, Schneider J, Arndt A, Heim D, Bockmayr M, Sachse C, Klauschen F. Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis. J Transl Med 2020; 100:1288-1299. [PMID: 32601356 PMCID: PMC7498367 DOI: 10.1038/s41374-020-0455-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 06/02/2020] [Accepted: 06/07/2020] [Indexed: 11/28/2022] Open
Abstract
Histomorphology and immunohistochemistry are the most common ways of cancer classification in routine cancer diagnostics, but often reach their limits in determining the organ origin in metastasis. These cancers of unknown primary, which are mostly adenocarcinomas or squamous cell carcinomas, therefore require more sophisticated methodologies of classification. Here, we report a multiplex protein profiling-based approach for the classification of fresh frozen and formalin-fixed paraffin-embedded (FFPE) cancer tissue samples using the digital western blot technique DigiWest. A DigiWest-compatible FFPE extraction protocol was developed, and a total of 634 antibodies were tested in an initial set of 16 FFPE samples covering tumors from different origins. Of the 303 detected antibodies, 102 yielded significant correlation of signals in 25 pairs of fresh frozen and FFPE primary tumor samples, including head and neck squamous cell carcinomas (HNSC), lung squamous cell carcinomas (LUSC), lung adenocarcinomas (LUAD), colorectal adenocarcinomas (COAD), and pancreatic adenocarcinomas (PAAD). For this signature of 102 analytes (covering 88 total proteins and 14 phosphoproteins), a support vector machine (SVM) algorithm was developed. This allowed for the classification of the tissue of origin for all five tumor types studied here with high overall accuracies in both fresh frozen (90.4%) and FFPE (77.6%) samples. In addition, the SVM classifier reached an overall accuracy of 88% in an independent validation cohort of 25 FFPE tumor samples. Our results indicate that DigiWest-based protein profiling represents a valuable method for cancer classification, yielding conclusive and decisive data not only from fresh frozen specimens but also FFPE samples, thus making this approach attractive for routine clinical applications.
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Affiliation(s)
- Teresa Bockmayr
- grid.7468.d0000 0001 2248 7639Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | | | - Denise Treue
- grid.7468.d0000 0001 2248 7639Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany ,Central Biobank Charité (ZeBanC), Berlin, Germany
| | - Philipp Jurmeister
- grid.7468.d0000 0001 2248 7639Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - Daniel Heim
- grid.7468.d0000 0001 2248 7639Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Michael Bockmayr
- grid.7468.d0000 0001 2248 7639Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany ,grid.13648.380000 0001 2180 3484Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany ,grid.470174.1Research Institute Children’s Cancer Center Hamburg, Hamburg, Germany
| | | | - Frederick Klauschen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany. .,German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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26
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Judd AM, Gutierrez DB, Moore JL, Patterson NH, Yang J, Romer CE, Norris JL, Caprioli RM. A recommended and verified procedure for in situ tryptic digestion of formalin-fixed paraffin-embedded tissues for analysis by matrix-assisted laser desorption/ionization imaging mass spectrometry. JOURNAL OF MASS SPECTROMETRY : JMS 2019; 54:716-727. [PMID: 31254303 PMCID: PMC6711785 DOI: 10.1002/jms.4384] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/20/2019] [Accepted: 06/20/2019] [Indexed: 05/06/2023]
Abstract
Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a molecular imaging technology uniquely capable of untargeted measurement of proteins, lipids, and metabolites while retaining spatial information about their location in situ. This powerful combination of capabilities has the potential to bring a wealth of knowledge to the field of molecular histology. Translation of this innovative research tool into clinical laboratories requires the development of reliable sample preparation protocols for the analysis of proteins from formalin-fixed paraffin-embedded (FFPE) tissues, the standard preservation process in clinical pathology. Although ideal for stained tissue analysis by microscopy, the FFPE process cross-links, disrupts, or can remove proteins from the tissue, making analysis of the protein content challenging. To date, reported approaches differ widely in process and efficacy. This tutorial presents a strategy derived from systematic testing and optimization of key parameters, for reproducible in situ tryptic digestion of proteins in FFPE tissue and subsequent MALDI IMS analysis. The approach describes a generalized method for FFPE tissues originating from virtually any source.
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Affiliation(s)
- Audra M. Judd
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
- Correspondence: Dr. Richard M. Caprioli, 9160 MRB III, Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA, Phone: (615) 322-4336, Fax: (615) 343-8372,
| | - Danielle B. Gutierrez
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
- Correspondence: Dr. Richard M. Caprioli, 9160 MRB III, Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA, Phone: (615) 322-4336, Fax: (615) 343-8372,
| | - Jessica L. Moore
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
| | - Junhai Yang
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
| | - Carrie E. Romer
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
| | - Jeremy L. Norris
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
- Departments of Chemistry, Vanderbilt University, Nashville TN, 37235
| | - Richard M. Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
- Departments of Chemistry, Vanderbilt University, Nashville TN, 37235
- Departments of Pharmacology, Vanderbilt University, Nashville TN, 37235
- Departments of Medicine, Vanderbilt University, Nashville TN, 37235
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27
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Ly A, Longuespée R, Casadonte R, Wandernoth P, Schwamborn K, Bollwein C, Marsching C, Kriegsmann K, Hopf C, Weichert W, Kriegsmann J, Schirmacher P, Kriegsmann M, Deininger SO. Site-to-Site Reproducibility and Spatial Resolution in MALDI-MSI of Peptides from Formalin-Fixed Paraffin-Embedded Samples. Proteomics Clin Appl 2019; 13:e1800029. [PMID: 30408343 PMCID: PMC6590241 DOI: 10.1002/prca.201800029] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 10/23/2018] [Indexed: 12/25/2022]
Abstract
Purpose To facilitate the transition of MALDI–MS Imaging (MALDI–MSI) from basic science to clinical application, it is necessary to analyze formalin‐fixed paraffin‐embedded (FFPE) tissues. The aim is to improve in situ tryptic digestion for MALDI–MSI of FFPE samples and determine if similar results would be reproducible if obtained from different sites. Experimental Design FFPE tissues (mouse intestine, human ovarian teratoma, tissue microarray of tumor entities sampled from three different sites) are prepared for MALDI–MSI. Samples are coated with trypsin using an automated sprayer then incubated using deliquescence to maintain a stable humid environment. After digestion, samples are sprayed with CHCA using the same spraying device and analyzed with a rapifleX MALDI Tissuetyper at 50 µm spatial resolution. Data are analyzed using flexImaging, SCiLS, and R. Results Trypsin application and digestion are identified as sources of variation and loss of spatial resolution in the MALDI–MSI of FFPE samples. Using the described workflow, it is possible to discriminate discrete histological features in different tissues and enabled different sites to generate images of similar quality when assessed by spatial segmentation and PCA. Conclusions and Clinical Relevance Spatial resolution and site‐to‐site reproducibility can be maintained by adhering to a standardized MALDI–MSI workflow.
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Affiliation(s)
- Alice Ly
- Bruker Daltonik GmbH, Bremen, Germany
| | - Rémi Longuespée
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | | | | | | | | | - Christian Marsching
- Center for Biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany
| | - Carsten Hopf
- Center for Biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Wilko Weichert
- Institute of Pathology, Technical University of Munich, Munich, Germany
| | | | - Peter Schirmacher
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
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28
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Cordero Hernandez Y, Boskamp T, Casadonte R, Hauberg‐Lotte L, Oetjen J, Lachmund D, Peter A, Trede D, Kriegsmann K, Kriegsmann M, Kriegsmann J, Maass P. Targeted Feature Extraction in MALDI Mass Spectrometry Imaging to Discriminate Proteomic Profiles of Breast and Ovarian Cancer. Proteomics Clin Appl 2018; 13:e1700168. [DOI: 10.1002/prca.201700168] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 11/29/2018] [Indexed: 01/10/2023]
Affiliation(s)
| | - Tobias Boskamp
- Center for Industrial MathematicsUniversity of Bremen Bremen 28359 Germany
- Department for Cell BiologyUniversity of Bremen Bremen 28359 Germany
| | | | - Lena Hauberg‐Lotte
- Center for Industrial MathematicsUniversity of Bremen Bremen 28359 Germany
| | - Janina Oetjen
- Center for Industrial MathematicsUniversity of Bremen Bremen 28359 Germany
| | - Delf Lachmund
- Center for Industrial MathematicsUniversity of Bremen Bremen 28359 Germany
| | - Annette Peter
- Department for Cell BiologyUniversity of Bremen Bremen 28359 Germany
| | - Dennis Trede
- SCiLS, Bruker Daltonik GmbH Bremen 28359 Germany
| | - Katharina Kriegsmann
- Department of Hematology Oncology and RheumatologyUniversity of Heidelberg Heidelberg 69120 Germany
| | - Mark Kriegsmann
- Institute of PathologyUniversity of Heidelberg Heidelberg 69120 Germany
| | - Jörg Kriegsmann
- Proteopath GmbH Trier 54296 Germany
- Center for Histology Cytology and Molecular Diagnostic Trier 54296 Germany
| | - Peter Maass
- Center for Industrial MathematicsUniversity of Bremen Bremen 28359 Germany
- SCiLS, Bruker Daltonik GmbH Bremen 28359 Germany
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29
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Casadonte R, Kriegsmann M, Perren A, Baretton G, Deininger S, Kriegsmann K, Welsch T, Pilarsky C, Kriegsmann J. Development of a Class Prediction Model to Discriminate Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor by MALDI Mass Spectrometry Imaging. Proteomics Clin Appl 2018; 13:e1800046. [DOI: 10.1002/prca.201800046] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/05/2018] [Indexed: 12/13/2022]
Affiliation(s)
| | - Mark Kriegsmann
- Institute of PathologyUniversity of Heidelberg Heidelberg 69120 Germany
| | - Aurel Perren
- Institute of PathologyUniversity of Bern Bern 3012 Switzerland
| | - Gustavo Baretton
- Institute of PathologyUniversity Hospital Carl Gustav Carus at the Technical University of Dresden Dresden 01307 Germany
| | | | - Katharina Kriegsmann
- Department of HematologyOncology and RheumatologyUniversity of Heidelberg Heidelberg 69120 Germany
| | - Thilo Welsch
- Institute of PathologyUniversity Hospital Carl Gustav Carus at the Technical University of Dresden Dresden 01307 Germany
| | - Christian Pilarsky
- Institute of PathologyUniversity Hospital Carl Gustav Carus at the Technical University of Dresden Dresden 01307 Germany
| | - Jörg Kriegsmann
- Proteopath GmbH Trier 54296 Germany
- MVZ for HistologyCytology and Molecular Diagnostics Trier 54296 Germany
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30
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Behrmann J, Etmann C, Boskamp T, Casadonte R, Kriegsmann J, Maaß P. Deep learning for tumor classification in imaging mass spectrometry. Bioinformatics 2018; 34:1215-1223. [PMID: 29126286 DOI: 10.1093/bioinformatics/btx724] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 11/07/2017] [Indexed: 11/14/2022] Open
Abstract
Motivation Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Results Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. Availability and implementation https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. Contact jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jens Behrmann
- Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
| | - Christian Etmann
- Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
| | - Tobias Boskamp
- Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
- SCiLS, 28359 Bremen, Germany
| | | | - Jörg Kriegsmann
- Proteopath GmbH, 54296 Trier, Germany
- Center for Histology, Cytology and Molecular Diagnosis, 54296 Trier, Germany
| | - Peter Maaß
- Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
- SCiLS, 28359 Bremen, Germany
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31
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Angel PM, Schwamborn K, Comte-Walters S, Clift CL, Ball LE, Mehta AS, Drake RR. Extracellular Matrix Imaging of Breast Tissue Pathologies by MALDI-Imaging Mass Spectrometry. Proteomics Clin Appl 2018; 13:e1700152. [PMID: 30251340 DOI: 10.1002/prca.201700152] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 08/31/2018] [Indexed: 12/28/2022]
Abstract
PURPOSE A new method accessing proteins from extracellular matrix by imaging mass spectrometry (ECM IMS) has been recently reported. ECM IMS is evaluated for use in exploring breast tissue pathologies. EXPERIMENTAL DESIGN A tissue microarray (TMA) is analyzed that has 176 cores of biopsies and lumpectomies spanning breast pathologies of inflammation, hyperplasia, fibroadenoma, invasive ductal carcinoma, and invasive lobular carcinoma and normal adjacent to tumor (NAT). NAT is compared to subtypes by area under the receiver operating curve (ROC) >0.7. A lumpectomy is also characterized for collagen organization by microscopy and stromal protein distribution by IMS. LC-based high-resolution accurate mass (HRAM) proteomics is used to identify proteins from the lumpectomy. RESULTS TMA analysis shows distinct spectral signatures reflecting a heterogeneous tissue microenvironment. Ninety-four peaks show an ROC > 0.7 compared to NAT; NAT has overall higher intensities. Lumpectomy analysis by IMS visualizes a complex central tumor region with distal tumor regions. A total of 39 stromal proteins are identified by HRAM LC-based proteomics. Accurate mass matches between image data and LC-based proteomics demonstrate a heterogeneous collagen type environment in the central tumor. CONCLUSIONS Data portray the heterogeneous stromal microenvironment of breast pathologies, including alteration of multiple collagen-type patterns. ECM IMS is a promising new tool for investigating the stromal microenvironment of breast tissue including cancer.
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Affiliation(s)
- Peggi M Angel
- Department of Cell and Molecular Pharmacology, MUSC Proteomics Center, Medical University of South Carolina, Charleston, SC, 29425
| | | | - Susana Comte-Walters
- Department of Cell and Molecular Pharmacology, MUSC Proteomics Center, Medical University of South Carolina, Charleston, SC, 29425
| | - Cassandra L Clift
- Department of Cell and Molecular Pharmacology, MUSC Proteomics Center, Medical University of South Carolina, Charleston, SC, 29425
| | - Lauren E Ball
- Department of Cell and Molecular Pharmacology, MUSC Proteomics Center, Medical University of South Carolina, Charleston, SC, 29425
| | - Anand S Mehta
- Department of Cell and Molecular Pharmacology, MUSC Proteomics Center, Medical University of South Carolina, Charleston, SC, 29425
| | - Richard R Drake
- Department of Cell and Molecular Pharmacology, MUSC Proteomics Center, Medical University of South Carolina, Charleston, SC, 29425
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32
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Kriegsmann J, Kriegsmann M, Kriegsmann K, Longuespée R, Deininger SO, Casadonte R. MALDI Imaging for Proteomic Painting of Heterogeneous Tissue Structures. Proteomics Clin Appl 2018; 13:e1800045. [PMID: 30471204 DOI: 10.1002/prca.201800045] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 11/07/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE To present matrix-assisted laser desorption/ionization (MALDI) imaging as a powerful method to highlight various tissue compartments. EXPERIMENTAL DESIGN Formalin-fixed paraffin-embedded (FFPE) tissue of a uterine cervix, a pancreas, a duodenum, a teratoma, and a breast cancer tissue microarray (TMA) are analyzed by MALDI imaging and by immunohistochemistry (IHC). Peptide images are visualized and analyzed using FlexImaging and SCiLS Lab software. Different histological compartments are compared by hierarchical cluster analysis. RESULTS MALDI imaging highlights tissue compartments comparable to IHC. In cervical tissue, normal epithelium can be discerned from intraepithelial neoplasia. In pancreatic and duodenal tissues, m/z signals from lymph follicles, vessels, duodenal mucosa, normal pancreas, and smooth muscle structures can be visualized. In teratoma, specific m/z signals to discriminate squamous epithelium, sebaceous glands, and soft tissue are detected. Additionally, tumor tissue can be discerned from the surrounding stroma in small tissue cores of TMAs. Proteomic data acquisition of complex tissue compartments in FFPE tissue requires less than 1 h with recent mass spectrometers. CONCLUSION AND CLINICAL RELEVANCE The simultaneous characterization of morphological and proteomic features in the same tissue section adds proteomic information for histopathological diagnostics, which relies at present on conventional hematoxylin and eosin staining, histochemical, IHC and molecular methods.
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Affiliation(s)
- Jörg Kriegsmann
- Proteopath GmbH, Trier 54296, Germany.,MVZ for Histology, Cytology and Molecular Diagnostics, Trier 54296, Germany
| | - Mark Kriegsmann
- Institute of Pathology, Heidelberg University, Heidelberg 69120, Germany
| | - Katharina Kriegsmann
- Department of Hematology, Oncology, and Rheumatology, Heidelberg University, Heidelberg 69120, Germany
| | - Rémi Longuespée
- Institute of Pathology, Heidelberg University, Heidelberg 69120, Germany
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33
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Longuespée R, Kriegsmann K, Cremer M, Zgorzelski C, Casadonte R, Kazdal D, Kriegsmann J, Weichert W, Schwamborn K, Fresnais M, Schirmacher P, Kriegsmann M. In MALDI-Mass Spectrometry Imaging on Formalin-Fixed Paraffin-Embedded Tissue Specimen Section Thickness Significantly Influences m/z Peak Intensity. Proteomics Clin Appl 2018; 13:e1800074. [PMID: 30216687 DOI: 10.1002/prca.201800074] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 08/03/2018] [Indexed: 12/18/2022]
Abstract
BACKGROUND In matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) standardized sample preparation is important to obtain reliable results. Herein, the impact of section thickness in formalin-fixed paraffin embedded (FFPE) tissue microarrays (TMA) on spectral intensities is investigated. PATIENTS AND METHODS TMAs consisting of ten different tissues represented by duplicates of ten patients (n = 200 cores) are cut at 1, 3, and 5 μm. MSI analysis is performed and mean intensities of all evaluable cores are extracted. Measurements are merged and mean m/z intensities are compared. RESULTS Visual inspection of spectral intensities between 1, 3, and 5 μm reveals generally higher intensities in thinner tissue sections. Specifically, higher intensities are observed in the vast majority of peaks (98.6%, p < 0.01) in 1 μm compared with 5 μm sections. Note that 28.4% and 2.1% of m/z values exhibit a at least two- and threefold intensity difference (p < 0.01) in 1 μm compared to 5 μm sections, respectively. CONCLUSION A section thickness of 1 μm results in higher spectral intensities compared with 5 μm. The results highlight the importance of standardized protocols in light of recent efforts to identify clinically relevant biomarkers using MSI. The use of TMAs for comparative analysis seems advantageous, as section thickness displays less variability.
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Affiliation(s)
- Rémi Longuespée
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
| | - Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
| | - Martin Cremer
- Department of Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
| | | | | | - Daniel Kazdal
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
| | - Jörg Kriegsmann
- Proteopath Trier, Trier, Germany.,Institute of Molecular Pathology Trier, Trier, Germany
| | | | | | - Margaux Fresnais
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany.,German Cancer Consortium (DKTK)German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Schirmacher
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
| | - Mark Kriegsmann
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
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Kriegsmann K, Longuespée R, Hundemer M, Zgorzelski C, Casadonte R, Schwamborn K, Weichert W, Schirmacher P, Harms A, Kazdal D, Leichsenring J, Stenzinger A, Warth A, Fresnais M, Kriegsmann J, Kriegsmann M. Combined Immunohistochemistry after Mass Spectrometry Imaging for Superior Spatial Information. Proteomics Clin Appl 2018; 13:e1800035. [PMID: 30035857 DOI: 10.1002/prca.201800035] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 07/04/2018] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Tissue slides analyzed by MS imaging (MSI) are stained by H&E (Haematoxylin and Eosin) to identify regions of interest. As it can be difficult to identify specific cells of interest by H&E alone, data analysis may be impaired. Immunohistochemistry (IHC) can highlight cells of interest but single or combined IHC on tissue sections analyzed by MSI have not been performed. METHODS We performed MSI on bone marrow biopsies from patients with multiple myeloma and stained different antibodies (CD38, CD138, MUM1, kappa- and lambda). A combination of CK5/6/TTF1 and Napsin-A/p40 is stained after MSI on adenocarcinoma and squamous cell carcinoma of the lung. Staining intensities of p40 after MSI and on a serial section are quantified on a tissue microarray (n = 44) by digital analysis. RESULTS Digital evaluation reveals weaker staining intensities after MSI as compared to serial sections. Staining quality and quantity after MSI enables to identify cells of interest. On the tissue microarray, one out of 44 tissue specimens shows no staining of p40 after MSI, but weak nuclear staining on a serial section. CONCLUSION We demonstrated that single and double IHC staining is feasible on tissue sections previously analyzed by MSI, with decreased staining intensities.
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Affiliation(s)
- Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, University of Heidelberg, 69117, Heidelberg, Germany
| | - Rémi Longuespée
- Institute of Pathology, University of Heidelberg, 69117 Heidelberg, Germany
| | - Michael Hundemer
- Department of Hematology, Oncology and Rheumatology, University of Heidelberg, 69117, Heidelberg, Germany
| | | | | | | | - Wilko Weichert
- Institute of Pathology, TU Munich, 80333 Munich, Germany
| | - Peter Schirmacher
- Institute of Pathology, University of Heidelberg, 69117 Heidelberg, Germany
| | - Alexander Harms
- Institute of Pathology, University of Heidelberg, 69117 Heidelberg, Germany
| | - Daniel Kazdal
- Institute of Pathology, University of Heidelberg, 69117 Heidelberg, Germany
| | - Jonas Leichsenring
- Institute of Pathology, University of Heidelberg, 69117 Heidelberg, Germany
| | | | - Arne Warth
- Institute of Pathology, University of Heidelberg, 69117 Heidelberg, Germany
| | - Margaux Fresnais
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, 69120 Heidelberg, Germany.,German Cancer Consortium (DKTK)-German Cancer Research Center (DKFZ), 69117 Heidelberg, Germany
| | | | - Mark Kriegsmann
- Institute of Pathology, University of Heidelberg, 69117 Heidelberg, Germany
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Kriegsmann J, Casadonte R, Kriegsmann K, Longuespée R, Kriegsmann M. Mass spectrometry in pathology - Vision for a future workflow. Pathol Res Pract 2018; 214:1057-1063. [PMID: 29910062 DOI: 10.1016/j.prp.2018.05.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2017] [Revised: 04/23/2018] [Accepted: 05/11/2018] [Indexed: 02/09/2023]
Abstract
Mass spectrometric (MS) techniques are applied in various areas of medical diagnostics. For the detection of microbiological germs and genetic mutations, MS is a method used in routine. Since MS also allows the analysis of proteins and peptides, it seems an ideal candidate to supplement histopatholological diagnostics. Matrix-assisted laser desorption/ionization time-of-flight Imaging MS links molecular analysis of numerous analytes with morphological information about their spatial distribution in cells or tissues. Herein, we review principle MS techniques as well as potential applications in pathology and discuss our vision for a future workflow.
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Affiliation(s)
- Jörg Kriegsmann
- MVZ for Histology, Cytology and Molecular Diagnostics Trier, Trier, Germany; Proteopath GmbH, Trier, Germany
| | | | - Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany
| | - Rémi Longuespée
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
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Rabe JH, A Sammour D, Schulz S, Munteanu B, Ott M, Ochs K, Hohenberger P, Marx A, Platten M, Opitz CA, Ory DS, Hopf C. Fourier Transform Infrared Microscopy Enables Guidance of Automated Mass Spectrometry Imaging to Predefined Tissue Morphologies. Sci Rep 2018; 8:313. [PMID: 29321555 PMCID: PMC5762902 DOI: 10.1038/s41598-017-18477-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 12/12/2017] [Indexed: 12/27/2022] Open
Abstract
Multimodal imaging combines complementary platforms for spatially resolved tissue analysis that are poised for application in life science and personalized medicine. Unlike established clinical in vivo multimodality imaging, automated workflows for in-depth multimodal molecular ex vivo tissue analysis that combine the speed and ease of spectroscopic imaging with molecular details provided by mass spectrometry imaging (MSI) are lagging behind. Here, we present an integrated approach that utilizes non-destructive Fourier transform infrared (FTIR) microscopy and matrix assisted laser desorption/ionization (MALDI) MSI for analysing single-slide tissue specimen. We show that FTIR microscopy can automatically guide high-resolution MSI data acquisition and interpretation without requiring prior histopathological tissue annotation, thus circumventing potential human-annotation-bias while achieving >90% reductions of data load and acquisition time. We apply FTIR imaging as an upstream modality to improve accuracy of tissue-morphology detection and to retrieve diagnostic molecular signatures in an automated, unbiased and spatially aware manner. We show the general applicability of multimodal FTIR-guided MALDI-MSI by demonstrating precise tumor localization in mouse brain bearing glioma xenografts and in human primary gastrointestinal stromal tumors. Finally, the presented multimodal tissue analysis method allows for morphology-sensitive lipid signature retrieval from brains of mice suffering from lipidosis caused by Niemann-Pick type C disease.
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Affiliation(s)
- Jan-Hinrich Rabe
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Denis A Sammour
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Sandra Schulz
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Bogdan Munteanu
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Martina Ott
- German Cancer Consortium (DKTK) CCU Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Katharina Ochs
- German Cancer Consortium (DKTK) CCU Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology and National Center of Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Peter Hohenberger
- University Medical Center Mannheim of Heidelberg University, Mannheim, Germany
| | - Alexander Marx
- University Medical Center Mannheim of Heidelberg University, Mannheim, Germany
| | - Michael Platten
- German Cancer Consortium (DKTK) CCU Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Center Mannheim of Heidelberg University, Mannheim, Germany
| | - Christiane A Opitz
- Brain Cancer Metabolism Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology and National Center of Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Daniel S Ory
- Diabetic Cardiovascular Disease Center and Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Carsten Hopf
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany.
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany.
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Angel PM, Comte-Walters S, Ball LE, Talbot K, Mehta A, Brockbank KGM, Drake RR. Mapping Extracellular Matrix Proteins in Formalin-Fixed, Paraffin-Embedded Tissues by MALDI Imaging Mass Spectrometry. J Proteome Res 2017; 17:635-646. [PMID: 29161047 DOI: 10.1021/acs.jproteome.7b00713] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Collagens and elastin form the fundamental framework of all tissues and organs, and their expression and post-translational processing are tightly regulated in disease and health. Because of their unique structural composition and properties, it is a recognized challenge to access these protein structures within the complex tissue microenvironment to understand how localized changes modulate tissue health. We describe a new workflow using a combination of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) with matrix metalloproteinase (MMP) enzymes to access and report on spatial localization of collagen and elastin sequences in formalin-fixed, paraffin-embedded (FFPE) tissues. The developed technology provides new access to collagens and elastin sequences localized to tissue features that were previously unattainable. This high-throughput technological advance should be applicable to any tissue regardless of disease type, tissue origin, or disease status and is thus relevant to all research: basic, translational, or clinical.
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Affiliation(s)
| | | | | | | | | | - Kelvin G M Brockbank
- Tissue Testing Technologies LLC , North Charleston, South Carolina 29406, United States.,Department of Bioengineering, Clemson University , Clemson, South Carolina 29634, United States
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Guran R, Vanickova L, Horak V, Krizkova S, Michalek P, Heger Z, Zitka O, Adam V. MALDI MSI of MeLiM melanoma: Searching for differences in protein profiles. PLoS One 2017; 12:e0189305. [PMID: 29220390 PMCID: PMC5722329 DOI: 10.1371/journal.pone.0189305] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 11/23/2017] [Indexed: 12/21/2022] Open
Abstract
Background Treatment of advanced cutaneous melanoma remains challenging, and new data on melanoma biology are required. The most widely accepted criteria for the prognostic evaluation of melanoma are histopathological and clinical parameters, and the identification of additional tumor markers is thus of paramount importance. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI), an important tool in cancer research, is useful for unraveling the molecular profile of melanoma. Methodology/Principal findings In this report, we used the melanoma-bearing Libechov minipig (MeLiM), a unique animal model that allows observation of the complete spontaneous regression of invasive cutaneous melanoma, to investigate i) the differences between melanoma and healthy skin protein profiles and ii) the proteins potentially involved in spontaneous regression. The MeLiM tissues were cryosected, histologically characterized, analyzed by MALDI MSI, and immunohistologically stained. Multivariate statistical analyses of the MALDI MSI data revealed ten relevant m/z ions, of which the expression levels varied significantly among the studied MeLiM tissues. These ion peaks were used to create mass ion images/maps and visualize the differences between tumor and healthy skin specimens, as well as among histologically characterized tissue regions. Conclusions/Significance Protein profiles comprising ten statistically significant mass ion peaks useful for differentiating cutaneous melanoma and healthy skin tissues were determined. Peaks at m/z 3044, 6011, 6140 and 10180 were overexpressed in melanoma compared with healthy skin tissue. More specifically, m/z 6140 was expressed at significantly (p < 0.05) higher levels in normally growing melanoma regions than in regions with early and late spontaneous regression. This study demonstrates the clinical utility of MALDI MSI for the analysis of tissue cryosections at a molecular level.
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Affiliation(s)
- Roman Guran
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Lucie Vanickova
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
| | - Vratislav Horak
- Institute of Animal Physiology and Genetics, Academy of Sciences of the Czech Republic, v.v.i., Libechov, Czech Republic
| | - Sona Krizkova
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Petr Michalek
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Ondrej Zitka
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
- * E-mail:
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Recent advances in sample pre-treatment for emerging methods in proteomic analysis. Talanta 2017; 174:738-751. [DOI: 10.1016/j.talanta.2017.06.056] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Revised: 06/14/2017] [Accepted: 06/19/2017] [Indexed: 12/21/2022]
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40
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Alberts D, Pottier C, Smargiasso N, Baiwir D, Mazzucchelli G, Delvenne P, Kriegsmann M, Kazdal D, Warth A, De Pauw E, Longuespée R. MALDI Imaging-Guided Microproteomic Analyses of Heterogeneous Breast Tumors-A Pilot Study. Proteomics Clin Appl 2017; 12. [DOI: 10.1002/prca.201700062] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 07/05/2017] [Indexed: 11/06/2022]
Affiliation(s)
- Deborah Alberts
- Laboratory of Mass Spectrometry (LSM) - MolSys; Department of Chemistry; University of Liège; Liege Belgium
| | - Charles Pottier
- Department of Pathology; GIGA Cancer; University of Liège Hospital; Liège Belgium
| | - Nicolas Smargiasso
- Laboratory of Mass Spectrometry (LSM) - MolSys; Department of Chemistry; University of Liège; Liege Belgium
| | | | - Gabriel Mazzucchelli
- Laboratory of Mass Spectrometry (LSM) - MolSys; Department of Chemistry; University of Liège; Liege Belgium
| | - Philippe Delvenne
- Department of Pathology; GIGA Cancer; University of Liège Hospital; Liège Belgium
| | - Mark Kriegsmann
- Institute of Pathology; University of Heidelberg; Heidelberg Germany
| | - Daniel Kazdal
- Institute of Pathology; University of Heidelberg; Heidelberg Germany
| | - Arne Warth
- Institute of Pathology; University of Heidelberg; Heidelberg Germany
| | - Edwin De Pauw
- Laboratory of Mass Spectrometry (LSM) - MolSys; Department of Chemistry; University of Liège; Liege Belgium
| | - Rémi Longuespée
- Laboratory of Mass Spectrometry (LSM) - MolSys; Department of Chemistry; University of Liège; Liege Belgium
- Institute of Pathology; University of Heidelberg; Heidelberg Germany
- Proteopath GmbH; Trier Germany
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Fujino Y, Minamizaki T, Hayashi I, Kawakami A, Miyaji T, Sakurai K, Yoshioka H, Kozai K, Okada M, Yoshiko Y. Comparative proteome analysis of wild-type and klotho
-knockout mouse kidneys using a combination of MALDI-IMS and LC-MS/MS. Proteomics Clin Appl 2017; 11. [DOI: 10.1002/prca.201600095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 02/08/2017] [Accepted: 03/03/2017] [Indexed: 11/09/2022]
Affiliation(s)
- Yoko Fujino
- Department of Special Care Dentistry; Hiroshima University Graduate School of Biomedical and Health Sciences; Hiroshima University; Hiroshima Japan
| | - Tomoko Minamizaki
- Department of Calcified Tissue Biology; Hiroshima University Graduate School of Biomedical and Health Sciences; Hiroshima University; Hiroshima Japan
| | - Ikue Hayashi
- Central Laboratory; Hiroshima University Faculty of Dentistry; Hiroshima Japan
| | - Asako Kawakami
- Advanced Science Research Center; Okayama University; Okayama Japan
| | - Takaaki Miyaji
- Advanced Science Research Center; Okayama University; Okayama Japan
| | - Kaoru Sakurai
- Department of Pediatric Dentistry; Hiroshima University Graduate School of Biomedical and Health Sciences; Hiroshima University; Hiroshima Japan
| | - Hirotaka Yoshioka
- Department of Calcified Tissue Biology; Hiroshima University Graduate School of Biomedical and Health Sciences; Hiroshima University; Hiroshima Japan
| | - Katsuyuki Kozai
- Department of Pediatric Dentistry; Hiroshima University Institute of Biomedical and Health Sciences; Hiroshima University; Hiroshima Japan
| | - Mitsugi Okada
- Special Care Dentistry; Hiroshima University Hospital; Hiroshima Japan
| | - Yuji Yoshiko
- Department of Calcified Tissue Biology; Hiroshima University Graduate School of Biomedical and Health Sciences; Hiroshima University; Hiroshima Japan
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Casadonte R, Longuespée R, Kriegsmann J, Kriegsmann M. MALDI IMS and Cancer Tissue Microarrays. Adv Cancer Res 2017; 134:173-200. [PMID: 28110650 DOI: 10.1016/bs.acr.2016.11.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) technology creates a link between the molecular assessment of numerous molecules and the morphological information about their special distribution. The application of MALDI IMS on formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMAs) is suitable for large-scale discovery analyses. Data acquired from FFPE TMA cancer samples in current research are very promising, and applications for routine diagnostics are under development. With the current rapid advances in both technology and applications, MALDI IMS technology is expected to enter into routine diagnostics soon. This chapter is intended to be comprehensive with respect to all aspects and considerations for the application of MALDI IMS on FFPE cancer TMAs with in-depth notes on technical aspects.
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Affiliation(s)
| | | | - J Kriegsmann
- Proteopath GmbH, Trier, Germany; Institute of Molecular Pathology, Trier, Germany; Center for Histology, Cytology and Molecular Diagnostics, Trier, Germany
| | - M Kriegsmann
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany.
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Ucal Y, Durer ZA, Atak H, Kadioglu E, Sahin B, Coskun A, Baykal AT, Ozpinar A. Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2017; 1865:795-816. [PMID: 28087424 DOI: 10.1016/j.bbapap.2017.01.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 12/08/2016] [Accepted: 01/06/2017] [Indexed: 12/25/2022]
Abstract
Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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Affiliation(s)
- Yasemin Ucal
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Zeynep Aslıhan Durer
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Hakan Atak
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Elif Kadioglu
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Betul Sahin
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Abdurrahman Coskun
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Ahmet Tarık Baykal
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey
| | - Aysel Ozpinar
- Acibadem University, Department of Medical Biochemistry, School of Medicine, Istanbul, Turkey.
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Arentz G, Mittal P, Zhang C, Ho YY, Briggs M, Winderbaum L, Hoffmann MK, Hoffmann P. Applications of Mass Spectrometry Imaging to Cancer. Adv Cancer Res 2017; 134:27-66. [PMID: 28110654 DOI: 10.1016/bs.acr.2016.11.002] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Pathologists play an essential role in the diagnosis and prognosis of benign and cancerous tumors. Clinicians provide tissue samples, for example, from a biopsy, which are then processed and thin sections are placed onto glass slides, followed by staining of the tissue with visible dyes. Upon processing and microscopic examination, a pathology report is provided, which relies on the pathologist's interpretation of the phenotypical presentation of the tissue. Targeted analysis of single proteins provide further insight and together with clinical data these results influence clinical decision making. Recent developments in mass spectrometry facilitate the collection of molecular information about such tissue specimens. These relatively new techniques generate label-free mass spectra across tissue sections providing nonbiased, nontargeted molecular information. At each pixel with spatial coordinates (x/y) a mass spectrum is acquired. The acquired mass spectrums can be visualized as intensity maps displaying the distribution of single m/z values of interest. Based on the sample preparation, proteins, peptides, lipids, small molecules, or glycans can be analyzed. The generated intensity maps/images allow new insights into tumor tissues. The technique has the ability to detect and characterize tumor cells and their environment in a spatial context and combined with histological staining, can be used to aid pathologists and clinicians in the diagnosis and management of cancer. Moreover, such data may help classify patients to aid therapy decisions and predict outcomes. The novel complementary mass spectrometry-based methods described in this chapter will contribute to the transformation of pathology services around the world.
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Affiliation(s)
- G Arentz
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - P Mittal
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - C Zhang
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - Y-Y Ho
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - M Briggs
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia; ARC Centre for Nanoscale BioPhotonics (CNBP), University of Adelaide, Adelaide, SA, Australia
| | - L Winderbaum
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - M K Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia
| | - P Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia; Institute for Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, SA, Australia.
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Quanico J, Franck J, Wisztorski M, Salzet M, Fournier I. Progress and Potential of Imaging Mass Spectrometry Applied to Biomarker Discovery. Methods Mol Biol 2017; 1598:21-43. [PMID: 28508356 DOI: 10.1007/978-1-4939-6952-4_2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Mapping provides a direct means to assess the impact of protein biomarkers and puts into context their relevance in the type of cancer being examined. To this end, mass spectrometry imaging (MSI) was developed to provide the needed spatial information which is missing in traditional liquid-based mass spectrometric proteomics approaches. Aptly described as a "molecular histology" technique, MSI gives an additional dimension in characterizing tumor biopsies, allowing for mapping of hundreds of molecules in a single analysis. A decade of developments focused on improving and standardizing MSI so that the technique can be translated into the clinical setting. This review describes the progress made in addressing the technological development that allows to bridge local protein detection by MSI to its identification and to illustrate its potential in studying various aspects of cancer biomarker discovery.
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Affiliation(s)
- Jusal Quanico
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France
| | - Julien Franck
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France
| | - Maxence Wisztorski
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France
| | - Michel Salzet
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France
| | - Isabelle Fournier
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), 59000, Lille, France.
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Kriegsmann M, Longuespée R, Wandernoth P, Mohanu C, Lisenko K, Weichert W, Warth A, Dienemann H, De Pauw E, Katzenberger T, Aust D, Baretton G, Kriegsmann J, Casadonte R. Typing of colon and lung adenocarcinoma by high throughput imaging mass spectrometry. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1865:858-864. [PMID: 27939606 DOI: 10.1016/j.bbapap.2016.11.018] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 11/21/2016] [Accepted: 11/23/2016] [Indexed: 10/20/2022]
Abstract
In advanced tumor stages, diagnosis is frequently made from metastatic tumor tissue. In some cases, the identification of the tumor of origin may be difficult by histology alone. In this setting, immunohistochemical and molecular biological methods are often required. In a subset of tumors definite diagnosis cannot be achieved. Thus, additional new diagnostic methods are required for precise tumor subtyping. Mass spectrometric methods are of special interest for the discrimination of different tumor types. We investigated whether it is possible to discern adenocarcinomas of colon and lung using high-throughput imaging mass spectrometry on formalin-fixed paraffin-embedded tissue microarrays. 101 primary adenocarcinoma of the colon and 91 primary adenocarcinoma of the lung were used to train a Linear Discriminant Analysis model. Results were validated on an independent set of 116 colonic and 75 lung adenocarcinomas. In the validation cohort 109 of 116 patients with colonic and 67 of 75 patients with lung adenocarcinomas were correctly classified. The ability to define proteomic profiles capable to discern different tumor types promises a valuable tool in cancer diagnostics and might complement current approaches. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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Affiliation(s)
- Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
| | | | | | | | - Katharina Lisenko
- Department of Internal Medicine V, University of Heidelberg, Heidelberg, Germany.
| | | | - Arne Warth
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research, Germany.
| | - Hendrik Dienemann
- Department of Thoracic Surgery, Thoraxklinik at Heidelberg University, Heidelberg, Germany.
| | - Edwin De Pauw
- Mass Spectrometry Laboratory, Systems Biology and Chemical Biology, GIGA-Research, University of Liège, Belgium.
| | | | - Daniela Aust
- Institute of Pathology, University Hospital Carl Gustav Carus, Dresden, Germany.
| | - Gustavo Baretton
- Institute of Pathology, University Hospital Carl Gustav Carus, Dresden, Germany.
| | - Joerg Kriegsmann
- Proteopath GmbH, Trier, Germany; Center for Histology, Cytology and Molecular Diagnostics Trier, Trier, Germany.
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Boskamp T, Lachmund D, Oetjen J, Cordero Hernandez Y, Trede D, Maass P, Casadonte R, Kriegsmann J, Warth A, Dienemann H, Weichert W, Kriegsmann M. A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1865:916-926. [PMID: 27836618 DOI: 10.1016/j.bbapap.2016.11.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 11/02/2016] [Accepted: 11/04/2016] [Indexed: 12/28/2022]
Abstract
Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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Affiliation(s)
- Tobias Boskamp
- Center for Industrial Mathematics, University of Bremen, Bremen, Germany; SCiLS GmbH, Bremen, Germany.
| | - Delf Lachmund
- Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Janina Oetjen
- MALDI Imaging Lab, University of Bremen, Bremen, Germany
| | | | | | - Peter Maass
- Center for Industrial Mathematics, University of Bremen, Bremen, Germany; MALDI Imaging Lab, University of Bremen, Bremen, Germany; SCiLS GmbH, Bremen, Germany
| | | | - Jörg Kriegsmann
- Proteopath GmbH, Trier, Germany; Center for Histology, Cytology and Molecular Diagnostic, Trier, Germany
| | - Arne Warth
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Hendrik Dienemann
- Thoraxklinik Heidelberg, University of Heidelberg, Heidelberg, Germany
| | - Wilko Weichert
- Institute of Pathology, Technical University of Munich, Munich, Germany
| | - Mark Kriegsmann
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
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48
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Schwamborn K, Kriegsmann M, Weichert W. MALDI imaging mass spectrometry - From bench to bedside. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1865:776-783. [PMID: 27810414 DOI: 10.1016/j.bbapap.2016.10.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 10/24/2016] [Accepted: 10/28/2016] [Indexed: 10/20/2022]
Abstract
Today, pathologists face many challenges in defining the precise morphomolecular diagnosis and in guiding clinicians to the optimal patients' treatment. To achieve this goal, increasingly, classical histomorphological methods have to be supplemented by high throughput molecular assays. Since MALDI imaging mass spectrometry (IMS) enables the assessment of spatial molecular arrangements in tissue sections, it goes far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. Thus, this technology has the potential to uncover new markers for diagnostic purposes or markers that correlate with disease severity as well as prognosis and therapeutic response. Additionally, in the future MALDI IMS based classifiers measured with this technology in real time in the diagnostic setting might be applicable in the routine diagnostic setting. In this review, recently published studies that show the usefulness, advantages, and applicability of MALDI IMS in different fields of pathology (diagnosis, prognosis and treatment response) are highlighted. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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Affiliation(s)
- Kristina Schwamborn
- Institute of Pathology, Technische Universität München (TUM), Munich, Germany.
| | - Mark Kriegsmann
- University of Heidelberg, Department of Pathology, Heidelberg, Germany
| | - Wilko Weichert
- Institute of Pathology, Technische Universität München (TUM), Munich, Germany
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49
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Kriegsmann M, Casadonte R, Kriegsmann J, Dienemann H, Schirmacher P, Hendrik Kobarg J, Schwamborn K, Stenzinger A, Warth A, Weichert W. Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens. Mol Cell Proteomics 2016; 15:3081-3089. [PMID: 27473201 PMCID: PMC5054336 DOI: 10.1074/mcp.m115.057513] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 07/27/2016] [Indexed: 12/24/2022] Open
Abstract
Histopathological subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (ADC), and squamous cell carcinoma (SqCC) is of utmost relevance for treatment stratification. However, current immunohistochemistry (IHC) based typing approaches on biopsies are imperfect, therefore novel analytical methods for reliable subtyping are needed. We analyzed formalin-fixed paraffin-embedded tissue cores of NSCLC by Matrix-assisted laser desorption/ionization (MALDI) imaging on tissue microarrays to identify and validate discriminating MALDI imaging profiles for NSCLC subtyping. 110 ADC and 98 SqCC were used to train a Linear Discriminant Analysis (LDA) model. Results were validated on a separate set of 58 ADC and 60 SqCC. Selected differentially expressed proteins were identified by tandem mass spectrometry and validated by IHC. The LDA classification model incorporated 339 m/z values. In the validation cohort, in 117 cases (99.1%) MALDI classification on tissue cores was in accordance with the pathological diagnosis made on resection specimen. Overall, three cases in the combined cohorts were discordant, after reevaluation two were initially misclassified by pathology whereas one was classified incorrectly by MALDI. Identification of differentially expressed peptides detected well-known IHC discriminators (CK5, CK7), but also less well known differentially expressed proteins (CK15, HSP27). In conclusion, MALDI imaging on NSCLC tissue cores as small biopsy equivalents is capable to discriminate lung ADC and SqCC with a very high accuracy. In addition, replacing multislide IHC by an one-slide MALDI approach may also save tissue for subsequent predictive molecular testing. We therefore advocate to pursue routine diagnostic implementation strategies for MALDI imaging in solid tumor typing.
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Affiliation(s)
- Mark Kriegsmann
- From the ‡Institute of Pathology, University Heidelberg, 69120 Heidelberg, Germany;
| | | | - Jörg Kriegsmann
- §Proteopath GmbH, 54296 Trier, Germany; ¶Center for Histology, Cytology and Molecular Diagnostics, 54296 Trier, Germany
| | - Hendrik Dienemann
- ‖Department of Thoracic Surgery, Thoraxklinik at Heidelberg University, 69126 Heidelberg, Germany
| | - Peter Schirmacher
- From the ‡Institute of Pathology, University Heidelberg, 69120 Heidelberg, Germany
| | | | - Kristina Schwamborn
- ‡‡Institute of Pathology, Technical University Munich (TUM), 81675 Munich, Germany
| | - Albrecht Stenzinger
- From the ‡Institute of Pathology, University Heidelberg, 69120 Heidelberg, Germany; §§German Cancer Consortium (DKTK)
| | - Arne Warth
- From the ‡Institute of Pathology, University Heidelberg, 69120 Heidelberg, Germany; ¶¶Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research
| | - Wilko Weichert
- From the ‡Institute of Pathology, University Heidelberg, 69120 Heidelberg, Germany; ‡‡Institute of Pathology, Technical University Munich (TUM), 81675 Munich, Germany; §§German Cancer Consortium (DKTK); ‖‖National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
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50
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An approach to optimize sample preparation for MALDI imaging MS of FFPE sections using fractional factorial design of experiments. Anal Bioanal Chem 2016; 408:6729-40. [DOI: 10.1007/s00216-016-9793-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 07/01/2016] [Accepted: 07/12/2016] [Indexed: 12/29/2022]
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