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Zickuhr GM, Um IH, Laird A, Harrison DJ, Dickson AL. DESI-MSI-guided exploration of metabolic-phenotypic relationships reveals a correlation between PI 38:3 and proliferating cells in clear cell renal cell carcinoma via single-section co-registration of multimodal imaging. Anal Bioanal Chem 2024:10.1007/s00216-024-05339-0. [PMID: 38780655 DOI: 10.1007/s00216-024-05339-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024]
Abstract
A workflow has been evaluated that utilizes a single tissue section to obtain spatially co-registered, molecular, and phenotypical information suitable for AI-enabled image analysis. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was used to obtain molecular information followed by conventional histological staining and immunolabelling. The impact of varying DESI-MSI conditions (e.g., heated transfer line (HTL) temperature, scan rate, acquisition time) on the detection of small molecules and lipids as well as on tissue integrity crucial for integration into typical clinical pathology workflows was assessed in human kidney. Increasing the heated transfer line temperature from 150 to 450 °C resulted in a 1.8-fold enhancement in lipid signal at a scan rate of 10 scans/s, while preserving histological features. Moreover, increasing the acquisition speed to 30 scans/s yielded superior lipid signal when compared to 10 scans/s at 150 °C. Tissue morphology and protein epitopes remained intact allowing full histological assessment and further multiplex phenotyping by immunofluorescence (mIF) and immunohistochemistry (mIHC) of the same section. The successful integration of the workflow incorporating DESI-MSI, H&E, and immunolabelling on a single tissue section revealed an accumulation of ascorbic acid in regions of focal chronic inflammatory cell infiltrate within non-cancerous kidney tissue. Additionally, a strong positive correlation between PI 38:3 and proliferating cells was observed in clear cell renal cell carcinoma (ccRCC) showing the utility of this approach in uncovering molecular associations in disease pathology.
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Affiliation(s)
- Greice M Zickuhr
- School of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF, UK
| | - In Hwa Um
- School of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF, UK
| | - Alexander Laird
- Department of Urology, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK
| | - David J Harrison
- School of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF, UK
- NuCana Plc, Lochside Way, Edinburgh, EH12 9DT, UK
| | - Alison L Dickson
- School of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF, UK.
- NuCana Plc, Lochside Way, Edinburgh, EH12 9DT, UK.
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2
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Sorokin AA, Pekov SI, Zavorotnyuk DS, Shamraeva MM, Bormotov DS, Popov IA. Modern machine-learning applications in ambient ionization mass spectrometry. MASS SPECTROMETRY REVIEWS 2024. [PMID: 38671553 DOI: 10.1002/mas.21886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/29/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024]
Abstract
This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.
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Affiliation(s)
- Anatoly A Sorokin
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Stanislav I Pekov
- Mass Spectrometry Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russia
- Translational Medicine Laboratory, Siberian State Medical University, Tomsk, Russia
- Department for Molecular and Biological Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Denis S Zavorotnyuk
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Mariya M Shamraeva
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Denis S Bormotov
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Igor A Popov
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Translational Medicine Laboratory, Siberian State Medical University, Tomsk, Russia
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3
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Ma X, Fernández FM. Advances in mass spectrometry imaging for spatial cancer metabolomics. MASS SPECTROMETRY REVIEWS 2024; 43:235-268. [PMID: 36065601 PMCID: PMC9986357 DOI: 10.1002/mas.21804] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 05/09/2023]
Abstract
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.
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Affiliation(s)
- Xin Ma
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
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4
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Djambazova KV, van Ardenne JM, Spraggins JM. Advances in Imaging Mass Spectrometry for Biomedical and Clinical Research. Trends Analyt Chem 2023; 169:117344. [PMID: 38045023 PMCID: PMC10688507 DOI: 10.1016/j.trac.2023.117344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Imaging mass spectrometry (IMS) allows for the untargeted mapping of biomolecules directly from tissue sections. This technology is increasingly integrated into biomedical and clinical research environments to supplement traditional microscopy and provide molecular context for tissue imaging. IMS has widespread clinical applicability in the fields of oncology, dermatology, microbiology, and others. This review summarizes the two most widely employed IMS technologies, matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), and covers technological advancements, including efforts to increase spatial resolution, specificity, and throughput. We also highlight recent biomedical applications of IMS, primarily focusing on disease diagnosis, classification, and subtyping.
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Affiliation(s)
- Katerina V. Djambazova
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
| | - Jacqueline M. van Ardenne
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Jeffrey M. Spraggins
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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Rainu SK, Ramachandran RG, Parameswaran S, Krishnakumar S, Singh N. Advancements in Intraoperative Near-Infrared Fluorescence Imaging for Accurate Tumor Resection: A Promising Technique for Improved Surgical Outcomes and Patient Survival. ACS Biomater Sci Eng 2023; 9:5504-5526. [PMID: 37661342 DOI: 10.1021/acsbiomaterials.3c00828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Clear surgical margins for solid tumor resection are essential for preventing cancer recurrence and improving overall patient survival. Complete resection of tumors is often limited by a surgeon's ability to accurately locate malignant tissues and differentiate them from healthy tissue. Therefore, techniques or imaging modalities are required that would ease the identification and resection of tumors by real-time intraoperative visualization of tumors. Although conventional imaging techniques such as positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), or radiography play an essential role in preoperative diagnostics, these cannot be utilized in intraoperative tumor detection due to their large size, high cost, long imaging time, and lack of cancer specificity. The inception of several imaging techniques has paved the way to intraoperative tumor margin detection with a high degree of sensitivity and specificity. Particularly, molecular imaging using near-infrared fluorescence (NIRF) based nanoprobes provides superior imaging quality due to high signal-to-noise ratio, deep penetration to tissues, and low autofluorescence, enabling accurate tumor resection and improved survival rates. In this review, we discuss the recent developments in imaging technologies, specifically focusing on NIRF nanoprobes that aid in highly specific intraoperative surgeries with real-time recognition of tumor margins.
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Affiliation(s)
- Simran Kaur Rainu
- Center for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Remya Girija Ramachandran
- L&T Ocular Pathology Department, Vision Research Foundation, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Chennai 600006, India
| | - Sowmya Parameswaran
- L&T Ocular Pathology Department, Vision Research Foundation, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Chennai 600006, India
| | - Subramanian Krishnakumar
- L&T Ocular Pathology Department, Vision Research Foundation, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Chennai 600006, India
| | - Neetu Singh
- Center for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
- Biomedical Engineering Unit, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
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Planque M, Igelmann S, Ferreira Campos AM, Fendt SM. Spatial metabolomics principles and application to cancer research. Curr Opin Chem Biol 2023; 76:102362. [PMID: 37413787 DOI: 10.1016/j.cbpa.2023.102362] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 05/07/2023] [Accepted: 06/06/2023] [Indexed: 07/08/2023]
Abstract
Mass spectrometry imaging (MSI) is an emerging technology in cancer metabolomics. Desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI) MSI are complementary techniques to identify hundreds of metabolites in space with close to single-cell resolution. This technology leap enables research focusing on tumor heterogeneity, cancer cell plasticity, and the communication signals between cancer and stromal cells in the tumor microenvironment (TME). Currently, unprecedented knowledge is generated using spatial metabolomics in fundamental cancer research. Yet, also translational applications are emerging, including the assessment of spatial drug distribution in organs and tumors. Moreover, clinical research investigates the use of spatial metabolomics as a rapid pathology tool during cancer surgeries. Here, we summarize MSI applications, the knowledge gained by this technology in space, future directions, and developments needed.
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Affiliation(s)
- Mélanie Planque
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Sebastian Igelmann
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Ana Margarida Ferreira Campos
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Sarah-Maria Fendt
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium.
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7
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Chen H, Li X, Li F, Li Y, Chen F, Zhang L, Ye F, Gong M, Bu H. Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging. J Pathol 2023; 261:125-138. [PMID: 37555360 DOI: 10.1002/path.6154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 05/16/2023] [Accepted: 06/07/2023] [Indexed: 08/10/2023]
Abstract
Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Metabolomics analysis using DESI-MS data revealed significant differences in metabolite levels, including small-molecule antioxidants, long-chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long-chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI-MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel-level prediction based on DESI-MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI-MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Hong Chen
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Key Laboratory of Transplant Engineering and Immunology of the National Health Commission, West China Hospital, Sichuan University, Chengdu, PR China
| | - Xin Li
- Laboratory of Clinical Proteomics and Metabolomics, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, PR China
| | - Fengling Li
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, PR China
| | - Yijie Li
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Key Laboratory of Transplant Engineering and Immunology of the National Health Commission, West China Hospital, Sichuan University, Chengdu, PR China
| | - Fei Chen
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
| | - Lu Zhang
- Image Processing and Parallel Computing Laboratory, School of Computer Science, Southwest Petroleum University, Chengdu, PR China
| | - Feng Ye
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Key Laboratory of Transplant Engineering and Immunology of the National Health Commission, West China Hospital, Sichuan University, Chengdu, PR China
| | - Meng Gong
- Laboratory of Clinical Proteomics and Metabolomics, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, PR China
| | - Hong Bu
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, PR China
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8
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Kumar BS. Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview. Mass Spectrom (Tokyo) 2023; 12:A0129. [PMID: 37789912 PMCID: PMC10542858 DOI: 10.5702/massspectrometry.a0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/25/2023] [Indexed: 10/05/2023] Open
Abstract
Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.
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Affiliation(s)
- Bharath S. Kumar
- Correspondence to: Bharath S. Kumar, 21, B2, 27th Street, Nanganallur, Chennai, India, e-mail:
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9
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Chappel JR, King ME, Fleming J, Eberlin LS, Reif DM, Baker ES. Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics. Anal Chem 2023; 95:12913-12922. [PMID: 37579019 PMCID: PMC10561690 DOI: 10.1021/acs.analchem.3c02389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often assessed and visualized using various supervised and unsupervised statistical approaches. However, these approaches tend to fall short in identifying and concisely visualizing subtle, phenotype-relevant molecular changes. To address these shortcomings, we developed aggregated molecular phenotype (AMP) scores. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores, therefore, allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes. Due to the ensembled approach, AMP scores are able to overcome limitations associated with individual models, leading to high diagnostic accuracy and interpretability. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization MSI. Initial comparisons of cancerous human tissues to their normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.
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Affiliation(s)
- Jessie R Chappel
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Mary E King
- Department of Surgery, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Jonathon Fleming
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Livia S Eberlin
- Department of Surgery, Baylor College of Medicine, Houston, Texas 77030, United States
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, North Carolina 27709, United States
| | - Erin S Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, United States
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10
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Kumar BS. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) in disease diagnosis: an overview. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:3768-3784. [PMID: 37503728 DOI: 10.1039/d3ay00867c] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Tissue analysis, which is essential to histology and is considered the benchmark for the diagnosis and prognosis of many illnesses, including cancer, is significant. During surgery, the surgical margin of the tumor is assessed using the labor-intensive, challenging, and commonly subjective technique known as frozen section histopathology. In the biopsy section, large numbers of molecules can now be visualized at once (ion images) following recent developments in [MSI] mass spectrometry imaging under atmospheric conditions. This is vastly superior to and different from the single optical tissue image processing used in traditional histopathology. This review article will focus on the advancement of desorption electrospray ionization mass spectrometry imaging [DESI-MSI] technique, which is label-free and requires little to no sample preparation. Since the proportion of molecular species in normal and abnormal tissues is different, DESI-MSI can capture ion images of the distributions of lipids and metabolites on biopsy sections, which can provide rich diagnostic information. This is not a systematic review but a summary of well-known, cutting-edge and recent DESI-MSI applications in cancer research between 2018 and 2023.
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Affiliation(s)
- Bharath Sampath Kumar
- Independent Researcher, 21, B2, 27th Street, Nanganallur, Chennai 61, TamilNadu, India.
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11
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King ME, Lin M, Spradlin M, Eberlin LS. Advances and Emerging Medical Applications of Direct Mass Spectrometry Technologies for Tissue Analysis. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:1-25. [PMID: 36944233 DOI: 10.1146/annurev-anchem-061020-015544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis.
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Affiliation(s)
- Mary E King
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA;
- Department of Surgery, Baylor College of Medicine, Houston, Texas, USA;
| | - Monica Lin
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA;
| | - Meredith Spradlin
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA;
| | - Livia S Eberlin
- Department of Surgery, Baylor College of Medicine, Houston, Texas, USA;
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12
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Chappel JR, King ME, Fleming J, Eberlin LS, Reif DM, Baker ES. Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.01.543306. [PMID: 37333214 PMCID: PMC10274704 DOI: 10.1101/2023.06.01.543306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.
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Affiliation(s)
- Jessie R. Chappel
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Mary E. King
- Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Jonathon Fleming
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Livia S. Eberlin
- Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - David M. Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Erin S. Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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13
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Shankar V, Vijayalakshmi K, Nolley R, Sonn GA, Kao CS, Zhao H, Wen R, Eberlin LS, Tibshirani R, Zare RN, Brooks JD. Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning. JCO Precis Oncol 2023; 7:e2200668. [PMID: 37285559 PMCID: PMC10309512 DOI: 10.1200/po.22.00668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/26/2023] [Accepted: 04/10/2023] [Indexed: 06/09/2023] Open
Abstract
PURPOSE Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. MATERIALS AND METHODS Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. RESULTS From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. CONCLUSION Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.
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Affiliation(s)
- Vishnu Shankar
- Program in Immunology, Stanford University School of Medicine, Stanford, CA
| | | | - Rosalie Nolley
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Geoffrey A. Sonn
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Chia-Sui Kao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA
| | - Hongjuan Zhao
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Ru Wen
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | | | - Robert Tibshirani
- Department of Biomedical Data Science, and Statistics, Stanford University, Stanford, CA
| | | | - James D. Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA
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14
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Soudah T, Zoabi A, Margulis K. Desorption electrospray ionization mass spectrometry imaging in discovery and development of novel therapies. MASS SPECTROMETRY REVIEWS 2023; 42:751-778. [PMID: 34642958 DOI: 10.1002/mas.21736] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 09/16/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is one of the least specimen destructive ambient ionization mass spectrometry tissue imaging methods. It enables rapid simultaneous mapping, measurement, and identification of hundreds of molecules from an unmodified tissue sample. Over the years, since its first introduction as an imaging technique in 2005, DESI-MSI has been extensively developed as a tool for separating tissue regions of various histopathologic classes for diagnostic applications. Recently, DESI-MSI has also emerged as a versatile technique that enables drug discovery and can guide the efficient development of drug delivery systems. For example, it has been increasingly employed for uncovering unique patterns of in vivo drug distribution, the discovery of potentially treatable biochemical pathways, revealing novel druggable targets, predicting therapeutic sensitivity of diseased tissues, and identifying early tissue response to pharmacological treatment. These and other recent advances in implementing DESI-MSI as the tool for the development of novel therapies are highlighted in this review.
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Affiliation(s)
- Terese Soudah
- The Faculty of Medicine, The School of Pharmacy, The Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Amani Zoabi
- The Faculty of Medicine, The School of Pharmacy, The Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Katherine Margulis
- The Faculty of Medicine, The School of Pharmacy, The Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, Israel
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15
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King ME, Yuan R, Chen J, Pradhan K, Sariol I, Li S, Chakraborty A, Ekpenyong O, Yearley JH, Wong JC, Zúñiga L, Tomazela D, Beaumont M, Han JH, Eberlin LS. Long-chain polyunsaturated lipids associated with responsiveness to anti-PD-1 therapy are colocalized with immune infiltrates in the tumor microenvironment. J Biol Chem 2023; 299:102902. [PMID: 36642178 PMCID: PMC9957763 DOI: 10.1016/j.jbc.2023.102902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 12/23/2022] [Accepted: 01/07/2023] [Indexed: 01/15/2023] Open
Abstract
The programmed cell death protein-1 (PD-1) is highly expressed on the surface of antigen-specific exhausted T cells and, upon interaction with its ligand PD-L1, can result in inhibition of the immune response. Anti-PD-1 treatment has been shown to extend survival and result in durable responses in several cancers, yet only a subset of patients benefit from this therapy. Despite the implication of metabolic alteration following cancer immunotherapy, mechanistic associations between antitumor responses and metabolic changes remain unclear. Here, we used desorption electrospray ionization mass spectrometry imaging to examine the lipid profiles of tumor tissue from three syngeneic murine models with varying treatment sensitivity at the baseline and at three time points post-anti-PD-1 therapy. These imaging experiments revealed specific alterations in the lipid profiles associated with the degree of response to treatment and allowed us to identify a significant increase of long-chain polyunsaturated lipids within responsive tumors following anti-PD-1 therapy. Immunofluorescence imaging of tumor tissues also demonstrated that the altered lipid profile associated with treatment response is localized to dense regions of tumor immune infiltrates. Overall, these results indicate that effective anti-PD-1 therapy modulates lipid metabolism in tumor immune infiltrates, and we thereby propose that further investigation of the related immune-metabolic pathways may be useful for better understanding success and failure of anti-PD-1 therapy.
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Affiliation(s)
- Mary E King
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA; Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Robert Yuan
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA
| | - Jeremy Chen
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA
| | - Komal Pradhan
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA
| | - Isabel Sariol
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA
| | - Shirley Li
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA
| | - Ashish Chakraborty
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA
| | - Oscar Ekpenyong
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA
| | - Jennifer H Yearley
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA
| | - Janica C Wong
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA
| | - Luis Zúñiga
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA
| | - Daniela Tomazela
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA
| | - Maribel Beaumont
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA.
| | - Jin-Hwan Han
- Merck Research Laboratories, Merck & Co, Inc, South San Francisco, California, USA.
| | - Livia S Eberlin
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA; Department of Surgery, Baylor College of Medicine, Houston, Texas, USA.
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16
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Martín-Saiz L, Abad-García B, Solano-Iturri JD, Mosteiro L, Martín-Allende J, Rueda Y, Pérez-Fernández A, Unda M, Coterón-Ochoa P, Goya A, Saiz A, Martínez J, Ochoa B, Fresnedo O, Larrinaga G, Fernández JA. Using the Synergy between HPLC-MS and MALDI-MS Imaging to Explore the Lipidomics of Clear Cell Renal Cell Carcinoma. Anal Chem 2023; 95:2285-2293. [PMID: 36638042 PMCID: PMC9893214 DOI: 10.1021/acs.analchem.2c03953] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Lipid imaging mass spectrometry (LIMS) has been tested in several pathological contexts, demonstrating its ability to segregate and isolate lipid signatures in complex tissues, thanks to the technique's spatial resolution. However, it cannot yet compete with the superior identification power of high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS), and therefore, very often, the latter is used to refine the assignment of the species detected by LIMS. Also, it is not clear if the differences in sensitivity and spatial resolution between the two techniques lead to a similar panel of biomarkers for a given disease. Here, we explore the capabilities of LIMS and HPLC-MS to produce a panel of lipid biomarkers to screen nephrectomy samples from 40 clear cell renal cell carcinoma patients. The same set of samples was explored by both techniques, and despite the important differences between them in terms of the number of detected and identified species (148 by LIMS and 344 by HPLC-MS in negative-ion mode) and the presence/absence of image capabilities, similar conclusions were reached: using the lipid fingerprint, it is possible to set up classifiers that correctly identify the samples as either healthy or tumor samples. The spatial resolution of LIMS enables extraction of additional information, such as the existence of necrotic areas or the existence of different tumor cell populations, but such information does not seem determinant for the correct classification of the samples, or it may be somehow compensated by the higher analytical power of HPLC-MS. Similar conclusions were reached with two very different techniques, validating their use for the discovery of lipid biomarkers.
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Affiliation(s)
- Lucía Martín-Saiz
- Department
of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), B. Sarriena, s/n, Leioa 48940, Spain
| | - Beatriz Abad-García
- Central
Analysis Service, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa 48940, Spain
| | - Jon D. Solano-Iturri
- Service
of Anatomic Pathology, Donostia University
Hospital, Donostia/San
Sebastian 20014, Spain,Biocruces
Bizkaia Health Research Institute, Barakaldo 48903, Spain
| | - Lorena Mosteiro
- Service
of Anatomic Pathology, Cruces University
Hospital, Barakaldo 48903, Spain
| | - Javier Martín-Allende
- Department
of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), B. Sarriena, s/n, Leioa 48940, Spain
| | - Yuri Rueda
- Lipids &
Liver, Department of Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), B. Sarriena, s/n, Leioa 48940, Spain
| | | | - Miguel Unda
- Service
of Urology, Basurto University Hospital, Bilbao 48003, Spain
| | - Pedro Coterón-Ochoa
- Service
of Urology, Galdakao-Usansolo University
Hospital, Galdakao 48960, Spain
| | - Aintzane Goya
- Service
of Urology, Galdakao-Usansolo University
Hospital, Galdakao 48960, Spain
| | - Alberto Saiz
- Service
of Anatomic Pathology, Galdakao-Usansolo
University Hospital, Galdakao 48960, Spain
| | - Jennifer Martínez
- Service
of Anatomic Pathology, Galdakao-Usansolo
University Hospital, Galdakao 48960, Spain
| | - Begoña Ochoa
- Lipids &
Liver, Department of Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), B. Sarriena, s/n, Leioa 48940, Spain
| | - Olatz Fresnedo
- Lipids &
Liver, Department of Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), B. Sarriena, s/n, Leioa 48940, Spain
| | - Gorka Larrinaga
- Biocruces
Bizkaia Health Research Institute, Barakaldo 48903, Spain,Department
of Nursing and Department of Physiology, Faculty of Medicine and Nursing (UPV/EHU), Leioa 48940, Spain
| | - José A. Fernández
- Department
of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), B. Sarriena, s/n, Leioa 48940, Spain,. Phone: +34 6015387
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17
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Manzi M, Zabalegui N, Monge ME. Postoperative Metabolic Phenoreversion in Clear Cell Renal Cell Carcinoma. J Proteome Res 2023; 22:1-15. [PMID: 36484409 DOI: 10.1021/acs.jproteome.2c00293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The ultimate goal of surgical treatment in cancer is to remove the tumor mass for restoring a healthy state. A 16-lipid panel that discriminated healthy controls from clear cell renal cell carcinoma (ccRCC) patients in a prior study was evaluated in the present work in paired-serum samples collected from patients (n = 41) before and after nephrectomy. Changes in the lipid and metabolite fingerprints from ccRCC patients were investigated and compared with fingerprints from healthy individuals obtained by means of ultra-performance liquid chromatography-high-resolution mass spectrometry. The lipid panel differentiated phenotypes associated with metabolic restoration after surgery, representing a serum signature of phenoreversion to a healthy metabolic state. In particular, PC 16:0/0:0, PC 18:2/18:2, and linoleic acid allowed discriminating serum samples from ccRCC patients with poor prognosis from those with an improved outcome during the follow-up period. Ratios of PC 16:0/0:0 and PC 18:2/18:2 with linoleic acid levels may contribute as prognostic tools to support decision-making during the patient follow-up care. The preliminary character of these results should be validated with larger cohorts, including subjects with different ethnicities, life style, and diets. MetaboLights study references: MTBLS1839, MTBLS3838, and MTBLS4629.
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Affiliation(s)
- Malena Manzi
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina.,Departamento de Fisiología, Biología molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Intendente Güiraldes 2160, C1428EGA Buenos Aires, Argentina
| | - Nicolás Zabalegui
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina.,Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA Buenos Aires, Argentina
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina
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18
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Hu H, Laskin J. Emerging Computational Methods in Mass Spectrometry Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203339. [PMID: 36253139 PMCID: PMC9731724 DOI: 10.1002/advs.202203339] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/17/2022] [Indexed: 05/10/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
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Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
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19
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Hu X, Wang Z, Chen H, Zhao A, Sun N, Deng C. Diagnosing, Typing, and Staging of Renal Cell Carcinoma by Designer Matrix-Based Urinary Metabolic Analysis. Anal Chem 2022; 94:14846-14853. [PMID: 36260912 DOI: 10.1021/acs.analchem.2c01563] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Molecular diagnosing, typing, and staging have been considered to be the ideal alternatives of imaging-based detection methods in clinics. Designer matrix-based analytical tools, with high speed, throughout, efficiency and low/noninvasiveness, have attracted much attention recently for in vitro metabolite detection. Herein, we develop an advanced metabolic analysis tool based on highly porous metal oxides derived from available metal-organic frameworks (MOFs), which elaborately inherit the morphology and porosity of MOFs and newly incorporate laser adsorption capacity of metal oxides. Through optimized conditions, direct high-quality fingerprinting spectra in 0.5 μL of urine are acquired. Using these fingerprinting spectra, we can discriminate the renal cell carcinoma (RCC) from healthy controls with higher than 0.99 of area under the curve (AUC) values (R2Y(cum) = 0.744, Q2 (cum) = 0.880), as well, from patients with other tumors (R2Y(cum) = 0.748, Q2(cum) = 0.871). We also realize the typing of three RCC subtypes, including clear cell RCC, chromophobe RCC (R2Y(cum) = 0.620, Q2(cum) = 0.656), and the staging of RCC (R2Y(cum) = 0.755, Q2(cum) = 0.857). Moreover, the tumor sizes (threshold value is 3 cm) can be remarkably recognized by this advanced metabolic analysis tool (R2Y(cum) = 0.710, Q2(cum) = 0.787). Our work brings a bright prospect for designer matrix-based analytical tools in disease diagnosis, typing and staging.
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Affiliation(s)
- Xufang Hu
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
| | - Zongping Wang
- Department of Urology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310000, China
| | - Haolin Chen
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
| | - An Zhao
- Experimental Research Center, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310000, China.,Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Nianrong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
| | - Chunhui Deng
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
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20
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Shi L, Habib A, Bi L, Hong H, Begum R, Wen L. Ambient Ionization Mass Spectrometry: Application and Prospective. Crit Rev Anal Chem 2022:1-50. [PMID: 36206159 DOI: 10.1080/10408347.2022.2124840] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
Abstract
Mass spectrometry (MS) is a formidable analytical tool for the analysis of non-polar to polar compounds individually and/or from mixtures, providing information on the molecular weights and chemical structures of the analytes. During the last more than one-decade, ambient ionization mass spectrometry (AIMS) has developed quickly, producing a wide range of platforms and proving scientific improvements in a variety of domains, from biological imaging to quick quality control. These methods have made it possible to detect target analytes in real time without sample preparation in an open environment, and they can be connected to any MS system with an atmospheric pressure interface. They also have the ability to analyze explosives, illicit drugs, disease diagnostics, drugs in biological samples, adulterants in food and agricultural products, reaction progress, and environmental monitoring. The development of novel ambient ionization techniques, such as probe electrospray ionization, paper spray ionization, and fiber spray ionization, employed even at picolitre to femtolitre solution levels to provide femtogram to attogram levels of the target analytes. The special characteristic of this ambient ion source, which has been extensively used, is the noninvasive property of PESI of examination of biological real samples. The results in the current review supports the idea that AIMS has emerged as a pioneer in MS-based approaches and that methods will continue to be developed along with improvements to existing ones in the near future.
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Affiliation(s)
- Lulu Shi
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
| | - Ahsan Habib
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
- Department of Chemistry, University of Dhaka, Dhaka, Bangladesh
| | - Lei Bi
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
| | - Huanhuan Hong
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
| | - Rockshana Begum
- Department of Chemistry, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Luhong Wen
- China Innovation Instrument Co., Ltd, Ningbo, Zhejiang, China
- The Research Institute of Advanced Technologies, Ningbo University, Ningbo, Zhejiang, China
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21
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Association of levels of metabolites with the safe margin of rectal cancer surgery: a metabolomics study. BMC Cancer 2022; 22:1043. [PMID: 36199039 PMCID: PMC9533537 DOI: 10.1186/s12885-022-10124-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 09/22/2022] [Indexed: 11/10/2022] Open
Abstract
Background Rectal cancer is one of the most lethal of gastrointestinal malignancies. Metabonomics has gradually developed as a convenient, inexpensive and non-destructive technique for the study of cancers. Methods A total of 150 tissue samples from 25 rectal cancer patients were analyzed by liquid chromatography–mass spectrometry (LC–MS), and 6 tissue samples were collected from each patient (group 1: tumor; group 2: 0.5 cm from tumor; group 3:1 cm from tumor; group 4:2 cm from tumor; group 5:3 cm from tumor and group 6:5 cm from tumor). The differential metabolites of tumor tissues and 5 cm from the tumor (normal tissues) were first selected. The differential metabolites between tumor tissues and normal tissues were regrouped by hierarchical clustering analysis, and further selected by discriminant analysis according to the regrouping of clustering results. The potential safe margin of clinical T(cT)1,cT2 stage rectal cancer and cT3,cT4 stage rectal cancer at the metabolomic level was further identified by observing the changes in the level of differential metabolites within the samples from group 1 to group 6. Results We found 22 specific metabolites to distinguish tumor tissue and normal tissue. The most significant changes in metabolite levels were observed at 0.5 cm (cT1, cT2) and 2.0 cm (cT3, cT4) from the tumor, while the changes in the tissues afterwards showed a stable trend. Conclusions There are differential metabolites between tumor tissues and normal tissues in rectal cancer. Based on our limited sample size, the safe distal incision margin for rectal cancer surgery in metabolites may be 0.5 cm in patients with cT1 and cT2 stage rectal cancer and 2.0 cm in patients with cT3 and cT4 stage rectal cancer.
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22
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Gao SQ, Zhao JH, Guan Y, Tang YS, Li Y, Liu LY. Mass Spectrometry Imaging technology in metabolomics: a systematic review. Biomed Chromatogr 2022:e5494. [PMID: 36044038 DOI: 10.1002/bmc.5494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/24/2022] [Accepted: 08/28/2022] [Indexed: 11/11/2022]
Abstract
Mass spectrometry imaging (MSI) is a powerful label-free analysis technique that can provide simultaneous spatial distribution of multiple compounds in a single experiment. By combining the sensitive and rapid screening of high-throughput mass spectrometry with spatial chemical information, metabolite analysis and morphological characteristics are presented in a single image. MSI can be used for qualitative and quantitative analysis of metabolic profiles and it can provide visual analysis of spatial distribution information of complex biological and microbial systems. Matrix assisted laser desorption ionization, laser ablation electrospray ionization and desorption electrospray ionization are commonly used in MSI. Here, we summarize and compare these three technologies, as well as the applications and prospects of MSI in metabolomics.
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Affiliation(s)
- Si-Qi Gao
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Jin-Hui Zhao
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Yue Guan
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Ying-Shu Tang
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Ying Li
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Li-Yan Liu
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
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23
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Zhang C, Qi F, Zheng Y, Xia X, Li X, Wang X. Comprehensive Genomic Characterization of Tumor Microenvironment and Relevant Signature in Clear Cell Renal Cell Carcinoma. Front Oncol 2022; 12:749119. [PMID: 35651807 PMCID: PMC9149313 DOI: 10.3389/fonc.2022.749119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To systematically investigate the characterization of tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC), we performed a comprehensive analysis incorporating genomic alterations, cellular interactions, infiltrating immune cells, and risk signature. Patients and Methods Multi-omics data including RNA-seq, single-nucleotide variant (SNV) data, copy number variation (CNV) data, miRNA, and corresponding prognostic data were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. The CIBERSORT algorithm was utilized to identify prognostic TME subclusters, and TMEscore was further quantified. Moreover, the mutational landscape of TCGA-KIRC was explored. Lastly, TIDE resource was applied to assess the significance of TMEscore in predicting immunotherapeutic benefits. Results We analyzed the TME infiltration patterns from 621 ccRCC patients and identified 5 specific TME subclusters associated with clinical outcomes. Then, we found that TMEcluster5 was significantly related to favorable prognosis and enriched memory B-cell infiltration. Accordingly, we depicted the clustering landscape of TMEclusters, TMEscore levels, tumor mutation burden (TMB), tumor grades, purity, and ploidy in all patients. Lastly, TIDE was used to assess the efficiency of immune checkpoint blockers (ICBs) and found that the TMEscore has superior predictive significance to TMB, making it an essential independent prognostic biomarker and drug indicator for clinical use. Conclusions Our study depicted the clustering landscape of TMEclusters, TMEscore levels, TMB, tumor grades, purity, and ploidy in total ccRCC patients. The TMEscore was proved to have promising significance for predicting prognosis and ICB responses, in accordance with the goal of developing rationally individualized therapeutic interventions.
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Affiliation(s)
- Chuanjie Zhang
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feng Qi
- Department of Urology, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Yuxiao Zheng
- Department of Urology, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Xin Xia
- Department of Anatomy, Nanjing Medical University, Nanjing, China
| | - Xiao Li
- Department of Urology, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Xinwei Wang
- Department of Medical Oncology, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
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24
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Armstrong N, Storey CM, Noll SE, Margulis K, Soe MH, Xu H, Yeh B, Fishbein L, Kebebew E, Howitt BE, Zare RN, Sage J, Annes JP. SDHB knockout and succinate accumulation are insufficient for tumorigenesis but dual SDHB/NF1 loss yields SDHx-like pheochromocytomas. Cell Rep 2022; 38:110453. [PMID: 35235785 PMCID: PMC8939053 DOI: 10.1016/j.celrep.2022.110453] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 11/03/2021] [Accepted: 02/07/2022] [Indexed: 12/29/2022] Open
Abstract
Inherited pathogenic succinate dehydrogenase (SDHx) gene mutations cause the hereditary pheochromocytoma and paraganglioma tumor syndrome. Syndromic tumors exhibit elevated succinate, an oncometabolite that is proposed to drive tumorigenesis via DNA and histone hypermethylation, mitochondrial expansion, and pseudohypoxia-related gene expression. To interrogate this prevailing model, we disrupt mouse adrenal medulla SDHB expression, which recapitulates several key molecular features of human SDHx tumors, including succinate accumulation but not 5hmC loss, HIF accumulation, or tumorigenesis. By contrast, concomitant SDHB and the neurofibromin 1 tumor suppressor disruption yields SDHx-like pheochromocytomas. Unexpectedly, in vivo depletion of the 2-oxoglutarate (2-OG) dioxygenase cofactor ascorbate reduces SDHB-deficient cell survival, indicating that SDHx loss may be better tolerated by tissues with high antioxidant capacity. Contrary to the prevailing oncometabolite model, succinate accumulation and 2-OG-dependent dioxygenase inhibition are insufficient for mouse pheochromocytoma tumorigenesis, which requires additional growth-regulatory pathway activation.
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Affiliation(s)
- Neali Armstrong
- Department of Medicine, Division of Endocrinology, Stanford University, Stanford, CA, USA
| | - Claire M Storey
- Department of Medicine, Division of Endocrinology, Stanford University, Stanford, CA, USA
| | - Sarah E Noll
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | | | - Myat Han Soe
- Department of Medicine, Division of Endocrinology, Stanford University, Stanford, CA, USA
| | - Haixia Xu
- Department of Medicine, Division of Endocrinology, Stanford University, Stanford, CA, USA
| | | | - Lauren Fishbein
- Department of Medicine, Division of Endocrinology, Metabolism, and Diabetes, Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Electron Kebebew
- Department of Surgery and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Brooke E Howitt
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - Richard N Zare
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Julien Sage
- Department of Pediatrics and Genetics, Stanford University, Stanford, CA, USA
| | - Justin P Annes
- Department of Medicine, Division of Endocrinology, Stanford University, Stanford, CA, USA; Endocrine Oncology Program, Stanford University, Stanford, CA, USA; Chemistry, Engineering, and Medicine for Human Health (ChEM-H) Institute, Stanford University, Stanford, CA, USA.
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25
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Prade VM, Sun N, Shen J, Feuchtinger A, Kunzke T, Buck A, Schraml P, Moch H, Schwamborn K, Autenrieth M, Gschwend JE, Erlmeier F, Hartmann A, Walch A. The synergism of spatial metabolomics and morphometry improves machine learning‐based renal tumour subtype classification. Clin Transl Med 2022; 12:e666. [PMID: 35184396 PMCID: PMC8858620 DOI: 10.1002/ctm2.666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/11/2021] [Accepted: 11/17/2021] [Indexed: 12/14/2022] Open
Affiliation(s)
- Verena M. Prade
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Na Sun
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Jian Shen
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Annette Feuchtinger
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Thomas Kunzke
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Achim Buck
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
| | - Peter Schraml
- Institute of Pathology and Molecular Pathology University Hospital Zurich Zurich Switzerland
| | - Holger Moch
- Institute of Pathology and Molecular Pathology University Hospital Zurich Zurich Switzerland
| | | | | | | | - Franziska Erlmeier
- Institute of Pathology, University Hospital Erlangen Friedrich‐Alexander‐University Erlangen‐Nürnberg Erlangen Germany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN) Erlangen Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen Friedrich‐Alexander‐University Erlangen‐Nürnberg Erlangen Germany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN) Erlangen Germany
| | - Axel Walch
- Research Unit Analytical Pathology Helmholtz Zentrum München – German Research Center for Environmental Health Neuherberg Germany
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26
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Rice MA, Kumar V, Tailor D, Garcia-Marques FJ, Hsu EC, Liu S, Bermudez A, Kanchustambham V, Shankar V, Inde Z, Alabi BR, Muruganantham A, Shen M, Pandrala M, Nolley R, Aslan M, Ghoochani A, Agarwal A, Buckup M, Kumar M, Going CC, Peehl DM, Dixon SJ, Zare RN, Brooks JD, Pitteri SJ, Malhotra SV, Stoyanova T. SU086, an inhibitor of HSP90, impairs glycolysis and represents a treatment strategy for advanced prostate cancer. Cell Rep Med 2022; 3:100502. [PMID: 35243415 PMCID: PMC8861828 DOI: 10.1016/j.xcrm.2021.100502] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 10/09/2021] [Accepted: 12/20/2021] [Indexed: 12/19/2022]
Abstract
Among men, prostate cancer is the second leading cause of cancer-associated mortality, with advanced disease remaining a major clinical challenge. We describe a small molecule, SU086, as a therapeutic strategy for advanced prostate cancer. We demonstrate that SU086 inhibits the growth of prostate cancer cells in vitro, cell-line and patient-derived xenografts in vivo, and ex vivo prostate cancer patient specimens. Furthermore, SU086 in combination with standard of care second-generation anti-androgen therapies displays increased impairment of prostate cancer cell and tumor growth in vitro and in vivo. Cellular thermal shift assay reveals that SU086 binds to heat shock protein 90 (HSP90) and leads to a decrease in HSP90 levels. Proteomic profiling demonstrates that SU086 binds to and decreases HSP90. Metabolomic profiling reveals that SU086 leads to perturbation of glycolysis. Our study identifies SU086 as a treatment for advanced prostate cancer as a single agent or when combined with second-generation anti-androgens.
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Affiliation(s)
- Meghan A. Rice
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Vineet Kumar
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Dhanir Tailor
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
- Department of Cell, Development and Cancer Biology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Center for Experimental Therapeutics, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Fernando Jose Garcia-Marques
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - En-Chi Hsu
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Shiqin Liu
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Abel Bermudez
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | | | - Vishnu Shankar
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Zintis Inde
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Busola Ruth Alabi
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Arvind Muruganantham
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Michelle Shen
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Mallesh Pandrala
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
- Department of Cell, Development and Cancer Biology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Center for Experimental Therapeutics, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Rosalie Nolley
- Department of Urology, Stanford University, Stanford, CA 94305, USA
| | - Merve Aslan
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Ali Ghoochani
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Arushi Agarwal
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Mark Buckup
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Manoj Kumar
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Catherine C. Going
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Donna M. Peehl
- Department of Urology, Stanford University, Stanford, CA 94305, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Scott J. Dixon
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Richard N. Zare
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - James D. Brooks
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
- Department of Urology, Stanford University, Stanford, CA 94305, USA
| | - Sharon J. Pitteri
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Sanjay V. Malhotra
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
- Department of Cell, Development and Cancer Biology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Center for Experimental Therapeutics, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Tanya Stoyanova
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
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27
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Solon E, Groseclose MR, Ho S, Tanaka K, Nakada N, Linehan S, Nishidate M, Yokoi H, Kaji H, Urasaki Y, Watanabe K, Ishida T, Komatsu R, Yoshida K, Yamazaki H, Saito K, Saito Y, Tanaka Y. Imaging Mass Spectrometry (IMS) for drug discovery and development survey: Results on methods, applications and regulatory compliance. Drug Metab Pharmacokinet 2021; 43:100438. [DOI: 10.1016/j.dmpk.2021.100438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 11/08/2021] [Accepted: 11/19/2021] [Indexed: 12/26/2022]
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28
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Recent Advances of Ambient Mass Spectrometry Imaging and Its Applications in Lipid and Metabolite Analysis. Metabolites 2021; 11:metabo11110780. [PMID: 34822438 PMCID: PMC8625079 DOI: 10.3390/metabo11110780] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/08/2021] [Accepted: 11/11/2021] [Indexed: 01/02/2023] Open
Abstract
Ambient mass spectrometry imaging (AMSI) has attracted much attention in recent years. As a kind of unlabeled molecular imaging technique, AMSI can enable in situ visualization of a large number of compounds in biological tissue sections in ambient conditions. In this review, the developments of various AMSI techniques are discussed according to one-step and two-step ionization strategies. In addition, recent applications of AMSI for lipid and metabolite analysis (from 2016 to 2021) in disease diagnosis, animal model research, plant science, drug metabolism and toxicology research, etc., are summarized. Finally, further perspectives of AMSI in spatial resolution, sensitivity, quantitative ability, convenience and software development are proposed.
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29
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Shankar V, Tibshirani R, Zare RN. MassExplorer: a computational tool for analyzing desorption electrospray ionization mass spectrometry data. Bioinformatics 2021; 37:btab282. [PMID: 34009252 DOI: 10.1093/bioinformatics/btab282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 04/04/2021] [Accepted: 04/23/2021] [Indexed: 11/13/2022] Open
Abstract
Summary In the last few years, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) has been increasingly used for simultaneous detection of thousands of metabolites and lipids from human tissues and biofluids. To successfully find the most significant differences between two sets of DESI-MSI data (e.g., healthy vs disease) requires the application of accurate computational and statistical methods that can pre-process the data under various normalization settings and help identify these changes among thousands of detected metabolites. Here, we report MassExplorer, a novel computational tool, to help pre-process DESI-MSI data, visualize raw data, build predictive models using the statistical lasso approach to select for a sparse set of significant molecular changes, and interpret selected metabolites. This tool, which is available for both online and offline use, is flexible for both chemists and biologists and statisticians as it helps in visualizing structure of DESI-MSI data and in analyzing the statistically significant metabolites that are differentially expressed across both sample types. Based on the modules in MassExplorer, we expect it to be immediately useful for various biological and chemical applications in mass spectrometry. Availability and implementation MassExplorer is available as an online R-Shiny application or Mac OS X compatible standalone application. The application, sample performance, source code and corresponding guide can be found at: https://zarelab.com/research/massexplorer-a-tool-to-help-guide-analysis-of-mass-spectrometry-samples/. Supplementary informationMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vishnu Shankar
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Robert Tibshirani
- Department of Statistics and Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Richard N Zare
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
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30
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Woolman M, Katz L, Tata A, Basu SS, Zarrine-Afsar A. Breaking Through the Barrier: Regulatory Considerations Relevant to Ambient Mass Spectrometry at the Bedside. Clin Lab Med 2021; 41:221-246. [PMID: 34020761 DOI: 10.1016/j.cll.2021.03.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Rapid characterization of tissue disorder using ambient mass spectrometry (MS) techniques, requiring little to no preanalytical preparations of sampled tissues, has been shown using a variety of ion sources and with many disease classes. A brief overview of ambient MS in clinical applications, the state of the art in regulatory affairs, and recommendations to facilitate adoption for use at the bedside are presented. Unique challenges in the validation of untargeted MS methods and additional safety and compliance requirements for deployment within a clinical setting are further discussed. Development of a harmonized validation strategy for ambient MS methods is emphasized.
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Affiliation(s)
- Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, Ontario M5G 1P5, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Lauren Katz
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, Ontario M5G 1P5, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Alessandra Tata
- Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico delle Venezie, Viale Fiume 78, 36100 Vicenza, Italy
| | - Sankha S Basu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Arash Zarrine-Afsar
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, Ontario M5G 1P5, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada; Department of Surgery, University of Toronto, 149 College Street, Toronto, Ontario M5T 1P5, Canada; Keenan Research Center for Biomedical Science & the Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada.
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31
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Manzi M, Palazzo M, Knott ME, Beauseroy P, Yankilevich P, Giménez MI, Monge ME. Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma. J Proteome Res 2020; 20:841-857. [PMID: 33207877 DOI: 10.1021/acs.jproteome.0c00663] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.
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Affiliation(s)
- Malena Manzi
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD CABA, Argentina.,Departamento de Química Biológica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD Buenos Aires, Argentina
| | - Martín Palazzo
- LM2S, Université de Technologie de Troyes, 12 rue Marie-Curie, CS42060 Troyes, France.,Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA), CONICET, Instituto Partner de la Sociedad Max Planck, Godoy Cruz 2390, C1425FQD CABA, Argentina
| | - María Elena Knott
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD CABA, Argentina
| | - Pierre Beauseroy
- LM2S, Université de Technologie de Troyes, 12 rue Marie-Curie, CS42060 Troyes, France
| | - Patricio Yankilevich
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA), CONICET, Instituto Partner de la Sociedad Max Planck, Godoy Cruz 2390, C1425FQD CABA, Argentina
| | - María Isabel Giménez
- Departamento de Diagnóstico y Tratamiento, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, C1199ABB CABA, Argentina
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD CABA, Argentina
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32
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Li N, Nie H, Jiang L, Ruan G, Du F, Liu H. Recent advances of ambient ionization mass spectrometry imaging in clinical research. J Sep Sci 2020; 43:3146-3163. [PMID: 32573988 DOI: 10.1002/jssc.202000273] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 05/03/2020] [Accepted: 05/06/2020] [Indexed: 02/06/2023]
Abstract
The structural information and spatial distribution of molecules in biological tissues are closely related to the potential molecular mechanisms of disease origin, transfer, and classification. Ambient ionization mass spectrometry imaging is an effective tool that provides molecular images while describing in situ information of biomolecules in complex samples, in which ionization occurs at atmospheric pressure with the samples being analyzed in the native state. Ambient ionization mass spectrometry imaging can directly analyze tissue samples at a fairly high resolution to obtain molecules in situ information on the tissue surface to identify pathological features associated with a disease, resulting in the wide applications in pharmacy, food science, botanical research, and especially clinical research. Herein, novel ambient ionization techniques, such as techniques based on spray and solid-liquid extraction, techniques based on plasma desorption, techniques based on laser desorption ablation, and techniques based on acoustic desorption were introduced, and the data processing of ambient ionization mass spectrometry imaging was briefly reviewed. Besides, we also highlight recent applications of this imaging technology in clinical researches and discuss the challenges in this imaging technology and the perspectives on the future of the clinical research.
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Affiliation(s)
- Na Li
- Guangxi Key Laboratory of Electrochemical and Magnetochemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, P. R. China
- College of Chemistry and Molecular Engineering, Peking University, Beijing, P. R. China
| | - Honggang Nie
- College of Chemistry and Molecular Engineering, Peking University, Beijing, P. R. China
| | - Liping Jiang
- Guangxi Key Laboratory of Electrochemical and Magnetochemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, P. R. China
| | - Guihua Ruan
- Guangxi Key Laboratory of Electrochemical and Magnetochemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, P. R. China
| | - Fuyou Du
- Guangxi Key Laboratory of Electrochemical and Magnetochemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin, P. R. China
- College of Biological and Environmental Engineering, Changsha University, Changsha, P. R. China
| | - Huwei Liu
- College of Chemistry and Molecular Engineering, Peking University, Beijing, P. R. China
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33
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Hale OJ, Cooper HJ. In situ mass spectrometry analysis of intact proteins and protein complexes from biological substrates. Biochem Soc Trans 2020; 48:317-326. [PMID: 32010951 PMCID: PMC7054757 DOI: 10.1042/bst20190793] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 01/09/2020] [Accepted: 01/09/2020] [Indexed: 12/15/2022]
Abstract
Advances in sample preparation, ion sources and mass spectrometer technology have enabled the detection and characterisation of intact proteins. The challenges associated include an appropriately soft ionisation event, efficient transmission and detection of the often delicate macromolecules. Ambient ion sources, in particular, offer a wealth of strategies for analysis of proteins from solution environments, and directly from biological substrates. The last two decades have seen rapid development in this area. Innovations include liquid extraction surface analysis, desorption electrospray ionisation and nanospray desorption electrospray ionisation. Similarly, developments in native mass spectrometry allow protein-protein and protein-ligand complexes to be ionised and analysed. Identification and characterisation of these large ions involves a suite of hyphenated mass spectrometry techniques, often including the coupling of ion mobility spectrometry and fragmentation techniques. The latter include collision, electron and photon-induced methods, each with their own characteristics and benefits for intact protein identification. In this review, recent developments for in situ protein analysis are explored, with a focus on ion sources and tandem mass spectrometry techniques used for identification.
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Affiliation(s)
- Oliver J. Hale
- School of Biosciences, University of Birmingham, Edgbaston B15 2TT, U.K
| | - Helen J. Cooper
- School of Biosciences, University of Birmingham, Edgbaston B15 2TT, U.K
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