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Brewer M, Migas LG, Clouthier KA, Allen JL, Anderson DM, Pingry E, Farrow M, Quardokus EM, Spraggins JM, Van de Plas R, de Caestecker MP. Validation of an organ mapping antibody panel for cyclical immunofluorescence microscopy on normal human kidneys. Am J Physiol Renal Physiol 2024; 327:F91-F102. [PMID: 38721662 DOI: 10.1152/ajprenal.00426.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/11/2024] [Accepted: 03/26/2024] [Indexed: 06/21/2024] Open
Abstract
The lack of standardization in antibody validation remains a major contributor to irreproducibility of human research. To address this, we have applied a standardized approach to validate a panel of antibodies to identify 18 major cell types and 5 extracellular matrix compartments in the human kidney by immunofluorescence (IF) microscopy. We have used these to generate an organ mapping antibody panel for two-dimensional (2-D) and three-dimensional (3-D) cyclical IF (CyCIF) to provide a more detailed method for evaluating tissue segmentation and volumes using a larger panel of markers than would normally be possible using standard fluorescence microscopy. CyCIF also makes it possible to perform multiplexed IF microscopy of whole slide images, which is a distinct advantage over other multiplexed imaging technologies that are applicable to limited fields of view. This enables a broader view of cell distributions across larger anatomical regions, allowing a better chance to capture localized regions of dysfunction in diseased tissues. These methods are broadly accessible to any laboratory with a fluorescence microscope, enabling spatial cellular phenotyping in normal and disease states. We also provide a detailed solution for image alignment between CyCIF cycles that can be used by investigators to perform these studies without programming experience using open-sourced software. This ability to perform multiplexed imaging without specialized instrumentation or computational skills opens the door to integration with more highly dimensional molecular imaging modalities such as spatial transcriptomics and imaging mass spectrometry, enabling the discovery of molecular markers of specific cell types, and how these are altered in disease.NEW & NOTEWORTHY We describe here validation criteria used to define on organ mapping panel of antibodies that can be used to define 18 cell types and five extracellular matrix compartments using cyclical immunofluorescence (CyCIF) microscopy. As CyCIF does not require specialized instrumentation, and image registration required to assemble CyCIF images can be performed by any laboratory without specialized computational skills, this technology is accessible to any laboratory with access to a fluorescence microscope and digital scanner.
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Affiliation(s)
- Maya Brewer
- Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Lukasz G Migas
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
| | - Kelly A Clouthier
- Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Jamie L Allen
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - David M Anderson
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Ellie Pingry
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Melissa Farrow
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Chemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Raf Van de Plas
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Mark P de Caestecker
- Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
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2
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Liao X, Scheidereit E, Kuppe C. New tools to study renal fibrogenesis. Curr Opin Nephrol Hypertens 2024; 33:420-426. [PMID: 38587103 PMCID: PMC11139246 DOI: 10.1097/mnh.0000000000000988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
PURPOSE OF REVIEW Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development. RECENT FINDINGS We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single-cell RNA sequencing (scRNA-seq) [1] . We analysed cells from healthy human kidneys and compared them to patient samples with CKD. We identified PDGFRα+/PDGFRβ+ mesenchymal cells as the primary cellular source of extracellular matrix (ECM) in human kidney fibrosis. We found several commonly shared cell states of fibroblasts and myofibroblasts and provided insights into molecular regulators. Novel single-cell and spatial multiomics tools are now available to shed light on cell lineages, the plasticity of kidney cells and cell-cell communication in fibrosis. SUMMARY As further single-cell and spatial multiomic approaches are being developed, opportunities to apply these methods to human kidney tissues expand similarly. Careful design and optimisation of the multiomic experiments are needed to answer questions related to cell lineages, plasticity and cell-cell communication in kidney fibrosis.
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Affiliation(s)
- Xian Liao
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
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3
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Rietjens RGJ, Wang G, van der Velden AIM, Koudijs A, Avramut MC, Kooijman S, Rensen PCN, van der Vlag J, Rabelink TJ, Heijs B, van den Berg BM. Phosphatidylinositol metabolism of the renal proximal tubule S3 segment is disturbed in response to diabetes. Sci Rep 2023; 13:6261. [PMID: 37069341 PMCID: PMC10110589 DOI: 10.1038/s41598-023-33442-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/12/2023] [Indexed: 04/19/2023] Open
Abstract
Diabetes is a main risk factor for kidney disease, causing diabetic nephropathy in close to half of all patients with diabetes. Metabolism has recently been identified to be decisive in cell fate decisions and repair. Here we used mass spectrometry imaging (MSI) to identify tissue specific metabolic dysregulation, in order to better understand early diabetes-induced metabolic changes of renal cell types. In our experimental diabetes mouse model, early glomerular glycocalyx barrier loss and systemic metabolic changes were observed. In addition, MSI targeted at small molecule metabolites and glycero(phospho)lipids exposed distinct changes upon diabetes in downstream nephron segments. Interestingly, the outer stripe of the outer medullar proximal tubular segment (PT_S3) demonstrated the most distinct response compared to other segments. Furthermore, phosphatidylinositol lipid metabolism was altered specifically in PT_S3, with one of the phosphatidylinositol fatty acid tails being exchanged from longer unsaturated fatty acids to shorter, more saturated fatty acids. In acute kidney injury, the PT_S3 segment and its metabolism are already recognized as important factors in kidney repair processes. The current study exposes early diabetes-induced changes in membrane lipid composition in this PT_S3 segment as a hitherto unrecognized culprit in the early renal response to diabetes.
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Affiliation(s)
- Rosalie G J Rietjens
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, The Netherlands
| | - Gangqi Wang
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, The Netherlands
| | - Anouk I M van der Velden
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Angela Koudijs
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - M Cristina Avramut
- Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Cell and Chemical Biology (Electron Microscopy), Leiden University Medical Center, Leiden, The Netherlands
| | - Sander Kooijman
- Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine (Endocrinology), Leiden University Medical Center, Leiden, The Netherlands
| | - Patrick C N Rensen
- Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine (Endocrinology), Leiden University Medical Center, Leiden, The Netherlands
| | - Johan van der Vlag
- Department of Nephrology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ton J Rabelink
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, The Netherlands
| | - Bram Heijs
- The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, The Netherlands
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Bernard M van den Berg
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, The Netherlands.
- Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands.
- The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, The Netherlands.
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4
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Mondal S, Singh MP, Kumar A, Chattopadhyay S, Nandy A, Sthanikam Y, Pandey U, Koner D, Marisiddappa L, Banerjee S. Rapid Molecular Evaluation of Human Kidney Tissue Sections by In Situ Mass Spectrometry and Machine Learning to Classify the Nephrotic Syndrome. J Proteome Res 2023; 22:967-976. [PMID: 36696358 DOI: 10.1021/acs.jproteome.2c00768] [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: 01/26/2023]
Abstract
Nephrotic syndrome (NS) is classified based on morphological changes of glomeruli in biopsied kidney tissues evaluated by time-consuming microscopy methods. In contrast, we employed desorption electrospray ionization mass spectrometry (DESI-MS) directly on renal biopsy specimens obtained from 37 NS patients to rapidly differentiate lipid profiles of three prevalent forms of NS: IgA nephropathy (n = 9), membranous glomerulonephritis (n = 7), and lupus nephritis (n = 8), along with other types of glomerular diseases (n = 13). As we noted molecular heterogeneity in regularly spaced renal tissue regions, multiple sections from each biopsy specimen were collected, providing a total of 973 samples for investigation. Using multivariate analysis, we report differential expressions of glycerophospholipids, sphingolipids, and glycerolipids among the above four classes of NS kidneys, which were otherwise overlooked in several past studies correlating lipid abnormalities with glomerular diseases. We developed machine learning (ML) models with the top 100 features using the support vector machine, which enabled us to discriminate the concerned glomerular diseases with 100% overall accuracy in the training, validation, and holdout test set. This DESI-MS/ML-based tissue analysis can be completed in a few minutes, in sharp contrast to a daylong procedure followed in the conventional histopathology of NS.
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Affiliation(s)
- Supratim Mondal
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Mithlesh Prasad Singh
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Anubhav Kumar
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Sutirtha Chattopadhyay
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Abhijit Nandy
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Yeswanth Sthanikam
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Uddeshya Pandey
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Debasish Koner
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Limesh Marisiddappa
- Department of Nephrology, St. John's Medical College Hospital, Bangalore 560034, India
| | - Shibdas Banerjee
- Department of Chemistry, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
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5
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Mou M, Pan Z, Lu M, Sun H, Wang Y, Luo Y, Zhu F. Application of Machine Learning in Spatial Proteomics. J Chem Inf Model 2022; 62:5875-5895. [PMID: 36378082 DOI: 10.1021/acs.jcim.2c01161] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.
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Affiliation(s)
- Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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6
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Li L, Sun C, Sun Y, Dong Z, Wu R, Sun X, Zhang H, Jiang W, Zhou Y, Cen X, Cai S, Xia H, Zhu Y, Guo T, Piatkevich KD. Spatially resolved proteomics via tissue expansion. Nat Commun 2022; 13:7242. [PMID: 36450705 PMCID: PMC9712279 DOI: 10.1038/s41467-022-34824-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 11/04/2022] [Indexed: 12/12/2022] Open
Abstract
Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity of biological samples, however, it remains technically challenging due to the complexity of the tissue microsampling techniques and mass spectrometry analysis of nanoscale specimen volumes. Here, we describe a spatially resolved proteomics method based on the combination of tissue expansion with mass spectrometry-based proteomics, which we call Expansion Proteomics (ProteomEx). ProteomEx enables quantitative profiling of the spatial variability of the proteome in mammalian tissues at ~160 µm lateral resolution, equivalent to the tissue volume of 0.61 nL, using manual microsampling without the need for custom or special equipment. We validated and demonstrated the utility of ProteomEx for streamlined large-scale proteomics profiling of biological tissues including brain, liver, and breast cancer. We further applied ProteomEx for identifying proteins associated with Alzheimer's disease in a mouse model by comparative proteomic analysis of brain subregions.
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Affiliation(s)
- Lu Li
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.13402.340000 0004 1759 700XCollege of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310024 Zhejiang China
| | - Cuiji Sun
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Yaoting Sun
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Zhen Dong
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Runxin Wu
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.21107.350000 0001 2171 9311Whiting School of Engineering, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
| | - Xiaoting Sun
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Hanbin Zhang
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Wenhao Jiang
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Yan Zhou
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Xufeng Cen
- grid.13402.340000 0004 1759 700XDepartment of Biochemistry & Molecular Medical Center, Zhejiang University School of Medicine, Hangzhou, 310058 China
| | - Shang Cai
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Hongguang Xia
- grid.13402.340000 0004 1759 700XDepartment of Biochemistry & Molecular Medical Center, Zhejiang University School of Medicine, Hangzhou, 310058 China ,grid.452661.20000 0004 1803 6319Research Center for Clinical Pharmacy & Key Laboratory for Drug Evaluation and Clinical Research of Zhejiang Province, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 China ,grid.13402.340000 0004 1759 700XZhejiang Laboratory for Systems & Precision Medicine, Zhejiang University Medical Center, 1369 West Wenyi Road, Hangzhou, 311121 China
| | - Yi Zhu
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Tiannan Guo
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Kiryl D. Piatkevich
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
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7
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Zemaitis KJ, Veličković D, Kew W, Fort KL, Reinhardt-Szyba M, Pamreddy A, Ding Y, Kaushik D, Sharma K, Makarov AA, Zhou M, Paša-Tolić L. Enhanced Spatial Mapping of Histone Proteoforms in Human Kidney Through MALDI-MSI by High-Field UHMR-Orbitrap Detection. Anal Chem 2022; 94:12604-12613. [PMID: 36067026 PMCID: PMC10064997 DOI: 10.1021/acs.analchem.2c01034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Core histones including H2A, H2B, H3, and H4 are key modulators of cellular repair, transcription, and replication within eukaryotic cells, playing vital roles in the pathogenesis of disease and cellular responses to environmental stimuli. Traditional mass spectrometry (MS)-based bottom-up and top-down proteomics allows for the comprehensive identification of proteins and of post-translational modification (PTM) harboring proteoforms. However, these methodologies have difficulties preserving near-cellular spatial distributions because they typically require laser capture microdissection (LCM) and advanced sample preparation techniques. Herein, we coupled a matrix-assisted laser desorption/ionization (MALDI) source with a Thermo Scientific Q Exactive HF Orbitrap MS upgraded with ultrahigh mass range (UHMR) boards for the first demonstration of complementary high-resolution accurate mass (HR/AM) measurements of proteoforms up to 16.5 kDa directly from tissues using this benchtop mass spectrometer. The platform achieved isotopic resolution throughout the detected mass range, providing confident assignments of proteoforms with low ppm mass error and a considerable increase in duty cycle over other Fourier transform mass analyzers. Proteoform mapping of core histones was demonstrated on sections of human kidney at near-cellular spatial resolution, with several key distributions of histone and other proteoforms noted within both healthy biopsy and a section from a renal cell carcinoma (RCC) containing nephrectomy. The use of MALDI-MS imaging (MSI) for proteoform mapping demonstrates several steps toward high-throughput accurate identification of proteoforms and provides a new tool for mapping biomolecule distributions throughout tissue sections in extended mass ranges.
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Affiliation(s)
- Kevin J Zemaitis
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Dušan Veličković
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - William Kew
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Kyle L Fort
- Thermo Fisher Scientific (Bremen) GmbH, 28199 Bremen, Germany
| | | | - Annapurna Pamreddy
- Center for Renal Precision Medicine, Department of Medicine, University of Texas Health, San Antonio, Texas 78284, United States
| | - Yanli Ding
- Department of Pathology and Laboratory Medicine, University of Texas Health, San Antonio, Texas 78284, United States
| | - Dharam Kaushik
- Department of Urology, University of Texas Health, San Antonio, Texas 78284, United States
| | - Kumar Sharma
- Center for Renal Precision Medicine, Department of Medicine, University of Texas Health, San Antonio, Texas 78284, United States.,Audie L. Murphy Memorial VA Hospital, South Texas Veterans Health Care System, San Antonio, Texas 78284, United States
| | - Alexander A Makarov
- Thermo Fisher Scientific (Bremen) GmbH, 28199 Bremen, Germany.,Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht 3584, The Netherlands
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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Applications of Tandem Mass Spectrometry (MS/MS) in Protein Analysis for Biomedical Research. Molecules 2022; 27:molecules27082411. [PMID: 35458608 PMCID: PMC9031286 DOI: 10.3390/molecules27082411] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 01/27/2023] Open
Abstract
Mass Spectrometry (MS) allows the analysis of proteins and peptides through a variety of methods, such as Electrospray Ionization-Mass Spectrometry (ESI-MS) or Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry (MALDI-MS). These methods allow identification of the mass of a protein or a peptide as intact molecules or the identification of a protein through peptide-mass fingerprinting generated upon enzymatic digestion. Tandem mass spectrometry (MS/MS) allows the fragmentation of proteins and peptides to determine the amino acid sequence of proteins (top-down and middle-down proteomics) and peptides (bottom-up proteomics). Furthermore, tandem mass spectrometry also allows the identification of post-translational modifications (PTMs) of proteins and peptides. Here, we discuss the application of MS/MS in biomedical research, indicating specific examples for the identification of proteins or peptides and their PTMs as relevant biomarkers for diagnostic and therapy.
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