1
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Gorman BL, Shafer CC, Ragi N, Sharma K, Neumann EK, Anderton CR. Imaging and spatially resolved mass spectrometry applications in nephrology. Nat Rev Nephrol 2025; 21:399-416. [PMID: 40148534 DOI: 10.1038/s41581-025-00946-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2025] [Indexed: 03/29/2025]
Abstract
The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. The complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.
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Affiliation(s)
- Brittney L Gorman
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Catelynn C Shafer
- Department of Chemistry, University of California, Davis, Davis, CA, 95695, USA
| | - Nagarjunachary Ragi
- Center for Precision Medicine, The University of Texas Health San Antonio, San Antonio, TX, USA
| | - Kumar Sharma
- Center for Precision Medicine, The University of Texas Health San Antonio, San Antonio, TX, USA
- Division of Nephrology, Department of Medicine, The University of Texas Health San Antonio, San Antonio, TX, USA
| | - Elizabeth K Neumann
- Department of Chemistry, University of California, Davis, Davis, CA, 95695, USA
| | - Christopher R Anderton
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
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2
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Kirchgaessner R, Watson C, Creason A, Keutler K, Goecks J. Imputing single-cell protein abundance in multiplex tissue imaging. Nat Commun 2025; 16:4747. [PMID: 40404617 PMCID: PMC12098973 DOI: 10.1038/s41467-025-59788-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 05/06/2025] [Indexed: 05/24/2025] Open
Abstract
Multiplex tissue imaging enables single-cell spatial proteomics and transcriptomics but remains limited by incomplete molecular profiling, tissue loss, and probe failure. Here, we apply machine learning to impute single-cell protein abundance using multiplex tissue imaging data from a breast cancer cohort. We evaluate regularized linear regression, gradient-boosted trees, and deep learning autoencoders, incorporating spatial context to enhance imputation accuracy. Our models achieve mean absolute errors between 0.05-0.3 on a [0,1] scale, closely approximating ground truth values. Using imputed data, we classify single cells as pre- or post-treatment, demonstrating their biological relevance. These findings establish the feasibility of imputing missing protein abundance, highlight the advantages of spatial information, and support machine learning as a powerful tool for improving single-cell tissue imaging.
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Affiliation(s)
- Raphael Kirchgaessner
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Cameron Watson
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Allison Creason
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Kaya Keutler
- Department of Chemical Physiology and Biochemistry, Oregon Health & Science University, Portland, OR, USA
| | - Jeremy Goecks
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
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3
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Veličković D, Purkerson J, Bhotika H, Huyck H, Clair G, Pryhuber GS, Anderton C. Integrating N-glycan and CODEX imaging reveal cell-specific protein glycosylation in healthy human lung. Mol Omics 2025. [PMID: 40392055 PMCID: PMC12090982 DOI: 10.1039/d4mo00230j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2024] [Accepted: 05/11/2025] [Indexed: 05/22/2025]
Abstract
Identifying cell-specific glycan structures in human lungs is critical for understanding the chemistry and mechanisms that guide cell-cell and cell-matrix interactions and determining nuanced functions of specific glycosylation. Our dual-modality omics platform, which uses matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to profile glycan chemistry at 50 μm × 50 μm scale, combined with co-detection by indexing (CODEX) to provide cell identification from the exact same tissue section, is a significant step in this direction. It enabled us to detect, differentiate, and reveal chemical properties of N-glycans in the various cell types of a human lung, suggesting the cell-specific function of distinct carbohydrate moieties. This innovative technological combination bridges the gap between the specific protein glycosylation and their cellular origin, paving the way for targeted studies in the lungs and many other human tissues where glycans mediate cell-cell recognition events.
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Affiliation(s)
- Dušan Veličković
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.
| | - Jeffrey Purkerson
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York, USA
| | - Harsh Bhotika
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.
| | - Heidie Huyck
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York, USA
| | - Geremy Clair
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.
| | - Gloria S Pryhuber
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York, USA
| | - Christopher Anderton
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.
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4
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Lee Y, Lee M, Shin Y, Kim K, Kim T. Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications. Int J Mol Sci 2025; 26:3949. [PMID: 40362187 PMCID: PMC12071594 DOI: 10.3390/ijms26093949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2025] [Revised: 04/17/2025] [Accepted: 04/17/2025] [Indexed: 05/15/2025] Open
Abstract
Spatial omics integrates molecular profiling with spatial tissue context, enabling high-resolution analysis of gene expression, protein interactions, and epigenetic modifications. This approach provides critical insights into disease mechanisms and therapeutic responses, with applications in cancer, neurology, and immunology. Spatial omics technologies, including spatial transcriptomics, proteomics, and epigenomics, facilitate the study of cellular heterogeneity, tissue organization, and cell-cell interactions within their native environments. Despite challenges in data complexity and integration, advancements in multi-omics pipelines and computational tools are enhancing data accuracy and biological interpretation. This review provides a comprehensive overview of key spatial omics technologies, their analytical methods, validation strategies, and clinical applications. By integrating spatially resolved molecular data with traditional omics, spatial omics is transforming precision medicine, biomarker discovery, and personalized therapy. Future research should focus on improving standardization, reproducibility, and multimodal data integration to fully realize the potential of spatial omics in clinical and translational research.
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Affiliation(s)
- Yoonji Lee
- College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; (Y.L.); (M.L.); (Y.S.)
| | - Mingyu Lee
- College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; (Y.L.); (M.L.); (Y.S.)
| | - Yoojin Shin
- College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; (Y.L.); (M.L.); (Y.S.)
| | - Kyuri Kim
- College of Medicine, Ewha Womans University, 25 Magokdong-ro 2-gil, Gangseo-gu, Seoul 07804, Republic of Korea;
| | - Taejung Kim
- Department of Hospital Pathology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 10, 63-ro, Yeongdeungpo-gu, Seoul 07345, Republic of Korea
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5
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Zemaitis KJ, Fulcher JM, Kumar R, Degnan DJ, Lewis LA, Liao YC, Veličković M, Williams SM, Moore RJ, Bramer LM, Veličković D, Zhu Y, Zhou M, Paša-Tolić L. Spatial top-down proteomics for the functional characterization of human kidney. Clin Proteomics 2025; 22:9. [PMID: 40045235 PMCID: PMC11881370 DOI: 10.1186/s12014-025-09531-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 09/04/2024] [Indexed: 03/09/2025] Open
Abstract
BACKGROUND The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. RESULTS Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. CONCLUSIONS We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) while discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.
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Affiliation(s)
- Kevin J Zemaitis
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - James M Fulcher
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Rashmi Kumar
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - David J Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Logan A Lewis
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Yen-Chen Liao
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Marija Veličković
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Sarah M Williams
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Dušan Veličković
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Ying Zhu
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
- Department of Proteomic and Genomic Technologies, San Francisco, CA, 94080, USA
| | - Mowei Zhou
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
- Department of Chemistry, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
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6
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Whittle JR, Kriel J, Fatunla OE, Lu T, Moffet JJD, Spiteri M, Best SA, Freytag S. Spatial omics shed light on the tumour organisation of glioblastoma. Semin Cell Dev Biol 2025; 167:1-9. [PMID: 39787997 DOI: 10.1016/j.semcdb.2024.12.006] [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: 06/27/2024] [Revised: 10/23/2024] [Accepted: 12/30/2024] [Indexed: 01/12/2025]
Abstract
The glioblastoma tumour microenvironment is characterised by immense heterogeneity, with malignant and non-malignant cells that interact in a complex ecosystem. Emerging evidence suggests that the tumour microenvironment is key in facilitating rapid proliferation, invasion, migration and cancer cell survival, crucial for treatment resistance. Spatial omics technologies have enabled the molecular characterisation of regions or individual cells within their spatial context, providing previously unattainable insights into the complex organisation of the glioblastoma tumour microenvironment. Understanding this organisation is crucial for the development of new therapeutics and novel diagnostic tools that guide patient care. This review explores spatial omics technologies and how they have contributed to the development of a model outlining the architecture of the glioblastoma tumour microenvironment.
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Affiliation(s)
- James R Whittle
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Australia; Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Jurgen Kriel
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | - Oluwaseun E Fatunla
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | - Tianyao Lu
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
| | - Joel J D Moffet
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | - Montana Spiteri
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
| | - Sarah A Best
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Australia.
| | - Saskia Freytag
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Australia.
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7
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Esselman AB, Moser FA, Tideman LEM, Migas LG, Djambazova KV, Colley ME, Pingry EL, Patterson NH, Farrow MA, Yang H, Fogo AB, de Caestecker M, Van de Plas R, Spraggins JM. In situ molecular profiles of glomerular cells by integrated imaging mass spectrometry and multiplexed immunofluorescence microscopy. Kidney Int 2025; 107:332-337. [PMID: 39571907 DOI: 10.1016/j.kint.2024.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 09/13/2024] [Accepted: 11/11/2024] [Indexed: 12/07/2024]
Abstract
Glomeruli filter blood through the coordination of podocytes, mesangial cells, fenestrated endothelial cells, and the glomerular basement membrane. Cellular changes, such as podocyte loss, are associated with pathologies like diabetic kidney disease. However, little is known regarding the in situ molecular profiles of specific cell types and how these profiles change with disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is well-suited for untargeted tissue mapping of a wide range of molecular classes. Importantly, additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Here, we integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 μm) was used to determine lipid distributions within human glomeruli from a normal portion of fresh-frozen kidney cancer nephrectomy tissue revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types and substructures through new methods that enable MxIF microscopy to be performed on the same tissue section following MALDI IMS, without sacrificing signal quality from either modality. Machine learning approaches were combined enabling cell type segmentation and identification based on MxIF data. This was followed by mining of cell type or cluster-associated MALDI IMS signatures using classification and interpretable machine learning. This allowed automated discovery of spatially specific molecular markers for glomerular cell types and substructures as well as lipids correlated to deep and superficial glomeruli. Overall, our work establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments providing a framework applicable to other kidney tissue features and organ systems.
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Affiliation(s)
- Allison B Esselman
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA
| | - Felipe A Moser
- Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
| | - Léonore E M Tideman
- Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
| | - Lukasz G Migas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
| | - Katerina V Djambazova
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Madeline E Colley
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA
| | - Ellie L Pingry
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA
| | - Melissa A Farrow
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Haichun Yang
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Agnes B Fogo
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Mark de Caestecker
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Raf Van de Plas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands; Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA.
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA; Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
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8
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Vanderschoot KA, Bender KJ, De Caro CM, Steineman KA, Neumann EK. Multimodal Mass Spectrometry Imaging in Atlas Building: A Review. Semin Nephrol 2024; 44:151578. [PMID: 40246671 DOI: 10.1016/j.semnephrol.2025.151578] [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: 04/19/2025]
Abstract
In the era of precision medicine, scientists are creating atlases of the human body to map cells at the molecular level, providing insight into what fundamentally makes each cell different. In these atlas efforts, multimodal imaging techniques that include mass spectrometry imaging (MSI) have revolutionized the way biomolecules, such as lipids, peptides, proteins, and small metabolites, are visualized in the native spatial context of biological tissue. As such, MSI has become a fundamental arm of major cell atlasing efforts, as it can analyze the spatial distribution of hundreds of molecules in diverse sample types. These rich molecular data are then correlated with orthogonal assays, including histologic staining, proteomics, and transcriptomics, to analyze molecular classes that are not traditionally detected by MSI. Additional computational methods enable further examination of the correlations between biomolecular classes and creation of visualizations that serve as a powerful resource for researchers and clinicians trying to understand human health and disease. In this review, we examine modern multimodal imaging methods and how they contribute to precision medicine and the understanding of fundamental disease mechanisms.
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Affiliation(s)
| | - Kayle J Bender
- Chemistry Department, University of California at Davis, Davis, CA
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9
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Tasca P, van den Berg BM, Rabelink TJ, Wang G, Heijs B, van Kooten C, de Vries APJ, Kers J. Application of spatial-omics to the classification of kidney biopsy samples in transplantation. Nat Rev Nephrol 2024; 20:755-766. [PMID: 38965417 DOI: 10.1038/s41581-024-00861-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2024] [Indexed: 07/06/2024]
Abstract
Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.
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Affiliation(s)
- Paola Tasca
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - Bernard M van den Berg
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Ton J Rabelink
- Department of Internal Medicine, Division of Nephrology, 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, Division of Nephrology, 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
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Cees van Kooten
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Aiko P J de Vries
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands.
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands.
| | - Jesper Kers
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
- Center for Analytical Sciences Amsterdam, Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, the Netherlands
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10
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Gorman BL, Lukowski JK. Spatial Metabolomics and Lipidomics in Kidney Disease. Semin Nephrol 2024; 44:151582. [PMID: 40234137 DOI: 10.1016/j.semnephrol.2025.151582] [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: 04/17/2025]
Abstract
Kidney disease is a global health issue that affects over 850 million people, and early detection is key to preventing severe disease and complications. Kidney diseases are associated with complex and dysregulation of lipid metabolism. Spatial metabolomics through mass spectrometry imaging (MSI) enables spatial mapping of the lipids in tissue and includes a variety of techniques that can be used to image lipids. In the kidney, MSI studies often seek to resolve individual functional tissue units such as glomeruli and proximal tubules. Several different MSI techniques, such as matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), have been used to characterize lipids and small molecules in chronic kidney disease, acute kidney injury, genetic kidney disease, and cancer. In this review we provide several examples of how spatial metabolomics data can provide critical information concerning the localization of changes in various disease states. Additionally, when combined with pathology, transcriptomics, or proteomics, the metabolomic changes can illuminate underlying mechanisms and provide new clinical insights into disease mechanisms.
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Affiliation(s)
| | - Jessica K Lukowski
- Mass Spectrometry Imaging Lead, Mass Spectrometry Technology Access Center at the McDonnell Genome Institute, Washington University in St. Louis School of Medicine, St. Louis, MO
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11
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Kaur H, Heiser CN, McKinley ET, Ventura-Antunes L, Harris CR, Roland JT, Farrow MA, Selden HJ, Pingry EL, Moore JF, Ehrlich LIR, Shrubsole MJ, Spraggins JM, Coffey RJ, Lau KS, Vandekar SN. Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM). Commun Biol 2024; 7:1295. [PMID: 39478141 PMCID: PMC11525554 DOI: 10.1038/s42003-024-06281-8] [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: 02/03/2024] [Accepted: 05/02/2024] [Indexed: 11/02/2024] Open
Abstract
Spatially resolved molecular assays provide high dimensional genetic, transcriptomic, proteomic, and epigenetic information in situ and at various resolutions. Pairing these data across modalities with histological features enables powerful studies of tissue pathology in the context of an intact microenvironment and tissue structure. Increasing dimensions across molecular analytes and samples require new data science approaches to functionally annotate spatially resolved molecular data. A specific challenge is data-driven cross-sample domain detection that allows for analysis within and between consensus tissue compartments across high volumes of multiplex datasets stemming from tissue atlasing efforts. Here, we present MILWRM (multiplex image labeling with regional morphology)-a Python package for rapid, multi-scale tissue domain detection and annotation at the image- or spot-level. We demonstrate MILWRM's utility in identifying histologically distinct compartments in human colonic polyps, lymph nodes, mouse kidney, and mouse brain slices through spatially-informed clustering in two different spatial data modalities from different platforms. We used tissue domains detected in human colonic polyps to elucidate the molecular distinction between polyp subtypes, and explored the ability of MILWRM to identify anatomical regions of the brain tissue and their respective distinct molecular profiles.
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Affiliation(s)
- Harsimran Kaur
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Cody N Heiser
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | - Coleman R Harris
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Joseph T Roland
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Melissa A Farrow
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Hilary J Selden
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Ellie L Pingry
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - John F Moore
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Lauren I R Ehrlich
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Martha J Shrubsole
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Jeffrey M Spraggins
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robert J Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ken S Lau
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA.
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA.
| | - Simon N Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
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12
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Kwon Y, Woo J, Yu F, Williams SM, Markillie LM, Moore RJ, Nakayasu ES, Chen J, Campbell-Thompson M, Mathews CE, Nesvizhskii AI, Qian WJ, Zhu Y. Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows. Mol Cell Proteomics 2024; 23:100841. [PMID: 39307423 PMCID: PMC11541776 DOI: 10.1016/j.mcpro.2024.100841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 08/19/2024] [Accepted: 08/23/2024] [Indexed: 09/25/2024] Open
Abstract
Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.
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Affiliation(s)
- Yumi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, United States
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Lye Meng Markillie
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, United States
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, United States
| | - Clayton E Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, United States
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, United States; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States.
| | - Ying Zhu
- Department of Proteomic and Genomic Technologies, Genentech Inc, South San Francisco, California, United States.
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13
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Kondo A, McGrady M, Nallapothula D, Ali H, Trevino AE, Lam A, Preska R, D'Angio HB, Wu Z, Lopez LN, Badhesha HK, Vargas CR, Ramesh A, Wiegley N, Han SS, Dall'Era M, Jen KY, Mayer AT, Afkarian M. Spatial proteomics of human diabetic kidney disease, from health to class III. Diabetologia 2024; 67:1962-1979. [PMID: 39037603 DOI: 10.1007/s00125-024-06210-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/30/2024] [Indexed: 07/23/2024]
Abstract
AIMS/HYPOTHESIS Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease in the USA and worldwide. Animal models have taught us much about DKD mechanisms, but translation of this knowledge into treatments for human disease has been slowed by the lag in our molecular understanding of human DKD. METHODS Using our Spatial TissuE Proteomics (STEP) pipeline (comprising curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools), we imaged and analysed the expression of 21 proteins in 23 tissue sections from individuals with diabetes and healthy kidneys (n=5), compared to those with DKDIIA, IIA-B and IIB (n=2 each) and DKDIII (n=1). RESULTS These analyses revealed the existence of 11 cellular clusters (kidney compartments/cell types): podocytes, glomerular endothelial cells, proximal tubules, distal nephron, peritubular capillaries, blood vessels (endothelial cells and vascular smooth muscle cells), macrophages, myeloid cells, other CD45+ inflammatory cells, basement membrane and the interstitium. DKD progression was associated with co-localised increases in inflammatory cells and collagen IV deposition, with concomitant loss of native proteins of each nephron segment. Cell-type frequency and neighbourhood analyses highlighted a significant increase in inflammatory cells and their adjacency to tubular and αSMA+ (α-smooth muscle actin-positive) cells in DKD. Finally, DKD progression showed marked regional variability within single tissue sections, as well as inter-individual variability within each DKD class. CONCLUSIONS/INTERPRETATION Using the STEP pipeline, we found alterations in protein expression, cellular phenotypic composition and microenvironment structure with DKD progression, demonstrating the power of this pipeline to reveal the pathophysiology of human DKD.
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Affiliation(s)
| | | | | | - Hira Ali
- Enable Medicine, Menlo Park, CA, USA
| | | | - Amy Lam
- Enable Medicine, Menlo Park, CA, USA
| | | | | | | | - Lauren N Lopez
- Division of Nephrology, University of California, Davis, CA, USA
| | | | - Chenoa R Vargas
- Division of Nephrology, University of California, Davis, CA, USA
| | | | - Nasim Wiegley
- Division of Nephrology, University of California, Davis, CA, USA
| | - Seung Seok Han
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Marc Dall'Era
- Department of Urologic Surgery, University of California-Davis Medical Center, Sacramento, CA, USA
| | - Kuang-Yu Jen
- Department of Pathology and Laboratory Medicine, University of California- Davis, Sacramento, CA, USA
| | | | - Maryam Afkarian
- Division of Nephrology, University of California, Davis, CA, USA.
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14
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Kirchgaessner R, Watson C, Creason A, Keutler K, Goecks J. Imputing Single-Cell Protein Abundance in Multiplex Tissue Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.05.570058. [PMID: 38106203 PMCID: PMC10723289 DOI: 10.1101/2023.12.05.570058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Multiplex tissue imaging are a collection of increasingly popular single-cell spatial proteomics and transcriptomics assays for characterizing biological tissues both compositionally and spatially. However, several technical issues limit the utility of multiplex tissue imaging, including the limited number of molecules (proteins and RNAs) that can be assayed, tissue loss, and protein probe failure. In this work, we demonstrate how machine learning methods can address these limitations by imputing protein abundance at the single-cell level using multiplex tissue imaging datasets from a breast cancer cohort. We first compared machine learning methods' strengths and weaknesses for imputing single-cell protein abundance. Machine learning methods used in this work include regularized linear regression, gradient-boosted regression trees, and deep learning autoencoders. We also incorporated cellular spatial information to improve imputation performance. Using machine learning, single-cell protein expression can be imputed with mean absolute error ranging between 0.05-0.3 on a [0,1] scale. Finally, we used imputed data to predict whether single cells were more likely to come from pre-treatment or post-treatment biopsies. Our results demonstrate (1) the feasibility of imputing single-cell abundance levels for many proteins using machine learning; (2) how including cellular spatial information can substantially enhance imputation results; and (3) the use of single-cell protein abundance levels in a use case to demonstrate biological relevance.
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15
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Kwon Y, Woo J, Yu F, Williams SM, Markillie LM, Moore RJ, Nakayasu ES, Chen J, Campbell-Thompson M, Mathews CE, Nesvizhskii AI, Qian WJ, Zhu Y. Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583367. [PMID: 38496682 PMCID: PMC10942300 DOI: 10.1101/2024.03.04.583367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.
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Affiliation(s)
- Yumi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Sarah M. Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lye Meng Markillie
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J. Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Clayton E. Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Alexey I. Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Department of Proteomic and Genomic Technologies, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
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16
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Zemaitis KJ, Fulcher JM, Kumar R, Degnan DJ, Lewis LA, Liao YC, Veličković M, Williams SM, Moore RJ, Bramer LM, Veličković D, Zhu Y, Zhou M, Paša-Tolić L. Spatial top-down proteomics for the functional characterization of human kidney. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580062. [PMID: 38405958 PMCID: PMC10888776 DOI: 10.1101/2024.02.13.580062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.
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Affiliation(s)
- Kevin J. Zemaitis
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - James M. Fulcher
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Rashmi Kumar
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - David J. Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Logan A. Lewis
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Yen-Chen Liao
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Marija Veličković
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Sarah M. Williams
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J. Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lisa M. Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Dušan Veličković
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
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17
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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 PMCID: PMC11390132 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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18
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Asowata EO, Romoli S, Sargeant R, Tan JY, Hoffmann S, Huang MM, Mahbubani KT, Krause FN, Jachimowicz D, Agren R, Koulman A, Jenkins B, Musial B, Griffin JL, Soderberg M, Ling S, Hansen PBL, Saeb-Parsy K, Woollard KJ. Multi-omics and imaging mass cytometry characterization of human kidneys to identify pathways and phenotypes associated with impaired kidney function. Kidney Int 2024; 106:85-97. [PMID: 38431215 DOI: 10.1016/j.kint.2024.01.041] [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: 11/18/2022] [Revised: 01/16/2024] [Accepted: 01/24/2024] [Indexed: 03/05/2024]
Abstract
Despite the recent advances in our understanding of the role of lipids, metabolites, and related enzymes in mediating kidney injury, there is limited integrated multi-omics data identifying potential metabolic pathways driving impaired kidney function. The limited availability of kidney biopsies from living donors with acute kidney injury has remained a major constraint. Here, we validated the use of deceased transplant donor kidneys as a good model to study acute kidney injury in humans and characterized these kidneys using imaging and multi-omics approaches. We noted consistent changes in kidney injury and inflammatory markers in donors with reduced kidney function. Neighborhood and correlation analyses of imaging mass cytometry data showed that subsets of kidney cells (proximal tubular cells and fibroblasts) are associated with the expression profile of kidney immune cells, potentially linking these cells to kidney inflammation. Integrated transcriptomic and metabolomic analysis of human kidneys showed that kidney arachidonic acid metabolism and seven other metabolic pathways were upregulated following diminished kidney function. To validate the arachidonic acid pathway in impaired kidney function we demonstrated increased levels of cytosolic phospholipase A2 protein and related lipid mediators (prostaglandin E2) in the injured kidneys. Further, inhibition of cytosolic phospholipase A2 reduced injury and inflammation in human kidney proximal tubular epithelial cells in vitro. Thus, our study identified cell types and metabolic pathways that may be critical for controlling inflammation associated with impaired kidney function in humans.
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Affiliation(s)
- Evans O Asowata
- Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom; Department of Surgery, University of Cambridge and NIHR Biomedical Research Centre, Cambridge, United Kingdom
| | - Simone Romoli
- Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Rebecca Sargeant
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
| | - Jennifer Y Tan
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
| | - Scott Hoffmann
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
| | - Margaret M Huang
- Department of Surgery, University of Cambridge and NIHR Biomedical Research Centre, Cambridge, United Kingdom
| | - Krishnaa T Mahbubani
- Department of Surgery, University of Cambridge and NIHR Biomedical Research Centre, Cambridge, United Kingdom
| | - Fynn N Krause
- Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Daniel Jachimowicz
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Rasmus Agren
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Albert Koulman
- NIHR BRC Core Metabolomics and Lipidomics Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Benjamin Jenkins
- NIHR BRC Core Metabolomics and Lipidomics Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Barbara Musial
- Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Julian L Griffin
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Magnus Soderberg
- Department of Pathology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Stephanie Ling
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
| | - Pernille B L Hansen
- Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Kourosh Saeb-Parsy
- Department of Surgery, University of Cambridge and NIHR Biomedical Research Centre, Cambridge, United Kingdom.
| | - Kevin J Woollard
- Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom.
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19
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Kozakowski N. The histomorphology of the senescent kidney - the clinical relevance of specimen and biopsy findings in the elderly native kidneys. Curr Opin Urol 2024; 34:170-175. [PMID: 38410848 DOI: 10.1097/mou.0000000000001164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
PURPOSE OF REVIEW Renal pathology is crucial in diagnosing the ageing kidney. Recent technological advances enabled high-resolution molecular investigations into the complex mechanisms of ageing and senescence. RECENT FINDINGS The pathological analysis of large kidney tissue collections coupled with computer-assisted morphometry contributed to the establishment of age-related reference values for glomerular or vascular sclerosis, interstitial fibrosis, and tubular atrophy. Furthermore, new high-throughput proteomic and transcriptomic platforms have entered the field of pathology. When coupled with morphology information, these techniques facilitated the study of extracellular matrix modifications and the senescent immune system in the ageing kidney. Finally, iatrogenic complications are now frequent indications for diagnostic kidney biopsies in older patients, potentially accelerating kidney senescence. SUMMARY Recent pathology literature supports identifying and prognosticating sclerosing processes in ageing kidneys.
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Affiliation(s)
- Nicolas Kozakowski
- Medical University of Vienna, Department of Pathology, Vienna, Austria; General Hospital, Waehringer Guertel, Vienna, Austria
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20
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Alexander MP, Zaidi M, Larson N, Mullan A, Pavelko KD, Stegall MD, Bentall A, Wouters BG, McKee T, Taner T. Exploring the single-cell immune landscape of kidney allograft inflammation using imaging mass cytometry. Am J Transplant 2024; 24:549-563. [PMID: 37979921 DOI: 10.1016/j.ajt.2023.11.008] [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: 07/26/2023] [Revised: 11/01/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Kidney allograft inflammation, mostly attributed to rejection and infection, is an important cause of graft injury and loss. Standard histopathological assessment of allograft inflammation provides limited insights into biological processes and the immune landscape. Here, using imaging mass cytometry with a panel of 28 validated biomarkers, we explored the single-cell landscape of kidney allograft inflammation in 32 kidney transplant biopsies and 247 high-dimensional histopathology images of various phenotypes of allograft inflammation (antibody-mediated rejection, T cell-mediated rejection, BK nephropathy, and chronic pyelonephritis). Using novel analytical tools, for cell segmentation, we segmented over 900 000 cells and developed a tissue-based classifier using over 3000 manually annotated kidney microstructures (glomeruli, tubules, interstitium, and arteries). Using PhenoGraph, we identified 11 immune and 9 nonimmune clusters and found a high prevalence of memory T cell and macrophage-enriched immune populations across phenotypes. Additionally, we trained a machine learning classifier to identify spatial biomarkers that could discriminate between the different allograft inflammatory phenotypes. Further validation of imaging mass cytometry in larger cohorts and with more biomarkers will likely help interrogate kidney allograft inflammation in more depth than has been possible to date.
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Affiliation(s)
- Mariam P Alexander
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Rochester, Minnesota, USA.
| | - Mark Zaidi
- Department of Medical Biophysics, University of Toronto, Canada
| | - Nicholas Larson
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Aidan Mullan
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Kevin D Pavelko
- Immune Monitoring Core Laboratory, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark D Stegall
- Departments of Surgery and Immunology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew Bentall
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
| | - Bradly G Wouters
- Department of Medical Biophysics, University of Toronto, Canada; Princess Margaret Cancer Center, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Trevor McKee
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Pathomics Inc., Toronto, Ontario, Canada
| | - Timucin Taner
- Departments of Surgery and Immunology, Mayo Clinic, Rochester, Minnesota, USA
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21
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Abraham MJ, Goncalves C, McCallum P, Gupta V, Preston SEJ, Huang F, Chou H, Gagnon N, Johnson NA, Miller WH, Mann KK, Del Rincon SV. Tunable PhenoCycler imaging of the murine pre-clinical tumour microenvironments. Cell Biosci 2024; 14:19. [PMID: 38311785 PMCID: PMC10840224 DOI: 10.1186/s13578-024-01199-4] [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: 10/18/2023] [Accepted: 01/19/2024] [Indexed: 02/06/2024] Open
Abstract
BACKGROUND The tumour microenvironment (TME) consists of tumour-supportive immune cells, endothelial cells, and fibroblasts. PhenoCycler, a high-plex single cell spatial biology imaging platform, is used to characterize the complexity of the TME. Researchers worldwide harvest and bank tissues from mouse models which are employed to model a plethora of human disease. With the explosion of interest in spatial biology, these panoplies of archival tissues provide a valuable resource to answer new questions. Here, we describe our protocols for developing tunable PhenoCycler multiplexed imaging panels and describe our open-source data analysis pipeline. Using these protocols, we used PhenoCycler to spatially resolve the TME of 8 routinely employed pre-clinical models of lymphoma, breast cancer, and melanoma preserved as FFPE. RESULTS Our data reveal distinct TMEs in the different cancer models that were imaged and show that cell-cell contacts differ depending on the tumour type examined. For instance, we found that the immune infiltration in a murine model of melanoma is altered in cellular organization in melanomas that become resistant to αPD-1 therapy, with depletions in a number of cell-cell interactions. CONCLUSIONS This work presents a valuable resource study seamlessly adaptable to any field of research involving murine models. The methodology described allows researchers to address newly formed hypotheses using archival materials, bypassing the new to perform new mouse studies.
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Affiliation(s)
- Madelyn J Abraham
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
- Division of Experimental Medicine, McGill University, Montreal, QC, Canada
| | | | - Paige McCallum
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
- Division of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Vrinda Gupta
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
- Division of Experimental Medicine, McGill University, Montreal, QC, Canada
- University of British Columbia, Vancouver, BC, Canada
| | - Samuel E J Preston
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
- Division of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Fan Huang
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
- Division of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Hsiang Chou
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
- Clinical Research Unit, Jewish General Hospital, Montreal, QC, Canada
| | - Natascha Gagnon
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
| | - Nathalie A Johnson
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
- Division of Experimental Medicine, McGill University, Montreal, QC, Canada
- Clinical Research Unit, Jewish General Hospital, Montreal, QC, Canada
| | - Wilson H Miller
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada.
- Division of Experimental Medicine, McGill University, Montreal, QC, Canada.
- Clinical Research Unit, Jewish General Hospital, Montreal, QC, Canada.
| | - Koren K Mann
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada.
- Division of Experimental Medicine, McGill University, Montreal, QC, Canada.
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada.
| | - Sonia V Del Rincon
- Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada.
- Division of Experimental Medicine, McGill University, Montreal, QC, Canada.
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22
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Schnuelle P. Renal Biopsy for Diagnosis in Kidney Disease: Indication, Technique, and Safety. J Clin Med 2023; 12:6424. [PMID: 37835066 PMCID: PMC10573674 DOI: 10.3390/jcm12196424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/29/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023] Open
Abstract
Renal biopsies are the gold standard for diagnosis, staging, and prognosis of underlying parenchymal kidney disease. This article provides an overview of the current indications and highlights ways to reduce bleeding complications in order to achieve optimal diagnostic yield with minimal risk to the patient. Novel indications have emerged from the increasing use of new molecularly targeted oncologic therapies in recent years, which often induce immune-mediated renal disease. On the other hand, the detection of specific antibodies against target antigens on podocytes in the sera of patients with new-onset nephrotic syndrome has now relativized the indication for biopsy in membranous nephropathy. The use of semi-automatic spring-loaded biopsy devices and real-time ultrasound considerably declined the complication rate and is the current standard. Percutaneous renal biopsies are overall a safe procedure if contraindications are considered. A coagulation disorder needs to be excluded beforehand, and an elevated blood pressure must be reduced to the normotensive range with medications. A laparoscopic approach or a radiology interventional procedure through the internal jugular vein may be considered for obtaining a kidney tissue sample if there is an urgent indication and a bleeding tendency cannot be adequately corrected. Major bleeding after a percutaneous renal biopsy can usually be managed with selective arterial embolization of the injured renal vessel. The use of a 16-gauge needle is the most reasonable compromise between diagnostic benefit and risk of complication. In the routine diagnostic, the biopsy specimen is examined with light microscopy, immunohistochemistry, and electron microscopy. Combination with modern molecular pathology techniques will contribute to more precise insights into the development and progression of kidney disease, which will likely refine future treatments in nephrology.
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Affiliation(s)
- Peter Schnuelle
- Center for Renal Diseases Weinheim, Academic Teaching Practice of the University Medical Center Mannheim, University of Heidelberg, D-69469 Weinheim, Germany
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23
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Marshall CR, Farrow MA, Djambazova KV, Spraggins JM. Untangling Alzheimer's disease with spatial multi-omics: a brief review. Front Aging Neurosci 2023; 15:1150512. [PMID: 37533766 PMCID: PMC10390637 DOI: 10.3389/fnagi.2023.1150512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/13/2023] [Indexed: 08/04/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.
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Affiliation(s)
- Cody R. Marshall
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Melissa A. Farrow
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Katerina V. Djambazova
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Jeffrey M. Spraggins
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
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24
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Border S, Lucarelli N, Eadon MT, El-Achkar TM, Jain S, Sarder P. Computational Pathology Fusing Spatial Technologies. Clin J Am Soc Nephrol 2023; 18:675-677. [PMID: 36913267 PMCID: PMC10278855 DOI: 10.2215/cjn.0000000000000146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 03/14/2023]
Affiliation(s)
- Samuel Border
- Department of Biomedical Engineering, University of Florida College of Engineering, Gainesville, Florida
| | - Nicholas Lucarelli
- Department of Biomedical Engineering, University of Florida College of Engineering, Gainesville, Florida
| | - Michael T. Eadon
- Department of Medicine–Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana
| | - Tarek M. El-Achkar
- Department of Medicine–Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana
| | - Sanjay Jain
- Department of Medicine–Division of Nephrology, Washington University School of Medicine, St. Louis, Missouri
| | - Pinaki Sarder
- Department of Biomedical Engineering, University of Florida College of Engineering, Gainesville, Florida
- Department of Medicine–Quantitative Health, University of Florida College of Medicine, Gainesville, Florida
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida
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25
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Yang M, Unsihuay D, Hu H, Meke FN, Qu Z, Zhang ZY, Laskin J. Nano-DESI Mass Spectrometry Imaging of Proteoforms in Biological Tissues with High Spatial Resolution. Anal Chem 2023; 95:5214-5222. [PMID: 36917636 PMCID: PMC11330692 DOI: 10.1021/acs.analchem.2c04795] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful tool for label-free mapping of the spatial distribution of proteins in biological tissues. We have previously demonstrated imaging of individual proteoforms in biological tissues using nanospray desorption electrospray ionization (nano-DESI), an ambient liquid extraction-based MSI technique. Nano-DESI MSI generates multiply charged protein ions, which is advantageous for their identification using top-down proteomics analysis. In this study, we demonstrate proteoform mapping in biological tissues with a spatial resolution down to 7 μm using nano-DESI MSI. A substantial decrease in protein signals observed in high-spatial-resolution MSI makes these experiments challenging. We have enhanced the sensitivity of nano-DESI MSI experiments by optimizing the design of the capillary-based probe and the thickness of the tissue section. In addition, we demonstrate that oversampling may be used to further improve spatial resolution at little or no expense to sensitivity. These developments represent a new step in MSI-based spatial proteomics, which complements targeted imaging modalities widely used for studying biological systems.
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Affiliation(s)
- Manxi Yang
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisy Unsihuay
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | - Hang Hu
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | - Frederick Nguele Meke
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, 47907, USA
| | - Zihan Qu
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, 47907, USA
| | - Zhong-Yin Zhang
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, 47907, USA
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
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26
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Barbetta A, Rocque B, Sarode D, Bartlett JA, Emamaullee J. Revisiting transplant immunology through the lens of single-cell technologies. Semin Immunopathol 2023; 45:91-109. [PMID: 35980400 PMCID: PMC9386203 DOI: 10.1007/s00281-022-00958-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022]
Abstract
Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell- and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT.
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Affiliation(s)
- Arianna Barbetta
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA
- University of Southern California, Los Angeles, CA, USA
| | - Brittany Rocque
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA
- University of Southern California, Los Angeles, CA, USA
| | - Deepika Sarode
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA
- University of Southern California, Los Angeles, CA, USA
| | - Johanna Ascher Bartlett
- Pediatric Gastroenterology, Hepatology and Nutrition, Children's Hospital of Los Angeles, Los Angeles, CA, USA
| | - Juliet Emamaullee
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA.
- University of Southern California, Los Angeles, CA, USA.
- Division of Hepatobiliary and Abdominal Organ Transplantation Surgery, Children's Hospital Los Angeles, Los Angeles, CA, USA.
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27
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Su P, McGee JP, Durbin KR, Hollas MAR, Yang M, Neumann EK, Allen JL, Drown BS, Butun FA, Greer JB, Early BP, Fellers RT, Spraggins JM, Laskin J, Camarillo JM, Kafader JO, Kelleher NL. Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry. SCIENCE ADVANCES 2022; 8:eabp9929. [PMID: 35947651 PMCID: PMC9365283 DOI: 10.1126/sciadv.abp9929] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/24/2022] [Indexed: 05/25/2023]
Abstract
Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by fourfold compared to reported methods and reveals tissue localization of proteoforms at <80-μm spatial resolution. PiMS advances proteoform imaging by combining ambient nanospray desorption electrospray ionization with ion detection using individual ion mass spectrometry. We demonstrate highly multiplexed proteoform imaging of human kidney, annotating 169 of 400 proteoforms of <70 kDa using top-down MS and a database lookup of ~1000 kidney candidate proteoforms, including dozens of key enzymes in primary metabolism. PiMS images reveal distinct spatial localizations of proteoforms to both anatomical structures and cellular neighborhoods in the vasculature, medulla, and cortex regions of the human kidney. The benefits of PiMS are poised to increase proteome coverage for label-free protein imaging of tissues.
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Affiliation(s)
- Pei Su
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - John P. McGee
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Kenneth R. Durbin
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Michael A. R. Hollas
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Manxi Yang
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Elizabeth K. Neumann
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
| | - Jamie L. Allen
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
| | - Bryon S. Drown
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | | | - Joseph B. Greer
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Bryan P. Early
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Ryan T. Fellers
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Jeffrey M. Spraggins
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry and Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Jeannie M. Camarillo
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
| | - Jared O. Kafader
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
| | - Neil L. Kelleher
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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28
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Santoriello D, Nasr SH. Novel approaches beyond standard immunofluorescence for kidney biopsies. Curr Opin Nephrol Hypertens 2022; 31:221-227. [PMID: 35256574 DOI: 10.1097/mnh.0000000000000783] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW Immunofluorescence on frozen tissue (IF-F) utilizing antibodies against immunoglobulin (Ig) heavy and light chains (IgA, IgG and IgM, kappa and lambda) and components of classical and alternative complement pathways (C1q, C3c and C4) is the standard of renal pathology. However, conventional IF-F has limitations, particularly in nephropathies associated with organized and/or monoclonal Ig deposits. This review will discuss new applications of established methods beyond conventional IF-F and recent novel immunohistochemical methods. RECENT FINDINGS The combined application of paraffin immunofluorescence (IF-P) and IgG subtype staining excluded monotypic deposits in 62-66% of DNA J homolog subfamily B member 9-associated fibrillary glomerulonephritis (FGN) with apparent monotypic deposits by IF-F, whereas IF-P unmasks IgG deposits in a subset of cases of immunotactoid glomerulopathy. A novel IF technique targeting epitopes at the junction of the Ig heavy and light chains was introduced and unmasked polytypic deposits in a subset of glomerulonephritis with apparent monotypic deposits on IF-F. A recent study described the successful application of co-detection by indexing (CODEX) multiplexed IF to visualize more than a dozen target antigens within a single kidney tissue section. Finally, immunohistochemical protocols for detection of the novel antigens in membranous nephropathy have already entered the clinical practice of renal pathology. SUMMARY Novel ancillary techniques in renal pathology have the potential to significantly enhance our ability to evaluate renal biopsies.
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Affiliation(s)
- Dominick Santoriello
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York
| | - Samih H Nasr
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
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29
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El-Achkar TM, Winfree S, Talukder N, Barwinska D, Ferkowicz MJ, Al Hasan M. Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data. Front Physiol 2022; 13:832457. [PMID: 35309077 PMCID: PMC8931540 DOI: 10.3389/fphys.2022.832457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/28/2022] [Indexed: 12/19/2022] Open
Abstract
Advances in cellular and molecular interrogation of kidney tissue have ushered a new era of understanding the pathogenesis of kidney disease and potentially identifying molecular targets for therapeutic intervention. Classifying cells in situ and identifying subtypes and states induced by injury is a foundational task in this context. High resolution Imaging-based approaches such as large-scale fluorescence 3D imaging offer significant advantages because they allow preservation of tissue architecture and provide a definition of the spatial context of each cell. We recently described the Volumetric Tissue Exploration and Analysis cytometry tool which enables an interactive analysis, quantitation and semiautomated classification of labeled cells in 3D image volumes. We also established and demonstrated an imaging-based classification using deep learning of cells in intact tissue using 3D nuclear staining with 4',6-diamidino-2-phenylindole (DAPI). In this mini-review, we will discuss recent advancements in analyzing 3D imaging of kidney tissue, and how combining machine learning with cytometry is a powerful approach to leverage the depth of content provided by high resolution imaging into a highly informative analytical output. Therefore, imaging a small tissue specimen will yield big scale data that will enable cell classification in a spatial context and provide novel insights on pathological changes induced by kidney disease.
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Affiliation(s)
- Tarek M. El-Achkar
- Division of Nephrology, Department of Medicine, Indiana University, Indianapolis, IN, United States
| | - Seth Winfree
- Department of Pathology and Microbiology, University of Nebraska Omaha, Omaha, NE, United States
| | - Niloy Talukder
- Department of Computer and Information Science, Indiana University–Purdue University Indianapolis, Indianapolis, IN, United States
| | - Daria Barwinska
- Division of Nephrology, Department of Medicine, Indiana University, Indianapolis, IN, United States
| | - Michael J. Ferkowicz
- Division of Nephrology, Department of Medicine, Indiana University, Indianapolis, IN, United States
| | - Mohammad Al Hasan
- Department of Computer and Information Science, Indiana University–Purdue University Indianapolis, Indianapolis, IN, United States
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30
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Hickey JW, Neumann EK, Radtke AJ, Camarillo JM, Beuschel RT, Albanese A, McDonough E, Hatler J, Wiblin AE, Fisher J, Croteau J, Small EC, Sood A, Caprioli RM, Angelo RM, Nolan GP, Chung K, Hewitt SM, Germain RN, Spraggins JM, Lundberg E, Snyder MP, Kelleher NL, Saka SK. Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging. Nat Methods 2022; 19:284-295. [PMID: 34811556 PMCID: PMC9264278 DOI: 10.1038/s41592-021-01316-y] [Citation(s) in RCA: 189] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 09/15/2021] [Indexed: 02/07/2023]
Abstract
Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.
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Affiliation(s)
- John W Hickey
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Elizabeth K Neumann
- Department of Biochemistry, Vanderbilt University, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
| | - Andrea J Radtke
- Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA.
| | - Jeannie M Camarillo
- Departments of Chemistry, Molecular Biosciences and the National Resource for Translational and Developmental Proteomics, Northwestern University, Evanston, IL, USA
| | - Rebecca T Beuschel
- Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Alexandre Albanese
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Boston Children's Hospital, Division of Hematology/Oncology, Boston, MA, USA
| | | | - Julia Hatler
- Antibody Development Department, Bio-Techne, Minneapolis, MN, USA
| | - Anne E Wiblin
- Department of Research and Development, Abcam, Cambridge, UK
| | - Jeremy Fisher
- Department of Research and Development, Cell Signaling Technology, Danvers, MA, USA
| | - Josh Croteau
- Department of Applications Science, BioLegend, San Diego, CA, USA
| | | | | | - Richard M Caprioli
- Department of Biochemistry, Vanderbilt University, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - R Michael Angelo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Chemical Engineering, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea
- Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea
| | - Stephen M Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ronald N Germain
- Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Neil L Kelleher
- Departments of Chemistry, Molecular Biosciences and the National Resource for Translational and Developmental Proteomics, Northwestern University, Evanston, IL, USA
| | - Sinem K Saka
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA.
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
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31
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Kruse ARS, Spraggins JM. Uncovering Molecular Heterogeneity in the Kidney With Spatially Targeted Mass Spectrometry. Front Physiol 2022; 13:837773. [PMID: 35222094 PMCID: PMC8874197 DOI: 10.3389/fphys.2022.837773] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/04/2022] [Indexed: 02/06/2023] Open
Abstract
The kidney functions through the coordination of approximately one million multifunctional nephrons in 3-dimensional space. Molecular understanding of the kidney has relied on transcriptomic, proteomic, and metabolomic analyses of kidney homogenate, but these approaches do not resolve cellular identity and spatial context. Mass spectrometry analysis of isolated cells retains cellular identity but not information regarding its cellular neighborhood and extracellular matrix. Spatially targeted mass spectrometry is uniquely suited to molecularly characterize kidney tissue while retaining in situ cellular context. This review summarizes advances in methodology and technology for spatially targeted mass spectrometry analysis of kidney tissue. Profiling technologies such as laser capture microdissection (LCM) coupled to liquid chromatography tandem mass spectrometry provide deep molecular coverage of specific tissue regions, while imaging technologies such as matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) molecularly profile regularly spaced tissue regions with greater spatial resolution. These technologies individually have furthered our understanding of heterogeneity in nephron regions such as glomeruli and proximal tubules, and their combination is expected to profoundly expand our knowledge of the kidney in health and disease.
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Affiliation(s)
- Angela R. S. Kruse
- Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, United States
| | - Jeffrey M. Spraggins
- Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- *Correspondence: Jeffrey M. Spraggins,
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