1
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Elishaev M, Li B, Zhou A, Salim K, Leeper NJ, Francis GA, Lai C, Wang Y. Multiplex Imaging for Cell Phenotyping of Early Human Atherosclerosis. J Am Heart Assoc 2024:e034990. [PMID: 38842292 DOI: 10.1161/jaha.123.034990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/14/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND Previous studies using animal models and cultured cells suggest that vascular smooth muscle cells (SMCs) and inflammatory cytokines are important players in atherogenesis. Validating these findings in human disease is critical to designing therapeutics that target these components. Multiplex imaging is a powerful tool for characterizing cell phenotypes and microenvironments using biobanked human tissue sections. However, this technology has not been applied to human atherosclerotic lesions and needs to first be customized and validated. METHODS AND RESULTS For validation, we created an 8-plex imaging panel to distinguish foam cells from SMC and leukocyte origins on tissue sections of early human atherosclerotic lesions (n=9). The spatial distribution and characteristics of these foam cells were further analyzed to test the association between SMC phenotypes and inflammation. Consistent with previous reports using human lesions, multiplex imaging showed that foam cells of SMC origin outnumbered those of leukocyte origin and were enriched in the deep intima, where the lipids accumulate in early atherogenesis. This new technology also found that apoptosis or the expression of pro-inflammatory cytokines were not more associated with foam cells than with nonfoam cells in early human lesions. More CD68+ SMCs were present among SMCs that highly expressed interleukin-1β. Highly inflamed SMCs showed a trend of increased apoptosis, whereas leukocytes expressing similar levels of cytokines were enriched in regions of extracellular matrix remodeling. CONCLUSIONS The multiplex imaging method can be applied to biobanked human tissue sections to enable proof-of-concept studies and validate theories based on animal models and cultured cells.
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Affiliation(s)
- Maria Elishaev
- Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada
- Centre for Heart Lung Innovation University of British Columbia Vancouver BC Canada
| | - Boaz Li
- Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada
- Centre for Heart Lung Innovation University of British Columbia Vancouver BC Canada
| | - Annie Zhou
- Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada
- Centre for Heart Lung Innovation University of British Columbia Vancouver BC Canada
| | - Kevin Salim
- British Columbia Children's Hospital Research Institute University of British Columbia Vancouver BC Canada
| | - Nicholas J Leeper
- Department of Surgery, Division of Vascular Surgery Stanford University School of Medicine Stanford CA USA
- Stanford Cardiovascular Institute Stanford University Stanford CA USA
| | - Gordon A Francis
- Centre for Heart Lung Innovation University of British Columbia Vancouver BC Canada
- Department of Medicine University of British Columbia Vancouver BC Canada
| | - Chi Lai
- Centre for Heart Lung Innovation University of British Columbia Vancouver BC Canada
- Division of Anatomical Pathology Providence Health Care, St. Paul's Hospital Vancouver BC Canada
| | - Ying Wang
- Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada
- Centre for Heart Lung Innovation University of British Columbia Vancouver BC Canada
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2
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Barlow GL, Schürch CM, Bhate SS, Phillips D, Young A, Dong S, Martinez HA, Kaber G, Nagy N, Ramachandran S, Meng J, Korpos E, Bluestone JA, Nolan GP, Bollyky PL. The Extra-Islet Pancreas Supports Autoimmunity in Human Type 1 Diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.03.15.23287145. [PMID: 36993739 PMCID: PMC10055577 DOI: 10.1101/2023.03.15.23287145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
In autoimmune Type 1 diabetes (T1D), immune cells infiltrate and destroy the islets of Langerhans - islands of endocrine tissue dispersed throughout the pancreas. However, the contribution of cellular programs outside islets to insulitis is unclear. Here, using CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples, we simultaneously examine islet and extra-islet inflammation in human T1D. We identify four sub-states of inflamed islets characterized by the activation profiles of CD8 + T cells enriched in islets relative to the surrounding tissue. We further find that the extra-islet space of lobules with extensive islet-infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. Finally, we identify lymphoid structures away from islets enriched in CD45RA + T cells - a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D.
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3
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Börner K, Blood PD, Silverstein JC, Ruffalo M, Teichmann SA, Pryhuber G, Misra R, Purkerson J, Fan J, Hickey JW, Molla G, Xu C, Zhang Y, Weber G, Jain Y, Qaurooni D, Kong Y, Bueckle A, Herr BW. Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587041. [PMID: 38826261 PMCID: PMC11142047 DOI: 10.1101/2024.03.27.587041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal ( https://portal.hubmapconsortium.org ) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal ( https://humanatlas.io ) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies. In addition, three workflows were developed to map new experimental data into the HRA's CCF. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and demonstrates first atlas usage applications and previews.
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4
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Zhu B, Gao S, Chen S, Yeung J, Bai Y, Huang AY, Yeo YY, Liao G, Mao S, Jiang ZG, Rodig SJ, Shalek AK, Nolan GP, Jiang S, Ma Z. Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.12.593710. [PMID: 38798592 PMCID: PMC11118457 DOI: 10.1101/2024.05.12.593710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sheng Gao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Shuxiao Chen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Y Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Guanrui Liao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Zhenghui G Jiang
- Division of Gastroenterology/Liver Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zongming Ma
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
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5
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Winter S, Götze KS, Hecker JS, Metzeler KH, Guezguez B, Woods K, Medyouf H, Schäffer A, Schmitz M, Wehner R, Glauche I, Roeder I, Rauner M, Hofbauer LC, Platzbecker U. Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche. Leukemia 2024; 38:936-946. [PMID: 38514772 PMCID: PMC11073997 DOI: 10.1038/s41375-024-02226-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024]
Abstract
Clonal hematopoiesis (CH) defines a premalignant state predominantly found in older persons that increases the risk of developing hematologic malignancies and age-related inflammatory diseases. However, the risk for malignant transformation or non-malignant disorders is variable and difficult to predict, and defining the clinical relevance of specific candidate driver mutations in individual carriers has proved to be challenging. In addition to the cell-intrinsic mechanisms, mutant cells rely on and alter cell-extrinsic factors from the bone marrow (BM) niche, which complicates the prediction of a mutant cell's fate in a shifting pre-malignant microenvironment. Therefore, identifying the insidious and potentially broad impact of driver mutations on supportive niches and immune function in CH aims to understand the subtle differences that enable driver mutations to yield different clinical outcomes. Here, we review the changes in the aging BM niche and the emerging evidence supporting the concept that CH can progressively alter components of the local BM microenvironment. These alterations may have profound implications for the functionality of the osteo-hematopoietic niche and overall bone health, consequently fostering a conducive environment for the continued development and progression of CH. We also provide an overview of the latest technology developments to study the spatiotemporal dependencies in the CH BM niche, ideally in the context of longitudinal studies following CH over time. Finally, we discuss aspects of CH carrier management in clinical practice, based on work from our group and others.
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Affiliation(s)
- Susann Winter
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Katharina S Götze
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medicine III, Technical University of Munich (TUM), School of Medicine and Health, Munich, Germany
- German MDS Study Group (D-MDS), Leipzig, Germany
| | - Judith S Hecker
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medicine III, Technical University of Munich (TUM), School of Medicine and Health, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich (TUM), Munich, Germany
| | - Klaus H Metzeler
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Hematology, Cellular Therapy, Hemostaseology and Infectious Disease, University of Leipzig Medical Center, Leipzig, Germany
| | - Borhane Guezguez
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Hematology and Oncology, University Medical Center Mainz, Mainz, Germany
| | - Kevin Woods
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Hematology and Oncology, University Medical Center Mainz, Mainz, Germany
| | - Hind Medyouf
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Frankfurt am Main, Germany
| | - Alexander Schäffer
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Marc Schmitz
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Immunology, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - Rebekka Wehner
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Immunology, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - Ingmar Glauche
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Ingo Roeder
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Martina Rauner
- Division of Endocrinology, Diabetes and Bone Diseases, Department of Medicine III, and Center for Healthy Aging, University Medical Center, TU Dresden, Dresden, Germany
| | - Lorenz C Hofbauer
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Division of Endocrinology, Diabetes and Bone Diseases, Department of Medicine III, and Center for Healthy Aging, University Medical Center, TU Dresden, Dresden, Germany.
| | - Uwe Platzbecker
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- German MDS Study Group (D-MDS), Leipzig, Germany.
- Department of Hematology, Cellular Therapy, Hemostaseology and Infectious Disease, University of Leipzig Medical Center, Leipzig, Germany.
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6
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Hickey JW, Agmon E, Horowitz N, Tan TK, Lamore M, Sunwoo JB, Covert MW, Nolan GP. Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy. Cell Syst 2024; 15:322-338.e5. [PMID: 38636457 PMCID: PMC11030795 DOI: 10.1016/j.cels.2024.03.004] [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: 12/14/2022] [Revised: 03/07/2023] [Accepted: 03/19/2024] [Indexed: 04/20/2024]
Abstract
Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Center for Cell Analysis and Modeling, University of Connecticut Health, Farmington, CT 06032, USA
| | - Nina Horowitz
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Tze-Kai Tan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew Lamore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - John B Sunwoo
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Otolaryngology, Head and Neck Surgery, Stanford Cancer Institute Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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7
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Watson SS, Duc B, Kang Z, de Tonnac A, Eling N, Font L, Whitmarsh T, Massara M, Bodenmiller B, Hausser J, Joyce JA. Microenvironmental reorganization in brain tumors following radiotherapy and recurrence revealed by hyperplexed immunofluorescence imaging. Nat Commun 2024; 15:3226. [PMID: 38622132 PMCID: PMC11018859 DOI: 10.1038/s41467-024-47185-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/22/2024] [Indexed: 04/17/2024] Open
Abstract
The tumor microenvironment plays a crucial role in determining response to treatment. This involves a series of interconnected changes in the cellular landscape, spatial organization, and extracellular matrix composition. However, assessing these alterations simultaneously is challenging from a spatial perspective, due to the limitations of current high-dimensional imaging techniques and the extent of intratumoral heterogeneity over large lesion areas. In this study, we introduce a spatial proteomic workflow termed Hyperplexed Immunofluorescence Imaging (HIFI) that overcomes these limitations. HIFI allows for the simultaneous analysis of > 45 markers in fragile tissue sections at high magnification, using a cost-effective high-throughput workflow. We integrate HIFI with machine learning feature detection, graph-based network analysis, and cluster-based neighborhood analysis to analyze the microenvironment response to radiation therapy in a preclinical model of glioblastoma, and compare this response to a mouse model of breast-to-brain metastasis. Here we show that glioblastomas undergo extensive spatial reorganization of immune cell populations and structural architecture in response to treatment, while brain metastases show no comparable reorganization. Our integrated spatial analyses reveal highly divergent responses to radiation therapy between brain tumor models, despite equivalent radiotherapy benefit.
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Affiliation(s)
- Spencer S Watson
- Department of Oncology, University of Lausanne, Lausanne, Switzerland.
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Agora Cancer Research Center, Lausanne, 1011, Switzerland.
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland.
| | - Benoit Duc
- Department of Oncology, University of Lausanne, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, 1011, Switzerland
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland
| | - Ziqi Kang
- Department of Cellular and Molecular Biology, Karolinska Institutet and SciLifeLab, Stockholm, Sweden
| | - Axel de Tonnac
- Department of Cellular and Molecular Biology, Karolinska Institutet and SciLifeLab, Stockholm, Sweden
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Laure Font
- Department of Oncology, University of Lausanne, Lausanne, Switzerland
- École Polytechnique Fédérale Lausanne, Lausanne, Switzerland
| | - Tristan Whitmarsh
- Machine Intelligence Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Matteo Massara
- Department of Oncology, University of Lausanne, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, 1011, Switzerland
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Jean Hausser
- Department of Cellular and Molecular Biology, Karolinska Institutet and SciLifeLab, Stockholm, Sweden
| | - Johanna A Joyce
- Department of Oncology, University of Lausanne, Lausanne, Switzerland.
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Agora Cancer Research Center, Lausanne, 1011, Switzerland.
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland.
- Cancer Research UK, Cancer Grand Challenges iMAXT Consortium, University of Cambridge, Cambridge, UK.
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8
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Zhang Y, Lee RY, Tan CW, Guo X, Yim WWY, Lim JC, Wee FY, Yang WU, Kharbanda M, Lee JYJ, Ngo NT, Leow WQ, Loo LH, Lim TK, Sobota RM, Lau MC, Davis MJ, Yeong J. Spatial omics techniques and data analysis for cancer immunotherapy applications. Curr Opin Biotechnol 2024; 87:103111. [PMID: 38520821 DOI: 10.1016/j.copbio.2024.103111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 03/01/2024] [Accepted: 03/03/2024] [Indexed: 03/25/2024]
Abstract
In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.
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Affiliation(s)
- Yue Zhang
- Duke-NUS Medical School, Singapore 169856, Singapore
| | - Ren Yuan Lee
- Yong Loo Lin School of Medicine, National University of Singapore, 169856 Singapore; Singapore Thong Chai Medical Institution, Singapore 169874, Singapore
| | - Chin Wee Tan
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Xue Guo
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Willa W-Y Yim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Jeffrey Ct Lim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Felicia Yt Wee
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - W U Yang
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Malvika Kharbanda
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Jia-Ying J Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Nye Thane Ngo
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Wei Qiang Leow
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Lit-Hsin Loo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Tony Kh Lim
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Radoslaw M Sobota
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Mai Chan Lau
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A⁎STAR), Singapore 138648, Singapore
| | - Melissa J Davis
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia; Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Joe Yeong
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore.
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9
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Schulte SJ, Fornace ME, Hall JK, Shin GJ, Pierce NA. HCR spectral imaging: 10-plex, quantitative, high-resolution RNA and protein imaging in highly autofluorescent samples. Development 2024; 151:dev202307. [PMID: 38415752 PMCID: PMC10941662 DOI: 10.1242/dev.202307] [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: 08/31/2023] [Accepted: 12/21/2023] [Indexed: 02/29/2024]
Abstract
Signal amplification based on the mechanism of hybridization chain reaction (HCR) provides a unified framework for multiplex, quantitative, high-resolution imaging of RNA and protein targets in highly autofluorescent samples. With conventional bandpass imaging, multiplexing is typically limited to four or five targets owing to the difficulty in separating signals generated by fluorophores with overlapping spectra. Spectral imaging has offered the conceptual promise of higher levels of multiplexing, but it has been challenging to realize this potential in highly autofluorescent samples, including whole-mount vertebrate embryos. Here, we demonstrate robust HCR spectral imaging with linear unmixing, enabling simultaneous imaging of ten RNA and/or protein targets in whole-mount zebrafish embryos and mouse brain sections. Further, we demonstrate that the amplified and unmixed signal in each of the ten channels is quantitative, enabling accurate and precise relative quantitation of RNA and/or protein targets with subcellular resolution, and RNA absolute quantitation with single-molecule resolution, in the anatomical context of highly autofluorescent samples.
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Affiliation(s)
- Samuel J. Schulte
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Mark E. Fornace
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - John K. Hall
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Grace J. Shin
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Niles A. Pierce
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Division of Engineering & Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
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10
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Livingston NK, Hickey JW, Sim H, Salathe SF, Choy J, Kong J, Silver AB, Stelzel JL, Omotoso MO, Li S, Chaisawangwong W, Roy S, Ariail EC, Lanis MR, Pradeep P, Bieler JG, Witte SE, Leonard E, Doloff JC, Spangler JB, Mao HQ, Schneck JP. In Vivo Stimulation of Therapeutic Antigen-Specific T Cells in an Artificial Lymph Node Matrix. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2310043. [PMID: 38358310 DOI: 10.1002/adma.202310043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/04/2024] [Indexed: 02/16/2024]
Abstract
T cells are critical mediators of antigen-specific immune responses and are common targets for immunotherapy. Biomaterial scaffolds have previously been used to stimulate antigen-presenting cells to elicit antigen-specific immune responses; however, structural and molecular features that directly stimulate and expand naïve, endogenous, tumor-specific T cells in vivo have not been defined. Here, an artificial lymph node (aLN) matrix is created, which consists of an extracellular matrix hydrogel conjugated with peptide-loaded-MHC complex (Signal 1), the co-stimulatory signal anti-CD28 (Signal 2), and a tethered IL-2 (Signal 3), that can bypass challenges faced by other approaches to activate T cells in situ such as vaccines. This dynamic immune-stimulating platform enables direct, in vivo antigen-specific CD8+ T cell stimulation, as well as recruitment and coordination of host immune cells, providing an immuno-stimulatory microenvironment for antigen-specific T cell activation and expansion. Co-injecting the aLN with naïve, wild-type CD8+ T cells results in robust activation and expansion of tumor-targeted T cells that kill target cells and slow tumor growth in several distal tumor models. The aLN platform induces potent in vivo antigen-specific CD8+ T cell stimulation without the need for ex vivo priming or expansion and enables in situ manipulation of antigen-specific responses for immunotherapies.
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Affiliation(s)
- Natalie K Livingston
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Institute for Cell Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, 21231, USA
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - John W Hickey
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Institute for Cell Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, 21231, USA
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Hajin Sim
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Sebastian F Salathe
- Department of Biology, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Joseph Choy
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, 21231, USA
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Materials Science and Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jiayuan Kong
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, 21231, USA
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Materials Science and Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Aliyah B Silver
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, 21231, USA
- Johns Hopkins Center for Translational ImmunoEngineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Molecular Microbiology and Immunology, Johns Hopkins University Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Jessica L Stelzel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, 21231, USA
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Mary O Omotoso
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Shuyi Li
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Worarat Chaisawangwong
- Graduate Program in Immunology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Sayantika Roy
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Emily C Ariail
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Mara R Lanis
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Pratibha Pradeep
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Joan Glick Bieler
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Savannah Est Witte
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Elissa Leonard
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Joshua C Doloff
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, 21231, USA
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Materials Science and Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, MD, 21205, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Jamie B Spangler
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Institute for Cell Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, 21231, USA
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
- Johns Hopkins Center for Translational ImmunoEngineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Molecular Microbiology and Immunology, Johns Hopkins University Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
- Graduate Program in Immunology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, MD, 21205, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Hai-Quan Mao
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, 21231, USA
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Materials Science and Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Johns Hopkins Center for Translational ImmunoEngineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Jonathan P Schneck
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Institute for Cell Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Johns Hopkins Center for Translational ImmunoEngineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
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11
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Purde MT, Cupovic J, Palmowski YA, Makky A, Schmidt S, Rochwarger A, Hartmann F, Stemeseder F, Lercher A, Abdou MT, Bomze D, Besse L, Berner F, Tüting T, Hölzel M, Bergthaler A, Kochanek S, Ludewig B, Lauterbach H, Orlinger KK, Bald T, Schietinger A, Schürch C, Ring SS, Flatz L. A replicating LCMV-based vaccine for the treatment of solid tumors. Mol Ther 2024; 32:426-439. [PMID: 38058126 PMCID: PMC10861942 DOI: 10.1016/j.ymthe.2023.11.026] [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: 04/17/2023] [Revised: 10/31/2023] [Accepted: 11/30/2023] [Indexed: 12/08/2023] Open
Abstract
Harnessing the immune system to eradicate tumors requires identification and targeting of tumor antigens, including tumor-specific neoantigens and tumor-associated self-antigens. Tumor-associated antigens are subject to existing immune tolerance, which must be overcome by immunotherapies. Despite many novel immunotherapies reaching clinical trials, inducing self-antigen-specific immune responses remains challenging. Here, we systematically investigate viral-vector-based cancer vaccines encoding a tumor-associated self-antigen (TRP2) for the treatment of established melanomas in preclinical mouse models, alone or in combination with adoptive T cell therapy. We reveal that, unlike foreign antigens, tumor-associated antigens require replication of lymphocytic choriomeningitis virus (LCMV)-based vectors to break tolerance and induce effective antigen-specific CD8+ T cell responses. Immunization with a replicating LCMV vector leads to complete tumor rejection when combined with adoptive TRP2-specific T cell transfer. Importantly, immunization with replicating vectors leads to extended antigen persistence in secondary lymphoid organs, resulting in efficient T cell priming, which renders previously "cold" tumors open to immune infiltration and reprograms the tumor microenvironment to "hot." Our findings have important implications for the design of next-generation immunotherapies targeting solid cancers utilizing viral vectors and adoptive cell transfer.
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Affiliation(s)
- Mette-Triin Purde
- Institute of Immunobiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | - Jovana Cupovic
- Institute of Immunobiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | - Yannick A Palmowski
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, 72076 Tübingen, Germany
| | - Ahmad Makky
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, 72076 Tübingen, Germany
| | | | - Alexander Rochwarger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, 72076 Tübingen, Germany
| | - Fabienne Hartmann
- Institute of Immunobiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | | | - Alexander Lercher
- Research Center for Molecular Medicine (CeMM) of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Marie-Therese Abdou
- Institute of Immunobiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | - David Bomze
- Institute of Immunobiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | - Lenka Besse
- Laboratory of Experimental Oncology, Department of Oncology and Hematology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | - Fiamma Berner
- Institute of Immunobiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | - Thomas Tüting
- Laboratory of Experimental Dermatology, Department of Dermatology, University Hospital Magdeburg, 39120 Magdeburg, Germany
| | - Michael Hölzel
- Institute of Experimental Oncology, University Hospital Bonn, 53127 Bonn, Germany
| | - Andreas Bergthaler
- Research Center for Molecular Medicine (CeMM) of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Stefan Kochanek
- Department of Gene Therapy, Ulm University, 89081 Ulm, Germany
| | - Burkhard Ludewig
- Institute of Immunobiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | | | | | - Tobias Bald
- QIMR Medical Research Institute, Herston, QLD 4006, Australia
| | | | - Christian Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, 72076 Tübingen, Germany
| | - Sandra S Ring
- Institute of Immunobiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | - Lukas Flatz
- Institute of Immunobiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland; Department of Dermatology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland.
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12
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Bejarano DA, Schlitzer A. Unveiling Macrophage Heterogeneity and Their Spatial Distribution Using Multiplexed Tissue Imaging. Methods Mol Biol 2024; 2713:281-296. [PMID: 37639130 DOI: 10.1007/978-1-0716-3437-0_19] [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: 08/29/2023]
Abstract
Macrophages display a high degree of phenotypic diversity and plasticity, which is influenced by their location within the tissue microenvironment. Co-Detection by Indexing (CODEX), a multiplexed imaging technique, allows the simultaneous detection of multiple membrane and cellular markers that enable the accurate identification of tissue-resident hematopoietic and non-hematopoietic cells, while conferring spatial information at a single-cell level. Here we describe the use of CODEX to visualize the phenotypic and spatial heterogeneity of murine tissue-resident macrophages in several organs, and a pipeline to characterize their cellular microenvironments and interactions.
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Affiliation(s)
| | - Andreas Schlitzer
- Quantitative Systems Biology, LIMES Institute, University of Bonn, Bonn, Germany.
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13
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Scheurer J, Sauer B, Focken J, Giampetraglia M, Jäger A, Schürch CM, Weigelin B, Schittek B. Histological and functional characterization of 3D human skin models mimicking the inflammatory skin diseases psoriasis and atopic dermatitis. Dis Model Mech 2024; 17:dmm050541. [PMID: 38251799 PMCID: PMC10846593 DOI: 10.1242/dmm.050541] [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: 10/09/2023] [Accepted: 12/18/2023] [Indexed: 01/23/2024] Open
Abstract
Three-dimensional (3D) human skin equivalents have emerged as valuable tools in skin research, replacing animal experimentation and precluding the need for patient biopsies. In this study, we advanced 3D skin equivalents to model the inflammatory skin diseases atopic dermatitis and psoriasis by cytokine stimulation, and were successful in integrating TH1 T cells into skin models to develop an immunocompetent 3D psoriasis model. We performed in-depth histological and functional characterization of 3D skin equivalents and validated them in terms of tissue architecture, pathological changes, expression of antimicrobial peptides and Staphylococcus aureus colonization using 3D reconstruction by multiphoton microscopy and phenotyping by highly multiplexed 'co-detection by indexing' (CODEX) microscopy. We show that our skin equivalents have a structural architecture with a well-developed dermis and epidermis, thus resembling human skin. In addition, the skin models of atopic dermatitis and psoriasis show several phenotypic features of inflammatory skin disease, including disturbed epidermal differentiation and alterations in the expression of epidermal barrier genes and antimicrobial peptides, and can be reliably used to test novel treatment strategies. Therefore, these 3D equivalents will be a valuable tool in experimental dermatological research.
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Affiliation(s)
- Jasmin Scheurer
- Department of Dermatology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Birgit Sauer
- Department of Dermatology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Jule Focken
- Department of Dermatology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Martina Giampetraglia
- Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Annika Jäger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, 72076 Tübingen, Germany
| | - Christian M. Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Bettina Weigelin
- Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Birgit Schittek
- Department of Dermatology, University Hospital Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
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14
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Hickey JW, Haist M, Horowitz N, Caraccio C, Tan Y, Rech AJ, Baertsch MA, Rovira-Clavé X, Zhu B, Vazquez G, Barlow G, Agmon E, Goltsev Y, Sunwoo JB, Covert M, Nolan GP. T cell-mediated curation and restructuring of tumor tissue coordinates an effective immune response. Cell Rep 2023; 42:113494. [PMID: 38085642 PMCID: PMC10765317 DOI: 10.1016/j.celrep.2023.113494] [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: 01/10/2023] [Revised: 09/06/2023] [Accepted: 11/10/2023] [Indexed: 12/30/2023] Open
Abstract
Antigen-specific T cells traffic to, are influenced by, and create unique cellular microenvironments. Here we characterize these microenvironments over time with multiplexed imaging in a melanoma model of adoptive T cell therapy and human patients with melanoma treated with checkpoint inhibitor therapy. Multicellular neighborhood analysis reveals dynamic immune cell infiltration and inflamed tumor cell neighborhoods associated with CD8+ T cells. T cell-focused analysis indicates T cells are found along a continuum of neighborhoods that reflect the progressive steps coordinating the anti-tumor immune response. More effective anti-tumor immune responses are characterized by inflamed tumor-T cell neighborhoods, flanked by dense immune infiltration neighborhoods. Conversely, ineffective T cell therapies express anti-inflammatory cytokines, resulting in regulatory neighborhoods, spatially disrupting productive T cell-immune and -tumor interactions. Our study provides in situ mechanistic insights into temporal tumor microenvironment changes, cell interactions critical for response, and spatial correlates of immunotherapy outcomes, informing cellular therapy evaluation and engineering.
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Affiliation(s)
- John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Maximillian Haist
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Nina Horowitz
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Chiara Caraccio
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yuqi Tan
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Andrew J Rech
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marc-Andrea Baertsch
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xavier Rovira-Clavé
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bokai Zhu
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gustavo Vazquez
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Graham Barlow
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Center for Cell Analysis and Modeling, University of Connecticut Health, Farmington, CT 06032, USA
| | - Yury Goltsev
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John B Sunwoo
- Department of Otolaryngology, Head and Neck Surgery, Stanford Cancer Institute, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Markus Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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15
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Shiomi T, Eichinger A, Chiriboga L. Hematoxylin and Eosin staining of PhenoCycler® Fusion flow cell slides. J Histotechnol 2023; 46:203-206. [PMID: 37584179 DOI: 10.1080/01478885.2023.2245182] [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: 06/26/2023] [Accepted: 08/01/2023] [Indexed: 08/17/2023]
Abstract
Multiplexed Imaging technologies are powerful techniques that enable ultrahigh-plex spatial phenotyping of whole tissue sections at single cell spatial resolution. Co-Detection by Indexing (CODEX) multiplexing can detect up to 100 proteins using cyclic detection of DNA conjugated antibodies applied to tissue sections. However, it is necessary to correlate multiplexed fluorescent (mIF) spatial images with Hematoxylin and Eosin (H&E) stained sections post analysis. To effectively correlate mIF spatial images with H&E morphology, an (H&E) staining protocol was developed that is directly applied to the CODEX Fusion flow-cell slide after analysis allowing for direct H&E correlation and annotation with mIF images.
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Affiliation(s)
- Tomoe Shiomi
- NYULH Center for Biospecimen Research and Development, New York, NY, USA
| | - Anna Eichinger
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Pediatrics, Dr von Hauner Children's Hospital, University Hospital, LMU, Munich, Germany
| | - Luis Chiriboga
- NYULH Center for Biospecimen Research and Development, New York, NY, USA
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
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16
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El Marrahi A, Lipreri F, Kang Z, Gsell L, Eroglu A, Alber D, Hausser J. NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology. Nat Commun 2023; 14:7182. [PMID: 37935691 PMCID: PMC10630431 DOI: 10.1038/s41467-023-42878-z] [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: 03/02/2023] [Accepted: 10/24/2023] [Indexed: 11/09/2023] Open
Abstract
Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP.
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Affiliation(s)
- Anissa El Marrahi
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Fabio Lipreri
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Ziqi Kang
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Louise Gsell
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Alper Eroglu
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - David Alber
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Jean Hausser
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden.
- SciLifeLab; Solna, Stockholm, 171 65, Sweden.
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17
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Focken J, Scheurer J, Jäger A, Schürch CM, Kämereit S, Riel S, Schaller M, Weigelin B, Schittek B. Neutrophil extracellular traps enhance S. aureus skin colonization by oxidative stress induction and downregulation of epidermal barrier genes. Cell Rep 2023; 42:113148. [PMID: 37733587 DOI: 10.1016/j.celrep.2023.113148] [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: 04/17/2023] [Revised: 06/28/2023] [Accepted: 08/31/2023] [Indexed: 09/23/2023] Open
Abstract
Staphylococcus aureus is the most common cause of bacterial skin infections in humans, including patients with atopic dermatitis (AD). Polymorphonuclear neutrophils (PMNs) are the first cells to infiltrate an infection site, where they usually provide an effective first line of defense, including neutrophil extracellular trap (NET) formation. Here, we show that infiltrating PMNs in inflamed human and mouse skin enhance S. aureus skin colonization and persistence. Mechanistically, we demonstrate that a crosstalk between keratinocytes and PMNs results in enhanced NET formation upon S. aureus infection, which in turn induces oxidative stress and expression of danger-associated molecular patterns such as high-mobility-group-protein B1 (HMGB1) in keratinocytes. In turn, HMGB1 enhances S. aureus skin colonization and persistence by promoting skin barrier dysfunctions by the downregulation of epidermal barrier genes. Using patient material, we show that patients with AD exhibit enhanced presence of PMNs, NETs, and HMGB1 in the skin, demonstrating the clinical relevance of our finding.
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Affiliation(s)
- Jule Focken
- Department of Dermatology, University Hospital Tuebingen, Tuebingen, Germany
| | - Jasmin Scheurer
- Department of Dermatology, University Hospital Tuebingen, Tuebingen, Germany
| | - Annika Jäger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Christian M Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sofie Kämereit
- Department of Dermatology, University Hospital Tuebingen, Tuebingen, Germany
| | - Simon Riel
- Electron-Microscopy, Department of Dermatology, University Hospital Tuebingen, Tuebingen, Germany
| | - Martin Schaller
- Electron-Microscopy, Department of Dermatology, University Hospital Tuebingen, Tuebingen, Germany
| | - Bettina Weigelin
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Birgit Schittek
- Department of Dermatology, University Hospital Tuebingen, Tuebingen, Germany.
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18
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Affiliation(s)
- Yury Goltsev
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
| | - Garry Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA.
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19
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Chen S, Zhu B, Huang S, Hickey JW, Lin KZ, Snyder M, Greenleaf WJ, Nolan GP, Zhang NR, Ma Z. Integration of spatial and single-cell data across modalities with weakly linked features. Nat Biotechnol 2023:10.1038/s41587-023-01935-0. [PMID: 37679544 DOI: 10.1038/s41587-023-01935-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/02/2023] [Indexed: 09/09/2023]
Abstract
Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori 'linked' features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.
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Affiliation(s)
- Shuxiao Chen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Bokai Zhu
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Sijia Huang
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - John W Hickey
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Kevin Z Lin
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | - Garry P Nolan
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
- Department of Pathology, Stanford University, Stanford, CA, USA.
| | - Nancy R Zhang
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
| | - Zongming Ma
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA.
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20
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Guldberg SM, Okholm TLH, McCarthy EE, Spitzer MH. Computational Methods for Single-Cell Proteomics. Annu Rev Biomed Data Sci 2023; 6:47-71. [PMID: 37040735 PMCID: PMC10621466 DOI: 10.1146/annurev-biodatasci-020422-050255] [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] [Indexed: 04/13/2023]
Abstract
Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.
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Affiliation(s)
- Sophia M Guldberg
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
| | - Trine Line Hauge Okholm
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
| | - Elizabeth E McCarthy
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA
- Institute for Human Genetics; Division of Rheumatology, Department of Medicine; Medical Scientist Training Program; and Biological and Medical Informatics Graduate Program, University of California, San Francisco, California, USA
| | - Matthew H Spitzer
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
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21
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Zidane M, Makky A, Bruhns M, Rochwarger A, Babaei S, Claassen M, Schürch CM. A review on deep learning applications in highly multiplexed tissue imaging data analysis. FRONTIERS IN BIOINFORMATICS 2023; 3:1159381. [PMID: 37564726 PMCID: PMC10410935 DOI: 10.3389/fbinf.2023.1159381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 07/12/2023] [Indexed: 08/12/2023] Open
Abstract
Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.
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Affiliation(s)
- Mohammed Zidane
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Ahmad Makky
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Matthias Bruhns
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Alexander Rochwarger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sepideh Babaei
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
| | - Manfred Claassen
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Christian M. Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
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22
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Hickey JW, Becker WR, Nevins SA, Horning A, Perez AE, Zhu C, Zhu B, Wei B, Chiu R, Chen DC, Cotter DL, Esplin ED, Weimer AK, Caraccio C, Venkataraaman V, Schürch CM, Black S, Brbić M, Cao K, Chen S, Zhang W, Monte E, Zhang NR, Ma Z, Leskovec J, Zhang Z, Lin S, Longacre T, Plevritis SK, Lin Y, Nolan GP, Greenleaf WJ, Snyder M. Organization of the human intestine at single-cell resolution. Nature 2023; 619:572-584. [PMID: 37468586 PMCID: PMC10356619 DOI: 10.1038/s41586-023-05915-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/02/2023] [Indexed: 07/21/2023]
Abstract
The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health1. The intesting has a length of over nine metres, along which there are differences in structure and function2. The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease.
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Affiliation(s)
- John W Hickey
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - Winston R Becker
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | | | - Aaron Horning
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | - Almudena Espin Perez
- Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - Bei Wei
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | - Roxanne Chiu
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | - Derek C Chen
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | - Daniel L Cotter
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | - Edward D Esplin
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | - Annika K Weimer
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | - Chiara Caraccio
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | | | - Christian M Schürch
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sarah Black
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - Maria Brbić
- Department of Computer Science, Stanford University, Stanford, CA, USA
- School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kaidi Cao
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Shuxiao Chen
- Department of Statistics and Data Science, University of Pennsylvania, Pennsylvania, PA, USA
| | - Weiruo Zhang
- Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, USA
| | - Emma Monte
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
| | - Nancy R Zhang
- Department of Statistics and Data Science, University of Pennsylvania, Pennsylvania, PA, USA
| | - Zongming Ma
- Department of Statistics and Data Science, University of Pennsylvania, Pennsylvania, PA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Zhengyan Zhang
- Department of Surgery, Washington University, St Louis, MO, USA
| | - Shin Lin
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Teri Longacre
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - Sylvia K Plevritis
- Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, USA
| | - Yiing Lin
- Department of Surgery, Washington University, St Louis, MO, USA
| | - Garry P Nolan
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA.
| | | | - Michael Snyder
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA.
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23
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Strasser MK, Gibbs DL, Gascard P, Bons J, Hickey JW, Schürch CM, Tan Y, Black S, Chu P, Ozkan A, Basisty N, Sangwan V, Rose J, Shah S, Camilleri-Broet S, Fiset PO, Bertos N, Berube J, Djambazian H, Li R, Oikonomopoulos S, Fels-Elliott DR, Vernovsky S, Shimshoni E, Collyar D, Russell A, Ragoussis I, Stachler M, Goldenring JR, McDonald S, Ingber DE, Schilling B, Nolan GP, Tlsty TD, Huang S, Ferri LE. Concerted epithelial and stromal changes during progression of Barrett's Esophagus to invasive adenocarcinoma exposed by multi-scale, multi-omics analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.08.544265. [PMID: 37333362 PMCID: PMC10274886 DOI: 10.1101/2023.06.08.544265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Esophageal adenocarcinoma arises from Barrett's esophagus, a precancerous metaplastic replacement of squamous by columnar epithelium in response to chronic inflammation. Multi-omics profiling, integrating single-cell transcriptomics, extracellular matrix proteomics, tissue-mechanics and spatial proteomics of 64 samples from 12 patients' paths of progression from squamous epithelium through metaplasia, dysplasia to adenocarcinoma, revealed shared and patient-specific progression characteristics. The classic metaplastic replacement of epithelial cells was paralleled by metaplastic changes in stromal cells, ECM and tissue stiffness. Strikingly, this change in tissue state at metaplasia was already accompanied by appearance of fibroblasts with characteristics of carcinoma-associated fibroblasts and of an NK cell-associated immunosuppressive microenvironment. Thus, Barrett's esophagus progresses as a coordinated multi-component system, supporting treatment paradigms that go beyond targeting cancerous cells to incorporating stromal reprogramming.
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24
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Klotz L, Antel J, Kuhlmann T. Inflammation in multiple sclerosis: consequences for remyelination and disease progression. Nat Rev Neurol 2023; 19:305-320. [PMID: 37059811 DOI: 10.1038/s41582-023-00801-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2023] [Indexed: 04/16/2023]
Abstract
Despite the large number of immunomodulatory or immunosuppressive treatments available to treat relapsing-remitting multiple sclerosis (MS), treatment of the progressive phase of the disease has not yet been achieved. This lack of successful treatment approaches is caused by our poor understanding of the mechanisms driving disease progression. Emerging concepts suggest that a combination of persisting focal and diffuse inflammation within the CNS and a gradual failure of compensatory mechanisms, including remyelination, result in disease progression. Therefore, promotion of remyelination presents a promising intervention approach. However, despite our increasing knowledge regarding the cellular and molecular mechanisms regulating remyelination in animal models, therapeutic increases in remyelination remain an unmet need in MS, which suggests that mechanisms of remyelination and remyelination failure differ fundamentally between humans and demyelinating animal models. New and emerging technologies now allow us to investigate the cellular and molecular mechanisms underlying remyelination failure in human tissue samples in an unprecedented way. The aim of this Review is to summarize our current knowledge regarding mechanisms of remyelination and remyelination failure in MS and in animal models of the disease, identify open questions, challenge existing concepts, and discuss strategies to overcome the translational roadblock in the field of remyelination-promoting therapies.
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Affiliation(s)
- Luisa Klotz
- Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Jack Antel
- Neuroimmunology Unit, Montreal Neurological Institute, McGill University, Québec, Canada
| | - Tanja Kuhlmann
- Neuroimmunology Unit, Montreal Neurological Institute, McGill University, Québec, Canada.
- Institute of Neuropathology, University Hospital Münster, Münster, Germany.
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25
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Yang M, Unsihuay D, Hu H, Nguele Meke F, 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 DOI: 10.1021/acs.analchem.2c04795] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [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, Indiana 47907, United States
| | - Daisy Unsihuay
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Hang Hu
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Frederick Nguele Meke
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Zihan Qu
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.,Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Zhong-Yin Zhang
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.,Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
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26
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Elhanani O, Ben-Uri R, Keren L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell 2023; 41:404-420. [PMID: 36800999 DOI: 10.1016/j.ccell.2023.01.010] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/01/2022] [Accepted: 01/26/2023] [Indexed: 02/18/2023]
Abstract
The tumor microenvironment (TME) is composed of many different cellular and acellular components that together drive tumor growth, invasion, metastasis, and response to therapies. Increasing realization of the significance of the TME in cancer biology has shifted cancer research from a cancer-centric model to one that considers the TME as a whole. Recent technological advancements in spatial profiling methodologies provide a systematic view and illuminate the physical localization of the components of the TME. In this review, we provide an overview of major spatial profiling technologies. We present the types of information that can be extracted from these data and describe their applications, findings and challenges in cancer research. Finally, we provide a future perspective of how spatial profiling could be integrated into cancer research to improve patient diagnosis, prognosis, stratification to treatment and development of novel therapeutics.
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Affiliation(s)
- Ofer Elhanani
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Raz Ben-Uri
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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27
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Einhaus J, Rochwarger A, Mattern S, Gaudillière B, Schürch CM. High-multiplex tissue imaging in routine pathology-are we there yet? Virchows Arch 2023; 482:801-812. [PMID: 36757500 PMCID: PMC10156760 DOI: 10.1007/s00428-023-03509-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/22/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023]
Abstract
High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.
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Affiliation(s)
- Jakob Einhaus
- Department of Anaesthesiology, Perioperative & Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Alexander Rochwarger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sven Mattern
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Brice Gaudillière
- Department of Anaesthesiology, Perioperative & Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian M Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
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28
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Chen S, Zhu B, Huang S, Hickey JW, Lin KZ, Snyder M, Greenleaf WJ, Nolan GP, Zhang NR, Ma Z. Integration of spatial and single-cell data across modalities with weak linkage. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.12.523851. [PMID: 36711792 PMCID: PMC9882150 DOI: 10.1101/2023.01.12.523851] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
single-cell sequencing methods have enabled the profiling of multiple types of molecular readouts at cellular resolution, and recent developments in spatial barcoding, in situ hybridization, and in situ sequencing allow such molecular readouts to retain their spatial context. Since no technology can provide complete characterization across all layers of biological modalities within the same cell, there is pervasive need for computational cross-modal integration (also called diagonal integration) of single-cell and spatial omics data. For current methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori "linked" features. When such linked features are few or uninformative, a scenario that we call "weak linkage", existing methods fail. We developed MaxFuse, a cross-modal data integration method that, through iterative co-embedding, data smoothing, and cell matching, leverages all information in each modality to obtain high-quality integration. MaxFuse is modality-agnostic and, through comprehensive benchmarks on single-cell and spatial ground-truth multiome datasets, demonstrates high robustness and accuracy in the weak linkage scenario. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, we demonstrate how MaxFuse enables the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.
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Matusiak M, Hickey JW, Luca B, Lu G, Kidziński L, Zhu S, Colburg DRC, Phillips DJ, Brubaker SW, Charville GW, Shen J, Nolan GP, Newman AM, West RB, van de Rijn M. A spatial map of human macrophage niches reveals context-dependent macrophage functions in colon and breast cancer. RESEARCH SQUARE 2023:rs.3.rs-2393443. [PMID: 36711732 PMCID: PMC9882614 DOI: 10.21203/rs.3.rs-2393443/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Tumor-associated macrophages (TAMs) display heterogeneous phenotypes. Yet the exact tissue cues that shape macrophage functional diversity are incompletely understood. Here we discriminate, spatially resolve and reveal the function of five distinct macrophage niches within malignant and benign breast and colon tissue. We found that SPP1 TAMs reside in hypoxic and necrotic tumor regions, and a novel subset of FOLR2 tissue resident macrophages (TRMs) supports the plasma cell tissue niche. We discover that IL4I1 macrophages populate niches with high cell turnover where they phagocytose dying cells. Significantly, IL4I1 TAMs abundance correlates with anti-PD1 treatment response in breast cancer. Furthermore, NLRP3 inflammasome activation in NLRP3 TAMs correlates with neutrophil infiltration in the tumors and is associated with poor outcome in breast cancer patients. This suggests the NLRP3 inflammasome as a novel cancer immunetherapy target. Our work uncovers context-dependent roles of macrophage subsets, and suggests novel predictive markers and macrophage subset-specific therapy targets.
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Affiliation(s)
| | - John W. Hickey
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Bogdan Luca
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Guolan Lu
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Lukasz Kidziński
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Shirley Zhu
- Department of Pathology, Stanford University, Stanford, California, USA
| | | | - Darci J. Phillips
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Sky W. Brubaker
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | | | - Jeanne Shen
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Garry P. Nolan
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Aaron M. Newman
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA
- Stanford Cancer Institute, Stanford University, Stanford, California, USA
| | - Robert B. West
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Matt van de Rijn
- Department of Pathology, Stanford University, Stanford, California, USA
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30
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Potier G, Doméné A, Paul-Gilloteaux P. A flexible open-source processing workflow for multiplexed fluorescence imaging based on cycles. F1000Res 2022; 11:1121. [PMID: 38249121 PMCID: PMC10799232 DOI: 10.12688/f1000research.124990.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/15/2022] [Indexed: 01/23/2024] Open
Abstract
Background Multiplexing tissue imaging is developing as a complement for single cell analysis, bringing the spatial information of cells in tissue in addition to multiple parameters measurements. More and more commercial or home-made systems are available. These techniques allow the imaging of tens of fluorescent reporters, where the spectral overlap is solved by imaging by cycles the fluorophores using microfluidics to change the reporters between each cycle. Methods For several systems, the acquisition system coupled to the microfluidic system is a wide field microscope, and the acquisition process is done by mosaicking to cover a large field of view, relying on image processing to obtain the data set to be analysed in intensity. The processed data set allows the identification of different populations, quite similarly to cytometry analysis, but with spatial information in addition. To obtain the final image for analysis from the raw acquisitions, several preprocessing steps are needed for inter-cycle registration, tissue autofluorescence correction or mosaicking. We propose a workflow for this preprocessing, implemented as an open source software (as a library, command line tool and standalone). Results We exemplify the workflow on the commercial system PhenoCycler TM (formerly named CODEX®) and provide a reduced size data set for testing. Conclusions We compare our processor with the commercially provided processor and show that we solve some problems also reported by other users.
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Affiliation(s)
- Guillaume Potier
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d’Angers, CRCI2NA, Nantes, F-44000, France
- LabEx IGO “Immunotherapy, Graft, Oncology”, Nantes, France
| | - Aurélie Doméné
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d’Angers, CRCI2NA, Nantes, F-44000, France
- LabEx IGO “Immunotherapy, Graft, Oncology”, Nantes, France
- Nantes Université, CHU Nantes, CNRS, Inserm, BioCore, US16 UAR 3556, SFR Bonamy, Nantes, F-44000, France
| | - Perrine Paul-Gilloteaux
- Nantes Université, CHU Nantes, CNRS, Inserm, BioCore, US16 UAR 3556, SFR Bonamy, Nantes, F-44000, France
- Nantes Université, , CNRS, INSERM, l’institut du thorax, Nantes, F-44000, France
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31
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Lu G, Baertsch MA, Hickey JW, Goltsev Y, Rech AJ, Mani L, Forgó E, Kong C, Jiang S, Nolan GP, Rosenthal EL. A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data. Front Immunol 2022; 13:981825. [PMID: 36211386 PMCID: PMC9539451 DOI: 10.3389/fimmu.2022.981825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/07/2022] [Indexed: 11/20/2022] Open
Abstract
Highly multiplexed, single-cell imaging has revolutionized our understanding of spatial cellular interactions associated with health and disease. With ever-increasing numbers of antigens, region sizes, and sample sizes, multiplexed fluorescence imaging experiments routinely produce terabytes of data. Fast and accurate processing of these large-scale, high-dimensional imaging data is essential to ensure reliable segmentation and identification of cell types and for characterization of cellular neighborhoods and inference of mechanistic insights. Here, we describe RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software for large-scale multiplexed fluorescence microscopy Data. RAPID deconvolves large-scale, high-dimensional fluorescence imaging data, stitches and registers images with axial and lateral drift correction, and minimizes tissue autofluorescence such as that introduced by erythrocytes. Incorporation of an open source CUDA-driven, GPU-assisted deconvolution produced results similar to fee-based commercial software. RAPID reduces data processing time and artifacts and improves image contrast and signal-to-noise compared to our previous image processing pipeline, thus providing a useful tool for accurate and robust analysis of large-scale, multiplexed, fluorescence imaging data.
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Affiliation(s)
- Guolan Lu
- Department of Otolaryngology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Marc A. Baertsch
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany
| | - John W. Hickey
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Yury Goltsev
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Andrew J. Rech
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Lucas Mani
- Department of Otolaryngology, Stanford University School of Medicine, Stanford, CA, United States
| | - Erna Forgó
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Christina Kong
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Garry P. Nolan
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
- *Correspondence: Garry P. Nolan, ; Eben L. Rosenthal,
| | - Eben L. Rosenthal
- Department of Otolaryngology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, CA, United States
- *Correspondence: Garry P. Nolan, ; Eben L. Rosenthal,
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32
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Schürch CM. [Characterization of the tumor microenvironment by highly multiplexed microscopy]. PATHOLOGIE (HEIDELBERG, GERMANY) 2022; 43:21-24. [PMID: 36222923 DOI: 10.1007/s00292-022-01129-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/12/2022] [Indexed: 06/16/2023]
Abstract
Malignant neoplasms are highly complex ecosystems consisting of tumor cells and the tumor microenvironment (TME), which is composed of structural elements (vessels, fibroblasts, extracellular matrix) and a wide variety of infiltrating immune cell types of the innate and adaptive immune systems. The TME is the main site of tumor cell-immune cell interactions and plays a critical role in antitumoral immunity. Immunotherapies can affect the interactions between immune cell types and tumor cells in the TME, boost immune responses, and lead to tumor elimination. Novel highly multiplexed microscopy techniques, which enable the detection of more than 50 simultaneous markers in tissues, facilitate the in-depth characterization of the TME at single-cell resolution in clinically relevant samples. Detailed knowledge about the cellular and spatial composition of the TME, the specific cell types and their functional properties, and cell-cell interactions-as revealed by highly multiplexed microscopy-will improve our understanding of immunotherapies' mechanisms of action and reveal new potential therapeutic targets and predictive biomarkers.
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Affiliation(s)
- Christian M Schürch
- Institut für Pathologie, Universitätsklinikum Tübingen, Liebermeisterstr. 8, 72076, Tübingen, Deutschland.
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33
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Cheng Y, Burrack RK, Li Q. Spatially Resolved and Highly Multiplexed Protein and RNA In Situ Detection by Combining CODEX With RNAscope In Situ Hybridization. J Histochem Cytochem 2022; 70:571-581. [PMID: 35848523 PMCID: PMC9393509 DOI: 10.1369/00221554221114174] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Highly multiplexed protein and RNA in situ detection on a single tissue section concurrently is highly desirable for both basic and applied biomedical research. CO-detection by inDEXing (CODEX) is a new and powerful platform to visualize up to 60 protein biomarkers in situ, and RNAscope in situ hybridization (RNAscope) is a novel RNA detection system with high sensitivity and unprecedent specificity at a single-cell level. Nevertheless, to our knowledge, the combination of CODEX and RNAscope remained unreported until this study. Here, we report a simple and reproducible combination of CODEX and RNAscope. We also determined the cross-reactivities of CODEX anti-human antibodies to rhesus macaques, a widely used animal model of human disease.
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Affiliation(s)
- Yilun Cheng
- Nebraska Center for Virology, School of Biological Sciences, University of Nebraska–Lincoln, Lincoln, Nebraska
| | - Rachel K. Burrack
- Nebraska Center for Virology, School of Biological Sciences, University of Nebraska–Lincoln, Lincoln, Nebraska
| | - Qingsheng Li
- Nebraska Center for Virology, School of Biological Sciences, University of Nebraska–Lincoln, Lincoln, Nebraska
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34
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Shekarian T, Zinner CP, Bartoszek EM, Duchemin W, Wachnowicz AT, Hogan S, Etter MM, Flammer J, Paganetti C, Martins TA, Schmassmann P, Zanganeh S, Le Goff F, Muraro MG, Ritz MF, Phillips D, Bhate SS, Barlow GL, Nolan GP, Schürch CM, Hutter G. Immunotherapy of glioblastoma explants induces interferon-γ responses and spatial immune cell rearrangements in tumor center, but not periphery. SCIENCE ADVANCES 2022; 8:eabn9440. [PMID: 35776791 PMCID: PMC10883360 DOI: 10.1126/sciadv.abn9440] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A patient-tailored, ex vivo drug response platform for glioblastoma (GBM) would facilitate therapy planning, provide insights into treatment-induced mechanisms in the immune tumor microenvironment (iTME), and enable the discovery of biomarkers of response. We cultured regionally annotated GBM explants in perfusion bioreactors to assess iTME responses to immunotherapy. Explants were treated with anti-CD47, anti-PD-1, or their combination, and analyzed by multiplexed microscopy [CO-Detection by indEXing (CODEX)], enabling the spatially resolved identification of >850,000 single cells, accompanied by explant secretome interrogation. Center and periphery explants differed in their cell type and soluble factor composition, and responses to immunotherapy. A subset of explants displayed increased interferon-γ levels, which correlated with shifts in immune cell composition within specified tissue compartments. Our study demonstrates that ex vivo immunotherapy of GBM explants enables an active antitumoral immune response within the tumor center and provides a framework for multidimensional personalized assessment of tumor response to immunotherapy.
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Affiliation(s)
- Tala Shekarian
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Carl P Zinner
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital and University of Basel, Basel, Switzerland
| | - Ewelina M Bartoszek
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Wandrille Duchemin
- sciCORE Center for Scientific Computing, University of Basel, Basel, Switzerland
| | - Anna T Wachnowicz
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Sabrina Hogan
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Manina M Etter
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Julia Flammer
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Chiara Paganetti
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Tomas A Martins
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Philip Schmassmann
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Steven Zanganeh
- Department of Bioengineering, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | | | - Manuele G Muraro
- Tissue Engineering Laboratory, Department of Biomedicine, University Hospital of Basel and University of Basel, Basel, Switzerland
| | - Marie-Françoise Ritz
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
- Department of Neurosurgery, University Hospital Basel, Basel, Switzerland
| | - Darci Phillips
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Salil S Bhate
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Graham L Barlow
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- 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
| | - Christian M Schürch
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Gregor Hutter
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of Basel, Basel, Switzerland
- Department of Neurosurgery, University Hospital Basel, Basel, Switzerland
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35
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A multiplexed epitope barcoding strategy that enables dynamic cellular phenotypic screens. Cell Syst 2022; 13:376-387.e8. [PMID: 35316656 DOI: 10.1016/j.cels.2022.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/27/2021] [Accepted: 02/25/2022] [Indexed: 12/16/2022]
Abstract
Pooled genetic libraries have improved screening throughput for mapping genotypes to phenotypes. However, selectable phenotypes are limited, restricting screening to outcomes with a low spatiotemporal resolution. Here, we integrated live-cell imaging with pooled library-based screening. To enable intracellular multiplexing, we developed a method called EPICode that uses a combination of short epitopes, which can also appear in various subcellular locations. EPICode thus enables the use of live-cell microscopy to characterize a phenotype of interest over time, including after sequential stimulatory/inhibitory manipulations, and directly connects behavior to the cellular genotype. To test EPICode's capacity against an important milestone-engineering and optimizing dynamic, live-cell reporters-we developed a live-cell PKA kinase translocation reporter with improved sensitivity and specificity. The use of epitopes as fluorescent barcodes introduces a scalable strategy for high-throughput screening broadly applicable to protein engineering and drug discovery settings where image-based phenotyping is desired.
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36
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Eadon MT, Dagher PC, El-Achkar TM. Cellular and molecular interrogation of kidney biopsy specimens. Curr Opin Nephrol Hypertens 2022; 31:160-167. [PMID: 34982521 PMCID: PMC8799512 DOI: 10.1097/mnh.0000000000000770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Traditional histopathology of the kidney biopsy specimen has been an essential and successful tool for the diagnosis and staging of kidney diseases. However, it is likely that the full potential of the kidney biopsy has not been tapped so far. Indeed, there is now a concerted worldwide effort to interrogate kidney biopsy samples at the cellular and molecular levels with unprecedented rigor and depth. This review examines these novel approaches to study kidney biopsy specimens and highlights their potential to refine our understanding of the pathophysiology of kidney disease and lead to precision-based diagnosis and therapy. RECENT FINDINGS Several consortia are now active at studying kidney biopsy samples from various patient cohorts with state-of-the art cellular and molecular techniques. These include advanced imaging approaches as well as deep molecular interrogation with tools such as epigenetics, transcriptomics, proteomics and metabolomics. The emphasis throughout is on rigor, reproducibility and quality control. SUMMARY Although these techniques to study kidney biopsies are complementary, each on its own can yield novel ways to define and classify kidney disease. Therefore, great efforts are needed in order to generate an integrated output that can propel the diagnosis and treatment of kidney disease into the realm of precision medicine.
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Affiliation(s)
- Michael T Eadon
- Department of Medicine, Division of Nephrology, Indiana University School of Medicine, Indianapolis, Indiana, USA
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37
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Radtke AJ, Chu CJ, Yaniv Z, Yao L, Marr J, Beuschel RT, Ichise H, Gola A, Kabat J, Lowekamp B, Speranza E, Croteau J, Thakur N, Jonigk D, Davis JL, Hernandez JM, Germain RN. IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues. Nat Protoc 2022; 17:378-401. [PMID: 35022622 DOI: 10.1038/s41596-021-00644-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/05/2021] [Indexed: 01/02/2023]
Abstract
High-content imaging is needed to catalog the variety of cellular phenotypes and multicellular ecosystems present in metazoan tissues. We recently developed iterative bleaching extends multiplexity (IBEX), an iterative immunolabeling and chemical bleaching method that enables multiplexed imaging (>65 parameters) in diverse tissues, including human organs relevant for international consortia efforts. IBEX is compatible with >250 commercially available antibodies and 16 unique fluorophores, and can be easily adopted to different imaging platforms using slides and nonproprietary imaging chambers. The overall protocol consists of iterative cycles of antibody labeling, imaging and chemical bleaching that can be completed at relatively low cost in 2-5 d by biologists with basic laboratory skills. To support widespread adoption, we provide extensive details on tissue processing, curated lists of validated antibodies and tissue-specific panels for multiplex imaging. Furthermore, instructions are included on how to automate the method using competitively priced instruments and reagents. Finally, we present a software solution for image alignment that can be executed by individuals without programming experience using open-source software and freeware. In summary, IBEX is a noncommercial method that can be readily implemented by academic laboratories and scaled to achieve high-content mapping of diverse tissues in support of a Human Reference Atlas or other such applications.
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Affiliation(s)
- Andrea J Radtke
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
| | - Colin J Chu
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.,Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ziv Yaniv
- Bioinformatics and Computational Bioscience Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Li Yao
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - James Marr
- Leica Microsystems Inc., Wetzlar, Germany
| | - Rebecca T Beuschel
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Hiroshi Ichise
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Anita Gola
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.,Howard Hughes Medical Institute, Robin Neustein Laboratory of Mammalian Cell Biology and Development, The Rockefeller University, New York, NY, USA
| | - Juraj Kabat
- Biological Imaging Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Bradley Lowekamp
- Bioinformatics and Computational Bioscience Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Emily Speranza
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.,Innate Immunity and Pathogenesis Section, Laboratory of Virology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA
| | - Joshua Croteau
- Department of Business Development, BioLegend, Inc, San Diego, CA, USA
| | - Nishant Thakur
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Danny Jonigk
- Institute of Pathology, Hannover Medical School, Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
| | - Jeremy L Davis
- Surgical Oncology Program, Metastasis Biology Section, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jonathan M Hernandez
- Surgical Oncology Program, Metastasis Biology Section, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ronald N Germain
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. .,Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
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38
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Graf J, Cho S, McDonough E, Corwin A, Sood A, Lindner A, Salvucci M, Stachtea X, Van Schaeybroeck S, Dunne PD, Laurent-Puig P, Longley D, Prehn JHM, Ginty F. FLINO: a new method for immunofluorescence bioimage normalization. Bioinformatics 2022; 38:520-526. [PMID: 34601553 PMCID: PMC8723144 DOI: 10.1093/bioinformatics/btab686] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/09/2021] [Accepted: 09/25/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Multiplexed immunofluorescence bioimaging of single-cells and their spatial organization in tissue holds great promise to the development of future precision diagnostics and therapeutics. Current multiplexing pipelines typically involve multiple rounds of immunofluorescence staining across multiple tissue slides. This introduces experimental batch effects that can hide underlying biological signal. It is important to have robust algorithms that can correct for the batch effects while not introducing biases into the data. Performance of data normalization methods can vary among different assay pipelines. To evaluate differences, it is critical to have a ground truth dataset that is representative of the assay. RESULTS A new immunoFLuorescence Image NOrmalization method is presented and evaluated against alternative methods and workflows. Multiround immunofluorescence staining of the same tissue with the nuclear dye DAPI was used to represent virtual slides and a ground truth. DAPI was restained on a given tissue slide producing multiple images of the same underlying structure but undergoing multiple representative tissue handling steps. This ground truth dataset was used to evaluate and compare multiple normalization methods including median, quantile, smooth quantile, median ratio normalization and trimmed mean of the M-values. These methods were applied in both an unbiased grid object and segmented cell object workflow to 24 multiplexed biomarkers. An upper quartile normalization of grid objects in log space was found to obtain almost equivalent performance to directly normalizing segmented cell objects by the middle quantile. The developed grid-based technique was then applied with on-slide controls for evaluation. Using five or fewer controls per slide can introduce biases into the data. Ten or more on-slide controls were able to robustly correct for batch effects. AVAILABILITY AND IMPLEMENTATION The data underlying this article along with the FLINO R-scripts used to perform the evaluation of image normalizations methods and workflows can be downloaded from https://github.com/GE-Bio/FLINO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- John Graf
- To whom correspondence should be addressed. or
| | - Sanghee Cho
- Department of Biology & Applied Physics, GE Research, Niskayuna, NY 12309, USA
| | - Elizabeth McDonough
- Department of Biology & Applied Physics, GE Research, Niskayuna, NY 12309, USA
| | - Alex Corwin
- Department of Biology & Applied Physics, GE Research, Niskayuna, NY 12309, USA
| | - Anup Sood
- Department of Biology & Applied Physics, GE Research, Niskayuna, NY 12309, USA
| | - Andreas Lindner
- Department of Physiology and Medical Physics, Centre of Systems Medicine, Royal College of Surgeons in Ireland University of Medicine and Health Sciences, 123 St. Stephen’s Green, Dublin 2, Ireland
| | - Manuela Salvucci
- Department of Physiology and Medical Physics, Centre of Systems Medicine, Royal College of Surgeons in Ireland University of Medicine and Health Sciences, 123 St. Stephen’s Green, Dublin 2, Ireland
| | - Xanthi Stachtea
- Department of Oncology, Centre for Cancer Research & Cell Biology, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | - Sandra Van Schaeybroeck
- Department of Oncology, Centre for Cancer Research & Cell Biology, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | - Philip D Dunne
- Department of Oncology, Centre for Cancer Research & Cell Biology, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | - Pierre Laurent-Puig
- Department of Biology, Hôpital Européen Georges-Pompidou, Assistance Publique - Hôpitaux de Paris, 3 Av. Victoria, 75004 Paris, France
| | - Daniel Longley
- Department of Oncology, Centre for Cancer Research & Cell Biology, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | - Jochen H M Prehn
- Department of Physiology and Medical Physics, Centre of Systems Medicine, Royal College of Surgeons in Ireland University of Medicine and Health Sciences, 123 St. Stephen’s Green, Dublin 2, Ireland
| | - Fiona Ginty
- To whom correspondence should be addressed. or
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Chen HY, Palendira U, Feng CG. Navigating the cellular landscape in tissue: Recent advances in defining the pathogenesis of human disease. Comput Struct Biotechnol J 2022; 20:5256-5263. [PMID: 36212528 PMCID: PMC9519395 DOI: 10.1016/j.csbj.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/04/2022] [Accepted: 09/04/2022] [Indexed: 11/19/2022] Open
Affiliation(s)
- Helen Y. Chen
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
| | - Umaimainthan Palendira
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
| | - Carl G. Feng
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
- Corresponding author at: Level 5 (East) The Charles Perkins Centre (D17), The University of Sydney, NSW, 2006, Australia
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40
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Jiang S, Mukherjee N, Bennett RS, Chen H, Logue J, Dighero-Kemp B, Kurtz JR, Adams R, Phillips D, Schürch CM, Goltsev Y, Hickey JW, McCaffrey EF, Delmastro A, Chu P, Reader JR, Keesler RI, Galván JA, Zlobec I, Van Rompay KKA, Liu DX, Hensley LE, Nolan GP, McIlwain DR. Rhesus Macaque CODEX Multiplexed Immunohistochemistry Panel for Studying Immune Responses During Ebola Infection. Front Immunol 2021; 12:729845. [PMID: 34938283 PMCID: PMC8685521 DOI: 10.3389/fimmu.2021.729845] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 11/16/2021] [Indexed: 11/13/2022] Open
Abstract
Non-human primate (NHP) animal models are an integral part of the drug research and development process. For some biothreat pathogens, animal model challenge studies may offer the only possibility to evaluate medical countermeasure efficacy. A thorough understanding of host immune responses in such NHP models is therefore vital. However, applying antibody-based immune characterization techniques to NHP models requires extensive reagent development for species compatibility. In the case of studies involving high consequence pathogens, further optimization for use of inactivated samples may be required. Here, we describe the first optimized CO-Detection by indEXing (CODEX) multiplexed tissue imaging antibody panel for deep profiling of spatially resolved single-cell immune responses in rhesus macaques. This 21-marker panel is composed of a set of 18 antibodies that stratify major immune cell types along with a set three Ebola virus (EBOV)-specific antibodies. We validated these two sets of markers using immunohistochemistry and CODEX in fully inactivated Formalin-Fixed Paraffin-Embedded (FFPE) tissues from mock and EBOV challenged macaques respectively and provide an efficient framework for orthogonal validation of multiple antibody clones using CODEX multiplexed tissue imaging. We also provide the antibody clones and oligonucleotide tag sequences as a valuable resource for other researchers to recreate this reagent set for future studies of tissue immune responses to EBOV infection and other diseases.
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Affiliation(s)
- Sizun Jiang
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Nilanjan Mukherjee
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Richard S. Bennett
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, United States
| | - Han Chen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - James Logue
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, United States
| | - Bonnie Dighero-Kemp
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, United States
| | - Jonathan R. Kurtz
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, United States
| | - Ricky Adams
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, United States
| | - Darci Phillips
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Christian M. Schürch
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Yury Goltsev
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - John W. Hickey
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Erin F. McCaffrey
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Alea Delmastro
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Pauline Chu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - J. Rachel Reader
- California National Primate Research Center, University of California, Davis, CA, United States
| | - Rebekah I. Keesler
- California National Primate Research Center, University of California, Davis, CA, United States
| | - José A. Galván
- Institute of Pathology, University of Bern, Bern, Switzerland
| | - Inti Zlobec
- Institute of Pathology, University of Bern, Bern, Switzerland
| | - Koen K. A. Van Rompay
- California National Primate Research Center, University of California, Davis, CA, United States
| | - David X. Liu
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, United States
| | - Lisa E. Hensley
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, United States
| | - Garry P. Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - David R. McIlwain
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
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41
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Jarosch S, Köhlen J, Sarker RS, Steiger K, Janssen KP, Christians A, Hennig C, Holler E, D'Ippolito E, Busch DH. Multiplexed imaging and automated signal quantification in formalin-fixed paraffin-embedded tissues by ChipCytometry. CELL REPORTS METHODS 2021; 1:100104. [PMID: 35475000 PMCID: PMC9017205 DOI: 10.1016/j.crmeth.2021.100104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/30/2021] [Accepted: 10/06/2021] [Indexed: 12/17/2022]
Abstract
Deciphering the spatial composition of cells in tissues is essential for detailed understanding of biological processes in health and disease. Recent technological advances enabled the assessment of the enormous complexity of tissue-derived parameters by highly multiplexed tissue imaging (HMTI), but elaborate machinery and data analyses are required. This severely limits broad applicability of HMTI. Here we demonstrate for the first time the application of ChipCytometry technology, which has unique features for widespread use, on formalin-fixed paraffin-embedded samples, the most commonly used storage technique of clinically relevant patient specimens worldwide. The excellent staining quality permits workflows for automated quantification of signal intensities, which we further optimized to compensate signal spillover from neighboring cells. In combination with the high number of validated markers, the reported platform can be used from unbiased analyses of tissue composition to detection of phenotypically complex rare cells, and can be easily implemented in both routine research and clinical pathology.
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Affiliation(s)
- Sebastian Jarosch
- Institute for Medical Microbiology, Immunology and Hygiene, Technical University of Munich (TUM), 81675 Munich, Germany
| | - Jan Köhlen
- Institute for Medical Microbiology, Immunology and Hygiene, Technical University of Munich (TUM), 81675 Munich, Germany
| | - Rim S.J. Sarker
- Comparative Experimental Pathology, Institute for Pathology, Technical University of Munich, 81675 Munich, Germany
| | - Katja Steiger
- Comparative Experimental Pathology, Institute for Pathology, Technical University of Munich, 81675 Munich, Germany
| | - Klaus-Peter Janssen
- Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | | | | | - Ernst Holler
- Department of Hematology/Oncology, University Medical Center, 93053 Regensburg, Germany
| | - Elvira D'Ippolito
- Institute for Medical Microbiology, Immunology and Hygiene, Technical University of Munich (TUM), 81675 Munich, Germany
| | - Dirk H. Busch
- Institute for Medical Microbiology, Immunology and Hygiene, Technical University of Munich (TUM), 81675 Munich, Germany
- German Center for Infection Research (DZIF), partner site Munich, 81675 Munich, Germany
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42
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Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma. Nat Commun 2021; 12:6726. [PMID: 34795254 PMCID: PMC8602403 DOI: 10.1038/s41467-021-26974-6] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 10/26/2021] [Indexed: 02/08/2023] Open
Abstract
Cutaneous T cell lymphomas (CTCL) are rare but aggressive cancers without effective treatments. While a subset of patients derive benefit from PD-1 blockade, there is a critically unmet need for predictive biomarkers of response. Herein, we perform CODEX multiplexed tissue imaging and RNA sequencing on 70 tumor regions from 14 advanced CTCL patients enrolled in a pembrolizumab clinical trial (NCT02243579). We find no differences in the frequencies of immune or tumor cells between responders and non-responders. Instead, we identify topographical differences between effector PD-1+ CD4+ T cells, tumor cells, and immunosuppressive Tregs, from which we derive a spatial biomarker, termed the SpatialScore, that correlates strongly with pembrolizumab response in CTCL. The SpatialScore coincides with differences in the functional immune state of the tumor microenvironment, T cell function, and tumor cell-specific chemokine recruitment and is validated using a simplified, clinically accessible tissue imaging platform. Collectively, these results provide a paradigm for investigating the spatial balance of effector and suppressive T cell activity and broadly leveraging this biomarker approach to inform the clinical use of immunotherapies.
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43
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Bhate SS, Barlow GL, Schürch CM, Nolan GP. Tissue schematics map the specialization of immune tissue motifs and their appropriation by tumors. Cell Syst 2021; 13:109-130.e6. [PMID: 34653369 DOI: 10.1016/j.cels.2021.09.012] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 05/28/2021] [Accepted: 09/22/2021] [Indexed: 12/31/2022]
Abstract
A schematic of a biological system, i.e., a representation of its pieces, how they are combined, and what they do, would facilitate understanding its essential organization and alteration in pathogenesis or evolution. We present a computational approach for constructing tissue schematics (TSs) from high-parameter imaging data and a biological model for interpreting them. TSs map the spatial assembly of cellular neighborhoods into tissue motifs, whose modular composition, we propose, enables the generation of complex outputs. We developed our approach in human lymphoid tissue (HLT), identifying the follicular outer zone as a potential relay between neighboring zones and a core lymphoid assembly with modifications characteristic of each HLT type. Applying the TS approach to the tumor microenvironment in human colorectal cancer identified a higher-order motif, whose mutated assembly was negatively associated with patient survival. TSs may therefore elucidate how immune architectures can be specialized and become vulnerable to reprogramming by tumors.
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Affiliation(s)
- Salil S Bhate
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University Schools of Medicine and Engineering, Stanford University, Stanford, CA 94305, USA
| | - Graham L Barlow
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; Program in Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Christian M Schürch
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Garry P Nolan
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA.
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44
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Hickey JW, Tan Y, Nolan GP, Goltsev Y. Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data. Front Immunol 2021; 12:727626. [PMID: 34484237 PMCID: PMC8415085 DOI: 10.3389/fimmu.2021.727626] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 08/02/2021] [Indexed: 12/29/2022] Open
Abstract
Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging.
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Affiliation(s)
- John W. Hickey
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Yuqi Tan
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Garry P. Nolan
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Yury Goltsev
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
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45
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Black S, Phillips D, Hickey JW, Kennedy-Darling J, Venkataraaman VG, Samusik N, Goltsev Y, Schürch CM, Nolan GP. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc 2021; 16:3802-3835. [PMID: 34215862 PMCID: PMC8647621 DOI: 10.1038/s41596-021-00556-8] [Citation(s) in RCA: 198] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 04/14/2021] [Indexed: 02/06/2023]
Abstract
Advances in multiplexed imaging technologies have drastically improved our ability to characterize healthy and diseased tissues at the single-cell level. Co-detection by indexing (CODEX) relies on DNA-conjugated antibodies and the cyclic addition and removal of complementary fluorescently labeled DNA probes and has been used so far to simultaneously visualize up to 60 markers in situ. CODEX enables a deep view into the single-cell spatial relationships in tissues and is intended to spur discovery in developmental biology, disease and therapeutic design. Herein, we provide optimized protocols for conjugating purified antibodies to DNA oligonucleotides, validating the conjugation by CODEX staining and executing the CODEX multicycle imaging procedure for both formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen tissues. In addition, we describe basic image processing and data analysis procedures. We apply this approach to an FFPE human tonsil multicycle experiment. The hands-on experimental time for antibody conjugation is ~4.5 h, validation of DNA-conjugated antibodies with CODEX staining takes ~6.5 h and preparation for a CODEX multicycle experiment takes ~8 h. The multicycle imaging and data analysis time depends on the tissue size, number of markers in the panel and computational complexity.
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Affiliation(s)
- Sarah Black
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Darci Phillips
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Julia Kennedy-Darling
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Akoya Biosciences, Menlo Park, CA, USA
| | - Vishal G Venkataraaman
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Nikolay Samusik
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Becton Dickinson, San Jose, CA, USA
| | - Yury Goltsev
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian M Schürch
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
| | - Garry P Nolan
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
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46
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Brockman AA, Mobley BC, Ihrie RA. Histological Studies of the Ventricular-Subventricular Zone as Neural Stem Cell and Glioma Stem Cell Niche. J Histochem Cytochem 2021; 69:819-834. [PMID: 34310246 DOI: 10.1369/00221554211032003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The neural stem cell niche of the ventricular-subventricular zone supports the persistence of stem and progenitor cells in the mature brain. This niche has many notable cytoarchitectural features that affect the activity of stem cells and may also support the survival and growth of invading tumor cells. Histochemical studies of the niche have revealed many proteins that, in combination, can help to reveal stem-like cells in the normal or cancer context, although many caveats persist in the quest to consistently identify these cells in the human brain. Here, we explore the complex relationship between the persistent proliferative capacity of the neural stem cell niche and the malignant proliferation of brain tumors, with a special focus on histochemical identification of stem cells and stem-like tumor cells and an eye toward the potential application of high-dimensional imaging approaches to the field.
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Affiliation(s)
- Asa A Brockman
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Bret C Mobley
- Departments of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Rebecca A Ihrie
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee.,Departments of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
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47
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Phillips D, Schürch CM, Khodadoust MS, Kim YH, Nolan GP, Jiang S. Highly Multiplexed Phenotyping of Immunoregulatory Proteins in the Tumor Microenvironment by CODEX Tissue Imaging. Front Immunol 2021; 12:687673. [PMID: 34093591 PMCID: PMC8170307 DOI: 10.3389/fimmu.2021.687673] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 04/27/2021] [Indexed: 01/26/2023] Open
Abstract
Immunotherapies are revolutionizing cancer treatment by boosting the natural ability of the immune system. In addition to antibodies against traditional checkpoint molecules or their ligands (i.e., CTLA-4, PD-1, and PD-L1), therapies targeting molecules such as ICOS, IDO-1, LAG-3, OX40, TIM-3, and VISTA are currently in clinical trials. To better inform clinical care and the design of therapeutic combination strategies, the co-expression of immunoregulatory proteins on individual immune cells within the tumor microenvironment must be robustly characterized. Highly multiplexed tissue imaging platforms, such as CO-Detection by indEXing (CODEX), are primed to meet this need by enabling >50 markers to be simultaneously analyzed in single-cells on formalin-fixed paraffin-embedded (FFPE) tissue sections. Assembly and validation of antibody panels is particularly challenging, with respect to the specificity of antigen detection and robustness of signal over background. Herein, we report the design, development, optimization, and application of a 56-marker CODEX antibody panel to eight cutaneous T cell lymphoma (CTCL) patient samples. This panel is comprised of structural, tumor, and immune cell markers, including eight immunoregulatory proteins that are approved or currently undergoing clinical trials as immunotherapy targets. Here we provide a resource to enable extensive high-dimensional, spatially resolved characterization of the tissue microenvironment across tumor types and imaging modalities. This framework provides researchers with a readily applicable blueprint to study tumor immunology, tissue architecture, and enable mechanistic insights into immunotherapeutic targets.
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Affiliation(s)
- Darci Phillips
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, United States
| | - Christian M. Schürch
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Michael S. Khodadoust
- Division of Oncology, Stanford University School of Medicine, Stanford, CA, United States
| | - Youn H. Kim
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, United States
- Division of Oncology, Stanford University School of Medicine, Stanford, CA, United States
| | - Garry P. Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Sizun Jiang
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
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