1
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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2
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Panten J, Heinen T, Ernst C, Eling N, Wagner RE, Satorius M, Marioni JC, Stegle O, Odom DT. The dynamic genetic determinants of increased transcriptional divergence in spermatids. Nat Commun 2024; 15:1272. [PMID: 38341412 PMCID: PMC10858866 DOI: 10.1038/s41467-024-45133-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
Cis-genetic effects are key determinants of transcriptional divergence in discrete tissues and cell types. However, how cis- and trans-effects act across continuous trajectories of cellular differentiation in vivo is poorly understood. Here, we quantify allele-specific expression during spermatogenic differentiation at single-cell resolution in an F1 hybrid mouse system, allowing for the comprehensive characterisation of cis- and trans-genetic effects, including their dynamics across cellular differentiation. Collectively, almost half of the genes subject to genetic regulation show evidence for dynamic cis-effects that vary during differentiation. Our system also allows us to robustly identify dynamic trans-effects, which are less pervasive than cis-effects. In aggregate, genetic effects were strongest in round spermatids, which parallels their increased transcriptional divergence we identified between species. Our approach provides a comprehensive quantification of the variability of genetic effects in vivo, and demonstrates a widely applicable strategy to dissect the impact of regulatory variants on gene regulation in dynamic systems.
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Affiliation(s)
- Jasper Panten
- Division of Regulatory Genomics and Cancer Evolution, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69117, Heidelberg, Germany
| | - Tobias Heinen
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- European Molecular Biology Laboratory, Genome Biology Unit, 69117, Heidelberg, Germany
| | - Christina Ernst
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Nils Eling
- University of Zurich, Department of Quantitative Biomedicine, Zurich, 8057, Switzerland
- ETH Zurich, Institute for Molecular Health Sciences, Zurich, 8093, Switzerland
| | - Rebecca E Wagner
- Faculty of Biosciences, Heidelberg University, 69117, Heidelberg, Germany
- Division of Mechanisms Regulating Gene Expression, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
| | - Maja Satorius
- Division of Regulatory Genomics and Cancer Evolution, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
| | - John C Marioni
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany.
- European Molecular Biology Laboratory, Genome Biology Unit, 69117, Heidelberg, Germany.
| | - Duncan T Odom
- Division of Regulatory Genomics and Cancer Evolution, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany.
- Faculty of Biosciences, Heidelberg University, 69117, Heidelberg, Germany.
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
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3
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Meyer L, Eling N, Bodenmiller B. cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data. BMC Bioinformatics 2024; 25:9. [PMID: 38172724 PMCID: PMC10765786 DOI: 10.1186/s12859-023-05546-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/27/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R. RESULTS Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples. CONCLUSIONS The cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis. It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html . The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer .
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Affiliation(s)
- Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich/University of Zurich, Zurich, Switzerland
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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4
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Windhager J, Zanotelli VRT, Schulz D, Meyer L, Daniel M, Bodenmiller B, Eling N. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc 2023; 18:3565-3613. [PMID: 37816904 DOI: 10.1038/s41596-023-00881-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 06/23/2023] [Indexed: 10/12/2023]
Abstract
Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .
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Affiliation(s)
- Jonas Windhager
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
- SciLifeLab BioImage Informatics Facility and Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Vito Riccardo Tomaso Zanotelli
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Division of Metabolism and Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Daniel Schulz
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Michelle Daniel
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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5
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Meyer L, Eling N, Bodenmiller B. cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data. bioRxiv 2023:2023.05.24.542115. [PMID: 37292939 PMCID: PMC10245846 DOI: 10.1101/2023.05.24.542115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive data visualization of multiplexed imaging data is necessary for quality control and hypothesis examination. Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. The package operates on SingleCellExperiment, SpatialExperiment and CytoImageList objects and therefore integrates with the Bioconductor framework for single-cell and image analysis. Users of cytoviewer need little coding expertise, and the graphical user interface allows user-friendly navigation. We showcase the functionality of cytoviewer by analysis of an imaging mass cytometry dataset of cancer patients.
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Affiliation(s)
- Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
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6
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Hoch T, Schulz D, Eling N, Gómez JM, Levesque MP, Bodenmiller B. Multiplexed imaging mass cytometry of the chemokine milieus in melanoma characterizes features of the response to immunotherapy. Sci Immunol 2022; 7:eabk1692. [PMID: 35363540 DOI: 10.1126/sciimmunol.abk1692] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Intratumoral immune cells are crucial for tumor control and antitumor responses during immunotherapy. Immune cell trafficking into tumors is mediated by binding of specific immune cell receptors to chemokines, a class of secreted chemotactic cytokines. To broadly characterize chemokine expression and function in melanoma, we used multiplexed mass cytometry-based imaging of protein markers and RNA transcripts to analyze the chemokine landscape and immune infiltration in metastatic melanoma samples. Tumors that lacked immune infiltration were devoid of most of the profiled chemokines and exhibited low levels of antigen presentation and markers of inflammation. Infiltrated tumors were characterized by expression of multiple chemokines. CXCL9 and CXCL10 were often localized in patches associated with dysfunctional T cells expressing the B lymphocyte chemoattractant CXCL13. In tumors with B cells but no B cell follicles, T cells were the sole source of CXCL13, suggesting that T cells play a role in B cell recruitment and potentially in B cell follicle formation. B cell patches and follicles were also enriched with TCF7+ naïve-like T cells, a cell type that is predictive of response to immune checkpoint blockade. Our data highlight the strength of targeted RNA and protein codetection to analyze tumor immune microenvironments based on chemokine expression and suggest that the formation of tertiary lymphoid structures may be accompanied by naïve and naïve-like T cell recruitment, which may contribute to antitumor activity.
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Affiliation(s)
- Tobias Hoch
- University of Zürich, Department of Quantitative Biomedicine, Zürich 8057, Switzerland.,ETH-Zürich, Institute for Molecular Health Sciences, Zürich 8093, Switzerland.,Particles-Biology Interactions Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology, St. Gallen, Switzerland
| | - Daniel Schulz
- University of Zürich, Department of Quantitative Biomedicine, Zürich 8057, Switzerland.,ETH-Zürich, Institute for Molecular Health Sciences, Zürich 8093, Switzerland
| | - Nils Eling
- University of Zürich, Department of Quantitative Biomedicine, Zürich 8057, Switzerland.,ETH-Zürich, Institute for Molecular Health Sciences, Zürich 8093, Switzerland
| | - Julia Martínez Gómez
- University Hospital Zürich, Department of Dermatology, Schlieren 8952, Switzerland
| | - Mitchell P Levesque
- University Hospital Zürich, Department of Dermatology, Schlieren 8952, Switzerland
| | - Bernd Bodenmiller
- University of Zürich, Department of Quantitative Biomedicine, Zürich 8057, Switzerland.,ETH-Zürich, Institute for Molecular Health Sciences, Zürich 8093, Switzerland
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7
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Nowicki-Osuch K, Zhuang L, Jammula S, Bleaney CW, Mahbubani KT, Devonshire G, Katz-Summercorn A, Eling N, Wilbrey-Clark A, Madissoon E, Gamble J, Di Pietro M, O'Donovan M, Meyer KB, Saeb-Parsy K, Sharrocks AD, Teichmann SA, Marioni JC, Fitzgerald RC. Molecular phenotyping reveals the identity of Barrett's esophagus and its malignant transition. Science 2021; 373:760-767. [PMID: 34385390 DOI: 10.1126/science.abd1449] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/26/2021] [Accepted: 06/07/2021] [Indexed: 12/12/2022]
Abstract
The origin of human metaplastic states and their propensity for cancer is poorly understood. Barrett's esophagus is a common metaplastic condition that increases the risk for esophageal adenocarcinoma, and its cellular origin is enigmatic. To address this, we harvested tissues spanning the gastroesophageal junction from healthy and diseased donors, including isolation of esophageal submucosal glands. A combination of single-cell transcriptomic profiling, in silico lineage tracing from methylation, open chromatin and somatic mutation analyses, and functional studies in organoid models showed that Barrett's esophagus originates from gastric cardia through c-MYC and HNF4A-driven transcriptional programs. Furthermore, our data indicate that esophageal adenocarcinoma likely arises from undifferentiated Barrett's esophagus cell types even in the absence of a pathologically identifiable metaplastic precursor, illuminating early detection strategies.
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Affiliation(s)
- Karol Nowicki-Osuch
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge CB2 0X2, UK
| | - Lizhe Zhuang
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge CB2 0X2, UK
| | - Sriganesh Jammula
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
| | - Christopher W Bleaney
- Faculty of Biology, Medicine and Health, Michael Smith Building, Oxford Road, University of Manchester, Manchester, UK
| | - Krishnaa T Mahbubani
- Cambridge Biorepository for Translational Medicine (CBTM), NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Ginny Devonshire
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
| | - Annalise Katz-Summercorn
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge CB2 0X2, UK
| | - Nils Eling
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Anna Wilbrey-Clark
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Elo Madissoon
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - John Gamble
- Cambridge Biorepository for Translational Medicine (CBTM), NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Massimiliano Di Pietro
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge CB2 0X2, UK
| | - Maria O'Donovan
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge CB2 0X2, UK
| | - Kerstin B Meyer
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Kourosh Saeb-Parsy
- Cambridge Biorepository for Translational Medicine (CBTM), NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Andrew D Sharrocks
- Faculty of Biology, Medicine and Health, Michael Smith Building, Oxford Road, University of Manchester, Manchester, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK
| | - John C Marioni
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Rebecca C Fitzgerald
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge CB2 0X2, UK.
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8
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Eling N, Damond N, Hoch T, Bodenmiller B. Cytomapper: an R/bioconductor package for visualisation of highly multiplexed imaging data. Bioinformatics 2020; 36:5706-5708. [PMID: 33367748 PMCID: PMC8023672 DOI: 10.1093/bioinformatics/btaa1061] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/20/2020] [Accepted: 12/10/2020] [Indexed: 01/08/2023] Open
Abstract
Summary Highly multiplexed imaging technologies enable spatial profiling of dozens of biomarkers in situ. Here, we describe cytomapper, a computational tool written in R, that enables visualization of pixel- and cell-level information obtained by multiplexed imaging. To illustrate its utility, we analysed 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients. In addition, cytomapper includes a Shiny application that allows hierarchical gating of cells based on marker expression and visualization of selected cells in corresponding images. Availability and implementation The cytomapper package can be installed via https://www.bioconductor.org/packages/release/bioc/html/cytomapper.html. Code for analysis and further instructions can be found at https://github.com/BodenmillerGroup/cytomapper_publication. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Nicolas Damond
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Hoch
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
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9
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Eling N, Richard AC, Richardson S, Marioni JC, Vallejos CA. Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data. Cell Syst 2019; 9:401-413. [PMID: 31647917 PMCID: PMC6838676 DOI: 10.1016/j.cels.2019.08.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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10
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Abstract
Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.
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Affiliation(s)
- Nils Eling
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, UK.
| | | | - John C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, UK.
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
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11
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Chen H, Eling N, Martinez‐Jimenez CP, O'Brien LM, Carbonaro V, Marioni JC, Odom DT, de la Roche M. IL-7-dependent compositional changes within the γδ T cell pool in lymph nodes during ageing lead to an unbalanced anti-tumour response. EMBO Rep 2019; 20:e47379. [PMID: 31283095 PMCID: PMC6680116 DOI: 10.15252/embr.201847379] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 05/29/2019] [Accepted: 06/05/2019] [Indexed: 12/21/2022] Open
Abstract
How the age-associated decline of immune function leads to increased cancer incidence is poorly understood. Here, we have characterised the cellular composition of the γδ T-cell pool in peripheral lymph nodes (pLNs) upon ageing. We find that ageing has minimal cell-intrinsic effects on function and global gene expression of γδ T cells, and γδTCR diversity remains stable. However, ageing alters TCRδ chain usage and clonal structure of γδ T-cell subsets. Importantly, IL-17-producing γδ17 T cells dominate the γδ T-cell pool of aged mice-mainly due to the selective expansion of Vγ6+ γδ17 T cells and augmented γδ17 polarisation of Vγ4+ T cells. Expansion of the γδ17 T-cell compartment is mediated by increased IL-7 expression in the T-cell zone of old mice. In a Lewis lung cancer model, pro-tumourigenic Vγ6+ γδ17 T cells are exclusively activated in the tumour-draining LN and their infiltration into the tumour correlates with increased tumour size in aged mice. Thus, upon ageing, substantial compositional changes in γδ T-cell pool in the pLN lead to an unbalanced γδ T-cell response in the tumour that is associated with accelerated tumour growth.
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MESH Headings
- Aging/genetics
- Aging/immunology
- Animals
- Carcinoma, Lewis Lung/genetics
- Carcinoma, Lewis Lung/immunology
- Carcinoma, Lewis Lung/pathology
- Cell Differentiation
- Cell Lineage/genetics
- Cell Lineage/immunology
- Gene Expression Regulation, Neoplastic
- Immunophenotyping
- Interleukin-17/genetics
- Interleukin-17/immunology
- Interleukin-7/genetics
- Interleukin-7/immunology
- Lymph Nodes/immunology
- Lymph Nodes/pathology
- Mice
- Mice, Inbred C57BL
- Receptors, Antigen, T-Cell, gamma-delta/classification
- Receptors, Antigen, T-Cell, gamma-delta/genetics
- Receptors, Antigen, T-Cell, gamma-delta/immunology
- Signal Transduction
- T-Lymphocyte Subsets/classification
- T-Lymphocyte Subsets/immunology
- T-Lymphocyte Subsets/pathology
- Tumor Burden/genetics
- Tumor Burden/immunology
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Affiliation(s)
- Hung‐Chang Chen
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
| | - Nils Eling
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Genome CampusCambridgeUK
| | - Celia Pilar Martinez‐Jimenez
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
- Wellcome Sanger Institute, Wellcome Genome CampusCambridgeUK
- Helmholtz Pioneer Campus, Helmholtz Zentrum MünchenNeuherbergGermany
| | | | | | - John C Marioni
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Genome CampusCambridgeUK
- Wellcome Sanger Institute, Wellcome Genome CampusCambridgeUK
| | - Duncan T Odom
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
- Wellcome Sanger Institute, Wellcome Genome CampusCambridgeUK
- Division of Signalling and Functional GenomicsGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Maike de la Roche
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
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12
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Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Affiliation(s)
- Nils Eling
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK. .,Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, UK.
| | | | - John C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK. .,Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, UK. .,Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
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13
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Ernst C, Eling N, Martinez-Jimenez CP, Marioni JC, Odom DT. Staged developmental mapping and X chromosome transcriptional dynamics during mouse spermatogenesis. Nat Commun 2019; 10:1251. [PMID: 30890697 PMCID: PMC6424977 DOI: 10.1038/s41467-019-09182-1] [Citation(s) in RCA: 132] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 02/15/2019] [Indexed: 12/21/2022] Open
Abstract
Male gametes are generated through a specialised differentiation pathway involving a series of developmental transitions that are poorly characterised at the molecular level. Here, we use droplet-based single-cell RNA-Sequencing to profile spermatogenesis in adult animals and at multiple stages during juvenile development. By exploiting the first wave of spermatogenesis, we both precisely stage germ cell development and enrich for rare somatic cell-types and spermatogonia. To capture the full complexity of spermatogenesis including cells that have low transcriptional activity, we apply a statistical tool that identifies previously uncharacterised populations of leptotene and zygotene spermatocytes. Focusing on post-meiotic events, we characterise the temporal dynamics of X chromosome re-activation and profile the associated chromatin state using CUT&RUN. This identifies a set of genes strongly repressed by H3K9me3 in spermatocytes, which then undergo extensive chromatin remodelling post-meiosis, thus acquiring an active chromatin state and spermatid-specific expression.
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Affiliation(s)
- Christina Ernst
- European Molecular Biology Laboratory, European Bioinformatics Institute, (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
| | - Nils Eling
- European Molecular Biology Laboratory, European Bioinformatics Institute, (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
| | - Celia P Martinez-Jimenez
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - John C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK.
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
| | - Duncan T Odom
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK.
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, 69120, Heidelberg, Germany.
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14
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Eling N, Richard AC, Richardson S, Marioni JC, Vallejos CA. Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data. Cell Syst 2018; 7:284-294.e12. [PMID: 30172840 PMCID: PMC6167088 DOI: 10.1016/j.cels.2018.06.011] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 05/02/2018] [Accepted: 06/25/2018] [Indexed: 01/10/2023]
Abstract
Cell-to-cell transcriptional variability in otherwise homogeneous cell populations plays an important role in tissue function and development. Single-cell RNA sequencing can characterize this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinder meaningful comparison of expression variability between cell populations. To address this problem, we introduce an analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. This includes a robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronization of biosynthetic machinery components in immune cells upon activation. In contrast to the uniform up-regulation of the biosynthetic machinery, CD4+ T cells show heterogeneous up-regulation of immune-related and lineage-defining genes during activation and differentiation.
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Affiliation(s)
- Nils Eling
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, UK
| | - Arianne C Richard
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, UK; Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0XY, UK
| | - Sylvia Richardson
- MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - John C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, UK.
| | - Catalina A Vallejos
- The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK; Department of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK; MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XY, UK.
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15
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Achim K, Eling N, Vergara HM, Bertucci PY, Musser J, Vopalensky P, Brunet T, Collier P, Benes V, Marioni JC, Arendt D. Whole-Body Single-Cell Sequencing Reveals Transcriptional Domains in the Annelid Larval Body. Mol Biol Evol 2018; 35:1047-1062. [PMID: 29373712 PMCID: PMC5913682 DOI: 10.1093/molbev/msx336] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Animal bodies comprise diverse arrays of cells. To characterize cellular identities across an entire body, we have compared the transcriptomes of single cells randomly picked from dissociated whole larvae of the marine annelid Platynereis dumerilii. We identify five transcriptionally distinct groups of differentiated cells, each expressing a unique set of transcription factors and effector genes that implement cellular phenotypes. Spatial mapping of cells into a cellular expression atlas, and wholemount in situ hybridization of group-specific genes reveals spatially coherent transcriptional domains in the larval body, comprising, for example, apical sensory-neurosecretory cells versus neural/epidermal surface cells. These domains represent new, basic subdivisions of the annelid body based entirely on differential gene expression, and are composed of multiple, transcriptionally similar cell types. They do not represent clonal domains, as revealed by developmental lineage analysis. We propose that the transcriptional domains that subdivide the annelid larval body represent families of related cell types that have arisen by evolutionary diversification. Their possible evolutionary conservation makes them a promising tool for evo-devo research.
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Affiliation(s)
- Kaia Achim
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Nils Eling
- EMBL-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | | | - Paola Yanina Bertucci
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Jacob Musser
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Pavel Vopalensky
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Thibaut Brunet
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Paul Collier
- Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Vladimir Benes
- Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - John C Marioni
- EMBL-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Detlev Arendt
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Centre for Organismal Studies, University of Heidelberg, Heidelberg, Germany
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16
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Martinez-Jimenez CP, Eling N, Chen HC, Vallejos CA, Kolodziejczyk AA, Connor F, Stojic L, Rayner TF, Stubbington MJT, Teichmann SA, de la Roche M, Marioni JC, Odom DT. Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science 2017; 355:1433-1436. [PMID: 28360329 PMCID: PMC5405862 DOI: 10.1126/science.aah4115] [Citation(s) in RCA: 190] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 12/07/2016] [Accepted: 02/23/2017] [Indexed: 12/24/2022]
Abstract
Aging is characterized by progressive loss of physiological and cellular functions, but the molecular basis of this decline remains unclear. We explored how aging affects transcriptional dynamics using single-cell RNA sequencing of unstimulated and stimulated naïve and effector memory CD4+ T cells from young and old mice from two divergent species. In young animals, immunological activation drives a conserved transcriptomic switch, resulting in tightly controlled gene expression characterized by a strong up-regulation of a core activation program, coupled with a decrease in cell-to-cell variability. Aging perturbed the activation of this core program and increased expression heterogeneity across populations of cells in both species. These discoveries suggest that increased cell-to-cell transcriptional variability will be a hallmark feature of aging across most, if not all, mammalian tissues.
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Affiliation(s)
- Celia Pilar Martinez-Jimenez
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Nils Eling
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Hung-Chang Chen
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
| | - Catalina A Vallejos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - Aleksandra A Kolodziejczyk
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Frances Connor
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
| | - Lovorka Stojic
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
| | - Timothy F Rayner
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
| | - Michael J T Stubbington
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sarah A Teichmann
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Maike de la Roche
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
| | - John C Marioni
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Duncan T Odom
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
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17
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Ernst C, Pike J, Aitken SJ, Long HK, Eling N, Stojic L, Ward MC, Connor F, Rayner TF, Lukk M, Klose RJ, Kutter C, Odom DT. Successful transmission and transcriptional deployment of a human chromosome via mouse male meiosis. eLife 2016; 5:e20235. [PMID: 27855777 PMCID: PMC5161449 DOI: 10.7554/elife.20235] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 11/14/2016] [Indexed: 12/12/2022] Open
Abstract
Most human aneuploidies originate maternally, due in part to the presence of highly stringent checkpoints during male meiosis. Indeed, male sterility is common among aneuploid mice used to study chromosomal abnormalities, and male germline transmission of exogenous DNA has been rarely reported. Here we show that, despite aberrant testis architecture, males of the aneuploid Tc1 mouse strain produce viable sperm and transmit human chromosome 21 to create aneuploid offspring. In these offspring, we mapped transcription, transcriptional initiation, enhancer activity, non-methylated DNA, and transcription factor binding in adult tissues. Remarkably, when compared with mice derived from female passage of human chromosome 21, the chromatin condensation during spermatogenesis and the extensive epigenetic reprogramming specific to male germline transmission resulted in almost indistinguishable patterns of transcriptional deployment. Our results reveal an unexpected tolerance of aneuploidy during mammalian spermatogenesis, and the surprisingly robust ability of mouse developmental machinery to accurately deploy an exogenous chromosome, regardless of germline transmission.
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Affiliation(s)
- Christina Ernst
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Jeremy Pike
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Sarah J Aitken
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Histopathology, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | - Hannah K Long
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
- Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United states
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, United States
| | - Nils Eling
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Lovorka Stojic
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Michelle C Ward
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Frances Connor
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Timothy F Rayner
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Margus Lukk
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Robert J Klose
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Claudia Kutter
- Department of Microbiology, Tumor and Cell Biology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Duncan T Odom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
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18
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Pinho AV, Mawson A, Gill A, Arshi M, Warmerdam M, Giry-Laterriere M, Eling N, Lie T, Kuster E, Camargo S, Biankin AV, Wu J, Rooman I. Sirtuin 1 stimulates the proliferation and the expression of glycolysis genes in pancreatic neoplastic lesions. Oncotarget 2016; 7:74768-74778. [PMID: 27494892 PMCID: PMC5342700 DOI: 10.18632/oncotarget.11013] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Accepted: 07/18/2016] [Indexed: 12/13/2022] Open
Abstract
Metabolic reprogramming is a feature of neoplasia and tumor growth. Sirtuin 1 (SIRT1) is a lysine deacetylase of multiple targets including metabolic regulators such as p53. SIRT1 regulates metaplasia in the pancreas. Nevertheless, it is unclear if SIRT1 affects the development of neoplastic lesions and whether metabolic gene expression is altered.To assess neoplastic lesion development, mice with a pancreas-specific loss of Sirt1 (Pdx1-Cre;Sirt1-lox) were bred into a KrasG12D mutant background (KC) that predisposes to the development of pancreatic intra-epithelial neoplasia (PanIN) and ductal adenocarcinoma (PDAC). Similar grade PanIN lesions developed in KC and KC;Sirt1-lox mice but specifically early mucinous PanINs occupied 40% less area in the KC;Sirt1-lox line, attributed to reduced proliferation. This was accompanied by reduced expression of proteins in the glycolysis pathway, such as GLUT1 and GAPDH.The stimulatory effect of SIRT1 on proliferation and glycolysis gene expression was confirmed in a human PDAC cell line. In resected PDAC samples, higher proliferation and expression of glycolysis genes correlated with poor patient survival. SIRT1 expression per se was not prognostic but low expression of Cell Cycle and Apoptosis Regulator 2 (CCAR2), a reported SIRT1 inhibitor, corresponded to poor patient survival.These findings open perspectives for novel targeted therapies in pancreatic cancer.
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Affiliation(s)
- Andreia V. Pinho
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
- St. Vincent's Clinical School, UNSW Australia, Sydney, Australia
- The Australian Pancreatic Cancer Genome Initiative, Darlinghurst, Australia
| | - Amanda Mawson
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
- The Australian Pancreatic Cancer Genome Initiative, Darlinghurst, Australia
| | - Anthony Gill
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
- The Australian Pancreatic Cancer Genome Initiative, Darlinghurst, Australia
- University of Sydney, Sydney, Australia
| | - Mehreen Arshi
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
| | - Max Warmerdam
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
| | - Marc Giry-Laterriere
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
- The Australian Pancreatic Cancer Genome Initiative, Darlinghurst, Australia
| | - Nils Eling
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
| | - Triyana Lie
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
| | | | | | - Andrew V. Biankin
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
- St. Vincent's Clinical School, UNSW Australia, Sydney, Australia
- The Australian Pancreatic Cancer Genome Initiative, Darlinghurst, Australia
- Wolfson Wohl Cancer Research Centre, University of Glasgow, Glasgow, Scotland
| | - Jianmin Wu
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
- St. Vincent's Clinical School, UNSW Australia, Sydney, Australia
- The Australian Pancreatic Cancer Genome Initiative, Darlinghurst, Australia
- Center for Cancer Bioinformatics, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ilse Rooman
- Cancer Division, The Garvan Institute of Medical Research, Sydney, Australia
- St. Vincent's Clinical School, UNSW Australia, Sydney, Australia
- The Australian Pancreatic Cancer Genome Initiative, Darlinghurst, Australia
- Oncology Research Centre, Vrije Universiteit Brussel, Brussels, Belgium
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19
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Giry-Laterriere M, Pinho AV, Eling N, Chantrill L, Rooman I. Emerging Drug Target In Pancreatic Cancer: Placing Sirtuin 1 on the Canvas. Curr Cancer Drug Targets 2016; 15:463-8. [PMID: 26282546 DOI: 10.2174/1568009615666150512102957] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Revised: 04/02/2015] [Accepted: 05/07/2015] [Indexed: 11/22/2022]
Abstract
Sirtuin 1 is a protein deacetylase that regulates a large number of proteins often functionally implicated in tumor development and progression. Its pleiotropic function has turned SIRT1 into an attractive chemotherapeutic target, underscored by very promising preclinical results with SIRT1 inhibitors in the treatment of chronic myeloid leukemia. Here, we revisit the studies on SIRT1 as an emerging target for therapy in pancreatic cancer, a tumor with dismal outcomes for which currently few therapeutic options are available. We highlight those potential SIRT1 target genes that are commonly affected in pancreatic cancer according to recent genomic analyses.
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Affiliation(s)
| | | | | | | | - Ilse Rooman
- The Garvan Institute of Medical Research, Sydney, Australia.
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20
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Eling N, Reuter L, Hazin J, Hamacher-Brady A, Brady NR. Identification of artesunate as a specific activator of ferroptosis in pancreatic cancer cells. Oncoscience 2015; 2:517-32. [PMID: 26097885 PMCID: PMC4468338 DOI: 10.18632/oncoscience.160] [Citation(s) in RCA: 363] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 04/28/2015] [Indexed: 12/12/2022] Open
Abstract
Oncogenic KRas reprograms pancreatic ductal adenocarcinoma (PDAC) cells to states which are highly resistant to apoptosis. Thus, a major preclinical goal is to identify effective strategies for killing PDAC cells. Artesunate (ART) is an anti-malarial that specifically induces programmed cell death in different cancer cell types, in a manner initiated by reactive oxygen species (ROS)-generation. In this study we demonstrate that ART specifically induced ROS- and lysosomal iron-dependent cell death in PDAC cell lines. Highest cytotoxicity was obtained in PDAC cell lines with constitutively-active KRas, and ART did not affect non-neoplastic human pancreatic ductal epithelial (HPDE) cells. We determined that ART did not induce apoptosis or necroptosis. Instead, ART induced ferroptosis, a recently described mode of ROS- and iron-dependent programmed necrosis which can be activated in Ras-transformed cells. Co-treatment with the ferroptosis inhibitor ferrostatin-1 blocked ART-induced lipid peroxidation and cell death, and increased long-term cell survival and proliferation. Importantly, analysis of PDAC patient mRNA expression indicates a dependency on antioxidant homeostasis and increased sensitivity to free intracellular iron, both of which correlate with Ras-driven sensitivity to ferroptosis. Overall, our findings suggest that ART activation of ferroptosis is an effective, novel pathway for killing PDAC cells.
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Affiliation(s)
- Nils Eling
- Lysosomal Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany ; Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ), Heidelberg, Germany ; BioQuant, University of Heidelberg, Germany
| | - Lukas Reuter
- Lysosomal Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany ; Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ), Heidelberg, Germany ; BioQuant, University of Heidelberg, Germany
| | - John Hazin
- Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ), Heidelberg, Germany ; Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany ; BioQuant, University of Heidelberg, Germany
| | - Anne Hamacher-Brady
- Lysosomal Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany ; BioQuant, University of Heidelberg, Germany
| | - Nathan R Brady
- Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ), Heidelberg, Germany ; Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany ; BioQuant, University of Heidelberg, Germany
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