1
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Zhou L, Wen R, Bai C, Li Z, Zheng K, Yu Y, Zhang T, Jia H, Peng Z, Zhu X, Lou Z, Hao L, Yu G, Yang F, Zhang W. Spatial transcriptomic revealed intratumor heterogeneity and cancer stem cell enrichment in colorectal cancer metastasis. Cancer Lett 2024; 602:217181. [PMID: 39159882 DOI: 10.1016/j.canlet.2024.217181] [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: 06/15/2024] [Revised: 07/30/2024] [Accepted: 08/11/2024] [Indexed: 08/21/2024]
Abstract
Metastasis is the main cause of mortality in colorectal cancer (CRC) patients. Exploring the mechanisms of metastasis is of great importance in both clinical and fundamental CRC research. CRC is a highly heterogeneous disease with variable therapeutic outcomes of treatment. In this study, we applied spatial transcriptomics (ST) to generate a tissue-wide transcriptome from two primary colorectal cancer tissues and their matched liver metastatic tissues. Spatial RNA information showed intratumoral heterogeneity (ITH) of both primary and metastatic tissues. The comparison of gene expressions across tissues revealed an apparent enrichment of cancer stem cells (CSCs) in metastatic tissues and identified FOXD1 as a novel metastatic CSC marker. Trajectory and pseudo-time analyses revealed distinct evolutionary trajectories and a dedifferentiation-differentiation process during metastasis. CellphoneDB analysis suggested a dominant interaction of CD74-MIF with tumor cells in metastatic tissues. Further analysis confirmed FOXD1 as a maker of CSCs and the predictor of patient survival, especially in metastatic diseases. Our study found ITH of primary and metastatic tissues and provides novel insights into the cellular mechanisms underlying liver metastasis of CRC and foundations for therapeutic strategies for CRC metastasis.
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Affiliation(s)
- Leqi Zhou
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Rongbo Wen
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chenguang Bai
- Department of Pathology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Zhixuan Li
- Translational Medicine Research Center, Medical Innovation Research Division and Fourth Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Kuo Zheng
- Department of Critical Care Medicine, Jinling Hospital, Medical School of Nanjing University, Jiangsu, China
| | - Yue Yu
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Tianshuai Zhang
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Hang Jia
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Zhiyin Peng
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xiaoming Zhu
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Zheng Lou
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Liqiang Hao
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Guanyu Yu
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.
| | - Fu Yang
- Department of Medical Genetics, Naval Medical University, Shanghai, China.
| | - Wei Zhang
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.
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2
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Lötstedt B, Stražar M, Xavier R, Regev A, Vickovic S. Spatial host-microbiome sequencing reveals niches in the mouse gut. Nat Biotechnol 2024; 42:1394-1403. [PMID: 37985876 PMCID: PMC11392810 DOI: 10.1038/s41587-023-01988-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/12/2023] [Indexed: 11/22/2023]
Abstract
Mucosal and barrier tissues, such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for understanding homeostasis and disease. Here we present spatial host-microbiome sequencing (SHM-seq), an all-sequencing-based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from a tissue by modifying spatially barcoded glass surfaces to enable simultaneous capture of host transcripts and hypervariable regions of the 16S bacterial ribosomal RNA. We applied our approach to the mouse gut as a model system, used a deep learning approach for data mapping and detected spatial niches defined by cellular composition and microbial geography. We show that subpopulations of gut cells express specific gene programs in different microenvironments characteristic of regional commensal bacteria and impact host-bacteria interactions. SHM-seq should enhance the study of native host-microbe interactions in health and disease.
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Affiliation(s)
- Britta Lötstedt
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
- New York Genome Center, New York, NY, USA
| | | | - Ramnik Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Molecular Biology, Center for Computational and Integrative Biology, Massachusetts, General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
| | - Sanja Vickovic
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- New York Genome Center, New York, NY, USA.
- Department of Biomedical Engineering and Herbert Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA.
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Beijer Laboratory for Gene and Neuro Research, Uppsala University, Uppsala, Sweden.
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3
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Saarenpää S, Shalev O, Ashkenazy H, Carlos V, Lundberg DS, Weigel D, Giacomello S. Spatial metatranscriptomics resolves host-bacteria-fungi interactomes. Nat Biotechnol 2024; 42:1384-1393. [PMID: 37985875 PMCID: PMC11392817 DOI: 10.1038/s41587-023-01979-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2023] [Indexed: 11/22/2023]
Abstract
The interactions of microorganisms among themselves and with their multicellular host take place at the microscale, forming complex networks and spatial patterns. Existing technology does not allow the simultaneous investigation of spatial interactions between a host and the multitude of its colonizing microorganisms, which limits our understanding of host-microorganism interactions within a plant or animal tissue. Here we present spatial metatranscriptomics (SmT), a sequencing-based approach that leverages 16S/18S/ITS/poly-d(T) multimodal arrays for simultaneous host transcriptome- and microbiome-wide characterization of tissues at 55-µm resolution. We showcase SmT in outdoor-grown Arabidopsis thaliana leaves as a model system, and find tissue-scale bacterial and fungal hotspots. By network analysis, we study inter- and intrakingdom spatial interactions among microorganisms, as well as the host response to microbial hotspots. SmT provides an approach for answering fundamental questions on host-microbiome interplay.
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Affiliation(s)
- Sami Saarenpää
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Or Shalev
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Systems Biology of Microbial Communities, University of Tübingen, Tübingen, Germany
| | - Haim Ashkenazy
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
| | - Vanessa Carlos
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany
| | - Derek Severi Lundberg
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Detlef Weigel
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Stefania Giacomello
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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4
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Hildebrandt F, Iturritza MU, Zwicker C, Vanneste B, Van Hul N, Semle E, Quin J, Pascini T, Saarenpää S, He M, Andersson ER, Scott CL, Vega-Rodriguez J, Lundeberg J, Ankarklev J. Host-pathogen interactions in the Plasmodium-infected mouse liver at spatial and single-cell resolution. Nat Commun 2024; 15:7105. [PMID: 39160174 PMCID: PMC11333755 DOI: 10.1038/s41467-024-51418-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 08/06/2024] [Indexed: 08/21/2024] Open
Abstract
Upon infecting its vertebrate host, the malaria parasite initially invades the liver where it undergoes massive replication, whilst remaining clinically silent. The coordination of host responses across the complex liver tissue during malaria infection remains unexplored. Here, we perform spatial transcriptomics in combination with single-nuclei RNA sequencing over multiple time points to delineate host-pathogen interactions across Plasmodium berghei-infected liver tissues. Our data reveals significant changes in spatial gene expression in the malaria-infected tissues. These include changes related to lipid metabolism in the proximity to sites of Plasmodium infection, distinct inflammation programs between lobular zones, and regions with enrichment of different inflammatory cells, which we term 'inflammatory hotspots'. We also observe significant upregulation of genes involved in inflammation in the control liver tissues of mice injected with mosquito salivary gland components. However, this response is considerably delayed compared to that observed in P. berghei-infected mice. Our study establishes a benchmark for investigating transcriptome changes during host-parasite interactions in tissues, it provides informative insights regarding in vivo study design linked to infection and offers a useful tool for the discovery and validation of de novo intervention strategies aimed at malaria liver stage infection.
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Affiliation(s)
- Franziska Hildebrandt
- Molecular Biosciences, the Wenner Gren Institute, Stockholm University, Svante Arrhenius Väg 20C, SE-106 91, Stockholm, Sweden.
| | - Miren Urrutia Iturritza
- Molecular Biosciences, the Wenner Gren Institute, Stockholm University, Svante Arrhenius Väg 20C, SE-106 91, Stockholm, Sweden
| | - Christian Zwicker
- Department of Biomedical Molecular Biology, Faculty of Sciences, Ghent University, Ghent, Belgium
- Laboratory of Myeloid Cell Biology in Tissue Damage and Inflammation, VIB-UGent Center for Inflammation Research, Technologiepark-Zwijnaarde 71, Ghent, 9052, Belgium
| | - Bavo Vanneste
- Department of Biomedical Molecular Biology, Faculty of Sciences, Ghent University, Ghent, Belgium
- Laboratory of Myeloid Cell Biology in Tissue Damage and Inflammation, VIB-UGent Center for Inflammation Research, Technologiepark-Zwijnaarde 71, Ghent, 9052, Belgium
- Laboratory of Myeloid Cell Biology in Tissue Homeostasis and Regeneration, VIB-UGent Center for Inflammation Research, Technologiepark-Zwijnaarde 71, Ghent, 9052, Belgium
| | - Noémi Van Hul
- Department of Cell and Molecular Biology, Karolinska Institutet Stockholm, SE-171 77, Solna, Sweden
| | - Elisa Semle
- Molecular Biosciences, the Wenner Gren Institute, Stockholm University, Svante Arrhenius Väg 20C, SE-106 91, Stockholm, Sweden
| | - Jaclyn Quin
- Molecular Biosciences, the Wenner Gren Institute, Stockholm University, Svante Arrhenius Väg 20C, SE-106 91, Stockholm, Sweden
| | - Tales Pascini
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 12735 Twinbrook Parkway, Rm 2E20A, Rockville, MD, 20852, USA
| | - Sami Saarenpää
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23a, SE-171 65, Solna, Sweden
| | - Mengxiao He
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23a, SE-171 65, Solna, Sweden
| | - Emma R Andersson
- Department of Cell and Molecular Biology, Karolinska Institutet Stockholm, SE-171 77, Solna, Sweden
| | - Charlotte L Scott
- Department of Biomedical Molecular Biology, Faculty of Sciences, Ghent University, Ghent, Belgium
- Laboratory of Myeloid Cell Biology in Tissue Damage and Inflammation, VIB-UGent Center for Inflammation Research, Technologiepark-Zwijnaarde 71, Ghent, 9052, Belgium
| | - Joel Vega-Rodriguez
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 12735 Twinbrook Parkway, Rm 2E20A, Rockville, MD, 20852, USA
| | - Joakim Lundeberg
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23a, SE-171 65, Solna, Sweden
| | - Johan Ankarklev
- Molecular Biosciences, the Wenner Gren Institute, Stockholm University, Svante Arrhenius Väg 20C, SE-106 91, Stockholm, Sweden.
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5
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Jing SY, Liu D, Feng N, Dong H, Wang HQ, Yan X, Chen XF, Qu MC, Lin P, Yi B, Feng F, Chen L, Wang HY, Li H, He YF. Spatial multiomics reveals a subpopulation of fibroblasts associated with cancer stemness in human hepatocellular carcinoma. Genome Med 2024; 16:98. [PMID: 39138551 PMCID: PMC11320883 DOI: 10.1186/s13073-024-01367-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 07/23/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Cancer-associated fibroblasts (CAFs) are the prominent cell type in the tumor microenvironment (TME), and CAF subsets have been identified in various tumors. However, how CAFs spatially coordinate other cell populations within the liver TME to promote cancer progression remains unclear. METHODS We combined multi-region proteomics (6 patients, 24 samples), 10X Genomics Visium spatial transcriptomics (11 patients, 25 samples), and multiplexed imaging (92 patients, 264 samples) technologies to decipher the expression heterogeneity, functional diversity, spatial distribution, colocalization, and interaction of fibroblasts. The newly identified CAF subpopulation was validated by cells isolated from 5 liver cancer patients and in vitro functional assays. RESULTS We identified a liver CAF subpopulation, marked by the expression of COL1A2, COL4A1, COL4A2, CTGF, and FSTL1, and named F5-CAF. F5-CAF is preferentially located within and around tumor nests and colocalizes with cancer cells with higher stemness in hepatocellular carcinoma (HCC). Multiplexed staining of 92 patients and the bulk transcriptome of 371 patients demonstrated that the abundance of F5-CAFs in HCC was associated with a worse prognosis. Further in vitro experiments showed that F5-CAFs isolated from liver cancer patients can promote the proliferation and stemness of HCC cells. CONCLUSIONS We identified a CAF subpopulation F5-CAF in liver cancer, which is associated with cancer stemness and unfavorable prognosis. Our results provide potential mechanisms by which the CAF subset in the TME promotes the development of liver cancer by supporting the survival of cancer stem cells.
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Affiliation(s)
- Si-Yu Jing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Dan Liu
- Molecular Pathology Laboratory, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Shanghai, 201800, People's Republic of China
- Cancer Institute, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People's Republic of China
| | - Na Feng
- Molecular Pathology Laboratory, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Shanghai, 201800, People's Republic of China
| | - Hui Dong
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Shanghai, 200438, People's Republic of China
| | - He-Qi Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Xi Yan
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Xu-Feng Chen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Min-Cheng Qu
- Molecular Pathology Laboratory, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Shanghai, 201800, People's Republic of China
| | - Ping Lin
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Bin Yi
- Department of Organ Transplantation, Eastern Hepatobiliary Surgery Hospital, Shanghai, 201800, People's Republic of China
| | - Feiling Feng
- Department of Biliary Tract Surgery I, Eastern Hepatobiliary Surgery Hospital, Shanghai, 201800, People's Republic of China
| | - Lei Chen
- National Center for Liver Cancer and International Cooperation Laboratory On Signal Transduction, Eastern Hepatobiliary Surgery Institute/Hospital, Shanghai, 200438, People's Republic of China.
| | - Hong-Yang Wang
- National Center for Liver Cancer and International Cooperation Laboratory On Signal Transduction, Eastern Hepatobiliary Surgery Institute/Hospital, Shanghai, 200438, People's Republic of China.
- Key Laboratory of Signaling Regulation and Targeting Therapy of Liver Cancer, Ministry of Education and Shanghai Key Laboratory of Hepatobiliary Tumor Biology, Shanghai, 200438, People's Republic of China.
| | - Hong Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China.
| | - Yu-Fei He
- Molecular Pathology Laboratory, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Shanghai, 201800, People's Republic of China.
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6
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Schmidt M, Avagyan S, Reiche K, Binder H, Loeffler-Wirth H. A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections. Curr Issues Mol Biol 2024; 46:4701-4720. [PMID: 38785552 PMCID: PMC11119626 DOI: 10.3390/cimb46050284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.
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Affiliation(s)
- Maria Schmidt
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
| | - Susanna Avagyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Perlickstrasse 1, 04103 Leipzig, Germany
- Institute for Clinical Immunology, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
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7
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Daly AC, Cambuli F, Äijö T, Lötstedt B, Marjanovic N, Kuksenko O, Smith-Erb M, Fernandez S, Domovic D, Van Wittenberghe N, Drokhlyansky E, Griffin GK, Phatnani H, Bonneau R, Regev A, Vickovic S. Tissue and cellular spatiotemporal dynamics in colon aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590125. [PMID: 38712088 PMCID: PMC11071407 DOI: 10.1101/2024.04.22.590125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.
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Affiliation(s)
- Aidan C. Daly
- New York Genome Center, New York, NY, USA
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | | | - Tarmo Äijö
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Britta Lötstedt
- New York Genome Center, New York, NY, USA
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Nemanja Marjanovic
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Olena Kuksenko
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | | | | | | | | | - Eugene Drokhlyansky
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabriel K Griffin
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Hemali Phatnani
- New York Genome Center, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Center for Data Science, New York University, New York, NY, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Aviv Regev
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Sanja Vickovic
- New York Genome Center, New York, NY, USA
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Engineering and Herbert Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Beijer Laboratory for Gene and Neuro Research, Uppsala University, Uppsala, Sweden
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8
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Bai Z, Zhang D, Gao Y, Tao B, Bao S, Enninful A, Zhang D, Su G, Tian X, Zhang N, Xiao Y, Liu Y, Gerstein M, Li M, Xing Y, Lu J, Xu ML, Fan R. Spatially Exploring RNA Biology in Archival Formalin-Fixed Paraffin-Embedded Tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.579143. [PMID: 38370833 PMCID: PMC10871202 DOI: 10.1101/2024.02.06.579143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Spatial transcriptomics has emerged as a powerful tool for dissecting spatial cellular heterogeneity but as of today is largely limited to gene expression analysis. Yet, the life of RNA molecules is multifaceted and dynamic, requiring spatial profiling of different RNA species throughout the life cycle to delve into the intricate RNA biology in complex tissues. Human disease-relevant tissues are commonly preserved as formalin-fixed and paraffin-embedded (FFPE) blocks, representing an important resource for human tissue specimens. The capability to spatially explore RNA biology in FFPE tissues holds transformative potential for human biology research and clinical histopathology. Here, we present Patho-DBiT combining in situ polyadenylation and deterministic barcoding for spatial full coverage transcriptome sequencing, tailored for probing the diverse landscape of RNA species even in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for five years. Furthermore, genome-wide single nucleotide RNA variants can be captured to distinguish different malignant clones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA-mRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis trajectory. High resolution Patho-DBiT at the cellular level reveals a spatial neighborhood and traces the spatiotemporal kinetics driving tumor progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to study human tissue biology and aid in clinical pathology evaluation.
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Affiliation(s)
- Zhiliang Bai
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Dingyao Zhang
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Yan Gao
- Center for Computational and Genomic Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Bo Tao
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Shuozhen Bao
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Daiwei Zhang
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Graham Su
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Xiaolong Tian
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Ningning Zhang
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Yang Xiao
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Yang Liu
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Mark Gerstein
- Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yi Xing
- Center for Computational and Genomic Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jun Lu
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Mina L. Xu
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale University School of Medicine, New Haven, CT 06520, USA
- Human and Translational Immunology, Yale University School of Medicine, New Haven, CT 06520, USA
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9
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Li Y, Di C, Song S, Zhang Y, Lu Y, Liao J, Lei B, Zhong J, Guo K, Zhang N, Su S. Choroid plexus mast cells drive tumor-associated hydrocephalus. Cell 2023; 186:5719-5738.e28. [PMID: 38056463 DOI: 10.1016/j.cell.2023.11.001] [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: 04/09/2023] [Revised: 09/04/2023] [Accepted: 11/01/2023] [Indexed: 12/08/2023]
Abstract
Tumor-associated hydrocephalus (TAH) is a common and lethal complication of brain metastases. Although other factors beyond mechanical obstructions have been suggested, the exact mechanisms are unknown. Using single-nucleus RNA sequencing and spatial transcriptomics, we find that a distinct population of mast cells locate in the choroid plexus and dramatically increase during TAH. Genetic fate tracing and intracranial mast-cell-specific tryptase knockout showed that choroid plexus mast cells (CPMCs) disrupt cilia of choroid plexus epithelia via the tryptase-PAR2-FoxJ1 pathway and consequently increase cerebrospinal fluid production. Mast cells are also found in the human choroid plexus. Levels of tryptase in cerebrospinal fluid are closely associated with clinical severity of TAH. BMS-262084, an inhibitor of tryptase, can cross the blood-brain barrier, inhibit TAH in vivo, and alleviate mast-cell-induced damage of epithelial cilia in a human pluripotent stem-cell-derived choroid plexus organoid model. Collectively, we uncover the function of CPMCs and provide an attractive therapy for TAH.
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Affiliation(s)
- Yiye Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Can Di
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Shijian Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Yubo Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Yiwen Lu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Jianyou Liao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Bingxi Lei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Department of Neurosurgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Jian Zhong
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Brain Function and Disease, Guangdong Translational Medicine Innovation Platform, Guangzhou 510080, China
| | - Kaihua Guo
- Department of Anatomy and Physiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - Nu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Brain Function and Disease, Guangdong Translational Medicine Innovation Platform, Guangzhou 510080, China; Department of Anatomy and Physiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - Shicheng Su
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China; Biotherapy Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
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10
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Wang T, Song Z, Zhao X, Wu Y, Wu L, Haghparast A, Wu H. Spatial transcriptomic analysis of the mouse brain following chronic social defeat stress. EXPLORATION (BEIJING, CHINA) 2023; 3:20220133. [PMID: 38264685 PMCID: PMC10742195 DOI: 10.1002/exp.20220133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 09/03/2023] [Indexed: 01/25/2024]
Abstract
Depression is a highly prevalent and disabling mental disorder, involving numerous genetic changes that are associated with abnormal functions in multiple regions of the brain. However, there is little transcriptomic-wide characterization of chronic social defeat stress (CSDS) to comprehensively compare the transcriptional changes in multiple brain regions. Spatial transcriptomics (ST) was used to reveal the spatial difference of gene expression in the control, resilient (RES) and susceptible (SUS) mouse brains, and annotated eight anatomical brain regions and six cell types. The gene expression profiles uncovered that CSDS leads to gene synchrony changes in different brain regions. Then it was identified that inhibitory neurons and synaptic functions in multiple regions were primarily affected by CSDS. The brain regions Hippocampus (HIP), Isocortex, and Amygdala (AMY) present more pronounced transcriptional changes in genes associated with depressive psychiatric disorders than other regions. Signalling communication between these three brain regions may play a critical role in susceptibility to CSDS. Taken together, this study provides important new insights into CSDS susceptibility at the ST level, which offers a new approach for understanding and treating depression.
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Affiliation(s)
- Ting Wang
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Zhihong Song
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Xin Zhao
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Yan Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Liying Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Abbas Haghparast
- Neuroscience Research Center, School of MedicineShahid Beheshti University of Medical SciencesTehranIran
| | - Haitao Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
- Key Laboratory of Neuroregeneration, Co‐innovation Center of NeuroregenerationNantong UniversityNantongChina
- Chinese Institute for Brain ResearchBeijingChina
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11
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Liu Y, DiStasio M, Su G, Asashima H, Enninful A, Qin X, Deng Y, Nam J, Gao F, Bordignon P, Cassano M, Tomayko M, Xu M, Halene S, Craft JE, Hafler D, Fan R. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat Biotechnol 2023; 41:1405-1409. [PMID: 36823353 PMCID: PMC10567548 DOI: 10.1038/s41587-023-01676-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 01/12/2023] [Indexed: 02/25/2023]
Abstract
In this study, we extended co-indexing of transcriptomes and epitopes (CITE) to the spatial dimension and demonstrated high-plex protein and whole transcriptome co-mapping. We profiled 189 proteins and whole transcriptome in multiple mouse tissue types with spatial CITE sequencing and then further applied the method to measure 273 proteins and transcriptome in human tissues, revealing spatially distinct germinal center reactions in tonsil and early immune activation in skin at the Coronavirus Disease 2019 mRNA vaccine injection site.
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Affiliation(s)
- Yang Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Marcello DiStasio
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Graham Su
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Hiromitsu Asashima
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Xiaoyu Qin
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Yanxiang Deng
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Jungmin Nam
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Fu Gao
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | | | | | - Mary Tomayko
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Dermatology, Yale School of Medicine, New Haven, CT, USA
| | - Mina Xu
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Halene
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Joseph E Craft
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT, USA
| | - David Hafler
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA.
- Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT, USA.
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12
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Cisar C, Keener N, Ruffalo M, Paten B. A unified pipeline for FISH spatial transcriptomics. CELL GENOMICS 2023; 3:100384. [PMID: 37719153 PMCID: PMC10504669 DOI: 10.1016/j.xgen.2023.100384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 06/23/2023] [Accepted: 07/24/2023] [Indexed: 09/19/2023]
Abstract
High-throughput spatial transcriptomics has emerged as a powerful tool for investigating the spatial distribution of mRNA expression and its effects on cellular function. There is a lack of standardized tools for analyzing spatial transcriptomics data, leading many groups to write their own in-house tools that are often poorly documented and not generalizable. To address this, we have expanded and improved the starfish library and used those tools to create PIPEFISH, a semi-automated and generalizable pipeline that performs transcript annotation for fluorescence in situ hybridization (FISH)-based spatial transcriptomics. We used this pipeline to annotate transcript locations from three real datasets from three different common types of FISH image-based experiments, MERFISH, seqFISH, and targeted in situ sequencing (ISS), and verified that the results were high quality using the internal quality metrics of the pipeline and also a comparison with an orthogonal method of measuring RNA expression. PIPEFISH is a publicly available and open-source tool.
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Affiliation(s)
- Cecilia Cisar
- Department of Biomolecular Engineering, School of Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Nicholas Keener
- Department of Biomolecular Engineering, School of Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Mathew Ruffalo
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Benedict Paten
- Department of Biomolecular Engineering, School of Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
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13
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Cheng M, Jiang Y, Xu J, Mentis AFA, Wang S, Zheng H, Sahu SK, Liu L, Xu X. Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges. J Genet Genomics 2023; 50:625-640. [PMID: 36990426 DOI: 10.1016/j.jgg.2023.03.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/11/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023]
Abstract
The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology, from the invention of the microscope 350 years ago to the recent emergence of single-cell sequencing, by which the scientific community has been able to visualize life at an unprecedented resolution. Most recently, the Spatially Resolved Transcriptomics (SRT) technologies have filled the gap in probing the spatial or even three-dimensional organization of the molecular foundation behind the molecular mysteries of life, including the origin of different cellular populations developed from totipotent cells and human diseases. In this review, we introduce recent progresses and challenges on SRT from the perspectives of technologies and bioinformatic tools, as well as the representative SRT applications. With the currently fast-moving progress of the SRT technologies and promising results from early adopted research projects, we can foresee the bright future of such new tools in understanding life at the most profound analytical level.
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Affiliation(s)
| | - Yujia Jiang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
| | | | | | - Shuai Wang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Sunil Kumar Sahu
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China; State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China
| | - Longqi Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xun Xu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; BGI-Shenzhen, Shenzhen, Guangdong 518103, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, Guangdong 518120, China.
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14
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Zheng J, Wu H, Wang X, Zhang G, Lu J, Xu W, Xu S, Fang Y, Zhang A, Shao A, Chen S, Zhao Z, Zhang J, Yu J. Temporal dynamics of microglia-astrocyte interaction in neuroprotective glial scar formation after intracerebral hemorrhage. J Pharm Anal 2023; 13:862-879. [PMID: 37719195 PMCID: PMC10499589 DOI: 10.1016/j.jpha.2023.02.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 02/02/2023] [Accepted: 02/16/2023] [Indexed: 02/25/2023] Open
Abstract
The role of glial scar after intracerebral hemorrhage (ICH) remains unclear. This study aimed to investigate whether microglia-astrocyte interaction affects glial scar formation and explore the specific function of glial scar. We used a pharmacologic approach to induce microglial depletion during different ICH stages and examine how ablating microglia affects astrocytic scar formation. Spatial transcriptomics (ST) analysis was performed to explore the potential ligand-receptor pair in the modulation of microglia-astrocyte interaction and to verify the functional changes of astrocytic scars at different periods. During the early stage, sustained microglial depletion induced disorganized astrocytic scar, enhanced neutrophil infiltration, and impaired tissue repair. ST analysis indicated that microglia-derived insulin like growth factor 1 (IGF1) modulated astrocytic scar formation via mechanistic target of rapamycin (mTOR) signaling activation. Moreover, repopulating microglia (RM) more strongly activated mTOR signaling, facilitating a more protective scar formation. The combination of IGF1 and osteopontin (OPN) was necessary and sufficient for RM function, rather than IGF1 or OPN alone. At the chronic stage of ICH, the overall net effect of astrocytic scar changed from protective to destructive and delayed microglial depletion could partly reverse this. The vital insight gleaned from our data is that sustained microglial depletion may not be a reasonable treatment strategy for early-stage ICH. Inversely, early-stage IGF1/OPN treatment combined with late-stage PLX3397 treatment is a promising therapeutic strategy. This prompts us to consider the complex temporal dynamics and overall net effect of microglia and astrocytes, and develop elaborate treatment strategies at precise time points after ICH.
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Affiliation(s)
- Jingwei Zheng
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
- Stroke Research Center for Diagnostic and Therapeutic Technologies of Zhejiang Province, Hangzhou, 310000, China
| | - Haijian Wu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
- Stroke Research Center for Diagnostic and Therapeutic Technologies of Zhejiang Province, Hangzhou, 310000, China
| | - Xiaoyu Wang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Guoqiang Zhang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Jia'nan Lu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Weilin Xu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
- Stroke Research Center for Diagnostic and Therapeutic Technologies of Zhejiang Province, Hangzhou, 310000, China
| | - Shenbin Xu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
- Stroke Research Center for Diagnostic and Therapeutic Technologies of Zhejiang Province, Hangzhou, 310000, China
| | - Yuanjian Fang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
- Stroke Research Center for Diagnostic and Therapeutic Technologies of Zhejiang Province, Hangzhou, 310000, China
| | - Anke Zhang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Anwen Shao
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
- Stroke Research Center for Diagnostic and Therapeutic Technologies of Zhejiang Province, Hangzhou, 310000, China
| | - Sheng Chen
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
- Stroke Research Center for Diagnostic and Therapeutic Technologies of Zhejiang Province, Hangzhou, 310000, China
| | - Zhen Zhao
- Center for Neurodegeneration and Regeneration, Zilkha Neurogenetic Institute and Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jianmin Zhang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou, 310000, China
- Stroke Research Center for Diagnostic and Therapeutic Technologies of Zhejiang Province, Hangzhou, 310000, China
| | - Jun Yu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
- Stroke Research Center for Diagnostic and Therapeutic Technologies of Zhejiang Province, Hangzhou, 310000, China
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15
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Geras A, Darvish Shafighi S, Domżał K, Filipiuk I, Rączkowska A, Szymczak P, Toosi H, Kaczmarek L, Koperski Ł, Lagergren J, Nowis D, Szczurek E. Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data. Genome Biol 2023; 24:120. [PMID: 37198601 PMCID: PMC10190053 DOI: 10.1186/s13059-023-02951-8] [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: 04/27/2022] [Accepted: 04/21/2023] [Indexed: 05/19/2023] Open
Abstract
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.
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Affiliation(s)
- Agnieszka Geras
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Shadi Darvish Shafighi
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR, Paris, France
| | - Kacper Domżał
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Igor Filipiuk
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Alicja Rączkowska
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Paulina Szymczak
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Hosein Toosi
- KTH Royal Institute of Technology, Stockholm, Sweden
| | - Leszek Kaczmarek
- BRAINCITY, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
| | - Łukasz Koperski
- Department of Pathology, Medical University of Warsaw, Warsaw, Poland
| | | | - Dominika Nowis
- Laboratory of Experimental Medicine, Medical University of Warsaw, Warsaw, Poland
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland.
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16
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Zhang D, Deng Y, Kukanja P, Agirre E, Bartosovic M, Dong M, Ma C, Ma S, Su G, Bao S, Liu Y, Xiao Y, Rosoklija GB, Dwork AJ, Mann JJ, Leong KW, Boldrini M, Wang L, Haeussler M, Raphael BJ, Kluger Y, Castelo-Branco G, Fan R. Spatial epigenome-transcriptome co-profiling of mammalian tissues. Nature 2023; 616:113-122. [PMID: 36922587 PMCID: PMC10076218 DOI: 10.1038/s41586-023-05795-1] [Citation(s) in RCA: 88] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 02/03/2023] [Indexed: 03/17/2023]
Abstract
Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context1-5. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.
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Affiliation(s)
- Di Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Yanxiang Deng
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology and Laboratory Medicine, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Petra Kukanja
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Eneritz Agirre
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Marek Bartosovic
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Mingze Dong
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Cong Ma
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Sai Ma
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Graham Su
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Shuozhen Bao
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Yang Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Yang Xiao
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Gorazd B Rosoklija
- Department of Psychiatry, Columbia University, New York, NY, USA
- Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Macedonian Academy of Sciences & Arts, Skopje, Republic of Macedonia
| | - Andrew J Dwork
- Department of Psychiatry, Columbia University, New York, NY, USA
- Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Macedonian Academy of Sciences & Arts, Skopje, Republic of Macedonia
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - J John Mann
- Department of Psychiatry, Columbia University, New York, NY, USA
- Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Department of Radiology, Columbia University, New York, NY, USA
| | - Kam W Leong
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Maura Boldrini
- Department of Psychiatry, Columbia University, New York, NY, USA
- Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
| | - Liya Wang
- AtlasXomics, Inc., New Haven, CT, USA
| | | | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Yuval Kluger
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Applied Mathematics Program, Yale University, New Haven, CT, USA
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
- Ming Wai Lau Centre for Reparative Medicine, Stockholm Node, Karolinska Institutet, Stockholm, Sweden.
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
- Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT, USA.
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17
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Kleino I, Frolovaitė P, Suomi T, Elo LL. Computational solutions for spatial transcriptomics. Comput Struct Biotechnol J 2022; 20:4870-4884. [PMID: 36147664 PMCID: PMC9464853 DOI: 10.1016/j.csbj.2022.08.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/18/2022] Open
Abstract
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.
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Key Words
- AOI, area of illumination
- BICCN, Brain Initiative Cell Census Network
- BOLORAMIS, barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses
- Baysor, Bayesian Segmentation of Spatial Transcriptomics Data
- BinSpect, Binary Spatial Extraction
- CCC, cell–cell communication
- CCI, cell–cell interactions
- CNV, copy-number variation
- Computational biology
- DSP, digital spatial profiling
- DbiT-Seq, Deterministic Barcoding in Tissue for spatial omics sequencing
- FA, factor analysis
- FFPE, formalin-fixed, paraffin-embedded
- FISH, fluorescence in situ hybridization
- FISSEQ, fluorescence in situ sequencing of RNA
- FOV, Field of view
- GRNs, gene regulation networks
- GSEA, gene set enrichment analysis
- GSVA, gene set variation analysis
- HDST, high definition spatial transcriptomics
- HMRF, hidden Markov random field
- ICG, interaction changed genes
- ISH, in situ hybridization
- ISS, in situ sequencing
- JSTA, Joint cell segmentation and cell type annotation
- KNN, k-nearest neighbor
- LCM, Laser Capture Microdissection
- LCM-seq, laser capture microdissection coupled with RNA sequencing
- LOH, loss of heterozygosity analysis
- MC, Molecular Cartography
- MERFISH, multiplexed error-robust FISH
- NMF (NNMF), Non-negative matrix factorization
- PCA, Principal Component Analysis
- PIXEL-seq, Polony (or DNA cluster)-indexed library-sequencing
- PL-lig, padlock ligation
- QC, quality control
- RNAseq, RNA sequencing
- ROI, region of interest
- SCENIC, Single-Cell rEgulatory Network Inference and Clustering
- SME, Spatial Morphological gene Expression normalization
- SPATA, SPAtial Transcriptomic Analysis
- ST Pipeline, Spatial Transcriptomics Pipeline
- ST, Spatial transcriptomics
- STARmap, spatially-resolved transcript amplicon readout mapping
- Single-cell analysis
- Spatial data analysis frameworks
- Spatial deconvolution
- Spatial transcriptomics
- TIVA, Transcriptome in Vivo Analysis
- TMA, tissue microarray
- TME, tumor micro environment
- UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction
- UMI, unique molecular identifier
- ZipSeq, zipcoded sequencing.
- scRNA-seq, single-cell RNA sequencing
- scvi-tools, single-cell variational inference tools
- seqFISH, sequential fluorescence in situ hybridization
- sequ-smFISH, sequential single-molecule fluorescent in situ hybridization
- smFISH, single molecule FISH
- t-SNE, t-distributed stochastic neighbor embedding
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Affiliation(s)
- Iivari Kleino
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Paulina Frolovaitė
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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18
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Erickson A, He M, Berglund E, Marklund M, Mirzazadeh R, Schultz N, Kvastad L, Andersson A, Bergenstråhle L, Bergenstråhle J, Larsson L, Alonso Galicia L, Shamikh A, Basmaci E, Díaz De Ståhl T, Rajakumar T, Doultsinos D, Thrane K, Ji AL, Khavari PA, Tarish F, Tanoglidi A, Maaskola J, Colling R, Mirtti T, Hamdy FC, Woodcock DJ, Helleday T, Mills IG, Lamb AD, Lundeberg J. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature 2022; 608:360-367. [PMID: 35948708 PMCID: PMC9365699 DOI: 10.1038/s41586-022-05023-2] [Citation(s) in RCA: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 06/23/2022] [Indexed: 12/28/2022]
Abstract
Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.
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Affiliation(s)
- Andrew Erickson
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Mengxiao He
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Emelie Berglund
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Maja Marklund
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Reza Mirzazadeh
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Niklas Schultz
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Linda Kvastad
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Alma Andersson
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Ludvig Bergenstråhle
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Joseph Bergenstråhle
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Ludvig Larsson
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Leire Alonso Galicia
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Alia Shamikh
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Elisa Basmaci
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Teresita Díaz De Ståhl
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Timothy Rajakumar
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | - Kim Thrane
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Andrew L Ji
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Paul A Khavari
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Firaz Tarish
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Anna Tanoglidi
- Department of Clinical Pathology, University Uppsala Hospital, Uppsala, Sweden
| | - Jonas Maaskola
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Richard Colling
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Tuomas Mirtti
- Department of Pathology, University of Helsinki & Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Urology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Dan J Woodcock
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Big Data Institute, University of Oxford, Oxford, UK
| | - Thomas Helleday
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
- Weston Park Cancer Centre, Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Ian G Mills
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Alastair D Lamb
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
- Department of Urology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden.
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19
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Sztanka-Toth TR, Jens M, Karaiskos N, Rajewsky N. Spacemake: processing and analysis of large-scale spatial transcriptomics data. Gigascience 2022; 11:giac064. [PMID: 35852420 PMCID: PMC9295369 DOI: 10.1093/gigascience/giac064] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/10/2022] [Accepted: 05/31/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Spatial sequencing methods increasingly gain popularity within RNA biology studies. State-of-the-art techniques quantify messenger RNA expression levels from tissue sections and at the same time register information about the original locations of the molecules in the tissue. The resulting data sets are processed and analyzed by accompanying software that, however, is incompatible across inputs from different technologies. FINDINGS Here, we present spacemake, a modular, robust, and scalable spatial transcriptomics pipeline built in Snakemake and Python. Spacemake is designed to handle all major spatial transcriptomics data sets and can be readily configured for other technologies. It can process and analyze several samples in parallel, even if they stem from different experimental methods. Spacemake's unified framework enables reproducible data processing from raw sequencing data to automatically generated downstream analysis reports. Spacemake is built with a modular design and offers additional functionality such as sample merging, saturation analysis, and analysis of long reads as separate modules. Moreover, spacemake employs novoSpaRc to integrate spatial and single-cell transcriptomics data, resulting in increased gene counts for the spatial data set. Spacemake is open source and extendable, and it can be seamlessly integrated with existing computational workflows.
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Affiliation(s)
- Tamas Ryszard Sztanka-Toth
- Systems Biology of Gene Regulatory Elements, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 10115 Berlin, Germany
- Humboldt-Universität zu Berlin, Institut für Biologie, 10099 Berlin, Germany
| | - Marvin Jens
- Systems Biology of Gene Regulatory Elements, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 10115 Berlin, Germany
| | - Nikos Karaiskos
- Systems Biology of Gene Regulatory Elements, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 10115 Berlin, Germany
| | - Nikolaus Rajewsky
- Systems Biology of Gene Regulatory Elements, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 10115 Berlin, Germany
- Humboldt-Universität zu Berlin, Institut für Biologie, 10099 Berlin, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Berlin, 10117 Berlin, Germany
- Department of Pediatric Oncology, Universitätsmedizin Charité, 13353 Berlin, Germany
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20
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Peng L, Wang F, Wang Z, Tan J, Huang L, Tian X, Liu G, Zhou L. Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies. Brief Bioinform 2022; 23:6618236. [PMID: 35753695 DOI: 10.1093/bib/bbac234] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/15/2022] [Accepted: 05/19/2022] [Indexed: 12/12/2022] Open
Abstract
Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China.,College of Life Sciences and Chemistry, Hunan University of Technology, 412007, Hunan, China
| | - Feixiang Wang
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Zhao Wang
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Jingwei Tan
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, 10084, Beijing, China.,The Future Laboratory, Tsinghua University, 10084, Beijing, China
| | - Xiongfei Tian
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, 412007, Hunan, China
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21
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Ospina OE, Wilson CM, Soupir AC, Berglund A, Smalley I, Tsai KY, Fridley BL. spatialGE: quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics. Bioinformatics 2022; 38:2645-2647. [PMID: 35258565 PMCID: PMC9890305 DOI: 10.1093/bioinformatics/btac145] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 02/04/2022] [Accepted: 03/07/2022] [Indexed: 02/05/2023] Open
Abstract
SUMMARY Spatially resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics that can be compared against clinical information, spot-level cell deconvolution and spatially informed clustering, all using a new data object to store data and resulting analyses simultaneously. AVAILABILITY AND IMPLEMENTATION The R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE. A script to reproduce the analyses in this manuscript is available in Supplementary information. The Thrane study data included in spatialGE was made available from the public available from the website https://www.spatialresearch.org/resources-published-datasets/doi-10-1158-0008-5472-can-18-0747/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Oscar E Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Christopher M Wilson
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Alex C Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Anders Berglund
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Inna Smalley
- Department of Tumor Biology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Kenneth Y Tsai
- Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, FL 33612, USA
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22
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Vickovic S, Schapiro D, Carlberg K, Lötstedt B, Larsson L, Hildebrandt F, Korotkova M, Hensvold AH, Catrina AI, Sorger PK, Malmström V, Regev A, Ståhl PL. Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium. Commun Biol 2022; 5:129. [PMID: 35149753 PMCID: PMC8837632 DOI: 10.1038/s42003-022-03050-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/10/2022] [Indexed: 12/14/2022] Open
Abstract
The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics, where tissue-resident RNA is spatially labeled in situ with barcodes in a transcriptome-wide fashion, to study local tissue interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-Seq data coupled to cell type-specific localization patterns at and around organized structures of infiltrating leukocyte cells in the synovium. Combining morphological features and high-throughput spatially resolved transcriptomics may be able to provide higher statistical power and more insights into monitoring disease severity and treatment-specific responses in seropositive and seronegative rheumatoid arthritis.
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Affiliation(s)
- Sanja Vickovic
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden. .,New York Genome Center, New York, NY, USA.
| | - Denis Schapiro
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.,Institute for Computational Biomedicine and Institute of Pathology, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
| | - Konstantin Carlberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Britta Lötstedt
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Franziska Hildebrandt
- Department of Molecular Biosciences, the Wenner Gren Institute, Stockholm University, Stockholm, Sweden
| | - Marina Korotkova
- Karolinska Institutet, Division of Rheumatology, Department of Medicine, Center for Molecular Medicine, Stockholm, Sweden.,Unit of Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Aase H Hensvold
- Karolinska Institutet, Division of Rheumatology, Department of Medicine, Center for Molecular Medicine, Stockholm, Sweden.,Unit of Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Anca I Catrina
- Karolinska Institutet, Division of Rheumatology, Department of Medicine, Center for Molecular Medicine, Stockholm, Sweden.,Unit of Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Vivianne Malmström
- Karolinska Institutet, Division of Rheumatology, Department of Medicine, Center for Molecular Medicine, Stockholm, Sweden.,Unit of Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.,Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Patrik L Ståhl
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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23
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Vickovic S, Lötstedt B, Klughammer J, Mages S, Segerstolpe Å, Rozenblatt-Rosen O, Regev A. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat Commun 2022; 13:795. [PMID: 35145087 PMCID: PMC8831571 DOI: 10.1038/s41467-022-28445-y] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/24/2022] [Indexed: 12/12/2022] Open
Abstract
The spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial transcriptomics has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of spatial transcriptomics at scale, by presenting Spatial Multi-Omics (SM-Omics) as a fully automated, high-throughput all-sequencing based platform for combined and spatially resolved transcriptomics and antibody-based protein measurements. SM-Omics uses DNA-barcoded antibodies, immunofluorescence or a combination thereof, to scale and combine spatial transcriptomics and spatial antibody-based multiplex protein detection. SM-Omics allows processing of up to 64 in situ spatial reactions or up to 96 sequencing-ready libraries, of high complexity, in a ~2 days process. We demonstrate SM-Omics in the mouse brain, spleen and colorectal cancer model, showing its broad utility as a high-throughput platform for spatial multi-omics.
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Affiliation(s)
- S Vickovic
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. .,New York Genome Center, New York, NY, USA. .,Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
| | - B Lötstedt
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - J Klughammer
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - S Mages
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Å Segerstolpe
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - O Rozenblatt-Rosen
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - A Regev
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Genentech, 1 DNA Way, South San Francisco, CA, USA.
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24
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Cox Jr KL, Gurazada SGR, Duncan KE, Czymmek KJ, Topp CN, Meyers BC. Organizing your space: The potential for integrating spatial transcriptomics and 3D imaging data in plants. PLANT PHYSIOLOGY 2022; 188:703-712. [PMID: 34726737 PMCID: PMC8825300 DOI: 10.1093/plphys/kiab508] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 10/04/2021] [Indexed: 05/31/2023]
Abstract
Plant cells communicate information for the regulation of development and responses to external stresses. A key form of this communication is transcriptional regulation, accomplished via complex gene networks operating both locally and systemically. To fully understand how genes are regulated across plant tissues and organs, high resolution, multi-dimensional spatial transcriptional data must be acquired and placed within a cellular and organismal context. Spatial transcriptomics (ST) typically provides a two-dimensional spatial analysis of gene expression of tissue sections that can be stacked to render three-dimensional data. For example, X-ray and light-sheet microscopy provide sub-micron scale volumetric imaging of cellular morphology of tissues, organs, or potentially entire organisms. Linking these technologies could substantially advance transcriptomics in plant biology and other fields. Here, we review advances in ST and 3D microscopy approaches and describe how these technologies could be combined to provide high resolution, spatially organized plant tissue transcript mapping.
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Affiliation(s)
- Kevin L Cox Jr
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA
| | - Sai Guna Ranjan Gurazada
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA
- Delaware Biotechnology Institute, University of Delaware, Newark, Delaware 19711, USA
| | - Keith E Duncan
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA
| | - Kirk J Czymmek
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA
- Advanced Bioimaging Laboratory, Donald Danforth Plant Science Center, St Louis, Missouri 63132, USA
| | | | - Blake C Meyers
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA
- Division of Plant Sciences and Technology, University of Missouri–Columbia, Columbia, MO 65211, USA
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25
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Valdebenito-Maturana B, Guatimosim C, Carrasco MA, Tapia JC. Spatially Resolved Expression of Transposable Elements in Disease and Somatic Tissue with SpatialTE. Int J Mol Sci 2021; 22:ijms222413623. [PMID: 34948421 PMCID: PMC8708317 DOI: 10.3390/ijms222413623] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 11/23/2022] Open
Abstract
Spatial transcriptomics (ST) is transforming the way we can study gene expression and its regulation through position-specific resolution within tissues. However, as in bulk RNA-Seq, transposable elements (TEs) are not being studied due to their highly repetitive nature. In recent years, TEs have been recognized as important regulators of gene expression, and thus, TE expression analysis in a spatially resolved manner could further help to understand their role in gene regulation within tissues. We present SpatialTE, a tool to analyze TE expression from ST datasets and show its application in somatic and diseased tissues. The results indicate that TEs have spatially regulated expression patterns and that their expression profiles are spatially altered in ALS disease, indicating that TEs might perform differential regulatory functions within tissue organs. We have made SpatialTE publicly available as open-source software under an MIT license.
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Affiliation(s)
- Braulio Valdebenito-Maturana
- Núcleo Científico Multidisciplinario, School of Medicine, Universidad de Talca, Campus Talca, Talca 3460000, Chile;
| | - Cristina Guatimosim
- Departamento de Morfologia, ICB, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil;
| | - Mónica Alejandra Carrasco
- School of Medicine, Universidad de Talca, Campus Talca, Talca 3460000, Chile
- Correspondence: (M.A.C.); (J.C.T.)
| | - Juan Carlos Tapia
- School of Medicine, Universidad de Talca, Campus Talca, Talca 3460000, Chile
- Correspondence: (M.A.C.); (J.C.T.)
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26
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Hildebrandt F, Andersson A, Saarenpää S, Larsson L, Van Hul N, Kanatani S, Masek J, Ellis E, Barragan A, Mollbrink A, Andersson ER, Lundeberg J, Ankarklev J. Spatial Transcriptomics to define transcriptional patterns of zonation and structural components in the mouse liver. Nat Commun 2021; 12:7046. [PMID: 34857782 PMCID: PMC8640072 DOI: 10.1038/s41467-021-27354-w] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 11/10/2021] [Indexed: 12/19/2022] Open
Abstract
Reconstruction of heterogeneity through single cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we apply Spatial Transcriptomics to perform transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development and regeneration as well as its potential in pre-clinical and clinical pathology.
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Affiliation(s)
- Franziska Hildebrandt
- Department of Molecular Biosciences, the Wenner-Gren Institute, Stockholm University, Svante Arrhenius Väg 20C, SE-106 91, Stockholm, Sweden.
| | - Alma Andersson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23a, SE-171 65, Solna, Sweden
| | - Sami Saarenpää
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23a, SE-171 65, Solna, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23a, SE-171 65, Solna, Sweden
| | - Noémi Van Hul
- Department of Cell and Molecular Biology, Karolinska Institutet Stockholm, SE-171 77, Solna, Sweden
| | - Sachie Kanatani
- Department of Molecular Biosciences, the Wenner-Gren Institute, Stockholm University, Svante Arrhenius Väg 20C, SE-106 91, Stockholm, Sweden
| | - Jan Masek
- Department of Cell and Molecular Biology, Karolinska Institutet Stockholm, SE-171 77, Solna, Sweden
- Department of Cell Biology, Faculty of Science, Charles University, Viničná 7, 128 00, Prague 2, Czech Republic
| | - Ewa Ellis
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 141-86, Stockholm, Sweden
| | - Antonio Barragan
- Department of Molecular Biosciences, the Wenner-Gren Institute, Stockholm University, Svante Arrhenius Väg 20C, SE-106 91, Stockholm, Sweden
| | - Annelie Mollbrink
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23a, SE-171 65, Solna, Sweden
| | - Emma R Andersson
- Department of Cell and Molecular Biology, Karolinska Institutet Stockholm, SE-171 77, Solna, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23a, SE-171 65, Solna, Sweden
| | - Johan Ankarklev
- Department of Molecular Biosciences, the Wenner-Gren Institute, Stockholm University, Svante Arrhenius Väg 20C, SE-106 91, Stockholm, Sweden.
- Microbial Single Cell Genomics facility, SciLifeLab, Biomedical Center (BMC) Uppsala University, SE-751 23, Uppsala, Sweden.
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27
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Music of metagenomics-a review of its applications, analysis pipeline, and associated tools. Funct Integr Genomics 2021; 22:3-26. [PMID: 34657989 DOI: 10.1007/s10142-021-00810-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 09/25/2021] [Accepted: 10/03/2021] [Indexed: 10/20/2022]
Abstract
This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."
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28
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Space: the final frontier — achieving single-cell, spatially resolved transcriptomics in plants. Emerg Top Life Sci 2021; 5:179-188. [DOI: 10.1042/etls20200274] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 01/05/2021] [Accepted: 01/11/2021] [Indexed: 01/13/2023]
Abstract
Single-cell RNA-seq is a tool that generates a high resolution of transcriptional data that can be used to understand regulatory networks in biological systems. In plants, several methods have been established for transcriptional analysis in tissue sections, cell types, and/or single cells. These methods typically require cell sorting, transgenic plants, protoplasting, or other damaging or laborious processes. Additionally, the majority of these technologies lose most or all spatial resolution during implementation. Those that offer a high spatial resolution for RNA lack breadth in the number of transcripts characterized. Here, we briefly review the evolution of spatial transcriptomics methods and we highlight recent advances and current challenges in sequencing, imaging, and computational aspects toward achieving 3D spatial transcriptomics of plant tissues with a resolution approaching single cells. We also provide a perspective on the potential opportunities to advance this novel methodology in plants.
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29
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Armingol E, Officer A, Harismendy O, Lewis NE. Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet 2021; 22:71-88. [PMID: 33168968 PMCID: PMC7649713 DOI: 10.1038/s41576-020-00292-x] [Citation(s) in RCA: 501] [Impact Index Per Article: 167.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2020] [Indexed: 12/13/2022]
Abstract
Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.
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Affiliation(s)
- Erick Armingol
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Adam Officer
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA
| | - Olivier Harismendy
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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Kvastad L, Carlberg K, Larsson L, Villacampa EG, Stuckey A, Stenbeck L, Mollbrink A, Zamboni M, Magnusson JP, Basmaci E, Shamikh A, Prochazka G, Schaupp AL, Borg Å, Fugger L, Nistér M, Lundeberg J. The spatial RNA integrity number assay for in situ evaluation of transcriptome quality. Commun Biol 2021; 4:57. [PMID: 33420318 PMCID: PMC7794352 DOI: 10.1038/s42003-020-01573-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 11/11/2020] [Indexed: 12/03/2022] Open
Abstract
The RNA integrity number (RIN) is a frequently used quality metric to assess the completeness of rRNA, as a proxy for the corresponding mRNA in a tissue. Current methods operate at bulk resolution and provide a single average estimate for the whole sample. Spatial transcriptomics technologies have emerged and shown their value by placing gene expression into a tissue context, resulting in transcriptional information from all tissue regions. Thus, the ability to estimate RNA quality in situ has become of utmost importance to overcome the limitation with a bulk rRNA measurement. Here we show a new tool, the spatial RNA integrity number (sRIN) assay, to assess the rRNA completeness in a tissue wide manner at cellular resolution. We demonstrate the use of sRIN to identify spatial variation in tissue quality prior to more comprehensive spatial transcriptomics workflows.
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Affiliation(s)
- Linda Kvastad
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Konstantin Carlberg
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Eva Gracia Villacampa
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Alexander Stuckey
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Linnea Stenbeck
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Annelie Mollbrink
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Margherita Zamboni
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Jens Peter Magnusson
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
- Bioengineering Department, Stanford University, Stanford, USA
| | - Elisa Basmaci
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Alia Shamikh
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Gabriela Prochazka
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Anna-Lena Schaupp
- Nuffield Department of Clinical Neurosciences, MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital University of Oxford, Oxford Centre for Neuroinflammation, Oxford, UK
| | - Åke Borg
- Division of Oncology and Pathology, Department of Clinical Sciences, Lund Lund University, Lund, Sweden
| | - Lars Fugger
- Nuffield Department of Clinical Neurosciences, MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital University of Oxford, Oxford Centre for Neuroinflammation, Oxford, UK
| | - Monica Nistér
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden.
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31
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Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantify Heterogeneity in Tumour Tissues. Methods Mol Biol 2021; 2190:209-228. [PMID: 32804368 DOI: 10.1007/978-1-0716-0826-5_10] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not only in radiology and cardiology but also oncology and other diseases. Machine learning methods, especially deep learning techniques, are already and broadly implemented in diverse technological and industrial sectors, but their applications in healthcare are just starting. Uniquely in biomedical research, a vast potential exists to integrate genomics data with histopathological imaging data. The integration has the potential to extend the pathologist's limits and boundaries, which may create breakthroughs in diagnosis, treatment, and monitoring at molecular and tissue levels. Moreover, the applications of genomics data are realizing the potential for personalized medicine, making diagnosis, treatment, monitoring, and prognosis more accurate. In this chapter, we discuss machine learning methods readily available for digital pathology applications, new prospects of integrating spatial genomics data on tissues with tissue morphology, and frontier approaches to combining genomics data with pathological imaging data. We present perspectives on how artificial intelligence can be synergized with molecular genomics and imaging to make breakthroughs in biomedical and translational research for computer-aided applications.
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32
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Grauel AL, Nguyen B, Ruddy D, Laszewski T, Schwartz S, Chang J, Chen J, Piquet M, Pelletier M, Yan Z, Kirkpatrick ND, Wu J, deWeck A, Riester M, Hims M, Geyer FC, Wagner J, MacIsaac K, Deeds J, Diwanji R, Jayaraman P, Yu Y, Simmons Q, Weng S, Raza A, Minie B, Dostalek M, Chikkegowda P, Ruda V, Iartchouk O, Chen N, Thierry R, Zhou J, Pruteanu-Malinici I, Fabre C, Engelman JA, Dranoff G, Cremasco V. TGFβ-blockade uncovers stromal plasticity in tumors by revealing the existence of a subset of interferon-licensed fibroblasts. Nat Commun 2020; 11:6315. [PMID: 33298926 PMCID: PMC7725805 DOI: 10.1038/s41467-020-19920-5] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 11/05/2020] [Indexed: 02/08/2023] Open
Abstract
Despite the increasing interest in targeting stromal elements of the tumor microenvironment, we still face tremendous challenges in developing adequate therapeutics to modify the tumor stromal landscape. A major obstacle to this is our poor understanding of the phenotypic and functional heterogeneity of stromal cells in tumors. Herein, we perform an unbiased interrogation of tumor mesenchymal cells, delineating the co-existence of distinct subsets of cancer-associated fibroblasts (CAFs) in the microenvironment of murine carcinomas, each endowed with unique phenotypic features and functions. Furthermore, our study shows that neutralization of TGFβ in vivo leads to remodeling of CAF dynamics, greatly reducing the frequency and activity of the myofibroblast subset, while promoting the formation of a fibroblast population characterized by strong response to interferon and heightened immunomodulatory properties. These changes correlate with the development of productive anti-tumor immunity and greater efficacy of PD1 immunotherapy. Along with providing the scientific rationale for the evaluation of TGFβ and PD1 co-blockade in the clinical setting, this study also supports the concept of plasticity of the stromal cell landscape in tumors, laying the foundation for future investigations aimed at defining pathways and molecules to program CAF composition for cancer therapy.
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Affiliation(s)
- Angelo L Grauel
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Beverly Nguyen
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - David Ruddy
- Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Tyler Laszewski
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Stephanie Schwartz
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Jonathan Chang
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Julie Chen
- Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Michelle Piquet
- Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Marc Pelletier
- Oncology Translational Research, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Zheng Yan
- Oncology Translational Research, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Nathaniel D Kirkpatrick
- Biotherapeutic and Analytical Technologies, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Jincheng Wu
- Oncology Data Science, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Antoine deWeck
- Oncology Data Science, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Markus Riester
- Oncology Data Science, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Matt Hims
- Oncology Translational Research, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Felipe Correa Geyer
- Oncology Translational Research, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Joel Wagner
- Oncology Data Science, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Kenzie MacIsaac
- Oncology Data Science, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - James Deeds
- Oncology Translational Research, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Rohan Diwanji
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Pushpa Jayaraman
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Yenyen Yu
- Oncology Translational Research, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Quincey Simmons
- Oncology Data Science, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Shaobu Weng
- Oncology Translational Research, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Alina Raza
- Oncology Translational Research, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Brian Minie
- Oncology Data Science, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Mirek Dostalek
- PKS Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Pavitra Chikkegowda
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Vera Ruda
- Chemical Biology and Therapeutics, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Oleg Iartchouk
- Chemical Biology and Therapeutics, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Naiyan Chen
- Oncology Data Science, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Raphael Thierry
- Biotherapeutic and Analytical Technologies, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Joseph Zhou
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Iulian Pruteanu-Malinici
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Claire Fabre
- Translational Clinical Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Jeffrey A Engelman
- Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Glenn Dranoff
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Viviana Cremasco
- Immuno-Oncology, Novartis Institutes for BioMedical Research, 250 Massachusetts Ave, Cambridge, MA, 02139, USA.
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Liu Y, Yang M, Deng Y, Su G, Enninful A, Guo CC, Tebaldi T, Zhang D, Kim D, Bai Z, Norris E, Pan A, Li J, Xiao Y, Halene S, Fan R. High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue. Cell 2020; 183:1665-1681.e18. [PMID: 33188776 DOI: 10.1016/j.cell.2020.10.026] [Citation(s) in RCA: 372] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 07/31/2020] [Accepted: 10/14/2020] [Indexed: 12/21/2022]
Abstract
We present deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) for co-mapping of mRNAs and proteins in a formaldehyde-fixed tissue slide via next-generation sequencing (NGS). Parallel microfluidic channels were used to deliver DNA barcodes to the surface of a tissue slide, and crossflow of two sets of barcodes, A1-50 and B1-50, followed by ligation in situ, yielded a 2D mosaic of tissue pixels, each containing a unique full barcode AB. Application to mouse embryos revealed major tissue types in early organogenesis as well as fine features like microvasculature in a brain and pigmented epithelium in an eye field. Gene expression profiles in 10-μm pixels conformed into the clusters of single-cell transcriptomes, allowing for rapid identification of cell types and spatial distributions. DBiT-seq can be adopted by researchers with no experience in microfluidics and may find applications in a range of fields including developmental biology, cancer biology, neuroscience, and clinical pathology.
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Affiliation(s)
- Yang Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Mingyu Yang
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Yanxiang Deng
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Graham Su
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Cindy C Guo
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Toma Tebaldi
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA; Section of Hematology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, USA
| | - Di Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Dongjoo Kim
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Zhiliang Bai
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Eileen Norris
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Alisia Pan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Jiatong Li
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Yang Xiao
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Stephanie Halene
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA; Section of Hematology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA; Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT 06520, USA.
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34
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Tan X, Su A, Tran M, Nguyen Q. SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. Bioinformatics 2020; 36:2293-2294. [PMID: 31808789 DOI: 10.1093/bioinformatics/btz914] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 11/22/2019] [Accepted: 12/04/2019] [Indexed: 12/16/2022] Open
Abstract
MOTIVATION Spatial transcriptomics (ST) technology is increasingly being applied because it enables the measurement of spatial gene expression in an intact tissue along with imaging morphology of the same tissue. However, current analysis methods for ST data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology. RESULTS We developed a user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially barcoded spots in a tissue. We show the integration approach outperforms the use of gene-count data alone or imaging data alone to build deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy. AVAILABILITY AND IMPLEMENTATION The SpaCell package is open source under an MIT licence and it is available at https://github.com/BiomedicalMachineLearning/SpaCell. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiao Tan
- Division of Genetics and Genomics, Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, QLD, Australia
| | - Andrew Su
- Division of Genetics and Genomics, Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, QLD, Australia
| | - Minh Tran
- Division of Genetics and Genomics, Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, QLD, Australia
| | - Quan Nguyen
- Division of Genetics and Genomics, Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, QLD, Australia
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35
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Nerurkar SN, Goh D, Cheung CCL, Nga PQY, Lim JCT, Yeong JPS. Transcriptional Spatial Profiling of Cancer Tissues in the Era of Immunotherapy: The Potential and Promise. Cancers (Basel) 2020; 12:E2572. [PMID: 32917035 PMCID: PMC7563386 DOI: 10.3390/cancers12092572] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/05/2020] [Accepted: 09/06/2020] [Indexed: 12/18/2022] Open
Abstract
Intratumoral heterogeneity poses a major challenge to making an accurate diagnosis and establishing personalized treatment strategies for cancer patients. Moreover, this heterogeneity might underlie treatment resistance, disease progression, and cancer relapse. For example, while immunotherapies can confer a high success rate, selective pressures coupled with dynamic evolution within a tumour can drive the emergence of drug-resistant clones that allow tumours to persist in certain patients. To improve immunotherapy efficacy, researchers have used transcriptional spatial profiling techniques to identify and subsequently block the source of tumour heterogeneity. In this review, we describe and assess the different technologies available for such profiling within a cancer tissue. We first outline two well-known approaches, in situ hybridization and digital spatial profiling. Then, we highlight the features of an emerging technology known as Visium Spatial Gene Expression Solution. Visium generates quantitative gene expression data and maps them to the tissue architecture. By retaining spatial information, we are well positioned to identify novel biomarkers and perform computational analyses that might inform on novel combinatorial immunotherapies.
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Affiliation(s)
- Sanjna Nilesh Nerurkar
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore;
| | - Denise Goh
- Institute of Molecular and Cell Biology (IMCB), Agency of Science, Technology and Research (A*STAR), Singapore 169856, Singapore; (D.G.); (P.Q.Y.N.); (J.C.T.L.)
| | | | - Pei Qi Yvonne Nga
- Institute of Molecular and Cell Biology (IMCB), Agency of Science, Technology and Research (A*STAR), Singapore 169856, Singapore; (D.G.); (P.Q.Y.N.); (J.C.T.L.)
| | - Jeffrey Chun Tatt Lim
- Institute of Molecular and Cell Biology (IMCB), Agency of Science, Technology and Research (A*STAR), Singapore 169856, Singapore; (D.G.); (P.Q.Y.N.); (J.C.T.L.)
| | - Joe Poh Sheng Yeong
- Institute of Molecular and Cell Biology (IMCB), Agency of Science, Technology and Research (A*STAR), Singapore 169856, Singapore; (D.G.); (P.Q.Y.N.); (J.C.T.L.)
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
- Singapore Immunology Network (SIgN), Agency of Science, Technology and Research (A*STAR), Singapore 138648, Singapore
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36
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Chen WT, Lu A, Craessaerts K, Pavie B, Sala Frigerio C, Corthout N, Qian X, Laláková J, Kühnemund M, Voytyuk I, Wolfs L, Mancuso R, Salta E, Balusu S, Snellinx A, Munck S, Jurek A, Fernandez Navarro J, Saido TC, Huitinga I, Lundeberg J, Fiers M, De Strooper B. Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer's Disease. Cell 2020; 182:976-991.e19. [PMID: 32702314 DOI: 10.1016/j.cell.2020.06.038] [Citation(s) in RCA: 408] [Impact Index Per Article: 102.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 04/17/2020] [Accepted: 06/25/2020] [Indexed: 12/16/2022]
Abstract
Although complex inflammatory-like alterations are observed around the amyloid plaques of Alzheimer's disease (AD), little is known about the molecular changes and cellular interactions that characterize this response. We investigate here, in an AD mouse model, the transcriptional changes occurring in tissue domains in a 100-μm diameter around amyloid plaques using spatial transcriptomics. We demonstrate early alterations in a gene co-expression network enriched for myelin and oligodendrocyte genes (OLIGs), whereas a multicellular gene co-expression network of plaque-induced genes (PIGs) involving the complement system, oxidative stress, lysosomes, and inflammation is prominent in the later phase of the disease. We confirm the majority of the observed alterations at the cellular level using in situ sequencing on mouse and human brain sections. Genome-wide spatial transcriptomics analysis provides an unprecedented approach to untangle the dysregulated cellular network in the vicinity of pathogenic hallmarks of AD and other brain diseases.
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Affiliation(s)
- Wei-Ting Chen
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium
| | - Ashley Lu
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium
| | - Katleen Craessaerts
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium
| | - Benjamin Pavie
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium; VIB Bio Imaging Core, Gent 9052, Belgium; VIB Bio Imaging Core, Leuven 3000, Belgium
| | - Carlo Sala Frigerio
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium; UK Dementia Research Institute at University College London, London WC1E 6BT, UK
| | - Nikky Corthout
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium; VIB Bio Imaging Core, Gent 9052, Belgium; VIB Bio Imaging Core, Leuven 3000, Belgium
| | | | | | | | - Iryna Voytyuk
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium
| | - Leen Wolfs
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium
| | - Renzo Mancuso
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium
| | - Evgenia Salta
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium
| | - Sriram Balusu
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium
| | - An Snellinx
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium
| | - Sebastian Munck
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium; VIB Bio Imaging Core, Gent 9052, Belgium; VIB Bio Imaging Core, Leuven 3000, Belgium
| | - Aleksandra Jurek
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Stockholm 17121, Sweden
| | - Jose Fernandez Navarro
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Stockholm 17121, Sweden
| | - Takaomi C Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Brain Science Institute, Wako-shi, Saitama 351-0198, Japan
| | - Inge Huitinga
- Department of Neuroimmunology, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam 1105BA, the Netherlands; Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam 1098XH, the Netherlands
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Stockholm 17121, Sweden
| | - Mark Fiers
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium; UK Dementia Research Institute at University College London, London WC1E 6BT, UK.
| | - Bart De Strooper
- VIB Center for Brain & Disease Research, Leuven 3000, Belgium; KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven 3000, Belgium; UK Dementia Research Institute at University College London, London WC1E 6BT, UK.
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37
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Ji AL, Rubin AJ, Thrane K, Jiang S, Reynolds DL, Meyers RM, Guo MG, George BM, Mollbrink A, Bergenstråhle J, Larsson L, Bai Y, Zhu B, Bhaduri A, Meyers JM, Rovira-Clavé X, Hollmig ST, Aasi SZ, Nolan GP, Lundeberg J, Khavari PA. Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Cell 2020; 182:497-514.e22. [PMID: 32579974 PMCID: PMC7391009 DOI: 10.1016/j.cell.2020.05.039] [Citation(s) in RCA: 411] [Impact Index Per Article: 102.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 04/09/2020] [Accepted: 05/20/2020] [Indexed: 12/13/2022]
Abstract
To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer.
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Affiliation(s)
- Andrew L Ji
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Adam J Rubin
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kim Thrane
- Science for Life Laboratory, KTH Royal Institute of Technology, Department of Gene Technology, Tomtebodavägen 23, 171 65 Solna, Sweden
| | - Sizun Jiang
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - David L Reynolds
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Robin M Meyers
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Margaret G Guo
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Benson M George
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Annelie Mollbrink
- Science for Life Laboratory, KTH Royal Institute of Technology, Department of Gene Technology, Tomtebodavägen 23, 171 65 Solna, Sweden
| | - Joseph Bergenstråhle
- Science for Life Laboratory, KTH Royal Institute of Technology, Department of Gene Technology, Tomtebodavägen 23, 171 65 Solna, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, KTH Royal Institute of Technology, Department of Gene Technology, Tomtebodavägen 23, 171 65 Solna, Sweden
| | - Yunhao Bai
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bokai Zhu
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Aparna Bhaduri
- Department of Neurology, University of California, San Francisco (UCSF), San Francisco, CA 94122, USA
| | - Jordan M Meyers
- Program in Epithelial Biology, 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
| | - S Tyler Hollmig
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sumaira Z Aasi
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Garry P Nolan
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Joakim Lundeberg
- Science for Life Laboratory, KTH Royal Institute of Technology, Department of Gene Technology, Tomtebodavägen 23, 171 65 Solna, Sweden
| | - Paul A Khavari
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA.
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38
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Asp M, Giacomello S, Larsson L, Wu C, Fürth D, Qian X, Wärdell E, Custodio J, Reimegård J, Salmén F, Österholm C, Ståhl PL, Sundström E, Åkesson E, Bergmann O, Bienko M, Månsson-Broberg A, Nilsson M, Sylvén C, Lundeberg J. A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart. Cell 2020; 179:1647-1660.e19. [PMID: 31835037 DOI: 10.1016/j.cell.2019.11.025] [Citation(s) in RCA: 361] [Impact Index Per Article: 90.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 09/06/2019] [Accepted: 11/14/2019] [Indexed: 10/25/2022]
Abstract
The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.
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Affiliation(s)
- Michaela Asp
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Stefania Giacomello
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden; Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Chenglin Wu
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Daniel Fürth
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Xiaoyan Qian
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Eva Wärdell
- Department of Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Joaquin Custodio
- Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Johan Reimegård
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Fredrik Salmén
- Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, the Netherlands
| | - Cecilia Österholm
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Patrik L Ståhl
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Erik Sundström
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, R&D Unit, Stockholms Sjukhem, Stockholm, Sweden
| | - Elisabet Åkesson
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, R&D Unit, Stockholms Sjukhem, Stockholm, Sweden
| | - Olaf Bergmann
- Center for Regenerative Therapies Dresden, TU-Dresden, Dresden, Germany; Karolinska Institutet, Cell and Molecular Biology, Stockholm, Sweden
| | - Magda Bienko
- Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Christer Sylvén
- Department of Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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39
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Ortiz C, Navarro JF, Jurek A, Märtin A, Lundeberg J, Meletis K. Molecular atlas of the adult mouse brain. SCIENCE ADVANCES 2020; 6:eabb3446. [PMID: 32637622 PMCID: PMC7319762 DOI: 10.1126/sciadv.abb3446] [Citation(s) in RCA: 146] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 04/28/2020] [Indexed: 05/21/2023]
Abstract
Brain maps are essential for integrating information and interpreting the structure-function relationship of circuits and behavior. We aimed to generate a systematic classification of the adult mouse brain based purely on the unbiased identification of spatially defining features by employing whole-brain spatial transcriptomics. We found that the molecular information was sufficient to deduce the complex and detailed neuroanatomical organization of the brain. The unsupervised (non-expert, data-driven) classification revealed new area- and layer-specific subregions, for example in isocortex and hippocampus, and new subdivisions of striatum. The molecular atlas further supports the characterization of the spatial identity of neurons from their single-cell RNA profile, and provides a resource for annotating the brain using a minimal gene set-a brain palette. In summary, we have established a molecular atlas to formally define the spatial organization of brain regions, including the molecular code for mapping and targeting of discrete neuroanatomical domains.
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Affiliation(s)
- Cantin Ortiz
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jose Fernandez Navarro
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Aleksandra Jurek
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Antje Märtin
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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40
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Berglund E, Saarenpää S, Jemt A, Gruselius J, Larsson L, Bergenstråhle L, Lundeberg J, Giacomello S. Automation of Spatial Transcriptomics library preparation to enable rapid and robust insights into spatial organization of tissues. BMC Genomics 2020; 21:298. [PMID: 32293264 PMCID: PMC7158132 DOI: 10.1186/s12864-020-6631-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 02/27/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Interest in studying the spatial distribution of gene expression in tissues is rapidly increasing. Spatial Transcriptomics is a novel sequencing-based technology that generates high-throughput information on the distribution, heterogeneity and co-expression of cells in tissues. Unfortunately, manual preparation of high-quality sequencing libraries is time-consuming and subject to technical variability due to human error during manual pipetting, which results in sample swapping and the accidental introduction of batch effects. All these factors complicate the production and interpretation of biological datasets. RESULTS We have integrated an Agilent Bravo Automated Liquid Handling Platform into the Spatial Transcriptomics workflow. Compared to the previously reported Magnatrix 8000+ automated protocol, this approach increases the number of samples processed per run, reduces sample preparation time by 35%, and minimizes batch effects between samples. The new approach is also shown to be highly accurate and almost completely free from technical variability between prepared samples. CONCLUSIONS The new automated Spatial Transcriptomics protocol using the Agilent Bravo Automated Liquid Handling Platform rapidly generates high-quality Spatial Transcriptomics libraries. Given the wide use of the Agilent Bravo Automated Liquid Handling Platform in research laboratories and facilities, this will allow many researchers to quickly create robust Spatial Transcriptomics libraries.
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Affiliation(s)
- Emelie Berglund
- Science for Life Laboratory, Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Sami Saarenpää
- Science for Life Laboratory, Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Anders Jemt
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Joel Gruselius
- Science for Life Laboratory, Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Ludvig Bergenstråhle
- Science for Life Laboratory, Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Stefania Giacomello
- Science for Life Laboratory, Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden.
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41
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Gregory JM, McDade K, Livesey MR, Croy I, Marion de Proce S, Aitman T, Chandran S, Smith C. Spatial transcriptomics identifies spatially dysregulated expression of GRM3 and USP47 in amyotrophic lateral sclerosis. Neuropathol Appl Neurobiol 2020; 46:441-457. [PMID: 31925813 DOI: 10.1111/nan.12597] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 12/21/2019] [Indexed: 02/02/2023]
Abstract
AIMS The mechanisms underlying the selective degeneration of motor neurones in amyotrophic lateral sclerosis (ALS) are poorly understood. The aim of this study was to implement spatially resolved RNA sequencing in human post mortem cortical tissue from an ALS patient harbouring the C9orf72 hexanucleotide repeat expansion to identify dysregulated transcripts that may account for differential vulnerabilities of distinct (i) cell types and (ii) brain regions in the pathogenesis of ALS. METHODS Using spatial transcriptomics (ST) we analysed the transcriptome of post mortem brain tissue, with spatial resolution down to 100 μm. Validation of these findings was then performed using BaseScope, an adapted, in situ hybridization technique with single-transcript single-cell-resolution, providing extensive regional and cell-type specific confirmation of these dysregulated transcripts. The validation cohort was then extended to include multiple post mortem brain regions and spinal cord tissue from an extended cohort of C9orf72, sporadic ALS (sALS) and SOD1 ALS cases. RESULTS We identified sixteen dysregulated transcripts of proteins that have roles within six disease-related pathways. Furthermore, these complementary molecular pathology techniques converged to identify two spatially dysregulated transcripts, GRM3 and USP47, that are commonly dysregulated across sALS, SOD1 and C9orf72 cases alike. CONCLUSIONS This study presents the first description of ST in human post mortem cortical tissue from an ALS patient harbouring the C9orf72 hexanucleotide repeat expansion. These data taken together highlight the importance of preserving spatial resolution, facilitating the identification of genes whose dysregulation may in part underlie regional susceptibilities to ALS, crucially highlighting potential therapeutic and diagnostic targets.
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Affiliation(s)
- J M Gregory
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Edinburgh Pathology, University of Edinburgh, Edinburgh, UK.,Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, UK
| | - K McDade
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Edinburgh Pathology, University of Edinburgh, Edinburgh, UK.,Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, UK
| | - M R Livesey
- Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, UK.,Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - I Croy
- Edinburgh Pathology, University of Edinburgh, Edinburgh, UK.,Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - S Marion de Proce
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - T Aitman
- Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, UK.,Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - S Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, UK.,Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - C Smith
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Edinburgh Pathology, University of Edinburgh, Edinburgh, UK.,Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, UK
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42
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Yoosuf N, Navarro JF, Salmén F, Ståhl PL, Daub CO. Identification and transfer of spatial transcriptomics signatures for cancer diagnosis. Breast Cancer Res 2020; 22:6. [PMID: 31931856 PMCID: PMC6958738 DOI: 10.1186/s13058-019-1242-9] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Accepted: 12/27/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification and visualization of transcriptomes in individual tissue sections. In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types. METHODS We used four publicly available ST breast cancer datasets from breast tissue sections annotated by pathologists as non-malignant, DCIS, or IDC. We trained and tested a machine learning method (support vector machine) based on the expert annotation as well as based on automatic selection of cell types by their transcriptome profiles. RESULTS We identified expression signatures for expert annotated regions (non-malignant, DCIS, and IDC) and build machine learning models. Classification results for 798 expression signature transcripts showed high coincidence with the expert pathologist annotation for DCIS (100%) and IDC (96%). Extending our analysis to include all 25,179 expressed transcripts resulted in an accuracy of 99% for DCIS and 98% for IDC. Further, classification based on an automatically identified expression signature covering all ST spots of tissue sections resulted in prediction accuracy of 95% for DCIS and 91% for IDC. CONCLUSIONS This concept study suggest that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on. Furthermore, the identified expression signatures can classify cancer regions in tissue sections not used for training with high accuracy. Expert-generated but even automatically generated cancer signatures from ST data might be able to classify breast cancer regions and provide clinical decision support for pathologists in the future.
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Affiliation(s)
- Niyaz Yoosuf
- Department of Biosciences and Nutrition, Karolinska Institutet, 141 83, Huddinge, Sweden. .,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - José Fernández Navarro
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Salmén
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.,Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, the Netherlands
| | - Patrik L Ståhl
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Carsten O Daub
- Department of Biosciences and Nutrition, Karolinska Institutet, 141 83, Huddinge, Sweden.
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43
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Carlberg K, Korotkova M, Larsson L, Catrina AI, Ståhl PL, Malmström V. Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics. Sci Rep 2019; 9:18975. [PMID: 31831833 PMCID: PMC6908624 DOI: 10.1038/s41598-019-55441-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 11/23/2019] [Indexed: 12/18/2022] Open
Abstract
Lately it has become possible to analyze transcriptomic profiles in tissue sections with retained cellular context. We aimed to explore synovial biopsies from rheumatoid arthritis (RA) and spondyloarthritis (SpA) patients, using Spatial Transcriptomics (ST) as a proof of principle approach for unbiased mRNA studies at the site of inflammation in these chronic inflammatory diseases. Synovial tissue biopsies from affected joints were studied with ST. The transcriptome data was subjected to differential gene expression analysis (DEA), pathway analysis, immune cell type identification using Xcell analysis and validation with immunohistochemistry (IHC). The ST technology allows selective analyses on areas of interest, thus we analyzed morphologically distinct areas of mononuclear cell infiltrates. The top differentially expressed genes revealed an adaptive immune response profile and T-B cell interactions in RA, while in SpA, the profiles implicate functions associated with tissue repair. With spatially resolved gene expression data, overlaid on high-resolution histological images, we digitally portrayed pre-selected cell types in silico. The RA displayed an overrepresentation of central memory T cells, while in SpA effector memory T cells were most prominent. Consequently, ST allows for deeper understanding of cellular mechanisms and diversity in tissues from chronic inflammatory diseases.
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Affiliation(s)
- Konstantin Carlberg
- Department of Gene Technology, Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.,Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Marina Korotkova
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Ludvig Larsson
- Department of Gene Technology, Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Anca I Catrina
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Patrik L Ståhl
- Department of Gene Technology, Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Vivianne Malmström
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
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44
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Vickovic S, Eraslan G, Salmén F, Klughammer J, Stenbeck L, Schapiro D, Äijö T, Bonneau R, Bergenstråhle L, Navarro JF, Gould J, Griffin GK, Borg Å, Ronaghi M, Frisén J, Lundeberg J, Regev A, Ståhl PL. High-definition spatial transcriptomics for in situ tissue profiling. Nat Methods 2019; 16:987-990. [PMID: 31501547 PMCID: PMC6765407 DOI: 10.1038/s41592-019-0548-y] [Citation(s) in RCA: 582] [Impact Index Per Article: 116.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 08/02/2019] [Indexed: 12/21/2022]
Abstract
Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2-μm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.
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Affiliation(s)
- Sanja Vickovic
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Gökcen Eraslan
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fredrik Salmén
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Johanna Klughammer
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Linnea Stenbeck
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Denis Schapiro
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Tarmo Äijö
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Richard Bonneau
- Center for Data Science, New York University, New York, NY, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Ludvig Bergenstråhle
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - José Fernandéz Navarro
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Joshua Gould
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabriel K Griffin
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Åke Borg
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | | | - Jonas Frisén
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Patrik L Ståhl
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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45
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Finotello F, Rieder D, Hackl H, Trajanoski Z. Next-generation computational tools for interrogating cancer immunity. Nat Rev Genet 2019; 20:724-746. [PMID: 31515541 DOI: 10.1038/s41576-019-0166-7] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2019] [Indexed: 12/17/2022]
Abstract
The remarkable success of cancer therapies with immune checkpoint blockers is revolutionizing oncology and has sparked intensive basic and translational research into the mechanisms of cancer-immune cell interactions. In parallel, numerous novel cutting-edge technologies for comprehensive molecular and cellular characterization of cancer immunity have been developed, including single-cell sequencing, mass cytometry and multiplexed spatial cellular phenotyping. In order to process, analyse and visualize multidimensional data sets generated by these technologies, computational methods and software tools are required. Here, we review computational tools for interrogating cancer immunity, discuss advantages and limitations of the various methods and provide guidelines to assist in method selection.
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Affiliation(s)
- Francesca Finotello
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Dietmar Rieder
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Hubert Hackl
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Zlatko Trajanoski
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria.
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46
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Maniatis S, Äijö T, Vickovic S, Braine C, Kang K, Mollbrink A, Fagegaltier D, Andrusivová Ž, Saarenpää S, Saiz-Castro G, Cuevas M, Watters A, Lundeberg J, Bonneau R, Phatnani H. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 2019; 364:89-93. [PMID: 30948552 DOI: 10.1126/science.aav9776] [Citation(s) in RCA: 239] [Impact Index Per Article: 47.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 03/01/2019] [Indexed: 12/12/2022]
Abstract
Paralysis occurring in amyotrophic lateral sclerosis (ALS) results from denervation of skeletal muscle as a consequence of motor neuron degeneration. Interactions between motor neurons and glia contribute to motor neuron loss, but the spatiotemporal ordering of molecular events that drive these processes in intact spinal tissue remains poorly understood. Here, we use spatial transcriptomics to obtain gene expression measurements of mouse spinal cords over the course of disease, as well as of postmortem tissue from ALS patients, to characterize the underlying molecular mechanisms in ALS. We identify pathway dynamics, distinguish regional differences between microglia and astrocyte populations at early time points, and discern perturbations in several transcriptional pathways shared between murine models of ALS and human postmortem spinal cords.
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Affiliation(s)
- Silas Maniatis
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
| | - Tarmo Äijö
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Sanja Vickovic
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Catherine Braine
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA.,Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Kristy Kang
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
| | - Annelie Mollbrink
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Delphine Fagegaltier
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
| | - Žaneta Andrusivová
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Sami Saarenpää
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Gonzalo Saiz-Castro
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Miguel Cuevas
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Aaron Watters
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden. .,Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, New York, NY, USA. .,Center for Data Science, New York University, New York, NY, USA
| | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA. .,Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
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47
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Salmén F, Ståhl PL, Mollbrink A, Navarro JF, Vickovic S, Frisén J, Lundeberg J. Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections. Nat Protoc 2019; 13:2501-2534. [PMID: 30353172 DOI: 10.1038/s41596-018-0045-2] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Spatial resolution of gene expression enables gene expression events to be pinpointed to a specific location in biological tissue. Spatially resolved gene expression in tissue sections is traditionally analyzed using immunohistochemistry (IHC) or in situ hybridization (ISH). These technologies are invaluable tools for pathologists and molecular biologists; however, their throughput is limited to the analysis of only a few genes at a time. Recent advances in RNA sequencing (RNA-seq) have made it possible to obtain unbiased high-throughput gene expression data in bulk. Spatial Transcriptomics combines the benefits of traditional spatially resolved technologies with the massive throughput of RNA-seq. Here, we present a protocol describing how to apply the Spatial Transcriptomics technology to mammalian tissue. This protocol combines histological staining and spatially resolved RNA-seq data from intact tissue sections. Once suitable tissue-specific conditions have been established, library construction and sequencing can be completed in ~5-6 d. Data processing takes a few hours, with the exact timing dependent on the sequencing depth. Our method requires no special instruments and can be performed in any laboratory with access to a cryostat, microscope and next-generation sequencing.
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Affiliation(s)
- Fredrik Salmén
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.,Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, The Netherlands
| | - Patrik L Ståhl
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Annelie Mollbrink
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - José Fernández Navarro
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Sanja Vickovic
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.,Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jonas Frisén
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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48
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Giacomello S, Lundeberg J. Preparation of plant tissue to enable Spatial Transcriptomics profiling using barcoded microarrays. Nat Protoc 2018; 13:2425-2446. [DOI: 10.1038/s41596-018-0046-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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49
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Thrane K, Eriksson H, Maaskola J, Hansson J, Lundeberg J. Spatially Resolved Transcriptomics Enables Dissection of Genetic Heterogeneity in Stage III Cutaneous Malignant Melanoma. Cancer Res 2018; 78:5970-5979. [PMID: 30154148 DOI: 10.1158/0008-5472.can-18-0747] [Citation(s) in RCA: 153] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/03/2018] [Accepted: 08/23/2018] [Indexed: 11/16/2022]
Abstract
Cutaneous malignant melanoma (melanoma) is characterized by a high mutational load, extensive intertumoral and intratumoral genetic heterogeneity, and complex tumor microenvironment (TME) interactions. Further insights into the mechanisms underlying melanoma are crucial for understanding tumor progression and responses to treatment. Here we adapted the technology of spatial transcriptomics (ST) to melanoma lymph node biopsies and successfully sequenced the transcriptomes of over 2,200 tissue domains. Deconvolution combined with traditional approaches for dimensional reduction of transcriptome-wide data enabled us to both visualize the transcriptional landscape within the tissue and identify gene expression profiles linked to specific histologic entities. Our unsupervised analysis revealed a complex spatial intratumoral composition of melanoma metastases that was not evident through morphologic annotation. Each biopsy showed distinct gene expression profiles and included examples of the coexistence of multiple melanoma signatures within a single tumor region as well as shared profiles for lymphoid tissue characterized according to their spatial location and gene expression profiles. The lymphoid area in close proximity to the tumor region displayed a specific expression pattern, which may reflect the TME, a key component to fully understanding tumor progression. In conclusion, using the ST technology to generate gene expression profiles reveals a detailed landscape of melanoma metastases. This should inspire researchers to integrate spatial information into analyses aiming to identify the factors underlying tumor progression and therapy outcome.Significance: Applying ST technology to gene expression profiling in melanoma lymph node metastases reveals a complex transcriptional landscape in a spatial context, which is essential for understanding the multiple components of tumor progression and therapy outcome. Cancer Res; 78(20); 5970-9. ©2018 AACR.
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Affiliation(s)
- Kim Thrane
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Hanna Eriksson
- Department of Oncology-Pathology, Karolinska Institutet, SE-17176 Stockholm, Sweden
- Department of Oncology, Karolinska University Hospital, SE-17176 Stockholm, Sweden
| | - Jonas Maaskola
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Johan Hansson
- Department of Oncology-Pathology, Karolinska Institutet, SE-17176 Stockholm, Sweden
- Department of Oncology, Karolinska University Hospital, SE-17176 Stockholm, Sweden
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden.
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50
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Fernández Navarro J, Lundeberg J, Ståhl PL. ST viewer: a tool for analysis and visualization of spatial transcriptomics datasets. Bioinformatics 2018; 35:1058-1060. [DOI: 10.1093/bioinformatics/bty714] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 04/20/2018] [Accepted: 08/20/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- José Fernández Navarro
- Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Joakim Lundeberg
- Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Patrik L Ståhl
- Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
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