1
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Bell ATF, Mitchell JT, Kiemen AL, Lyman M, Fujikura K, Lee JW, Coyne E, Shin SM, Nagaraj S, Deshpande A, Wu PH, Sidiropoulos DN, Erbe R, Stern J, Chan R, Williams S, Chell JM, Ciotti L, Zimmerman JW, Wirtz D, Ho WJ, Zaidi N, Thompson E, Jaffee EM, Wood LD, Fertig EJ, Kagohara LT. PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration. Cell Syst 2024; 15:753-769.e5. [PMID: 39116880 DOI: 10.1016/j.cels.2024.07.001] [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: 07/07/2023] [Revised: 02/06/2024] [Accepted: 07/08/2024] [Indexed: 08/10/2024]
Abstract
This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.
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Affiliation(s)
- Alexander T F Bell
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jacob T Mitchell
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Ashley L Kiemen
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Melissa Lyman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kohei Fujikura
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jae W Lee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Erin Coyne
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sarah M Shin
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sushma Nagaraj
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Atul Deshpande
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Dimitrios N Sidiropoulos
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rossin Erbe
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Lauren Ciotti
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jacquelyn W Zimmerman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Denis Wirtz
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA; Department of Materials Science and Engineering, The Johns Hopkins University, Baltimore, MD, USA; Johns Hopkins Physical Sciences - Oncology Center, The Johns Hopkins University, Baltimore, MD, USA
| | - Won Jin Ho
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Neeha Zaidi
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Elizabeth Thompson
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Laura D Wood
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Elana J Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA.
| | - Luciane T Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA.
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2
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Ruan Z, Zhou W, Liu H, Wei J, Pan Y, Yan C, Wei X, Xiang W, Yan C, Chen S, Liu J. Precise detection of cell-type-specific domains in spatial transcriptomics. CELL REPORTS METHODS 2024; 4:100841. [PMID: 39127046 DOI: 10.1016/j.crmeth.2024.100841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/17/2024] [Accepted: 07/17/2024] [Indexed: 08/12/2024]
Abstract
Cell-type-specific domains are the anatomical domains in spatially resolved transcriptome (SRT) tissues where particular cell types are enriched coincidentally. It is challenging to use existing computational methods to detect specific domains with low-proportion cell types, which are partly overlapped with or even inside other cell-type-specific domains. Here, we propose De-spot, which synthesizes segmentation and deconvolution as an ensemble to generate cell-type patterns, detect low-proportion cell-type-specific domains, and display these domains intuitively. Experimental evaluation showed that De-spot enabled us to discover the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. We further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma.
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Affiliation(s)
- Zhihan Ruan
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Weijun Zhou
- Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Hong Liu
- The Second Surgical Department of Breast Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
| | - Jinmao Wei
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Yichen Pan
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Chaoyang Yan
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Xiaoyi Wei
- Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China
| | - Wenting Xiang
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Chengwei Yan
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Shengquan Chen
- School of Mathematical Sciences, Nankai University, Tianjin 300350, China
| | - Jian Liu
- State Key Laboratory of Medicinal Chemical Biology, College of Computer Science, Nankai University, Tianjin 300350, China.
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3
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Harnik Y, Yakubovsky O, Hoefflin R, Novoselsky R, Bahar Halpern K, Barkai T, Korem Kohanim Y, Egozi A, Golani O, Addadi Y, Kedmi M, Keidar Haran T, Levin Y, Savidor A, Keren-Shaul H, Mayer C, Pencovich N, Pery R, Shouval DS, Tirosh I, Nachmany I, Itzkovitz S. A spatial expression atlas of the adult human proximal small intestine. Nature 2024; 632:1101-1109. [PMID: 39112711 DOI: 10.1038/s41586-024-07793-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 07/05/2024] [Indexed: 08/17/2024]
Abstract
The mouse small intestine shows profound variability in gene expression along the crypt-villus axis1,2. Whether similar spatial heterogeneity exists in the adult human gut remains unclear. Here we use spatial transcriptomics, spatial proteomics and single-molecule fluorescence in situ hybridization to reconstruct a comprehensive spatial expression atlas of the adult human proximal small intestine. We describe zonated expression and cell type representation for epithelial, mesenchymal and immune cell types. We find that migrating enterocytes switch from lipid droplet assembly and iron uptake at the villus bottom to chylomicron biosynthesis and iron release at the tip. Villus tip cells are pro-immunogenic, recruiting γδ T cells and macrophages to the tip, in contrast to their immunosuppressive roles in mouse. We also show that the human small intestine contains abundant serrated and branched villi that are enriched at the tops of circular folds. Our study presents a detailed resource for understanding the biology of the adult human small intestine.
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Affiliation(s)
- Yotam Harnik
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Oran Yakubovsky
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- Department of General Surgery and Transplantation, Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Rouven Hoefflin
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Roy Novoselsky
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Keren Bahar Halpern
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Tal Barkai
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- Sheba Medical Center, Ramat Gan, Israel
| | - Yael Korem Kohanim
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Adi Egozi
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ofra Golani
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Yoseph Addadi
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Merav Kedmi
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Tal Keidar Haran
- Department of Pathology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Yishai Levin
- The De Botton Institute for Protein Profiling, The Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Alon Savidor
- The De Botton Institute for Protein Profiling, The Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Hadas Keren-Shaul
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Chen Mayer
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Institute of Pathology, Sheba Medical Center, Ramat Gan, Israel
| | - Niv Pencovich
- Department of General Surgery and Transplantation, Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ron Pery
- Department of General Surgery and Transplantation, Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Dror S Shouval
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Institute of Gastroenterology, Nutrition and Liver Diseases, Schneider Children's Medical Center of Israel, Petach Tikva, Israel
| | - Itay Tirosh
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ido Nachmany
- Department of General Surgery and Transplantation, Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Shalev Itzkovitz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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4
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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5
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Chen J, Zhou M, Wu W, Zhang J, Li Y, Li D. STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics. ARXIV 2024:arXiv:2406.06393v2. [PMID: 38947920 PMCID: PMC11213178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000 - 30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.
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Affiliation(s)
| | | | - Wenrong Wu
- University of North Carolina at Chapel Hill
| | | | - Yun Li
- University of North Carolina at Chapel Hill
| | - Didong Li
- University of North Carolina at Chapel Hill
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6
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Mohanty C, Singh CK, Daccache JA, Damsky W, Kendziorski C, Yan D, Prasad A, Zhang D, Keenan T, Drolet B, Ahmad N, Shields BE. Granuloma Annulare Exhibits Mixed Immune and Macrophage Polarization Profiles with Spatial Transcriptomics. J Invest Dermatol 2024:S0022-202X(24)00389-0. [PMID: 38844128 DOI: 10.1016/j.jid.2024.04.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 04/11/2024] [Accepted: 04/27/2024] [Indexed: 07/16/2024]
Abstract
Granuloma annulare (GA) is an idiopathic condition characterized by granulomatous inflammation in the skin. Prior studies have suggested that GA develops from various triggers, leading to a complex interplay involving innate and adaptive immunity, tissue remodeling, and fibrosis. Macrophages are the major immune cells comprising GA granulomas; however, the molecular drivers and inflammatory signaling cascade behind macrophage activation are poorly understood. Histologically, GA exhibits both palisaded and interstitial patterns on histology; however, the molecular composition of GA at the spatial level remains unexplored. GA is a condition without Food and Drug Administration-approved therapies despite the significant impact of GA on QOL. Spatial transcriptomics is a valuable tool for profiling localized, genome-wide gene expression changes across tissues, with emerging applications in clinical medicine. To improve our understanding of the spatially localized gene expression patterns underlying GA, we profiled the spatial gene expression landscape from 6 patients with GA. Our findings revealed mixed T helper 1 and T helper 2 signals comprising the GA microenvironment and spatially distinct M1 and M2 macrophage polarization characteristics. IFN-γ and TNF signals emerged as important regulators of GA granulomatous inflammation, and IL-32 emerged as a key driver of granulomatous inflammation. Overall, our spatial transcriptomics data indicate that GA exhibits mixed immune and macrophage polarization.
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Affiliation(s)
- Chitrasen Mohanty
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Chandra K Singh
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Joseph A Daccache
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA; Department of Pathology, NYU Langone Health, New York, New York, USA
| | - William Damsky
- Department of Pathology, NYU Langone Health, New York, New York, USA; Department of Dermatology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Di Yan
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Aman Prasad
- Department of Dermatology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Donglin Zhang
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Tom Keenan
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Beth Drolet
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Nihal Ahmad
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA; Department of Dermatology, William S. Middleton Memorial Veterans' Hospital, Madison, Wisconsin, USA
| | - Bridget E Shields
- Department of Dermatology, The School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA.
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7
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Mohanty C, Prasad A, Cheng L, Arkin LM, Shields BE, Drolet B, Kendziorski C. SpatialView: an interactive web application for visualization of multiple samples in spatial transcriptomics experiments. Bioinformatics 2024; 40:btae117. [PMID: 38444087 PMCID: PMC10957517 DOI: 10.1093/bioinformatics/btae117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/06/2023] [Accepted: 03/04/2024] [Indexed: 03/07/2024] Open
Abstract
MOTIVATION Spatial transcriptomics (ST) experiments provide spatially localized measurements of genome-wide gene expression allowing for an unprecedented opportunity to investigate cellular heterogeneity and organization within a tissue. Statistical and computational frameworks exist that implement robust methods for pre-processing and analyzing data in ST experiments. However, the lack of an interactive suite of tools for visualizing ST data and results currently limits the full potential of ST experiments. RESULTS To fill the gap, we developed SpatialView, an open-source web browser-based interactive application for visualizing data and results from multiple 10× Genomics Visium ST experiments. We anticipate SpatialView will be useful to a broad array of clinical and basic science investigators utilizing ST to study disease. AVAILABILITY AND IMPLEMENTATION SpatialView is available at https://github.com/kendziorski-lab/SpatialView (and https://doi.org/10.5281/zenodo.10223907); a demo application is available at https://www.biostat.wisc.edu/˜kendzior/spatialviewdemo/.
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Affiliation(s)
- Chitrasen Mohanty
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, United States
| | - Aman Prasad
- Department of Dermatology, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Lingxin Cheng
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, United States
| | - Lisa M Arkin
- Department of Dermatology, University of Wisconsin-Madison, Madison, WI 53715, United States
| | - Bridget E Shields
- Department of Dermatology, University of Wisconsin-Madison, Madison, WI 53715, United States
| | - Beth Drolet
- Department of Dermatology, University of Wisconsin-Madison, Madison, WI 53715, United States
| | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, United States
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8
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Mason K, Sathe A, Hess PR, Rong J, Wu CY, Furth E, Susztak K, Levinsohn J, Ji HP, Zhang N. Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions. Genome Biol 2024; 25:14. [PMID: 38217002 PMCID: PMC10785550 DOI: 10.1186/s13059-023-03159-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 12/22/2023] [Indexed: 01/14/2024] Open
Abstract
Existing methods for analysis of spatial transcriptomic data focus on delineating the global gene expression variations of cell types across the tissue, rather than local gene expression changes driven by cell-cell interactions. We propose a new statistical procedure called niche-differential expression (niche-DE) analysis that identifies cell-type-specific niche-associated genes, which are differentially expressed within a specific cell type in the context of specific spatial niches. We further develop niche-LR, a method to reveal ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. Niche-DE and niche-LR are applicable to low-resolution spot-based spatial transcriptomics data and data that is single-cell or subcellular in resolution.
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Affiliation(s)
- Kaishu Mason
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Anuja Sathe
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Paul R Hess
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Jiazhen Rong
- Genomics and Computational Biology Graduate Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Chi-Yun Wu
- The Gladstone Institute, San Francisco, USA
| | - Emma Furth
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Katalin Susztak
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jonathan Levinsohn
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nancy Zhang
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA.
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9
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Shireman JM, Cheng L, Goel A, Garcia DM, Partha S, Quiñones-Hinojosa A, Kendziorski C, Dey M. Spatial transcriptomics in glioblastoma: is knowing the right zip code the key to the next therapeutic breakthrough? Front Oncol 2023; 13:1266397. [PMID: 37916170 PMCID: PMC10618006 DOI: 10.3389/fonc.2023.1266397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/27/2023] [Indexed: 11/03/2023] Open
Abstract
Spatial transcriptomics, the technology of visualizing cellular gene expression landscape in a cells native tissue location, has emerged as a powerful tool that allows us to address scientific questions that were elusive just a few years ago. This technological advance is a decisive jump in the technological evolution that is revolutionizing studies of tissue structure and function in health and disease through the introduction of an entirely new dimension of data, spatial context. Perhaps the organ within the body that relies most on spatial organization is the brain. The central nervous system's complex microenvironmental and spatial architecture is tightly regulated during development, is maintained in health, and is detrimental when disturbed by pathologies. This inherent spatial complexity of the central nervous system makes it an exciting organ to study using spatial transcriptomics for pathologies primarily affecting the brain, of which Glioblastoma is one of the worst. Glioblastoma is a hyper-aggressive, incurable, neoplasm and has been hypothesized to not only integrate into the spatial architecture of the surrounding brain, but also possess an architecture of its own that might be actively remodeling the surrounding brain. In this review we will examine the current landscape of spatial transcriptomics in glioblastoma, outline novel findings emerging from the rising use of spatial transcriptomics, and discuss future directions and ultimate clinical/translational avenues.
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Affiliation(s)
- Jack M. Shireman
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Lingxin Cheng
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Amiti Goel
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Diogo Moniz Garcia
- Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, United States
| | - Sanil Partha
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | | | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Mahua Dey
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
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10
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Rahman MN, Noman AA, Turza AM, Abrar MA, Samee MAH, Rahman MS. ScribbleDom: using scribble-annotated histology images to identify domains in spatial transcriptomics data. Bioinformatics 2023; 39:btad594. [PMID: 37756699 PMCID: PMC10564617 DOI: 10.1093/bioinformatics/btad594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 09/03/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
MOTIVATION Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. RESULTS In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot microenvironment through convolution filters of varying sizes, in the form of "Inception" blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods-between 1.82% to 169.38% improvements in adjusted Rand index for 9 of the 12 human dorsolateral prefrontal cortex samples, and 15.54% improvement in the melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. AVAILABILITY AND IMPLEMENTATION Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669).
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Affiliation(s)
- Mohammad Nuwaisir Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
| | - Abdullah Al Noman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
| | - Abir Mohammad Turza
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
| | - Mohammed Abid Abrar
- Department of Computer Science and Engineering, Brac University, Dhaka 1212, Bangladesh
| | - Md Abul Hassan Samee
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, United States
| | - M Saifur Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
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11
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Yousuf S, Qiu M, Voith von Voithenberg L, Hulkkonen J, Macinkovic I, Schulz AR, Hartmann D, Mueller F, Mijatovic M, Ibberson D, AlHalabi KT, Hetzer J, Anders S, Brüne B, Mei HE, Imbusch CD, Brors B, Heikenwälder M, Gaida MM, Büchler MW, Weigert A, Hackert T, Roth S. Spatially Resolved Multi-Omics Single-Cell Analyses Inform Mechanisms of Immune Dysfunction in Pancreatic Cancer. Gastroenterology 2023; 165:891-908.e14. [PMID: 37263303 DOI: 10.1053/j.gastro.2023.05.036] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND & AIMS As pancreatic ductal adenocarcinoma (PDAC) continues to be recalcitrant to therapeutic interventions, including poor response to immunotherapy, albeit effective in other solid malignancies, a more nuanced understanding of the immune microenvironment in PDAC is urgently needed. We aimed to unveil a detailed view of the immune micromilieu in PDAC using a spatially resolved multimodal single-cell approach. METHODS We applied single-cell RNA sequencing, spatial transcriptomics, multiplex immunohistochemistry, and mass cytometry to profile the immune compartment in treatment-naïve PDAC tumors and matched adjacent normal pancreatic tissue, as well as in the systemic circulation. We determined prognostic associations of immune signatures and performed a meta-analysis of the immune microenvironment in PDAC and lung adenocarcinoma on single-cell level. RESULTS We provided a spatially resolved fine map of the immune landscape in PDAC. We substantiated the exhausted phenotype of CD8 T cells and immunosuppressive features of myeloid cells, and highlighted immune subsets with potentially underappreciated roles in PDAC that diverged from immune populations within adjacent normal areas, particularly CD4 T cell subsets and natural killer T cells that are terminally exhausted and acquire a regulatory phenotype. Differential analysis of immune phenotypes in PDAC and lung adenocarcinoma revealed the presence of extraordinarily immunosuppressive subtypes in PDAC, along with a distinctive immune checkpoint composition. CONCLUSIONS Our study sheds light on the multilayered immune dysfunction in PDAC and presents a holistic view of the immune landscape in PDAC and lung adenocarcinoma, providing a comprehensive resource for functional studies and the exploration of therapeutically actionable targets in PDAC.
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Affiliation(s)
- Suhail Yousuf
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Mengjie Qiu
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Johannes Hulkkonen
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Igor Macinkovic
- Institute of Biochemistry I, Faculty of Medicine, Goethe-University Frankfurt, Frankfurt, Germany
| | | | - Domenic Hartmann
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Florian Mueller
- Division of Chronic Inflammation and Cancer, German Cancer Research Center, Heidelberg, Germany
| | - Margarete Mijatovic
- Institute of Biochemistry I, Faculty of Medicine, Goethe-University Frankfurt, Frankfurt, Germany
| | - David Ibberson
- Deep Sequencing Core Facility, BioQuant, Heidelberg University, Heidelberg, Germany
| | - Karam T AlHalabi
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Jenny Hetzer
- Division of Chronic Inflammation and Cancer, German Cancer Research Center, Heidelberg, Germany
| | - Simon Anders
- BioQuant Center, Heidelberg University, Heidelberg, Germany
| | - Bernhard Brüne
- Institute of Biochemistry I, Faculty of Medicine, Goethe-University Frankfurt, Frankfurt, Germany; Frankfurt Cancer Institute, Goethe-University Frankfurt, Frankfurt, Germany; German Cancer Consortium, Partner Site Frankfurt, Germany
| | - Henrik E Mei
- German Rheumatism Research Center, Berlin, Germany
| | - Charles D Imbusch
- Division of Applied Bioinformatics, German Cancer Research Center, Heidelberg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center, Heidelberg, Germany
| | - Mathias Heikenwälder
- Division of Chronic Inflammation and Cancer, German Cancer Research Center, Heidelberg, Germany
| | - Matthias M Gaida
- Institute of Pathology, University Medical Center Mainz, Johannes Gutenberg University, Mainz, Germany; Research Center for Immunotherapy, University Medical Center Mainz, Johannes Gutenberg University, Mainz, Germany; Joint Unit Immunopathology, Institute of Pathology, University Medical Center, Johannes Gutenberg University and Translational Oncology, University Medical Center Mainz, Mainz, Germany
| | - Markus W Büchler
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Andreas Weigert
- Institute of Biochemistry I, Faculty of Medicine, Goethe-University Frankfurt, Frankfurt, Germany; Frankfurt Cancer Institute, Goethe-University Frankfurt, Frankfurt, Germany; German Cancer Consortium, Partner Site Frankfurt, Germany
| | - Thilo Hackert
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Susanne Roth
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany.
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12
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Cheng C, Chen W, Jin H, Chen X. A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication. Cells 2023; 12:1970. [PMID: 37566049 PMCID: PMC10417635 DOI: 10.3390/cells12151970] [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: 06/13/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.
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Affiliation(s)
- Changde Cheng
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Xiang Chen
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
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13
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Zhang H, Hunter MV, Chou J, Quinn JF, Zhou M, White RM, Tansey W. BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment. Cell Syst 2023; 14:605-619.e7. [PMID: 37473731 PMCID: PMC10368078 DOI: 10.1016/j.cels.2023.06.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/09/2023] [Accepted: 06/09/2023] [Indexed: 07/22/2023]
Abstract
Spatial variation in cellular phenotypes underlies heterogeneity in immune recognition and response to therapy in cancer and many other diseases. Spatial transcriptomics holds the potential to quantify such variation, but existing analysis methods are limited by their focus on individual tasks such as spot deconvolution. We present BayesTME, an end-to-end Bayesian method for analyzing spatial transcriptomics data. BayesTME unifies several previously distinct analysis goals under a single, holistic generative model. This unified approach enables BayesTME to deconvolve spots into cell phenotypes without any need for paired single-cell RNA-seq. BayesTME then goes beyond spot deconvolution to uncover spatial expression patterns among coordinated subsets of genes within phenotypes, which we term spatial transcriptional programs. BayesTME achieves state-of-the-art performance across myriad benchmarks. On human and zebrafish melanoma tissues, BayesTME identifies spatial transcriptional programs that capture fundamental biological phenomena such as bilateral symmetry and tumor-associated fibroblast and macrophage reprogramming. BayesTME is open source.
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Affiliation(s)
- Haoran Zhang
- Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA
| | - Miranda V Hunter
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jacqueline Chou
- Department of Physiology, Biophysics, & Systems Biology, Weill Cornell Medical College, New York, NY 10065, USA
| | - Jeffrey F Quinn
- Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Mingyuan Zhou
- McCombs School of Business, University of Texas at Austin, Austin, TX 78712, USA
| | - Richard M White
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, UK
| | - Wesley Tansey
- Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
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14
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Park H, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206939. [PMID: 37026425 PMCID: PMC10238226 DOI: 10.1002/advs.202206939] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/10/2023] [Indexed: 06/04/2023]
Abstract
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.
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Affiliation(s)
- Han‐Eol Park
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
- School of Biological SciencesSeoul National UniversitySeoul08826Republic of Korea
| | - Song Hyun Jo
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Rosalind H. Lee
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Christian P. Macks
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
| | - Taeyun Ku
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Jihwan Park
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Chung Whan Lee
- Department of ChemistryGachon UniversitySeongnamGyeonggi‐do13120Republic of Korea
| | - Junho K. Hur
- Department of GeneticsCollege of MedicineHanyang UniversitySeoul04763Republic of Korea
| | - Chang Ho Sohn
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
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15
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Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment. Biosci Rep 2023; 43:232194. [PMID: 36459212 PMCID: PMC9950536 DOI: 10.1042/bsr20221680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 12/03/2022] Open
Abstract
Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.
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16
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Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas. Nat Commun 2023; 14:1028. [PMID: 36823172 PMCID: PMC9950149 DOI: 10.1038/s41467-023-36707-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 02/14/2023] [Indexed: 02/25/2023] Open
Abstract
Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.
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17
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Chen J, Liu W, Luo T, Yu Z, Jiang M, Wen J, Gupta GP, Giusti P, Zhu H, Yang Y, Li Y. A comprehensive comparison on cell-type composition inference for spatial transcriptomics data. Brief Bioinform 2022; 23:6618233. [PMID: 35753702 PMCID: PMC9294426 DOI: 10.1093/bib/bbac245] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/20/2022] [Accepted: 05/25/2022] [Indexed: 01/11/2023] Open
Abstract
Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional profiles along with maintained positional information. Such spatially resolved transcriptional characterization of intact tissue samples provides an integrated view of gene expression in its natural spatial and functional context. However, high-throughput sequencing-based ST technologies cannot yet reach single cell resolution. Thus, similar to bulk RNA-seq data, gene expression data at ST spot-level reflect transcriptional profiles of multiple cells and entail the inference of cell-type composition within each ST spot for valid and powerful subsequent analyses. Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is to review state-of-the-art methods for ST deconvolution, comparing their strengths and weaknesses. In particular, we construct ST spots from single-cell level ST data to assess the performance of 10 methods, with either ideal reference or non-ideal reference. Furthermore, we examine the performance of these methods on spot- and bead-level ST data by comparing estimated cell-type proportions to carefully matched single-cell ST data. In comparing the performance on various tissues and technological platforms, we concluded that RCTD and stereoscope achieve more robust and accurate inferences.
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Affiliation(s)
| | | | | | - Zhentao Yu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Minzhi Jiang
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, North Carolina, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Gaorav P Gupta
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Paola Giusti
- Department of Psychiatry, University of Florida, Gainesville, Florida, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yuchen Yang
- State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, 510275 Guangzhou, China
| | - Yun Li
- Corresponding author. Yun Li, Department of Genetics, 120 Mason Farm Road, Campus Box 7264, University North Carolina, Chapel Hill, NC 27599, USA. Tel: (919) 843-2832; Fax: (919) 843-4682; E-mail:
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