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Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
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Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
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2
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Croizer H, Mhaidly R, Kieffer Y, Gentric G, Djerroudi L, Leclere R, Pelon F, Robley C, Bohec M, Meng A, Meseure D, Romano E, Baulande S, Peltier A, Vincent-Salomon A, Mechta-Grigoriou F. Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer. Nat Commun 2024; 15:2806. [PMID: 38561380 PMCID: PMC10984943 DOI: 10.1038/s41467-024-47068-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Although heterogeneity of FAP+ Cancer-Associated Fibroblasts (CAF) has been described in breast cancer, their plasticity and spatial distribution remain poorly understood. Here, we analyze trajectory inference, deconvolute spatial transcriptomics at single-cell level and perform functional assays to generate a high-resolution integrated map of breast cancer (BC), with a focus on inflammatory and myofibroblastic (iCAF/myCAF) FAP+ CAF clusters. We identify 10 spatially-organized FAP+ CAF-related cellular niches, called EcoCellTypes, which are differentially localized within tumors. Consistent with their spatial organization, cancer cells drive the transition of detoxification-associated iCAF (Detox-iCAF) towards immunosuppressive extracellular matrix (ECM)-producing myCAF (ECM-myCAF) via a DPP4- and YAP-dependent mechanism. In turn, ECM-myCAF polarize TREM2+ macrophages, regulatory NK and T cells to induce immunosuppressive EcoCellTypes, while Detox-iCAF are associated with FOLR2+ macrophages in an immuno-protective EcoCellType. FAP+ CAF subpopulations accumulate differently according to the invasive BC status and predict invasive recurrence of ductal carcinoma in situ (DCIS), which could help in identifying low-risk DCIS patients eligible for therapeutic de-escalation.
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Affiliation(s)
- Hugo Croizer
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Rana Mhaidly
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Yann Kieffer
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Geraldine Gentric
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Lounes Djerroudi
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, Rue d'Ulm, F-75248, Paris, France
| | - Renaud Leclere
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, Rue d'Ulm, F-75248, Paris, France
| | - Floriane Pelon
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Catherine Robley
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Mylene Bohec
- Institut Curie, PSL Research University, ICGex Next-Generation Sequencing Platform, 75005, Paris, France
- Institut Curie, PSL Research University, Single Cell Initiative, 75005, Paris, France
| | - Arnaud Meng
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Didier Meseure
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, Rue d'Ulm, F-75248, Paris, France
| | - Emanuela Romano
- Department of Medical Oncology, Center for Cancer Immunotherapy, Institut Curie, 26, Rue d'Ulm, F-75248, Paris, France
| | - Sylvain Baulande
- Institut Curie, PSL Research University, ICGex Next-Generation Sequencing Platform, 75005, Paris, France
- Institut Curie, PSL Research University, Single Cell Initiative, 75005, Paris, France
| | - Agathe Peltier
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Anne Vincent-Salomon
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, Rue d'Ulm, F-75248, Paris, France
| | - Fatima Mechta-Grigoriou
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France.
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France.
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Wang J, Li Q, Liang F, Du X, Song P, Wu T, Chen R, Lin X, Liu Q, Hu H, Han P, Huang X. Dickkopf-1 drives perineural invasion via PI3K-AKT signaling pathway in head and neck squamous cancer. MedComm (Beijing) 2024; 5:e518. [PMID: 38525111 PMCID: PMC10959454 DOI: 10.1002/mco2.518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 01/16/2024] [Accepted: 01/29/2024] [Indexed: 03/26/2024] Open
Abstract
Perineural invasion (PNI) leads to the poor prognosis of head and neck squamous cancer (HNSCC) patients, but the mechanism of PNI remains unclear. Dickkopf-1 (DKK1), a secretory protein in the Wnt signaling pathway, was found indeed upregulated in HNSCC cells and tissues. Higher expression of DKK1 was statistically relevant to T stage, N stage, PNI, and poor prognosis of HNSCC. DKK1 overexpression enhanced the migration abilities of cancer cells. Moreover, DKK1-overexpressing cancer cells promoted cancer cells invasion of peripheral nerves in vitro and in vivo. Mechanistically, DKK1 could promote the PI3K-AKT signaling pathway. The migration abilities of neuroblastoma cells, which were enhanced by DKK1-overexpressing HNSCC cell lines, could be reversed by an inhibitor of Akt (MK2206). The association of DKK1 with PNI was also confirmed in HNSCC samples. Variables, including T stage, N stage, DKK1 expression, and PNI, were used to establish a nomogram to predict the survival probability and disease-free probability at 3 and 5 years. In summary, DKK1 can promote the PI3K-AKT signaling pathway in tumor cells and then could induce neuritogenesis and facilitate PNI. MK2206 may be a potential therapeutic target drug for HNSCC patients with PNI.
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Affiliation(s)
- Jingyi Wang
- Department of Otolaryngology‐Head and Neck SurgerySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangzhouChina
| | - Qianying Li
- Department of Otolaryngology‐Head and Neck SurgerySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangzhouChina
| | - Faya Liang
- Department of Otolaryngology‐Head and Neck SurgerySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangzhouChina
| | - Xin Du
- Department of Oncology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Pan Song
- Department of Otolaryngology‐Head and Neck SurgerySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangzhouChina
| | - Taowei Wu
- Department of Otolaryngology‐Head and Neck SurgerySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangzhouChina
| | - Renhui Chen
- Department of Otolaryngology‐Head and Neck SurgerySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangzhouChina
| | - Xiaorong Lin
- Diagnosis and Treatment Center of Breast DiseasesShantou Central HospitalShantouChina
| | - Qinglian Liu
- Department of Oncology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Hai Hu
- Department of Oncology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Ping Han
- Department of Otolaryngology‐Head and Neck SurgerySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangzhouChina
| | - Xiaoming Huang
- Department of Otolaryngology‐Head and Neck SurgerySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangzhouChina
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4
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Yuan Z, Zhao F, Lin S, Zhao Y, Yao J, Cui Y, Zhang XY, Zhao Y. Benchmarking spatial clustering methods with spatially resolved transcriptomics data. Nat Methods 2024; 21:712-722. [PMID: 38491270 DOI: 10.1038/s41592-024-02215-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 02/16/2024] [Indexed: 03/18/2024]
Abstract
Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
| | - Fangyuan Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Senlin Lin
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yu Zhao
- Tencent AI Lab, Shenzhen, China
| | | | - Yan Cui
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - Xiao-Yong Zhang
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China
| | - Yi Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
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5
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Wu R, Horimoto Y, Oshi M, Benesch MGK, Khoury T, Takabe K, Ishikawa T. Emerging measurements for tumor-infiltrating lymphocytes in breast cancer. Jpn J Clin Oncol 2024:hyae033. [PMID: 38521965 DOI: 10.1093/jjco/hyae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/01/2024] [Indexed: 03/25/2024] Open
Abstract
Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.
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Affiliation(s)
- Rongrong Wu
- Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo, Japan
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Yoshiya Horimoto
- Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo, Japan
- Department of Breast Oncology, Juntendo University Hospital, Tokyo, Japan
| | - Masanori Oshi
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
- Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Matthew G K Benesch
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Thaer Khoury
- Department of Pathology & Laboratory Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Kazuaki Takabe
- Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo, Japan
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
- Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama, Japan
- Department of Surgery, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, The State University of New York, Buffalo, NY, USA
- Department of Surgery, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Department of Breast Surgery, Fukushima Medical University, Fukushima, Japan
| | - Takashi Ishikawa
- Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo, Japan
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Guo X, Ning J, Chen Y, Liu G, Zhao L, Fan Y, Sun S. Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies. Brief Funct Genomics 2024; 23:95-109. [PMID: 37022699 DOI: 10.1093/bfgp/elad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/09/2022] [Accepted: 03/10/2023] [Indexed: 04/07/2023] Open
Abstract
Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.
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Affiliation(s)
- Xiya Guo
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Jin Ning
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yuanze Chen
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Guoliang Liu
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Liyan Zhao
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yue Fan
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Shiquan Sun
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
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7
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Albarrán V, San Román M, Pozas J, Chamorro J, Rosero DI, Guerrero P, Calvo JC, González C, García de Quevedo C, Pérez de Aguado P, Moreno J, Cortés A, Soria A. Adoptive T cell therapy for solid tumors: current landscape and future challenges. Front Immunol 2024; 15:1352805. [PMID: 38550594 PMCID: PMC10972864 DOI: 10.3389/fimmu.2024.1352805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/04/2024] [Indexed: 04/02/2024] Open
Abstract
Adoptive cell therapy (ACT) comprises different strategies to enhance the activity of T lymphocytes and other effector cells that orchestrate the antitumor immune response, including chimeric antigen receptor (CAR) T-cell therapy, T-cell receptor (TCR) gene-modified T cells, and therapy with tumor-infiltrating lymphocytes (TILs). The outstanding results of CAR-T cells in some hematologic malignancies have launched the investigation of ACT in patients with refractory solid malignancies. However, certain characteristics of solid tumors, such as their antigenic heterogeneity and immunosuppressive microenvironment, hamper the efficacy of antigen-targeted treatments. Other ACT modalities, such as TIL therapy, have emerged as promising new strategies. TIL therapy has shown safety and promising activity in certain immunogenic cancers, mainly advanced melanoma, with an exciting rationale for its combination with immune checkpoint inhibitors. However, the implementation of TIL therapy in clinical practice is hindered by several biological, logistic, and economic challenges. In this review, we aim to summarize the current knowledge, available clinical results, and potential areas of future research regarding the use of T cell therapy in patients with solid tumors.
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Affiliation(s)
- Víctor Albarrán
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
| | - María San Román
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
| | - Javier Pozas
- Department of Medical Oncology, The Royal Marsden Hospital, London, United Kingdom
| | - Jesús Chamorro
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
| | - Diana Isabel Rosero
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
| | - Patricia Guerrero
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
| | - Juan Carlos Calvo
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
| | - Carlos González
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
| | | | | | - Jaime Moreno
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
| | - Alfonso Cortés
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
| | - Ainara Soria
- Department of Medical Oncology, Ramon y Cajal University Hospital, Madrid, Spain
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8
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Yang S, Wang M, Hua Y, Li J, Zheng H, Cui M, Huang N, Liu Q, Liao Q. Advanced insights on tumor-associated macrophages revealed by single-cell RNA sequencing: The intratumor heterogeneity, functional phenotypes, and cellular interactions. Cancer Lett 2024; 584:216610. [PMID: 38244910 DOI: 10.1016/j.canlet.2024.216610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 11/28/2023] [Accepted: 12/18/2023] [Indexed: 01/22/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) is an emerging technology used for cellular transcriptome analysis. The application of scRNA-seq has led to profoundly advanced oncology research, continuously optimizing novel therapeutic strategies. Intratumor heterogeneity extensively consists of all tumor components, contributing to different tumor behaviors and treatment responses. Tumor-associated macrophages (TAMs), the core immune cells linking innate and adaptive immunity, play significant roles in tumor progression and resistance to therapies. Moreover, dynamic changes occur in TAM phenotypes and functions subject to the regulation of the tumor microenvironment. The heterogeneity of TAMs corresponding to the state of the tumor microenvironment has been comprehensively recognized using scRNA-seq. Herein, we reviewed recent research and summarized variations in TAM phenotypes and functions from a developmental perspective to better understand the significance of TAMs in the tumor microenvironment.
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Affiliation(s)
- Sen Yang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Mengyi Wang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Yuze Hua
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Jiayi Li
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Huaijin Zheng
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Ming Cui
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Nan Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Qiaofei Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China.
| | - Quan Liao
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China.
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9
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Pang JMB, Byrne DJ, Bergin ART, Caramia F, Loi S, Gorringe KL, Fox SB. Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology. Histopathology 2024; 84:577-586. [PMID: 37991396 DOI: 10.1111/his.15093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/23/2023]
Abstract
In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.
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Affiliation(s)
- Jia-Min B Pang
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - David J Byrne
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Alice R T Bergin
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Franco Caramia
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Sherene Loi
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Kylie L Gorringe
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Stephen B Fox
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
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10
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Yan K, Liu Q, Huang R, Jiang Y, Bian Z, Li S, Li L, Shen F, Tsuneyama K, Zhang Q, Lian Z, Guan H, Xu B. Spatial transcriptomics reveals prognosis-associated cellular heterogeneity in the papillary thyroid carcinoma microenvironment. Clin Transl Med 2024; 14:e1594. [PMID: 38426403 PMCID: PMC10905537 DOI: 10.1002/ctm2.1594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Papillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study. METHODS In conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC-associated cells within the tissue sections. The spatial RNA-clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand-receptor analysis and Gene Ontology enrichment analysis. RESULTS The spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse-free survival in PTC patients. We discovered key ligand-receptor interactions, including LAMB3-ITGA2, FN1-ITGA3 and FN1-SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse-free survival. In the tumour immune microenvironment, reduced interaction between myeloid-derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA-clinical analysis method developed here revealed prognosis-associated cellular heterogeneity in the PTC microenvironment. CONCLUSIONS The occurrence of tumour foci No. 2 and three enhanced ligand-receptor interactions in the AFC area/tumour foci reduced the relapse-free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients.
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Affiliation(s)
- Kai Yan
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Qing‐Zhi Liu
- Chronic Disease LaboratoryInstitutes for Life SciencesSouth China University of TechnologyGuangzhouChina
| | - Rong‐Rong Huang
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Yi‐Hua Jiang
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangzhouChina
| | - Zhen‐Hua Bian
- School of Biomedical Sciences and EngineeringSouth China University of TechnologyGuangzhou International CampusGuangzhouChina
| | - Si‐Jin Li
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Liang Li
- Medical Research InstituteGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Fei Shen
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Koichi Tsuneyama
- Department of Pathology and Laboratory MedicineInstitute of Biomedical SciencesTokushima University Graduate SchoolTokushimaJapan
| | - Qing‐Ling Zhang
- Department of PathologyGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Zhe‐Xiong Lian
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Haixia Guan
- Department of EndocrinologyGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Bo Xu
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
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11
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Zhi Y, Wang Q, Zi M, Zhang S, Ge J, Liu K, Lu L, Fan C, Yan Q, Shi L, Chen P, Fan S, Liao Q, Guo C, Wang F, Gong Z, Xiong W, Zeng Z. Spatial Transcriptomic and Metabolomic Landscapes of Oral Submucous Fibrosis-Derived Oral Squamous Cell Carcinoma and its Tumor Microenvironment. Adv Sci (Weinh) 2024; 11:e2306515. [PMID: 38229179 DOI: 10.1002/advs.202306515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/19/2023] [Indexed: 01/18/2024]
Abstract
In South and Southeast Asia, the habit of chewing betel nuts is prevalent, which leads to oral submucous fibrosis (OSF). OSF is a well-established precancerous lesion, and a portion of OSF cases eventually progress to oral squamous cell carcinoma (OSCC). However, the specific molecular mechanisms underlying the malignant transformation of OSCC from OSF are poorly understood. In this study, the leading-edge techniques of Spatial Transcriptomics (ST) and Spatial Metabolomics (SM) are integrated to obtain spatial location information of cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes in OSF-derived OSCC tissues. This work reveals for the first time that some OSF-derived OSCC cells undergo partial epithelial-mesenchymal transition (pEMT) within the in situ carcinoma (ISC) region, eventually acquiring fibroblast-like phenotypes and participating in collagen deposition. Complex interactions among epithelial cells, fibroblasts, and immune cells in the tumor microenvironment are demonstrated. Most importantly, significant metabolic reprogramming in OSF-derived OSCC, including abnormal polyamine metabolism, potentially playing a pivotal role in promoting tumorigenesis and immune evasion is discovered. The ST and SM data in this study shed new light on deciphering the mechanisms of OSF-derived OSCC. The work also offers invaluable clues for the prevention and treatment of OSCC.
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Affiliation(s)
- Yuan Zhi
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410078, China
| | - Qian Wang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, 410078, China
| | - Moxin Zi
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410078, China
| | - Shanshan Zhang
- Department of Stomatology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Junshang Ge
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, 410078, China
| | - Keyue Liu
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Linsong Lu
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Chunmei Fan
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, 410078, China
| | - Qijia Yan
- Department of Stomatology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Lei Shi
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Pan Chen
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410078, China
| | - Songqing Fan
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Qianjin Liao
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410078, China
| | - Can Guo
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, 410078, China
| | - Fuyan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, 410078, China
| | - Zhaojian Gong
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, 410078, China
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, 410078, China
| | - Zhaoyang Zeng
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, 410078, China
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12
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Zhou P, Xiao Y, Zhou X, Fang J, Zhang J, Liu J, Guo L, Zhang J, Zhang N, Chen K, Zhao C. Mapping Spatiotemporal Heterogeneity in Multifocal Breast Tumor Progression by Noninvasive Ultrasound Elastography-Guided Mass Spectrometry Imaging Strategy. JACS Au 2024; 4:465-475. [PMID: 38425919 PMCID: PMC10900218 DOI: 10.1021/jacsau.3c00589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 01/10/2024] [Accepted: 01/24/2024] [Indexed: 03/02/2024]
Abstract
Spatiotemporal heterogeneity of tumors provides an escape mechanism for breast cancer cells, which can obstruct the investigation of tumor progression. While molecular profiling obtained from mass spectrometry imaging (MSI) is rich in biochemical information, it lacks the capacity for in vivo analysis. Ultrasound diagnosis has a high diagnostic accuracy but low chemical specificity. Here, we describe a noninvasive ultrasound elastography (UE)-guided MSI strategy (UEg-MSI) that integrates physical and biochemical characteristics of tumors acquired from both in vivo and in vitro imaging. Using UEg-MSI, both elasticity histopathology metabolism "fingerprints" and reciprocal crosstalk are revealed, indicating the intact, multifocal spatiotemporal heterogeneity of spontaneous tumorigenesis of the breast from early, middle, and late stages. Our results demonstrate a gradual increase in malignant degree of primary focus in cervical and thoracic mammary glands. This progression is characterized by increased stiffness according to elasticity scores, histopathological changes from hyperplasia to increased nests of neoplastic cells and necrotic areas, and regional metabolic heterogeneity and reprogramming at the spatiotemporal level. De novo fatty acid (FA) synthesis focused on independent (such as ω-9 FAs) and dependent (such as ω-6 FAs) dietary FA intake in the core cancerous nest areas in the middle and late stages of tumor or in the peripheral microareas in the early stage of the tumor. SM-Cer signaling pathway and GPs biosynthesis and degradation, as well as glycerophosphoinositol intensity, changed in multiple characteristic microareas. The UEg-MSI strategy holds the potential to expand MSI applications and enhance ultrasound-mediated cancer diagnosis. It offers new insight into early cancer discovery and the occurrence of metastasis.
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Affiliation(s)
- Peng Zhou
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Yu Xiao
- Department
of Thyroid and Breast department, First Affiliated Hospital of Shenzhen
University, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Xin Zhou
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jinghui Fang
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Jingwen Zhang
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Jianjun Liu
- Shenzhen
Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline
of Health Toxicology (2020-2024), Shenzhen
Center for Disease Control and Prevention, 518054, Shenzhen, China
| | - Ling Guo
- Shenzhen
Key Laboratory of Epigenetics and Precision Medicine for Cancers,
National Cancer Center/National Clinical Research Center for Cancer/Cancer
Hospital & Shenzhen Hospital, Chinese
Academic of Medical Sciences & Peking Union Medical College, Shenzhen 518172, China
| | - Jiuhong Zhang
- Shenzhen
Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline
of Health Toxicology (2020-2024), Shenzhen
Center for Disease Control and Prevention, 518054, Shenzhen, China
| | - Ning Zhang
- College
of Chemistry and Chemical Engineering, Dezhou
University, Dezhou 253026, Shandong, China
| | - Ke Chen
- Key
Laboratory of Resources Conversion and Pollution Control of the State
Ethnic Affairs Commission, College of Resources and Environmental
Science, South-Central Minzu University, Wuhan 430074, China
| | - Chao Zhao
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
- Shenzhen
Key Laboratory of Precision Diagnosis and Treatment of Depression, Shenzhen Institute of Advanced Technology, Chinese
Academy of Sciences, Shenzhen 518055, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
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13
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Kojima Y, Mii S, Hayashi S, Hirose H, Ishikawa M, Akiyama M, Enomoto A, Shimamura T. Single-cell colocalization analysis using a deep generative model. Cell Syst 2024; 15:180-192.e7. [PMID: 38387441 DOI: 10.1016/j.cels.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 03/06/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024]
Abstract
Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, and cellular responses to external stimuli and their biological functions in diseases and tissues. However, existing computational methodologies identified the colocalization patterns between predefined cell populations, which can obscure the molecular signatures arising from intercellular communication. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers intercellular colocalization networks with single-cell resolution by the integration of single-cell and spatial transcriptomes. Along with colocalized population detection accuracy that is superior to existing methods in simulated dataset, DeepCOLOR identified plausible cell-cell interaction candidates between colocalized single cells and segregated cell populations defined by the colocalization relationships in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2. DeepCOLOR is applicable to studying cell-cell interactions behind various spatial niches. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Yasuhiro Kojima
- Laboratory of Computational Life Science, National Cancer Center Research Institute, Chuo-ku, Tokyo 104-0045, Japan; Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.
| | - Shinji Mii
- Department of Pathology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Shuto Hayashi
- Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Haruka Hirose
- Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Masato Ishikawa
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, Kyoto 606-8507, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - Masashi Akiyama
- Department of Dermatology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Atsushi Enomoto
- Department of Pathology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Teppei Shimamura
- Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.
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14
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Di Ceglie I, Carnevale S, Rigatelli A, Grieco G, Molisso P, Jaillon S. Immune cell networking in solid tumors: focus on macrophages and neutrophils. Front Immunol 2024; 15:1341390. [PMID: 38426089 PMCID: PMC10903099 DOI: 10.3389/fimmu.2024.1341390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
The tumor microenvironment is composed of tumor cells, stromal cells and leukocytes, including innate and adaptive immune cells, and represents an ecological niche that regulates tumor development and progression. In general, inflammatory cells are considered to contribute to tumor progression through various mechanisms, including the formation of an immunosuppressive microenvironment. Macrophages and neutrophils are important components of the tumor microenvironment and can act as a double-edged sword, promoting or inhibiting the development of the tumor. Targeting of the immune system is emerging as an important therapeutic strategy for cancer patients. However, the efficacy of the various immunotherapies available is still limited. Given the crucial importance of the crosstalk between macrophages and neutrophils and other immune cells in the formation of the anti-tumor immune response, targeting these interactions may represent a promising therapeutic approach against cancer. Here we will review the current knowledge of the role played by macrophages and neutrophils in cancer, focusing on their interaction with other immune cells.
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Affiliation(s)
| | | | | | - Giovanna Grieco
- IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Piera Molisso
- IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Sebastien Jaillon
- IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
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15
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Quail DF, Park M, Welm AL, Ekiz HA. Breast Cancer Immunity: It is TIME for the Next Chapter. Cold Spring Harb Perspect Med 2024; 14:a041324. [PMID: 37188526 PMCID: PMC10835621 DOI: 10.1101/cshperspect.a041324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Our ability to interrogate the tumor immune microenvironment (TIME) at an ever-increasing granularity has uncovered critical determinants of disease progression. Not only do we now have a better understanding of the immune response in breast cancer, but it is becoming possible to leverage key mechanisms to effectively combat this disease. Almost every component of the immune system plays a role in enabling or inhibiting breast tumor growth. Building on early seminal work showing the involvement of T cells and macrophages in controlling breast cancer progression and metastasis, single-cell genomics and spatial proteomics approaches have recently expanded our view of the TIME. In this article, we provide a detailed description of the immune response against breast cancer and examine its heterogeneity in disease subtypes. We discuss preclinical models that enable dissecting the mechanisms responsible for tumor clearance or immune evasion and draw parallels and distinctions between human disease and murine counterparts. Last, as the cancer immunology field is moving toward the analysis of the TIME at the cellular and spatial levels, we highlight key studies that revealed previously unappreciated complexity in breast cancer using these technologies. Taken together, this article summarizes what is known in breast cancer immunology through the lens of translational research and identifies future directions to improve clinical outcomes.
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Affiliation(s)
- Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec H3A 1A3, Canada
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Morag Park
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec H3A 1A3, Canada
- Departments of Biochemistry, Oncology, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Alana L Welm
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - H Atakan Ekiz
- Department of Molecular Biology and Genetics, Izmir Institute of Technology, Gulbahce, 35430 Urla, Izmir, Turkey
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16
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Liu PW, Lin J, Hou R, Cai Z, Gong Y, He PA, Yang J. Single-cell RNA-seq reveals the metabolic status of immune cells response to immunotherapy in triple-negative breast cancer. Comput Biol Med 2024; 169:107926. [PMID: 38183706 DOI: 10.1016/j.compbiomed.2024.107926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/09/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024]
Abstract
Immune checkpoint blockade (ICB) therapy offers promise in the treatment of triple-negative breast cancer (TNBC); however, its limited efficacy in certain TNBC patients poses a challenge. In this study, we elucidated the metabolic mechanism at 'sub-subtype' resolution underlying the non-response to ICB therapy in TNBC. Here, an analytic pipeline was developed to reveal the metabolic heterogeneity, which is correlated with the ICB outcomes, within each immune cell subtype. First, we identified metabolic 'sub-subtypes' within certain cell subtypes, predominantly T cell subsets, which are enriched in ICB non-responders and named as non-responder-enriched (NR-E) clusters. Notably, most of NR-E T metabolic cells exhibit globally higher metabolic activities compared to other cells within the same individual subtype. Further, we investigated the extra-cellular signals that trigger the metabolic status of NR-E T cells. In detail, the prediction of cell-to-cell communication indicated that NR-E T cells are regulated by plasmatic dendritic cells (pDCs) through TNFSF9, as well as by macrophages expressing SIGLEC9. In addition, we also validate the communication between TNFSF9+ pDCs and NR-E T cells utilizing deconvolution of spatial transcriptomics analysis. In summary, our research identified specific metabolic 'sub-subtypes' associated with ICB non-response and uncovered the mechanisms of their regulation in TNBC. And the proposed analytical pipeline can be used to examine metabolic heterogeneity within cell types that correlate with diverse phenotypes.
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Affiliation(s)
- Pei-Wen Liu
- School of Science, Zhejiang Sci-Tech University, Hangzhou, China; Geneis Beijing Co., Ltd., Beijing, China
| | - Jun Lin
- Depatment of Pathology, The People's Hospital of QuZhou City, ZheJiang, China
| | - Rui Hou
- Geneis Beijing Co., Ltd., Beijing, China
| | - Zhe Cai
- Extendcity (Shanghai) Co., Ltd., Shanghai, China
| | - Yue Gong
- Geneis Beijing Co., Ltd., Beijing, China
| | - Ping-An He
- School of Science, Zhejiang Sci-Tech University, Hangzhou, China.
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17
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Lee S, Kim G, Lee J, Lee AC, Kwon S. Mapping cancer biology in space: applications and perspectives on spatial omics for oncology. Mol Cancer 2024; 23:26. [PMID: 38291400 PMCID: PMC10826015 DOI: 10.1186/s12943-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.
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Affiliation(s)
- Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea
| | - Gyeongjun Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - JinYoung Lee
- Division of Engineering Science, University of Toronto, Toronto, Ontario, ON, M5S 3H6, Canada
| | - Amos C Lee
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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18
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Ji S, Shi Y, Yin B. Macrophage barrier in the tumor microenvironment and potential clinical applications. Cell Commun Signal 2024; 22:74. [PMID: 38279145 PMCID: PMC10811890 DOI: 10.1186/s12964-023-01424-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/05/2023] [Indexed: 01/28/2024] Open
Abstract
The tumor microenvironment (TME) constitutes a complex microenvironment comprising a diverse array of immune cells and stromal components. Within this intricate context, tumor-associated macrophages (TAMs) exhibit notable spatial heterogeneity. This heterogeneity contributes to various facets of tumor behavior, including immune response modulation, angiogenesis, tissue remodeling, and metastatic potential. This review summarizes the spatial distribution of macrophages in both the physiological environment and the TME. Moreover, this paper explores the intricate interactions between TAMs and diverse immune cell populations (T cells, dendritic cells, neutrophils, natural killer cells, and other immune cells) within the TME. These bidirectional exchanges form a complex network of immune interactions that influence tumor immune surveillance and evasion strategies. Investigating TAM heterogeneity and its intricate interactions with different immune cell populations offers potential avenues for therapeutic interventions. Additionally, this paper discusses therapeutic strategies targeting macrophages, aiming to uncover novel approaches for immunotherapy. Video Abstract.
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Affiliation(s)
- Shuai Ji
- Department of Urinary Surgery, The Shengjing Hospital of China Medical University, Shenyang, 110022, China
| | - Yuqing Shi
- Department of Respiratory Medicine, Shenyang 10th People's Hospital, Shenyang, 110096, China
| | - Bo Yin
- Department of Urinary Surgery, The Shengjing Hospital of China Medical University, Shenyang, 110022, China.
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19
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Hu Y, Xiao K, Yang H, Liu X, Zhang C, Shi Q. Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics. Brief Bioinform 2024; 25:bbae016. [PMID: 38324623 PMCID: PMC10849194 DOI: 10.1093/bib/bbae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/29/2023] [Indexed: 02/09/2024] Open
Abstract
Recent advances in spatially resolved transcriptomics (SRT) have brought ever-increasing opportunities to characterize expression landscape in the context of tissue spatiality. Nevertheless, there still exist multiple challenges to accurately detect spatial functional regions in tissue. Here, we present a novel contrastive learning framework, SPAtially Contrastive variational AutoEncoder (SpaCAE), which contrasts transcriptomic signals of each spot and its spatial neighbors to achieve fine-grained tissue structures detection. By employing a graph embedding variational autoencoder and incorporating a deep contrastive strategy, SpaCAE achieves a balance between spatial local information and global information of expression, enabling effective learning of representations with spatial constraints. Particularly, SpaCAE provides a graph deconvolutional decoder to address the smoothing effect of local spatial structure on expression's self-supervised learning, an aspect often overlooked by current graph neural networks. We demonstrated that SpaCAE could achieve effective performance on SRT data generated from multiple technologies for spatial domains identification and data denoising, making it a remarkable tool to obtain novel insights from SRT studies.
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Affiliation(s)
- Yaofeng Hu
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, Hangzhou 310024; University of Chinese Academy of Sciences, China
| | - Kai Xiao
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hengyu Yang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, Hangzhou 310024; University of Chinese Academy of Sciences, China
| | - Xiaoping Liu
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, Hangzhou 310024; University of Chinese Academy of Sciences, China
| | - Chuanchao Zhang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, Hangzhou 310024; University of Chinese Academy of Sciences, China
| | - Qianqian Shi
- Hubei Engineering Technology Research Center of Agricultural Big Data, Huazhong Agricultural University, Wuhan 430070, Hubei, China
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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20
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Valdeolivas A, Amberg B, Giroud N, Richardson M, Gálvez EJC, Badillo S, Julien-Laferrière A, Túrós D, Voith von Voithenberg L, Wells I, Pesti B, Lo AA, Yángüez E, Das Thakur M, Bscheider M, Sultan M, Kumpesa N, Jacobsen B, Bergauer T, Saez-Rodriguez J, Rottenberg S, Schwalie PC, Hahn K. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. NPJ Precis Oncol 2024; 8:10. [PMID: 38200223 PMCID: PMC10781769 DOI: 10.1038/s41698-023-00488-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 12/04/2023] [Indexed: 01/12/2024] Open
Abstract
The consensus molecular subtypes (CMS) of colorectal cancer (CRC) is the most widely-used gene expression-based classification and has contributed to a better understanding of disease heterogeneity and prognosis. Nevertheless, CMS intratumoral heterogeneity restricts its clinical application, stressing the necessity of further characterizing the composition and architecture of CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) to decipher the spatially resolved cellular and molecular composition of CRC. In addition to mapping the intratumoral heterogeneity of CMS and their microenvironment, we identified cell communication events in the tumor-stroma interface of CMS2 carcinomas. This includes tumor growth-inhibiting as well as -activating signals, such as the potential regulation of the ETV4 transcriptional activity by DCN or the PLAU-PLAUR ligand-receptor interaction. Our study illustrates the potential of ST to resolve CRC molecular heterogeneity and thereby help advance personalized therapy.
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Affiliation(s)
- Alberto Valdeolivas
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
| | - Bettina Amberg
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Nicolas Giroud
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Marion Richardson
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Eric J C Gálvez
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Solveig Badillo
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Alice Julien-Laferrière
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Demeter Túrós
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | | | - Isabelle Wells
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Benedek Pesti
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Amy A Lo
- Genentech, Inc, San Francisco, CA, USA
| | - Emilio Yángüez
- Roche Pharma Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland
| | | | - Michael Bscheider
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Marc Sultan
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Nadine Kumpesa
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Björn Jacobsen
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Tobias Bergauer
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Julio Saez-Rodriguez
- Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany
| | - Sven Rottenberg
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
- Bern Center for Precision Medicine (BCPM), University of Bern, Bern, Switzerland
| | - Petra C Schwalie
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Kerstin Hahn
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
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21
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Hao M, Luo E, Chen Y, Wu Y, Li C, Chen S, Gao H, Bian H, Gu J, Wei L, Zhang X. STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning. Commun Biol 2024; 7:56. [PMID: 38184694 PMCID: PMC10771471 DOI: 10.1038/s42003-023-05640-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/27/2023] [Indexed: 01/08/2024] Open
Abstract
Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. Single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We develop the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer SC-ST mapping and predict pseudo-spatial adjacency between cells in SC data. Semi-simulation and real data experiments verify that the embeddings preserved spatial information and eliminated technical biases between SC and ST data. We apply STEM to human squamous cell carcinoma and hepatic lobule datasets to uncover the localization of rare cell types and reveal cell-type-specific gene expression variation along a spatial axis. STEM is powerful for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.
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Affiliation(s)
- Minsheng Hao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Erpai Luo
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Yixin Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Yanhong Wu
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Chen Li
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Sijie Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Haoxiang Gao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Haiyang Bian
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Jin Gu
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Lei Wei
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
- School of Life Sciences and School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.
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22
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Pozniak J, Pedri D, Landeloos E, Van Herck Y, Antoranz A, Vanwynsberghe L, Nowosad A, Roda N, Makhzami S, Bervoets G, Maciel LF, Pulido-Vicuña CA, Pollaris L, Seurinck R, Zhao F, Flem-Karlsen K, Damsky W, Chen L, Karagianni D, Cinque S, Kint S, Vandereyken K, Rombaut B, Voet T, Vernaillen F, Annaert W, Lambrechts D, Boecxstaens V, Saeys Y, van den Oord J, Bosisio F, Karras P, Shain AH, Bosenberg M, Leucci E, Paschen A, Rambow F, Bechter O, Marine JC. A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma. Cell 2024; 187:166-183.e25. [PMID: 38181739 DOI: 10.1016/j.cell.2023.11.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 08/23/2023] [Accepted: 11/29/2023] [Indexed: 01/07/2024]
Abstract
To better understand intrinsic resistance to immune checkpoint blockade (ICB), we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem and studied its evolution under ICB. Using single-cell, spatial multi-omics, we showed that the tumor microenvironment promotes the emergence of a complex melanoma transcriptomic landscape. Melanoma cells harboring a mesenchymal-like (MES) state, a population known to confer resistance to targeted therapy, were significantly enriched in early on-treatment biopsies from non-responders to ICB. TCF4 serves as the hub of this landscape by being a master regulator of the MES signature and a suppressor of the melanocytic and antigen presentation transcriptional programs. Targeting TCF4 genetically or pharmacologically, using a bromodomain inhibitor, increased immunogenicity and sensitivity of MES cells to ICB and targeted therapy. We thereby uncovered a TCF4-dependent regulatory network that orchestrates multiple transcriptional programs and contributes to resistance to both targeted therapy and ICB in melanoma.
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Affiliation(s)
- Joanna Pozniak
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium.
| | - Dennis Pedri
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium; Laboratory for Membrane Trafficking, Center for Brain and Disease Research, VIB, Leuven, Belgium
| | - Ewout Landeloos
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium; Department of General Medical Oncology, UZ Leuven, Leuven, Belgium
| | | | - Asier Antoranz
- Laboratory of Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven and UZ Leuven, Leuven, Belgium
| | - Lukas Vanwynsberghe
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ada Nowosad
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Niccolò Roda
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Samira Makhzami
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Greet Bervoets
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Lucas Ferreira Maciel
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Carlos Ariel Pulido-Vicuña
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Lotte Pollaris
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Ruth Seurinck
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Fang Zhao
- Laboratory of Molecular Tumor Immunology, Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Essen, Germany
| | - Karine Flem-Karlsen
- Department of Dermatology, Yale University, 15 York Street, New Haven, CT 05610, USA
| | - William Damsky
- Departments of Dermatology and Pathology, Yale University, 15 York Street, New Haven, CT 05610, USA
| | - Limin Chen
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Despoina Karagianni
- Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London WC1E 6DD, UK
| | - Sonia Cinque
- Laboratory for RNA Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Sam Kint
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | - Katy Vandereyken
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | - Benjamin Rombaut
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Thierry Voet
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | | | - Wim Annaert
- Laboratory for Membrane Trafficking, Center for Brain and Disease Research, VIB, Leuven, Belgium
| | - Diether Lambrechts
- Laboratory of Translational Genetics, Center for Cancer Biology, VIB, Leuven, Belgium; Center for Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Yvan Saeys
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Joost van den Oord
- Laboratory of Translational Cell and Tissue Research, Department of Pathology, UZ Leuven, Leuven, Belgium
| | - Francesca Bosisio
- Laboratory of Translational Cell and Tissue Research, Department of Pathology, UZ Leuven, Leuven, Belgium
| | - Panagiotis Karras
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - A Hunter Shain
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Marcus Bosenberg
- Departments of Dermatology, Pathology and Immunobiology, Yale University, New Haven, CT 05610, USA
| | - Eleonora Leucci
- Laboratory for RNA Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Annette Paschen
- Laboratory of Molecular Tumor Immunology, Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Essen, Germany
| | - Florian Rambow
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium; Department of Applied Computational Cancer Research, Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany; University Duisburg-Essen, Essen, Germany.
| | - Oliver Bechter
- Department of General Medical Oncology, UZ Leuven, Leuven, Belgium.
| | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium.
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23
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Zhang D, Schroeder A, Yan H, Yang H, Hu J, Lee MYY, Cho KS, Susztak K, Xu GX, Feldman MD, Lee EB, Furth EE, Wang L, Li M. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat Biotechnol 2024:10.1038/s41587-023-02019-9. [PMID: 38168986 DOI: 10.1038/s41587-023-02019-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/04/2023] [Indexed: 01/05/2024]
Abstract
Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.
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Affiliation(s)
- Daiwei Zhang
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Amelia Schroeder
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hanying Yan
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochen Yang
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jian Hu
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Michelle Y Y Lee
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kyung S Cho
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - George X Xu
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael D Feldman
- Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emma E Furth
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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24
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Rodríguez-Bejarano OH, Roa L, Vargas-Hernández G, Botero-Espinosa L, Parra-López C, Patarroyo MA. Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate. Biochim Biophys Acta Rev Cancer 2024; 1879:189064. [PMID: 38158026 DOI: 10.1016/j.bbcan.2023.189064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]
Abstract
The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.
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Affiliation(s)
- Oscar Hernán Rodríguez-Bejarano
- Health Sciences Faculty, Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A), Calle 222#55-37, Bogotá 111166, Colombia; Molecular Biology and Immunology Department, Fundacion Instituto de Inmunología de Colombia (FIDIC), Carrera 50#26-20, Bogotá 111321, Colombia; PhD Programme in Biotechnology, Faculty of Sciences, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Leonardo Roa
- Veterinary Clinic, Faculty of Agricultural Sciences, Universidad de La Salle, Carrera 7 #179-03, Bogotá 110141, Colombia
| | - Giovanni Vargas-Hernández
- Animal Health Department, Faculty of Veterinary Medicine and Zootechnics, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Lucía Botero-Espinosa
- Animal Health Department, Faculty of Veterinary Medicine and Zootechnics, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Carlos Parra-López
- Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia.
| | - Manuel Alfonso Patarroyo
- Molecular Biology and Immunology Department, Fundacion Instituto de Inmunología de Colombia (FIDIC), Carrera 50#26-20, Bogotá 111321, Colombia; Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia.
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25
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Xiao X, Kong Y, Li R, Wang Z, Lu H. Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image. Med Image Anal 2024; 91:103040. [PMID: 38007979 DOI: 10.1016/j.media.2023.103040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 11/04/2023] [Accepted: 11/17/2023] [Indexed: 11/28/2023]
Abstract
Inferring gene expressions from histopathological images has long been a fascinating yet challenging task, primarily due to the substantial disparities between the two modality. Existing strategies using local or global features of histological images are suffering model complexity, GPU consumption, low interpretability, insufficient encoding of local features, and over-smooth prediction of gene expressions among neighboring sites. In this paper, we develop TCGN (Transformer with Convolution and Graph-Node co-embedding method) for gene expression estimation from H&E-stained pathological slide images. TCGN comprises a combination of convolutional layers, transformer encoders, and graph neural networks, and is the first to integrate these blocks in a general and interpretable computer vision backbone. Notably, TCGN uniquely operates with just a single spot image as input for histopathological image analysis, simplifying the process while maintaining interpretability. We validate TCGN on three publicly available spatial transcriptomic datasets. TCGN consistently exhibited the best performance (with median PCC 0.232). TCGN offers superior accuracy while keeping parameters to a minimum (just 86.241 million), and it consumes minimal memory, allowing it to run smoothly even on personal computers. Moreover, TCGN can be extended to handle bulk RNA-seq data while providing the interpretability. Enhancing the accuracy of omics information prediction from pathological images not only establishes a connection between genotype and phenotype, enabling the prediction of costly-to-measure biomarkers from affordable histopathological images, but also lays the groundwork for future multi-modal data modeling. Our results confirm that TCGN is a powerful tool for inferring gene expressions from histopathological images in precision health applications.
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Affiliation(s)
- Xiao Xiao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China; Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, United States
| | - Yan Kong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Ronghan Li
- SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China; Zhiyuan College, Shanghai Jiao Tong University, Shanghai, China
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, United States
| | - Hui Lu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China; NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai, China.
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26
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Alhaddad H, Ospina OE, Khaled ML, Ren Y, Forsyth P, Pina Y, Macaulay R, Law V, Tsai KY, Cress WD, Fridley B, Smalley I. Spatial transcriptomics analysis identifies a unique tumor-promoting function of the meningeal stroma in melanoma leptomeningeal disease. bioRxiv 2023:2023.12.18.572266. [PMID: 38187574 PMCID: PMC10769278 DOI: 10.1101/2023.12.18.572266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Leptomeningeal disease (LMD) remains a rapidly lethal complication for late-stage melanoma patients. The inaccessible nature of the disease site and lack of understanding of the biology of this unique metastatic site are major barriers to developing efficacious therapies for patients with melanoma LMD. Here, we characterize the tumor microenvironment of the leptomeningeal tissues and patient-matched extra-cranial metastatic sites using spatial transcriptomic analyses with in vitro and in vivo validation. We show the spatial landscape of melanoma LMD to be characterized by a lack of immune infiltration and instead exhibit a higher level of stromal involvement. We show that the tumor-stroma interactions at the leptomeninges activate pathways implicated in tumor-promoting signaling, mediated through upregulation of SERPINA3 at the tumor-stroma interface. Our functional experiments establish that the meningeal stroma is required for melanoma cells to survive in the CSF environment and that these interactions lead to a lack of MAPK inhibitor sensitivity in the tumor. We show that knocking down SERPINA3 or inhibiting the downstream IGR1R/PI3K/AKT axis results in re-sensitization of the tumor to MAPK-targeting therapy and tumor cell death in the leptomeningeal environment. Our data provides a spatial atlas of melanoma LMD, identifies the tumor-promoting role of meningeal stroma, and demonstrates a mechanism for overcoming microenvironment-mediated drug resistance unique to this metastatic site.
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Affiliation(s)
- Hasan Alhaddad
- Department of Metabolism and Physiology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Oscar E. Ospina
- Department of Biostatistics and Bioinformatics at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Mariam Lotfy Khaled
- Department of Metabolism and Physiology at the Moffitt Cancer Center, Tampa, Florida, USA
- Department of Biochemistry, Faculty of Pharmacy, Cairo University, Egypt
| | - Yuan Ren
- Department of Metabolism and Physiology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Peter Forsyth
- Department of Tumor Biology at the Moffitt Cancer Center, Tampa, Florida, USA
- Department of NeuroOncology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Yolanda Pina
- Department of NeuroOncology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Robert Macaulay
- Department of Pathology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Vincent Law
- Department of Tumor Biology at the Moffitt Cancer Center, Tampa, Florida, USA
- Department of NeuroOncology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Kenneth Y. Tsai
- Department of Pathology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - W Douglas Cress
- Department of Molecular Oncology at the Moffitt Cancer Center, Tampa, Florida, USA
| | - Brooke Fridley
- Department of Biostatistics and Bioinformatics at the Moffitt Cancer Center, Tampa, Florida, USA
- Division of Health Services & Outcomes Research, Children’s Mercy Hospital, Kansas City, MO 64108
| | - Inna Smalley
- Department of Metabolism and Physiology at the Moffitt Cancer Center, Tampa, Florida, USA
- Department of Cutaneous Oncology at the Moffitt Cancer Center, Tampa, Florida, USA
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27
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Engblom C, Thrane K, Lin Q, Andersson A, Toosi H, Chen X, Steiner E, Lu C, Mantovani G, Hagemann-Jensen M, Saarenpää S, Jangard M, Saez-Rodriguez J, Michaëlsson J, Hartman J, Lagergren J, Mold JE, Lundeberg J, Frisén J. Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics. Science 2023; 382:eadf8486. [PMID: 38060664 DOI: 10.1126/science.adf8486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 10/23/2023] [Indexed: 12/18/2023]
Abstract
The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed spatial transcriptomics of variable, diversity, and joining (VDJ) sequences (Spatial VDJ), a method that maps B cell and T cell receptor sequences in human tissue sections. Spatial VDJ captures lymphocyte clones that match canonical B and T cell distributions and amplifies clonal sequences confirmed by orthogonal methods. We found spatial congruency between paired receptor chains, developed a computational framework to predict receptor pairs, and linked the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity and lineage trajectories within their anatomical niche. Thus, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, providing a platform to harness clonal sequences for therapy.
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Affiliation(s)
- Camilla Engblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Kim Thrane
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Qirong Lin
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Alma Andersson
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Hosein Toosi
- SciLifeLab, Computational Science and Technology department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xinsong Chen
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Embla Steiner
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Chang Lu
- Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Giulia Mantovani
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | | | - Sami Saarenpää
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Mattias Jangard
- ENT Unit, Sophiahemmet University Research Laboratory and Sophiahemmet Hospital, Stockholm, Sweden
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Jakob Michaëlsson
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Johan Hartman
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Jens Lagergren
- SciLifeLab, Computational Science and Technology department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Joakim Lundeberg
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jonas Frisén
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
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28
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Li Z, Patel ZM, Song D, Yan G, Li JJ, Pinello L. Benchmarking computational methods to identify spatially variable genes and peaks. bioRxiv 2023:2023.12.02.569717. [PMID: 38076922 PMCID: PMC10705556 DOI: 10.1101/2023.12.02.569717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran's I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.
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Affiliation(s)
- Zhijian Li
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Zain M. Patel
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Dongyuan Song
- Interdepartmental Program of Bioinformatics, University of California, Los Angeles, CA, USA
| | - Guanao Yan
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Luca Pinello
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
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29
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Sheikh E, Agrawal K, Roy S, Burk D, Donnarumma F, Ko YH, Guttula PK, Biswal NC, Shukla HD, Gartia MR. Multimodal Imaging of Pancreatic Cancer Microenvironment in Response to an Antiglycolytic Drug. Adv Healthc Mater 2023; 12:e2301815. [PMID: 37706285 PMCID: PMC10842640 DOI: 10.1002/adhm.202301815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Indexed: 09/15/2023]
Abstract
Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3-bromopyruvate (3-BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor-associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody-functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface-enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC-MS) analysis. The study reveals the efficacy of 3-BP in treating pancreatic cancer and identifies drug treatment-induced lipid species remodeling and associated pathways through bioinformatics analysis.
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Affiliation(s)
- Elnaz Sheikh
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Kirti Agrawal
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Sanjit Roy
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - David Burk
- Department of Cell Biology and Bioimaging, Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA
| | - Fabrizio Donnarumma
- Department of Chemistry, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Young H Ko
- NewG Lab Pharma, 701 East Pratt Street, Columbus Center, Baltimore, MD, 21202, USA
| | - Praveen Kumar Guttula
- Sprott Center for Stem Cell Research, Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON, K1H 8L6, Canada
- Department of Biochemistry, Microbiology, and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, K1H 8M5, Canada
| | - Nrusingh C Biswal
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Hem D Shukla
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Manas Ranjan Gartia
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
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30
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Toninelli M, Rossetti G, Pagani M. Charting the tumor microenvironment with spatial profiling technologies. Trends Cancer 2023; 9:1085-1096. [PMID: 37673713 DOI: 10.1016/j.trecan.2023.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 09/08/2023]
Abstract
In recent years technologies that can achieve readouts at cellular resolution such as single-cell RNA sequencing (scRNA-seq) have provided a comprehensive characterization of the cellular proportions and phenotypes that populate the tumor microenvironment (TME). However, because of the sample dissociation steps required by these protocols, they fail to capture information related to the intricate spatial context in which cells operate as well as their dense networks of interactions. Spatial profiling technologies have recently emerged as a valuable way to investigate the physical organization of cells crowding the TME in intact tissues. In this review we first discuss how spatial profiling technologies have propelled TME characterization, and then explore their potential to improve both diagnosis and prognosis for cancer patients in the clinic.
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Affiliation(s)
- Mattia Toninelli
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Grazisa Rossetti
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Massimiliano Pagani
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy; Department of Medical Biotechnology and Translational Medicine (BIOMETRA), Università degli Studi, Milan, Italy.
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31
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Jia Y, Liu J, Chen L, Zhao T, Wang Y. THItoGene: a deep learning method for predicting spatial transcriptomics from histological images. Brief Bioinform 2023; 25:bbad464. [PMID: 38145948 PMCID: PMC10749789 DOI: 10.1093/bib/bbad464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/10/2023] [Accepted: 11/18/2023] [Indexed: 12/27/2023] Open
Abstract
Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https://github.com/yrjia1015/THItoGene.
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Affiliation(s)
- Yuran Jia
- Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150040, China
| | - Junliang Liu
- Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150040, China
| | - Li Chen
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Tianyi Zhao
- School of Medicine and Health, Harbin Institute of Technology, Harbin, 150040, China
| | - Yadong Wang
- School of Medicine and Health, Harbin Institute of Technology, Harbin, 150040, China
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32
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Tippani M, Divecha HR, Catallini JL, Kwon SH, Weber LM, Spangler A, Jaffe AE, Hyde TM, Kleinman JE, Hicks SC, Martinowich K, Collado-Torres L, Page SC, Maynard KR. VistoSeg: Processing utilities for high-resolution images for spatially resolved transcriptomics data. Biol Imaging 2023; 3:e23. [PMID: 38510173 PMCID: PMC10951916 DOI: 10.1017/s2633903x23000235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/14/2023] [Accepted: 09/19/2023] [Indexed: 03/22/2024]
Abstract
Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.
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Affiliation(s)
- Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Heena R. Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Joseph L. Catallini
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang H. Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Lukas M. Weber
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Andrew E. Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Stephanie C. Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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33
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Robles-Remacho A, Sanchez-Martin RM, Diaz-Mochon JJ. Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling. Anal Chem 2023; 95:15450-15460. [PMID: 37814884 PMCID: PMC10603609 DOI: 10.1021/acs.analchem.3c02029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/21/2023] [Indexed: 10/11/2023]
Abstract
In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, in situ sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.
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Affiliation(s)
- Agustín Robles-Remacho
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Rosario M. Sanchez-Martin
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Juan J. Diaz-Mochon
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
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Abstract
Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial structure through coexpression hotspots-regions exhibiting localized spatial coexpression of some set of genes. Coexpression hotspots identify structure on any spatial scale, over any possible subset of genes, and are highly explainable. NeST also performs spatial analysis of cell-cell interactions via ligand-receptor, identifying active areas de novo without restriction of cell type or other groupings, in both two and three dimensions. Through application on ST datasets of varying type and resolution, we demonstrate the ability of NeST to reveal a new level of biological structure.
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Affiliation(s)
- Benjamin L Walker
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA
| | - Qing Nie
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, 92627, USA.
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35
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Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol 2023; 95:25-41. [PMID: 37400044 DOI: 10.1016/j.semcancer.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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Wang Q, Zhi Y, Zi M, Mo Y, Wang Y, Liao Q, Zhang S, Gong Z, Wang F, Zeng Z, Guo C, Xiong W. Spatially Resolved Transcriptomics Technology Facilitates Cancer Research. Adv Sci (Weinh) 2023; 10:e2302558. [PMID: 37632718 PMCID: PMC10602551 DOI: 10.1002/advs.202302558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/16/2023] [Indexed: 08/28/2023]
Abstract
Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.
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Affiliation(s)
- Qian Wang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Yuan Zhi
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Moxin Zi
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Yongzhen Mo
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Qianjin Liao
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
| | - Shanshan Zhang
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Zhaojian Gong
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Fuyan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Zhaoyang Zeng
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Can Guo
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
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Riley-Gillis B, Tsaih SW, King E, Wollenhaupt S, Reeb J, Peck AR, Wackman K, Lemke A, Rui H, Dezso Z, Flister MJ. Machine learning reveals genetic modifiers of the immune microenvironment of cancer. iScience 2023; 26:107576. [PMID: 37664640 PMCID: PMC10470213 DOI: 10.1016/j.isci.2023.107576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/01/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.
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Affiliation(s)
- Bridget Riley-Gillis
- Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA
| | - Shirng-Wern Tsaih
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Emily King
- Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA
| | - Sabrina Wollenhaupt
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Jonas Reeb
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Amy R. Peck
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Kelsey Wackman
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Angela Lemke
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Hallgeir Rui
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Zoltan Dezso
- Genomics Research Center, AbbVie Bay Area, 1000 Gateway Boulevard, South San Francisco, CA 94080, USA
| | - Michael J. Flister
- Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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38
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Du J, An ZJ, Huang ZF, Yang YC, Zhang MH, Fu XH, Shi WY, Hou J. Novel insights from spatial transcriptome analysis in solid tumors. Int J Biol Sci 2023; 19:4778-4792. [PMID: 37781515 PMCID: PMC10539699 DOI: 10.7150/ijbs.83098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/03/2023] [Indexed: 10/03/2023] Open
Abstract
Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.
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Affiliation(s)
- Jun Du
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China
| | - Zhi-Jie An
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Zou-Fang Huang
- Ganzhou Key Laboratory of Hematology, Department of Hematology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, 341000, China
| | - Yu-Chen Yang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Ming-Hui Zhang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Xue-Hang Fu
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China
| | - Wei-Yang Shi
- Ministry of Education Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Jian Hou
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China
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Halawani R, Buchert M, Chen YPP. Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity. Comput Biol Med 2023; 164:107274. [PMID: 37506451 DOI: 10.1016/j.compbiomed.2023.107274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 07/03/2023] [Accepted: 07/16/2023] [Indexed: 07/30/2023]
Abstract
Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.
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Affiliation(s)
- Raid Halawani
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
| | - Michael Buchert
- School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia; Olivia Newton-John Cancer Research Institute, Melbourne, Victoria, Australia
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.
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Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Justet A, Adams TS, Homer R, Amei A, Rosas IO, Kaminski N, Wang Z, Yan X. A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data. bioRxiv 2023:2023.08.24.554722. [PMID: 37662370 PMCID: PMC10473707 DOI: 10.1101/2023.08.24.554722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER uses a machine learning approach to remove the systematic difference between ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile. It also considers sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. Based on the estimated cell type proportions, SDePER imputes cell type compositions and gene expression at unmeasured locations in a tissue map with enhanced resolution. Applications to coarse-grained simulated data and four real datasets showed that SDePER achieved more accurate and robust results than existing methods, suggesting the importance of considering platform effects, sparsity and spatial correlation in cell type deconvolution.
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41
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Orsini A, Diquigiovanni C, Bonora E. Omics Technologies Improving Breast Cancer Research and Diagnostics. Int J Mol Sci 2023; 24:12690. [PMID: 37628869 PMCID: PMC10454385 DOI: 10.3390/ijms241612690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.
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Affiliation(s)
| | - Chiara Diquigiovanni
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40131 Bologna, Italy; (A.O.); (E.B.)
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42
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Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J, Wang L, Yang X, Yang M, Liu G. Applications of multi-omics analysis in human diseases. MedComm (Beijing) 2023; 4:e315. [PMID: 37533767 PMCID: PMC10390758 DOI: 10.1002/mco2.315] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 08/04/2023] Open
Abstract
Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.
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Affiliation(s)
- Chongyang Chen
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
| | - Jing Wang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Donghui Pan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xinyu Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Yuping Xu
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Junjie Yan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Lizhen Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xifei Yang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Min Yang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Gong‐Ping Liu
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
- Department of PathophysiologySchool of Basic MedicineKey Laboratory of Ministry of Education of China and Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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Thakur S, Haider S, Natrajan R. Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer. J Pathol 2023; 260:621-636. [PMID: 37587096 DOI: 10.1002/path.6158] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 08/18/2023]
Abstract
Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Shefali Thakur
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
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Gong S, Sun N, Meyer LS, Tetti M, Koupourtidou C, Krebs S, Masserdotti G, Blum H, Rainey WE, Reincke M, Walch A, Williams TA. Primary Aldosteronism: Spatial Multiomics Mapping of Genotype-Dependent Heterogeneity and Tumor Expansion of Aldosterone-Producing Adenomas. Hypertension 2023; 80:1555-1567. [PMID: 37125608 PMCID: PMC10330203 DOI: 10.1161/hypertensionaha.123.20921] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/10/2023] [Indexed: 05/02/2023]
Abstract
BACKGROUND Primary aldosteronism is frequently caused by an adrenocortical aldosterone-producing adenoma (APA) carrying a somatic mutation that drives aldosterone overproduction. APAs with a mutation in KCNJ5 (APA-KCNJ5MUT) are characterized by heterogeneous CYP11B2 (aldosterone synthase) expression, a particular cellular composition and larger tumor diameter than those with wild-type KCNJ5 (APA-KCNJ5WT). We exploited these differences to decipher the roles of transcriptome and metabolome reprogramming in tumor pathogenesis. METHODS Consecutive adrenal cryosections (7 APAs and 7 paired adjacent adrenal cortex) were analyzed by spatial transcriptomics (10x Genomics platform) and metabolomics (in situ matrix-assisted laser desorption/ionization mass spectrometry imaging) co-integrated with CYP11B2 immunohistochemistry. RESULTS We identified intratumoral transcriptional heterogeneity that delineated functionally distinct biological pathways. Common transcriptomic signatures were established across all APA specimens which encompassed 2 distinct transcriptional profiles in CYP11B2-immunopositive regions (CYP11B2-type 1 or 2). The CYP11B2-type 1 signature was characterized by zona glomerulosa gene markers and was detected in both APA-KCNJ5MUT and APA-KCNJ5WT. The CYP11B2-type 2 signature displayed markers of the zona fasciculata or reticularis and predominated in APA-KCNJ5MUT. Metabolites that promote oxidative stress and cell death accumulated in APA-KCNJ5WT. In contrast, antioxidant metabolites were abundant in APA-KCNJ5MUT. Finally, APA-like cell subpopulations-negative for CYP11B2 gene expression-were identified in adrenocortical tissue adjacent to APAs suggesting the existence of tumor precursor states. CONCLUSIONS Our findings provide insight into intra- and intertumoral transcriptional heterogeneity and support a role for prooxidant versus antioxidant systems in APA pathogenesis highlighting genotype-dependent capacities for tumor expansion.
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Affiliation(s)
- Siyuan Gong
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU München, München, Germany
| | - Na Sun
- Research Unit Analytical Pathology, German Research Center for Environmental Health, Helmholtz Zentrum München, Germany
| | - Lucie S Meyer
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU München, München, Germany
| | - Martina Tetti
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU München, München, Germany
| | - Christina Koupourtidou
- Department for Cell Biology and Anatomy, Biomedical Center, Ludwig-Maximilians-Universität (LMU), Planegg-Martinsried, Germany
- Graduate School Systemic Neurosciences, Ludwig-Maximilians-Universität (LMU), Planegg-Martinsried, Germany
| | - Stefan Krebs
- Laboratory for Functional Genome Analysis, Gene Center, LMU Munich, 81377 Munich, Germany
| | - Giacomo Masserdotti
- Institute of Stem Cell Research, Helmholtz Center Munich, Neuherberg, Germany
- Physiological Genomics, Biomedical Center (BMC), Ludwig-Maximilians-Universität (LMU), Planegg-Martinsried, Germany
| | - Helmut Blum
- Laboratory for Functional Genome Analysis, Gene Center, LMU Munich, 81377 Munich, Germany
| | - William E. Rainey
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
- Division of Metabolism, Endocrine, and Diabetes, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Martin Reincke
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU München, München, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, German Research Center for Environmental Health, Helmholtz Zentrum München, Germany
| | - Tracy Ann Williams
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU München, München, Germany
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Chu Y, Dai E, Li Y, Han G, Pei G, Ingram DR, Thakkar K, Qin JJ, Dang M, Le X, Hu C, Deng Q, Sinjab A, Gupta P, Wang R, Hao D, Peng F, Yan X, Liu Y, Song S, Zhang S, Heymach JV, Reuben A, Elamin YY, Pizzi MP, Lu Y, Lazcano R, Hu J, Li M, Curran M, Futreal A, Maitra A, Jazaeri AA, Ajani JA, Swanton C, Cheng XD, Abbas HA, Gillison M, Bhat K, Lazar AJ, Green M, Litchfield K, Kadara H, Yee C, Wang L. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. Nat Med 2023; 29:1550-1562. [PMID: 37248301 DOI: 10.1038/s41591-023-02371-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, TSTR, characterized by heat shock gene expression. TSTR cells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene expression in intratumoral CD4/CD8+ cells following immune checkpoint blockade treatment, particularly in nonresponsive tumors, suggesting a potential role of TSTR cells in immunotherapy resistance. Our well-annotated T cell reference maps, web portal and automatic alignment/annotation tool could provide valuable resources for T cell therapy optimization and biomarker discovery.
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Affiliation(s)
- Yanshuo Chu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Enyu Dai
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yating Li
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Guangchun Han
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Guangsheng Pei
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Davis R Ingram
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Krupa Thakkar
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK
| | - Jiang-Jiang Qin
- Department of Gastric Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | - Minghao Dang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Can Hu
- Department of Gastric Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | - Qing Deng
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ansam Sinjab
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pravesh Gupta
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ruiping Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dapeng Hao
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fuduan Peng
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinmiao Yan
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shumei Song
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shaojun Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yasir Y Elamin
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Melissa P Pizzi
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yang Lu
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rossana Lazcano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jian Hu
- Department of Human Genetics, Emory School of Medicine, Atlanta, GA, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Curran
- Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anirban Maitra
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amir A Jazaeri
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jaffer A Ajani
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Xiang-Dong Cheng
- Department of Gastric Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | - Hussein A Abbas
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Maura Gillison
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Krishna Bhat
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexander J Lazar
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Michael Green
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kevin Litchfield
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Cassian Yee
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
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Du J, Yang YC, An ZJ, Zhang MH, Fu XH, Huang ZF, Yuan Y, Hou J. Advances in spatial transcriptomics and related data analysis strategies. J Transl Med 2023; 21:330. [PMID: 37202762 PMCID: PMC10193345 DOI: 10.1186/s12967-023-04150-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/25/2023] [Indexed: 05/20/2023] Open
Abstract
Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.
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Affiliation(s)
- Jun Du
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
| | - Yu-Chen Yang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Zhi-Jie An
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Ming-Hui Zhang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Xue-Hang Fu
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
| | - Zou-Fang Huang
- Ganzhou Key Laboratory of Hematology, Department of Hematology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000 Jiangxi China
| | - Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240 China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240 China
| | - Jian Hou
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
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47
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Sun C, Wang A, Zhou Y, Chen P, Wang X, Huang J, Gao J, Wang X, Shu L, Lu J, Dai W, Bu Z, Ji J, He J. Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer. Nat Commun 2023; 14:2692. [PMID: 37164975 PMCID: PMC10172194 DOI: 10.1038/s41467-023-38360-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 04/27/2023] [Indexed: 05/12/2023] Open
Abstract
Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated "tumor-normal interface" region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level.
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Affiliation(s)
- Chenglong Sun
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Anqiang Wang
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yanhe Zhou
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Panpan Chen
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Xiangyi Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jianpeng Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jiamin Gao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xiao Wang
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Liebo Shu
- Shanghai Luming Biological Technology co.Ltd, Shanghai, 201102, China
| | - Jiawei Lu
- Shanghai Luming Biological Technology co.Ltd, Shanghai, 201102, China
| | - Wentao Dai
- NHC Key Lab of Reproduction Regulation (Shanghai Institute for Biomedical and Pharmaceutical Technologies) & Shanghai Engineering Research Center of Pharmaceutical Translation, Fudan University, Shanghai, 200080, China.
- Shanghai Key Laboratory of Gastric Neoplasms, Department of General Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Zhaode Bu
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, 100142, China.
| | - Jiafu Ji
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, 100142, China.
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
- NMPA Key Laboratory of safety research and evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
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48
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Hu J, Coleman K, Zhang D, Lee EB, Kadara H, Wang L, Li M. Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA. Cell Syst 2023; 14:404-417.e4. [PMID: 37164011 DOI: 10.1016/j.cels.2023.03.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/06/2023] [Accepted: 03/30/2023] [Indexed: 05/12/2023]
Abstract
Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.
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Affiliation(s)
- Jian Hu
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
| | - Kyle Coleman
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daiwei Zhang
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences (GSBS), Houston, TX 77030, USA.
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Villemin JP, Bassaganyas L, Pourquier D, Boissière F, Cabello-Aguilar S, Crapez E, Tanos R, Cornillot E, Turtoi A, Colinge J. Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR. Nucleic Acids Res 2023:7152875. [PMID: 37144485 DOI: 10.1093/nar/gkad352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/24/2023] [Accepted: 04/22/2023] [Indexed: 05/06/2023] Open
Abstract
The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.
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Affiliation(s)
- Jean-Philippe Villemin
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Inserm U 1194, Montpellier, France
- Université de Montpellier, Montpellier, France
- Institut régional du Cancer Montpellier (ICM), Montpellier, France
| | - Laia Bassaganyas
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Inserm U 1194, Montpellier, France
- Université de Montpellier, Montpellier, France
- Institut régional du Cancer Montpellier (ICM), Montpellier, France
| | - Didier Pourquier
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Inserm U 1194, Montpellier, France
- Institut régional du Cancer Montpellier (ICM), Montpellier, France
| | | | - Simon Cabello-Aguilar
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Inserm U 1194, Montpellier, France
- Université de Montpellier, Montpellier, France
- Institut régional du Cancer Montpellier (ICM), Montpellier, France
| | - Evelyne Crapez
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Inserm U 1194, Montpellier, France
- Institut régional du Cancer Montpellier (ICM), Montpellier, France
| | - Rita Tanos
- Institut régional du Cancer Montpellier (ICM), Montpellier, France
| | - Emmanuel Cornillot
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Inserm U 1194, Montpellier, France
- Université de Montpellier, Montpellier, France
- Institut régional du Cancer Montpellier (ICM), Montpellier, France
- Faculté de Pharmacie, Université de Montpellier, Montpellier, France
| | - Andrei Turtoi
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Inserm U 1194, Montpellier, France
- Université de Montpellier, Montpellier, France
- Institut régional du Cancer Montpellier (ICM), Montpellier, France
| | - Jacques Colinge
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Inserm U 1194, Montpellier, France
- Université de Montpellier, Montpellier, France
- Institut régional du Cancer Montpellier (ICM), Montpellier, France
- Faculté de Médecine, Université de Montpellier, Montpellier, France
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50
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Qian J, Liao J, Liu Z, Chi Y, Fang Y, Zheng Y, Shao X, Liu B, Cui Y, Guo W, Hu Y, Bao H, Yang P, Chen Q, Li M, Zhang B, Fan X. Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace. Nat Commun 2023; 14:2484. [PMID: 37120608 PMCID: PMC10148590 DOI: 10.1038/s41467-023-38121-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 04/17/2023] [Indexed: 05/01/2023] Open
Abstract
Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.
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Affiliation(s)
- Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China.
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China.
| | - Ziqi Liu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Ying Chi
- DAMO Academy, Alibaba group, 310052, Hangzhou, China
| | - Yin Fang
- College of Computer Science and Technology, Zhejiang University, 310013, Hangzhou, China
| | - Yanrong Zheng
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 310053, Hangzhou, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, 310006, Hangzhou, China
| | - Bingqi Liu
- School of Mathematical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Yongjin Cui
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Wenbo Guo
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Yining Hu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Hudong Bao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Penghui Yang
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Qian Chen
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Mingxiao Li
- Institute of Microelectronics of the Chinese Academy of Sciences, 100029, Beijing, China
| | - Bing Zhang
- DAMO Academy, Alibaba group, 310052, Hangzhou, China.
- iMedicine Lab, Alibaba-Zhejiang University Joint Research Center for Future Digital Healthcare, 310058, Hangzhou, China.
- Alibaba Cloud, Alibaba Group, 310052, Hangzhou, China.
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China.
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China.
- iMedicine Lab, Alibaba-Zhejiang University Joint Research Center for Future Digital Healthcare, 310058, Hangzhou, China.
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