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Cao J, Li C, Cui Z, Deng S, Lei T, Liu W, Yang H, Chen P. Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery. Theranostics 2024; 14:2946-2968. [PMID: 38773973 PMCID: PMC11103497 DOI: 10.7150/thno.95908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/25/2024] [Indexed: 05/24/2024] Open
Abstract
Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.
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Affiliation(s)
- Junxian Cao
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Caifeng Li
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhao Cui
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Shiwen Deng
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Tong Lei
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Wei Liu
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongjun Yang
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Peng Chen
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
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Wang S, Wang X, Shan Y, Tan Z, Su Y, Cao Y, Wang S, Dong J, Gu J, Wang Y. Region-specific cellular and molecular basis of liver regeneration after acute pericentral injury. Cell Stem Cell 2024; 31:341-358.e7. [PMID: 38402618 DOI: 10.1016/j.stem.2024.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/08/2023] [Accepted: 01/30/2024] [Indexed: 02/27/2024]
Abstract
Liver injuries often occur in a zonated manner. However, detailed regenerative responses to such zonal injuries at cellular and molecular levels remain largely elusive. By using a fate-mapping strain, Cyp2e1-DreER, to elucidate liver regeneration after acute pericentral injury, we found that pericentral regeneration is primarily compensated by the expansion of remaining pericentral hepatocytes, and secondarily by expansion of periportal hepatocytes. Employing single-cell RNA sequencing, spatial transcriptomics, immunostaining, and in vivo functional assays, we demonstrated that the upregulated expression of the mTOR/4E-BP1 axis and lactate dehydrogenase A in hepatocytes contributes to pericentral regeneration, while activation of transforming growth factor β (TGF-β1) signaling in the damaged area mediates fibrotic responses and inhibits hepatocyte proliferation. Inhibiting the pericentral accumulation of monocytes and monocyte-derived macrophages through an Arg-Gly-Asp (RGD) peptide-based strategy attenuates these cell-derived TGF-β1 signalings, thus improving pericentral regeneration. Our study provides integrated and high-resolution spatiotemporal insights into the cellular and molecular basis of pericentral regeneration.
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Affiliation(s)
- Shuyong Wang
- Hepatopancreatobiliary Center, Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Beijing 102218, China; Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing 100091, China
| | - Xuan Wang
- Hepatopancreatobiliary Center, Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Beijing 102218, China
| | - Yiran Shan
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Zuolong Tan
- Department of Stem Cell and Regenerative Medicine, Beijing Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuxin Su
- Hepatopancreatobiliary Center, Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Beijing 102218, China
| | - Yannan Cao
- Hepatopancreatobiliary Center, Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Beijing 102218, China
| | - Shuang Wang
- Hepatopancreatobiliary Center, Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Beijing 102218, China
| | - Jiahong Dong
- Hepatopancreatobiliary Center, Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Beijing 102218, China; School of Clinical Medicine, Tsinghua University, Beijing 100084, China
| | - Jin Gu
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China.
| | - Yunfang Wang
- Hepatopancreatobiliary Center, Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Beijing 102218, China; School of Clinical Medicine, Tsinghua University, Beijing 100084, China.
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Tao Y, Sun X, Wang F. BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices. Brief Bioinform 2024; 25:bbae045. [PMID: 38385877 PMCID: PMC10883416 DOI: 10.1093/bib/bbae045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/10/2024] [Accepted: 01/23/2024] [Indexed: 02/23/2024] Open
Abstract
Recent advancements in spatial transcriptomics technology have revolutionized our ability to comprehensively characterize gene expression patterns within the tissue microenvironment, enabling us to grasp their functional significance in a spatial context. One key field of research in spatial transcriptomics is the identification of spatial domains, which refers to distinct regions within the tissue where specific gene expression patterns are observed. Diverse methodologies have been proposed, each with its unique characteristics. As the availability of spatial transcriptomics data continues to expand, there is a growing need for methods that can integrate information from multiple slices to discover spatial domains. To extend the applicability of existing single-slice analysis methods to multi-slice clustering, we introduce BiGATAE (Bipartite Graph Attention Auto Encoder) that leverages gene expression information from adjacent tissue slices to enhance spatial transcriptomics data. BiGATAE comprises two steps: aligning slices to generate an adjacency matrix for different spots in consecutive slices and constructing a bipartite graph. Subsequently, it utilizes a graph attention network to integrate information across different slices. Then it can seamlessly integrate with pre-existing techniques. To evaluate the performance of BiGATAE, we conducted benchmarking analyses on three different datasets. The experimental results demonstrate that for existing single-slice clustering methods, the integration of BiGATAE significantly enhances their performance. Moreover, single-slice clustering methods integrated with BiGATAE outperform methods specifically designed for multi-slice integration. These results underscore the proficiency of BiGATAE in facilitating information transfer across multiple slices and its capacity to broaden the applicability and sustainability of pre-existing methods.
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Affiliation(s)
- Yuhao Tao
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan University Handan Street, 200433 Shanghai, China
| | - Xiaoang Sun
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan University Handan Street, 200433 Shanghai, China
| | - Fei Wang
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan University Handan Street, 200433 Shanghai, China
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Huo Y, Guo Y, Wang J, Xue H, Feng Y, Chen W, Li X. Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network. J Genet Genomics 2023; 50:720-733. [PMID: 37356752 DOI: 10.1016/j.jgg.2023.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 06/27/2023]
Abstract
Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in situ. Spatial transcriptomics can provide multimodal and complementary information simultaneously, including gene expression profiles, spatial locations, and histology images. However, most existing methods have limitations in efficiently utilizing spatial information and matched high-resolution histology images. To fully leverage the multi-modal information, we propose a SPAtially embedded Deep Attentional graph Clustering (SpaDAC) method to identify spatial domains while reconstructing denoised gene expression profiles. This method can efficiently learn the low-dimensional embeddings for spatial transcriptomics data by constructing multi-view graph modules to capture both spatial location connectives and morphological connectives. Benchmark results demonstrate that SpaDAC outperforms other algorithms on several recent spatial transcriptomics datasets. SpaDAC is a valuable tool for spatial domain detection, facilitating the comprehension of tissue architecture and cellular microenvironment. The source code of SpaDAC is freely available at Github (https://github.com/huoyuying/SpaDAC.git).
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Affiliation(s)
- Yuying Huo
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Yilang Guo
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Jiakang Wang
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Huijie Xue
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Yujuan Feng
- School of Software Engineering, Beijing University of Technology, Beijing 100124, China
| | | | - Xiangyu Li
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.
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Cheng C, Chen W, Jin H, Chen X. A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication. Cells 2023; 12:1970. [PMID: 37566049 PMCID: PMC10417635 DOI: 10.3390/cells12151970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.
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Affiliation(s)
- Changde Cheng
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Xiang Chen
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
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Gao F, Huang K, Xing Y. Artificial Intelligence in Omics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:811-813. [PMID: 36640826 PMCID: PMC10025753 DOI: 10.1016/j.gpb.2023.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 12/20/2022] [Accepted: 01/08/2023] [Indexed: 01/13/2023]
Affiliation(s)
- Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China.
| | - Kun Huang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA; IUPUI Fairbanks School of Public Health, Indianapolis, IN 46202, USA; Regenstrief Institute, Indianapolis, IN 46202, USA.
| | - Yi Xing
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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