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Junaid M, Lee EJ, Lim SB. Single-cell and spatial omics: exploring hypothalamic heterogeneity. Neural Regen Res 2025; 20:1525-1540. [PMID: 38993130 DOI: 10.4103/nrr.nrr-d-24-00231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/03/2024] [Indexed: 07/13/2024] Open
Abstract
Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.
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Affiliation(s)
- Muhammad Junaid
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
| | - Eun Jeong Lee
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
- Department of Brain Science, Ajou University School of Medicine, Suwon, South Korea
| | - Su Bin Lim
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
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2
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Cheng X, Meng X, Chen R, Song Z, Li S, Wei S, Lv H, Zhang S, Tang H, Jiang Y, Zhang R. The molecular subtypes of autoimmune diseases. Comput Struct Biotechnol J 2024; 23:1348-1363. [PMID: 38596313 PMCID: PMC11001648 DOI: 10.1016/j.csbj.2024.03.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/11/2024] Open
Abstract
Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.
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Affiliation(s)
| | | | | | - Zerun Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuai Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuhao Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hao Tang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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3
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Wang X, Yang J, Ren B, Yang G, Liu X, Xiao R, Ren J, Zhou F, You L, Zhao Y. Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma. Genes Dis 2024; 11:101143. [PMID: 39253579 PMCID: PMC11382047 DOI: 10.1016/j.gendis.2023.101143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/04/2023] [Accepted: 09/10/2023] [Indexed: 09/11/2024] Open
Abstract
Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.
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Affiliation(s)
- Xing Wang
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
| | - Jinshou Yang
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
| | - Bo Ren
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
| | - Gang Yang
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
| | - Xiaohong Liu
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
| | - Ruiling Xiao
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
| | - Jie Ren
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
| | - Feihan Zhou
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
| | - Lei You
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
| | - Yupei Zhao
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China
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4
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Mao S, Wang Y, Chao N, Zeng L, Zhang L. Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals immune suppression subtypes and establishes a novel signature for determining the prognosis in lung adenocarcinoma. Cell Oncol (Dordr) 2024; 47:1697-1713. [PMID: 38616208 DOI: 10.1007/s13402-024-00948-4] [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] [Accepted: 04/07/2024] [Indexed: 04/16/2024] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with lower survival rates. Recent advancements in targeted therapies and immunotherapies targeting immune checkpoints have achieved remarkable success, there is still a large percentage of LUAD that lacks available therapeutic options. Due to tumor heterogeneity, the diagnosis and treatment of LUAD are challenging. Exploring the biology of LUAD and identifying new biomarker and therapeutic targets options are essential. METHOD We performed single-cell RNA sequencing (scRNA-seq) of 6 paired primary and adjacent LUAD tissues, and integrative omics analysis of the scRNA-seq, bulk RNA-seq and whole-exome sequencing data revealed molecular subtype characteristics. Our experimental results confirm that CDC25C gene can serve as a potential marker for poor prognosis in LUAD. RESULTS We investigated aberrant gene expression in diverse cell types in LUAD via the scRNA-seq data. Moreover, multi-omics clustering revealed four subgroups defined by transcriptional profile and molecular subtype 4 (MS4) with poor survival probability, and immune cell infiltration signatures revealed that MS4 tended to be the immunosuppressive subtype. Our study revealed that the CDC25C gene can be a distinct prognostic biomarker that indicates immune infiltration levels and response to immunotherapy in LUAD patients. Our experimental results concluded that CDC25C expression affects lung cancer cell invasion and migration, might play a key role in regulating Epithelial-Mesenchymal Transition (EMT) pathways. CONCLUSIONS Our multi-omics result revealed a comprehensive set of molecular attributes associated with prognosis-related genes in LUAD at the cellular and tissue level. Identification of a subtype of immunosuppressive TME and prognostic signature for LUAD. We identified the cell cycle regulation gene CDC25C affects lung cancer cell invasion and migration, which can be used as a potential biomarker for LUAD.
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Affiliation(s)
- Shengqiang Mao
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Yilong Wang
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Ningning Chao
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Lingyan Zeng
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Li Zhang
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
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5
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Wang N, Hong W, Wu Y, Chen Z, Bai M, Wang W, Zhu J. Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology. MedComm (Beijing) 2024; 5:e765. [PMID: 39376738 PMCID: PMC11456678 DOI: 10.1002/mco2.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 08/30/2024] [Accepted: 09/03/2024] [Indexed: 10/09/2024] Open
Abstract
The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.
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Affiliation(s)
- Nan Wang
- Cosmos Wisdom Biotech Co. LtdHangzhouChina
| | - Weifeng Hong
- Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
- Hangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouChina
- Zhejiang Key Laboratory of Radiation OncologyHangzhouChina
| | - Yixing Wu
- Department of Pulmonary and Critical Care MedicineZhongshan HospitalFudan UniversityShanghaiChina
| | - Zhe‐Sheng Chen
- Department of Pharmaceutical SciencesCollege of Pharmacy and Health SciencesInstitute for BiotechnologySt. John's UniversityQueensNew YorkUSA
| | - Minghua Bai
- Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
- Hangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouChina
- Zhejiang Key Laboratory of Radiation OncologyHangzhouChina
| | | | - Ji Zhu
- Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
- Hangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouChina
- Zhejiang Key Laboratory of Radiation OncologyHangzhouChina
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6
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Wang A, Zhu XX, Bie Y, Zhang B, Ji W, Lou J, Huang M, Zhou X, Ren Y. Single-cell RNA-sequencing reveals a profound immune cell response in human cytomegalovirus-infected humanized mice. Virol Sin 2024:S1995-820X(24)00133-0. [PMID: 39153545 DOI: 10.1016/j.virs.2024.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 08/12/2024] [Indexed: 08/19/2024] Open
Abstract
Human cytomegalovirus (HCMV) is a common herpesvirus that persistently infects a large portion of the world's population. Despite the robust host immune response, HCMV is able to replicate, evade host defenses, and establish latency throughout the lifespan by developing multiple immunomodulatory strategies, making the studies on the interaction between HCMV infection and host response particularly important. HCMV has a strict host specificity that specifically infects humans. Therefore, most of the in vivo researches of HCMV rely on clinical samples. Fortunately, the establishment of humanized mouse models allows for convenient in-lab animal experiments involving HCMV infection. Single-cell RNA sequencing enables the study of the relationship between viral and host gene expressions at the single-cell level within host cells. In this study, we assessed the gene expression alterations of PBMCs at the single-cell level within HCMV-infected humanized mice, which sheds light onto the virus-host interactions in the context of HCMV infection of humanized mice and provides a valuable dataset for the related researches.
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Affiliation(s)
- An Wang
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiao-Xu Zhu
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuanyuan Bie
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bowen Zhang
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wenting Ji
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Jing Lou
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China
| | - Muhan Huang
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China
| | - Xi Zhou
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; University of Chinese Academy of Sciences, Beijing, 100049, China; School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
| | - Yujie Ren
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
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7
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Zeng Z, Ma Y, Hu L, Tan B, Liu P, Wang Y, Xing C, Xiong Y, Du H. OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing. Nat Commun 2024; 15:5983. [PMID: 39013860 PMCID: PMC11252408 DOI: 10.1038/s41467-024-50194-3] [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: 06/19/2023] [Accepted: 06/28/2024] [Indexed: 07/18/2024] Open
Abstract
Single-cell sequencing is frequently affected by "omission" due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly "omitted" cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of "omitted" cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.
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Affiliation(s)
- Zehua Zeng
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.
- Daxing Research Institute, University of Science and Technology Beijing, Beijing, China.
| | - Yuqing Ma
- Center of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong Province, China
- Institute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong Province, China
| | - Lei Hu
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Bowen Tan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
| | - Peng Liu
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China
| | - Yixuan Wang
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China
| | - Cencan Xing
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.
- Daxing Research Institute, University of Science and Technology Beijing, Beijing, China.
| | - Yuanyan Xiong
- Key Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun-Yat-Sen University, Guangzhou, Guangdong, China.
| | - Hongwu Du
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.
- Daxing Research Institute, University of Science and Technology Beijing, Beijing, China.
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8
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Li C, Shao X, Zhang S, Wang Y, Jin K, Yang P, Lu X, Fan X, Wang Y. scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network. Cell Rep Med 2024; 5:101568. [PMID: 38754419 PMCID: PMC11228399 DOI: 10.1016/j.xcrm.2024.101568] [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: 05/05/2023] [Revised: 12/27/2023] [Accepted: 04/21/2024] [Indexed: 05/18/2024]
Abstract
Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.
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Affiliation(s)
- Chengyu Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
| | - Shujing Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Yingchao Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Kaiyu Jin
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Penghui Yang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xiaoyan Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China; Jinhua Institute of Zhejiang University, Jinhua 321299, China; Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
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9
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Chelu A, Cartwright EJ, Dobrzynski H. Empowering artificial intelligence in characterizing the human primary pacemaker of the heart at single cell resolution. Sci Rep 2024; 14:14041. [PMID: 38890395 PMCID: PMC11189420 DOI: 10.1038/s41598-024-63542-6] [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: 05/14/2024] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
The sinus node (SN) serves as the primary pacemaker of the heart and is the first component of the cardiac conduction system. Due to its anatomical properties and sample scarcity, the cellular composition of the human SN has been historically challenging to study. Here, we employed a novel deep learning deconvolution method, namely Bulk2space, to characterise the cellular heterogeneity of the human SN using existing single-cell datasets of non-human species. As a proof of principle, we used Bulk2Space to profile the cells of the bulk human right atrium using publicly available mouse scRNA-Seq data as a reference. 18 human cell populations were identified, with cardiac myocytes being the most abundant. Each identified cell population correlated to its published experimental counterpart. Subsequently, we applied the deconvolution to the bulk transcriptome of the human SN and identified 11 cell populations, including a population of pacemaker cardiomyocytes expressing pacemaking ion channels (HCN1, HCN4, CACNA1D) and transcription factors (SHOX2 and TBX3). The connective tissue of the SN was characterised by adipocyte and fibroblast populations, as well as key immune cells. Our work unravelled the unique single cell composition of the human SN by leveraging the power of a novel machine learning method.
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Affiliation(s)
- Alexandru Chelu
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK.
| | - Elizabeth J Cartwright
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK
| | - Halina Dobrzynski
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK
- Department of Anatomy, Jagiellonian University Medical College, 31-008, Kraków, Poland
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10
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Qian J, Bao H, Shao X, Fang Y, Liao J, Chen Z, Li C, Guo W, Hu Y, Li A, Yao Y, Fan X, Cheng Y. Simulating multiple variability in spatially resolved transcriptomics with scCube. Nat Commun 2024; 15:5021. [PMID: 38866768 PMCID: PMC11169532 DOI: 10.1038/s41467-024-49445-0] [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: 09/28/2023] [Accepted: 06/03/2024] [Indexed: 06/14/2024] Open
Abstract
A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.
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Affiliation(s)
- Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Hudong Bao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yin Fang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310013, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Zhuo Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310013, China
| | - Chengyu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Wenbo Guo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yining Hu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Anyao Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yue Yao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Yiyu Cheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China.
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11
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Zhang J, Zhang L, Gongol B, Hayes J, Borowsky A, Bailey-Serres J, Girke T. spatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images. NAR Genom Bioinform 2024; 6:lqae006. [PMID: 38312938 PMCID: PMC10836942 DOI: 10.1093/nargab/lqae006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/14/2023] [Accepted: 01/18/2024] [Indexed: 02/06/2024] Open
Abstract
Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.
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Affiliation(s)
- Jianhai Zhang
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Le Zhang
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Brendan Gongol
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Jordan Hayes
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Alexander T Borowsky
- Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA 92521, USA
| | - Julia Bailey-Serres
- Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA 92521, USA
| | - Thomas Girke
- Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
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12
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Lin S, Zhao F, Wu Z, Yao J, Zhao Y, Yuan Z. Streamlining spatial omics data analysis with Pysodb. Nat Protoc 2024; 19:831-895. [PMID: 38135744 DOI: 10.1038/s41596-023-00925-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 10/02/2023] [Indexed: 12/24/2023]
Abstract
Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website ( https://protocols-pysodb.readthedocs.io/ ) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.
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Affiliation(s)
- Senlin Lin
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | | | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Zhiyuan Yuan
- 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.
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
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13
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Yang G, Cheng J, Xu J, Shen C, Lu X, He C, Huang J, He M, Cheng J, Wang H. Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics. J Transl Med 2024; 22:210. [PMID: 38414015 PMCID: PMC10900752 DOI: 10.1186/s12967-024-04848-x] [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: 09/06/2023] [Accepted: 12/31/2023] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment. METHODS Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis. RESULTS Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation. CONCLUSIONS The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.
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Affiliation(s)
- Guanwen Yang
- Department of Urology, Zhongshan Hospital, Fudan University, 180Th Fengling Rd, Xuhui District, Shanghai, 200032, China
| | - Jiangting Cheng
- Department of Urology, Zhongshan Hospital, Fudan University, 180Th Fengling Rd, Xuhui District, Shanghai, 200032, China
| | - Jiayi Xu
- Department of Urology, Zhongshan Hospital, Fudan University, 180Th Fengling Rd, Xuhui District, Shanghai, 200032, China
| | - Chenyang Shen
- Department of Urology, Zhongshan Hospital, Fudan University, 180Th Fengling Rd, Xuhui District, Shanghai, 200032, China
| | - Xuwei Lu
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, 201199, China
| | - Chang He
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, 201199, China
| | - Jiaqi Huang
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, 201199, China
| | - Minke He
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, 201199, China
| | - Jie Cheng
- Department of Urology, Xuhui Hospital, Fudan University, 966Th Huaihai Middle Rd, Xuhui District, Shanghai, 200031, China.
| | - Hang Wang
- Department of Urology, Zhongshan Hospital, Fudan University, 180Th Fengling Rd, Xuhui District, Shanghai, 200032, China.
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14
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Fang Z, Chen J, Zheng Y, Chen Z. Targeting Histamine and Histamine Receptors for Memory Regulation: An Emotional Perspective. Curr Neuropharmacol 2024; 22:1846-1869. [PMID: 38288837 PMCID: PMC11284729 DOI: 10.2174/1570159x22666240128003108] [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: 06/14/2023] [Revised: 08/23/2023] [Accepted: 09/13/2023] [Indexed: 07/23/2024] Open
Abstract
Histamine has long been accepted as a pro-cognitive agent. However, lines of evidence have suggested that the roles of histamine in learning and memory processes are much more complex than previously thought. When explained by the spatial perspectives, there are many contradictory results. However, using emotional memory perspectives, we suspect that the histaminergic system may interplay with stress, reward inhibition, and attention to modulate emotional memory formation. The functional diversity of histamine makes it a viable target for clinical management of neuropsychiatric disorders. Here, we update the current knowledge about the functions of histamine in emotional memory and summarize the underlying molecular and neural circuit mechanisms. Finally, we review the main clinical studies about the impacts of histamine-related compounds on memory and discuss insights into future research on the roles of histamine in emotional memory. Despite the recent progress in histamine research, the histaminergic emotional memory circuits are poorly understood, and it is also worth verifying the functions of histamine receptors in a more spatiotemporally specific manner.
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Affiliation(s)
- Zhuowen Fang
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiahui Chen
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yanrong Zheng
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Zhong Chen
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China
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15
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Zhang C, Sheng M, Lv J, Cao Y, Chen D, Jia L, Sun Y, Ren Y, Li L, Weng Y, Yu W. Single-cell analysis reveals the immune heterogeneity and interactions in lungs undergoing hepatic ischemia-reperfusion. Int Immunopharmacol 2023; 124:111043. [PMID: 37844464 DOI: 10.1016/j.intimp.2023.111043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/02/2023] [Accepted: 10/08/2023] [Indexed: 10/18/2023]
Abstract
Hepatic ischemia-reperfusion IR (HIR) is an unavoidable pathophysiological process during liver transplantation, resulting in systematic sterile inflammation and remote organ injury. Acute lung injury (ALI) is a serious complication after liver transplantation with high postoperative morbidity and mortality. However, the underlying mechanism is still unclear. To assess the phenotype and plasticity of various cell types in the lung tissue microenvironment after HIR at the single-cell level, single-cell RNA sequencing (scRNA-seq) was performed using the lungs from HIR-induced mice. In our results, we identified 23 cell types in the lungs after HIR and found that this highly complex ecosystem was formed by subpopulations of bone marrow-derived cells that signaled each other and mediated inflammatory responses in different states and different intervals. We described the unique transcriptional profiles of lung cell clusters and discovered two novel cell subtypes (Tspo+Endothelial cells and Vcan+ monocytes), as well as the endothelial cell-immune cell and immune cell-T cell clusters interactome. In addition, we found that S100 calcium binding protein (S100a8/a9), specifically and highly expressed in immune cell clusters of lung tissues and exhibited detrimental effects. Finally, the cellular landscape of the lung tissues after HIR was established, highlighting the heterogeneity and cellular interactions between major immune cells in HIR-induced lungs. Our findings provided new insights into the mechanisms of HIR-induced ALI and offered potential therapeutic target to prevent ALI after liver transplantation.
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Affiliation(s)
- Chen Zhang
- The First Central Clinical School, Tianjin Medical University, Tianjin 300052, China; Department of Anesthesiology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Mingwei Sheng
- Department of Anesthesiology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Jingshu Lv
- Department of Anesthesiology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Yingli Cao
- School of Medical, Nankai University, Tianjin 300071, China
| | - Dapeng Chen
- The First Central Clinical School, Tianjin Medical University, Tianjin 300052, China
| | - Lili Jia
- Department of Anesthesiology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Ying Sun
- Department of Anesthesiology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Yinghui Ren
- Department of Anesthesiology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Lian Li
- College of Life Sciences, Nankai University, Tianjin 300071, China
| | - Yiqi Weng
- Department of Anesthesiology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Wenli Yu
- The First Central Clinical School, Tianjin Medical University, Tianjin 300052, China; Department of Anesthesiology, Tianjin First Central Hospital, Tianjin 300192, China.
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16
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Yang H, Lin H, Sun X. Multiscale modeling of drug resistance in glioblastoma with gene mutations and angiogenesis. Comput Struct Biotechnol J 2023; 21:5285-5295. [PMID: 37941656 PMCID: PMC10628546 DOI: 10.1016/j.csbj.2023.10.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023] Open
Abstract
Drug resistance is a prominent impediment to the efficacy of targeted therapies across various cancer types, including glioblastoma (GBM). However, comprehending the intricate intracellular and extracellular mechanisms underlying drug resistance remains elusive. Empirical investigations have elucidated that genetic aberrations, such as gene mutations, along with microenvironmental adaptation, notably angiogenesis, act as pivotal drivers of tumor progression and drug resistance. Nonetheless, mathematical models frequently compartmentalize these factors in isolation. In this study, we present a multiscale agent-based model of GBM, encompassing cellular dynamics, intricate signaling pathways, gene mutations, angiogenesis, and therapeutic interventions. This integrative framework facilitates an exploration of the interplay between genetic mutations and the vascular microenvironment in shaping the dynamic evolution of tumors during treatment with tyrosine kinase inhibitor. Our simulations unveil that mutations influencing the migration and proliferation of tumor cells expedite the emergence of phenotype heterogeneity, thereby exacerbating tumor invasion under both treated and untreated conditions. Moreover, angiogenesis proximate to the tumor fosters a protumoral milieu, augmenting mutation-induced drug resistance by increasing the survival rate of tumor cells. Collectively, our findings underscore the dual roles of intrinsic genetic mutations and extrinsic microenvironmental adaptations in steering tumor growth and drug resistance. Finally, we substantiate our model predictions concerning the impact of gene mutations and angiogenesis on the responsiveness of targeted therapies by integrating single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, and clinical data from GBM patients. The multidimensional approach enhances our understanding of the complexities governing drug resistance in glioma and offers insights into potential therapeutic strategies.
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Affiliation(s)
- Heng Yang
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Haofeng Lin
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Xiaoqiang Sun
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
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17
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Guo W, Hu Y, Qian J, Zhu L, Cheng J, Liao J, Fan X. Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution. J Genet Genomics 2023; 50:641-651. [PMID: 37544594 DOI: 10.1016/j.jgg.2023.07.011] [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: 04/28/2023] [Revised: 07/28/2023] [Accepted: 07/28/2023] [Indexed: 08/08/2023]
Abstract
Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.
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Affiliation(s)
- Wenbo Guo
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Yining Hu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Jingyang Qian
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Lidan Zhu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Junyun Cheng
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China.
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18
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Berson E, Sreenivas A, Phongpreecha T, Perna A, Grandi FC, Xue L, Ravindra NG, Payrovnaziri N, Mataraso S, Kim Y, Espinosa C, Chang AL, Becker M, Montine KS, Fox EJ, Chang HY, Corces MR, Aghaeepour N, Montine TJ. Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature. Nat Commun 2023; 14:4947. [PMID: 37587197 PMCID: PMC10432546 DOI: 10.1038/s41467-023-40611-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.
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Affiliation(s)
- Eloise Berson
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| | - Anjali Sreenivas
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Thanaphong Phongpreecha
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Amalia Perna
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Fiorella C Grandi
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Lei Xue
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Neal G Ravindra
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Neelufar Payrovnaziri
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Samson Mataraso
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Yeasul Kim
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Alan L Chang
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Edward J Fox
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
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19
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Zhou Y, Jiang X, Wang X, Huang J, Li T, Jin H, He J. Promise of spatially resolved omics for tumor research. J Pharm Anal 2023; 13:851-861. [PMID: 37719191 PMCID: PMC10499658 DOI: 10.1016/j.jpha.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.
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Affiliation(s)
- 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
| | - Xinyi Jiang
- 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
| | - 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
| | - Tong Li
- 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
| | - Hongtao Jin
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, 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 for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, China
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20
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O'Connor LM, O'Connor BA, Lim SB, Zeng J, Lo CH. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective. J Pharm Anal 2023; 13:836-850. [PMID: 37719197 PMCID: PMC10499660 DOI: 10.1016/j.jpha.2023.06.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/19/2023] Open
Abstract
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
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Affiliation(s)
- Lance M. O'Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Blake A. O'Connor
- School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
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21
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Fangma Y, Liu M, Liao J, Chen Z, Zheng Y. Dissecting the brain with spatially resolved multi-omics. J Pharm Anal 2023; 13:694-710. [PMID: 37577383 PMCID: PMC10422112 DOI: 10.1016/j.jpha.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 08/15/2023] Open
Abstract
Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.
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Affiliation(s)
- Yijia Fangma
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Mengting Liu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yanrong Zheng
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
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22
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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] [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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23
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Rabjohns EM, Rampersad RR, Ghosh A, Hurst K, Eudy AM, Brozowski JM, Lee HH, Ren Y, Mirando A, Gladman J, Bowser JL, Berg K, Wani S, Ralston SH, Hilton MJ, Tarrant TK. Aged G Protein-Coupled Receptor Kinase 3 (Grk3)-Deficient Mice Exhibit Enhanced Osteoclastogenesis and Develop Bone Lesions Analogous to Human Paget's Disease of Bone. Cells 2023; 12:981. [PMID: 37048054 PMCID: PMC10093054 DOI: 10.3390/cells12070981] [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: 02/07/2023] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 03/29/2023] Open
Abstract
Paget's Disease of Bone (PDB) is a metabolic bone disease that is characterized by dysregulated osteoclast function leading to focal abnormalities of bone remodeling. It can lead to pain, fracture, and bone deformity. G protein-coupled receptor kinase 3 (GRK3) is an important negative regulator of G protein-coupled receptor (GPCR) signaling. GRK3 is known to regulate GPCR function in osteoblasts and preosteoblasts, but its regulatory function in osteoclasts is not well defined. Here, we report that Grk3 expression increases during osteoclast differentiation in both human and mouse primary cells and established cell lines. We also show that aged mice deficient in Grk3 develop bone lesions similar to those seen in human PDB and other Paget's Disease mouse models. We show that a deficiency in Grk3 expression enhances osteoclastogenesis in vitro and proliferation of hematopoietic osteoclast precursors in vivo but does not affect the osteoclast-mediated bone resorption function or cellular senescence pathway. Notably, we also observe decreased Grk3 expression in peripheral blood mononuclear cells of patients with PDB compared with age- and gender-matched healthy controls. Our data suggest that GRK3 has relevance to the regulation of osteoclast differentiation and that it may have relevance to the pathogenesis of PDB and other metabolic bone diseases associated with osteoclast activation.
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Affiliation(s)
- Emily M. Rabjohns
- Division of Rheumatology and Immunology, Duke University Department of Medicine, Durham, NC 27710, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rishi R. Rampersad
- Division of Rheumatology and Immunology, Duke University Department of Medicine, Durham, NC 27710, USA
| | - Arin Ghosh
- College of Arts and Sciences, Duke University, Durham, NC 27510, USA
| | - Katlyn Hurst
- College of Arts and Sciences, Duke University, Durham, NC 27510, USA
| | - Amanda M. Eudy
- Division of Rheumatology and Immunology, Duke University Department of Medicine, Durham, NC 27710, USA
| | - Jaime M. Brozowski
- Division of Rheumatology and Immunology, Duke University Department of Medicine, Durham, NC 27710, USA
| | - Hyun Ho Lee
- Division of Rheumatology and Immunology, Duke University Department of Medicine, Durham, NC 27710, USA
| | - Yinshi Ren
- Department of Orthopaedic Surgery, University of Texas Southwestern, Dallas, TX 75390, USA
- Scottish Rite Hospital, Dallas, TX 75219, USA
- Department of Orthopedics, Duke University, Durham, NC 27710, USA
| | - Anthony Mirando
- Department of Orthopedics, Duke University, Durham, NC 27710, USA
| | - Justin Gladman
- Pratt School of Engineering, Duke University, Durham, NC 27710, USA
| | - Jessica L. Bowser
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kathryn Berg
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Sachin Wani
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Stuart H. Ralston
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | | | - Teresa K. Tarrant
- Division of Rheumatology and Immunology, Duke University Department of Medicine, Durham, NC 27710, USA
- Durham Veterans Hospital, Durham, NC 27710, USA
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24
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Yue L, Liu F, Hu J, Yang P, Wang Y, Dong J, Shu W, Huang X, Wang S. A guidebook of spatial transcriptomic technologies, data resources and analysis approaches. Comput Struct Biotechnol J 2023; 21:940-955. [PMID: 38213887 PMCID: PMC10781722 DOI: 10.1016/j.csbj.2023.01.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.
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Affiliation(s)
- Liangchen Yue
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Feng Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Jiongsong Hu
- University of South China, Hengyang, Hunan 421001, China
| | - Pin Yang
- Anhui Medical University, Hefei 230022, Anhui, China
| | - Yuxiang Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Junguo Dong
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Wenjie Shu
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Xingxu Huang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310029, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shengqi Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
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