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Ye F, Wang J, Li J, Mei Y, Guo G. Mapping Cell Atlases at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305449. [PMID: 38145338 PMCID: PMC10885669 DOI: 10.1002/advs.202305449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/01/2023] [Indexed: 12/26/2023]
Abstract
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative MedicineDr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative MedicineHangzhouZhejiang310058China
- Institute of HematologyZhejiang UniversityHangzhouZhejiang310000China
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202
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Tian G, Zhang J, Bao Y, Li Q, Hou J. A prognostic model based on Scissor + cancer associated fibroblasts identified from bulk and single cell RNA sequencing data in head and neck squamous cell carcinoma. Cell Signal 2024; 114:110984. [PMID: 38029947 DOI: 10.1016/j.cellsig.2023.110984] [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: 08/31/2023] [Revised: 10/23/2023] [Accepted: 11/15/2023] [Indexed: 12/01/2023]
Abstract
Head and neck squamous cell carcinoma (HNSCC) is one of the most lethal diseases in the world, which often recur after multimodality treatment approaches, leading to a poor prognosis. Fibroblasts, a heterogeneous component of the tumor microenvironment, can modulate numerous aspects of tumor biology and have been increasingly acknowledged in dictating the clinical outcome of patients with HNSCC. However, the subpopulation of fibroblasts that are related to the prognosis of HNSCC has not yet been fully explored. To do so, we combined a single-cell RNA sequencing (scRNA-seq) dataset and bulk RNA-sequencing dataset with clinical information, identifying the fibroblast population that are related to poor prognosis of HNSCC. We found these specific population of fibroblasts are less differentiated. In addition, to identify the prognostic signatures of HNSCC, bioinformatics analysis included least absolute shrinkage and selection operator (LASSO) analyses and univariate cox and were performed. We selected 12 prognosis-related genes for constructing a risk model using The Cancer Genome Atlas (TCGA). The AUC values and calibration plots of this model indicated good prognostic prediction efficacy. This model also was validated in two Gene Expression Omnibus (GEO) datasets. In conclusion, we constructed an optimal model that was derived from single cell RNA-seq and bulk RNA-seq to predict the survival probability of HNSCC patients. Among this model, AKR1C3 higher expression in cancer associated fibroblasts (CAFs) of HNSCC has been confirmed by preliminary experiments.
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Affiliation(s)
- Guoli Tian
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Jiaqiang Zhang
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Yong Bao
- Department of Medical Oncology; Institute of Precision Medicine; Center for Translational Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
| | - Qiuli Li
- Department of Head and Neck Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
| | - Jinsong Hou
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China.
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203
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Zhang H, Wang Y, Lian B, Wang Y, Li X, Wang T, Shang X, Yang H, Aziz A, Hu J. Scbean: a python library for single-cell multi-omics data analysis. Bioinformatics 2024; 40:btae053. [PMID: 38290765 PMCID: PMC10868338 DOI: 10.1093/bioinformatics/btae053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 02/01/2024] Open
Abstract
SUMMARY Single-cell multi-omics technologies provide a unique platform for characterizing cell states and reconstructing developmental process by simultaneously quantifying and integrating molecular signatures across various modalities, including genome, transcriptome, epigenome, and other omics layers. However, there is still an urgent unmet need for novel computational tools in this nascent field, which are critical for both effective and efficient interrogation of functionality across different omics modalities. Scbean represents a user-friendly Python library, designed to seamlessly incorporate a diverse array of models for the examination of single-cell data, encompassing both paired and unpaired multi-omics data. The library offers uniform and straightforward interfaces for tasks, such as dimensionality reduction, batch effect elimination, cell label transfer from well-annotated scRNA-seq data to scATAC-seq data, and the identification of spatially variable genes. Moreover, Scbean's models are engineered to harness the computational power of GPU acceleration through Tensorflow, rendering them capable of effortlessly handling datasets comprising millions of cells. AVAILABILITY AND IMPLEMENTATION Scbean is released on the Python Package Index (PyPI) (https://pypi.org/project/scbean/) and GitHub (https://github.com/jhu99/scbean) under the MIT license. The documentation and example code can be found at https://scbean.readthedocs.io/en/latest/.
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Affiliation(s)
- Haohui Zhang
- School of Computer Science, Northwestern Polytechnical University, 710129 Xi'an, Shaanxi, China
| | - Yuwei Wang
- School of Computer Science, Northwestern Polytechnical University, 710129 Xi'an, Shaanxi, China
| | - Bin Lian
- School of Computer Science, Northwestern Polytechnical University, 710129 Xi'an, Shaanxi, China
| | - Yiran Wang
- School of Computer Science, Northwestern Polytechnical University, 710129 Xi'an, Shaanxi, China
| | - Xingyi Li
- School of Computer Science, Northwestern Polytechnical University, 710129 Xi'an, Shaanxi, China
| | - Tao Wang
- School of Computer Science, Northwestern Polytechnical University, 710129 Xi'an, Shaanxi, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, 710129 Xi'an, Shaanxi, China
| | - Hui Yang
- School of Life Science, Northwestern Polytechnical University, 710072 Xi'an, Shaanxi, China
| | - Ahmad Aziz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, 53105 Bonn, Germany
| | - Jialu Hu
- School of Computer Science, Northwestern Polytechnical University, 710129 Xi'an, Shaanxi, China
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
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204
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Bawa G, Liu Z, Yu X, Tran LSP, Sun X. Introducing single cell stereo-sequencing technology to transform the plant transcriptome landscape. TRENDS IN PLANT SCIENCE 2024; 29:249-265. [PMID: 37914553 DOI: 10.1016/j.tplants.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 11/03/2023]
Abstract
Single cell RNA-sequencing (scRNA-seq) advancements have helped detect transcriptional heterogeneities in biological samples. However, scRNA-seq cannot currently provide high-resolution spatial transcriptome information or identify subcellular organs in biological samples. These limitations have led to the development of spatially enhanced-resolution omics-sequencing (Stereo-seq), which combines spatial information with single cell transcriptomics to address the challenges of scRNA-seq alone. In this review, we discuss the advantages of Stereo-seq technology. We anticipate that the application of such an integrated approach in plant research will advance our understanding of biological process in the plant transcriptomics era. We conclude with an outlook of how such integration will enhance crop improvement.
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Affiliation(s)
- George Bawa
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Key Laboratory of Plant Stress Biology, School of Life Sciences, Henan University, 85 Minglun Street, Kaifeng 475001, PR China
| | - Zhixin Liu
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Key Laboratory of Plant Stress Biology, School of Life Sciences, Henan University, 85 Minglun Street, Kaifeng 475001, PR China
| | - Xiaole Yu
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Key Laboratory of Plant Stress Biology, School of Life Sciences, Henan University, 85 Minglun Street, Kaifeng 475001, PR China
| | - Lam-Son Phan Tran
- Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA.
| | - Xuwu Sun
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Key Laboratory of Plant Stress Biology, School of Life Sciences, Henan University, 85 Minglun Street, Kaifeng 475001, PR China.
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205
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Camunas-Soler J. Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca 2+ imaging and electrophysiology. Biophys Rev 2024; 16:89-107. [PMID: 38495444 PMCID: PMC10937895 DOI: 10.1007/s12551-023-01174-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/29/2023] [Indexed: 03/19/2024] Open
Abstract
I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.
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Affiliation(s)
- Joan Camunas-Soler
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, 405 30 Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
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206
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Yang J, Wang W, Zhang X. scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision. Bioinformatics 2024; 40:btae091. [PMID: 38366925 PMCID: PMC10904148 DOI: 10.1093/bioinformatics/btae091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/14/2024] [Accepted: 02/14/2024] [Indexed: 02/19/2024] Open
Abstract
MOTIVATION Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell data analysis. Although a host of works have been developed, the low signal-to-noise-ratio single-cell RNA-sequencing data that suffers from batch effects and dropout still poses obstacles in discovering grouped patterns for cell types by unsupervised learning and its alternative-semi-supervised learning that utilizes a few labeled cells as guidance for cell-type annotation. RESULTS We propose a robust cell-type annotation method scSemiGCN based on graph convolutional networks. Built upon a denoised network structure that characterizes reliable cell-to-cell connections, scSemiGCN generates pseudo labels for unannotated cells. Then supervised contrastive learning follows to refine the noisy single-cell data. Finally, message passing with the refined features over the denoised network structure is conducted for semi-supervised cell-type annotation. Comparison over several datasets with six methods under extremely limited supervision validates the effectiveness and efficiency of scSemiGCN for cell-type annotation. AVAILABILITY AND IMPLEMENTATION Implementation of scSemiGCN is available at https://github.com/Jane9898/scSemiGCN.
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Affiliation(s)
- Jue Yang
- School of Mathematics, Sun Yat-sen University, Guangzhou 510000, China
| | - Weiwen Wang
- Department of Mathematics, School of Information Science and Technology, Jinan University, Guangzhou 510000, China
| | - Xiwen Zhang
- Department of Bioinformatics, College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510000, China
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207
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Lee S, Kim G, Lee J, Lee AC, Kwon S. Mapping cancer biology in space: applications and perspectives on spatial omics for oncology. Mol Cancer 2024; 23:26. [PMID: 38291400 PMCID: PMC10826015 DOI: 10.1186/s12943-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.
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Affiliation(s)
- Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea
| | - Gyeongjun Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - JinYoung Lee
- Division of Engineering Science, University of Toronto, Toronto, Ontario, ON, M5S 3H6, Canada
| | - Amos C Lee
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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208
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Liu J, Jiang P, Lu Z, Yu Z, Qian P. Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance. Exp Hematol Oncol 2024; 13:12. [PMID: 38291542 PMCID: PMC10826069 DOI: 10.1186/s40164-024-00479-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 01/16/2024] [Indexed: 02/01/2024] Open
Abstract
Leukemias are refractory hematological malignancies, characterized by marked intrinsic heterogeneity which poses significant obstacles to effective treatment. However, traditional bulk sequencing techniques have not been able to effectively unravel the heterogeneity among individual tumor cells. With the emergence of single-cell sequencing technology, it has bestowed upon us an unprecedented resolution to comprehend the mechanisms underlying leukemogenesis and drug resistance across various levels, including the genome, epigenome, transcriptome and proteome. Here, we provide an overview of the currently prevalent single-cell sequencing technologies and a detailed summary of single-cell studies conducted on leukemia, with a specific focus on four key aspects: (1) leukemia's clonal architecture, (2) frameworks to determine leukemia subtypes, (3) tumor microenvironment (TME) and (4) the drug-resistant mechanisms of leukemia. This review provides a comprehensive summary of current single-cell studies on leukemia and highlights the markers and mechanisms that show promising clinical implications for the diagnosis and treatment of leukemia.
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Affiliation(s)
- Jianche Liu
- Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- International Campus, Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, 718 East Haizhou Road, Haining, 314400, China
| | - Penglei Jiang
- Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- Institute of Hematology, Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Zhejiang University, Hangzhou, 310058, China
| | - Zezhen Lu
- Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- International Campus, Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, 718 East Haizhou Road, Haining, 314400, China
| | - Zebin Yu
- Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- Institute of Hematology, Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Zhejiang University, Hangzhou, 310058, China
| | - Pengxu Qian
- Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310058, China.
- Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China.
- Institute of Hematology, Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Zhejiang University, Hangzhou, 310058, China.
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209
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Chen G, Xu W, Long Z, Chong Y, Lin B, Jie Y. Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology. J Clin Transl Hepatol 2024; 12:79-90. [PMID: 38250462 PMCID: PMC10794276 DOI: 10.14218/jcth.2023.00224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/25/2023] [Accepted: 07/12/2023] [Indexed: 01/23/2024] Open
Abstract
The liver is the largest glandular organ in the body and has a unique distribution of cells and biomolecules. However, the treatment outcome of end-stage liver disease is extremely poor. Single-cell sequencing is a new advanced and powerful technique for identifying rare cell populations and biomolecules by analyzing the characteristics of gene expression between individual cells. These cells and biomolecules might be used as potential targets for immunotherapy of liver diseases and contribute to the development of precise individualized treatment. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) or other single-cell histological techniques have solved the problem of cell population heterogeneity and characterize molecular changes associated with liver diseases with higher accuracy and resolution. In this review, we comprehensively summarized single-cell approaches including transcriptomic, spatial transcriptomic, immunomic, proteomic, epigenomic, and multiomic technologies, and described their application in liver physiology and pathology. We also discussed advanced techniques and recent studies in the field of single-cell; our review might provide new insights into the pathophysiological mechanisms of the liver to achieve precise and individualized treatment of liver diseases.
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Affiliation(s)
| | | | - Zhicong Long
- Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yutian Chong
- Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Bingliang Lin
- Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yusheng Jie
- Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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210
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Li J, Choi J, Cheng X, Ma J, Pema S, Sanes JR, Mardon G, Frankfort BJ, Tran NM, Li Y, Chen R. Comprehensive single-cell atlas of the mouse retina. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.24.577060. [PMID: 38328114 PMCID: PMC10849744 DOI: 10.1101/2024.01.24.577060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity at the single-cell resolution by classifying and characterizing cell types in multiple tissues and species. While several mouse retinal scRNA-seq reference datasets have been published, each dataset either has a relatively small number of cells or is focused on specific cell classes, and thus is suboptimal for assessing gene expression patterns across all retina types at the same time. To establish a unified and comprehensive reference for the mouse retina, we first generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse whole retinas. This dataset was generated through the targeted enrichment of rare population cells via antibody-based magnetic cell sorting. By integrating this new dataset with public datasets, we conducted an integrated analysis to construct the Mouse Retina Cell Atlas (MRCA) for wild-type mice, which encompasses over 330,000 single cells. The MRCA characterizes 12 major classes and 138 cell types. It captured consensus cell type characterization from public datasets and identified additional new cell types. To facilitate the public use of the MRCA, we have deposited it in CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal for visualization and gene expression exploration. The comprehensive MRCA serves as an easy-to-use, one-stop data resource for the mouse retina communities.
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Affiliation(s)
- Jin Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Jongsu Choi
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Xuesen Cheng
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Justin Ma
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Shahil Pema
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Joshua R. Sanes
- Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02130, USA
| | - Graeme Mardon
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas 77030, USA
- Departments of Ophthalmology and Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Benjamin J. Frankfort
- Departments of Ophthalmology and Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Nicholas M. Tran
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Yumei Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
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211
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Xie Y, Chen L, Wang L, Liu T, Zheng Y, Si L, Ge H, Xu H, Xiao L, Wang G. Single-nucleus transcriptomic analysis reveals the relationship between gene expression in oligodendrocyte lineage and major depressive disorder. J Transl Med 2024; 22:109. [PMID: 38281050 PMCID: PMC10822185 DOI: 10.1186/s12967-023-04727-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: 04/08/2023] [Accepted: 11/13/2023] [Indexed: 01/29/2024] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a common mental illness that affects millions of people worldwide and imposes a heavy burden on individuals, families and society. Previous studies on MDD predominantly focused on neurons and employed bulk homogenates of brain tissues. This paper aims to decipher the relationship between oligodendrocyte lineage (OL) development and MDD at the single-cell resolution level. METHODS Here, we present the use of a guided regularized random forest (GRRF) algorithm to explore single-nucleus RNA sequencing profiles (GSE144136) of the OL at four developmental stages, which contains dorsolateral prefrontal cortex of 17 healthy controls (HC) and 17 MDD cases, generated by Nagy C et al. We prioritized and ordered differentially expressed genes (DEGs) based on Nagy et al., which could predominantly discriminate cells in the four developmental stages and two adjacent developmental stages of the OL. We further screened top-ranked genes that distinguished between HC and MDD in four developmental stages. Moreover, we estimated the performance of the GRRF model via the area under the curve value. Additionally, we validated the pivotal candidate gene Malat1 in animal models. RESULTS We found that, among the four developmental stages, the onset development of OL (OPC2) possesses the best predictive power for distinguishing HC and MDD, and long noncoding RNA MALAT1 has top-ranked importance value in candidate genes of four developmental stages. In addition, results of fluorescence in situ hybridization assay showed that Malat1 plays a critical role in the occurrence of depression. CONCLUSIONS Our work elucidates the mechanism of MDD from the perspective of OL development at the single-cell resolution level and provides novel insight into the occurrence of depression.
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Affiliation(s)
- Yinping Xie
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lijuan Chen
- Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Leimin Wang
- School of Automation, China University of Geosciences, Wuhan, China
| | - Tongou Liu
- The First Clinical College of Hubei University of Chinese Medicine, Wuhan, China
| | - Yage Zheng
- Judicial Appraisal Institute, Renmin Hospital of Hubei Province, Wuhan, China
| | - Lujia Si
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hailong Ge
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hong Xu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ling Xiao
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
| | - Gaohua Wang
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
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212
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Han T, Wu J, Liu Y, Zhou J, Miao R, Guo J, Xu Z, Xing Y, Bai Y, Hu D. Integrating bulk-RNA sequencing and single-cell sequencing analyses to characterize adenosine-enriched tumor microenvironment landscape and develop an adenosine-related prognostic signature predicting immunotherapy in lung adenocarcinoma. Funct Integr Genomics 2024; 24:19. [PMID: 38265702 DOI: 10.1007/s10142-023-01281-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 12/19/2023] [Accepted: 12/29/2023] [Indexed: 01/25/2024]
Abstract
The adenosine-signaling axis has been recognized as an important immunomodulatory pathway in tumor immunity. However, the biological role of the adenosine-signaling axis in the remodeling of the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) remains unclear. Here, we quantified adenosine signaling (ado_sig) in LUAD samples using the GSVA method and assessed the prognostic value of adenosine in LUAD. Afterward, we explored the heterogeneity of the tumor-immune microenvironment at different adenosine levels. In addition, we analyzed the potential biological pathways engaged by adenosine. Next, we established single-cell transcriptional profiles of LUAD and analyzed cellular composition and cell-cell communication analysis under different adenosine microenvironments. Moreover, we established adenosine-related prognostic signatures (ARS) based on comprehensive bioinformatics analysis and evaluated the efficacy of ARS in predicting immunotherapy. The results demonstrated that adenosine signaling adversely impacted the survival of immune-enriched LUAD. The high-adenosine microenvironment exhibited elevated pro-tumor-immune infiltration, including M2 macrophages and displayed notably increased epithelial-mesenchymal transition (EMT) transformation. Furthermore, adenosine signaling displayed significant associations with the expression patterns and prognostic value of immunomodulators within the TME. Single-cell sequencing data revealed increased fibroblast occupancy and a prominent activation of the SPP1 signaling pathway in the high adenosine-signaling microenvironment. The ARS exhibited promising effectiveness in prognostication and predicting immunotherapy response in LUAD. In summary, overexpression of adenosine can cause a worsened prognosis in the LUAD with abundant immune infiltration. Moreover, increased adenosine levels are associated with pro-tumor-immune infiltration, active EMT transformation, pro-tumor angiogenesis, and other factors promoting cancer progression, which collectively contribute to the formation of an immunosuppressive microenvironment. Importantly, the ARS developed in this study demonstrate high efficacy in evaluating the response to immunotherapy.
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Affiliation(s)
- Tao Han
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China.
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, People's Republic of China.
| | - Jing Wu
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, People's Republic of China
| | - Yafeng Liu
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, People's Republic of China
| | - Jiawei Zhou
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, People's Republic of China
| | - Rui Miao
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China
| | - Jianqiang Guo
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, People's Republic of China
| | - Zhi Xu
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China
| | - Yingru Xing
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China
- Department of Clinical Laboratory, Anhui Zhongke Gengjiu Hospital, Hefei, People's Republic of China
| | - Ying Bai
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China.
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, People's Republic of China.
| | - Dong Hu
- School of Medicine, Anhui University of Science and Technology, Chongren Building, No 168, Taifeng St, Huainan, People's Republic of China.
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, People's Republic of China.
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, People's Republic of China.
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China.
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213
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Gong L, Qiu L, Hao M. Novel Insights into the Initiation, Evolution, and Progression of Multiple Myeloma by Multi-Omics Investigation. Cancers (Basel) 2024; 16:498. [PMID: 38339250 PMCID: PMC10854875 DOI: 10.3390/cancers16030498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/08/2024] [Accepted: 01/15/2024] [Indexed: 02/12/2024] Open
Abstract
The evolutionary history of multiple myeloma (MM) includes malignant transformation, followed by progression to pre-malignant stages and overt malignancy, ultimately leading to more aggressive and resistant forms. Over the past decade, large effort has been made to identify the potential therapeutic targets in MM. However, MM remains largely incurable. Most patients experience multiple relapses and inevitably become refractory to treatment. Tumor-initiating cell populations are the postulated population, leading to the recurrent relapses in many hematological malignancies. Clonal evolution of tumor cells in MM has been identified along with the disease progression. As a consequence of different responses to the treatment of heterogeneous MM cell clones, the more aggressive populations survive and evolve. In addition, the tumor microenvironment is a complex ecosystem which plays multifaceted roles in supporting tumor cell evolution. Emerging multi-omics research at single-cell resolution permits an integrative and comprehensive profiling of the tumor cells and microenvironment, deepening the understanding of biological features of MM. In this review, we intend to discuss the novel insights into tumor cell initiation, clonal evolution, drug resistance, and tumor microenvironment in MM, as revealed by emerging multi-omics investigations. These data suggest a promising strategy to unravel the pivotal mechanisms of MM progression and enable the improvement in treatment, both holistically and precisely.
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Affiliation(s)
- Lixin Gong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 288 Nanjing Road, Tianjin 300020, China;
- Tianjin Institutes of Health Science, Tianjin 300020, China
| | - Lugui Qiu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 288 Nanjing Road, Tianjin 300020, China;
- Tianjin Institutes of Health Science, Tianjin 300020, China
- Gobroad Healthcare Group, Beijing 100072, China
| | - Mu Hao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 288 Nanjing Road, Tianjin 300020, China;
- Tianjin Institutes of Health Science, Tianjin 300020, China
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214
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Fang W, Liu X, Maiga M, Cao W, Mu Y, Yan Q, Zhu Q. Digital PCR for Single-Cell Analysis. BIOSENSORS 2024; 14:64. [PMID: 38391982 PMCID: PMC10886679 DOI: 10.3390/bios14020064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 02/24/2024]
Abstract
Single-cell analysis provides an overwhelming strategy for revealing cellular heterogeneity and new perspectives for understanding the biological function and disease mechanism. Moreover, it promotes the basic and clinical research in many fields at a single-cell resolution. A digital polymerase chain reaction (dPCR) is an absolute quantitative analysis technology with high sensitivity and precision for DNA/RNA or protein. With the development of microfluidic technology, digital PCR has been used to achieve absolute quantification of single-cell gene expression and single-cell proteins. For single-cell specific-gene or -protein detection, digital PCR has shown great advantages. So, this review will introduce the significance and process of single-cell analysis, including single-cell isolation, single-cell lysis, and single-cell detection methods, mainly focusing on the microfluidic single-cell digital PCR technology and its biological application at a single-cell level. The challenges and opportunities for the development of single-cell digital PCR are also discussed.
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Affiliation(s)
- Weibo Fang
- Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; (W.F.); (X.L.); (M.M.); (W.C.); (Y.M.)
| | - Xudong Liu
- Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; (W.F.); (X.L.); (M.M.); (W.C.); (Y.M.)
| | - Mariam Maiga
- Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; (W.F.); (X.L.); (M.M.); (W.C.); (Y.M.)
| | - Wenjian Cao
- Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; (W.F.); (X.L.); (M.M.); (W.C.); (Y.M.)
| | - Ying Mu
- Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; (W.F.); (X.L.); (M.M.); (W.C.); (Y.M.)
| | - Qiang Yan
- Department of Hepatobiliary and Pancreatic Surgery, Huzhou Central Hospital, Huzhou Key Laboratory of Intelligent and Digital Precision Surgery, Department of General Surgery, Affiliated Huzhou Hospital, School of Medicine, Zhejiang University, Huzhou 313000, China
| | - Qiangyuan Zhu
- Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; (W.F.); (X.L.); (M.M.); (W.C.); (Y.M.)
- Huzhou Institute of Zhejiang University, Huzhou 313002, China
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215
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Wang L, Nie R, Miao X, Cai Y, Wang A, Zhang H, Zhang J, Cai J. InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation. BMC Bioinformatics 2024; 25:41. [PMID: 38267858 PMCID: PMC10809631 DOI: 10.1186/s12859-024-05656-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/15/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND With the development of single-cell technology, many cell traits can be measured. Furthermore, the multi-omics profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed. RESULTS Here, we present inClust+, a deep generative framework for the multi-omics. It's built on previous inClust that is specific for transcriptome data, and augmented with two mask modules designed for multimodal data processing: an input-mask module in front of the encoder and an output-mask module behind the decoder. InClust+ was first used to integrate scRNA-seq and MERFISH data from similar cell populations, and to impute MERFISH data based on scRNA-seq data. Then, inClust+ was shown to have the capability to integrate the multimodal data (e.g. tri-modal data with gene expression, chromatin accessibility and protein abundance) with batch effect. Finally, inClust+ was used to integrate an unlabeled monomodal scRNA-seq dataset and two labeled multimodal CITE-seq datasets, transfer labels from CITE-seq datasets to scRNA-seq dataset, and generate the missing modality of protein abundance in monomodal scRNA-seq data. In the above examples, the performance of inClust+ is better than or comparable to the most recent tools in the corresponding task. CONCLUSIONS The inClust+ is a suitable framework for handling multimodal data. Meanwhile, the successful implementation of mask in inClust+ means that it can be applied to other deep learning methods with similar encoder-decoder architecture to broaden the application scope of these models.
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Affiliation(s)
- Lifei Wang
- Shulan (Hangzhou) Hospital, Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China.
| | - Rui Nie
- China National Center for Bioinformation, Beijing, China
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xuexia Miao
- China National Center for Bioinformation, Beijing, China
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yankai Cai
- School of Economic and Management, China University of Geoscience, Wuhan, China
| | - Anqi Wang
- Shulan (Hangzhou) Hospital, Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
| | - Hanwen Zhang
- Shulan (Hangzhou) Hospital, Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
| | - Jiang Zhang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China.
| | - Jun Cai
- China National Center for Bioinformation, Beijing, China.
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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216
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Lin H, Zhang M, Hu M, Zhang Y, Jiang W, Tang W, Ouyang Y, Jiang L, Mi Y, Chen Z, He P, Zhao G, Ouyang X. Emerging applications of single-cell profiling in precision medicine of atherosclerosis. J Transl Med 2024; 22:97. [PMID: 38263066 PMCID: PMC10804726 DOI: 10.1186/s12967-023-04629-y] [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: 06/12/2023] [Accepted: 10/14/2023] [Indexed: 01/25/2024] Open
Abstract
Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.
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Affiliation(s)
- Huiling Lin
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
- Department of Physiology, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China
| | - Ming Zhang
- Affiliated Qingyuan Hospital, Guangzhou Medical University (Qingyuan People's Hospital), Qingyuan, 511518, Guangdong, China
| | - Mi Hu
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
| | - Yangkai Zhang
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
| | - WeiWei Jiang
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Wanying Tang
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
| | - Yuxin Ouyang
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
| | - Liping Jiang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yali Mi
- Affiliated Qingyuan Hospital, Guangzhou Medical University (Qingyuan People's Hospital), Qingyuan, 511518, Guangdong, China
| | - Zhi Chen
- College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
| | - Pingping He
- Department of Nursing, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China.
| | - Guojun Zhao
- Affiliated Qingyuan Hospital, Guangzhou Medical University (Qingyuan People's Hospital), Qingyuan, 511518, Guangdong, China.
| | - Xinping Ouyang
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China.
- Department of Physiology, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China.
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, 410081, Hunan, Changsha, China.
- The Engineering Research Center of Reproduction and Translational Medicine of Hunan Province, School of Medicine, Hunan Normal University, 410081, Hunan, Changsha, China.
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217
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Pang Z, Cravatt BF, Ye L. Deciphering Drug Targets and Actions with Single-Cell and Spatial Resolution. Annu Rev Pharmacol Toxicol 2024; 64:507-526. [PMID: 37722721 DOI: 10.1146/annurev-pharmtox-033123-123610] [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] [Indexed: 09/20/2023]
Abstract
Recent advances in chemical, molecular, and genetic approaches have provided us with an unprecedented capacity to identify drug-target interactions across the whole proteome and genome. Meanwhile, rapid developments of single-cell and spatial omics technologies are revolutionizing our understanding of the molecular architecture of biological systems. However, a significant gap remains in how we align our understanding of drug actions, traditionally based on molecular affinities, with the in vivo cellular and spatial tissue heterogeneity revealed by these newer techniques. Here, we review state-of-the-art methods for profiling drug-target interactions and emerging multiomics tools to delineate the tissue heterogeneity at single-cell resolution. Highlighting the recent technical advances enabling high-resolution, multiplexable in situ small-molecule drug imaging (clearing-assisted tissue click chemistry, or CATCH), we foresee the integration of single-cell and spatial omics platforms, data, and concepts into the future framework of defining and understanding in vivo drug-target interactions and mechanisms of actions.
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Affiliation(s)
- Zhengyuan Pang
- Department of Neuroscience, The Scripps Research Institute, La Jolla, California, USA;
| | - Benjamin F Cravatt
- Department of Chemistry, The Scripps Research Institute, La Jolla, California, USA;
| | - Li Ye
- Department of Neuroscience, The Scripps Research Institute, La Jolla, California, USA;
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
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218
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Grujčić V, Saarenpää S, Sundh J, Sennblad B, Norgren B, Latz M, Giacomello S, Foster RA, Andersson AF. Towards high-throughput parallel imaging and single-cell transcriptomics of microbial eukaryotic plankton. PLoS One 2024; 19:e0296672. [PMID: 38241213 PMCID: PMC10798536 DOI: 10.1371/journal.pone.0296672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 12/13/2023] [Indexed: 01/21/2024] Open
Abstract
Single-cell transcriptomics has the potential to provide novel insights into poorly studied microbial eukaryotes. Although several such technologies are available and benchmarked on mammalian cells, few have been tested on protists. Here, we applied a microarray single-cell sequencing (MASC-seq) technology, that generates microscope images of cells in parallel with capturing their transcriptomes, on three species representing important plankton groups with different cell structures; the ciliate Tetrahymena thermophila, the diatom Phaeodactylum tricornutum, and the dinoflagellate Heterocapsa sp. Both the cell fixation and permeabilization steps were adjusted. For the ciliate and dinoflagellate, the number of transcripts of microarray spots with single cells were significantly higher than for background spots, and the overall expression patterns were correlated with that of bulk RNA, while for the much smaller diatom cells, it was not possible to separate single-cell transcripts from background. The MASC-seq method holds promise for investigating "microbial dark matter", although further optimizations are necessary to increase the signal-to-noise ratio.
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Affiliation(s)
- Vesna Grujčić
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Sami Saarenpää
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - John Sundh
- Science for Life Laboratory, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Stockholm University, Solna, Sweden
| | - Bengt Sennblad
- Science for Life Laboratory, Dept of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Benjamin Norgren
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Meike Latz
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Stefania Giacomello
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Rachel A. Foster
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Anders F. Andersson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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219
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Liu K, Han B. Role of immune cells in the pathogenesis of myocarditis. J Leukoc Biol 2024; 115:253-275. [PMID: 37949833 DOI: 10.1093/jleuko/qiad143] [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: 08/15/2023] [Revised: 10/15/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
Myocarditis is an inflammatory heart disease that mostly affects young people. Myocarditis involves a complex immune network; however, its detailed pathogenesis is currently unclear. The diversity and plasticity of immune cells, either in the peripheral blood or in the heart, have been partially revealed in a number of previous studies involving patients and several kinds of animal models with myocarditis. It is the complexity of immune cells, rather than one cell type that is the culprit. Thus, recognizing the individual intricacies within immune cells in the context of myocarditis pathogenesis and finding the key intersection of the immune network may help in the diagnosis and treatment of this condition. With the vast amount of cell data gained on myocarditis and the recent application of single-cell sequencing, we summarize the multiple functions of currently recognized key immune cells in the pathogenesis of myocarditis to provide an immune background for subsequent investigations.
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Affiliation(s)
- Keyu Liu
- Department of Pediatric Cardiology, Shandong Provincial Hospital, Shandong University, Cheeloo Colledge of Medicine, No. 324 Jingwu Road, 250021, Jinan, China
| | - Bo Han
- Department of Pediatric Cardiology, Shandong Provincial Hospital, Shandong University, Cheeloo Colledge of Medicine, No. 324 Jingwu Road, 250021, Jinan, China
- Department of Pediatric Cardiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, 250021, Jinan, China
- Shandong Provincial Hospital, Shandong Provincial Clinical Research Center for Children' s Health and Disease office, No. 324 Jingwu Road, 250021, Jinan, China
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220
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Zhao L, Wang Q, Yang C, Ye Y, Shen Z. Application of Single-Cell Sequencing Technology in Research on Colorectal Cancer. J Pers Med 2024; 14:108. [PMID: 38248808 PMCID: PMC10820918 DOI: 10.3390/jpm14010108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/04/2024] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
Colorectal cancer (CRC) is the third most prevalent and second most lethal cancer globally, with gene mutations and tumor metastasis contributing to its poor prognosis. Single-cell sequencing technology enables high-throughput analysis of the genome, transcriptome, and epigenetic landscapes at the single-cell level. It offers significant insights into analyzing the tumor immune microenvironment, detecting tumor heterogeneity, exploring metastasis mechanisms, and monitoring circulating tumor cells (CTCs). This article provides a brief overview of the technical procedure and data processing involved in single-cell sequencing. It also reviews the current applications of single-cell sequencing in CRC research, aiming to enhance the understanding of intratumoral heterogeneity, CRC development, CTCs, and novel drug targets. By exploring the diverse molecular and clinicopathological characteristics of tumor heterogeneity using single-cell sequencing, valuable insights can be gained into early diagnosis, therapy, and prognosis of CRC. Thus, this review serves as a valuable resource for identifying prognostic markers, discovering new therapeutic targets, and advancing personalized therapy in CRC.
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Affiliation(s)
- Long Zhao
- Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing 100044, China; (L.Z.); (C.Y.); (Y.Y.)
- Laboratory of Surgical Oncology, Peking University People’s Hospital, Beijing 100044, China
| | - Quan Wang
- Department of Ambulatory Surgery Center, Xijing Hospital, Air Force Military Medical University, Xi’an 710032, China;
| | - Changjiang Yang
- Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing 100044, China; (L.Z.); (C.Y.); (Y.Y.)
- Laboratory of Surgical Oncology, Peking University People’s Hospital, Beijing 100044, China
| | - Yingjiang Ye
- Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing 100044, China; (L.Z.); (C.Y.); (Y.Y.)
- Laboratory of Surgical Oncology, Peking University People’s Hospital, Beijing 100044, China
| | - Zhanlong Shen
- Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing 100044, China; (L.Z.); (C.Y.); (Y.Y.)
- Laboratory of Surgical Oncology, Peking University People’s Hospital, Beijing 100044, China
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221
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Saxena P, Sinha A, Singh SK. Computer-assisted interpretation, in-depth exploration and single cell type annotation of RNA sequence data using k-means clustering algorithm. Comput Methods Biomech Biomed Engin 2024:1-11. [PMID: 38235728 DOI: 10.1080/10255842.2023.2300685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 12/24/2023] [Indexed: 01/19/2024]
Abstract
At now, the majority of approaches rely on manual techniques for annotating cell types subsequent to clustering the data obtained from single-cell RNA sequencing (scRNA-seq). These approaches require a significant amount of physical exertion and depend substantially on the user's skill, perhaps resulting in uneven outcomes and inconsistency in treatment. In this paper, we provide a computer-assisted interpretation of every single cell of a tissue sample, along with an in-depth exploration of an individual cell's molecular, phenotypic and functional attributes. The paper will also perform k-means clustering followed by silhouette validation based on similar phenotype and functional attributes, and also, cell type annotation is performed, where we match a cell's gene profile against some known database by applying certain statistical conditions. Finally, all the genes are mapped spatially on the tissue sample. This paper is an aid to medicine to know which cells are expressed/not expressed in a tissue sample and their spatial location on the tissue sample.
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Affiliation(s)
- Pranshu Saxena
- Department of Information Technology, ABES Engineering College, Ghaziabad, India
| | - Amit Sinha
- Department of Information Technology, ABES Engineering College, Ghaziabad, India
| | - Sanjay Kumar Singh
- University School of Automation and Robotics, Guru Gobind Singh Indraprastha University, Surajmal Vihar, Delhi, India
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222
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Kiessling P, Kuppe C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med 2024; 16:14. [PMID: 38238823 PMCID: PMC10795303 DOI: 10.1186/s13073-024-01282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
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Affiliation(s)
- Paul Kiessling
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany
| | - Christoph Kuppe
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany.
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223
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Peng D, Jackson D, Palicha B, Kernfeld E, Laughner N, Shoemaker A, Celniker SE, Loganathan R, Cahan P, Andrew DJ. Organogenetic transcriptomes of the Drosophila embryo at single cell resolution. Development 2024; 151:dev202097. [PMID: 38174902 PMCID: PMC10820837 DOI: 10.1242/dev.202097] [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: 06/16/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024]
Abstract
To gain insight into the transcription programs activated during the formation of Drosophila larval structures, we carried out single cell RNA sequencing during two periods of Drosophila embryogenesis: stages 10-12, when most organs are first specified and initiate morphological and physiological specialization; and stages 13-16, when organs achieve their final mature architectures and begin to function. Our data confirm previous findings with regards to functional specialization of some organs - the salivary gland and trachea - and clarify the embryonic functions of another - the plasmatocytes. We also identify two early developmental trajectories in germ cells and uncover a potential role for proteolysis during germline stem cell specialization. We identify the likely cell type of origin for key components of the Drosophila matrisome and several commonly used Drosophila embryonic cell culture lines. Finally, we compare our findings with other recent related studies and with other modalities for identifying tissue-specific gene expression patterns. These data provide a useful community resource for identifying many new players in tissue-specific morphogenesis and functional specialization of developing organs.
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Affiliation(s)
- Da Peng
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Dorian Jackson
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Bianca Palicha
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Eric Kernfeld
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Nathaniel Laughner
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Ashleigh Shoemaker
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Susan E. Celniker
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Rajprasad Loganathan
- Department of Biological Sciences, Wichita State University, Wichita, KS 67260, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Deborah J. Andrew
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Chen L, Wan Y, Yang T, Zhang Q, Zeng Y, Zheng S, Ling Z, Xiao Y, Wan Q, Liu R, Yang C, Huang G, Zeng Q. Bibliometric and visual analysis of single-cell sequencing from 2010 to 2022. Front Genet 2024; 14:1285599. [PMID: 38274109 PMCID: PMC10808606 DOI: 10.3389/fgene.2023.1285599] [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: 09/04/2023] [Accepted: 12/31/2023] [Indexed: 01/27/2024] Open
Abstract
Background: Single-cell sequencing (SCS) is a technique used to analyze the genome, transcriptome, epigenome, and other genetic data at the level of a single cell. The procedure is commonly utilized in multiple fields, including neurobiology, immunology, and microbiology, and has emerged as a key focus of life science research. However, a thorough and impartial analysis of the existing state and trends of SCS-related research is lacking. The current study aimed to map the development trends of studies on SCS during the years 2010-2022 through bibliometric software. Methods: Pertinent papers on SCS from 2010 to 2022 were obtained using the Web of Science Core Collection. Research categories, nations/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords were analyzed using VOSviewer, the R package "bibliometric", and CiteSpace. Results: The bibliometric analysis included 9,929 papers published between 2010 and 2022, and showed a consistent increase in the quantity of papers each year. The United States was the source of the highest quantity of articles and citations in this field. The majority of articles were published in the periodical Nature Communications. Butler A was the most frequently quoted author on this topic, and his article "Integrating single-cell transcriptome data across diverse conditions, technologies, and species" has received numerous citations to date. The literature and keyword analysis showed that studies involving single-cell RNA sequencing (scRNA-seq) were prominent in this discipline during the study period. Conclusion: This study utilized bibliometric techniques to visualize research in SCS-related domains, which facilitated the identification of emerging patterns and future directions in the field. Current hot topics in SCS research include COVID-19, tumor microenvironment, scRNA-seq, and neuroscience. Our results are significant for scholars seeking to identify key issues and generate new research ideas.
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Affiliation(s)
- Ling Chen
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yantong Wan
- Guangdong Provincial Key Laboratory of Proteomics, Department of Pathophysiology, School of BasicMedical Sciences, Southern Medical University, Guangzhou, China
| | - Tingting Yang
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Qi Zhang
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Yuting Zeng
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Shuqi Zheng
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Zhishan Ling
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Yupeng Xiao
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Qingyi Wan
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Ruili Liu
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Chun Yang
- Dongguan Key Laboratory of Stem Cell and Regenerative Tissue Engineering, Guangdong Medical University, Dongguan, China
| | - Guozhi Huang
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Qing Zeng
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
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225
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Jang WJ, Lee S, Jeong CH. Uncovering transcriptomic biomarkers for enhanced diagnosis of methamphetamine use disorder: a comprehensive review. Front Psychiatry 2024; 14:1302994. [PMID: 38260797 PMCID: PMC10800441 DOI: 10.3389/fpsyt.2023.1302994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Methamphetamine use disorder (MUD) is a chronic relapsing disorder characterized by compulsive Methamphetamine (MA) use despite its detrimental effects on physical, psychological, and social well-being. The development of MUD is a complex process that involves the interplay of genetic, epigenetic, and environmental factors. The treatment of MUD remains a significant challenge, with no FDA-approved pharmacotherapies currently available. Current diagnostic criteria for MUD rely primarily on self-reporting and behavioral assessments, which have inherent limitations owing to their subjective nature. This lack of objective biomarkers and unidimensional approaches may not fully capture the unique features and consequences of MA addiction. Methods We performed a literature search for this review using the Boolean search in the PubMed database. Results This review explores existing technologies for identifying transcriptomic biomarkers for MUD diagnosis. We examined non-invasive tissues and scrutinized transcriptomic biomarkers relevant to MUD. Additionally, we investigated transcriptomic biomarkers identified for diagnosing, predicting, and monitoring MUD in non-invasive tissues. Discussion Developing and validating non-invasive MUD biomarkers could address these limitations, foster more precise and reliable diagnostic approaches, and ultimately enhance the quality of care for individuals with MA addiction.
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Affiliation(s)
| | | | - Chul-Ho Jeong
- College of Pharmacy, Keimyung University, Daegu, Republic of Korea
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226
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Liu X, Yi J, Li T, Wen J, Huang K, Liu J, Wang G, Kim P, Song Q, Zhou X. DRMref: comprehensive reference map of drug resistance mechanisms in human cancer. Nucleic Acids Res 2024; 52:D1253-D1264. [PMID: 37986230 PMCID: PMC10767840 DOI: 10.1093/nar/gkad1087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/18/2023] [Accepted: 10/30/2023] [Indexed: 11/22/2023] Open
Abstract
Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal transition, cell-cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug's Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user's exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.
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Affiliation(s)
- Xiaona Liu
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jiahao Yi
- Bioinformatics and Biomedical Big Data Mining Laboratory, Department of Medical Informatics, School of Big Health, Guizhou Medical University, Guiyang 550025, China
| | - Tina Li
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jianguo Wen
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Kexin Huang
- West China Biomedical Big Data Centre, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiajia Liu
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Grant Wang
- Department of Bioengineering, Rice University, Houston, TX 77005, USA
| | - Pora Kim
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Qianqian Song
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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227
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Clough E, Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim I, Tomashevsky M, Marshall K, Phillippy K, Sherman P, Lee H, Zhang N, Serova N, Wagner L, Zalunin V, Kochergin A, Soboleva A. NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update. Nucleic Acids Res 2024; 52:D138-D144. [PMID: 37933855 PMCID: PMC10767856 DOI: 10.1093/nar/gkad965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
The Gene Expression Omnibus (GEO) is an international public repository that archives gene expression and epigenomics data sets generated by next-generation sequencing and microarray technologies. Data are typically submitted to GEO by researchers in compliance with widespread journal and funder mandates to make generated data publicly accessible. The resource handles raw data files, processed data files and descriptive metadata for over 200 000 studies and 6.5 million samples, all of which are indexed, searchable and downloadable. Additionally, GEO offers web-based tools that facilitate analysis and visualization of differential gene expression. This article presents the current status and recent advancements in GEO, including the generation of consistently computed gene expression count matrices for thousands of RNA-seq studies, and new interactive graphical plots in GEO2R that help users identify differentially expressed genes and assess data set quality. The GEO repository is built and maintained by the National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), and is publicly accessible at https://www.ncbi.nlm.nih.gov/geo/.
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Affiliation(s)
- Emily Clough
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Tanya Barrett
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Stephen E Wilhite
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Pierre Ledoux
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Carlos Evangelista
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Irene F Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Maxim Tomashevsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kimberly A Marshall
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Katherine H Phillippy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patti M Sherman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Hyeseung Lee
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Naigong Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nadezhda Serova
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lukas Wagner
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Vadim Zalunin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Andrey Kochergin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alexandra Soboleva
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
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228
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Umbaugh DS, Jaeschke H. Biomarker discovery in acetaminophen hepatotoxicity: leveraging single-cell transcriptomics and mechanistic insight. Expert Rev Clin Pharmacol 2024; 17:143-155. [PMID: 38217408 PMCID: PMC10872301 DOI: 10.1080/17512433.2024.2306219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 01/12/2024] [Indexed: 01/15/2024]
Abstract
INTRODUCTION Acetaminophen (APAP) overdose is the leading cause of drug-induced liver injury and can cause a rapid progression to acute liver failure (ALF). Therefore, the identification of prognostic biomarkers to determine which patients will require a liver transplant is critical for APAP-induced ALF. AREAS COVERED We begin by relating the mechanistic investigations in mouse models of APAP hepatotoxicity to the human APAP overdose pathophysiology. We draw insights from the established sequence of molecular events in mice to understand the progression of events in the APAP overdose patient. Through this mechanistic understanding, several new biomarkers, such as CXCL14, have recently been evaluated. We also explore how single-cell RNA sequencing, spatial transcriptomics, and other omics approaches have been leveraged for identifying novel biomarkers and how these approaches will continue to push the field of biomarker discovery forward. EXPERT OPINION Recent investigations have elucidated several new biomarkers or combination of markers such as CXCL14, a regenerative miRNA signature, a cell death miRNA signature, hepcidin, LDH, CPS1, and FABP1. While these biomarkers are promising, they all require further validation. Larger cohort studies analyzing these new biomarkers in the same patient samples, while adding these candidate biomarkers to prognostic models will further support their clinical utility.
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Affiliation(s)
- David S Umbaugh
- Department of Pharmacology, Toxicology & Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Hartmut Jaeschke
- Department of Pharmacology, Toxicology & Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA
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229
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Yamashita N, Kramann R. Mechanisms of kidney fibrosis and routes towards therapy. Trends Endocrinol Metab 2024; 35:31-48. [PMID: 37775469 DOI: 10.1016/j.tem.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/01/2023] [Accepted: 09/04/2023] [Indexed: 10/01/2023]
Abstract
Kidney fibrosis is the final common pathway of virtually all chronic kidney diseases (CKDs) and is therefore considered to be a promising therapeutic target for these conditions. However, despite great progress in recent years, no targeted antifibrotic therapies for the kidney have been approved, likely because the complex mechanisms that initiate and drive fibrosis are not yet completely understood. Recent single-cell genomic approaches have allowed novel insights into kidney fibrosis mechanisms in mouse and human, particularly the heterogeneity and differentiation processes of myofibroblasts, the role of injured epithelial cells and immune cells, and their crosstalk mechanisms. In this review we summarize the key mechanisms that drive kidney fibrosis, including recent advances in understanding the mechanisms, as well as potential routes for developing novel targeted antifibrotic therapeutics.
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Affiliation(s)
- Noriyuki Yamashita
- Department of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany; Department of Nephrology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Rafael Kramann
- Department of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany; Department of Internal Medicine, Nephrology, and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands.
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230
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Momoi MY. Overview: Research on the Genetic Architecture of the Developing Cerebral Cortex in Norms and Diseases. Methods Mol Biol 2024; 2794:1-12. [PMID: 38630215 DOI: 10.1007/978-1-0716-3810-1_1] [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] [Indexed: 04/19/2024]
Abstract
The human brain is characterized by high cell numbers, diverse cell types with diverse functions, and intricate connectivity with an exceedingly broad surface of the cortex. Human-specific brain development was accomplished by a long timeline for maturation from the prenatal period to the third decade of life. The long timeline makes complicated architecture and circuits of human cerebral cortex possible, and it makes human brain vulnerable to intrinsic and extrinsic insults resulting in the development of variety of neuropsychiatric disorders. Unraveling the molecular and cellular processes underlying human brain development under the elaborate regulation of gene expression in a spatiotemporally specific manner, especially that of the cortex will provide a biological understanding of human cognition and behavior in health and diseases. Global research consortia and the advancing technologies in brain science including functional genomics equipped with emergent neuroinformatics such as single-cell multiomics, novel human models, and high-volume databases with high-throughput computation facilitate the biological understanding of the development of the human brain cortex. Knowing the process of interplay of the genome and the environment in cortex development will lead us to understand the human-specific cognitive function and its individual diversity. Thus, it is worthwhile to overview the recent progress in neurotechnology to foresee further understanding of the human brain and norms and diseases.
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Affiliation(s)
- Mariko Y Momoi
- Ryomo Seishi Ryogoen Rehabilitation Hospital for Children with Disabilities, Gunma, Japan
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231
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Rodríguez-Bejarano OH, Roa L, Vargas-Hernández G, Botero-Espinosa L, Parra-López C, Patarroyo MA. Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate. Biochim Biophys Acta Rev Cancer 2024; 1879:189064. [PMID: 38158026 DOI: 10.1016/j.bbcan.2023.189064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]
Abstract
The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.
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Affiliation(s)
- Oscar Hernán Rodríguez-Bejarano
- Health Sciences Faculty, Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A), Calle 222#55-37, Bogotá 111166, Colombia; Molecular Biology and Immunology Department, Fundacion Instituto de Inmunología de Colombia (FIDIC), Carrera 50#26-20, Bogotá 111321, Colombia; PhD Programme in Biotechnology, Faculty of Sciences, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Leonardo Roa
- Veterinary Clinic, Faculty of Agricultural Sciences, Universidad de La Salle, Carrera 7 #179-03, Bogotá 110141, Colombia
| | - Giovanni Vargas-Hernández
- Animal Health Department, Faculty of Veterinary Medicine and Zootechnics, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Lucía Botero-Espinosa
- Animal Health Department, Faculty of Veterinary Medicine and Zootechnics, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Carlos Parra-López
- Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia.
| | - Manuel Alfonso Patarroyo
- Molecular Biology and Immunology Department, Fundacion Instituto de Inmunología de Colombia (FIDIC), Carrera 50#26-20, Bogotá 111321, Colombia; Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia.
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232
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Kumar A, Dixson J, Azad RK. RNA-Seq Analysis of Mammalian Prion Disease. Methods Mol Biol 2024; 2812:367-377. [PMID: 39068373 DOI: 10.1007/978-1-0716-3886-6_20] [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] [Indexed: 07/30/2024]
Abstract
A protein, which can attain a prion state, differs from standard proteins in terms of structural conformation and aggregation propensity. High-throughput sequencing technology provides an opportunity to gain insight into the prion disease condition when coupled with single-cell RNA-Seq analysis to reveal transcriptional changes during prion-based pathogenicity. In this chapter, we present a protocol for RNA-Seq analysis of mammalian prion disease using a single-cell RNA sequencing dataset procured from the NCBI GEO database. This protocol is a tool that can assist researchers in characterizing mammalian prion disease in a reproducible and reusable manner. Further, the resulting output has the potential to provide transcript biomarkers for mammalian prion diseases, which can be employed for diagnostic and prognostic purposes.
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Affiliation(s)
- Ambarish Kumar
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA
| | - Jamie Dixson
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA
| | - Rajeev K Azad
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA.
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233
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Pandey A, Bhutani N. Profiling joint tissues at single-cell resolution: advances and insights. Nat Rev Rheumatol 2024; 20:7-20. [PMID: 38057475 DOI: 10.1038/s41584-023-01052-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2023] [Indexed: 12/08/2023]
Abstract
Advances in the profiling of human joint tissues at single-cell resolution have provided unique insights into the organization and function of these tissues in health and disease. Data generated by various single-cell technologies, including single-cell RNA sequencing and cytometry by time-of-flight, have identified the distinct subpopulations that constitute these tissues. These timely studies have provided the building blocks for the construction of single-cell atlases of joint tissues including cartilage, bone and synovium, leading to the identification of developmental trajectories, deciphering of crosstalk between cells and discovery of rare populations such as stem and progenitor cells. In addition, these studies have revealed unique pathogenetic populations that are potential therapeutic targets. The use of these approaches in synovial tissues has helped to identify how distinct cell subpopulations can orchestrate disease initiation and progression and be responsible for distinct pathological outcomes. Additionally, repair of tissues such as cartilage and meniscus remains an unmet medical need, and single-cell methodologies can be invaluable in providing a blueprint for both effective tissue-engineering strategies and therapeutic interventions for chronic joint diseases such as osteoarthritis and rheumatoid arthritis.
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Affiliation(s)
- Akshay Pandey
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA, USA
| | - Nidhi Bhutani
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA, USA.
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234
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Lang PF, Penas DR, Banga JR, Weindl D, Novak B. Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells. PLoS Comput Biol 2024; 20:e1011151. [PMID: 38190398 PMCID: PMC10773963 DOI: 10.1371/journal.pcbi.1011151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 11/24/2023] [Indexed: 01/10/2024] Open
Abstract
The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. By combining both state-of-the-art multiplexed experimental methods and powerful computational tools, this work aims at improving on these models along four dimensions: model structure, validation data, validation methodology and model reusability. We developed a comprehensive model structure of the full cell cycle that qualitatively explains the behaviour of human retinal pigment epithelial-1 cells. To estimate the model parameters, time courses of eight cell cycle regulators in two compartments were reconstructed from single cell snapshot measurements. After optimisation with a parallel global optimisation metaheuristic we obtained excellent agreements between simulations and measurements. The PEtab specification of the optimisation problem facilitates reuse of model, data and/or optimisation results. Future perturbation experiments will improve parameter identifiability and allow for testing model predictive power. Such a predictive model may aid in drug discovery for cell cycle-related disorders.
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Affiliation(s)
- Paul F. Lang
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - David R. Penas
- Computational Biology Lab, MBG-CSIC (Spanish National Research Council), Pontevedra, Spain
| | - Julio R. Banga
- Computational Biology Lab, MBG-CSIC (Spanish National Research Council), Pontevedra, Spain
| | - Daniel Weindl
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
| | - Bela Novak
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
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235
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Cadwell CR, Tolias AS. Patch-seq: Multimodal Profiling of Single-Cell Morphology, Electrophysiology, and Gene Expression. Methods Mol Biol 2024; 2752:227-243. [PMID: 38194038 DOI: 10.1007/978-1-0716-3621-3_15] [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] [Indexed: 01/10/2024]
Abstract
Cells exhibit diverse morphologic phenotypes, biophysical and functional properties, and gene expression patterns. Understanding how these features are interrelated at the level of single cells has been challenging due to the lack of techniques for multimodal profiling of individual cells. We recently developed Patch-seq, a technique that combines whole-cell patch clamp recording, immunohistochemistry, and single-cell RNA-sequencing (scRNA-seq) to comprehensively profile single cells. Here we present a detailed step-by-step protocol for obtaining high-quality morphological, electrophysiological, and transcriptomic data from single cells. Patch-seq enables researchers to explore the rich, multidimensional phenotypic variability among cells and to directly correlate gene expression with phenotype at the level of single cells.
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Affiliation(s)
- Cathryn R Cadwell
- Departments of Neurological Surgery and Pathology, School of Medicine, University of California San Francisco, San Francisco, CA, USA.
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
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236
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Swaminath S, Russell AB. The use of single-cell RNA-seq to study heterogeneity at varying levels of virus-host interactions. PLoS Pathog 2024; 20:e1011898. [PMID: 38236826 PMCID: PMC10796064 DOI: 10.1371/journal.ppat.1011898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2024] Open
Abstract
The outcome of viral infection depends on the diversity of the infecting viral population and the heterogeneity of the cell population that is infected. Until almost a decade ago, the study of these dynamic processes during viral infection was challenging and limited to certain targeted measurements. Presently, with the use of single-cell sequencing technology, the complex interface defined by the interactions of cells with infecting virus can now be studied across the breadth of the transcriptome in thousands of individual cells simultaneously. In this review, we will describe the use of single-cell RNA sequencing (scRNA-seq) to study the heterogeneity of viral infections, ranging from individual virions to the immune response between infected individuals. In addition, we highlight certain key experimental limitations and methodological decisions that are critical to analyzing scRNA-seq data at each scale.
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Affiliation(s)
- Sharmada Swaminath
- School of Biological Sciences, University of California, San Diego, La Jolla, California, United States of America
| | - Alistair B. Russell
- School of Biological Sciences, University of California, San Diego, La Jolla, California, United States of America
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237
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Bhattachan P, Jeschke MG. SINGLE-CELL TRANSCRIPTOME ANALYSIS IN HEALTH AND DISEASE. Shock 2024; 61:19-27. [PMID: 37962963 PMCID: PMC10883422 DOI: 10.1097/shk.0000000000002274] [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] [Indexed: 11/16/2023]
Abstract
ABSTRACT The analysis of the single-cell transcriptome has emerged as a powerful tool to gain insights on the basic mechanisms of health and disease. It is widely used to reveal the cellular diversity and complexity of tissues at cellular resolution by RNA sequencing of the whole transcriptome from a single cell. Equally, it is applied to discover an unknown, rare population of cells in the tissue. The prime advantage of single-cell transcriptome analysis is the detection of stochastic nature of gene expression of the cell in tissue. Moreover, the availability of multiple platforms for the single-cell transcriptome has broadened its approaches to using cells of different sizes and shapes, including the capture of short or full-length transcripts, which is helpful in the analysis of challenging biological samples. And with the development of numerous packages in R and Python, new directions in the computational analysis of single-cell transcriptomes can be taken to characterize healthy versus diseased tissues to obtain novel pathological insights. Downstream analysis such as differential gene expression analysis, gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, cell-cell interaction analysis, and trajectory analysis has become standard practice in the workflow of single-cell transcriptome analysis to further examine the biology of different cell types. Here, we provide a broad overview of single-cell transcriptome analysis in health and disease conditions currently applied in various studies.
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238
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Deng X, Hu Z, Zhou S, Wu Y, Fu M, Zhou C, Sun J, Gao X, Huang Y. Perspective from single-cell sequencing: Is inflammation in acute ischemic stroke beneficial or detrimental? CNS Neurosci Ther 2024; 30:e14510. [PMID: 37905592 PMCID: PMC10805403 DOI: 10.1111/cns.14510] [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: 07/05/2023] [Revised: 09/24/2023] [Accepted: 10/08/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Acute ischemic stroke (AIS) is a common cerebrovascular event associated with high incidence, disability, and poor prognosis. Studies have shown that various cell types, including microglia, astrocytes, oligodendrocytes, neurons, and neutrophils, play complex roles in the early stages of AIS and significantly affect its prognosis. Thus, a comprehensive understanding of the mechanisms of action of these cells will be beneficial for improving stroke prognosis. With the rapid development of single-cell sequencing technology, researchers have explored the pathophysiological mechanisms underlying AIS at the single-cell level. METHOD We systematically summarize the latest research on single-cell sequencing in AIS. RESULT In this review, we summarize the phenotypes and functions of microglia, astrocytes, oligodendrocytes, neurons, neutrophils, monocytes, and lymphocytes, as well as their respective subtypes, at different time points following AIS. In particular, we focused on the crosstalk between microglia and astrocytes, oligodendrocytes, and neurons. Our findings reveal diverse and sometimes opposing roles within the same cell type, with the possibility of interconversion between different subclusters. CONCLUSION This review offers a pioneering exploration of the functions of various glial cells and cell subclusters after AIS, shedding light on their regulatory mechanisms that facilitate the transformation of detrimental cell subclusters towards those that are beneficial for improving the prognosis of AIS. This approach has the potential to advance the discovery of new specific targets and the development of drugs, thus representing a significant breakthrough in addressing the challenges in AIS treatment.
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Affiliation(s)
- Xinpeng Deng
- Department of NeurosurgeryThe First Affiliated Hospital of Ningbo UniversityNingboChina
- Key Laboratory of Precision Medicine for Atherosclerotic Diseases of Zhejiang ProvinceNingboChina
| | - Ziliang Hu
- Department of NeurosurgeryThe First Affiliated Hospital of Ningbo UniversityNingboChina
- Key Laboratory of Precision Medicine for Atherosclerotic Diseases of Zhejiang ProvinceNingboChina
| | - Shengjun Zhou
- Department of NeurosurgeryThe First Affiliated Hospital of Ningbo UniversityNingboChina
| | - Yiwen Wu
- Department of NeurosurgeryThe First Affiliated Hospital of Ningbo UniversityNingboChina
| | - Menglin Fu
- School of Economics and ManagementChina University of GeosciencesWuhanChina
| | - Chenhui Zhou
- Department of NeurosurgeryThe First Affiliated Hospital of Ningbo UniversityNingboChina
| | - Jie Sun
- Department of NeurosurgeryThe First Affiliated Hospital of Ningbo UniversityNingboChina
| | - Xiang Gao
- Department of NeurosurgeryThe First Affiliated Hospital of Ningbo UniversityNingboChina
| | - Yi Huang
- Department of NeurosurgeryThe First Affiliated Hospital of Ningbo UniversityNingboChina
- Key Laboratory of Precision Medicine for Atherosclerotic Diseases of Zhejiang ProvinceNingboChina
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239
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Yoshida H. Dissecting the Immune System through Gene Regulation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1444:219-235. [PMID: 38467983 DOI: 10.1007/978-981-99-9781-7_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.
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Affiliation(s)
- Hideyuki Yoshida
- YCI Laboratory for Immunological Transcriptomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
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240
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Kalsan M, Jabeen A, Ahmad S. Incorporating Sequence-Dependent DNA Shape and Dynamics into Transcriptome Data Analysis. Methods Mol Biol 2024; 2812:317-343. [PMID: 39068371 DOI: 10.1007/978-1-0716-3886-6_18] [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] [Indexed: 07/30/2024]
Abstract
Differentially expressed genes in a cellular context may be co-regulated by the same transcription factor. However, in the absence of a concurrent transcription factor binding data, such interactions are difficult to detect, especially at the single cell expression level. Motif enrichments in such genes can be used to gain insight into differential expressions caused by the shared upstream TFs. However, it is now established that many genes are co-regulated by the same TF due to a shared DNA shape or sequence-dependent conformational dynamics instead of sequence motif. In this work, we demonstrate how, starting from a gene expression data, such DNA shape and dynamics signatures can be potentially detected using publicly available tools, including DynaSeq, developed in our group for predicting the sequence-dependent components of these DNA shape features.
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Affiliation(s)
- Manisha Kalsan
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Almas Jabeen
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Shandar Ahmad
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
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241
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Sun C, Shao Y, Iqbal J. Insect Insights at the Single-Cell Level: Technologies and Applications. Cells 2023; 13:91. [PMID: 38201295 PMCID: PMC10777908 DOI: 10.3390/cells13010091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/23/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Single-cell techniques are a promising way to unravel the complexity and heterogeneity of transcripts at the cellular level and to reveal the composition of different cell types and functions in a tissue or organ. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have further changed our view of biological systems. The application of scRNA-seq in insects enables the comprehensive characterization of both common and rare cell types and cell states, the discovery of new cell types, and revealing how cell types relate to each other. The recent application of scRNA-seq techniques to insect tissues has led to a number of exciting discoveries. Here we provide an overview of scRNA-seq and its application in insect research, focusing on biological applications, current challenges, and future opportunities to make new discoveries with scRNA-seq in insects.
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Affiliation(s)
- Chao Sun
- Analysis Center of Agrobiology and Environmental Sciences, Zhejiang University, Hangzhou 310058, China;
| | - Yongqi Shao
- Institute of Sericulture and Apiculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Junaid Iqbal
- Institute of Sericulture and Apiculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
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242
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Wang X, Pei J, Xiong L, Kang Y, Guo S, Cao M, Ding Z, Bao P, Chu M, Liang C, Yan P, Guo X. Single-cell RNA sequencing and UPHLC-MS/MS targeted metabolomics offer new insights into the etiological basis for male cattle-yak sterility. Int J Biol Macromol 2023; 253:126831. [PMID: 37716658 DOI: 10.1016/j.ijbiomac.2023.126831] [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: 05/26/2023] [Revised: 08/18/2023] [Accepted: 09/06/2023] [Indexed: 09/18/2023]
Abstract
The variety of species can be efficiently increased by interspecific hybridization. However, because the males in the hybrid progeny are usually sterile, this heterosis cannot be employed when other cattle and yaks are hybridized. While some system-level studies have sought to explore the etiological basis for male cattle-yak sterility, no systematic cellular analyses of this phenomenon have yet been performed. Here, single-cell RNA sequencing and UPHLC-MS/MS targeted metabolomics methods were used to study the differences in testicular tissue between 4-year-old male yak and 4-year-old male cattle-yak, providing new and comprehensive insights into the causes of male cattle-yak sterility. Cattle-yak testes samples detected 6 somatic cell types and one mixed germ cell type. Comparisons of these cell types revealed the more significant differences in Sertoli cells (SCs) and [Leydig cells and myoid cells (LCs_MCs)] between yak and cattle-yak samples compared to other somatic cell clusters. Even though the LCs and MCs from yaks and cattle-yaks were derived from the differentiation of the same progenitor cells, a high degree of overlap between LCs and MCs was observed in yak samples. Still, only a small overlap between LCs and MCs was observed in cattle-yak samples. Functional enrichment analyses revealed that genes down-regulated in cattle-yak SCs were primarily enriched in biological activity, whereas up-regulated genes in these cells were enriched for apoptotic activity. Furthermore, the genes of up-regulated in LCs_MCs of cattle-yak were significantly enriched in enzyme inhibitor and molecular function inhibitor activity. On the other hand, the genes of down-regulated in these cells were enriched for signal receptor binding, molecular function regulation, positive regulation of biological processes, and regulation of cell communication activity. The most significant annotated differences between yak and cattle-yak LCs_MCs were associated with cell-to-cell communication. While yak LCs_MCs regulated spermatogenic cells at spermatogonia, spermatocyte, and spermatid levels, no such relationships were found between cattle-yak LCs_MCs and germ cells. This may suggest that the somatic niche in male cattle-yak testes is a microenvironment that is ultimately not favorable for spermatogenesis.
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Affiliation(s)
- Xingdong Wang
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Jie Pei
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Lin Xiong
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Yandong Kang
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Shaoke Guo
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Mengli Cao
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Ziqiang Ding
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Pengjia Bao
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Min Chu
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Chunnian Liang
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Ping Yan
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China
| | - Xian Guo
- Key Laboratory of Yak Breeding Engineering of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou 730050, China.
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243
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Shao M, Zhang W, Li Y, Tang L, Hao ZZ, Liu S. Patch-seq: Advances and Biological Applications. Cell Mol Neurobiol 2023; 44:8. [PMID: 38123823 DOI: 10.1007/s10571-023-01436-3] [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: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023]
Abstract
Multimodal analysis of gene-expression patterns, electrophysiological properties, and morphological phenotypes at the single-cell/single-nucleus level has been arduous because of the diversity and complexity of neurons. The emergence of Patch-sequencing (Patch-seq) directly links transcriptomics, morphology, and electrophysiology, taking neuroscience research to a multimodal era. In this review, we summarized the development of Patch-seq and recent applications in the cortex, hippocampus, and other nervous systems. Through generating multimodal cell type atlases, targeting specific cell populations, and correlating transcriptomic data with phenotypic information, Patch-seq has provided new insight into outstanding questions in neuroscience. We highlight the challenges and opportunities of Patch-seq in neuroscience and hope to shed new light on future neuroscience research.
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Affiliation(s)
- Mingting Shao
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, China
| | - Wei Zhang
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, China
| | - Ye Li
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, China
| | - Lei Tang
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, China
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou, 510080, China.
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244
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Karmele EP, Moldoveanu AL, Kaymak I, Jugder BE, Ursin RL, Bednar KJ, Corridoni D, Ort T. Single cell RNA-sequencing profiling to improve the translation between human IBD and in vivo models. Front Immunol 2023; 14:1291990. [PMID: 38179052 PMCID: PMC10766350 DOI: 10.3389/fimmu.2023.1291990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/29/2023] [Indexed: 01/06/2024] Open
Abstract
Inflammatory bowel disease (IBD) is an umbrella term for two conditions (Crohn's Disease and Ulcerative Colitis) that is characterized by chronic inflammation of the gastrointestinal tract. The use of pre-clinical animal models has been invaluable for the understanding of potential disease mechanisms. However, despite promising results of numerous therapeutics in mouse colitis models, many of these therapies did not show clinical benefits in patients with IBD. Single cell RNA-sequencing (scRNA-seq) has recently revolutionized our understanding of complex interactions between the immune system, stromal cells, and epithelial cells by mapping novel cell subpopulations and their remodeling during disease. This technology has not been widely applied to pre-clinical models of IBD. ScRNA-seq profiling of murine models may provide an opportunity to increase the translatability into the clinic, and to choose the most appropriate model to test hypotheses and novel therapeutics. In this review, we have summarized some of the key findings at the single cell transcriptomic level in IBD, how specific signatures have been functionally validated in vivo, and highlighted the similarities and differences between scRNA-seq findings in human IBD and experimental mouse models. In each section of this review, we highlight the importance of utilizing this technology to find the most suitable or translational models of IBD based on the cellular therapeutic target.
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Affiliation(s)
- Erik P. Karmele
- Bioscience Immunology, Research and Early Development, Respiratory and Immunology, Biopharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Ana Laura Moldoveanu
- Bioscience Immunology, Research and Early Development, Respiratory and Immunology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Irem Kaymak
- Bioscience Immunology, Research and Early Development, Respiratory and Immunology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Bat-Erdene Jugder
- Bioscience Immunology, Research and Early Development, Respiratory and Immunology, Biopharmaceuticals R&D, AstraZeneca, Waltham, MA, United States
| | - Rebecca L. Ursin
- Bioscience Immunology, Research and Early Development, Respiratory and Immunology, Biopharmaceuticals R&D, AstraZeneca, Waltham, MA, United States
| | - Kyle J. Bednar
- Bioscience Immunology, Research and Early Development, Respiratory and Immunology, Biopharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Daniele Corridoni
- Bioscience Immunology, Research and Early Development, Respiratory and Immunology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Tatiana Ort
- Bioscience Immunology, Research and Early Development, Respiratory and Immunology, Biopharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
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Dawood M, Eastwood M, Jahanifar M, Young L, Ben-Hur A, Branson K, Jones L, Rajpoot N, Minhas FUAA. Cross-linking breast tumor transcriptomic states and tissue histology. Cell Rep Med 2023; 4:101313. [PMID: 38118424 PMCID: PMC10783602 DOI: 10.1016/j.xcrm.2023.101313] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 09/08/2023] [Accepted: 11/14/2023] [Indexed: 12/22/2023]
Abstract
Identification of the gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from WSIs using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinical and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining biological insights from routine pathology imaging directly.
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Affiliation(s)
- Muhammad Dawood
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK.
| | - Mark Eastwood
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | | | - Lawrence Young
- Warwick Medical School, University of Warwick, Coventry, UK; Cancer Research Centre, University of Warwick, Coventry, UK
| | - Asa Ben-Hur
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - Kim Branson
- Artificial Intelligence & Machine Learning, GlaxoSmithKline, San Francisco, CA, USA
| | - Louise Jones
- Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK; The Alan Turing Institute, London, UK
| | - Fayyaz Ul Amir Afsar Minhas
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK; Cancer Research Centre, University of Warwick, Coventry, UK.
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246
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Guo ZH, Wu Y, Wang S, Zhang Q, Shi JM, Wang YB, Chen ZH. scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data. BMC Bioinformatics 2023; 24:481. [PMID: 38104057 PMCID: PMC10724984 DOI: 10.1186/s12859-023-05579-4] [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] [Received: 09/29/2023] [Accepted: 11/23/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND The rapid emergence of single-cell RNA-seq (scRNA-seq) data presents remarkable opportunities for broad investigations through integration analyses. However, most integration models are black boxes that lack interpretability or are hard to train. RESULTS To address the above issues, we propose scInterpreter, a deep learning-based interpretable model. scInterpreter substantially outperforms other state-of-the-art (SOTA) models in multiple benchmark datasets. In addition, scInterpreter is extensible and can integrate and annotate atlas scRNA-seq data. We evaluated the robustness of scInterpreter in a variety of situations. Through comparison experiments, we found that with a knowledge prior, the training process can be significantly accelerated. Finally, we conducted interpretability analysis for each dimension (pathway) of cell representation in the embedding space. CONCLUSIONS The results showed that the cell representations obtained by scInterpreter are full of biological significance. Through weight sorting, we found several new genes related to pathways in PBMC dataset. In general, scInterpreter is an effective and interpretable integration tool. It is expected that scInterpreter will bring great convenience to the study of single-cell transcriptomics.
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Affiliation(s)
- Zhen-Hao Guo
- College of Electronics and Information Engineering, Tongji University, Shanghai, 200000, China
- Department of Clinical Anesthesiology, Faculty of Anesthesiology, Second Military Medical University / Naval Medical University, Shanghai, 200433, China
| | - Yan Wu
- College of Electronics and Information Engineering, Tongji University, Shanghai, 200000, China.
| | - Siguo Wang
- EIT Institute for Advanced Study, Ningbo, 315201, Zhejiang, China
| | - Qinhu Zhang
- EIT Institute for Advanced Study, Ningbo, 315201, Zhejiang, China
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning, 530007, China
| | - Jin-Ming Shi
- Department of Endocrinology, Aviation General Hospital, Beijing, 100000, China
| | - Yan-Bin Wang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, Zhejiang, China
| | - Zhan-Heng Chen
- Department of Clinical Anesthesiology, Faculty of Anesthesiology, Second Military Medical University / Naval Medical University, Shanghai, 200433, China.
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning, 530007, China.
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247
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Hornung BVH, Azmani Z, den Dekker AT, Oole E, Ozgur Z, Brouwer RWW, van den Hout MCGN, van IJcken WFJ. Comparison of Single Cell Transcriptome Sequencing Methods: Of Mice and Men. Genes (Basel) 2023; 14:2226. [PMID: 38137048 PMCID: PMC10743076 DOI: 10.3390/genes14122226] [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: 10/31/2023] [Revised: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Single cell RNAseq has been a big leap in many areas of biology. Rather than investigating gene expression on a whole organism level, this technology enables scientists to get a detailed look at rare single cells or within their cell population of interest. The field is growing, and many new methods appear each year. We compared methods utilized in our core facility: Smart-seq3, PlexWell, FLASH-seq, VASA-seq, SORT-seq, 10X, Evercode, and HIVE. We characterized the equipment requirements for each method. We evaluated the performances of these methods based on detected features, transcriptome diversity, mitochondrial RNA abundance and multiplets, among others and benchmarked them against bulk RNA sequencing. Here, we show that bulk transcriptome detects more unique transcripts than any single cell method. While most methods are comparable in many regards, FLASH-seq and VASA-seq yielded the best metrics, e.g., in number of features. If no equipment for automation is available or many cells are desired, then HIVE or 10X yield good results. In general, more recently developed methods perform better. This also leads to the conclusion that older methods should be phased out, and that the development of single cell RNAseq methods is still progressing considerably.
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Affiliation(s)
- Bastian V. H. Hornung
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Zakia Azmani
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Alexander T. den Dekker
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Edwin Oole
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Zeliha Ozgur
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Rutger W. W. Brouwer
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Mirjam C. G. N. van den Hout
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Wilfred F. J. van IJcken
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
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248
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Song Y, Wang L, Zhang Y. Identification of central genes for endometriosis through integration of single-cell RNA sequencing and bulk RNA sequencing analysis. Medicine (Baltimore) 2023; 102:e36707. [PMID: 38115253 PMCID: PMC10727599 DOI: 10.1097/md.0000000000036707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023] Open
Abstract
This study aimed to identify the key genes involved in the development of endometriosis and construct an accurate predictive model to provide new directions for the diagnosis and treatment of endometriosis. Using bioinformatics analysis, we employed the single-cell cell communication method to identify the key cell subtypes. By combining chip data and integrating differential analysis, WGCNA analysis, and the least absolute shrinkage and selection operator (LASSO) model, key genes were identified for immune infiltration and functional enrichment analyses. Cell communication analysis identified tissue stem cells as the key subtype. Differential analysis revealed 1879 differentially expressed genes, whereas WGCNA identified 357 module genes. The LASSO model further selects 4 key genes: Adipocyte Enhancer Binding Protein 1(AEBP1), MBNL1, GREM1, and DES. All 4 key genes showed significant correlations with immune cell content. Moreover, these genes were significantly expressed in single cells. The predictive model demonstrated good diagnostic performance. Through scRNA-seq, WGCNA, and LASSO methodologies, DES, GREM1, MBNL1, and AEBP1 emerged as crucial core genes linked to tissue stem cell markers in endometriosis. These genes have promising applications as diagnostic markers and therapeutic targets for endometriosis.
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Affiliation(s)
- Yulin Song
- Department of obstetrics and gynecology, Qinhuangdao Maternal and Child Health Hospital, Qinhuangdao, Hebei, China
| | - Le Wang
- Department of Neurology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
| | - Yu Zhang
- Department of Gynecology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, 710068, China
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249
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Levenson MT, Barrere-Cain R, Truong L, Chen YW, Shuck K, Panter B, Reich E, Yang X, Allard P. Protocol for nuclear dissociation of the adult C. elegans for single-nucleus RNA sequencing and its application for mapping environmental responses. STAR Protoc 2023; 4:102756. [PMID: 38043054 PMCID: PMC10730361 DOI: 10.1016/j.xpro.2023.102756] [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: 08/22/2023] [Revised: 10/17/2023] [Accepted: 11/16/2023] [Indexed: 12/05/2023] Open
Abstract
Caenorhabditis elegans is a valuable model to study organ, tissue, and cell-type responses to external cues. However, the nematode comprises multiple syncytial tissues with spatial coordinates corresponding to distinct nuclear transcriptomes. Here, we present a single-nucleus RNA sequencing (snRNA-seq) protocol that aims to overcome difficulties encountered with single-cell RNA sequencing in C. elegans. We describe steps for isolating C. elegans nuclei for downstream applications including snRNA-seq applied to the context of alcohol exposure. For complete details on the use and execution of this protocol, please refer to Truong et al. (2023).1.
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Affiliation(s)
- Max T Levenson
- Molecular Toxicology Inter-Departmental Program, UCLA, Los Angeles, CA 90095, USA
| | - Rio Barrere-Cain
- Institute for Society & Genetics, UCLA, Los Angeles, CA 90095, USA
| | - Lisa Truong
- Human Genetics Graduate Program, UCLA, Los Angeles, CA 90095, USA
| | - Yen-Wei Chen
- Molecular Toxicology Inter-Departmental Program, UCLA, Los Angeles, CA 90095, USA
| | - Karissa Shuck
- Institute for Society & Genetics, UCLA, Los Angeles, CA 90095, USA
| | - Blake Panter
- Institute for Society & Genetics, UCLA, Los Angeles, CA 90095, USA
| | - Ella Reich
- Institute for Society & Genetics, UCLA, Los Angeles, CA 90095, USA
| | - Xia Yang
- Molecular Toxicology Inter-Departmental Program, UCLA, Los Angeles, CA 90095, USA; Integrative Biology and Physiology Department, UCLA, Los Angeles, CA 90095, USA
| | - Patrick Allard
- Molecular Toxicology Inter-Departmental Program, UCLA, Los Angeles, CA 90095, USA; Institute for Society & Genetics, UCLA, Los Angeles, CA 90095, USA; Molecular Biology Institute, UCLA, Los Angeles, CA 90095, USA.
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250
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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