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Garmire LX, Li Y, Huang Q, Xu C, Teichmann SA, Kaminski N, Pellegrini M, Nguyen Q, Teschendorff AE. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods 2024; 21:391-400. [PMID: 38374264 DOI: 10.1038/s41592-023-02166-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/26/2023] [Indexed: 02/21/2024]
Abstract
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
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Affiliation(s)
- Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Yijun Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Naftali Kaminski
- Pulmonary, Critical Care & Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Matteo Pellegrini
- Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland and QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- UCL Cancer Institute, University College London, London, UK
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2
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Piao M, Feng K, Liu X, Bai X, Zheng Y, Sun M, Zhao P, Wang Y, Ban X, Xiong J, Shi C, Meng L, Liu Y, Yu L, Li J, Zhong S, Jiang X, Chen Y, Sun X, Zheng Y, Tian J. AgingReG: a curated database of aging regulatory relationships in humans. Database (Oxford) 2023; 2023:baad064. [PMID: 37805704 PMCID: PMC10558184 DOI: 10.1093/database/baad064] [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: 12/26/2022] [Revised: 07/15/2023] [Accepted: 09/14/2023] [Indexed: 10/09/2023]
Abstract
Aging and cellular senescence are characterized by a progressive loss of physiological integrity, which could be triggered by aging factors such as physiological, pathological and external factors. Numerous studies have shown that gene regulatory events play crucial roles in aging, increasing the need for a comprehensive repository of regulatory relationships during aging. Here, we established a manually curated database of aging factors (AgingReG, https://bio.liclab.net/Aging-ReG/), focusing on the regulatory relationships during aging with experimental evidence in humans. By curating thousands of published literature, 2157 aging factor entries (1345 aging gene entries, 804 external factor entries and eight aging-related pathway entries) and related regulatory information were manually curated. The regulatory relationships were classified into four types according to their functions: (i) upregulation, which indicates that aging factors upregulate the expression of target genes during aging; (ii) downregulation, which indicates that aging factors downregulate the expression of target genes during aging; (iii) activation, which indicates that aging factors influence the activity of target genes during aging and (iv) inhibition, which indicates that aging factors inhibit the activation of target molecule activity, leading to declined or lost target activity. AgingReG involves 651 upregulating pairs, 632 downregulating pairs, 330 activation-regulating pairs and 34 inhibition-regulating pairs, covering 195 disease types and more than 800 kinds of cells and tissues from 1784 published literature studies. AgingReG provides a user-friendly interface to query, browse and visualize detailed information about the regulatory relationships during aging. We believe that AgingReG will serve as a valuable resource database in the field of aging research. Database URL: https://bio.liclab.net/Aging-ReG/.
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Affiliation(s)
- Minghui Piao
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
- The Key Laboratory of Myocardial Ischemia, Harbin Medical University, Ministry of Education, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Ke Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin 150086, China
| | - Xinyu Liu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, No. 39 Xinyang Road, High Tech Zone, Daqing 163319, China
| | - Xuefeng Bai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, No. 39 Xinyang Road, High Tech Zone, Daqing 163319, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, No. 2005 Songhu Road, Yangpu District, Shanghai 200438, China
| | - Yuqi Zheng
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Meiling Sun
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Peng Zhao
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Yani Wang
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Xiaofang Ban
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Jie Xiong
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Chengyu Shi
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Li Meng
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Yuxin Liu
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Li Yu
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Jing Li
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Shan Zhong
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Xinjian Jiang
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Yu Chen
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
| | - Xin Sun
- Department of Cardiology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), No. 1017 Dongmen North Road, Luohu District, Shenzhen 518000, China
| | - Yan Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, No. 2005 Songhu Road, Yangpu District, Shanghai 200438, China
| | - Jinwei Tian
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
- The Key Laboratory of Myocardial Ischemia, Harbin Medical University, Ministry of Education, No. 246 Xuefu Road, Nangang District, Harbin 150086, China
- Key Laboratory of Emergency and Trauma of Ministry of Education, Hainan Medical University, No. 3 Xueyuan Road, Longhua District, Haikou 571199, China
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3
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Jiang S, Qian Q, Zhu T, Zong W, Shang Y, Jin T, Zhang Y, Chen M, Wu Z, Chu Y, Zhang R, Luo S, Jing W, Zou D, Bao Y, Xiao J, Zhang Z. Cell Taxonomy: a curated repository of cell types with multifaceted characterization. Nucleic Acids Res 2022; 51:D853-D860. [PMID: 36161321 PMCID: PMC9825571 DOI: 10.1093/nar/gkac816] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/24/2022] [Indexed: 01/12/2023] Open
Abstract
Single-cell studies have delineated cellular diversity and uncovered increasing numbers of previously uncharacterized cell types in complex tissues. Thus, synthesizing growing knowledge of cellular characteristics is critical for dissecting cellular heterogeneity, developmental processes and tumorigenesis at single-cell resolution. Here, we present Cell Taxonomy (https://ngdc.cncb.ac.cn/celltaxonomy), a comprehensive and curated repository of cell types and associated cell markers encompassing a wide range of species, tissues and conditions. Combined with literature curation and data integration, the current version of Cell Taxonomy establishes a well-structured taxonomy for 3,143 cell types and houses a comprehensive collection of 26,613 associated cell markers in 257 conditions and 387 tissues across 34 species. Based on 4,299 publications and single-cell transcriptomic profiles of ∼3.5 million cells, Cell Taxonomy features multifaceted characterization for cell types and cell markers, involving quality assessment of cell markers and cell clusters, cross-species comparison, cell composition of tissues and cellular similarity based on markers. Taken together, Cell Taxonomy represents a fundamentally useful reference to systematically and accurately characterize cell types and thus lays an important foundation for deeply understanding and exploring cellular biology in diverse species.
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Affiliation(s)
| | | | | | - Wenting Zong
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yunfei Shang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tong Jin
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuansheng Zhang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ming Chen
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zishan Wu
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuan Chu
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rongqin Zhang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sicheng Luo
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Jing
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dong Zou
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China
| | - Yiming Bao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingfa Xiao
- Correspondence may also be addressed to Jingfa Xiao.
| | - Zhang Zhang
- To whom correspondence should be addressed. Tel: +86 10 84097261; Fax: +86 10 84097720;
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4
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Xu M, Bai X, Ai B, Zhang G, Song C, Zhao J, Wang Y, Wei L, Qian F, Li Y, Zhou X, Zhou L, Yang Y, Chen J, Liu J, Shang D, Wang X, Zhao Y, Huang X, Zheng Y, Zhang J, Wang Q, Li C. TF-Marker: a comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human. Nucleic Acids Res 2022; 50:D402-D412. [PMID: 34986601 PMCID: PMC8728118 DOI: 10.1093/nar/gkab1114] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 12/26/2022] Open
Abstract
Transcription factors (TFs) play key roles in biological processes and are usually used as cell markers. The emerging importance of TFs and related markers in identifying specific cell types in human diseases increases the need for a comprehensive collection of human TFs and related markers sets. Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/), aiming to provide cell/tissue-specific TFs and related markers for human. By manually curating thousands of published literature, 5905 entries including information about TFs and related markers were classified into five types according to their functions: (i) TF: TFs which regulate expression of the markers; (ii) T Marker: markers which are regulated by the TF; (iii) I Marker: markers which influence the activity of TFs; (iv) TFMarker: TFs which play roles as markers and (v) TF Pmarker: TFs which play roles as potential markers. The 5905 entries of TF-Marker include 1316 TFs, 1092 T Markers, 473 I Markers, 1600 TFMarkers and 1424 TF Pmarkers, involving 383 cell types and 95 tissue types in human. TF-Marker further provides a user-friendly interface to browse, query and visualize the detailed information about TFs and related markers. We believe TF-Marker will become a valuable resource to understand the regulation patterns of different tissues and cells.
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Affiliation(s)
- Mingcong Xu
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China.,The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Xuefeng Bai
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Bo Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Guorui Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Chao Song
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jun Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yuezhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Ling Wei
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Fengcui Qian
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yanyu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Xinyuan Zhou
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Liwei Zhou
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yongsan Yang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jiaxin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jiaqi Liu
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,School of Computer, University of South China, Hengyang, Hunan 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
| | - Desi Shang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,School of Computer, University of South China, Hengyang, Hunan 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
| | - Xuan Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yu Zhao
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,School of Computer, University of South China, Hengyang, Hunan 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
| | - Xuemei Huang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,School of Computer, University of South China, Hengyang, Hunan 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
| | - Yan Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China.,School of Computer, University of South China, Hengyang, Hunan 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
| | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China.,The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,School of Computer, University of South China, Hengyang, Hunan 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China.,General Surgery Department, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.,Guangxi Key Laboratory of Diabetic Systems Medicine, Guilin Medical University, Guilin, Guangxi 541199, China
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5
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Phillips W, Willms E, Hill AF. Understanding extracellular vesicle and nanoparticle heterogeneity: Novel methods and considerations. Proteomics 2021; 21:e2000118. [PMID: 33857352 PMCID: PMC8365743 DOI: 10.1002/pmic.202000118] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/22/2021] [Accepted: 04/12/2021] [Indexed: 12/20/2022]
Abstract
Extracellular vesicles (EVs) are a heterogeneous population of membrane-enclosed nanoparticles released by cells. They play a role in intercellular communication and are involved in numerous physiological and pathological processes. Cells release subpopulations of EVs with distinct composition and inherent biological function which overlap in size. Current size-based isolation methods are, therefore, not optimal to discriminate between functional EV subpopulations. In addition, EVs overlap in size with several other biological nanoparticles, such as lipoproteins and viruses. Proteomic analysis has allowed for more detailed study of EV composition, and EV isolation approaches based on this could provide a promising alternative for purification based on size. Elucidating EV heterogeneity and the characteristics and role of EV subpopulations will advance our understanding of EV biology and the role of EVs in health and disease. Here, we discuss current knowledge of EV composition, EV heterogeneity and advances in affinity based EV isolation tools.
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Affiliation(s)
- William Phillips
- Department of Biochemistry and GeneticsLa Trobe Institute for Molecular ScienceLa Trobe UniversityBundooraVictoriaAustralia
| | - Eduard Willms
- Department of Biochemistry and GeneticsLa Trobe Institute for Molecular ScienceLa Trobe UniversityBundooraVictoriaAustralia
| | - Andrew F. Hill
- Department of Biochemistry and GeneticsLa Trobe Institute for Molecular ScienceLa Trobe UniversityBundooraVictoriaAustralia
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6
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He X, Liu L, Chen B, Wu C. Using Cell Type-Specific Genes to Identify Cell-Type Transitions Between Different in vitro Culture Conditions. Front Cell Dev Biol 2021; 9:644261. [PMID: 34249906 PMCID: PMC8267371 DOI: 10.3389/fcell.2021.644261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 04/09/2021] [Indexed: 11/13/2022] Open
Abstract
In vitro differentiation or expansion of stem and progenitor cells under chemical stimulation or genetic manipulation is used for understanding the molecular mechanisms of cell differentiation and self-renewal. However, concerns around the cell identity of in vitro-cultured cells exist. Bioinformatics methods, which rely heavily on signatures of cell types, have been developed to estimate cell types in bulk samples. The Tabula Muris Senis project provides an important basis for the comprehensive identification of signatures for different cell types. Here, we identified 46 cell type-specific (CTS) gene clusters for 83 mouse cell types. We conducted Gene Ontology term enrichment analysis on the gene clusters and revealed the specific functions of the relevant cell types. Next, we proposed a simple method, named CTSFinder, to identify different cell types between bulk RNA-Seq samples using the 46 CTS gene clusters. We applied CTSFinder on bulk RNA-Seq data from 17 organs and from developing mouse liver over different stages. We successfully identified the specific cell types between organs and captured the dynamics of different cell types during liver development. We applied CTSFinder with bulk RNA-Seq data from a growth factor-induced neural progenitor cell culture system and identified the dynamics of brain immune cells and nonimmune cells during the long-time cell culture. We also applied CTSFinder with bulk RNA-Seq data from reprogramming induced pluripotent stem cells and identified the stage when those cells were massively induced. Finally, we applied CTSFinder with bulk RNA-Seq data from in vivo and in vitro developing mouse retina and captured the dynamics of different cell types in the two development systems. The CTS gene clusters and CTSFinder method could thus serve as promising toolkits for assessing the cell identity of in vitro culture systems.
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Affiliation(s)
- Xuelin He
- Department of Nephrology, Beilun People's Hospital, Ningbo, China.,Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Kidney Disease Immunology Laboratory, The Third Grade Laboratory, State Administration of Traditional Chinese Medicine of China, Hangzhou, China
| | - Li Liu
- Department of Library, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baode Chen
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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7
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Almeida-González FR, González-Vázquez A, Mithieux SM, O'Brien FJ, Weiss AS, Brougham CM. A step closer to elastogenesis on demand; Inducing mature elastic fibre deposition in a natural biomaterial scaffold. MATERIALS SCIENCE & ENGINEERING. C, MATERIALS FOR BIOLOGICAL APPLICATIONS 2021; 120:111788. [PMID: 33545914 DOI: 10.1016/j.msec.2020.111788] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/20/2020] [Accepted: 12/02/2020] [Indexed: 12/28/2022]
Abstract
Elastic fibres play a key role in bodily functions where fatigue resistance and elastic recovery are necessary while regulating phenotype, proliferation and migration in cells. While in vivo elastic fibres are created at a late foetal stage, a major obstacle in the development of engineered tissue is that human vascular smooth muscle cells (hVSMCs), one of the principal elastogenic cells, are unable to spontaneously promote elastogenesis in vitro. Therefore, the overall aim of this study was to activate elastogenesis in vitro by hVSMCs seeded in fibrin, collagen, glycosaminoglycan (FCG) scaffolds, following the addition of recombinant human tropoelastin. This combination of scaffold, tropoelastin and cells induced the deposition of elastin and formation of lamellar maturing elastic fibres, similar to those found in skin, blood vessels and heart valves. Furthermore, higher numbers of maturing branched elastic fibres were synthesised when a higher cell density was used and by drop-loading tropoelastin onto cell-seeded FCG scaffolds prior to adding growth medium. The addition of tropoelastin showed no effect on cell proliferation or mechanical properties of the scaffold which remained dimensionally stable throughout. With these results, we have established a natural biomaterial scaffold that can undergo controlled elastogenesis on demand, suitable for tissue engineering applications.
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Affiliation(s)
- Francisco R Almeida-González
- Biomedical Research Group, School of Mechanical and Design Engineering, Technological University Dublin, Bolton St, Dublin 1, Ireland; Tissue Engineering Research Group, Dept. of Anatomy and Regenerative Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin 2, Ireland
| | - Arlyng González-Vázquez
- Tissue Engineering Research Group, Dept. of Anatomy and Regenerative Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin 2, Ireland; Advanced Materials and Bioengineering Research (AMBER) Centre, RCSI, Ireland
| | - Suzanne M Mithieux
- Charles Perkins Centre, University of Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, University of Sydney, NSW 2006, Australia; Bosch Institute, University of Sydney, NSW 2006, Australia
| | - Fergal J O'Brien
- Tissue Engineering Research Group, Dept. of Anatomy and Regenerative Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin 2, Ireland; Advanced Materials and Bioengineering Research (AMBER) Centre, RCSI, Ireland
| | - Anthony S Weiss
- Charles Perkins Centre, University of Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, University of Sydney, NSW 2006, Australia; Bosch Institute, University of Sydney, NSW 2006, Australia
| | - Claire M Brougham
- Biomedical Research Group, School of Mechanical and Design Engineering, Technological University Dublin, Bolton St, Dublin 1, Ireland; Tissue Engineering Research Group, Dept. of Anatomy and Regenerative Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin 2, Ireland.
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8
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Panina Y, Karagiannis P, Kurtz A, Stacey GN, Fujibuchi W. Human Cell Atlas and cell-type authentication for regenerative medicine. Exp Mol Med 2020; 52:1443-1451. [PMID: 32929224 PMCID: PMC8080834 DOI: 10.1038/s12276-020-0421-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/05/2020] [Accepted: 03/09/2020] [Indexed: 12/22/2022] Open
Abstract
In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines.
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Affiliation(s)
- Yulia Panina
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Peter Karagiannis
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Andreas Kurtz
- BIH Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Glyn N Stacey
- International Stem Cell Banking Initiative, 2 High Street, Barley, Herts, SG88HZ, UK
- National Stem Cell Resource Centre, Institute of Zoology, Chinese Academy of Sciences, 100190, Beijing, China
- Innovation Academy for Stem Cell and Regeneration, Chinese Academy of Sciences, 100101, Beijing, China
| | - Wataru Fujibuchi
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
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9
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Zhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, Luo T, Xu L, Liao G, Yan M, Ping Y, Li F, Shi A, Bai J, Zhao T, Li X, Xiao Y. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res 2020; 47:D721-D728. [PMID: 30289549 PMCID: PMC6323899 DOI: 10.1093/nar/gky900] [Citation(s) in RCA: 721] [Impact Index Per Article: 180.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 09/25/2018] [Indexed: 12/13/2022] Open
Abstract
One of the most fundamental questions in biology is what types of cells form different tissues and organs in a functionally coordinated fashion. Larger-scale single-cell sequencing and biology experiment studies are now rapidly opening up new ways to track this question by revealing substantial cell markers for distinguishing different cell types in tissues. Here, we developed the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/ or http://bio-bigdata.hrbmu.edu.cn/CellMarker/), aiming to provide a comprehensive and accurate resource of cell markers for various cell types in tissues of human and mouse. By manually curating over 100 000 published papers, 4124 entries including the cell marker information, tissue type, cell type, cancer information and source, were recorded. At last, 13 605 cell markers of 467 cell types in 158 human tissues/sub-tissues and 9148 cell makers of 389 cell types in 81 mouse tissues/sub-tissues were collected and deposited in CellMarker. CellMarker provides a user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Furthermore, a summarized marker prevalence in each cell type is graphically and intuitively presented through a vivid statistical graph. We believe that CellMarker is a comprehensive and valuable resource for cell researches in precisely identifying and characterizing cells, especially at the single-cell level.
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Affiliation(s)
- Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yujia Lan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Jinyuan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Fei Quan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Erjie Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Chunyu Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Tao Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Liwen Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Gaoming Liao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Min Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Aiai Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Jing Bai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Tingting Zhao
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
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Abstract
The human endometrium is essential in providing the site for implantation and maintaining the growth and survival of the conceptus. An unreceptive endometrium and disrupted maternal-conceptus interactions can cause infertility due to pregnancy loss or later pregnancy complications. Despite this, the role of uterine glands in first trimester human pregnancy is little understood. An established organoid protocol was used to generate and comprehensively analyze 3-dimensional endometrial epithelial organoid (EEO) cultures from human endometrial biopsies. The derived EEO expand long-term, are genetically stable, and can be cryopreserved. Using endometrium from 2 different donors, EEO were derived and then treated with estrogen (E2) for 2 d or E2 and medroxyprogesterone acetate (MPA) for 6 d. EEO cells were positive for the gland marker, FOXA2, and exhibited appropriate hormonal regulation of steroid hormone receptor expression. Real-time qPCR and bulk RNA-sequencing analysis revealed effects of hormone treatment on gene expression that recapitulated changes in proliferative and secretory phase endometrium. Single-cell RNA sequencing analysis revealed that several different epithelial cell types are present in the EEO whose proportion and gene expression changed with hormone treatment. The EEO model serves as an important platform for studying the physiology and pathology of the human endometrium.
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11
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Kurtz A, Elsallab M, Sanzenbacher R, Abou-El-Enein M. Linking Scattered Stem Cell-Based Data to Advance Therapeutic Development. Trends Mol Med 2019; 25:8-19. [DOI: 10.1016/j.molmed.2018.10.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 10/20/2018] [Accepted: 10/22/2018] [Indexed: 02/07/2023]
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12
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Roy AL, Conroy RS. Toward mapping the human body at a cellular resolution. Mol Biol Cell 2018; 29:1779-1785. [PMID: 30058989 PMCID: PMC6085824 DOI: 10.1091/mbc.e18-04-0260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 06/01/2018] [Accepted: 06/07/2018] [Indexed: 12/21/2022] Open
Abstract
The adult human body is composed of nearly 37 trillion cells, each with potentially unique molecular characteristics. This Perspective describes some of the challenges and opportunities faced in mapping the molecular characteristics of these cells in specific regions of the body and highlights areas for international collaboration toward the broader goal of comprehensively mapping the human body with cellular resolution.
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Affiliation(s)
- Ananda L. Roy
- Office of Strategic Coordination, Division of Program Coordination, Planning, and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD 20892
| | - Richard S. Conroy
- Office of Strategic Coordination, Division of Program Coordination, Planning, and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD 20892
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13
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Bakken T, Cowell L, Aevermann BD, Novotny M, Hodge R, Miller JA, Lee A, Chang I, McCorrison J, Pulendran B, Qian Y, Schork NJ, Lasken RS, Lein ES, Scheuermann RH. Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics 2017; 18:559. [PMID: 29322913 PMCID: PMC5763450 DOI: 10.1186/s12859-017-1977-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. Results In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including “context annotations” in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. Conclusion The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.
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Affiliation(s)
- Trygve Bakken
- Allen Institute for Brain Science, Seattle, Washington, 98103, USA
| | - Lindsay Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA
| | - Brian D Aevermann
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Mark Novotny
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Rebecca Hodge
- Allen Institute for Brain Science, Seattle, Washington, 98103, USA
| | - Jeremy A Miller
- Allen Institute for Brain Science, Seattle, Washington, 98103, USA
| | - Alexandra Lee
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Ivan Chang
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Jamison McCorrison
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Bali Pulendran
- Department of Pathology and Laboratory Medicine, Emory University, 201 Dowman Dr, Atlanta, GA, 30322, USA
| | - Yu Qian
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Nicholas J Schork
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Roger S Lasken
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, Washington, 98103, USA
| | - Richard H Scheuermann
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA. .,Department of Pathology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
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14
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Fu X, He F, Li Y, Shahveranov A, Hutchins AP. Genomic and molecular control of cell type and cell type conversions. CELL REGENERATION 2017; 6:1-7. [PMID: 29348912 PMCID: PMC5769489 DOI: 10.1016/j.cr.2017.09.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 09/06/2017] [Accepted: 09/18/2017] [Indexed: 12/17/2022]
Abstract
Organisms are made of a limited number of cell types that combine to form higher order tissues and organs. Cell types have traditionally been defined by their morphologies or biological activity, yet the underlying molecular controls of cell type remain unclear. The onset of single cell technologies, and more recently genomics (particularly single cell genomics), has substantially increased the understanding of the concept of cell type, but has also increased the complexity of this understanding. These new technologies have added a new genome wide molecular dimension to the description of cell type, with genome-wide expression and epigenetic data acting as a cell type ‘fingerprint’ to describe the cell state. Using these genomic fingerprints cell types are being increasingly defined based on specific genomic and molecular criteria, without necessarily a distinct biological function. In this review, we will discuss the molecular definitions of cell types and cell type control, and particularly how endogenous and exogenous transcription factors can control cell types and cell type conversions.
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15
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Sakurai K, Kurtz A, Stacey G, Sheldon M, Fujibuchi W. First Proposal of Minimum Information About a Cellular Assay for Regenerative Medicine. Stem Cells Transl Med 2016; 5:1345-1361. [PMID: 27405781 PMCID: PMC5031183 DOI: 10.5966/sctm.2015-0393] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Accepted: 04/18/2016] [Indexed: 12/27/2022] Open
Abstract
: Advances in stem cell research have triggered scores of studies in regenerative medicine in a large number of institutions and companies around the world. However, reproducibility and data exchange among laboratories or cell banks are constrained by the lack of a standardized format for experiments. To enhance information flow in stem cell and derivative cell research, here we propose a minimum information standard to describe cellular assay data to facilitate practical regenerative medicine. Based on the existing Minimum Information About a Cellular Assay, we developed Minimum Information About a Cellular Assay for Regenerative Medicine (MIACARM), which allows for the description of advanced cellular experiments with defined taxonomy of human cell types. By using controlled terms, such as ontologies, MIACARM will provide a platform for cellular assay data exchange among cell banks or registries that have been established at more than 20 sites in the world. SIGNIFICANCE Currently, there are more than 20 human cell information storage sites around the world. However, reproducibility and data exchange among different laboratories or cell information providers are usually inadequate or nonexistent because of the lack of a standardized format for experiments. This study, which is the fruit of collaborative work by scientists at stem cell banks and cellular information registries worldwide, including those in the U.S., the U.K., Europe, and Japan, proposes new minimum information guidelines, Minimum Information About a Cellular Assay for Regenerative Medicine (MIACARM), for cellular assay data deposition. MIACARM is intended to promote data exchange and facilitation of practical regenerative medicine.
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Affiliation(s)
- Kunie Sakurai
- Center for iPS Cell Research and Application, Kyoto University, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Andreas Kurtz
- Charité-Universitätsmedizin Berlin, Berlin-Brandenburg Center for Regenerative Therapies, Berlin, Germany
| | - Glyn Stacey
- National Institute for Biological Standards and Control, an Operating Centre of the Medicines and Healthcare Products Regulatory Agency, South Mimms, United Kingdom
| | - Michael Sheldon
- Department of Genetics and Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Wataru Fujibuchi
- Center for iPS Cell Research and Application, Kyoto University, Shogoin, Sakyo-ku, Kyoto, Japan
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16
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Diehl AD, Meehan TF, Bradford YM, Brush MH, Dahdul WM, Dougall DS, He Y, Osumi-Sutherland D, Ruttenberg A, Sarntivijai S, Van Slyke CE, Vasilevsky NA, Haendel MA, Blake JA, Mungall CJ. The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability. J Biomed Semantics 2016; 7:44. [PMID: 27377652 PMCID: PMC4932724 DOI: 10.1186/s13326-016-0088-7] [Citation(s) in RCA: 145] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 06/23/2016] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies. CONSTRUCTION AND CONTENT Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning. UTILITY AND DISCUSSION The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs. CONCLUSIONS The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.
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Affiliation(s)
- Alexander D. Diehl
- />Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY 14203 USA
| | - Terrence F. Meehan
- />European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Yvonne M. Bradford
- />ZFIN, the Zebrafish Model Organism Database, 5291 University of Oregon, Eugene, OR 97403 USA
| | - Matthew H. Brush
- />Ontology Development Group, Library, Oregon Health and Science University, Portland, Oregon 97239 USA
| | - Wasila M. Dahdul
- />Department of Biology, University of South Dakota, Vermillion, SD 57069 USA
- />National Evolutionary Synthesis Center, Durham, NC 27705 USA
| | - David S. Dougall
- />Southwestern Medical Center, University of Texas, Dallas, TX 75235 USA
| | - Yongqun He
- />Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - David Osumi-Sutherland
- />European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Alan Ruttenberg
- />Oral Diagnostics Sciences, University at Buffalo School of Dental Medicine, Buffalo, NY 14210 USA
| | - Sirarat Sarntivijai
- />European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Ceri E. Van Slyke
- />ZFIN, the Zebrafish Model Organism Database, 5291 University of Oregon, Eugene, OR 97403 USA
| | - Nicole A. Vasilevsky
- />Ontology Development Group, Library, Oregon Health and Science University, Portland, Oregon 97239 USA
| | - Melissa A. Haendel
- />Ontology Development Group, Library, Oregon Health and Science University, Portland, Oregon 97239 USA
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Tanaka H, Ogishima S. Network biology approach to epithelial-mesenchymal transition in cancer metastasis: three stage theory. J Mol Cell Biol 2015; 7:253-66. [PMID: 26103982 DOI: 10.1093/jmcb/mjv035] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2014] [Accepted: 05/20/2015] [Indexed: 11/12/2022] Open
Abstract
Epithelial-mesenchymal transition (EMT) plays a critical role in promoting cancer metastasis. In this study, cancer EMT is considered as an overall structural change in the gene regulatory network (GRN), and its essential features are elucidated by the network biology approach. We first defined the state space of GRN as a set of all possible activation patterns of GRN, and then introduced the quasi-potential field into this space to show the relative stability distribution of each state. The quasi-potential was determined empirically by collecting gene expression profiles from public databases. Changes of GRN states during the EMT process were traced in the state space, by using time-course data of gene expression profiles of a cell line inducing EMT from the database. It was found that cancer EMT occurred in three sequential stable stages, each of which formed a potential basin along the EMT trajectory. As confirmation, structural changes of GRN were estimated by applying the ARACNe algorithm to the same time-course data, and then applying master regulator analysis to extract the main regulations. Each group of master regulators was found to be alternatively active in the subsequent three stages to cause overall structural changes of GRN during cancer EMT.
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Affiliation(s)
- Hiroshi Tanaka
- Department of Bioinformatics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan Department of Bioclinical Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Soichi Ogishima
- Department of Bioclinical Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
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18
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Databases and collaboration require standards for human stem cell research. Drug Discov Today 2014; 20:247-54. [PMID: 25449658 DOI: 10.1016/j.drudis.2014.10.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Revised: 09/26/2014] [Accepted: 10/20/2014] [Indexed: 11/20/2022]
Abstract
Stem cell research is at an important juncture: despite significant potential for human health and several countries with key initiatives to expedite commercialization, there are gaps in capturing and exploiting the results of past and current research. Here, we propose a concerted plan that could be taken to foster a more collaborative approach and ensure that all research efforts can be leveraged across the community. The creation of a definitive centralized database repository, or at least harmonized data repositories, for stem cell groups in academia and industry, enabling secure selective sharing of data when needed, could provide the core structure that is sought globally and protect intellectual property. The development of minimum information about stem cell experiments (MIASCE) could be key to this development.
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19
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Stachelscheid H, Seltmann S, Lekschas F, Fontaine JF, Mah N, Neves M, Andrade-Navarro MA, Leser U, Kurtz A. CellFinder: a cell data repository. Nucleic Acids Res 2013; 42:D950-8. [PMID: 24304896 PMCID: PMC3965082 DOI: 10.1093/nar/gkt1264] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
CellFinder (http://www.cellfinder.org) is a comprehensive one-stop resource for molecular data characterizing mammalian cells in different tissues and in different development stages. It is built from carefully selected data sets stemming from other curated databases and the biomedical literature. To date, CellFinder describes 3394 cell types and 50 951 cell lines. The database currently contains 3055 microscopic and anatomical images, 205 whole-genome expression profiles of 194 cell/tissue types from RNA-seq and microarrays and 553 905 protein expressions for 535 cells/tissues. Text mining of a corpus of >2000 publications followed by manual curation confirmed expression information on ∼900 proteins and genes. CellFinder’s data model is capable to seamlessly represent entities from single cells to the organ level, to incorporate mappings between homologous entities in different species and to describe processes of cell development and differentiation. Its ontological backbone currently consists of 204 741 ontology terms incorporated from 10 different ontologies unified under the novel CELDA ontology. CellFinder’s web portal allows searching, browsing and comparing the stored data, interactive construction of developmental trees and navigating the partonomic hierarchy of cells and tissues through a unique body browser designed for life scientists and clinicians.
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Affiliation(s)
- Harald Stachelscheid
- Berlin Brandenburg Center for Regenerative Medicine, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Max Delbrück Center for Molecular Medicine, Computational Biology and Data Mining, Berlin 13125, Germany, Humboldt Universität zu Berlin, Institute for Computer Science, Berlin 10099, Germany and Seoul National University, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul 151-742, Republic of Korea
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Tholey A, Treitz C, Kussmann M, Bendixen E, Schrimpf SP, Hengartner MO. Model Organisms Proteomics-From Holobionts to Human Nutrition. Proteomics 2013; 13:2537-41. [DOI: 10.1002/pmic.201370144] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Andreas Tholey
- Division of Systematic Proteome Research; Institute for Experimental Medicine; Christian-Albrechts-Universität zu Kiel; Kiel; Germany
| | - Christian Treitz
- Division of Systematic Proteome Research; Institute for Experimental Medicine; Christian-Albrechts-Universität zu Kiel; Kiel; Germany
| | | | - Emöke Bendixen
- Department of Molecular Biology and Genetics; Laboratory of Proteomics and Mass spectrometry; Aarhus University; Arhus; Denmark
| | - Sabine P. Schrimpf
- Institute of Molecular Life Sciences; University of Zurich; Zurich; Switzerland
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LifeMap Discovery™: the embryonic development, stem cells, and regenerative medicine research portal. PLoS One 2013; 8:e66629. [PMID: 23874394 PMCID: PMC3714290 DOI: 10.1371/journal.pone.0066629] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2013] [Accepted: 04/29/2013] [Indexed: 11/19/2022] Open
Abstract
LifeMap Discovery™ provides investigators with an integrated database of embryonic development, stem cell biology and regenerative medicine. The hand-curated reconstruction of cell ontology with stem cell biology; including molecular, cellular, anatomical and disease-related information, provides efficient and easy-to-use, searchable research tools. The database collates in vivo and in vitro gene expression and guides translation from in vitro data to the clinical utility, and thus can be utilized as a powerful tool for research and discovery in stem cell biology, developmental biology, disease mechanisms and therapeutic discovery. LifeMap Discovery is freely available to academic nonprofit institutions at http://discovery.lifemapsc.com.
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Seltmann S, Stachelscheid H, Damaschun A, Jansen L, Lekschas F, Fontaine JF, Nguyen-Dobinsky TN, Leser U, Kurtz A. CELDA -- an ontology for the comprehensive representation of cells in complex systems. BMC Bioinformatics 2013; 14:228. [PMID: 23865855 PMCID: PMC3722091 DOI: 10.1186/1471-2105-14-228] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2012] [Accepted: 07/15/2013] [Indexed: 01/29/2023] Open
Abstract
Background The need for detailed description and modeling of cells drives the continuous generation of large and diverse datasets. Unfortunately, there exists no systematic and comprehensive way to organize these datasets and their information. CELDA (Cell: Expression, Localization, Development, Anatomy) is a novel ontology for the association of primary experimental data and derived knowledge to various types of cells of organisms. Results CELDA is a structure that can help to categorize cell types based on species, anatomical localization, subcellular structures, developmental stages and origin. It targets cells in vitro as well as in vivo. Instead of developing a novel ontology from scratch, we carefully designed CELDA in such a way that existing ontologies were integrated as much as possible, and only minimal extensions were performed to cover those classes and areas not present in any existing model. Currently, ten existing ontologies and models are linked to CELDA through the top-level ontology BioTop. Together with 15.439 newly created classes, CELDA contains more than 196.000 classes and 233.670 relationship axioms. CELDA is primarily used as a representational framework for modeling, analyzing and comparing cells within and across species in CellFinder, a web based data repository on cells (http://cellfinder.org). Conclusions CELDA can semantically link diverse types of information about cell types. It has been integrated within the research platform CellFinder, where it exemplarily relates cell types from liver and kidney during development on the one hand and anatomical locations in humans on the other, integrating information on all spatial and temporal stages. CELDA is available from the CellFinder website: http://cellfinder.org/about/ontology.
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Affiliation(s)
- Stefanie Seltmann
- Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
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Yu J, Xing X, Zeng L, Sun J, Li W, Sun H, He Y, Li J, Zhang G, Wang C, Li Y, Xie L. SyStemCell: a database populated with multiple levels of experimental data from stem cell differentiation research. PLoS One 2012; 7:e35230. [PMID: 22807998 PMCID: PMC3396617 DOI: 10.1371/journal.pone.0035230] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2010] [Accepted: 03/13/2012] [Indexed: 11/18/2022] Open
Abstract
Elucidation of the mechanisms of stem cell differentiation is of great scientific interest. Increasing evidence suggests that stem cell differentiation involves changes at multiple levels of biological regulation, which together orchestrate the complex differentiation process; many related studies have been performed to investigate the various levels of regulation. The resulting valuable data, however, remain scattered. Most of the current stem cell-relevant databases focus on a single level of regulation (mRNA expression) from limited stem cell types; thus, a unifying resource would be of great value to compile the multiple levels of research data available. Here we present a database for this purpose, SyStemCell, deposited with multi-level experimental data from stem cell research. The database currently covers seven levels of stem cell differentiation-associated regulatory mechanisms, including DNA CpG 5-hydroxymethylcytosine/methylation, histone modification, transcript products, microRNA-based regulation, protein products, phosphorylation proteins and transcription factor regulation, all of which have been curated from 285 peer-reviewed publications selected from PubMed. The database contains 43,434 genes, recorded as 942,221 gene entries, for four organisms (Homo sapiens, Mus musculus, Rattus norvegicus, and Macaca mulatta) and various stem cell sources (e.g., embryonic stem cells, neural stem cells and induced pluripotent stem cells). Data in SyStemCell can be queried by Entrez gene ID, symbol, alias, or browsed by specific stem cell type at each level of genetic regulation. An online analysis tool is integrated to assist researchers to mine potential relationships among different regulations, and the potential usage of the database is demonstrated by three case studies. SyStemCell is the first database to bridge multi-level experimental information of stem cell studies, which can become an important reference resource for stem cell researchers. The database is available at http://lifecenter.sgst.cn/SyStemCell/.
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Affiliation(s)
- Jian Yu
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Xiaobin Xing
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Lingyao Zeng
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Tongji University, Shanghai, China
| | - Jiehuan Sun
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Huazhong Science and Technology University, Wuhan, Hubei, China
| | - Wei Li
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Huazhong Science and Technology University, Wuhan, Hubei, China
| | - Han Sun
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ying He
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jing Li
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Huazhong Science and Technology University, Wuhan, Hubei, China
| | - Guoqing Zhang
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Chuan Wang
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Yixue Li
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- * E-mail: (LX); (YL)
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai, China
- * E-mail: (LX); (YL)
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