CellMarker: a manually curated resource of cell markers in human and mouse
Open accessAbstract
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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Well-documented marker lists associated with well-defined cell types from CellMarker were used to avoid bias introduced by marker selection.
“To avoid bias introduced by marker selection, welldocumented marker lists associated with well-defined cell types from CellMarker 56 were used.”
From scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data (2021) · cites this paper as [56]
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- Authors
- 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.
- Affiliations
- Harbin Medical University (CN)
- Corresponding author
- Zhao, Tingting
- Li, Xia
- Xiao, Yun
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-10-05
- Volume
- 47
- Issue
- D1
- Pages
- D721-D728
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC6323899
- OpenAlex
- W2895456557
- Cited by
- 1,524
- References
- 23
- Citations by year
- 2026: 129 · 2025: 224 · 2024: 303 · 2023: 343 · 2022: 244 · 2021: 181 · 2020: 74 · 2019: 25
- FWCI
- 24.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National High Technology Research and Development Program of China (2014AA021102)
- National Program on Key Basic Research (2014CB910504)
- National Natural Science Foundation of China (61473106, 61573122, 31601076)
- China Postdoctoral Science Foundation (2016M600260)
- Harbin Medical University (WLD-QN1407)
- Construction of Higher Education in Heilongjiang Province (UNPYSCT-2016049)
- Heilongjiang Postdoctoral Foundation (LBHZ16098)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- cell markers, cell type annotation, human tissue, mouse tissues, manual curation, single-cell sequencing
- MeSH terms
- Animals, Humans, Software, Sequence Analysis, Databases, Genetic, Mice, Single-Cell Analysis
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