scImmOmics: a manually curated resource of single-cell multi-omics immune data

Single-cell sequencing technology has enabled the discovery and characterization of subpopulations of immune cells with unique functions, which is critical for revealing immune responses under healthy or disease conditions. Efforts have been made to collect and curate single-cell RNA sequencing (scRNA-seq) data, yet an immune-specific single-cell multi-omics atlas with harmonized metadata is still lacking. Here, we present scImmOmics (https://bio.liclab.net/scImmOmics/home), a manually curated single-cell multi-omics immune database constructed based on high-quality immune cells with known immune cell labels. Currently, scImmOmics documents >2.9 million cell-type labeled immune cells derived from seven single-cell sequencing technologies, involving 131 immune cell types, 47 tissues and 4 species. To ensure data consistency, we standardized the nomenclature of immune cell types and presented them in a hierarchical tree structure to clearly describe the lineage relationships within the immune system. scImmOmics also provides comprehensive immune regulatory information, including T-cell/B-cell receptor sequencing clonotype information, cell-specific regulatory information (e.g. gene/chromatin accessibility/protein/transcription factor states within known cell types, cell-to-cell communication and co-expression networks) and immune cell responses to cytokines. Collectively, scImmOmics is a comprehensive and valuable platform for unraveling the heterogeneity and diversity of immune cells and elucidating the specific regulatory mechanisms at the single-cell level.

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Publication Details

Journal
Nucleic Acids Research
Published
2024-11-04
DOI
https://doi.org/10.1093/nar/gkae985
Citations
33
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
7.72

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article

scImmOmics: a manually curated resource of single-cell multi-omics immune data

Lidong Li, Fengcui Qian, Li‐Wei Zhou, Huifang Tang et al.
33 citations
Nucleic Acids Research
Single-cell and spatial transcriptomics
7.72
article

scImmOmics: a manually curated resource of single-cell multi-omics immune data

Lidong Li, Fengcui Qian, Li‐Wei Zhou, Huifang Tang, Zhengmin Yu, Baoyang Hu, Tingting Yu, Qiuyu Wang, Ting Cui, Chunquan Li, Fu-Juan Dong, Yanyu Li, Yanbing Zhu, Fu-Hong Cai, Qiao-Li Fang
article en
33 citations

Abstract

Single-cell sequencing technology has enabled the discovery and characterization of subpopulations of immune cells with unique functions, which is critical for revealing immune responses under healthy or disease conditions. Efforts have been made to collect and curate single-cell RNA sequencing (scRNA-seq) data, yet an immune-specific single-cell multi-omics atlas with harmonized metadata is still lacking. Here, we present scImmOmics (https://bio.liclab.net/scImmOmics/home), a manually curated single-cell multi-omics immune database constructed based on high-quality immune cells with known immune cell labels. Currently, scImmOmics documents >2.9 million cell-type labeled immune cells derived from seven single-cell sequencing technologies, involving 131 immune cell types, 47 tissues and 4 species. To ensure data consistency, we standardized the nomenclature of immune cell types and presented them in a hierarchical tree structure to clearly describe the lineage relationships within the immune system. scImmOmics also provides comprehensive immune regulatory information, including T-cell/B-cell receptor sequencing clonotype information, cell-specific regulatory information (e.g. gene/chromatin accessibility/protein/transcription factor states within known cell types, cell-to-cell communication and co-expression networks) and immune cell responses to cytokines. Collectively, scImmOmics is a comprehensive and valuable platform for unraveling the heterogeneity and diversity of immune cells and elucidating the specific regulatory mechanisms at the single-cell level.

Nucleic Acids ResearchVol. 53(D1)
Capital Medical University (CN), Chinese Academy of Sciences (CN), Clinical Research Institute (US), Beijing Friendship Hospital (CN), University of South China (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hunan Province, University of South China
Openalex Percentile: Top 2%
Single-cell and spatial transcriptomics
7.72
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