CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data

Hu, Congxue; Li, Tengyue; Xu, Yingqi; Zhang, Xinxin; Li, Feng; Bai, Jing; Chen, Jing; Jiang, Wenqi; Yang, Kaiyue; Ou, Qi; Li, Xia; Wang, Peng; Zhang, Yunpeng

doi:10.1093/nar/gkac947

Cited by 334 publications

(242 citation statements)

References 33 publications

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“…We subsequently applied the UMAP and t-SNE algorithms on the top 30 principal components to visualize the high dimensional scRNA-seq data and 14 clusters were obtained. Previous canonical cell markers [ 19 , 20 , 21 ] and CellMarker 2.0 [ 22 ] helped us to identify seven T cell subsets ( Figure 1 A,E). Namely, exhausted CD8 + T cells, cytotoxic CD8 + T cells, resident memory CD4 + T cells, cluster1-resident memory CD8 + T cells, cluster2-resident memory CD8 + T cells, CD4 + CD25 + FOXP3 + regulatory T cells, and naive CD4 + T cells.…”

Section: Resultsmentioning

confidence: 99%

A New Signature That Predicts Progression-Free Survival of Clear Cell Renal Cell Carcinoma with Anti-PD-1 Therapy

Lin

Cai

et al. 2023

IJMS

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Immunotherapy has greatly improved the survival time and quality of life of patients with renal cell carcinoma, but the benefits are limited to a small portion of patients. There are too few new biomarkers that can be used to identify molecular subtypes of renal clear cell carcinoma and predict survival time with anti-PD-1 treatment. Single-cell RNA data of clear cell renal cell carcinoma (ccRCC) treated with anti-PD-1 were obtained from public databases, then 27,707 high-quality CD4 + T and CD8 + T cells were obtained for subsequent analysis. Firstly, genes set variation analysis and CellChat algorithm were used to explore potential molecular pathway differences and intercellular communication between the responder and non-responder groups. Additionally, differentially expressed genes (DEGs) between the responder and non-responder groups were obtained using the “edgeR” package, and ccRCC samples from TCGA-KIRC (n = 533) and ICGA-KIRC (n = 91) were analyzed by the unsupervised clustering algorithm to recognize molecular subtypes with different immune characteristics. Finally, using univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression, and multivariate Cox regression, the prognosis model of immunotherapy was established and verified to predict the progression-free survival of ccRCC patients treated with anti-PD-1. At the single cell level, there are different signal pathways and cell communication between the immunotherapy responder and non-responder groups. In addition, our research also confirms that the expression level of PDCD1/PD-1 is not an effective marker for predicting the response to immune checkpoint inhibitors (ICIs). The new prognostic immune signature (PIS) enabled the classification of ccRCC patients with anti-PD-1 therapy into high- and low-risk groups, and the progression-free survival times (PFS) and immunotherapy responses were significantly different between these two groups. In the training group, the area under the ROC curve (AUC) for predicting 1-, 2- and 3-year progression-free survival was 0.940 (95% CI: 0.894–0.985), 0.981 (95% CI: 0.960–1.000), and 0.969 (95% CI: 0.937–1.000), respectively. Validation sets confirm the robustness of the signature. This study revealed the heterogeneity between the anti-PD-1 responder and non-responder groups from different angles and established a robust PIS to predict the progression-free survival of ccRCC patients receiving immune checkpoint inhibitors.

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Section: Resultsmentioning

confidence: 99%

A New Signature That Predicts Progression-Free Survival of Clear Cell Renal Cell Carcinoma with Anti-PD-1 Therapy

Lin

Cai

et al. 2023

IJMS

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“…Cell clusters were identi ed by a shared nearest neighbor (SNN) modularity optimization based original Louvain algorithm. Then, cell types were annotated by crossvalidating the cell-type speci cally expressed genes, the pre-existing cell annotations from published studies, and the known cell markers from the CellMarker databases [22]. The CellChat [23] R package was utilized for cell communication analysis.…”

Section: Discussionmentioning

confidence: 99%

RAD-Blood: a database to identify RNAs in blood as potential biomarkers of Alzheimer's disease by integrating bulk- and single-cell transcriptome profile

Duan

Chu

et al. 2023

Preprint

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Background The early diagnosis of Alzheimer's disease (AD) in large-scale high-risk population is a major challenge. Blood-based biomarkers could enable widespread testing for AD. RNA-seq technology is becoming an effective method in investigating diagnostic biomarkers for diseases, but platforms exploring RNA-seq data in AD blood are lacking. Methods We collected the raw RNA-seq data in the blood of AD patients or AD mouse models, mild cognitive impairment (MCI) patients, and normal people or wild type mouse from the Gene Expression Omnibus (GEO) and Synapse databases. And the RNA-seq data was analyzed by the standard pipeline. We applied R-Shiny to develop the website of RAD-Blood (RNA-seq analysis of AD blood, http://www.bioinform.cn/RAD-Blood/) to present the plentiful analysis results. Results RAD-Blood was specifically designed to analyze existing blood RNA-seq data sets (mRNA-seq, miRNA-seq, and scRNA-seq) from patients and mouse models with AD pathology. The RAD-Blood provides differential expression, gene set enrichment, immune abundance and its correlation with gene expression, cell type annotation, T cell receptor, and cell communication analyses for RNA-seq data in AD/MCI/normal blood, with rich results forms and colorful figures. We used a case study to show the capacity of RAD-Blood in finding blood biomarkers in AD/MCI blood. By using RAD-Blood, we found 274 protein-coding genes whose mRNA expression was consistently up-regulated or down-regulated from normal to MCI to AD. Among the consistently down-regulated genes, four are the markers of the blood erythroid cell. Compared with normal people, the population of erythroid cells in AD patients decreased. Despite the reduction in cell count, interactions between blood erythroid cells with other cells increased dramatically, which is mainly mediated through the major histocompatibility complex I (MHC-I) signaling pathway. These findings have not been reported by existing studies, which suggests that RAD-Blood is a solution for finding potential novel signatures in the blood of AD and MCI. Conclusions RAD-Blood is a user-friendly web server for multi-level analysis and visualization of gene/miRNA expression and immune profile in AD blood, and allows broad utility in exploring potential AD blood biomarkers, testing hypotheses, guiding experiment design, and investigating the peripheral pathogenic mechanisms and proposing potential early diagnosis standard of AD.

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“…Harmony package ( 29 ) was used to integrate the scRNA-seq samples and control batch effects. Major cell types were annotated preliminarily based on gene sets from CellMarker 2.0 ( 30 ): epithelial cells ( EPCAM , KRT19 ); myeloid cells ( CD68 , CD163 ); fibroblasts ( COL1A1,PDGFRB , ACTA2 ); T and NK cells ( CD3D , CD4 , NKG7 ); plasma cells ( JCHAIN , IGHG1 , MZB1 ); endothelial cells ( PECAM1 , VWF ); B cells (CD79A , MS4A1 ).…”

Section: Methodsmentioning

confidence: 99%

Integrating bulk and single-cell RNA sequencing data reveals the relationship between intratumor microbiome signature and host metabolic heterogeneity in breast cancer

et al. 2023

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IntroductionNowadays, it has been recognized that gut microbiome can indirectly modulate cancer susceptibility or progression. However, whether intratumor microbes are parasitic, symbiotic, or merely bystanders in breast cancer is not fully understood. Microbial metabolite plays a pivotal role in the interaction of host and microbe via regulating mitochondrial and other metabolic pathways. And the relationship between tumor-resident microbiota and cancer metabolism remains an open question.Methods1085 breast cancer patients with normalized intratumor microbial abundance data and 32 single-cell RNA sequencing samples were retrieved from public datasets. We used the gene set variation analysis to evaluate the various metabolic activities of breast cancer samples. Furthermore, we applied Scissor method to identify microbe-associated cell subpopulations from single-cell data. Then, we conducted comprehensive bioinformatic analyses to explore the association between host and microbe in breast cancer.ResultsHere, we found that the metabolic status of breast cancer cells was highly plastic, and some microbial genera were significantly correlated with cancer metabolic activity. We identified two distinct clusters based on microbial abundance and tumor metabolism data. And dysregulation of the metabolic pathway was observed among different cell types. Metabolism-related microbial scores were calculated to predict overall survival in patients with breast cancer. Furthermore, the microbial abundance of the specific genus was associated with gene mutation due to possible microbe-mediated mutagenesis. The infiltrating immune cell compositions, including regulatory T cells and activated NK cells, were significantly associated with the metabolism-related intratumor microbes, as indicated in the Mantel test analysis. Moreover, the mammary metabolism-related microbes were related to T cell exclusion and response to immunotherapy.ConclusionsOverall, the exploratory study shed light on the potential role of the metabolism-related microbiome in breast cancer patients. And the novel treatment will be realized by further investigating the metabolic disturbance in host and intratumor microbial cells.

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CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data

Cited by 334 publications

References 33 publications

A New Signature That Predicts Progression-Free Survival of Clear Cell Renal Cell Carcinoma with Anti-PD-1 Therapy

A New Signature That Predicts Progression-Free Survival of Clear Cell Renal Cell Carcinoma with Anti-PD-1 Therapy

RAD-Blood: a database to identify RNAs in blood as potential biomarkers of Alzheimer's disease by integrating bulk- and single-cell transcriptome profile

Integrating bulk and single-cell RNA sequencing data reveals the relationship between intratumor microbiome signature and host metabolic heterogeneity in breast cancer

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