单细胞和 bulk 转录组学的整合揭示了瘢痕疙瘩中的免疫相关特征。

Integration of single-cell and bulk transcriptomics reveals immune-related signatures in keloid.

机构信息

Department of Burn Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

出版信息

J Cosmet Dermatol. 2023 Jun;22(6):1893-1905. doi: 10.1111/jocd.15649. Epub 2023 Jan 26.

DOI:10.1111/jocd.15649

PMID:36701151

Abstract

BACKGROUND

Keloid is a pathological dermatological condition that manifests as an overgrowth scar secondary to skin trauma. This study endeavored to excavate immune-related signatures of keloid based on single-cell RNA (scRNA) sequencing data and bulk RNA sequencing data.

METHOD

The keloid-relevant scRNA sequencing dataset GSE163973 and bulk RNA sequencing dataset GSE113619 were mined from the GEO database. The "Seurat" R package was utilized for data quality control, cell clustering, and investigation of marker genes of each cell cluster. The "SingleR" package helped match the marker genes of the corresponding cluster to specific cell types. Moreover, the R package "Monocle" was deployed for pseudotemporal ordering analysis, and the "clusterProfiler" was applied for functional and pathway enrichment analysis. The immune-related signatures were then identified, and potential targeted drugs were predicted via the DGIdb database. Verification of the immune-related signatures in clinical validation samples was implemented by RT-qPCR.

RESULTS

Totally 23 cell clusters were screened and classified into 10 cell types based on the scRNA sequencing data. The keloid group had a significantly higher endothelial cell proportion than the control group. As enrichment analysis was applied in both differentially expressed genes (DEGs) of scRNA and bulk RNA sequencing data, we found they were enriched in multiple common immune-related pathways and biological processes. Meanwhile, we acquired three immune-related signatures (VCAM1, CALCRL, and HLA-DPB1) by intersecting the above DEGs with immune-related genes (IRGs). Then, we predicted 16 drugs potentially targeting the biomarkers through the DGIdb database. Finally, the outcome of RT-qPCR of clinical validation samples further verified the results.

CONCLUSION

In conclusion, we analyzed the cell types and functional differences in the keloid through scRNA and bulk RNA sequencing data. We identified three immune-related signatures (VCAM1, CALCRL, and HLA-DPB1) in keloid, providing a basis for further in-depth investigation of the molecular mechanisms of keloid and exploration of therapeutic targets.

摘要

背景

瘢痕疙瘩是一种病理性皮肤疾病，表现为皮肤创伤后过度生长的瘢痕。本研究旨在基于单细胞 RNA（scRNA）测序数据和批量 RNA 测序数据挖掘瘢痕疙瘩的免疫相关特征。

方法

从 GEO 数据库中挖掘与瘢痕疙瘩相关的 scRNA 测序数据集 GSE163973 和批量 RNA 测序数据集 GSE113619。使用“Seurat”R 包进行数据质量控制、细胞聚类，并研究每个细胞簇的标记基因。“SingleR”包帮助将相应簇的标记基因与特定细胞类型相匹配。此外，使用 R 包“Monocle”进行伪时间排序分析，并使用“clusterProfiler”进行功能和途径富集分析。然后确定免疫相关特征，并通过 DGIdb 数据库预测潜在的靶向药物。通过 RT-qPCR 验证临床验证样本中的免疫相关特征。

结果

总共筛选出 23 个细胞簇，并根据 scRNA 测序数据将其分为 10 种细胞类型。与对照组相比，瘢痕疙瘩组内皮细胞比例明显更高。对 scRNA 和批量 RNA 测序数据的差异表达基因（DEGs）进行富集分析，发现它们富集在多个共同的免疫相关途径和生物学过程中。同时，通过将上述 DEGs 与免疫相关基因（IRGs）相交，我们获得了三个免疫相关特征（VCAM1、CALCRL 和 HLA-DPB1）。然后，我们通过 DGIdb 数据库预测了 16 种潜在针对生物标志物的药物。最后，临床验证样本的 RT-qPCR 结果进一步验证了结果。