Skin sc-RNASeq from seven body sites (face, scalp, axilla, palmoplantar, arm, leg, and back)

This sc-RNAseq dataset is composed of samples from 96 skin biopsies from seven body sites (face, scalp, axilla, palmoplantar, arm, leg, and back). The skin biopsies were separated into epidermis and dermis before dissociated and enriched for various cell fractions (keratinocytes, fibroblasts, and endothelial cells) and immune cells (myeloid and lymphoid cells) to up sample rare cell types. In total, across body sites, 274,834 cells were profiled, including 96,194 keratinocytes. <i>Seurat v3.0.</i> was utilized to normalize, scale, and reduce the dimensionality of the data. Low quality cells containing less than 200 genes per cell as well as greater than 5,000 genes per cell were filtered out. Cells containing more mitochondrial genes than the permitted quantile of 0.05 were removed. Ambient RNA was removed using R package <i>SoupX</i> v1.6.2. Doublets were removed using <i>scDblFinder</i> v1.12.0. Principal components (PC) were obtained from the topmost 2,000 variable genes, and the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction technique was applied to the 30 topmost variable PC-reduced dataset. Batch effect correction was performed utilizing <i>harmony</i> v1.0, using donor as batch. After batch correction, cells were clustered using shared nearest neighbor modularity optimization-based clustering. Cluster marker genes were identified with <i>FindAllMarkers</i>; cluster corresponding cell type was identified by comparing marker genes to curated cell-type signature genes. Differential expression by keratinocyte subtype was performed with Seurat (v4.3.0) <i>FindMarkers</i> function by comparing keratinocyte subtype to non-keratinocyte clusters. The log fold-change of the average expression between a keratinocyte subtype cluster compared to the rest of clusters is utilized as keratinocyte-subtype gene expression statistic. Biopsy sample details, curated cell-type signature genes and processed Seurat object are provided herein. Raw data are available in SRA (id PRJNA1054546)

本单细胞RNA测序（single-cell RNA sequencing, sc-RNAseq）数据集包含来自7个身体部位（面部、头皮、腋窝、掌跖、手臂、腿部及背部）的96份皮肤活检样本。在将皮肤活检样本解离并富集各类细胞组分（角质形成细胞、成纤维细胞、内皮细胞）及免疫细胞（髓系细胞与淋巴系细胞）以提升稀有细胞类型的采样比例前，先将其分离为表皮层与真皮层。整体而言，覆盖所有身体部位的样本中共完成274834个细胞的转录组分析，其中包含96194个角质形成细胞。本研究使用Seurat v3.0对测序数据进行标准化、缩放及降维处理。过滤掉每细胞基因数少于200或多于5000的低质量细胞，同时移除线粒体基因占比超过0.05分位数的细胞。使用R语言包SoupX v1.6.2去除环境RNA污染，并通过scDblFinder v1.12.0去除双细胞。基于前2000个高可变基因计算主成分（Principal Components, PC），并对经前30个主成分降维后的数据集应用均匀流形近似与投影（Uniform Manifold Approximation and Projection, UMAP）进行降维可视化。以样本供体为批次因素，使用harmony v1.0完成批次效应校正。批次效应校正完成后，基于共享最近邻模块化优化算法对细胞进行聚类。使用FindAllMarkers函数鉴定各聚类簇的标记基因，并将标记基因与预先整理的细胞类型特征基因集进行比对，以注释各聚类簇对应的细胞类型。针对角质形成细胞亚型的差异表达分析，采用Seurat v4.3.0的FindMarkers函数，将角质形成细胞亚型聚类簇与非角质形成细胞聚类簇进行比对完成。以角质形成细胞亚型聚类簇与其余所有聚类簇的平均表达量对数倍变化值，作为角质形成细胞亚型的基因表达统计量。本数据集附带活检样本详细信息、整理后的细胞类型特征基因集以及处理完成的Seurat对象文件。原始测序数据可在SRA数据库（登录号PRJNA1054546）获取。