DOI: 10.5061/dryad.mcvdnck85
title: Human dermal microvascular arterial and venous blood endothelial cells and their use in bioengineered dermo-epidermal skin substitutes in vitro and in vivo

description: The bioengineering of vascular networks is pivotal to creating complex
 tissues and organs for regenerative medicine applications. However,
 bioengineered tissues comprising an arterial and venous plexus alongside a
 lymphatic capillary network have not been explored yet. Here, we first
 employed scRNA-seq to investigate the arterio-venous endothelial cell
 marker patterning in human fetal and juvenile skin. Transcriptomic
 analysis revealed that arterial and venous endothelial cell markers NRP1
 and NR2F2 are broadly expressed in fetal and juvenile skin. In contrast,
 expression of NRP1 and NR2F2 on the protein level was cell-type specific
 and was retained in 2D cultures in vitro. Finally, we bioengineered
 distinct arterial and venous capillaries in 3D hydrogels and demonstrated
 rapid anastomosis with the host vasculature in vivo. In summary, we
 established the bioengineering of human arterial, venous, and lymphatic
 capillaries, hence paving the way for these cells to be used in
 regenerative medicine and future clinical applications.
description: # Human dermal microvascular arterial and venous blood endothelial cells
 and their use in bioengineered dermo-epidermal skin substitutes in vitro
 and in vivo
 [https://doi.org/10.5061/dryad.mcvdnck85](https://doi.org/10.5061/dryad.mcvdnck85) ## Description of the data and file structure The RDS file is a Single Cell Experiment R object that contains the normalized counts of the endothelial cells for the fetal and foreskin samples. All annotations for cell type (eg A, C1, C2, V, P, and L1/L2) are contained in colData Dimensional reduction for PCA and UMAP is in ReducedDim() We have submitted our processed single-cell RNAseq data as a single-cell experiment (SCE) object (.rds). The column data for the SCE object contains: * Sample: name of the sample * Barcode: cell barcode of the sample * sum: total number of RNA counts for the sample * detected: unique number of RNA detected for the sample * percent.top_100: [percentage of RNA transcripts that are in the top 100 expressed genes * subsets_Mito_sum: total number of mitochondrial RNA counts for the sample * subsets_Mito_detected: unique number of mitochondrial RNA detected for the sample * subsets_Mito_percent: percentage of mitochondrial RNA counts in relation to total RNA counts * total: total number of RNA counts for the sample  * scDblFinder.sample: sample name for the doublet detection algorithm * scDblFinder.cluster: cluster identity from the doublet detection algorithm * scDblFinder.class: output of the doublet detection algorithm as "singlet" or "doublet" * scDblFinder.score: score from the doublet detection algorithm * scDblFinder.weighted: weighted score from the doublet detection algorithm * scDblFinder.difficulty: difficulty output from the doublet detection algorithm * scDblFinder.cxds_score: cxds score from the doublet detection algorithm * scDblFinder: mostLikelyOrigin: most likely origin identifier from the double detection algorithm * scDblFinder.originAmbiguous: ambiguous origin output as "TRUE" or "FALSE" from the doublet detection algorithm * keep: column to identify cells for analysis as "TRUE" or "FALSE" * phases: output of cell cycle analysis * G1_score_norm: G1 score from cell cycle analysis * S_score_norm: S score from cell cycle analysis * G2M_score_norm: G2M score from cell cycle analysis * sizeFactor: library size factor for normalization * batch: batch ID for the sample * keep2: same as keep column * Type: group ID of the sample as "Fetal skin" or "Foreskin" * BPE_celltype: cell annotation from SingleR using the blueprint encode reference * fixedPCA_UMAP1: first dimension UMAP * fixedPCA_UMAP2: second dimension UMAP * fixPCA_recluster_UMAP1: first dimension UMAP after reclustering * fixedPCA_recluster_UMAP2: second dimension UMAP after reclustering * cluster_walktrap: cluster id from walktrap algorithm * cluster_walktrap_harmony: cluster id after harmony algorithm * cluster_walktrap_harmony2: cluster id after harmony algorithm second iteration * cluster_walktrap_harmony3: cluster id after harmony algorithm third iteration * cluster_walktrap_harmony4: cluster id after harmony algorithm fourth iteration * Cell_ident: cell annotation from SingleR using the endothelial reference from Li et al., (2021) *Theranostics* The row data for the SCE object contains the raw and log normalized counts of 16413 genes. ## Sharing/Access information Links to other publicly accessible locations of the data: * [https://zenodo.org/doi/10.5281/zenodo.8333705](https://zenodo.org/doi/10.5281/zenodo.8333705 "https://zenodo.org/doi/10.5281/zenodo.8333705") ## Code/Software The RDS file can be read into R with readRDS with the scater and scran packages loaded. R is required to read and import the RDS file. R packages scran and scater are required to access the SCE object.