Investigation of public single cell transcriptomics datasets to identify disease-related cellular phenotypes and molecular alterations of perivascular cells in multiple sclerosis

OBJECTIVE: In this thesis research, we aimed to investigate the molecular changes that occur in perivascular cells during Multiple Sclerosis, specifically pericytes and fibroblasts. For this purpose, bioinformatic tools are utilized to analyze the publicly available single-cell RNA sequencing datasets. METHODS: Firstly, we replicated the analysis presented in the "A human brain vascular atlas reveals diverse mediators of Alzheimer's risk" by Yang et al. to hold a view into molecular characteristics of mural cells in humans. Then, we analyzed the data presented in the article named "A lymphocyte–microglia–astrocyte axis in chronic active multiple sclerosis" by Absinta et al. We focused on the vascular cell cluster that was not studied in the article. We isolated this cluster from the other cells and then subclustered to study the characteristics of mural cells and fibroblasts in chronic MS lesions. In both studies the datasets are provided as fasq files. Our workflow has 9 steps including quality control, normalization, feature selection, dimensionality reduction, integration, 2-dimensional embedding, clustering, differential gene expression, and gene set enrichment analysis. RESULTS: First, the UMAP and t-SNE graphs were generated. Then, the identity of each cluster was determined according to the marker genes that are highly expressed by these cells in differential gene expression analysis. Marker genes were shown on UMAP graphs. Differentially expressed genes in mural cells, fibroblasts and endothelial cells were shown with volcano plots. KEGG pathway analysis was performed to identify the cellular pathways that are enriched in specific cell types. CONCLUSIONS: In the dataset of Absinta et al., sub-clustering of the vascular cluster (Cluster 11 and 13) revealed that this cluster is actually a combination of endothelial cells, pericytes, vascular smooth muscle cells, fibroblasts and a small contaminating population of oligodendrocytes. In the fibroblast cluster, enrichment of fibrosis related genes including Loxl4, Col12a1, Col15a1, Col6a3, Col11a1, Col13a1, FBN1, Col1a2 and Col1a1 was striking, and these genes were not described in the Alzheimer's disease dataset. DGE analysis of mural cell cluster revealed enriched expression of mostly the marker genes and several signaling pathways. KEY WORDS: Bioinformatics, single-cell RNA sequencing, Multiple Sclerosis, pericytes, fibroblasts