title: GSM7937407: WT, aortic root and ascending aorta, sc-RNA seq; Mus musculus; RNA-Seq
library_construction_protocol: The mice were sacrificed by sodium pentobarbital, followed by perfusion with cold PBS. The aortic root and ascending aortas were collected in a tube with 1 ml Kreb's buffer on ice. Perivascular adipose were carefully removed. After collecting all arteries，the arteries were washed with PBS for three times and cutted into 1-2 mm2 in tube and digested with aortic dissociation enzyme solution (Collagenase I, 450 U/ml; Hyaluronidase type I-S, 60 U/ml; DNase I, 60 U/ml; Collagenase XI, 125 U/ml) . The digestion was performed at 37℃ shaker for 1-2 h, and the tube was gently invert every 15 mins21. Detached cells were collected until the tissue dissolved completely. After sufficient digestion, single cell suspension was strained through a 70-µm strainer. And the collected cells were centrifuged 10 min at 700 g speed at 4 ℃. For scRNA-seq, single cell suspensions from 3 mice were pooled as one sample. Single cell suspensions of aortic root and ascending aortas from WT and Fbn1C1041G/+ mice were stained with a LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit (1:1000) and Hoechst 33342 (Invitrogen, H3570; 1:1000) for 20 min on ice. After being washed in PBS, cells were resuspended in PBS. Single live cells (Hoechst+/Dead Cell Stain−) were sorted into PBS with 0.04% BSA using a BD FACS Symphony S6 (BD Biosciences). A Chromium Single Cell 3' Reagent Kit v3 was used, and Standard 10X Chromium Single Cell 3' v3 (10X Genomics GemCode Technology) protocols were followed for scRNA-seq. Briefly, single cells with specific 10X barcodes and unique molecular identifiers were generated by partitioning the cells into Gel Bead-in-Emulsions. A cDNA library was generated and sequenced with a Novaseq (S4) 6000 instrument (PE150). Raw sequencing data were processed using Cell Ranger (version 3.1) to demultiplex raw data and generate FASTQ files, and the resulting data were aligned and counted with Cell Ranger pipelines. The R package Seurat (version 3.2.2) was used for analyzing scRNA-seq data. Briefly, gene features expressed in at least three cells and cells with at least 100 detected genes were retained; then, cells displaying fewer than 200 gene features or having more than 5% mitochondrial counts were filtered. After passing the quality control metrics, 3888 cells in WT group and 6409 cells in Fbn1C1041G/+ group were further analyzed. Then, gene expression was normalized by “LogNormalize.”. Integrative analysis of the two groups was performed using canonical correlation analysis to remove batch effects. A standard integrated analysis was performed with default parameters. The top 2000 highly variable genes were selected and scaled by “ScaleData.”. Principal components analysis was then performed, and the first 15 principle components with a resolution of 0.2 were used for unbiased clustering. Clusters were visualized with tSNE. The markers for every cluster compared to all remaining cells were identified by “FindAllMarkers” (min.pct = 0.25, logfc.threshold = 0.25, only.pos = TRUE) and were used as DEGs in the following analyses. Reported Padjusted values were calculated using Benjamini-Hochberg correction. Heatmaps of scRNA-seq data were plotted with R package ggplot2.