RNA-Seq analysis

We extracted RNAs from SCo and SCj as well as nSCo and nSCj (3 replicates per condition) obtained from a 74-year old donor to perform RNA-Seq analysis. To evaluate the quality of the raw RNA-seq data, we initially performed a quality assessment using FastQC (v0.12.1; https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). To remove adapter sequences and low-quality reads, we used TrimGalore (v4.3; https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) [19]. The resulting trimmed reads were aligned to the reference genome (GRCh38) using HISAT2 (v2.2.1) [20]. Subsequently, we converted the output SAM files to sorted BAM files using SAMtools (v1.12) [21] for downstream analysis. Mapped reads were assigned to their corresponding genes using FeatureCounts (v2.0.3) [22], which generated count data for each gene. The raw read counts were then normalized using DESeq2 (v1.8.3) [23]. For exploratory analysis, differential gene expression analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and enrichment analysis, we utilized iDEP [24]. Differential gene expression analysis was performed using DESeq2 (v1.8.3) in R (v4.2.3; https://www.R-project.org/) and RStudio (v2023.03.0 + 386; https://www.rstudio.com/). Gene ontology (GO) and pathway analysis for the identified differentially expressed genes (DEGs) were conducted using the clusterProfiler (v4.6.2) package [25].