Raw Bulk RNA-seq counts of atherosclerotic carotid artery samples from patients undergoing carotid endarterectomy

General description of data

Bulk-RNA-seq count matrix of human atherosclerotic carotid artery samples.

Human samples were obtained from patients diagnosed with carotid stenosis and sourced from the Munich Vascular Biobank. Plaque samples were collected from patients undergoing carotid endarterectomy (CEA), the surgical removal of atherosclerotic plaque from the carotid artery. Immediately after excision, samples were transferred into pre-chilled PBS for transport. The carotid plaques were then sectioned, and the most advanced (i.e., unstable) region, as well as the least diseased region when available, were selected for analysis.

Tissue samples for Bulk RNA Sequencing were transferred to chilled RNALater (Thermo Scientific, Germany), and stored at -80°C awaiting further processing. Total (bulk) RNA was extracted from human carotid artery specimens provided by the Munich Vascular Biobank and generated libraries that were sequenced (RNA-seq) via the Illumina NovaSeq platform (NovaSeq6000, Illumina, San Diego, USA). RNA was purified using poly-T-oligo-attached magnetic beads and the TruSeq stranded total RNA kit and TruSeq stranded mRNA kit (Illumina, San Diego, USA) were used to prepare the short-read sequencing libraries. RNA and library integrity was assessed via Qubit (Thermo Fisher, USA), as well as real-time PCR for quantification and tape station (Agilent, USA) for size distribution detection.

Stranded RNA-Seq was conducted using the Illumina NovaSeq 6000 platform, with 101 base pair (bp) paired-end reads. Sequencing depth ranged from 20 million to 140 million reads per sample, depending on RNA quality. The RNA-Seq reads were demultiplexed and aligned to the hg19 genome assembly (UCSC Genome Browser build) using STAR (v2.7.0a). Read quantification was performed with HTSeq-count (v0.6.0) to determine the number of reads mapping to annotated genes. Genes with an FPKM (Fragments Per Kilobase of transcript per Million mapped reads) below the 95th percentile of 1 were filtered out as insufficiently expressed.

Files contained

Raw_counts.csv

• Raw counts of bulk RNA-seq

Patient_metadata.csv

• Dataframe containing patient metadata along with information about mapping.

Bulk_data_column_descriptions.md

• Readme file contining short descriptions of the main patient metadata variables.

Detailed description of preprocessing steps can be found in our manuscript.