Human femoral atheroma exhibit inflammation-resolving myeloid and lymphoid bias compared with carotid atheroma

Pathologic and clinical investigations suggest that femoral artery plaques are less inflammatory than other vascular beds, including carotid arteries. However, limited data exist regarding comparative immune landscapes and inflammatory polarization that may underlie such differences in femoral versus carotid plaque. Femoral or carotid artery plaques obtained from endarterectomy of patients at Northwestern Medicine were processed into single-cell suspensions and underwent single-cell ribonucleic acid sequencing (scRNA-seq). ScRNA-seq was performed on a total of 65,920 leukocytes from 13 individuals undergoing femoral (N=9; 35265 CD45+ cells) or carotid (N=4; 30655 cells) endarterectomy. For scRNA-seq analyses, suspensions were then enriched for live cells and CD45+ cells. Dead cells were removed with a dead cell removal kit (Miltenyi Biotec 130-090-101 or Stem Cell Technologies 17899) according to manufacturer’s instructions. Suspensions were then enriched for immune cells with CD45 positive selection using a CD45+ enrichment kit (Miltenyi, 130-045-801) and cells were counted. The CD45+ enriched single cell suspensions of plaque were converted to barcoded scRNA-seq libraries using the Chromium Single Cell 3ʹ Library, Gel Bead, and Chip Kit from 10X Genomics. The CD45+ enriched single cell suspensions of plaque were converted to barcoded scRNA-seq libraries using the Chromium Single Cell 3ʹ Library, Gel Bead, and Chip G from 10X Genomics. The Chromium Single Cell 3’ V3.1 Reagent (10x Genomics, PN-1000286) kit was used to prepare scRNA-seq libraries. Reverse transcription, barcoding, complementary DNA amplification and purification for library preparation were performed according to the manufacturer’s instructions. Sequencing was performed on a NovaSeq 6000 platform with Read 1 of 28nt and Read 2 of 90bp (Illumina). Sequencing reads were demultiplexed and aligned to the human GRCh38 transcriptome using the CellRanger V3 software (10X Genomics). Filtering, unsupervised clustering, differential expression and additional analysis were completed using Seurat V4 and ClusterProfiler packages for R. To remain consistent with the institutionally approved protocol and due to identifiability concerns, data are shared here down to the level of gene counts tables/matrices. *** Raw data is not included. Data are provided down to the level of de-identified gene lists and counts tables to be compliant with the institutionally approved human subjects protocol used to collect the samples used in this study. ***