Single Cell RNA Sequencing in Atherosclerosis Research

Abstract

Technological advances in characterizing molecular heterogeneity at the single cell level have ushered in a deeper understanding of the biological diversity of cells present in tissues including atherosclerotic plaques. New subsets of cells have been discovered among cell types previously considered homogenous. The commercial availability of systems to obtain transcriptomes and matching surface phenotypes from thousands of single cells is rapidly changing our understanding of cell types and lineage identity. Emerging methods to infer cellular functions are beginning to shed new light on the interplay of components involved in multifaceted disease responses, like atherosclerosis. Here, we provide a technical guide for design, implementation, assembly, and interpretations of current single cell transcriptomics approaches from the perspective of employing these tools for advancing cardiovascular disease research.

Keywords: atherosclerosis; cardiovascular disease; immunology; phenotype; transcriptomic.

Figure 1.

Experimental workflow for CITE-seq and AB-seq experiments. Single cell suspensions derived from artery wall (top) or PBMCs (bottom) are sorted for live cells and other desired markers (here: CD45 for leukocytes), hash-tagged, washed and incubated with the oligonucleotide-marked antibody panel (currently 50 mAbs are possible). After 3 more washes, the cells are loaded into the BD Rhapsody scanner or the 10x Chromium controller. Beads are retrieved and processed for library preparation. Pre-sequencing quality control (QC) is done by TapeStation. DNA fragments are removed, and the library is sequenced using NGS sequencers like the Illumina NovaSeq. The figure was created with BioRender.

Figure 2.

Bioinformatic analysis workflow of CITE-seq and AB-seq experiments The output of the sequencer is a FASTQ file with information about read quality. Further post-sequencing QC steps are indicated. If batch effects are detected, they can be removed if the experiment was designed to do so. Missing transcripts may be imputed. Dimensionality reduction (here by UMAP) and clustering (here by Louvain) result in 2 D maps of cells segregated by surface markers. Each antibody and transcript can be displayed as a heat map projected onto the UMAP. Differential gene expression is determined between clusters, or between samples from healthy and diseased individuals, or one cluster against all other cells. From the gene signature, pathway analysis is performed. The figure was created with BioRender.