Single Cell RNA Sequencing in Atherosclerosis Research

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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.

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Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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