Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments

Abstract

Gene expression in multiple individual cells from a tissue or culture sample varies according to cell-cycle, genetic, epigenetic and stochastic differences between the cells. However, single-cell differences have been largely neglected in the analysis of the functional consequences of genetic variation. Here we measure the expression of 92 genes affected by Wnt signaling in 1,440 single cells from 15 individuals to associate single-nucleotide polymorphisms (SNPs) with gene-expression phenotypes, while accounting for stochastic and cell-cycle differences between cells. We provide evidence that many heritable variations in gene function--such as burst size, burst frequency, cell cycle-specific expression and expression correlation/noise between cells--are masked when expression is averaged over many cells. Our results demonstrate how single-cell analyses provide insights into the mechanistic and network effects of genetic variability, with improved statistical power to model these effects on gene expression.

Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity

“…By single-cell PCR, Chiu et al identified six subgroups of DRG neurons [25]. Single-cell RNA-seq enables a better understanding of a cell's transcriptome [26][27][28][29][30][31][32][33]. Usoskin et al performed low-coverage single-cell RNA-seq (3 574 ± 2 010 genes per neuron) and classified the mouse DRG neurons into two PEP types, three NP types, TH type and five NF200-positive types within the traditional classification framework [34].…”

Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia

“…Single-cell RNA profiling, via qPCR (Guo et al 2010(Guo et al , 2013Dalerba et al 2011;Buganim et al 2012;Wills et al 2013) or sequencing (Shalek et al 2013, has emerged as a powerful technology for establishing cell state (Treutlein et al 2014;Klein et al 2015;Zeisel et al 2015). Our single-cell RNA-seq data clearly delineated transcriptional states but could not be used to reliably detect mutations.…”

“…By leveraging the Biomark microfluidic technology that has been successfully used for analysis of single-cell RNA expression (Guo et al 2010(Guo et al , 2013Buganim et al 2012;Wills et al 2013), we implemented a two-stage RT-PCR amplification strategy (Fig. 1A) to include mutation detection.…”

Visualization of Biomedical Data

“…First accomplished with microarray experiments (42), accurate measurements of the abundance of transcripts in biological samples and single cells (43) are now taken with RNA sequencing (RNA-seq) (44).…”