Single cell expression data reveal human genes that escape X-chromosome inactivation 

Background: In mammals, sex chromosomes are a source of an inherent genetic difference between the sexes. A balance between the sexes is reached by random inactivation of one of the X-chromosomes, in each female somatic cell. Thus resulting in a tissue mosaic such that some of the cells express one of the X-chromosomes and some the other. While most genes from the inactivated X-chromosome are silenced, about 15-25% of them have been shown to escape inactivation (referred as escapees). These escapees have so far been identified using multiple indirect methods considering the mosaic nature of female tissues. Results: We use single-cell RNA-seq to directly quantify the extent of escape from X-inactivation phenomenon. Analyzing data from single cells is preferable as in each cell only one of the X chromosomes is inactivated. Our method relies on allelic specific expression of genes with heterozygous SNPs, thus enabling us to discriminate between the expression of genes from the active X-chromosome (Xa) and from the inactive (Xi) one. We apply our method to datasets from (i) primary fibroblasts without genomic phasing (n=104), and (ii) clonal lymphoblasts cells with phased parental genomes (n=25). Applying our single-cell analysis we identify 27 and 34 escapees from fibroblasts and lymphoblasts, respectively. On the other hand, when analyzing a pool of lymphoblasts, only 14 escapees are discovered. Altogether, we report on 51 escapees, many of which are known escapees discovered by indirect methods (p=2.74e-06). We identify a few overlooked escapees, and propose to revise the annotations associated with some others. Conclusions: Chromosome X-inactivation and escaping from it are robust phenomena detected at a single-cell resolution. These phenomena are apparent in isolated primary fibroblasts as well as in cell-lines. Genomic phasing substantially improves the detection of escapees. Cumulative data from individual cells increases the sensitivity of detecting escapees compared to data extracted from a pooled sample. Our results support the notion that different cell types express different escapees with only a handful of consistent escapees across cell types and experimental settings.