Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells

Abstract

Expression from both alleles is generally observed in analyses of diploid cell populations, but studies addressing allelic expression patterns genome-wide in single cells are lacking. Here, we present global analyses of allelic expression across individual cells of mouse preimplantation embryos of mixed background (CAST/EiJ × C57BL/6J). We discovered abundant (12 to 24%) monoallelic expression of autosomal genes and that expression of the two alleles occurs independently. The monoallelic expression appeared random and dynamic because there was considerable variation among closely related embryonic cells. Similar patterns of monoallelic expression were observed in mature cells. Our allelic expression analysis also demonstrates the de novo inactivation of the paternal X chromosome. We conclude that independent and stochastic allelic transcription generates abundant random monoallelic expression in the mammalian cell.

Detection of Imprinted Genes by Single-Cell Allele-Specific Gene Expression

“…Previously, our lab and others demonstrated that single-cell RNA-seq facilitates the identification of ASE patterns. [21][22][23][24] In this study, we sequenced the RNA of 1,084 single fibroblasts from 5 individuals. For 2 of these individuals, the parental DNA was available.…”

netSmooth: Network-smoothing based imputation for single cell RNA-seq

“…Next, we test netSmooth on 269 isolated cells from mouse embryos at different stages of pre-implantation development between oocyte and blastocyst, as well as 5 liver cells and 10 fibroblast cells 18 . The authors of this study demonstrated that lower dimension embeddings capture much of the developmental trajectory ( Figure 4a, Figure S4a, Figure S5a).…”

“…The embryonic 18 and glioblastoma 19 datasets were obtained from conquer 22 , a repository of uniformly processed scRNA-seq datasets. The datasets are available publicly, see Table 1.…”

“…While some approaches directly take into account the zero-inflation of scRNA-seq data 5 , other studies use traditional methods 18 . There is no standard method for clustering single cell RNAseq data, as different studies produce data with different topologies, which respond differently to the various clustering algorithms.…”

Single‐cell technologies to study the immune system

“…Cellular transitions can be studied by defining cell states using hierarchical clustering or principal component analysis‐like methods. The approach has been applied to show how cells change gradually along the developmental pathway from zygote to the late blastocyst 53. In future, a similar approach can be applied to the in vivo immune cell activation/inactivation dynamics and their dysfunction.…”