Santoni Federico A, Stamoulis Georgios, Garieri Marco, Falconnet Emilie, Ribaux Pascale, Borel Christelle, Antonarakis Stylianos E
Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland; University Hospitals of Geneva, Geneva 1211, Switzerland.
Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland.
Am J Hum Genet. 2017 Mar 2;100(3):444-453. doi: 10.1016/j.ajhg.2017.01.028. Epub 2017 Feb 9.
Genomic imprinting results in parental-specific gene expression. Imprinted genes are involved in the etiology of rare syndromes and have been associated with common diseases such as diabetes and cancer. Standard RNA bulk cell sequencing applied to whole-tissue samples has been used to detect imprinted genes in human and mouse models. However, lowly expressed genes cannot be detected by using RNA bulk approaches. Here, we report an original and robust method that combines single-cell RNA-seq and whole-genome sequencing into an optimized statistical framework to analyze genomic imprinting in specific cell types and in different individuals. Using samples from the probands of 2 family trios and 3 unrelated individuals, 1,084 individual primary fibroblasts were RNA sequenced and more than 700,000 informative heterozygous single-nucleotide variations (SNVs) were genotyped. The allele-specific coverage per gene of each SNV in each single cell was used to fit a beta-binomial distribution to model the likelihood of a gene being expressed from one and the same allele. Genes presenting a significant aggregate allelic ratio (between 0.9 and 1) were retained to identify of the allelic parent of origin. Our approach allowed us to validate the imprinting status of all of the known imprinted genes expressed in fibroblasts and the discovery of nine putative imprinted genes, thereby demonstrating the advantages of single-cell over bulk RNA-seq to identify imprinted genes. The proposed single-cell methodology is a powerful tool for establishing a cell type-specific map of genomic imprinting.
基因组印记导致亲本特异性基因表达。印记基因参与罕见综合征的病因学研究，并与糖尿病和癌症等常见疾病有关。应用于全组织样本的标准RNA大量细胞测序已被用于在人类和小鼠模型中检测印记基因。然而，低表达基因无法通过RNA大量方法检测到。在这里，我们报告了一种原始且强大的方法，该方法将单细胞RNA测序和全基因组测序结合到一个优化的统计框架中，以分析特定细胞类型和不同个体中的基因组印记。使用来自2个家庭三联体和3个无关个体的先证者样本，对1084个个体原代成纤维细胞进行RNA测序，并对超过70万个信息丰富的杂合单核苷酸变异（SNV）进行基因分型。每个单细胞中每个SNV的每个基因的等位基因特异性覆盖率用于拟合β-二项分布，以模拟基因从同一个等位基因表达的可能性。保留呈现显著聚集等位基因比率（介于0.9和1之间）的基因，以确定等位基因的亲本来源。我们的方法使我们能够验证成纤维细胞中表达的所有已知印记基因的印记状态，并发现9个推定的印记基因，从而证明了单细胞RNA测序相对于大量RNA测序在识别印记基因方面的优势。所提出的单细胞方法是建立细胞类型特异性基因组印记图谱的有力工具。
