Gur E Ravza, Hughes Jim R
MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK.
MRC Molecular Haematology Unit, Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, OX3 9DS, UK.
Sci Rep. 2025 Jan 29;15(1):3665. doi: 10.1038/s41598-025-87351-7.
Bulk ATAC-seq assays have been used to map and profile the chromatin accessibility of regulatory elements such as enhancers, promoters, and insulators. This has provided great insight into the regulation of gene expression in many cell types in a variety of organisms. To date, ATAC-seq has most often been used to provide an average evaluation of chromatin accessibility in populations of cells. The development of a single cell approach (scATAC-seq) assay enables researchers to evaluate chromatin accessibility in individual cells and identify sub-groups in mixed populations of cells. To investigate the full potential of single-cell epigenomic data, we have comprehensively compared the information derived from bulk ATAC-seq and scATAC-seq in populations of cells. We found that the chromatin architecture signal is the same using bulk ATAC-seq and scATAC-seq to analyse aliquots of the same cell population. However, scATAC-seq provides substantially higher data quality compared to bulk ATAC-seq improving the sensitivity to detect relatively weak, but functionally important ATAC-seq signals. Furthermore, we found that scATAC-seq identified differences in what was previously assumed to be a homogenous population of cells. Finally, we determined the number of cells required to generate aggregated open chromatin profiles from single cells and to identify biologically meaningful clusters after pseudo-bulking of data. This study illustrates the added value of using scATAC-seq rather than bulk ATAC-seq in evaluating both homogeneous and heterogeneous populations of cells. This paper provides a comprehensive guide on the benefits of using scATAC-seq data to study gene regulation.
大规模转座酶可接近染色质测序（ATAC-seq）分析已被用于绘制和分析增强子、启动子和绝缘子等调控元件的染色质可及性。这为深入了解多种生物体中许多细胞类型的基因表达调控提供了重要线索。迄今为止，ATAC-seq最常用于对细胞群体的染色质可及性进行平均评估。单细胞方法（scATAC-seq）分析的发展使研究人员能够评估单个细胞中的染色质可及性，并识别混合细胞群体中的亚群。为了探究单细胞表观基因组数据的全部潜力，我们全面比较了从细胞群体的大规模ATAC-seq和scATAC-seq中获得的信息。我们发现，使用大规模ATAC-seq和scATAC-seq分析同一细胞群体的等分样本时，染色质结构信号是相同的。然而，与大规模ATAC-seq相比，scATAC-seq提供的数据质量显著更高，提高了检测相对较弱但功能重要的ATAC-seq信号的灵敏度。此外，我们发现scATAC-seq揭示了先前被认为是同质细胞群体中的差异。最后，我们确定了从单细胞生成聚集的开放染色质图谱以及在数据伪批量处理后识别具有生物学意义的聚类所需的细胞数量。这项研究说明了在评估同质和异质细胞群体时使用scATAC-seq而非大规模ATAC-seq的附加价值。本文提供了一份关于使用scATAC-seq数据研究基因调控益处的全面指南。
