Reyes Miguel, Billman Kianna, Hacohen Nir, Blainey Paul C
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Adv Biosyst. 2019 Nov;3(11). doi: 10.1002/adbi.201900065. Epub 2019 Aug 28.
Profiling multiple omic layers in a single cell enables the discovery and analysis of biological phenomena that are not apparent from analysis of mono-omic data. While methods for multi-omic profiling have been reported, their adoption has been limited due to high cost and complex workflows. Here, we present a simple method for joint profiling of gene expression and chromatin accessibility in tens to hundreds of single cells. We assess the quality of resulting single cell ATAC- and RNA-seq data across three cell types, examine the link between accessibility and expression at the and loci in human primary T cells and monocytes, and compare the accuracy of clustering solutions for mono-omic and combined data. The new method allows biological laboratories to perform simultaneous profiling of gene expression and chromatin accessibility using standard reagents and instrumentation. This technique, in conjunction with other advances in multi-omic profiling, will enable highly-resolved cell state classification and more specific mechanistic hypothesis generation than is possible with mono-omic analysis.
在单个细胞中对多个组学层面进行分析,能够发现和分析单一组学数据分析中不明显的生物学现象。虽然已经报道了多组学分析方法,但由于成本高昂和工作流程复杂,其应用受到了限制。在这里,我们提出了一种简单的方法,用于在数十到数百个单细胞中联合分析基因表达和染色质可及性。我们评估了三种细胞类型中所得单细胞ATAC和RNA测序数据的质量,研究了人类原代T细胞和单核细胞中特定基因座处可及性与表达之间的联系,并比较了单一组学数据和组合数据聚类解决方案的准确性。这种新方法使生物实验室能够使用标准试剂和仪器同时分析基因表达和染色质可及性。与多组学分析的其他进展相结合,这项技术将实现比单一组学分析更高度解析的细胞状态分类和更具体的机制假设生成。