Nature CommunicationsGenomicsPeer-reviewed journal article
Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity
Longqi LiuiD, Chuanyu LiuiD, Andrés QuinteroiD, Liang Li WuiD, Yue YuaniD, Mingyue WangiD, Mengnan Cheng, Lizhi Leng, Liqin XuiD, Guoyi DongiD, Rui LiiD, Yang LiuiD, Xiaoyu WeiiD, Jiangshan XuiD, Xiaowei CheniD, Haorong LuiD, Dongsheng CheniD, Quanlei WangiD, Qing ZhouiD, Xinxin LiniD, Guibo LiiD, Shiping LiuiD, Qi WangiD, Hongru WangiD, J. Lynn FinkiD, , Xun XuiD
Abstract
Integrative analysis of multi-omics layers at single cell level is critical for accurate dissection of cell-to-cell variation within certain cell populations. Here we report scCAT-seq, a technique for simultaneously assaying chromatin accessibility and the transcriptome within the same single cell. We show that the combined single cell signatures enable accurate construction of regulatory relationships between cis-regulatory elements and the target genes at single-cell resolution, providing a new dimension of features that helps direct discovery of regulatory patterns specific to distinct cell identities. Moreover, we generated the first single cell integrated maps of chromatin accessibility and transcriptome in human pre-implantation embryos and demonstrated the robustness of scCAT-seq in the precise dissection of master transcription factors in cells of distinct states during embryo development. The ability to obtain these two layers of omics data will help provide more accurate definitions of \"single cell state\" and enable the deconvolution of regulatory heterogeneity from complex cell populations.
- organism: human
- assay: scATAC-seq
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 243
- FWCI
- 9.27
- Field percentile
- top 1%
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