Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples

单细胞和 FFPE 组织样本中 DNase I 高敏位点的全基因组检测

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作者:Wenfei Jin, Qingsong Tang, Mimi Wan, Kairong Cui, Yi Zhang, Gang Ren, Bing Ni, Jeffrey Sklar, Teresa M Przytycka, Richard Childs, David Levens, Keji Zhao

Abstract

DNase I hypersensitive sites (DHSs) provide important information on the presence of transcriptional regulatory elements and the state of chromatin in mammalian cells. Conventional DNase sequencing (DNase-seq) for genome-wide DHSs profiling is limited by the requirement of millions of cells. Here we report an ultrasensitive strategy, called single-cell DNase sequencing (scDNase-seq) for detection of genome-wide DHSs in single cells. We show that DHS patterns at the single-cell level are highly reproducible among individual cells. Among different single cells, highly expressed gene promoters and enhancers associated with multiple active histone modifications display constitutive DHS whereas chromatin regions with fewer histone modifications exhibit high variation of DHS. Furthermore, the single-cell DHSs predict enhancers that regulate cell-specific gene expression programs and the cell-to-cell variations of DHS are predictive of gene expression. Finally, we apply scDNase-seq to pools of tumour cells and pools of normal cells, dissected from formalin-fixed paraffin-embedded tissue slides from patients with thyroid cancer, and detect thousands of tumour-specific DHSs. Many of these DHSs are associated with promoters and enhancers critically involved in cancer development. Analysis of the DHS sequences uncovers one mutation (chr18: 52417839G>C) in the tumour cells of a patient with follicular thyroid carcinoma, which affects the binding of the tumour suppressor protein p53 and correlates with decreased expression of its target gene TXNL1. In conclusion, scDNase-seq can reliably detect DHSs in single cells, greatly extending the range of applications of DHS analysis both for basic and for translational research, and may provide critical information for personalized medicine.

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