Iterative epigenomic analyses in the same single cell
- 1Laboratory of Cellular Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
- 2Biostatistics and Data Management Section, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Rockville, Maryland 20850, USA;
- 3CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
- 4Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland 21702, USA
Abstract
Gene expression in individual cells is epigenetically regulated by DNA modifications, histone modifications, transcription factors, and other DNA-binding proteins. It has been shown that multiple histone modifications can predict gene expression and reflect future responses of bulk cells to extracellular cues. However, the predictive ability of epigenomic analysis is still limited for mechanistic research at a single cell level. To overcome this limitation, it would be useful to acquire reliable signals from multiple epigenetic marks in the same single cell. Here, we propose a new approach and a new method for analysis of several components of the epigenome in the same single cell. The new method allows reanalysis of the same single cell. We found that reanalysis of the same single cell is feasible, provides confirmation of the epigenetic signals, and allows application of statistical analysis to identify reproduced reads using data sets generated only from the single cell. Reanalysis of the same single cell is also useful to acquire multiple epigenetic marks from the same single cells. The method can acquire at least five epigenetic marks: H3K27ac, H3K27me3, mediator complex subunit 1, a DNA modification, and a DNA-interacting protein. We can predict active signaling pathways in K562 single cells using the epigenetic data and confirm that the predicted results strongly correlate with actual active signaling pathways identified by RNA-seq results. These results suggest that the new method provides mechanistic insights for cellular phenotypes through multilayered epigenome analysis in the same single cells.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.269068.120.
- Received July 20, 2020.
- Accepted January 14, 2021.
This is a work of the US Government.
