DNA cytosine methylation (5mC) is an important epigenetic modification, which is involved in important processes such as cell-specific regulation of gene expression and maintenance of genome stability. In mammalian cells, DNA cytosine methylation can be oxidized by TET family proteins, and then demethylated, thereby dynamically regulating cytosine modification in the genome (Figure 1a, b)【1】 . Cytosine hydroxymethylation modification (5hmC) is the most abundant demethylation intermediate in the genome (especially in nerve cells) and may play an important function [2] . However, because the traditional bisulfite sequencing technology (Bisulfite sequencing) cannot distinguish between 5mC and 5hmC modifications (Figure 1c) , the research team developed ACE-seq technology in 2018 to identify 5hmC modifications at single-base resolution [3] , However, how to quantitatively detect these two DNA modifications at the single-cell level is still a challenge.
Fig. 1 Dynamic regulation of DNA cytosine methylation and sequencing-based detection technology [1,2] .
On August 28, 2023, Wu Hao's laboratory at the University of Pennsylvania published a research paper Joint single-cell profiling resolves 5mC and 5hmC and reveals their distinct gene regulatory effects in the journal Nature Biotechnology , reporting a simultaneous detection of genomic DNA 5hmC and 5mC A modified single-cell sequencing technology, Joint-snhmC-seq (full name Joint single-nucleus (hydroxy)methylcytosine sequencing).
The core of this technology is the conversion of unmodified cytosine (C) and methylated cytosine (5mC) into Uracil (U) or thymine (T), and converted to thymine (T) in the final sequencing results; while the 5hmC modification site is not deaminated, and remains in the cytosine state (C) in the sequencing results. The research team first added the step of enzymatic deamination to the snmC-seq library construction process [4] , and developed the snhmC-seq technology for detecting 5hmC sites in single cells (Fig. 2a) . By systematically optimizing the deamination reaction conditions and the key steps of library construction, the authors improved the detection sensitivity and stability (snhmC-seq2 technology), and further developed the Joint-snhmC-seq technology for simultaneous detection of 5mC and 5hmC (Figure 2b ) . Using the Joint-snhmC-seq technology, the authors determined the modification levels of 5hmC and real 5mC in different cell types in the mouse cerebral cortex, and further studied the relationship between these two modifications and gene transcription levels, and found that some cell types specifically expressed Long genes unexpectedly tend to maintain higher gene body 5mC levels and maintain an active transcriptional state.
Figure 2 Flowchart of snhmC-seq and Joint-snhmC-seq.
In order to evaluate the accuracy of the cell clustering results of the Joint-snhmC-seq technology, the authors first applied the immunofluorescence-assisted nuclear sorting technique to divide the nuclei of the mouse cerebral cortex into non-neural cells (NeuN-), inhibitory neurons ( NeuN+ & Neurod6-) and excitatory neurons (NeuN+ & Neurod6+), and then perform Joint-snhmC-seq analysis on the collected nuclei. Using the similarity of single-cell nuclear CG methylation for clustering, the authors identified 6 cell types, and the clustering results were highly consistent with the results of nuclear sorting. Further, the authors jointly analyzed the two modules of the Joint-snhmC-seq data to quantitatively analyze the levels of cytosine (unmodified C), 5mC and 5hmC in different cell types. Consistent with previous studies, the 5hmC levels (23.8-29.4%) in the three types of neuronal cells were significantly higher than those in the three types of non-neural cells. At the same time, the authors also discovered a phenomenon that has not been reported: there are significant differences in 5hmC levels between different non-neuronal cells [Astrocytes, 12.7%; Microglia, 3.58%].
The authors re-clustered neuronal cells using the similarity of mononuclear CH methylation, further identified eight cell subtypes, and quantified 5mC and 5hmC levels in these cell subtypes.
In summary, Joint-snhmC-seq is a new technology that can accurately quantify DNA cytosine 5mC and 5hmC modifications at the single-cell level. Compared with the existing 5hmC sequencing technology [5,6] , this new technology has the advantages of low sample input, high accuracy, and simultaneous detection of two modifications. It is expected that this technology will be used to study important biological issues such as the role of 5mC and 5hmC modification in brain development and maturation, and disease formation-related processes.
Professor Wu Hao from the University of Pennsylvania is the corresponding author, and Dr. Emily Fabyanic (graduated), Dr. Hu Peng (currently a professor at the School of Fisheries and Life Sciences of Shanghai Ocean University) and Dr. Qiu Qi are the co-first authors. Dr. Emily Fabyanic and Dr. Qiu Qi established and optimized the Joint-snhmC-seq library construction method, and Dr. Hu Peng developed the Joint-snhmC-seq data analysis process. The labs of Zhaolan Zhou and Rahul Kohli at the University of Pennsylvania also made important contributions to the research.
Original link:
https://www.nature.com/articles/s41587-023-01909-2
Maker: Eleven
references
1. Kohli, R. & Zhang, Y. TET enzymes, TDG and the dynamics of DNA demethylation. Nature 502, 472–479 (2013).
2. Wei, A. & Wu, H. Mammalian DNA methylome dynamics: mechanisms, functions and new frontiers.Development 149, dev182683 (2022).
3. Schutsky, E.K. et al. Nondestructive, base-resolution sequencing of 5-hydroxymethylcytosine using a DNA deaminase. Nat Biotechnol 36, 1083-1090 (2018).
4. Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).
5. Yu, M. et al. Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome. Cell 149, 1368–1380 (2012).
6. Booth, M. J. et al. Quantitative sequencing of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution. Science 336, 934–937 (2012).
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