Epigenetic regulation of neuronal cell specification inferred with single cell "Omics" data.

The brain is a highly complex organ consisting of numerous types of cells with ample diversity at the epigenetic level to achieve distinct gene expression profiles. During neuronal cell specification, transcription factors (TFs) form regulatory modules with chromatin remodeling proteins to initiate the cascade of epigenetic changes. Currently, little is known about brain epigenetic regulatory modules and how they regulate gene expression in a cell-type specific manner. To infer TFs involved in neuronal specification, we applied a recursive motif search approach on the differentially methylated regions identified from single-cell methylomes. The epigenetic transcription regulatory modules (ETRM), including EGR1 and MEF2C, were predicted and the co-expression of TFs in ETRMs were examined with RNA-seq data from single or sorted brain cells using a conditional probability matrix. Lastly, computational predications were validated with EGR1 ChIP-seq data. In addition, methylome and RNA-seq data generated from Egr1 knockout mice supported the essential role of EGR1 in brain epigenome programming, in particular for excitatory neurons. In summary, we demonstrated that brain single cell methylome and RNA-seq data can be integrated to gain a better understanding of how ETRMs control cell specification. The analytical pipeline implemented in this study is freely accessible in the Github repository (https://github.com/Gavin-Yinld/brain_TF).

BRIF-Seq: Bisulfite-Converted Randomly Integrated Fragments Sequencing at the Single-Cell Level.

Single-cell bisulfite sequencing (scBS-seq) was developed to assess DNA methylation heterogeneity in human and mouse. However, the reads are under-represented in regions with high DNA methylation, because these regions are usually fragmented into long segments and are seldom sequenced on the Illumina platform. To reduce the read distribution bias and maximize the use of these long segments, we developed bisulfite-converted randomly integrated fragments sequencing (BRIF-seq), a method with high rates of read mapping and genome coverage. Single microspore of maize, which has a highly methylated and repetitive genome, was used to perform BRIF-seq. High coverage of the haploid genome was obtained to evaluate the methylation states of CG, CHG, and CHH (H = A, C, or T). Compared with scBS-seq, BRIF-seq produced reads that were distributed more evenly across the genome, including regions with high DNA methylation. Surprisingly, the methylation rates among the four microspores within one tetrad were similar, but differed significantly among tetrads, suggesting that non-simultaneous methylation reprogramming could occur among tetrads. Similar levels of heterogeneity, which often occur in low-copy regions, were detected in different genetic backgrounds. These results suggest that BRIF-seq can be applied for single-cell methylome analysis of any species with diverse genetic backgrounds.

Virtual methylome dissection facilitated by single-cell analyses.

BACKGROUND: Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm. RESULTS: In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld). CONCLUSIONS: We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

Simultaneous profiling of transcriptome and DNA methylome from a single cell.

BACKGROUND: Single-cell transcriptome and single-cell methylome technologies have become powerful tools to study RNA and DNA methylation profiles of single cells at a genome-wide scale. A major challenge has been to understand the direct correlation of DNA methylation and gene expression within single-cells. Due to large cell-to-cell variability and the lack of direct measurements of transcriptome and methylome of the same cell, the association is still unclear. RESULTS: Here, we describe a novel method (scMT-seq) that simultaneously profiles both DNA methylome and transcriptome from the same cell. In sensory neurons, we consistently identify transcriptome and methylome heterogeneity among single cells but the majority of the expression variance is not explained by proximal promoter methylation, with the exception of genes that do not contain CpG islands. By contrast, gene body methylation is positively associated with gene expression for only those genes that contain a CpG island promoter. Furthermore, using single nucleotide polymorphism patterns from our hybrid mouse model, we also find positive correlation of allelic gene body methylation with allelic expression. CONCLUSIONS: Our method can be used to detect transcriptome, methylome, and single nucleotide polymorphism information within single cells to dissect the mechanisms of epigenetic gene regulation.