Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain

Abstract

Cytosine DNA methylation is essential in brain development and has been implicated in various neurological disorders. A comprehensive understanding of DNA methylation diversity across the entire brain in the context of the brain's 3D spatial organization is essential for building a complete molecular atlas of brain cell types and understanding their gene regulatory landscapes. To this end, we employed optimized single-nucleus methylome (snmC-seq3) and multi-omic (snm3C-seq) sequencing technologies to generate 301,626 methylomes and 176,003 chromatin conformation/methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell type taxonomy that contains 4,673 cell groups and 261 cross-modality-annotated subclasses. We identified millions of differentially methylated regions (DMRs) across the genome, representing potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide multiplexed error-robust fluorescence in situ hybridization (MERFISH) data validated the association of this spatial epigenetic diversity with transcription and allowed the mapping of the DNA methylation and topology information into anatomical structures more precisely than our dissections. Furthermore, multi-scale chromatin conformation diversities occur in important neuronal genes, highly associated with DNA methylation and transcription changes. Brain-wide cell type comparison allowed us to build a regulatory model for each gene, linking transcription factors, DMRs, chromatin contacts, and downstream genes to establish regulatory networks. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a companion whole-brain SMART-seq dataset. Our study establishes the first brain-wide, single-cell resolution DNA methylome and 3D multi-omic atlas, providing an unparalleled resource for comprehending the mouse brain's cellular-spatial and regulatory genome diversity.

Epigenome erosion in Alzheimer’s disease brain cells and induced neurons

“…After addition of 25 μl of conversion reagent (Zymo Research) was added to each well of a 384-well plate, the following bisulfite conversion and library preparation was based on snmC-seq2 (described previously 68 ) and on an updated version snmC-seq3 used in BICCN. 69…”

“…Then the automation handling of plates and library preparation for both snmCT-seq and snm3C-seq libraries followed the same bisulfite conversion-based methylation sequencing pipelines described previously 18,68 and an updated version snmC-seq3 used in BICCN. 69 To facilitate large-scale profiling, Beckman Biomek i7 instrument was used and running scripts were shared. 69 The snm3C-seq, snmCT-seq and snmC-seq libraries were sequenced on an illumina NovaSeq 6000 instrument using one S4 flow cell per 16 384-well plates on 150-bp paired-end mode.…”

“…5a) Tab-delimited (ALLC) files containing methylation level for every cytosine position were generated using allcools 69 (v1.0.8) bam-to-allc function on the BAM file from step 4a.…”

“…We performed clustering analysis with the mCH and mCG fractions of chrom100k matrices described previously. 8 Most functions were derived from allcools 69 , scanpy 80 and scikit-learn 81 packages. In general, the major steps in the clustering included: (1) feature filtering based on coverage, exclude ENCODE blacklist and located in autosomes; (2) Highly Variable Feature (HVF) selection; (3) generation of posterior chrom100k mCH and mCG fraction matrices; (4) clustering with HVF and calculating Cluster Enriched Features (CEF) of the HVF clusters with “ALLCools.clustering.cluster_enriched_features” function; (5) calculating PC in the selected cell-by-CEF matrices and generating the t-SNE 82 and UMAP 83 embeddings for visualization; and (6) consensus clustering process using “ALLCools.clustering.ConsensusClustering” function.…”

“…After addition of 25 μl of conversion reagent (Zymo Research) was added to each well of a 384-well plate, the following bisulfite conversion and library preparation was based on snmC-seq2 (described previously 68 ) and on an updated version snmC-seq3 used in BICCN. 69 Nuclei purification from human postmortem tissues for snm3C-seq Brain blocks were ground in liquid nitrogen with cold mortar and pestle and then aliquoted and stored at -80 ℃. Approximately 100 mg of ground tissue was resuspended in 3 mL NIBT as above.…”