Biology subjects

Nichols, R. V.

Publications and source records attributed to Nichols, R. V..

4 recordsLinked to original sources

bioRxiv2026

Early postnatal DNA methylation dynamics define neuronal subtypes and are disrupted by MECP2 loss

DNA methylation is the foundational layer of epigenetic regulation underlying mammalian tissue specification. During synaptogenesis, neurons accumulate high levels of a unique type of methylation in which a cytosine precedes a C, A, or T (mCH) instead of the canonical guanine (mCG). Disruption of the mCH reader methyl-CpG binding protein 2 (MECP2) causes the devastating neurodevelopmental disorder Rett syndrome, yet the role of mCH in neurodevelopment remains unclear. In this study, we generated the first large-scale single-cell methylation atlas of the early postnatal mouse brain and resolved subtype-specific trajectories of canonical and noncanonical methylation changes underlying neuronal subtype specification. Each identified population undergoes a rapid maturation event of the noncanonical methylome occurring between the first and second postnatal weeks, with highly subtype-specific changes concentrated at genes involved in synaptic partner establishment. Trajectory analysis resolved that subtype diversification, such as the separation of parvalbumin and somatostatin-positive GABAergic interneurons, is further facilitated by a hierarchical sequence of methylation changes in genes involved in regulation of membrane potential. In parallel, we applied single-cell methylation analysis to a mouse model of Rett syndrome that recapitulates severe human symptoms. We show that while noncanonical methylation accumulation proceeds in each subtype in the absence of MECP2, populations that have been consistently implicated in Rett pathology such as GABAergic interneurons have an increasing number of differentially methylated regions with age and fail to accumulate typical global levels of mCH. Together, our study defines the methylation dynamics that facilitate neuronal subtype specification and resolves subtype-specific, global disruptions of noncanonical methylation in Rett syndrome.

neuroscience↗

bioRxiv2024

Atlas-scale Single-cell DNA Methylation Profiling with sciMETv3

Single-cell methods to assess DNA methylation have not yet achieved the same level of cell throughput compared to other modalities. Here, we describe sciMETv3, a combinatorial indexing-based technique that builds on our prior technology, sciMETv2. SciMETv3 achieves nearly a 100-fold improvement in cell throughput by increasing the index space while simultaneously reducing hands-on time and total costs per experiment. To reduce the sequencing burden of the assay, we demonstrate compatibility of sciMETv3 with capture techniques that enrich for regulatory regions, as well as the ability to leverage enzymatic conversion which can yield higher library diversity. We showcase the throughput of sciMETv3 by producing a >140k cell library from human middle frontal gyrus split across four multiplexed individuals using both Illumina and Ultima sequencing instrumentation. This library was prepared over two days by one individual and required no expensive equipment (e.g. a flow sorter, as required by sciMETv2). The same experiment produced an estimated 650k additional cells that were not sequenced, representing the power of sciMETv3 to meet the throughput needs of the most demanding atlas-scale projects. Finally, we demonstrate the compatibility of sciMETv3 with multimodal assays by introducing sciMET+ATAC, which will enable high- throughput exploration of the interplay between two layers of epigenetic regulation within the same cell, as well as the ability to directly integrate single-cell methylation datasets with existing single-cell ATAC-seq. HighlightsO_LIAtlas-scale production of single-cell DNA methylation libraries in a single experiment C_LIO_LIProtocols and evaluation using both Illumina and Ultima Genomics sequencing platforms C_LIO_LICompatibility of sciMETv3 with capture techniques to reduce sequencing burden C_LIO_LICompatibility of sciMETv3 with enzymatic conversion methods C_LIO_LIGeneration of an integrated >140,000 cell dataset from human middle frontal gyrus across four individuals C_LIO_LIAbility to profile both ATAC and genome-wide DNA methylation from the same cells and integration with datasets from each modality C_LIO_LIA novel implementation of the s3-ATAC technology that leverages a nanowell chip for increased throughput C_LI MotivationDNA methylation forms a basal layer of epigenomic regulatory control, shaping the genomic permissiveness of mammalian cells during lineage specification and development. Aberrant DNA methylation has been associated with myriad health conditions ranging from developmental disorders to cancer. The high cell type specificity necessitates analysis at the single-cell level, much like transcription or other epigenomic properties. However, robust and cost-effective techniques to produce atlas-scale datasets have not been realized for DNA methylation. Here, we directly meet this need by introducing sciMETv3, a high-throughput protocol capable of producing hundreds of thousands of single-cell DNA methylation profiles in a single experiment.

genetics↗

bioRxiv2024

Single-cell DNA methylation analysis tool Amethyst reveals distinct noncanonical methylation patterns in human glial cells

Single-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties in highly heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are much more limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and expedites batch integration, doublet detection, dimensionality reduction, clustering, cell type annotation, differentially methylated region calling, and interpretation of results, facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell (PBMC) and human cortex data. We further leverage Amethyst on an atlas-scale brain dataset to describe a noncanonical methylation pattern in human astrocytes and oligodendrocytes, challenging the notion that this form of methylation is principally relevant to neurons in the brain. Tools such as Amethyst will increase accessibility to single-cell methylation data analysis, catalyzing research progress across diverse contexts.

neuroscience↗

bioRxiv2023

sciMET-cap: High-throughput single-cell methylation analysis with a reduced sequencing burden

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Sufficient off-target coverage further enables the production of near-complete methylomes for individual cell types. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

genetics↗