bioRxiv2024
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.