These include the standard Read 2 sequencing primer, a unique index, and the mosaic end (ME) sequence recognized by the Tn5 transposase to form tagmentation complexes (Fig. The first optimization shared by both sciMETv2 workflows is the use of fully-methylated indexed tagmentation adapters that are modeled after our recently-published s3 complexes (symmetrical-strand single-cell combinatorial indexing Supplementary Data 1) 12. SciMETv2 enables high-throughput and high-coverage single-cell methylomes from brain tissue Here, we directly address each of these shortcomings by developing two complementary technologies: sciMETv2.LA (linear amplification), a highly-optimized version of the original workflow, and sciMETv2.SL (splint ligation), a rapid workflow that involves far less processing time, and a reduction in reagent usage resulting in a 10-fold lower cost per cell. Furthermore, sciMET sequencing libraries require a custom sequencing recipe and custom sequencing primers, making the technology inaccessible to users that are not able to implement these components. However, the original sciMET technology suffers from lower per-cell coverage when compared to other techniques that rely on one or more processing steps to be carried out in individual wells 6. This technique enabled ~1000 single-cell methylomes to be produced from a single 96-well plate-based experiment, without requiring any cells to be processed in an individual reaction compartment, cutting costs and enabling high throughput. Nuclei are then pooled and sorted with a limited number of pre-indexed nuclei per well followed by bisulfite conversion, reverse adapter incorporation using random priming similar to post-bisulfite adapter tagging (PBAT) 11, indexed PCR amplification, and sequencing. This process enables uniform access to genomic DNA contained within the nucleus for indexed transposase-based library preparation (tagmentation) using C-depleted adapters, while maintaining nuclear integrity. To accomplish this, sciMET relies on the fixation of nuclei followed by the disruption of nucleosomes, as first described in sci-WGS for single-cell whole genome sequencing using combinatorial indexing 10. Subsequent processing, including bisulfite conversion, adapter tagging, and PCR amplification is then carried out on pools of cells greatly reducing costs and enabling high throughput. This technique enables the encapsulation of genomic DNA within intact nuclei during an initial round of cell barcoding. To address this, we previously described a single-cell combinatorial indexed assay for the assessment of DNA methylation (sciMET) 9. As such, nearly every single-cell DNA methylation assay begins with isolating individual cells or nuclei into individual reaction wells for bisulfite or enzymatic conversion and subsequent processing imposing significant challenges for both cost and scaling 2, 3, 4, 5, 6, 7, 8. This paradigm requires a multi-stage process with buffer exchanges and cleanups, imposing a significant hurdle for high-throughput single-cell assessment. Currently the primary means of assessing DNA methylation is using strategies to convert non-methylated cytosine bases into uracil, which are in turn sequenced as thymine, whether chemical in the form of sodium bisulfite, or enzymatic. Much like other genomic properties, the benefits of single-cell DNA methylation assays are substantial: providing the ability to identify cell types and states within a complex tissue 1. ![]() Finally, we demonstrate the ability to determine cell types using CG methylation alone, which is the dominant context for DNA methylation in most cell types other than neurons and the most applicable analysis outside of brain tissue. These datasets are able to be directly integrated with one another as well as with existing snmC-seq2 datasets with little discernible bias. We demonstrate two versions of sciMETv2 on primary human cortex, a high coverage and rapid version, identifying distinct cell types using CH methylation patterns. Here, we describe a greatly improved version that generates high-coverage profiles (~15-fold increase) using a robust protocol that does not require custom sequencing capabilities, includes multiple stopping points, and exhibits minimal adapter contamination. We previously described a proof-of-principle technique that enabled high cell throughput however, it produced only low-coverage profiles and was a difficult protocol that required custom sequencing primers and recipes and frequently produced libraries with excessive adapter contamination. Current single-cell methods that produce high quality methylomes are expensive and low throughput without the aid of extensive automation. DNA methylation is a key epigenetic property that drives gene regulatory programs in development and disease.
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