Accel-NGS Adaptase Module for Single-Cell Methyl-Seq
- > 2-fold higher mapping rate
- > 2-fold longer library insert size
- Single random priming cycle: easier workflow, low bias
- 384-plex single-cell sequencing for large volume sequencers
- Greater data output per run, lower sequencing cost
Better DNA methylation sequencing data from single cells
Construct better NGS libraries from bisulfite-converted DNA from single cells. Because bisulfite treatment results in lower quantity and quality DNA, the Accel-NGS Adaptase Module is optimized to maximize the recovery of uracil-containing DNA and low concentrations of AT-rich template. The resultant single-cell methylation sequencing libraries consistently exhibit higher complexity with reduced composition bias to provide a more faithful representation of the methylome.
DNA methylation profiling of up to 384 single cells per run
The Accel-NGS Adaptase Module is an excellent choice for single-cell DNA methylation profiling applications. This approach incorporates a three-dimensional indexing strategy to generate 96 individual libraries that can be pooled and sequenced up to 384 individual cells in parallel. The published results demonstrated greater than two-fold increase in read mapping rate as compared to other methods and significantly improves the data output per run while reducing the sample sequencing cost.
Easy workflow for genome-wide bisulfite sequencing of individual cells
After bisulfite conversion of DNA from single cells, random priming incorporates a truncated P5 adapter to the 5’ ends of synthesized fragments (see schematic drawing on the right). This step also effectively reduces fragment size to about 400 base pairs for compatibility with Illumina® platforms. Optionally, you can use indexed random primers for downstream multiplexing. The highly efficient Adaptase step then simultaneously tails and ligates the truncated P7 adapter to the 3’ end of single-stranded products. The completed library is subsequently amplified with custom primers that incorporate dual indexing.