High-throughput sample processing for methylation analysis in an automated, enclosed environment. Alejandro Stark, Thomas R. Pisanic, James G. Herman, Tza-Huei Wang. SLAS Technology Volume 27, Issue 3, June 2022, Pages 172-179.
作者单位:美国约翰霍普金斯大学、匹兹堡大学联合研究组。

(Figure 4) - "Most notably, the Apostle particles outperformed all others, achieving almost 2-fold higher recovery yields than the particles supplied in the X kit. "
论文第6页图4 结果, " 尤为值得关注的是,Apostle 纳米磁珠表现优于其他所有方法,取得了比 X 试剂中提供的纳米磁珠几乎2倍的回收率。“
论文摘要
Variation in methylcytosine is perhaps the most well-studied epigenetic mechanism of gene regulation. Methods that have been developed and implemented for assessing DNA methylation require sample DNA to be extracted, purified and chemically-processed through bisulfite conversion before downstream analysis. While some automated solutions exist for each of these individual process steps, a fully integrated solution for accomplishing the entire process in a high-throughput manner has yet to be demonstrated. Thus, sample processing methods still require numerous manual steps that may reduce sample throughput and precision, while increasing the risk of contamination and human error. In this work, we present an integrated, automated solution for performing the entire sample preparation process, including DNA extraction, purification, bisulfite conversion and PCR plate preparation within in an enclosed environment. The method employs silica-coated magnetic particles that eliminate the need for a centrifuge or vacuum manifold, thereby reducing the complexity and cost of the required automation platform. Toward this end, we also compare commercial DNA extraction and bisulfite conversion kits to identify a protocol suitable for automation to significantly improve genomic and bisulfite-treated DNA yields over manufacturer protocols. Overall, this research demonstrated development of an automated protocol that offers the ability to generate high-quality, bisulfite-treated DNA samples in a high-throughput and clean environment with minimal user intervention and comparable yields to manual processing.