Back to Search Start Over

High-throughput robust single-cell DNA methylation profiling with sciMETv2.

Authors :
Nichols, Ruth V.
O'Connell, Brendan L.
Mulqueen, Ryan M.
Thomas, Jerushah
Woodfin, Ashley R.
Acharya, Sonia
Mandel, Gail
Pokholok, Dmitry
Steemers, Frank J.
Adey, Andrew C.
Source :
Nature Communications; 12/9/2022, Vol. 13 Issue 1, p1-10, 10p
Publication Year :
2022

Abstract

DNA methylation is a key epigenetic property that drives gene regulatory programs in development and disease. Current single-cell methods that produce high quality methylomes are expensive and low throughput without the aid of extensive automation. 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. 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 demonstrate two versions of sciMETv2 on primary human cortex, a high coverage and rapid version, identifying distinct cell types using CH methylation patterns. These datasets are able to be directly integrated with one another as well as with existing snmC-seq2 datasets with little discernible bias. 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. Despite the importance of DNA methylation, accessible and high-throughput methods to profile methylation at the single-cell level are lacking. Here, the authors present sciMETv2, a high-throughput workflow that provides high-quality single-cell methylomes in a robust and simple workflow. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20411723
Volume :
13
Issue :
1
Database :
Complementary Index
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
160705397
Full Text :
https://doi.org/10.1038/s41467-022-35374-3