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

Ruth V. Nichols, Brendan L. O’Connell, Ryan M. Mulqueen, Jerushah Thomas, Ashley R. Woodfin, Sonia Acharya, Gail Mandel, Dmitry Pokholok, Frank J. Steemers and Andrew C. Adey ()
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Ruth V. Nichols: Oregon Health & Science University
Brendan L. O’Connell: Oregon Health & Science University
Ryan M. Mulqueen: Oregon Health & Science University
Jerushah Thomas: Scale Biosciences
Ashley R. Woodfin: Scale Biosciences
Sonia Acharya: Oregon Health & Science University
Gail Mandel: Oregon Health & Science University
Dmitry Pokholok: Scale Biosciences
Frank J. Steemers: Scale Biosciences
Andrew C. Adey: Oregon Health & Science University

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-35374-3

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