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

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.

Single-cell multiomics reveals the interplay of clonal evolution and cellular plasticity in hepatoblastoma

“…In addition, ATAC-seq only gives access to chromatin accessibility profiles. Profiling other epigenetic features, like DNA methylation 35 or histone modifications 36 , will be useful to understand how the different layers of epigenetic regulation are orchestrated and their relative roles in HB cell plasticity.…”

Scalable ultra-high-throughput single-cell chromatin and RNA sequencing reveals gene regulatory dynamics linking macrophage polarization to autoimmune disease

“…We anticipate that through minor optimisations 1) gene expression complexity will further improve and 2) SUMseq can expand to incorporate surface proteomic readouts by use of single cells instead of single nuclei 51 . Furthermore, it should be noted that although the current format measures chromatin accessibility and gene expression, the strategy can be adopted for the measurement of other omic layers such as TF binding and histone modifications (Cut&Tag 52 ), DNA methylation (sci-MET 53 ), and whole genome sequencing (s3-WGS 54 ). We applied SUM-seq to hiPSC-derived macrophages in a time-course of M1 and M2 polarization, as a prototypical model of cellular polarization.…”

Advances in single-cell omics and multiomics for high-resolution molecular profiling

“…Single-cell combinatorial indexing for methylation analysis (sci-MET) is a scWGBS method that incorporates a combinatorial indexing strategy 33 . Furthermore, an enhanced version of sci-MET, sci-METv2 34 , exhibits high methylome coverage for sci-METv2.LA (linear amplification) and reduced costs and preparation time for sci-METv2.SL (splint ligation). However, it has a low read insert size due to damage during bisulfite conversion, which can be addressed using enzymatic conversion methods 34 .…”