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Related Experiment Video

Updated: Jun 8, 2026

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

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

Ruth V Nichols1, Brendan L O'Connell1,2, Ryan M Mulqueen2

  • 1Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA.

Nature Communications
|December 9, 2022
PubMed
Summary
This summary is machine-generated.

A new DNA methylation method, sciMETv2, offers high-quality, high-throughput single-cell profiling. This advance enables precise cell type identification using DNA methylation patterns, crucial for understanding development and disease.

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Automated Sample Preparation for the Multiplexed Analysis of Single-Cell Histone Post-Translational Modifications (sc-hPTM2)

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Related Experiment Videos

Last Updated: Jun 8, 2026

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

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Published on: February 24, 2015

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Published on: February 24, 2015

Automated Sample Preparation for the Multiplexed Analysis of Single-Cell Histone Post-Translational Modifications (sc-hPTM2)
07:21

Automated Sample Preparation for the Multiplexed Analysis of Single-Cell Histone Post-Translational Modifications (sc-hPTM2)

Published on: December 19, 2025

Area of Science:

  • Epigenetics and Genomics
  • Molecular Biology
  • Cellular and Developmental Biology

Background:

  • DNA methylation is critical for gene regulation in development and disease.
  • Existing single-cell methylome methods are costly, low-throughput, and technically demanding.
  • Previous proof-of-principle methods achieved high throughput but yielded low-coverage profiles with technical challenges.

Purpose of the Study:

  • To present a significantly improved version of the single-cell DNA methylation sequencing technique (sciMETv2).
  • To enable high-coverage, high-throughput, and robust single-cell methylome profiling without custom sequencing requirements.
  • To demonstrate the utility of sciMETv2 for cell type identification and data integration.

Main Methods:

  • Development and validation of sciMETv2, an enhanced single-cell DNA methylation sequencing protocol.
  • Application of sciMETv2 to primary human cortex samples, generating high-coverage methylomes.
  • Comparative analysis of methylation patterns (CG and CH contexts) for cell type classification.

Main Results:

  • sciMETv2 achieves approximately a 15-fold increase in coverage compared to previous methods.
  • The protocol is robust, requires no custom sequencing, offers stopping points, and minimizes adapter contamination.
  • Distinct cell types were identified in human cortex using CH methylation patterns.
  • Generated datasets are highly compatible with each other and existing snmC-seq2 data.
  • Cell type determination using CG methylation alone was demonstrated as a viable approach, particularly outside neuronal contexts.

Conclusions:

  • sciMETv2 provides a powerful and accessible tool for high-resolution single-cell epigenomic studies.
  • The method facilitates accurate cell type identification and enables multi-dataset integration.
  • DNA methylation analysis, especially using CG context, offers broad applicability for cell type classification across various tissues.