Analyzing single-cell bisulfite sequencing data with MethSCAn

• Verfasst von: Kremer, Lukas P. M. [VerfasserIn]
• Verfasst von: Braun, Martina M. [VerfasserIn]
• Verfasst von: Ovchinnikova, Svetlana [VerfasserIn]
• Verfasst von: Küchenhoff, Leonie [VerfasserIn]
• Verfasst von: Cerrizuela, Santiago [VerfasserIn]
• Verfasst von: Martín-Villalba, Ana [VerfasserIn]
• Verfasst von: Anders, Simon [VerfasserIn]
• Titel: Analyzing single-cell bisulfite sequencing data with MethSCAn
• Verf.angabe: Lukas P.M. Kremer, Martina M. Braun, Svetlana Ovchinnikova, Leonie Küchenhoff, Santiago Cerrizuela, Ana Martin-Villalba & Simon Anders
• E-Jahr: 2024
• Jahr: September 2024
• Umfang: 23 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 07.01.2025 ; Veröffentlicht: 31. Juli 2024
• Titel Quelle: Enthalten in: Nature methods
• Ort Quelle: London [u.a.] : Nature Publishing Group, 2004
• Jahr Quelle: 2024
• Band/Heft Quelle: 21(2024), 9 vom: Sept., Seite 1616-1623, [7], 8
• ISSN Quelle: 1548-7105
• DOI: doi:10.1038/s41592-024-02347-x
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Epigenetics
• Sach-SW: Epigenomics
• Sach-SW: Statistical methods
• K10plus-PPN: 1913728781

Abstract

Single-cell bisulfite sequencing (scBS) is a technique that enables the assessment of DNA methylation at single-base pair and single-cell resolution. The analysis of large datasets obtained from scBS requires preprocessing to reduce the data size, improve the signal-to-noise ratio and provide interpretability. Typically, this is achieved by dividing the genome into large tiles and averaging the methylation signals within each tile. Here we demonstrate that this coarse-graining approach can lead to signal dilution. We propose improved strategies to identify more informative regions for methylation quantification and a more accurate quantitation method than simple averaging. Our approach enables better discrimination of cell types and other features of interest and reduces the need for large numbers of cells. We also present an approach to detect differentially methylated regions between groups of cells and demonstrate its ability to identify biologically meaningful regions that are associated with genes involved in the core functions of specific cell types. Finally, we present the software tool MethSCAn for scBS data analysis (https://anders-biostat.github.io/MethSCAn).