Kuechenhoff, L. | bioRxiv Science

bioRxiv2022

Striated muscle-specific base editing enables correction of mutations causing dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is the second most common cause for heart failure with no cure except a high-risk heart transplantation. Approximately 30% of DCM patients harbor heritable mutations which are amenable to CRISPR-based gene therapy1. However, challenges related to delivery of the editing complex and off-target concerns hamper the broad applicability of CRISPR agents in the heart2. We employed a combination of the viral gene transfer vector AAVMYO with superior targeting specificity of heart muscle tissue3 and CRISPR base editors to repair patient mutations in the cardiac splice factor Rbm20, which cause aggressive and arrhythmogenic DCM4. Using optimized conditions, we could improve splice defects in human iPSC-derived cardiomyocytes (iPSC-CMs) and repair >70% of cardiomyocytes in two Rbm20 knock-in mouse models that we generated to serve as an in vivo platform of our editing strategy. Treatment of juvenile mice restored the localization defect of RBM20 in 75% of cells and splicing of RBM20 targets including TTN. Three months after injection, cardiac dilation and ejection fraction reached wildtype levels. Single-nuclei RNA sequencing (snRNA-seq) uncovered restoration of the transcriptional profile across all major cardiac cell types and whole-genome sequencing (WGS) revealed no evidence for aberrant off-target editing. Our study highlights the potential of base editors combined with AAVMYO to achieve gene repair for treatment of hereditary cardiac diseases.

genetics↗

bioRxiv2022

Analyzing single-cell bisulfite sequencing data with scbs

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 data size, improve 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. As an alternative, 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 (DMRs) 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. To facilitate the analysis of scBS data, we have developed a software tool called Meth-SCAn that implements these methods and provides additional functionality.

bioinformatics↗