Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia

DNA methylation (DNAm) is a crucial epigenetic mark and plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across five single-nucleus DNAm datasets from the human brain and mouse generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. We applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, advancing our understanding of the epigenetic bases of schizophrenia within specific brain cell types. Single-nucleus profiling of the human prefrontal cortex in four schizophrenia patients and four neurotypical controls *************************************************************** Raw files for human/patient samples were not submitted to GEO due to concerns about submitting personally identifiable sequence data for open access. ***************************************************************