Developing Computational Models on Single-Cell DNA-Methylation Data for Characterizing Functional Heterogeneity of Stem Cells in Mammalian Hematopoiesis

• Applicant: Dr. Michael Scherer
• Subject Area: General Genetics and Functional Genome Biology ; Bioinformatics and Theoretical Biology
• Term: from 2022 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 493935791

DNA methylation, the reversible addition of a methyl group to CpG dinucleotides in the DNA, is an important layer of epigenetic regulation and indispensable for cellular differentiation in mammals. In the context of the differentiation of blood stem cells, differences in DNA methylation have been associated with long-term fate biases of hematopoietic stem cells (HSCs) that are not visible on other, more dynamic epigenetic layers including the transcriptome. Dynamics of DNA methylation are further associated with the age-related decline in HSC function as well as early steps of leukemia formation. However, the heterogeneity of DNA-methylation patterns in HSCs and their immediate progeny as well as associated functional consequences are unknown, mostly due to the lack of appropriate single-cell methods. Recently, profiling DNA methylation of single cells has become feasible, but data generated using genome-wide approaches is noisy and sparse. Furthermore, an integration with lineage tracing approaches that track the cellular fate of HSCs is required for associating such DNA-methylation dynamics with its functional consequences. In this project, I will contribute to the development of a targeted single-cell DNA-methylation assay (scTAM-seq). This targeted approach allows for generating high-resolution profiles of up to 700 genomic positions with low sequencing effort, and is characterized by small dropout rates and the ability to capture lineage-tracing barcodes. A challenge exclusive to scTAM-seq is the selection of potentially informative regions. Thus, I will develop scalable software solutions for the selection of regions of interest using tools that I co-developed in my PhD (e.g., RnBeads, MeDeCom). To further facilitate the analysis of a biological system using scTAM-seq, I will develop a machine-learning model for denoising the data and for extracting single-cell methylation states. Computational models such as deep autoencoders can be leveraged for understanding the data generated by scTAM-seq and for extracting a low-dimensional visualization of the data. Using this toolset, I will explore lineage biases in HSCs. Particularly, I will investigate DNA methylation dynamics in a mouse model system that allows for tracking cell fates and lineage potential of HSCs. In summary, I will develop tools for the analysis of targeted single-cell DNA-methylation data and apply these tools to characterize the functional heterogeneity of methylation states in hematopoiesis.

• DFG Programme: WBP Fellowship
• International Connection: Spain
• Host: Dr. Lars Velten