Entwicklung computergestützter Modelle auf Einzelzell-DNA-Methylierungsdaten zur Charakterisierung von funktioneller Heterogenität von Stammzellen während der Hämatopoese in Säugetieren
Bioinformatik und Theoretische Biologie
Zusammenfassung der Projektergebnisse
Analyzing DNA methylation at the single-cell level is challenging due to the lack of an appropriate technology and associated computational tools. For investigating cellular heterogeneity attributed to e.g., clonal origin or heterogeneous differentiation states, single-cell methods are essential. Such methods require high cellular throughput, low dropout rates of individual CpGs and reasonable sequencing effort. During this project, we (i) developed a new single-cell DNA methylation method (scTAM-seq), (ii) created computational tools for designing and analyzing experiments with scTAM-seq, and (iii) employed the developed toolset to characterize functional heterogeneity of hematopoietic stem cell clones. ScTAM-seq allows for characterizing the DNA methylation state of up to 1,000 CpGs in up to 10,000 cells in a single experiment. Since it leverages the Mission Bio Tapestri platform, it is the first high-throughput, single-cell DNA methylation method accessible to a broad range of researchers. The selection of CpGs to be analyzed with scTAM-seq is crucial, and we developed a computational pipeline for selecting CpGs from bulk DNA methylation data. This pipeline is universally applicable to any system where bulk DNA methylation data is available. As a prime use-case of scTAM-seq, we profiled DNA methylation in hematopoietic stem cells to specifically profile clone-specific behaviors, including lineage bias. To readout lineage information from scTAM-seq data, we used genetically heritable barcodes in HSCs that can be read-out using scTAM-seq. While we found that DNA methylation is associated with the cellular differentiation state as expected, a subset of the investigated CpGs was associated with clonal identity. Based on this finding, we developed a computational method, EPI-clone, to predict clonal identity only from DNA methylation data, without the need for genetic barcoding. Additionally, DNA methylation data provides a high-resolution map of murine hematopoiesis. Using the joint readout of clonal identity and cell state, we were able to investigate functional heterogeneity of hematopoietic stem cell clones. With this unique combination of reading out clonal identity and cell state, EPI-clone might be among the first methods that enables characterizing functions of stem cell clones in humans, where genetic barcoding is not applicable. The toolbox that we developed during this project facilitates studying DNA methylation at the single-cell level and especially fuels studies characterizing clonal behaviors across different cellular systems.
Projektbezogene Publikationen (Auswahl)
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scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells. Genome Biology, 23(1).
Bianchi, Agostina; Scherer, Michael; Zaurin, Roser; Quililan, Kimberly; Velten, Lars & Beekman, Renée
