Integrative Modelle für Chromatinregulation und Zellidentität
Allgemeine Genetik und funktionelle Genomforschung
Zusammenfassung der Projektergebnisse
Although virtually all cells in an organism share the same genome, regulatory mechanisms give rise to hundreds of different, highly specialized cell types. These mechanisms are governed by chromatin signatures which determine DNA packaging, spatial organization, interactions with regulatory enzymes as well as RNA expression and which ultimately reflect the state of each individual cell. Various epigenetic, chromatin-associated marks can be charted using genomewide DNA sequencing. The increasing amount of available single-cell data allows for characterizing cell-to-cell heterogeneity and identifying relevant subpopulations of cells. Yet, while bioinformatics tools and standards for processing and interpreting data generated for individual epigenetic marks are widely established, computational approaches that simultaneously capture the genome-wide dynamics of multiple levels of regulation across cell states are just emerging. This project aims at a more integrated understanding of chromatin regulation. We established user-friendly analysis pipelines for the integrative processing of chromatin and gene expression data. We developed ChrAccR, a software package for the start-to-finish analysis of large chromatin accessibility datasets and their integration with gene expression profiles. We developed and employed integrative computational approaches for dissecting the generegulatory patterns of chromatin that characterize and drive dynamic cell states. We charted the chromatin accessibility landscape in human immune cells in resting and stimulated conditions, identified epigenetic patterns of immune memory formation and characterize genotype-driven dynamics in chromatin regulation. We derived supervised models for the chromatin-based prediction of gene-expression dynamics. Interpreting these models, we identified chromatin patterns associated with characteristic signatures of transcription factor binding that are potentially involved in the transcriptional regulation of individual genes. Furthermore, to quantify and characterize epigenetic heterogeneity in cell identity, we evaluated and improved on computational methods for the genome-wide quantification of within-sample DNA methylation heterogeneity in a systematic benchmarking effort. Finally, we created a single-cell atlas of gene expression and chromatin accessibility of the human developing cortex during midgestation. In this integrated atlas, we observed waves of gene regulation by key transcription factors across a nearly continuous differentiation trajectory into glutamatergic neurons, distinguished the expression programs of glial lineages, identified lineage-determining TFs, and we explored the impact of genetic mutations associated with neurodevelopmental disorders on chromatin accessibility and associated gene regulation using deep learning methods. In summary, our computational approaches provide a framework for an epigenetic definition of chromatin-regulated cell identity. They are generally applicable across biological systems characterized by dynamic cell states and they provide insights into the regulatory programs governing cell differentiation as well as in disease onset and progression.
Projektbezogene Publikationen (Auswahl)
- (2019). Landscape of stimulation-responsive chromatin across diverse human immune cells. Nature Genetics, 51(10), 1494–1505
Calderon, Nguyen, Mezger, Kathiria, Müller, Nguyen, Lescano, Wu, Trombetta, Ribado, Knowles, Gao, Blaeschke, Parent, Burt, Anderson, Criswell, Greenleaf, Marson, & Pritchard
(Siehe online unter https://doi.org/10.1038/s41588-019-0505-9) - (2020). Quantitative comparison of within-sample heterogeneity scores for DNA methylation data. Nucleic acids research
Scherer, Nebel, Franke, Walter, Lengauer, Bock, Müller, & List
(Siehe online unter https://doi.org/10.1093/nar/gkaa120) - (2021). Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell
Trevino, Müller, Andersen, Sundaram, Kathiria, Shcherbina, et al.
(Siehe online unter https://doi.org/10.1016/j.cell.2021.07.039)
