Molecular Regulation of Stem Cell Aging

Project Summary Bioinformatics Core C (Pellegrini). The Bioinformatics core will provide a central hub for data analysis for the program projects. We will process next-generation sequence data to enable the measurement of transcriptomes and epigenomes. This will be carried out for both bulk and single cell genomic approaches. Moreover, we will also process spatial data and provide estimates of cell types present across a tissue. As the focus of our program is to elucidate age dependent changes in stem cells and other cell types, the core will also develop models of age associated changes in genomic data. To this end, our core not only has expertise in the use of standard machine learning methodologies to construct epigenetic clocks but has also developed new methodologies to model non-linear associations between epigenetic data and age. This is of relevance, as age associated genomic changes have been shown to often follow non-linear trajectories that capture rapid changes early in development, followed by slower ones later in the later part of the lifespan. Finally, the core will also assist the program by providing data visualization interfaces to facilitate the analysis of complex single cell genomic data. The director and scientists of the core not only have extensive expertise in the application of standard data analysis platforms but have also developed multiple methodologies that will be provided as a service within the core. This includes methods to align DNA methylation data (i.e. BSBolt), model non-linear epigenetic trajectories (i.e. Epigenetic Pacemaker), deconvolute bulk transcriptomic data to identify the cell type composition (i.e. GEDIT) as well as assign cell types in single cell data (i.e. ACTINN) and finally to analyze transcriptomic data using signature databases (i.e. SAVANT). In conclusion, this core will provide cutting edge data analysis support to maximize the interpretability of the genomic data that will be collected throughout this program and advance the understanding of age associated transcriptomic and epigenomic changes.