Data Analysis Core

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PROJECT SUMMARY The Data Analysis Core will computationally define and characterize transcriptomic and epigenomic signatures of cellular senescence in murine brain, bone marrow, colon, breast and liver cell types from healthy male and female young and old mice. We will use established, scalable pipelines to process data generated by the Biological Analysis Core and create an integrated map of brain, bone marrow, colon, breast and liver cell types using cellular profiles derived from all single cell sequencing and imaging assays together. Using this integrated map, we will identify populations of senescent cells within each tissue-resident cell type based on gene expression and epigenomic profiles of known cellular senescence markers, and define both heterogeneous sub- types of senescent cells as well as ‘senescent-like’ cells with non-canonical profiles. For each senescent and senescent-like sub-type, we will define its cis-regulatory programs including candidate cis-regulatory elements (cCREs), chromatin states, transcriptional regulators and target genes of cCRE activity (i.e., gene enhancer – promoter networks), as well as their spatial orientation and micro-environment. We will characterize changes in the abundance and cis-regulation of senescent and senescent-like cell sub-types as well as cell types in the senescent niche across life span and linked to sex, cell type and tissue region. This integrated transcriptomic and epigenomic map of senescent cells will define and resolve senescent cells better than transcriptome alone. We will evaluate the effects of genetic and pharmacologic clearance of senescent cells and anti-inflammatory senomorphics. We will establish a pipeline to determine epigenetic age of single senescent cells based on their DNA methylation profile, a candidate predictor of beneficial versus detrimental senescent cells. Throughout the project we will work closely with the UCSD Center for Epigenomics and the Biological Analysis Core of this project to track data quality, link study design to downstream analyses by incorporating batch and other technical covariates, inform selection of targets for validation studies, and organize meta-data and associated data for all experiments. Finally, we will create a meta-data repository based on open-source software employed by our group in other large-scale projects to organize all raw and processed data, provide integrated results files, processing pipelines and analytical tools used by the project, and ensure all project data is FAIR, interoperable and adheres to community standard formats. Using this repository, we will transfer project data to the Consortium Organization and Data Coordination Center (CODCC) and collaborate with other groups in the consortium to share data and resources.