DATA ANALYSIS CORE (DAC): PROJECT SUMMARY To characterize signatures of senescence and their functional implications in multiple human tissues across the life span, the Columbia University Senescence Tissue Mapping (CUSTMAP) Center proposes a novel combination of genome-wide and targeted molecular panels to map cellular composition with spatial context. The Data Analysis Core correspondingly plays a key role in all aspects of data processing and analysis, tissue mapping, and identification of markers of senscence, as well as data harmonization, coordination, and dissemination through the SenNet Consortium Data Coordination Center (CODCC). To achieve these goals, the DAC will use an integrative analysis approach to combine multi-modal data consisting of transcriptome- wide and targeted proteomics profiling in the tissues being examined across the adult human lifespan. This includes the three major data modalities described in the Biological Analysis Core: large-scale high-resolution Iterative Indirect Immunofluorescence Imaging (4i) data, genome-wide spatially resolved Spatial Transcriptomics (ST) data, and transcriptome-wide single-nucleus RNA-seq data. These three data modalities in concert allow for comprehensive (genome-wide) molecular characterization at single-cell resolution in space; this combination of attributes has not been demonstrated by any single experimental technique at scale currently in human tissue. By integrating these modalities using established processing and analysis workflows, the Data Analysis Core will generate maps of known and novel senescence- associated markers, senescent cells, and the effects of senescent cells on their surroundings in each tissue type. To achieve these goals, the Data Analysis Core will implement modality-specific data processing workflows, followed by cross-modal data analysis, cross-individual map-building, and identification of novel, cell type-specific senescence signatures in brain, spinal cord, and skin. This includes cross-referencing tissue-based signatures to data from ongoing efforts to identify senescence-related signatures in cerebrospinal fluid and blood, the primary biofluids associated with central nervous system and skin. Finally, the Data Analysis Core will work closely with the Administrative Core to interface with the SenNet CODCC, in order to harmonize all aspects of data management and analysis with other members of the consortium.