The KAPP-Sen Tissue Mapping Center (TMC) Biological Analysis Core will be responsible for generating high- resolution and high-content datasets to define senescent cells and their microenvironment in aged non-diseased human tissues, and measure how such cells compare across a range of ages. We will utilize state-of-the-art single cell technologies on dissociated tissues and on intact tissue sections to study this biology. We will coordinate with our KAPP-Sen Biospecimen Core to obtain high-quality human normal kidney, pancreas, placenta, and adipose tissue. By employing unbiased, sequencing-based, single-cell resolution methods, we will generate high-content spatially resolved data to enable the identification of senescent cells. We will work with our KAPP-Sen Data Analysis Core to discover comprehensive mRNA biomarkers for human senescent cells. A selection of target epitopes derived from these biomarkers will be detected within tissue sections at high resolution (1 µm) utilizing a highly multiplex antibody imaging approach. Additional tangential experiments in human tissues and ex vivo and induced pluripotent stem cell (iPSC) models will further inform and validate senescence signatures, and identify associated epigenomic features, within intact human tissues. The Biological Analysis Core will achieve its objectives through the following Aims: Aim 1. To establish optimal tissue dissociation and preparation techniques to implement both dissociative and spatially-resolved single-cell transcriptome methods for the identification of senescent cells in human tissues. Aim 2. To scale and standardize the pipeline to generate high-quality, high-resolution, and high-throughput datasets and construct maps of cellular senescence in the four target tissues. Aim 3. To identify mRNA biomarkers of human senescent cells and construct and apply a multiplex antibody panel derived from these. Aim 4. Leverage ex vivo human models to further characterize the functional features of senescent cells. Together, this analytic approach will define the comprehensive tissue signature of senescence at 1 µm resolution and begin to uncover the molecular foundations of the senescent cell and its response to therapy. In addition, the data set generated will provide insight into senescence-associated secreted proteins that may inform the design of blood biomarker of senescence. Altogether, our approach and its associated tools will be applicable across a wide array of human tissues types.