Summary The hallmark of cellular senescence is the inability of cells to progress through the cell cycle. With increasing age, senescent cells accumulate in multiple tissues; their abundance increases in aging and several age-related diseases such as atherosclerosis, diabetes, lung disease, and many others. Senescence is both a physiologically fundamental and pathologically relevant program, with its role depending on the context and the specific situation. Evidence linking senescence to common age-associated human diseases has emerged. Despite these advances, little is known about the abundance, spatial distribution, precise nature, and functional relevance of senescent cells in human tissues. The main challenge is paused by the extraordinarily complex heterogeneity of the 3D architecture of tissues like the pancreas, even in non-diseased conditions, which greatly complicates the spatially resolved identification of senescent cells. In this project, we will use the pancreas as a testbed and will expand our 3D spatial analysis of cellular senescence to the human breast and ovaries of similar complex heterogeneity. The development of spatial -omics approaches such as spatial transcriptomics/proteomics may help overcome the challenge paused by 3D tissue heterogeneity. But these approaches only provide high- content molecular information in a spatially resolved manner in 2D tissue sections. Here, we propose to establish a new integrated 3D imaging platform that can map both the 3D architecture of large volumes (> 1cm3) of tissues and can determine the location of senescent cells within a 3D tissue sample. Given its versatility, our proposed integrated platform could readily be used to study spatial senescence in other tissues (besides the pancreas, breast, and ovaries studied here) and to incorporate protein-based and RNA-based senescence signatures that will be discovered by other centers and projects of the SenNet network. To produce the first comprehensive 3D multiscale senescence atlas of tissues across ages, we propose the following aims in the UG3 and the UH3 phases. In the UG3 phase, in a small set of pancreatic and breast tissues, we will first integrate our new AI- based 3D tissue reconstruction platform CODA with immunofluorescence (the CODA+IF platform) for multiple labeling of senescence markers in FFPE tissue samples of non-diseased human pancreas and breast. We will then integrate CODA with DBiT-seq (the CODA+DBiT-seq platform) and apply this integrated platform in regions of the tissue sample that are poor and rich in cellular senescence, as identified by CODA+IF. In the UH3 phase, we will apply these 3D cellular senescence mapping methods to large cohorts of pancreatic, breast, and ovarian tissue. We will measure the distribution and variability and associated molecular signatures of senescent cells within different tissue compartments in non-diseased tissue volumes as a function of age.