SUMMARY Standard immunostaining or RNA in situ hybridization can examine only one or a handful of target molecular species at a time; therefore, the amount of information obtained from a single experimental session is limited. To overcome this, emerging Spatial Transcriptomics (ST) techniques aim to examine all genes expressed from the genome from a single histological slide. There are three major methodologies of experimentally implementing ST: the sequential in situ hybridization method, in situ sequencing method and spatial barcoding method. Among these, the spatial barcoding method is the most straightforward, comprehensive and so far the only method scalable for large amount of samples. The spatial barcoding method reveals both the RNA sequence and their spatial locations by capturing tissue RNA using a spatially-barcoded oligonucleotide array. The current spatial barcoding method, however, are intrinsically limited by their low resolution and low RNA capture efficiencies; correspondingly, all currently available technologies failed to reveal the microscopic details of the spatial transcriptome. We recently developed a technology named Seq-Scope, which overcomes all these limitations. Seq-Scope has an effective resolution of 0.5-1 μm, and reveals over 20 transcripts per μm2 area (~50 transcripts/μm2 estimated at library saturation). Both resolution and transcriptome capture output of Seq-Scope are the best among all available technologies described in the literature so far. With this unprecedented performance, Seq-Scope visualized spatial transcriptome heterogeneity at multiple histological scales, including tissue zonation according to the portal-central (liver), crypt-surface (colon) and inflammation- fibrosis (injured liver) axes, cellular components including single cell types and subtypes, and subcellular architectures of nucleus, cytoplasm and mitochondria. Seq-Scope also has a potential to improve and complement current scRNA-seq approaches. In response to the SenNet announcement, we propose to adapt and utilize Seq-Scope to provide atlases of cellular senescence in multiple tissues during normal human aging, focusing on the following three aims: (1) Identify and Characterize Hepatic Senescent Cell Population during Liver Disease. (2) Characterize Age-Associated Changes of Hepatic Spatial Transcriptome. (3) Combine Seq- Scope with Detection of Senescence Protein and Cell-type Marker Proteins. For all aims, we will begin with analyzing mouse tissue to establish the feasibility of monitoring cell and tissue senescence (UG3), and then expand the work in human tissues using the tissue obtained from diverse resources including the SenNet tissue mapping center (TMC) (UH3). Seq-Scope is a versatile technology, which is quick, straightforward, scalable and adaptable. Once optimized, a single researcher can process 5-10 frozen tissue blocks every week to generate a high-quality spatial single cell transcriptome data. Therefore, our team is con...