Biological Analysis Core

PROJECT SUMMARY Senescent cells (SnCs) accumulate with age and contribute to morbidity and mortality in model systems. SnCs also play a role in normal physiology, e.g., wound healing. Currently it is unclear when and where SnCs arise in tissues with age, how heterogenous SnCs are in vivo, and how to best identify them and their role in physiology

vs. pathology, especially in humans. The goal of the Midwest Murine-Tissue Mapping Center (MM-TMC) Biological Analysis Core (BAC) is to leverage the utility of the mouse as a model organism to map SnCs, which will help inform the human SnC atlases under development by SenNet. We propose to validate, optimize, and

apply state-of-the-art methodologies for bulk and single cell characterization and spatiotemporal analysis of SnCs in healthy mouse tissues over a range of ages in two genetic backgrounds. The MM-TMC BAC will focus on adipose, skeletal muscle, liver, brain, and lung tissues from inbred C57BL/6J and f1 hybrid (C57BL/6J:FVB/n)

mice. The data generated by the BAC will be delivered to the Data Analysis Core (DAC) for integration to develop SnC atlases for the five tissues. The BAC will be led by Nathan LeBrasseur, an expert in the identification and characterization of SnCs in skeletal muscle and lung in mice and humans, and in biomarker discovery; Paul

Robbins, an expert in senolytic development; and Laura Niedernhofer, an expert in the study of SnCs in transgenic mice. The three MPIs are part of a P01 led by Overall PI Sundeep Khosla, which develops, characterizes, and utilizes innovative transgenic mice that permit the induction of SnCs in a particular organ or

cell type, report expression of the SnC-driving genes p16Ink4a or p21Cip1, or specifically kill cells expressing those genes. These mice will be important tools in SenNet for mapping efforts and validating probes to detect SnCs. The BAC analytical workflow will be based within existing cores at Mayo Clinic and University of Minnesota

(UMN) to guarantee a stable infrastructure and high quality control standards: the UMN Imaging Centers, the UMN Genomics Center, Mayo CyTOF Core, the UMN Center for Mass Spectrometry and Proteomics (CMSP), the UMN Cytokine Reference Laboratory, and Minnesota Supercomputing Institute. These cores contain state-

of-the-art instrumentation available for mapping SnCs: Ionpath Multiplexed Ion Beam Mass Imaging, Visium Spatial Gene Expression, and NanoString GeoMx Digital Spatial Profiling. In addition, the CMSP will use a proteogenomic approach to identify novel SnC-specific protein sequences as biomarkers. These unique

resources, together with the MPIs’ expertise, will be valuable for building the 4D tissue atlases. Broadly, the BAC proposes to: 1) Establish a pipeline of reproducible, validated, and quantitative assays to detect and characterize SnCs in whole tissues and single cell preparations; 2) Use primary mouse cells as a controlled model for

validating analytical tools, studying the evolution of SnCs over time, and identifying novel SnC biomarkers; 3) Scale-up the data generation pipeline and incorporate emerging technologies; and 4) Perform spatiotemporal analysis of SnCs in the five tissues to enable the DAC to generate 4D SnC atlases.