Biological Analysis Core

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Project Summary Senescent cells (SnCs) accumulate with age and contribute to driving morbidity and mortality in model systems. SnCs also play a role in normal physiology – for example, wound healing. It is currently unclear when and where SnCs arise in tissues as we age, how heterogenous SnCs are in vivo, and how to best identify them, especially in humans. The overall goal of the Minnesota Tissue Mapping Center (MN TMC) Biological Analysis Core (BAC) is to validate, optimize, and apply state-of-the-art methods for bulk and single cell characterization and spatio- temporal analysis of SnCs in healthy human tissues over a range of ages. The MN BAC will focus on adipose, skeletal muscle, liver, and ovarian tissues from healthy humans, which will be provided by the Biospecimen Core (BSP). The analytical data generated by the BAC will be delivered to the Data Analysis Core (DAC) via a web portal for integration with health data to develop 4D SnC tissue atlases and, eventually, models/predictions of SnC health impact. The BAC will be co-directed by Paul Robbins, an expert in characterizing SnCs and in the development of senolytics, and Andrew Nelson, a board-certified anatomic and molecular pathologist with extensive experience in bulk and single cell analysis of human tissue. The BAC analytical workflow will be based entirely within existing cores and institutes at UMN to guarantee stable infrastructure and high quality control standards: the University Imaging Centers (directed by Mark Sanders), the UMN Genomics Center (directed by Kenny Beckman), the Center for Mass Spectrometry and Proteomics (CMSP, directed by Tim Griffin) and multiple labs at Mayo Clinic in Minnesota (coordinated by Nathan LeBrasseur). State-of-the-art technologies to be applied to mapping SnCs include digital droplet PCR, single cell and single nucleus RNAseq, tissue clearing, RNAScope, CyTOF, 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. The BAC will also model early and deep senescence in vitro using induced pluripotent stem cells (iPSCs) differentiated into hepatocytes, cholangiocytes, granulosa cells, and myogenic and adipocyte progenitors. These cells will be induced to undergo senescence by a variety of stressors to validate SnC probes, identify new SnC biomarkers, and characterize the evolution of senescence over time. Broadly, the BAC proposes to: 1) Establish a pipeline of reproducible, validated, and quantitative assays to detect and characterize SnCs in bulk tissues and single cell preparations; 2) Use iPSC-derived differentiated cells of multi-lineages as a controlled model for validating analytical tools and expanding the repertoire of SnC biomarkers; 3) Scale-up the data generation pipeline and incorporate emerging technologies; and 4) Perform spatiotemporal analysis of SnCs in the four tissues in order for the DAC to generate a 4D atlas of SnCs.