# Data Analysis Core

> **NIH NIH U54** · SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE · 2022 · $577,305

## Abstract

PROJECT SUMMARY
The Data Analysis Core will computationally define and characterize transcriptomic and epigenomic signatures
of cellular senescence in murine brain, bone marrow, colon, breast and liver cell types from healthy male and
female young and old mice. We will use established, scalable pipelines to process data generated by the
Biological Analysis Core and create an integrated map of brain, bone marrow, colon, breast and liver cell types
using cellular profiles derived from all single cell sequencing and imaging assays together. Using this integrated
map, we will identify populations of senescent cells within each tissue-resident cell type based on gene
expression and epigenomic profiles of known cellular senescence markers, and define both heterogeneous sub-
types of senescent cells as well as ‘senescent-like’ cells with non-canonical profiles. For each senescent and
senescent-like sub-type, we will define its cis-regulatory programs including candidate cis-regulatory elements
(cCREs), chromatin states, transcriptional regulators and target genes of cCRE activity (i.e., gene enhancer –
promoter networks), as well as their spatial orientation and micro-environment. We will characterize changes in
the abundance and cis-regulation of senescent and senescent-like cell sub-types as well as cell types in the
senescent niche across life span and linked to sex, cell type and tissue region. This integrated transcriptomic
and epigenomic map of senescent cells will define and resolve senescent cells better than transcriptome alone.
We will evaluate the effects of genetic and pharmacologic clearance of senescent cells and anti-inflammatory
senomorphics. We will establish a pipeline to determine epigenetic age of single senescent cells based on their
DNA methylation profile, a candidate predictor of beneficial versus detrimental senescent cells. Throughout the
project we will work closely with the UCSD Center for Epigenomics and the Biological Analysis Core of this
project to track data quality, link study design to downstream analyses by incorporating batch and other technical
covariates, inform selection of targets for validation studies, and organize meta-data and associated data for all
experiments. Finally, we will create a meta-data repository based on open-source software employed by our
group in other large-scale projects to organize all raw and processed data, provide integrated results files,
processing pipelines and analytical tools used by the project, and ensure all project data is FAIR, interoperable
and adheres to community standard formats. Using this repository, we will transfer project data to the Consortium
Organization and Data Coordination Center (CODCC) and collaborate with other groups in the consortium to
share data and resources.

## Key facts

- **NIH application ID:** 10553047
- **Project number:** 1U54AG079758-01
- **Recipient organization:** SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE
- **Principal Investigator:** Bing Ren
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $577,305
- **Award type:** 1
- **Project period:** 2022-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10553047

## Citation

> US National Institutes of Health, RePORTER application 10553047, Data Analysis Core (1U54AG079758-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10553047. Licensed CC0.

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