# Leveraging Multi-Scale Deep Phenotyping and Applied Machine Learning to Predict Senescent Cell Burden in Humans

> **NIH NIH U54** · BUCK INSTITUTE FOR RESEARCH ON AGING · 2024 · $302,384

## Abstract

DATA ANALYSIS CORE - PROJECT SUMMARY
Senescent cells (SCs) are long-lived inflammatory cells that ensue from the exposure to certain cellular
stressors. These cells have been found to increase with aging and in many age-related chronic diseases and
some studies have mechanistically linked SC function with a variety of disease phenotypes. New therapies
targetingSCs (senolytics) can act by transiently disabling anti-apoptotic networks on SCs and causing apoptosis
of those SCs within a tissue. In animal models, senolytics can delay, prevent or improve frailty,
cardiovascular pathology, neuropsychiatric conditions and liver, kidney, musculoskeletal, lung, eye,
haematological, metabolic and skin disorders, among other clinical phenotypes. Current methods to
quantify SC burden in tissues rely on a few canonical markers such as p16, p21, SA-βgal, etc. but their
specificity is still debatable and these markers are seldom co-expressed in human tissues. Thus, at present, we
lack a complete picture of the estimated SC burden across tissues in humans in health and during
aging. Here, we aim to unbiasedly characterize SCs from different human tissues using different technological
platforms and advanced analytics to develop at Atlas of SCs in human tissues. Since the accumulation of SCs
is thought to precede disease phenotypes, robust diagnostic methods to identify SCs will also enable early
detection of those at risk for developing chronic disease. Without robust diagnostics to estimate SC burden in
human tissues, (1) assessment of therapies targeting SCs will continue to be a bottleneck in senolytic drug
development and (2) chronic disease will continue to be a growing public health concern which will lead to a
steady reduction in the populations' health span. In this proposal, we will construct molecular and
morphological maps for tissue-resident SCs in humans and create multiple mechanisms to share these
results with the scientific community. The Data Analysis Core of the Tissue Mapping Center will harness its
ability to unbiasedly profile human tissues and blood to predict the senescent cell burden in humans and create
an Atlas of SC biomarkers in tissues. To achieve our goals, our Data Analysis Core will provide pipelines for
data processing, algorithms for data analysis, construct and share a map of human SCs in tissues, and general
coordination of data through the Consortium Organization and Data Coordination Center (CODCC) and with
Cellular Senescence Network (SenNet). We will build, curate, and annotate a SCs atlas across human tissues
and implement data sharing and coordinate protocols and analytic pipelines with SenNet. The database will
allow users to view and download SCs signatures, and provide a controlled access system for de-identified
individual-level single nuclei expression, imaging and proteomic data. This resource will also serve for many
additional kinds of analyses throughout the consortium.

## Key facts

- **NIH application ID:** 10895604
- **Project number:** 5U54AG075932-04
- **Recipient organization:** BUCK INSTITUTE FOR RESEARCH ON AGING
- **Principal Investigator:** David Furman
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $302,384
- **Award type:** 5
- **Project period:** 2021-09-30 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10895604

## Citation

> US National Institutes of Health, RePORTER application 10895604, Leveraging Multi-Scale Deep Phenotyping and Applied Machine Learning to Predict Senescent Cell Burden in Humans (5U54AG075932-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10895604. Licensed CC0.

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