# Development of machine learning software to quantitatively map telomere induced senescence in tissue sections during aging

> **NIH NIH UG3** · MAYO CLINIC ROCHESTER · 2022 · $556,500

## Abstract

PROJECT SUMMARY/ABSTRACT
Cellular senescence is a pillar of aging, acting as a key driver of aging and age-related diseases.
Telomeres play a major role in cellular senescence. When telomeres become critically short or
damaged, they elicit a DNA damage response (DDR) that drives senescence. Our laboratory
developed sophisticated methods to detect senescent cells in tissues based on the co-localization
between telomeres and the DDR. Furthermore, we recently developed SenoQuant, a software
that simplifies the measure of Telomere associated foci (TAF), reducing quantification time from
weeks to hours. We now propose to apply emerging technologies such as machine learning and
deep learning to map TAF more accurately and robustly in human tissue sections. This will
address several challenges inherent to TAF analysis, including nuclei detection, staining artifacts,
and quantification time. Furthermore, in collaboration with the Tissue Mapping Centers we will
tailor SenoQuant to the analysis of specific human tissues. Finally, we propose to integrate TAF
with multiplexed imaging methods such as Imaging Mass Cytometry (IMC), allowing the detection
of multiple senescence markers in tissues simultaneously. We anticipate that this technology will
greatly advance the spatially-resolved mapping of senescent cells in human tissues and will be a
great resource for the aging and cell senescence community.

## Key facts

- **NIH application ID:** 10491929
- **Project number:** 5UG3CA268103-02
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Joao Passos
- **Activity code:** UG3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $556,500
- **Award type:** 5
- **Project period:** 2021-09-21 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10491929

## Citation

> US National Institutes of Health, RePORTER application 10491929, Development of machine learning software to quantitatively map telomere induced senescence in tissue sections during aging (5UG3CA268103-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10491929. Licensed CC0.

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