# Imaging spatial transcriptome at single cell resolution to study eye regeneration in old age

> **NIH NIH R21** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $195,000

## Abstract

ABSTRACT
To cure age-related diseases is of critical importance for the life quality of elderly people. One promising strategy
is to slow down aging or reverse aging with regenerative medicine. However, the regenerative capacity of stem
cells and their supportive microenvironments are drastically impaired in old age. Hence, to rejuvenate functions
of aged adult stem cells and their microenvironments or to maintain the youthful functions of adult stem cells in
old age are critical research directions to empower regenerative medicine to fight age-associated diseases. Here,
we propose to use a cutting-edge spatial transcriptomics method, Seq-Scope, to comprehensively examine the
aging, regeneration and rejuvenation of eye progenitor cells and their niche cells in the sexual planarians, a
champion of tissue regeneration. We aim to determine the molecular pathways that facilitate regeneration and
rejuvenation in old age. The proposed experiments will define datasets of fundamental importance in the
identification of niche cells and changes in aging and regeneration that will fuel long-term mechanistic studies.

## Key facts

- **NIH application ID:** 10933548
- **Project number:** 5R21AG084959-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Longhua Guo
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $195,000
- **Award type:** 5
- **Project period:** 2023-09-30 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10933548

## Citation

> US National Institutes of Health, RePORTER application 10933548, Imaging spatial transcriptome at single cell resolution to study eye regeneration in old age (5R21AG084959-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10933548. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*