# Mapping the landscape of the aged human brain for neurodegenerative disease models

> **NIH NIH R21** · UNIVERSITY OF WASHINGTON · 2024 · $205,619

## Abstract

Summary
Aging is the main contributor to many human neurodegenerative diseases, such as Alzheimer’s disease (AD),
that are common, progressive, and have limited therapeutic options. Our lack of understanding the biology of
human brain aging remains a major challenge in the study of age-associated disorders including AD and other
neurodegenerative disorders. While multiple laboratory models such as flies, worms and mice, have uncovered
major pathways in the biology of aging, translating these findings to humans is still incomplete, in part due to
important cross-species differences. Over the past decade, human induced pluripotent stem cell (hiPSC) models
have emerged as an experimental platform to model many human diseases and have contributed great insights
into AD and neurodegeneration broadly. However, one major caveat to hiPSC models is the fetal nature of the
cells. Several methods have emerged to try and integrate ‘aging’ factors into hiPSCs or to directly
transdifferentiate ‘aged’ cells. However, these protocols often rely on atypical aging programs or lack the flexibility
of the hiPSC system.
 One advance over the last 5-7 years is the advent of multi-omic data sets from human post-mortem brain.
The vast amount of data generated by omics technology has great potential to fill the gap in our understanding
of brain aging and age-dependent, cell-type specific genetic programs. A major current challenge, however, is
how to leverage these large, unbiased datasets to identify specific genes that regulate aging pathways.
Manipulating candidate genes in a human neural cell experimental system would enable understanding and in
vitro modeling of cellular brain aging in a tractable experimental system. Such experiments may reveal targets
that can be modified to improve aging phenotypes in human brain cells.
 In order to address these challenges, we have assembled a team of experts in explainable artificial
intelligence (XAI) technology (S-I. Lee), human brain ‘omic studies (S. Jayadev), and hiPSC disease modeling
for AD (J. Young). We hypothesize that by applying XAI methods to human brain data sets, we can identify a
tractable set of molecular drivers of brain aging. We further hypothesize that we can manipulate these drivers
in hiPSC models using CRISPR technology to generate aging phenotypes in hiPSC-derived cells. In this two-
pronged proposal, we will first perform proof-of-concept experiments to modulate expression of XAI-identified
genes in hiPSC-derived neurons and glia (microglia and astrocytes) and perform phenotypic assays to assess
cellular hallmarks of aging (R21 phase). Next, we will increase the complexity of our model by integrating
additional omics layers, further developing and refining the XAI techniques and modulating candidate aging
drivers in hiPSC-AD models in a multi-cellular context (R33 phase). These experiments will improve experimental
platforms to study human brain aging and further identify pathways that may be developed...

## Key facts

- **NIH application ID:** 10931824
- **Project number:** 1R21AG087874-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Su-In Lee
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $205,619
- **Award type:** 1
- **Project period:** 2024-09-15 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10931824

## Citation

> US National Institutes of Health, RePORTER application 10931824, Mapping the landscape of the aged human brain for neurodegenerative disease models (1R21AG087874-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10931824. Licensed CC0.

---

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