# Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer Disease

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $253,798

## Abstract

PROJECT SUMMARY
Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects 6.2M Americans, yet current
therapies are not effective at preventing or slowing the cognitive decline1. Neuropsychiatric symptoms (NPS) are
core features of AD and related dementias that are associated with major adverse effects on daily function and
quality of life, and accelerate time to institutionalization. The overarching goal of the parent grant R01AG067025
is to integrate single nucleus transcriptome profiles with detailed NPS phenotype data from each donor and
identify dysregulated genes associated with disease trajectory, identify clusters of donors with different gene
expression disease signatures, and nominate genes and pathways for targeting with novel therapeutics.
The compendium of single nucleus transcriptome profiles comprising ~7.2M nuclei from ~1,800 total donors
generated by the parent grant R01AG067025 is a remarkable resource. Yet mining these transcriptome profiles
to advance knowledge of AD etiology requires analytical workflows that scale to the unprecedented size of these
and other emerging data. Existing workflows for multi-donor single cell and nucleus transcriptome data have
either been 1) designed for a small number of donors and so cannot take advantage of the large-scale and
complex study design used here, or 2) adapted from bulk transcriptome analyses and do not currently scale to
hundreds of donors, dozens of cell types and millions of cells. The objective of addressing pressing biological
hypotheses about AD biology necessitates the development of analytical workflows designed and engineered
with the challenges of multi-donor single cell and nucleus transcriptome data in mind.
In this Supplement, we propose developing a scalable, open source analytical workflow for multi-donor single
cell/nucleus transcriptome data motivated by our previous work on linear mixed models2,3. We have previously
applied linear mixed models to analyze bulk transcriptome profiles, and developed the open source
variancePartition package to perform differential expression testing, account for technical batch effects and
characterize the multiple biological and technical sources of expression variation. While the current software
has facilitated analysis of bulk transcriptomic and epigenomic profiles by our group and many others, applying it
to the multi-donor single nucleus data is currently limited by the ad hoc design of the variancePartition codebase.
To address these limitations, here we propose (Aim 1) Scaling this analytical workflow to emerging datasets
using best practices in software engineering, code refactoring, and empirical testing across multiple computing
environments; and (Aim 2) Enabling broader use by (a) computational biologists by developing vignettes to
illustrate applications of the software on public datasets, and by (b) open source developers by improving code
design and documentation. Overall, reconceiving the analyti...

## Key facts

- **NIH application ID:** 10406707
- **Project number:** 3R01AG067025-03S1
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** STEVEN M FINKBEINER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $253,798
- **Award type:** 3
- **Project period:** 2019-09-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10406707

## Citation

> US National Institutes of Health, RePORTER application 10406707, Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer Disease (3R01AG067025-03S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10406707. Licensed CC0.

---

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