# Interpretable Deep Learning Methods to Investigate Genetics and Epigenetics of Alzheimer's Disease at a Single-Cell Resolution

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA-IRVINE · 2023 · $634,315

## Abstract

Alzheimer's disease and related dementias (ADRDs) are complex multifactorial disorders characterized
by progressive memory loss, confusion, and impaired cognitive abilities in older adults. In addition to
genetic variants, studies have reported that certain epigenetic, network, and genome organizational
perturbations, and their complex interplay, contribute to ADRD progression, informing new cellular
etiologies. The recent single-cell revolution, especially multimodal genomic profiling, makes it possible
to scrutinize multi-scale dysregulations in ADRDs at the finest possible resolution. However, few
methods have been developed to address this critical yet challenging task due to the high missingness,
dimensionality, and complex feature interactions in single-cell data. In this project, we will develop
interpretable deep learning methods and software tools to highlight multi-scale dysregulations
contributing to ADRDs, including genetic, epigenetic, network, and chromatin structural alterations at
a single-cell resolution.
 Distinct from previous efforts reporting a set of one-dimensional (1D) functional cis-regulatory
elements (CREs) from only one genome and applying it to all samples, we aim to construct personal,
compact, gene-centric, and cell-type-specific brain regulome from sc-multiome data. Specifically,
we will first propose a scalable multimodal deep generative model to integrate large-scale,
heterogeneous ADRD single-cell data with single-, multi-, and hybrid modalities. Distinct to existing
methods, we will include an invariant representation learning scheme to derive latent cell
representations uncorrelated with confounding factors (e.g., age, gender, read depth, and batch effects)
for bias-free transcriptome and epigenome reconstruction (Aim 1). Then, we will go beyond the 1D
genome annotation by deciphering the multi-scale gene regulation code (Aim 2), including cell-type-
specific chromatin compartmentation, CREs and their target genes for functional interpretation, and
transcription factor (TF) regulatory networks (TRNs). Lastly, we will develop interpretable deep learning
models to link multi-scale dysregulations to ADRD with mechanistic explanation (Aim 3).
 This proposal is built on an existing multi-year collaboration among the Zhang, Won, and
Gerstein labs that originated from the ENCODE and PsychENCODE projects, with diverse expertise in
computer science, neuroscience, and genomics. Upon completion, our proposal will significantly
accelerate research in a broader scientific community by providing essential tools to investigate
functional regions in the genome and prioritize multi-scale risk factors for ADRD.

## Key facts

- **NIH application ID:** 10698166
- **Project number:** 5R01NS128523-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** JING ZHANG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $634,315
- **Award type:** 5
- **Project period:** 2022-08-30 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10698166

## Citation

> US National Institutes of Health, RePORTER application 10698166, Interpretable Deep Learning Methods to Investigate Genetics and Epigenetics of Alzheimer's Disease at a Single-Cell Resolution (5R01NS128523-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10698166. Licensed CC0.

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