# Harmonization and Joint Analysis of Human Brain Single-Cell Datasets from Neurotypical Aging Controls and Alzheimer's Disease Patients

> **NIH NIH R01** · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · 2024 · $366,174

## Abstract

This supplement to our R01AG076949 aims to harmonize and jointly analyze our multiome datasets from 100
neurotypical normally aging (NA) subjects from adolescence into the 90s, and 40 Alzheimer’s Disease (AD)
brains, with BRAIN Initiative-generated datasets from human brain tissue. We are implementing high resolution
mass spectrometry (HRMS) (Stephens et al., 2018; Wobma et al., 2018a; Wobma et al., 2018b), 10X Genomics
single nuclei RNA sequencing (snRNA-seq), sn Assay for Transposase-Accessible Chromatin sequencing
(snATAC-seq), Visium spatial transcriptomics (10X Genomics), and our custom-made slide-seq technology,
using deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq), for co-mapping mRNAs
and proteins (Liu et al., 2020), ATAC spatial sequencing (Deng et al., 2022) and RNA-ATAC co-profiling (Zhang
et al., 2023). We will integrate our data with publicly available datasets from GEO (Gene Expression Omnibus),
SCORCH (Single Cell Opioid Responses in the Context of HIV), PsychENCODE Consortium, NeMO
(Neuroscience Multi-Omic Data Archive), SEA-AD (Seattle Alzheimer’s Disease Brain Cell Atlas), Psych-AD
(Neuropsychiatric Symptoms in Alzheimer’s Disease) and the BICCN (BRAIN Initiative Cell Census Network).
We will include external datasets on hippocampus and other brain regions, to implement analyses on gene
regulatory networks (GRNs) that are common to neurons and glial cells across brain regions, and identify GRNs
that synergistically regulate functional brain circuits involved in cognitive, emotional, and behavioral changes in
AD. The hippocampus, fundamental for memory, the amygdala, regulating emotional responses to stress and
“fight or flight” reactions, and the prefrontal cortex (PFC), involved in set shifting, attentional control, and decision
making, are all progressively affected in AD, and they are involved in AD neuropsychiatric symptoms (NPS),
such as disinhibition, impulsivity, anxiety, emotional distress, aggression, paranoid ideation, and other psychotic
symptoms. Understanding how those brain regions are regulated by cell-type specific epigenetic and gene
expression changes, will shed light on molecular mechanisms of AD pathogenesis and provide targets for new
therapeutic approaches to AD and NPS in AD. This supplement is within the scope of the active award and
improves the discovery potential of our dataset. This project will provide scalable pipelines for analyzing
multimodal and spatial omics cell profiling datasets, and will deliver a developmental and adult cell atlas of cell
type diversity. It will provide information on the regulome, the interaction between regulatory components that
control the state of expression of genes and their isoforms, their subcellular location, tissue state, development,
and pathology. These regulatory mechanisms include DNA accessibility for gene transcription, DNA methylation,
transcription factors that regulate gene transcription, RNA-protein interactions, and non-coding...

## Key facts

- **NIH application ID:** 10835660
- **Project number:** 3R01AG076949-02S1
- **Recipient organization:** NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
- **Principal Investigator:** Maura Boldrini Dupont
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $366,174
- **Award type:** 3
- **Project period:** 2022-05-15 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10835660

## Citation

> US National Institutes of Health, RePORTER application 10835660, Harmonization and Joint Analysis of Human Brain Single-Cell Datasets from Neurotypical Aging Controls and Alzheimer's Disease Patients (3R01AG076949-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10835660. Licensed CC0.

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