---
title: "NG00178 – Multi-brain region mRNA-seq of sporadic ALS patients with pTDP-43 pathological staging"
id: "8125"
type: "dataset"
slug: "ng00178"
published_at: "2025-04-24T21:12:43+00:00"
modified_at: "2026-07-28T14:30:37+00:00"
url: "https://dss.niagads.org/datasets/ng00178/"
markdown_url: "https://dss.niagads.org/datasets/ng00178.md"
excerpt: "To access this data, please log into DSS and submit an application.Within the application, add this dataset (accession NG00178) in the “Choose a Dataset” section.Once approved, you will be able to log in and access the data within the DARM..."
taxonomy_dataset_categories:
  - "ALS"
  - "Controlled Access"
  - "Data Type"
  - "Disease"
  - "Illumina NovaSeq 6000"
  - "mRNA Sequencing"
  - "Open and Controlled Access Data"
  - "Platform"
---

## Overview

To access this data, please log into DSS and submit an application.  
Within the application, add this dataset (accession NG00178) in the “Choose a Dataset” section.  
Once approved, you will be able to log in and access the data within the DARM portal.

#### Description

Post-mortem fresh-frozen brain tissue was obtained for 22 sporadic amyotrophic lateral sclerosis (ALS) cases and 11 neurologically healthy control participants from Sydney Brain Bank and the New South Wales Brain Tissue Resource Centre. ALS cases were clinically diagnosed according to El Escorial criteria and were classified as sporadic based on the absence of a family history of neurodegenerative disease. ALS patients were confirmed to not carry a pathogenic repeat expansion in C9orf72, an intermediate or pathogenic repeat expansion in ATXN2 or a pathogenic variant in SOD1. ALS patients underwent post-mortem pTDP-43 pathological staging by an experienced neuropathologist. ALS patients were classified as stage 1 (n=6), stage 2 (n=7), stage 3 (n=2) or stage 4 (n=7) pTDP-43 pathology.

RNA was isolated from fresh-frozen post-mortem brain tissue using the AllPrep DNA/RNA mini kit (Qiagen, Hilden, Germany) as previously described (PMID: 34939123). RNA integrity was measured using the Agilent RNA 6000 Nano assay on the Agilent 2100 Bioanalyzer system (Agilent Technologies, Santa Clara, CA). All sequenced samples had an RNA integrity number (RIN) ≥ 6. RNA-seq libraries were prepared from total RNA using the TruSeq Stranded mRNA Library Prep Kit (Illumina, CA, USA). Sequencing was performed on an Illumina NovaSeq 6000 platform (151bp paired-end reads) generating raw sequencing reads in FASTQ format (Macrogen, South Korea). Samples had an average read depth of 27 million paired-end reads (range: 20 – 45 million).

#### Sample Summary per Data Type

| Sample Set | Accession | Data Type | Number of Samples |
| --- | --- | --- | --- |
| ALS mRNA Sequencing | snd10119 | mRNA Sequencing | 165 |

#### Available Filesets

| Name | Accession | Latest Release | Description |
| --- | --- | --- | --- |
| ALS mRNA Sequencing | fsa000137 | NG00178.v1 | Fastq Files, Phenotypes, Readme |

View the [File Manifest](https://st1.niagads.org/portal/download-public/NG00178.v1/fm)
 for a full list of files released in this dataset.

## Participant Information

The first release (April 24, 2025) includes 22 sporadic ALS patients with pTDP-43 pathological staging and 11 non-neurological controls. For each individual, five brain regions were examined (n=165 samples total): motor cortex (pTDP-43 inclusions always present), prefrontal cortex and hippocampus (pTDP-43 inclusions sometimes present), and occipital cortex and cerebellum (pTDP-43 inclusions rarely present). Samples had an average read depth of 27 million 151bp paired-end reads (range: 20 - 45 million).

| Sample Set | Accession Number | Number of Participants | Number of Samples |
| --- | --- | --- | --- |
| ALS mRNA Sequencing | snd10119 | 33 | 165 |

## Related Studies

- [sa000068 - Multi-region brain transcriptomic analysis of amyotrophic lateral sclerosis reveals widespread RNA alterations and substantial cerebellum involvement](https://dss.niagads.org/studies/sa000068/) Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that primarily affects the motor neurons, causing progressive muscle weakness and paralysis. While research has focused on understanding pathological mechanisms in the… [Learn more](https://dss.niagads.org/studies/sa000068/)

## Cohorts

| Cohort | Number of Participants | Number of Samples |
| --- | --- | --- |
| New South Wales Brain Tissue Resource Centre | 6 | 30 |
| Sydney Brain Bank | 27 | 135 |

## Consent Levels

| Consent Level | Number of Participants | Number of Samples |
| --- | --- | --- |
| GRU-IRB-PUB | 33 | 165 |

Visit the [Data Use Limitations page](/documentation/policies-and-guidelines/data-use-limitations/)
 for definitions of the consent levels above.

## Acknowledgement

### Acknowledgment statement for any data distributed by NIAGADS:

Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689), funded by the National Institute on Aging.

Use the study-specific acknowledgement statements below (as applicable):

### For investigators using any data from this dataset:

Please cite/reference the use of NIAGADS data by including the accession [NG00178](https://archive.niagads.org/datasets/NG00178)
.

### For investigators using Multi-region brain transcriptomic analysis of amyotrophic lateral sclerosis reveals widespread RNA alterations and substantial cerebellum involvement (sa000068) data:

Post-mortem tissues were received from the Sydney Brain Bank at Neuroscience Research Australia and the New South Wales Brain Tissue Resource Centre at the University of Sydney. Tissues samples were collected by Heather McCann and Julia Stevens. pTDP-43 pathological stage classification of post-mortem brain tissues including post-mortem diagnosis was performed by Claire E. Shephard. Dominic B. Rowe and Matthew C. Kiernan ascertained patients and clinical data. Genetic screening and extraction of RNA from post-mortem brain tissues was performed by Natalie Grima. Funding to support this project was acquired by Kelly L. Williams, Ian P. Blair and Natalie Grima. We sincerely thank the study participants for their invaluable contribution to this study.

## Publications

- Grima N. **Multi-region brain transcriptomic analysis of amyotrophic lateral sclerosis reveals widespread RNA alterations and substantial cerebellum involvement.***Molecular neurodegeneration. 2025 Apr 25.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/40275359/)

## Approved Users

**Total number of approved DARs:** 7

[JSON Export](https://st1.niagads.org/portal/v1/adars/NG00178)
|[CSV Export](https://dss.niagads.org/wp-admin/admin-post.php?action=niagads_export_dars_csv&accession=NG00178)

- Investigator: Cheng, Feixiong Institution: Cleveland Clinic Project Title: A Multimodal Infrastructure for Alzheimer’s MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery Date of Initial Approval: June 9, 2021 Current Status: Approved Publications: Show publications (13) - Martin W. **Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease.***Current opinion in structural biology. 2022 Feb.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34628220/) - Fang J. **Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease.***Alzheimer's research & therapy. 2022 Jan 10.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/35012639/) - Fang J. **Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease.***Nature aging. 2021 Dec.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/35572351/) - Zhou Y. **The Alzheimer's Cell Atlas (TACA): A single-cell molecular map for translational therapeutics accelerator in Alzheimer's disease.***Alzheimer's & dementia (New York, N. Y.). 2022.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/36254161/) - Zhang P. **Population-based discovery and Mendelian randomization analysis identify telmisartan as a candidate medicine for Alzheimer's disease in African Americans.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023 May.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/36331056/) - Xu J. **Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease.***Cell reports. 2022 Nov 29.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/36450252/) - Hou Y. **Microglial immunometabolism endophenotypes contribute to sex difference in Alzheimer's disease.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 Feb.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/37985399/) - Qiu Y. **Artificial intelligence for drug discovery and development in Alzheimer's disease.***Current opinion in structural biology. 2024 Apr.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38335558/) - Cheng F. **Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease.***Cell reports. Medicine. 2024 Feb 20.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38382465/) - Gohel D. **Sildenafil as a Candidate Drug for Alzheimer's Disease: Real-World Patient Data Observation and Mechanistic Observations from Patient-Induced Pluripotent Stem Cell-Derived Neurons.***Journal of Alzheimer's disease : JAD. 2024.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38427489/) - Qiu Y. **Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease.***Cell reports. 2024 May 28.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38652661/) - Cummings J. **Alzheimer's disease drug development pipeline: 2024.***Alzheimer's & dementia (New York, N. Y.). 2024 Apr-Jun.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38659717/) - Xu J. **Single-microglia transcriptomic transition network-based prediction and real-world patient data validation identifies ketorolac as a repurposable drug for Alzheimer's disease.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2025 Jan.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/39641322/) Research Use Statements: Show statements Technical Research Use Statement: We propose to develop capable and intelligent computer-based toolboxes that enable searching, sharing, visualizing, querying, and analyzing genetics, genomics, multi-omics, and clinical data for AD. The central unifying hypothesis of this project (1U01AG073323-01 [pending for Council meeting at May/2021) is that a genome-wide, multimodal artificial intelligence (AI) framework to identify novel risk genes and networks from human WGS/WES and multi-omics findings will offer drug targets for targeted therapeutic development in AD. Aim 1 will identify rare coding variant-based risk genes using a sequence and structure-based deep learning model. Aim 2 will identify rare non-coding variant-based risk genes using a multiple kernel learning approach. Aim 3 will test whether GWAS common variants linked to AD pathobiology and endophenotypes are enriched in gene regulatory networks in a cell-type specific manner using a Bayesian framework. These analyses will leverage variants from ethnically diverse WGS/WES and clinical data (i.e., imaging, biomarkers, and cognitive measures) from Alzheimer's Disease Sequencing Project (ADSP), and publicly available chromatin interactomic data from NIH RoadMap, FANTOM5, and NIH 4D Nucleome. We will validate our findings using WGS/WES data and protein expression data from our existing cohorts: The Cleveland Clinic Lou Ruvo Center for Brain Health Aging and Neurodegenerative Disease Biobank (CBH-Biobank) and the Cleveland Alzheimer's Disease Research Center (CADRC). We will compile information for clinical data harmonization, including functional imaging, AD biomarkers, and cognitive measures for all integrative analyses. There are no any PHI information will collected or used in the data analysis. We don’t have the planned collaboration with researchers outside Cleveland Clinic in the current analytic plans. Non-Technical Research Use Statement: It is estimated that more than 16 million people with AD live in the United States by 2050 and the predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. This project will develop intelligent computer-based network medicine and systems biology tools, capable of identifying and validating human genome sequencing findings for novel risk gene discoveries and targeted therapeutic development in AD. The innovative network-based, artificial intelligence toolboxes and novel risk genes and biologically relevant targeted therapeutic approaches developed in this proposal will prove to be novel and effective ways to improve outcomes in long-term brain care for the rapidly growing AD population, an essential goal of AD precision medicine.
- Investigator: Cruchaga, Carlos Institution: Washington University School of Medicine Project Title: The Familial Alzheimer Sequencing (FASe) Project Date of Initial Approval: January 23, 2019 Current Status: Approved Publications: Show publications (10) - Cruchaga C. **Polygenic risk score of sporadic late-onset Alzheimer's disease reveals a shared architecture with the familial and early-onset forms.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2018 Feb.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/28943286/) - Fernández MV. **Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease.***PLoS genetics. 2017 Nov.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/29091718/) - Ridge PG. **Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer's disease resilience.***Genome medicine. 2017 Nov 29.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/29183403/) - Fernández MV. **Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease.***Frontiers in neuroscience. 2018.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/29670507/) - Olive C. **Examination of the Effect of Rare Variants in TREM2, ABI3, and PLCG2 in LOAD Through Multiple Phenotypes.***Journal of Alzheimer's disease : JAD. 2020.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/32894242/) - Kirola L. **Lack of evidence supporting a role for DPP6 sequence variants in Alzheimer's disease in the European American population.***Acta neuropathologica. 2021 Apr.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/33591372/) - Moreno-Grau S. **Long runs of homozygosity are associated with Alzheimer's disease.***Translational psychiatry. 2021 Feb 24.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/33627629/) - Cruchaga C. **Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer's disease.***Research square. 2023 Jun 9.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/37333337/) - Wang L. **Proteo-genomics of soluble TREM2 in cerebrospinal fluid provides novel insights and identifies novel modulators for Alzheimer's disease.***Molecular neurodegeneration. 2024 Jan 3.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38172904/) - Patel M. **Whole-genome sequencing reveals the impact of lipid pathway and APOE genotype on brain amyloidosis.***Human molecular genetics. 2025 Apr 6.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/39927718/) Research Use Statements: Show statements Technical Research Use Statement: The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studies Non-Technical Research Use Statement: Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease.
- Investigator: Fernandez, Victoria Institution: ACE Alzheimer Center Project Title: GADIR Date of Initial Approval: February 10, 2026 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: The objective of this study is to contribute to our understanding of neurodegenerative diseases by examining the genetic contributors of major dementia neuropathological hallmarks (amyloid-β deposition, tau pathology, TDP-43, hippocampal sclerosis, Lewy body pathology, and cerebrovascular disease, among others. We will generate the largest Iberian database(N=3500) of neuropathologically curated brains (Aim 1) with a subset of those (N≈350) undergoing deep digital phenotyping (Aim 3). We will generate an associated genetic map (Aim 2) order to elucidate how common and rare genetic variants contribute to specific pathologies. We additionally aim to determine how polygenic risk scores (PRS) and pathway-specific PRS correspond to single and mixed neuropathological profiles, and to clarify the genetic architecture driving co-pathologies that frequently complicate clinical diagnosis. Eventually, we will replicate and fine-map our findings (Aim 4) leveraging available datasets at NIAGADS and other public repositories. Our analysis plan includes genome-wide association testing of ordinal, binary, and quantitative neuropathological traits; rare-variant burden analyses for coding and non-coding regions; PRS and pathway-PRS modeling across multiple dementia-related diseases; unsupervised clustering to identify variant sets defining specific endophenotypes; and pathway and network analyses to interpret significant signals. Colocalization and functional annotation approaches will integrate genomic findings with transcriptomic and proteomic resources. Data obtained from NIAGADS will be used to strengthen replication, broaden meta-analytic power, validate associations across independent neuropathology cohorts, and support functional interpretation using available genetic, expression, and multi-omic datasets. All analyses will use de-identified data in compliance with ethical and data-sharing standards. Non-Technical Research Use Statement: Dementia is an immensely challenging and prevalent condition, deeply impacting the lives of over 55 million individuals worldwide. While Alzheimer's disease stands as the most commonly recognized form of dementia, there exist other conditions that present comparable symptoms but distinct underlying pathological characteristics. To provide more effective support to patients and their families, we need to better understand the genetic causes associated to each of these brain pathologies, and to develop advanced tools for early classification and diagnosis. This grant proposal aims to tackle these challenges by establishing the largest Iberian (Spanish and Portuguese) database of dementia neuropathological cases, marked by a modernized and standardized neuropathological classification alongside comprehensive genomic data. Our goal is to delve further into the genetic architecture underpinning these pathological features and to refine existing risk assessment tools for more accurate diagnoses.
- Investigator: Kamboh, M. Ilyas Institution: University of Pittsburgh Project Title: Genetics of Alzheimer's Disease and Endophenotypes Date of Initial Approval: January 7, 2025 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: Objectives: We are requesting access to the NIAGADS datasets to augment our ongoing studies on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes being carried out by Kamboh and his group since 1995. We are doing GWAS using array genotypes, whole-exome sequencing and whole-genome sequencing on datasets derived from University of Pittsburgh ADRC and ancillary population-based longitudinal studies on dementia and biomarkers. Different available phenotypes include AD and non-AD dementia, age-at-set, disease progression and survival, neuroimaging, cognitive decline, plasma biomarkers for the core ATN and non-ATN pathologies. We also plan to expand on gene-gene interaction and sex-stratified analyses which require the actual genotype data. The NIAGADS datasets will be used for replication and meta-analysis, and for gene-gene interaction and sex-stratified analyses. Study Design: A case-control design will incorporate a diverse cohort of individuals with AD and age-matched controls. For quantitative traits (neuroimaging and plasma biomarkers, cognitive performance measures, indicators of disease progression), linear regression analyses will be performed to identify genetic loci. To ensure the findings are robust and inclusive, participants from diverse demographic backgrounds will be included, enabling the exploration of potential genetic variations across populations. Analysis Plan: We will conduct GWAS and targeted analyses on candidate genes on different AD and AD-related phenotypes. Primary phenotypic variables include AD disease status, age-at-onset, last age for controls, APOE genotype, cognitive decline trajectories, sex, and race. Analyses will evaluate the influence of specific genetic variants on disease risk, cognitive performance, and biomarker levels, considering both individual and interactive effects of the APOE genotype. Results will be adjusted for potential confounders, such as demographic factors, to ensure valid associations. Detail analytical methods are described in our published papers for case-control (PMID: 32651314;35694926), quantitative traits (PMID: 30361487;37666928), and cognitive decline (PMID: 37089073; 30954325). Non-Technical Research Use Statement: Our research group at the University of Pittsburgh (Pitt), has been working on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes for almost three decades, on data derived largely from the University of Pittsburgh Alzheimer’s Disease Research Center and ancillary dementia studies. We are requesting access to the NIAGADS genotype and phenotype datasets to augment our sample size to increase power to detect novel genetic associations with AD and related endophenotypes.
- Investigator: Konermann, Silvana Institution: Arc institute Project Title: Modeling Alzheimer’s disease risk and associated molecular phenotypes Date of Initial Approval: August 8, 2025 Current Status: Expired Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: The objective of the proposed research is to determine the relationship between Alzheimer’s disease (AD) genetic risk and associated molecular phenotypes. Genotype data will be used to compute a polygenic risk score (PRS) for disease-affected and control (non-disease-affected) participants. Statistical regression and mediation analyses will be used to model variation of molecular phenotypes with respect to PRS and, where available, pathology stage or cognitive impairment. Molecular phenotypes to be analyzed include bulk/single-cell/single-nucleus transcriptome, epigenome, proteome, metabolome, lipidome, amyloid, and tau. Molecular phenotypes of participants, including controls, will be matched with molecular phenotypes of in vitro cellular models, informing the design of in vitro perturbation experiments that recapitulate the genetic drivers of AD risk. Non-Technical Research Use Statement: Our goal is to determine the relationship between human genetic profiles associated with Alzheimer’s disease (AD) risk and specific measurable characteristics of human cells. Using multiple statistical analysis methods, we will build quantitative models that describe how those characteristics vary as a function of AD genetic risk. The models we build will help us design in vitro cellular systems that reflect different levels of AD risk, enabling experiments that inform new strategies for treating or preventing AD.
- Investigator: Swindell, William Institution: University of Texas Southwestern Medical Center Project Title: Meta-analysis of gene expression differences in post-mortem cortex samples from ALS patients and control subjects Date of Initial Approval: March 30, 2026 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: Gene expression profiling has been used to understand transcriptional alterations that distinguish tissues from ALS patients and normal control individuals [1, 2], as well as to identify molecular subtypes and substages contributing to the heterogeneity observed among ALS patients [3]. This work will use a random effect inverse variance meta-analysis model to identify genes with robustly altered expression in post-mortem ALS tissues [2]. The model will generate differential expression summary statistics for all protein-coding genes having detectable expression within the CNS, identifying those transcriptional alterations that are most repeatable across a broad range of expression profiling platforms and technologies (i.e., microarray, bulk RNA-seq, snRNA-seq). These genes will then be further investigated to identify over-represented annotations, based upon multiple databases, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathways. A comparison will be made to genes located near previously identified ALS genetic risk loci, with the goal of identifying functional genomics changes that correspond to genetic alterations predisposing patients to disease. A mixed-effects meta-regression model will be used to identify moderating variables associated with between-study variation in effect size. Data will only be accessed by Dr. William Swindell in accordance with NIAGADS. appropriate use policies, without further access by any other investigators. [1] Swindell WR. 2025. Meta-analysis of gene expression in bulk-processed post-mortem spinal cord from ALS patients and normal controls. NeuroSci 6(3): 65. [2] Swindell WR. 2024. Meta-analysis of differential gene expression in lower motor neurons isolated by laser capture microdissection from post-mortem ALS spinal cords. Frontiers in Genetics 15:1385114. [3] Swindell WR, Kruse CPS, List EO, Berryman DE, Kopchick JJ. 2019. ALS blood expression profiling identifies new biomarkers, patient subgroups, and evidence for neutrophilia and hypoxia. Journal of Translational Medicine 17(1):170. Non-Technical Research Use Statement: ALS is a debilitating neurodegenerative disease with genetic and sporadic forms. Prognosis is poor although there is substantial heterogeneity among patients and new treatments have been developed and approved for those with certain types of familial disease (e.g., SOD1 mutations). This study will use gene expression profiling to identify mRNA transcripts having expression that is consistently altered in post-mortem cortex samples from ALS patients. The approach will involve large-scale integration between multiple datasets that have been generated in over decades and deposited within the public domain. The goal of this work is to highlight highly reproducible findings in post-mortem tissue samples from ALS patients, which can help focus investigational effort on genes and pathways most robustly altered in end-stage disease.
- Investigator: Zhao, Dongjiao Institution: Messiah University Project Title: Title: Gene Expression and Biological Pathway Associations with Clinical Phenotypes in Alzheimer’s Disease Date of Initial Approval: June 26, 2026 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: This project will examine how genetic variants, gene expression profiles, transcriptomic patterns, APOE status where available, and biological pathways are associated with Alzheimer’s disease and related cognitive, functional, and neuropathological phenotypes. The goal is to identify genomic and pathway-level factors that may help explain cognitive decline, daily functional changes, neuropathological burden, and vulnerability or resilience in Alzheimer’s disease and related neurodegenerative or cognitive aging conditions. This study is a secondary analysis of existing coded/de-identified controlled-access data obtained through NIAGADS DSS. No new participants will be recruited, no participant contact will occur, and no new biospecimens or data will be collected. The requested datasets include Alzheimer’s disease sequencing data, oldest-old clinical and pathological data, microglia single-nuclei RNA-seq data, multi-brain-region mRNA-seq data from sporadic ALS, and genomic data related to cognitive ability. All analyses will comply with NIAGADS data use limitations, NIH Genomic Data Sharing Policy, NIA requirements, and Messiah University institutional oversight. Analyses will evaluate associations between genetic or gene-level features and approved phenotypes, including AD diagnosis, case-control status, cognitive performance, general cognitive ability, functional status, daily activity measures, neuropathological findings, microglia-related expression profiles, brain-region-specific expression patterns, and disease stage where available. Methods may include quality control, phenotype harmonization, regression-based association testing, differential gene expression analysis, variant-to-gene annotation, gene-level aggregation, pathway enrichment, and aggregate or polygenic measures if appropriate. Models will adjust for relevant covariates such as age, sex, ancestry, relatedness, brain region, cell type, disease stage, batch effects, and technical factors. Only aggregate or summary-level results will be reported. Non-Technical Research Use Statement: This project will use existing de-identified research data to study why some people develop Alzheimer’s disease or cognitive decline while others remain more resilient. The study will look at genetic information, gene activity, and biological pathways that may be related to memory, thinking ability, daily function, and brain changes seen in Alzheimer’s disease and related conditions. No new participants will be recruited, and no new samples will be collected. The project will only use approved research data from NIAGADS. The goal is to better understand how inherited genetic differences and changes in gene expression may contribute to Alzheimer’s disease, cognitive decline, and functional changes in daily life. This research may help identify biological patterns that could support future studies on earlier detection, risk prediction, and better understanding of neurodegenerative diseases. All data used in this project will be coded or de-identified. The study will not attempt to identify any individual participant, and results will only be reported in summary form.

### Total number of participants: 33

SexRaceEthnicityDiagnosisAPOE

Female1442.4 %

Male1957.6 %

| White | 33 |
| --- | --- |

- NA33 (100.0%)

| Amyotrophic Lateral Sclerosis (ALS) |  |  |
| --- | --- | --- |
| Control | 11 | 33.3% |
| Case | 22 | 66.7% |

Not Applicable/Not Available

33

100.0%