Math 574: Final Project

Elucidating Changes in the Expression of Acetylcholine-related Genes during Alzheimer’s Disease Progression

Jorge Mato Frontela, Jeffrey Bohrer

2025-12-03

Background & Motivation

Alzheimer’s Disease (AD) At a Glance

  • 55M+ people affected as of 2020; characterized by progressive neurodegeneration.

The Cholinergic System in The Brain

  • Uses acetylcholine (ACh) to support learning, memory, and attention.
  • Cholinergic neurons project widely across the brain.
  • ACh signaling shapes cognition, synaptic plasticity, and brain regulation.
  • Cholinergic Hypothesis for AD: Early Theory

Why Cholinergic Signaling Matters For Alzheimer’s

  • Despite decades of AD research, the cellular and spatial organization of cholinergic gene expression during AD progression is poorly defined.
  • Past studies lacked single-cell and spatial resolution; new methods capable of including protein markers.

Objective

Understanding how acetylcholine genetic regulation changes as Alzheimer’s disease progresses

Methods

Dataset Source

Experimental Setup

What is STARMap Plus & Why This Dataset

  • 72,165 cells and 2,766 genes
  • Four different groups: 13 month (AD/CTRL) and 8 month (AD/CTRL)

Data Preparation

Analysis

Results

Initial Heatmaps Show Subtle Differences in Gene Expression Across Samples


  • Mice at 13 Months with AD

Gene Expression PCA Showcases Weak Separation

Gene Ontology Level Analysis

Obtained GO-terms Associated with Acetyl Choline



  • Obtained acetylcholine-related genes from AnnotatioDBi, provides access to GO databases.

  • Mapped GO terms to genes in the dataset

  • Found a GO score by finding the mean expression of the genes associated with a GO term

GO-Level Heatmaps Shows More Stark Differences of GO-Term Representation across Conditions


13 months mice / AD


13 months mice / Control

8 months mice / AD


8 months mice / Control

Differential GO Analysis with Wilcoxon Test



Goal: Identify gene programs that are up or downrefulated across conditions.


Why the Wilcoxon Test?

  • Single-cell gene expression is not normally distributed.
  • Sample sizes per gene per condition vary.
  • It yields:
    • Adjusted p-values using FDR.
    • Calculated log2 fold-change.

Differential GO Representation Results

Disease Progression from 8 months to 13 months


Effect of aging in control samples

Accounting for Aging Effects Shows Clear Differences in GO-Term Representation Across Cell Types



Cell-Type-Specific Progression Modeling

  • For significant genes, computed:
    • log2FC aging for control
    • log2FC progression for disease
    • log2FC_relative = disease_progression – control_progression
  • This quantifies whether disease-associated changes exceed normal aging.

Disease Progression Heatmap Shows Different Gene Programs Being Expressed

Spatial Mapping

Procedure

  • Joined differential expression data with 13-month spatial X and Y coordinates.

  • Mapped Acetylcholine-related GO-terms and genes across brain regions & cell types.

Spatial Cell Map Resembles the Original Tissue Architechture


Image from the Zeng et al. to benchmark our mapping


Spatial Representation of GO Terms Across Cell Types

Spatial Differential Gene Expression Across Cell Types


GO: Choline Transport


GO: Acetylcholine receptor signaling pathway

Conclusions

  • GO-level analysis allowed to capture significant gene programs across acetylcholine reduced subset.
  • Aforementioned gene programs differed across cell types showcasing distinct up/downregulation patterns through disease progression.
  • Inclusion of spatial coordinates contextualizes findings within tissue architecture.

Limitations

  • Gene set is relatively small → many ACh-related genes sparse, may conflate signal by subsetting
  • STARmap detects limited gene panels → missing many cholinergic regulators.
  • Mouse model ≠ human AD.
  • Need a better way to get a GO-Term score
  • Wilcoxon Test may inflate false discovery rate.

Future Directions

  • Integrate spatial analysis relative to plaque distances.
  • Explore effect of neurofibrillary tau tangles.

Sources

  1. Huang Q, Liao C, Ge F, Ao J, Liu T. Acetylcholine bidirectionally regulates learnineg and memory. J Neurorestoratology. 2022;10(2):100002. doi:10.1016/j.jnrt.2022.100002 (https://www.zotero.org/google-docs/?NLtrnF)

  2. Zhang J, Zhang Y, Wang J, Xia Y, Zhang J, Chen L. Recent advances in Alzheimer’s disease: mechanisms, clinical trials and new drug development strategies. Signal Transduct Target Ther. 2024;9(1):211. doi:10.1038/s41392-024-01911-3 (https://www.zotero.org/google-docs/?NLtrnF)

  3. Chen Y, Yu Y. Tau and neuroinflammation in Alzheimer’s disease: interplay mechanisms and clinical translation. J Neuroinflammation. 2023;20(1):165. doi:10.1186/s12974-023-02853-3 (https://www.zotero.org/google-docs/?NLtrnF)

  4. Azargoonjahromi A. The duality of amyloid-β: its role in normal and Alzheimer’s disease states. Mol Brain. 2024;17(1):44. doi:10.1186/s13041-024-01118-1 (https://www.zotero.org/google-docs/?NLtrnF)

  5. Bloom GS. Amyloid-β and Tau: The Trigger and Bullet in Alzheimer Disease Pathogenesis. JAMA Neurol. 2014;71(4):505. doi:10.1001/jamaneurol.2013.5847(https://www.zotero.org/google-docs/?NLtrnF)

  6. Gajendra K, Pratap GK, Poornima DV, Shantaram M, Ranjita G. Natural acetylcholinesterase inhibitors: A multi-targeted therapeutic potential in Alzheimer’s disease. Eur J Med Chem Rep. 2024;11:100154. doi:10.1016/j.ejmcr.2024.100154(https://www.zotero.org/google-docs/?NLtrnF)

  7. Abe Y, Aoyagi A, Hara T, et al. Pharmacological Characterization of RS-1259, an Orally Active Dual Inhibitor of Acetylcholinesterase and Serotonin Transporter, in Rodents: Possible Treatment of Alzheimer’s Disease. J Pharmacol Sci. 2003;93(1):95-105. doi:10.1254/jphs.93.95(https://www.zotero.org/google-docs/?NLtrnF)

  8. Haque A, Engel J, Teichmann SA, Lönnberg T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 2017;9(1):75. doi:10.1186/s13073-017-0467-4(https://www.zotero.org/google-docs/?NLtrnF)

  9. Williams CG, Lee HJ, Asatsuma T, Vento-Tormo R, Haque A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 2022;14(1):68. doi:10.1186/s13073-022-01075-1(https://www.zotero.org/google-docs/?NLtrnF)

  10. Marx V. Method of the Year: spatially resolved transcriptomics. Nat Methods. 2021;18(1):9-14. doi:10.1038/s41592-020-01033-y (https://www.zotero.org/google-docs/?NLtrnF)

  11. Zeng H, Jiahao Huang, Haowen Zhou, et al. Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in an Alzheimer disease model. Published online November 17, 2022. doi:10.5281/ZENODO.7332091(https://www.zotero.org/google-docs/?NLtrnF)

  12. Wilcoxon Rank Sum and Signed Rank Tests: wilcox.test. Documentation for the R ‘stats’ package.](https://rdrr.io/r/stats/wilcox.test.html)

  13. Breijyeh Z, Karaman R. Comprehensive Review on Alzheimer’s Disease: Causes and Treatment. Molecules 2020; 25(24): 5789. doi:10.3390/molecules25245789(https://www.mdpi.com/1420-3049/25/24/5789)

  14. Chen ZR, Huang JB, Yang SL, Hong FF. Role of Cholinergic Signaling in Alzheimer’s Disease. Molecules. 2022; 27(6):1816. PMCID: PMC8949236(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949236/)