Longitudinal Single-Cell Transcriptomic Analysis of Sex-Dependent Changes from THY-Tau22 mouse model of AD

Table of contents

• Introduction
• Content
• Data
• Requirements
• License
• Instructions

Introduction

This repository contains the code for bioinformatics analyses described in the article "Longitudinal single-cell transcriptomic analysis reveals sex-dependent changes in THY-Tau22 mice at early and late stages of tau pathology".

This project investigated sex-dependent molecular changes in the THY-Tau22 mouse model of Alzheimer's disease through single-cell RNA sequencing analysis, comparing transcriptomic profiles at 7 and 17 months of age to understand disease progression and sex-specific responses.

Content

The code covers the following main analysis steps:

1. QC, Normalization (SCT), Clustering, and Cell Type Annotation
2. Differential Expression Analysis (bulk and cell type-specific)
3. DEA using edgeR for sex-interaction analysis
4. Pathway Enrichment Analysis
5. Gene Regulatory Network (GRN) Analysis
6. Analysis of DEG Overlaps between Datasets
7. Longitudinal Analysis (7 to 17 months temporal changes)

Data

The single-cell RNA sequencing data is available in the NCBI Gene Expression Omnibus (GEO) database:

• THY-Tau22 mice (7 months): GSE245035
• THY-Tau22 mice (17 months): GSE285506
• Tg2576 mouse model: [accession pending]
• Human AD cortical tissue: GSE138852

Requirements

The code was written in R (version 4.2.2) and relies on multiple R and Bioconductor packages, including:

• Seurat (v4.3.0)
• clusterProfiler
• enrichplot
• cowplot
• ggplot2
• HGNChelper
• ggVennDiagram
• Additional packages listed at the beginning of each R script

License

The code is available under the MIT License.

Instructions

The code was tested on R 4.2.2 on both current Mac and Linux operating systems, but should be compatible with later versions of R installed on current Mac, Linux or Windows systems.

Required R packages can be installed using:

For CRAN packages:

install.packages("BiocManager::install("ADD_NAME_OF_THE_PACKAGE")

R packages from Bioconductor can be installed with the following commands:

if (!require("BiocManager", quietly = TRUE))

    install.packages("BiocManager")

BiocManager::install("ADD_NAME_OF_THE_PACKAGE")

To run the code, the correct working directory containing the input data must be specified at the beginning of the R-scripts, otherwise the scripts can be run as-is.

The scripts should be run in the following order:

quality_control_clustering.R

differential_expression.R

sex_interaction_analysis.R

pathway_enrichment.R

grn_analysis.R

deg_overlap_analysis.R

longitudinal_analysis.R