lasseignelab/230418_TS_AgingCCC: Evaluation of altered cell-cell communication between glia and neurons in the hippocampus of 3xTg-AD mice at two time points

The 230418_TS_AgingCCC GitHub project includes the following directories and files:

• bin/

• docker/

• Dockerfile: The original dockerfile used to build the initial docker image. Future images were pulled from this initial docker image. 

• Rstudio-prefs.json: JSON file with information on RStudio preferences.

• data/: Contains all processed data generated during this project. The GitHub repository includes a file tree that showcases all files within this directory. Additionally, the processed data is deposited on Zenodo. The processed data Zenodo has descriptions of all files.

• doc/: Contains any files that were downloaded/accessed for this study. The GitHub repository contains a README with detailed information. 

• HP_ALZHEIMER_DISEASE.v2023.2.Hs.tsv: HP AD gene set downloaded from MSigDB. Accessed on 240126.

• KEGG_ALZHEIMERS_DISEASE.v2023.2.Hs.tsv: KEGG AG gene set downloaded from MSigDB. Accessed on 240126.

• README: README contains file-specific information in this directory.

• ad_gene_list.csv: GWAS AD gene set from Bellenguez et al. 2022

• ad_genes_mm.rds: AD gene set compiled from HP, KEGG, and GWAS. Converted human gene names to mouse.

• collectri_prior_mm.csv: Mouse CollecTRI prior for TF activity analyses. Accessed on 240208.

• g2m_genes_mm.rds: cell cycle genes converted from human to mouse for Seurat pre-processing. Genes were converted on 231219.

• s_genes_mm.rds: cell cycle genes converted from human to mouse for Seurat pre-processing. Genes were converted on 231219.

• sample_sheet.rds: document with time point and condition information for each sample.

• results/

• final_outputs/: includes all final outputs. These can be accessed through the GitHub repository or seen in the paper.

• 01_figures: Includes all final figures

• 02_seurat: Includes individual plots of final figure 2.

• 03_ccc: Includes individual plots of final figures for CCC and JI analyses.

• 04_dea: Includes individual plots of final figure 4 (DEA and FEA)

• 05_bio_activity: Includes files used to plot Figure 6 heatmap.

• 06_gene_targeting: Includes individual plots of final Figure 6.

• 07_protein_quant: Includes final box plots of figure S1

• intermediate_outputs/

• 01_soupX: includes rho density plots.

• 02_seurat: Includes intermediate and QC plots.

• 03_dea: includes CSV files with DEA results for every cell type by time point. There is a file for all genes and another file for significant genes. These DEA outputs were generated using the apeglm shrinkage. Below is one cell type to illustrate what should be seen for all twelve cell types:

• astrocytes_group_12mAD_vs_12mWT_all_genes.csv

• astrocytes_group_12mAD_vs_12mWT_sig_genes.csv

• astrocytes_group_6mAD_vs_6mWT_all_genes.csv

• astrocytes_group_6mAD_vs_6mWT_sig_genes.csv

• 04_ccc: Includes individual plots of final figure 3.

• 05_grn: 

• 12m/: Includes pngs of 93 signaling networks of predicted ligand-receptor-target pairings.

• 6m/: Includes pngs of two signaling networks of predicted ligand-receptor-target pairings.

• igraph_objects_list.rds: List of all igraph objects.

• signaling_grn_properties_12m.rds: network topology metrics for each GRN of every ligand-receptor-target pair at 12 months.

• signaling_grn_properties_6m.rds: network topology metrics for each GRN of every ligand-receptor-target pair at 6 months.

• 06_bio_activity: includes CSV files with DEA results for every ezcitatory and inhibitory neurons by time point. These DEA outputs were generated with the normal shrinkage to preserve the Wald statistic for TF activity analyses. There is a file for all genes and another file for significant genes.

• excitatory_neurons_group_12mAD_vs_12mWT_all_genes.csv

• excitatory_neurons_group_12mAD_vs_12mWT_sig_genes.csv

• excitatory_neurons_group_6mAD_vs_6mWT_all_genes.csv

• excitatory_neurons_group_6mAD_vs_6mWT_sig_genes.csv

• inhibitory_neurons_group_12mAD_vs_12mWT_all_genes.csv

• inhibitory_neurons_group_12mAD_vs_12mWT_sig_genes.csv

• inhibitory_neurons_group_6mAD_vs_6mWT_all_genes.csv

• inhibitory_neurons_group_6mAD_vs_6mWT_sig_genes.csv

• 07_gene_targeting: 

• ad_enriched.rds: AD enriched gene targeting output.

• filtered_targeting_ad.rds: AD enriched gene targeting filtered by quartiles.

• 08_protein_quant: 

• ab40_fitted_curve.png: fitted 4PL curve for Ab40

• ab40_point_plot.png: plot of OD and concentration values for Ab40 

• ab42_fitted_curve.png: fitted 4PL curve for Ab42

• ab42_point_plot.png: plot of OD and concentration values for Ab42 

• tau_fitted_curve.png: fitted 4PL curve for Total Tau

• tau_point_plot.png: plot of OD and concentration values for Total Tau 

• src/

• bio_activity/

• 01_tf_activity.Rmd: Differential transcription factor activity analysis in 12m excitatory and inhibitory neurons.

• ccc/

• 01_differential_ccc.Rmd: Code to perform differential CCC between 3xTg-AD and WT mice at 6 and 12 months.

• 02_pathway_analysis_targets.Rmd: Code for pathway/overrepresentation analysis of predicted downstream target genes identified in 01_differential_ccc.Rmd

• 03_jaccard_similarity.Rmd: Code to calculate ligand, receptor, and target similarity between cell types.

• 04_grn.Rmd: Code to make signaling networks for every ligand-receptor-target pairing to identify signaling mediators. 

• cellranger/

• id_list.txt: List of output names for each sample.

• run_cellranger.sh: Array bash script code to run Cell Ranger for each sample.

• sample_list.txt: List of fastq prefixes for each sample.

• elisa/

• 01_protein_quantification.Rmd: Script to calculate concentration of measured OD values for Ab40, Ab42, and Total Tau ELISAs. Plots Figure S1.

• figures/

• figure2.Rmd: Code to make manuscript figure 2.

• figure3.Rmd: Code to make manuscript figure 3.

• figure4.Rmd: Code to make manuscript figure 4.

• figure5.Rmd: Code to make manuscript figure 5.

• figure6.Rmd: Code to make manuscript figure 6.

• figure_S2.Rmd: Code to make manuscript supplementary figure 2.

• figure_S3.Rmd: Code to make manuscript supplementary figure 3.

• figure_S4.Rmd: Code to make manuscript supplementary figure 4.

• figure_S5.Rmd: Code to make manuscript supplementary figure 5.

• gene_targeting/

• 01_input_preparation.Rmd: Code to create PANDA network gene expression input files.

• 02_PANDA.R: Code to create cell-type-specific PANDA gene regulatory networks. Submitted as an array bash job using 02_PANDA_array.sh

• 02_PANDA_array.sh: Bash array job to submit 02_PANDA.R

• 03_gene_targeting.Rmd: Code to calculate differential gene targeting of signaling mediators between 3xTg-AD and WT mice at 12 months.

• job_outs/

• Omnipathr-log/: OmnipathR log for CCC inference with MultiNicheNet

• PANDA/: directory which includes bash array error and output files from PANDA gene regulatory network construction. 

• pre_processing/: bash error and output files from 02_clustering.sh

• pseudobulk_dea/

• 01_pseudobulk_dea.Rmd: Code to generate cell-type specific count matrices and perform DEA between 3xTg-AD and WT samples at each time point using the apeglm shrinkage. 

• 02_dea_bio_activity.Rmd:  Code to perform DEA between 3xTg-AD and WT samples at 12 months using the normal shrinkage for TF activity analyses.

• seurat_preprocessing/

• 01_3xtgad_seurat_preprocessing.Rmd: Code to determine data quality by calculating quality control metrics and filtering accordingly.

• 02_3xtgad_clustering.R: Code to cluster at multiple resolutions to determine best resolution. Was submitted as a bash job using 02_3xtgad_clustering.sh

• 02_3xtgad_clustering.sh: Bash script used to submit 02_3xtgad_clustering.R

• 03_3xtgad_celltype_assignment.Rmd

• soupX/

• 01_ambient_RNA_removal.Rmd: Code to remove ambient RNA from all samples in the 3xTg-AD snRNA-seq dataset.

• functions_CCCin3xTgAD.R: Functions written and used for the majority of analyses by Tabea M. Soelter.

• functions_JW_PANDA.R: Functions written and used for PANDA gene regulatory network construction by Jordan H. Whitlock.

Raw data publicly available on GEO at series accession: GSE261596