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This repo accompanies the Soelter et al., 2024 manuscript "Altered Glia-Neuron Communication in Alzheimer’s Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq"

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README

2024-06-12

Altered Glia-Neuron Communication in Alzheimer’s Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq

Peer-reviewed manuscript:
DOI

Pre-print:
DOI:10.1101/2023.11.29.569304

Authors

Tabea M. Soelter, Timothy C. Howton, Amanda D. Clark, Vishal H. Oza, Brittany N. Lasseigne

The University of Alabama at Birmingham, Heersink School of Medicine, Department of Cell, Developmental and Integrative Biology

Project Overview

We used publicly available snRNA-seq AD data (Morabito et al., 2021) generated from postmortem human PFC to study altered glia-neuron interactions and their downstream effects in AD. We inferred differential CCC interactions between astrocytes, microglia, oligodendrocytes, or OPCs (sender cell types) and inhibitory or excitatory neurons (receiver cell types). We also investigated whether CCC shows high similarity across cell types by calculating the Jaccard Similarity Index (JI) of ligands, receptors, and target genes across cell types. Since CCC inference methodologies are known to produce false positives, we validated our interactions using two independent human PFC AD snRNA-seq datasets (Lau et al., 2020 & Sadick et al., 2022). We further investigated the resulting high-confidence ligand-receptor pairs from both data sets, their predicted downstream target genes, and signaling modulators through transcription factor (TF) and canonical signaling pathway activity.

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Datasets

The datasets used in this study can be found on GEO:

  1. Morabito et al., 2021
  2. Lau et al., 2020
  3. Sadick et al., 2022

Scripts

Data Alignment (Cell Ranger):

## src/cellranger/
## +-- geo
## |   +-- SAMN19128593_S19_CTRL.sh
## |   +-- SAMN19128594_S18_CTRL.sh
## |   +-- SAMN19128595_S17_CTRL.sh
## |   +-- SAMN19128596_S16_CTRL.sh
## |   +-- SAMN19128597_S15_CTRL.sh
## |   +-- SAMN19128598_S14_CTRL.sh
## |   +-- SAMN19128599_S13_AD.sh
## |   +-- SAMN19128600_S12_AD.sh
## |   +-- SAMN19128601_S11_AD.sh
## |   +-- SAMN19128602_S10_AD.sh
## |   +-- SAMN19128603_S9_AD.sh
## |   +-- SAMN19128604_S8_AD.sh
## |   +-- SAMN19128605_S6_AD.sh
## |   +-- SAMN19128606_S5_AD.sh
## |   +-- SAMN19128607_S4_AD.sh
## |   +-- SAMN19128608_S3_AD.sh
## |   +-- SAMN19128609_S2_CTRL.sh
## |   +-- SAMN19128610_S1_CTRL.sh
## |   \-- SAMN19128611_S7_AD.sh
## \-- gse
##     +-- GSE157827_all_array.sh
##     +-- id_sheet.csv
##     \-- sample_sheet.csv

Ambient RNA removal (SoupX):

## src/soupX/
## \-- 01_ambientRNA_removal.Rmd

Pre-processing (Seurat):

## src/seurat_preprocessing/
## +-- 01_geo_seurat_preprocessing.Rmd
## +-- 02_gse_seurat_preprocessing.Rmd
## \-- 03_sadick_seurat_preprocessing.Rmd

Cell-cell communication inference (MultiNicheNet) and JI calculations:

## src/ccc/
## +-- 01_differential_ccc_multinichenet.Rmd
## +-- 02_jaccard_index_geo.Rmd
## +-- 03_jaccard_index_gse.Rmd
## \-- 04_gene_regulatory_networks.Rmd

Biological Activity (decoupleR):

## src/biological_activity/
## +-- 01_pseudobulk_dea.Rmd
## +-- 02_tf_activity.Rmd
## \-- 03_pathway_activity.Rmd

Manuscript figures:

## src/manuscript_figures/
## +-- figure_2.Rmd
## +-- figure_3.Rmd
## +-- figure_4.Rmd
## +-- figure_5.Rmd
## +-- figure_S3.Rmd
## +-- figure_S4.Rmd
## +-- figure_S5.Rmd
## +-- figure_S6.Rmd
## +-- figure_S7.Rmd
## \-- figure_S8.Rmd

Code and Data Availability

Data: DOI
Docker Images: DOI
GitHub Repository: DOI

Docker

We performed all analyses in docker with R version 4.1.3. The repository with all the Docker images used for this project can be found on Docker Hub at tsoelter/rstudio_ccc_ad. While individual docker image tags are noted in every script, we provide an overview of analyses and their associated tags below:

Tag Associated Analyses
1.0.1 Pre-processing of Morabito et al., 2021
1.0.3 Pre-processing of Lau et al., 2020, CCC, and JI
1.0.5 GRNs, pseudo-bulking, DEA, biological activity, and plotting
1.0.6 Pre-processing of Sadick et al., 2022

Lasseigne Lab

What is Happening in the Lasseigne Lab?

Funding

This work was supported in part by the UAB Lasseigne Lab funds, the NIA R00HG009678-04S1, the Alzheimer’s of Central Alabama Lindy Harrell Predoctoral Scholar Program.

Acknowledgements

We would also like to thank the members of the Lasseigne Lab, specifically Jordan H. Whitlock, Emma F. Jones, and Elizabeth J. Wilk for their valuable input throughout this study.

License

MIT License