2024-04-29
Altered Glia-Neuron Communication in Alzheimer’s Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq
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
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.
The datasets used in this study can be found on GEO:
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
Data:
Docker Images:
GitHub Repository:
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 |
What is Happening in the Lasseigne Lab?
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.
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.
