Data to create figure 3: Comparing independent datasets using SCA pseudo-bulk. A) Pearson similarity matrix generated comparing RNA-5c (X1-5) and RNA-3c (Y1-4) clusters, together with the bulk cell lines transcriptome. Black arrows associate clusters on the basis higher similarity depicted among clusters. B) Seaurat integration table. On the colums are shown the integration clusters and on the rows the number of cells from each RNA-5c and RNA-3c cluster present in the integration clusters. C) UMAP plot of the Seurat integrated clusters. Cell line association is given by the hierarchical clustering shown in Figure 2. Y1 cell line association is indicated with a questin mark since by Figure 2D, Y1 seems to be associated to H1975, as instead by seurat integration and SCA pseudo bulk Y1 is more similar to HCC827 than to H1975Figure 3A somewhere_in_your_computer/fig3/psbulk_integration/old_psAE/c5c3.pngFigure 3Csomewhere_in_your_computer/fig3/seurat_integration/Rplots.pdf

processed_data.tar.gz contains all files from the data directory associated with the 230418_TS_AgingCCC GitHub project and includes the following:

• CellRangerCounts/

• post_soupX/ : contains 12 directories for 12 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but the post_soupX directory contains one directory for each of the 12 samples:

• S01_6m_AD/

• barcodes.tsv

• genes.tsv

• matrix.mtx

• pre_soupX/ : contains 12 directories for 12 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this directory contains 1 directory for each individual sample:

• S01_6m_AD/outs/

• filtered_feature_ bc_matrix.h5

• raw_feature_bc_matrix.h5

• PANDA_inputs/

• PANDA_exp_files_array.txt: Text files with files paths to expression inputs for PANDA gene regulatory networks.

• mm10_TFmotifs.txt: Mouse TF motif input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023

• mm10_ppi.txt: Mouse protein-protein interaction information from SringDB input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023

• ccc/

• nichenet_v2_prior/

• gr_network_mouse_21122021.rds : accessed in December 2023, gene regulation network – gene regulatory information from MultiNicheNet

• ligand_target_matrix_nsga2r_final_mouse.rds: accessed in December 2023, ligand target matrix for mouse from MultiNicheNet.

• ligand_tf_matrix_nsga2r_final_mouse.rds: accessed in December 2023, mouse ligand tf matrix for signaling path determination from MultiNicheNet

• lr_network_mouse_21122021.rds : accessed in December 2023, ligand-receptor matrix from MultiNicheNet

• signaling_network_mouse_21122021.rds : accessed in December 2023, signaling network – protein-protein interaction information from MultiNicheNet for mouse

• weighted_networks_nsga2r_final_mouse.rds : accessed in October 2023, networks weighted by literature evidence from MultiNicheNet for mouse

• multinichenet_output.rds : MultiNicheNet output for 3xTg-AD snRNA-seq data

• 12m_signaling_igraph_objects.rds : list of igraph objects for 93 LRTs and their signaling mediators at 12 months

• 6m_signaling_igraph_objects.rds :list of igraph objects for 2 LRTs and their signaling mediators at 6 months

• elisa/: CSV files of measured OD values for every ELISA.

• 240319_ELISA_Ab40.csv: OD measurements for Ab40

• 240319_ELISA_Ab42.csv: OD measurements for Ab42

• 240319_ELISA_total_tau.csv: OD measurements for Total Tau

• panda/: PANDA gene regulatory networks for each time point and condition in excitatory and inhibitory neurons. Used for differential gene targeting.

• excitatory_neurons_AD12.Rdata

• excitatory_neurons_AD6.Rdata

• excitatory_neurons_WT12.Rdata

• excitatory_neurons_WT6.Rdata

• inhibitory_neurons_AD12.Rdata

• inhibitory_neurons_AD6.Rdata

• inhibitory_neurons_WT12.Rdata

• inhibitory_neurons_WT6.Rdata

• pseudobulk/: includes pseudo bulk matrices for every cell type which were used for downstream analyses. Each matrix also includes metadata information on condition and time point.

• all_counts_ls.rds: List of all the pseudo bulk matrices (below).

• astrocytes.rds: pseudobulk matrix for astrocytes. Include time point and condition information for downstream analyses.

• endothelial_cells.rds: pseudobulk matrix for endothelial cells. Include time point and condition information for downstream analyses.

• ependymal_cells.rds: pseudobulk matrix for ependymal cells. Include time point and condition information for downstream analyses.

• excitatory_neurons.rds: pseudobulk matrix for excitatory neurons. Include time point and condition information for downstream analyses.

• fibroblasts.rds: pseudobulk matrix for fibroblasts. Include time point and condition information for downstream analyses.

• inhibitory_neurons.rds: pseudobulk matrix for inhibitory neurons. Include time point and condition information for downstream analyses.

• meningeal_cells.rds: pseudobulk matrix for meningeal cells. Include time point and condition information for downstream analyses.

• microglia.rds: pseudobulk matrix for microglia. Include time point and condition information for downstream analyses.

• oligodendrocytes.rds: pseudobulk matrix for oligodendrocytes. Include time point and condition information for downstream analyses.

• opcs.rds: pseudobulk matrix for oligodendrocyte progenitor cells. Include time point and condition information for downstream analyses.

• percicytes.rds: pseudobulk matrix for pericytes. Include time point and condition information for downstream analyses.

• rgcs.rds: pseudobulk matrix for retinal ganglion cells. Include time point and condition information for downstream analyses.

• pseudobulk_split/: Includes pseudo bulk count matrices split by time point and condition. Used for input to PANDA for gene regulatory network construction.

• excitatory_neurons_AD12.Rdata

• excitatory_neurons_AD6.Rdata

• excitatory_neurons_WT12.Rdata

• excitatory_neurons_WT6.Rdata

• inhibitory_neurons_AD12.Rdata

• inhibitory_neurons_AD6.Rdata

• inhibitory_neurons_WT12.Rdata

• inhibitory_neurons_WT6.Rdata

• seurat_preprocessing/

• filtered_seurat.rds : merged and filtered seurat object

• integrated_seurat.rds : seurat object integrated using harmony

• clustered_seurat.rds : clustered seurat object

• processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution

Raw data publicly available on GEO under series accession: GSE261596

Data used to build figure 2: Assignment of cell line type to clusters generated with Seurat, implemented in rCASC. A) RNA-5c clustering, five clusters generated with Seurat (resolution=0.1), using 2500 genes seected as the most variant within the 5000 most expressed (rCASC topx function). . B) RNA-3c clustering, four clusters generated with Seurat (resolution=0.1), using the 2500 genes selected for RNA-5c. C) RNA-5c hierarchical clustering (Euclidean distance, average linkage) of log2 CPM clusters’ pseudo-bulk expression (rCASC bulkClusters function), row-mean centered, and CCLE lung cell lines A449, NCIH838, NCIH2228, NCIH1975 and HCC827 log2 TPM row-mean centered. D) RNA-3c hierarchical clustering (Euclidean distance, average linkage) of log2 CPM clusters’ pseudo-bulk expression (rCASC bulkClusters function), row-mean centered, and CCLE lung cell lines A449, NCIH838, NCIH2228, NCIH1975 and HCC827 log2 TPM row-mean centered.Figure 2Asomewhere_in_your_computer/fig2/RNA2500-5c/VandE/Results_0.1/VandE/5/VandE_Stability_Plot.pdfFigure 2Bsomewhere_in_your_computer/fig2/RNA2500-3c/VandE/Results/VandE/4/VandE_Stability_Plot.pdf

Additional file 1. Sc-RNAseq information table. Data pertaining to the Seurat analysis of the scRNA-seq data contained in Fig. 1. Tables include samples basics and Seurat analysis parameters, cluster barcodes, results of differential gene expression analysis for each cluster, pseudobulk analysis and the foam cell gene signature.