DOI: 10.5061/dryad.m63xsj4ck
title: Single-cell spatial transcriptomics and proteomics of APOE Christchurch in 5xFAD and PS19 mice

description: This collection of datasets comprises results from four single-cell
 spatial experiments conducted on mouse brains: two spatial transcriptomics
 experiments and two spatial proteomics experiments. These experiments were
 performed using the Bruker Nanostring CosMx technology on 10µm coronal
 brain sections from the following mouse models: (1) 14-month-old
 male 5xFAD;ApoeCh mice and genotype controls, and (2) 9-month-old
 PS19;ApoeCh mice and genotype controls. Each dataset is provided as an RDS
 file which includes raw and corrected counts for the RNA data and mean
 fluorescent intensity for the protein data, along with comprehensive
 metadata. Metadata includes mouse genotype, sample ID, cell type
 annotations, sex (for PS19;ApoeCh dataset), and X-Y coordinates of each
 cell. Results from differential gene expression analysis for each cell
 type between genotypes using MAST are also included as .csv files.
description: <strong>Sample preparation: </strong>Isopentane
 fresh-frozen brain hemispheres were embedded in optimal cutting
 temperature (OCT) compound (Tissue-Tek, Sakura Fintek, Torrance, CA), and
 10µm thick coronal sections were prepared using a cryostat (CM1950,
 LeicaBiosystems, Deer Park, IL). Six hemibrains were mounted onto each VWR
 Superfrost Plus microscope slide (Avantor, 48311-703) and kept at -80°C
 until fixation. For both 5xFAD (14 months old, males) and PS19 (9 months
 old, females and 1 male <em>ApoeCh</em>) models, n=3 mice per
 genotype except for n=2 for PS19;<em>ApoeCh</em> (wild-type,
 <em>ApoeCh</em> HO, 5xFAD HEMI or PS19 HEMI, and 5xFAD HEMI;
 <em>ApoeCh</em> HO or PS19 HEMI;<em>ApoeCh</em>
 HO) were used for transcriptomics and proteomics. The same mice were used
 for both transcriptomics and proteomics. Tissues were processed according
 to the Nanostring CosMx fresh-frozen slide preparation manual for RNA and
 protein assays (NanoString University).
 <strong>Data processing: </strong>Spatial
 transcriptomics datasets were filtered using the AtoMx<sup>
 </sup>RNA Quality Control module to flag outlier negative probes
 (control probes targeting non-existent sequences to quantify non-specific
 hybridization), lowly-expressing cells, FOVs, and target genes. Datasets
 were then normalized and scaled using Seurat 5.0.1
 <em>SCTransform</em> to account for differences in library
 size across cell types [31]. Principal component analysis (PCA) and
 uniform manifold approximation and projection (UMAP) analysis were
 performed to reduce dimensionality and visualize clusters in space.
 Unsupervised clustering at 1.0 resolution yielded 33 clusters for the
 5xFAD dataset and 40 clusters for the PS19 dataset. Clusters were manually
 annotated based on gene expression and spatial location.
 Spatial proteomics data were filtered using the AtoMx Protein
 Quality Control module to flag unreliable cells based on segmented cell
 area, negative probe expression, and overly high/low protein expression.
 Mean fluorescence intensity data were hyperbolic arcsine transformed with
 the AtoMx Protein Normalization module. Cell types were automatically
 annotated based on marker gene expression using the CELESTA
 algorithm.
description: # Single-cell spatial transcriptomics and proteomics of APOE Christchurch
 in 5xFAD and PS19 mice
 [https://doi.org/10.5061/dryad.m63xsj4ck](https://doi.org/10.5061/dryad.m63xsj4ck) ## Description of the data and file structure We have submitted all processed RDS files (5xApoeCh_Protein_annotated_seurat.rds, PS19ApoeCh_Protein_annotated_seurat.rds, 5xApoeCh_RNA_annotated_seurat.rds, PS19ApoeCh_RNA_annotated_seurat.rds) analyzed using the R package Seurat. Sample metadata are stored in [seurat@meta.data](mailto:seurat@meta.data) and organized in the same way for the 5xFAD and PS19 cohorts. For spatial proteomics, we have included the .csv files containing the parameters used to perform automated cell typing with the CELESTA algorithm (mouse_signature_matrix.csv, mouse_tuning_params.csv). Finally, we have submitted a .zip file containing the outputs from differential gene expression analysis (DGE_files.zip). ### Files and variables #### **Single-cell spatial proteomics datasets** ###### **5xApoeCh\_Protein\_annotated\_seurat.rds, PS19ApoeCh\_Protein\_annotated\_seurat.rds** Rownames of metadata (accessed using rownames([seurat@meta.data](mailto:seurat@meta.data))) contain unique identifiers for each single cell, formatted as c_[slide]*[fov]*[cell]. Additional relevant metadata columns are described below: * fov: Field Of View (FOV) the cell is in * Area: Number of pixels assigned to a given cell * AspectRatio: Width divided by height  * x_FOV_px: x position of the cell center within the FOV, measured in pixels * y_FOV_px: y position of the cell center within the FOV, measured in pixels * Width: Cell’s maximum length in x dimension (pixels) * Height: Cell’s maximum length in y dimension (pixels) * Mean.DAPI: Mean fluorescence intensity within a given cell (AU) * Max.DAPI: Max fluorescence intensity within a given cell (AU) * Run_Tissue_name: Flowcell name * slide_ID_numeric: SlideID * x_slide_mm: x position of the cell center within the slide, measured in mm * y_slide_mm: y position of the cell center within the slide, measured in mm * nCount_RNA: Mean fluorescence intensity ("RNA" is a misnomer) * nFeature_RNA: Number of unique proteins detected ("RNA" is a misnomer) * nCount_negprobes: Number of Negative counts * nFeature_negprobes: Number of unique Negative targets * Area.um2: Area of cell (um^2) * celesta_R1: Cell type annotations from round 1 of CELESTA cell typing * celesta_R2: Cell type annotations from round 2 of CELESTA cell typing (All microglia are further classified into DAM or homeostatic) * celesta_final: Final annotations from CELESTA * celesta_broad: Broad cell type annotations (e.g., all neurons grouped together) * celesta_cell_type_n: Number associated with cell type (from mouse_signature_matrix.csv) #### ***mouse\_signature\_matrix-updated.csv*** User-defined cell-type signature matrix. (1) The first column has to contain the cell types to be inferred (2) The second column has the lineage information for each cell type. The lineage information has three numbers connected by “_” (underscore). The first number indicates round. Cell types with the same lineage level are inferred at the same round. The increasing number indicates increased cell-type resolution. Here, All_Microglia -&gt; DAM and Microglia (homeostatic) (3) Starting from column three, each column is a protein marker. If the protein marker is known to be expressed for that cell type, then it is denoted by “1”. If the protein marker is known to not express for a cell type, then it is denoted by “0”. If the protein marker is irrelevant or uncertain to express for a cell type, then it is left blank. For example, CD11c is expressed in some but not all microglia, so is left blank for All_Microglia.  #### **Single-cell spatial transcriptomics datasets** ###### **5xApoeCh\_RNA\_annotated\_seurat.rds, PS19ApoeCh\_RNA\_annotated\_seurat.rds** Rownames of metadata (accessed using rownames([seurat@meta.data](mailto:seurat@meta.data))) contain unique identifiers for each single cell, formatted as c_[slide]*[fov]*[cell]. Additional metadata columns are described below: * fov: Field Of View (FOV) the cell is in * Area: Number of pixels assigned to a given cell * AspectRatio: Width divided by height * x_FOV_px: x position of the cell center within the FOV, measured in pixels * y_FOV_px: y position of the cell center within the FOV, measured in pixels * Width: Cell’s maximum length in x dimension (pixels) * Height: Cell’s maximum length in y dimension (pixels) * Mean.DAPI: Mean fluorescence intensity within a given cell (AU) * Max.DAPI: Max fluorescence intensity within a given cell (AU) * Run_Tissue_name: Flowcell name * slide_ID_numeric: SlideID * x_slide_mm: x position of the cell center within the slide, measured in mm * y_slide_mm: y position of the cell center within the slide, measured in mm * nCount_RNA: Number of RNA counts * nFeature_RNA: Number of unique RNA targets * nCount_negprobes: Number of Negative counts * nFeature_negprobes: Number of unique Negative targets * Area.um2: Area of cell (um^2) * annotation: Manual cell type annotation based on marker genes and location in space * annotation_broad: Broad cell type annotation (e.g., all excitatory neurons grouped together) * group: Genotype name * sample_n: Sample name * sex: Sample sex #### ***DGE\_files.zip*** The .zip file contains output from differential gene expression analysis with the MAST test ([https://github.com/RGLab/MAST](https://github.com/RGLab/MAST)) using the FindMarkers() function. Files are organized by mouse data (single-cell spatial transcriptomics) and human data (snRNA-seq). Each filename contains metadata on the specific comparison and the cell type that was analyzed (eg. "5xApoeCh_vs_5xFAD_All_microglia.csv" contains the DGE results from the 5xFAD;ApoeCh vs. 5xFAD comparison for all microglia grouped together, including homeostatic and disease-associated microglia). Column names in each .csv file are described below: * p_val: Raw p-value to determine if there is a significant difference between ident1 and ident2. * avg_logFC: log fold-change of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group. If the slot is scale.data or a reduction is specified, the average difference is returned instead of the log fold change, and the column is named "avg_diff". * pct.1: The percentage of cells where the gene is detected in the first group * pct.2: The percentage of cells where the gene is detected in the second group * p_val_adj: Adjusted p-value, based on Bonferroni correction using all genes in the dataset * gene: Names of the genes analyzed in the DGE analysis * group: Specific cell type being analyzed in the DGE analysis (should match the file name) * ident1: Represents the first genotype in the analysis. The direction of avg_logFC or avg_diff values indicates expression in this genotype relative to ident2 * ident2: Represents the second genotype in the analysis, serving as the comparison baseline for ident1