DOI: 10.5061/dryad.z612jm6pw
title: Single-cell spatial transcriptomics of ACAN cKO in WT and 5xFAD mice

description: This dataset comprises results from one single-cell spatial experiment
 conducted on mouse brains. This experiment was performed using the Bruker
 Nanostring CosMx technology on 10 µm coronal brain sections from
 8-month-old female WT, WT ACAN cKO, 5xFAD, and 5x ACAN cKO mice. The
 dataset is provided as two separate RDS files split by flowcell which
 include raw and corrected counts for the RNA data, along with
 comprehensive metadata. Metadata includes mouse genotype, sample ID, cell
 type annotations, and X-Y coordinates of each cell.
description: Sample preparation: Isopentane fresh-frozen brain hemispheres
 were embedded in optimal cutting temperature (OCT) compound (Tissue-Tek,
 Sakura Fintek, Torrance, CA), and 10 µm 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 transcriptomic
 analysis, n=3 mice per genotype were used for both the 5xFAD and 5xFAD
 ACAN cKO groups, while n=4 for WT and n=2 for WT ACAN cKO. Tissues were
 processed according to the Nanostring CosMx fresh-frozen slide preparation
 manual for RNA assay (NanoString University). Data
 processing: 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. 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 38 clusters. Clusters
 were manually annotated based on gene expression and spatial
 location.
description: # Single-cell spatial transcriptomics of ACAN cKO in WT and 5xFAD mice
 Dataset DOI: [10.5061/dryad.z612jm6pw](10.5061/dryad.z612jm6pw) ##
 Description of the data and file structure Due to the large file size, the
 R object has been split by flowcell into two separate files
 (5xACANcKO_RNA_slide1.rds and 5xACANcKO_RNA_slide2.rds). The two files
 should be loaded into the R workspace and combined using the merge()
 function. For merging and downstream analysis, we recommend using a high
 performance computing system and at least 64GB of RAM for optimal
 performance. Data were analyzed using the R package Seurat. Sample
 metadata are stored in `seurat@meta.data`. ### Files and variables
 Single-cell spatial transcriptomics dataset Rownames of metadata (accessed
 using rownames(`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) * reannotation: Manual cell type annotation based on marker
 genes and location in space * reannotation_broad: Broad cell type
 annotation (e.g., all excitatory neurons grouped together) * group:
 Genotype name * sample_n: Sample name