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@MISC{Lee:279461,
      author       = {Lee, Jaehyun and Danzer, Karin M},
      title        = {{D}ataset: {V}isium {S}patial and sn{RNA} data of {B}rain
                      section from {P}arkinson {M}ouse {M}odel based on inducible
                      expression of human a-syn constructs: 20-months + sn{RNA} 23
                      months},
      publisher    = {Zenodo},
      reportid     = {DZNE-2025-00788},
      year         = {2025},
      abstract     = {Using 23-months old mice of a inducible expression of human
                      a-syn constructs based Parkinson mouse model, we produced a
                      single nucleus RNA dataset by cutting 0mm Bregma to -5mm
                      Bregma. The Chromium 3’ Single Cell Library Kit (10x
                      Genomics) was used and Sequencing was performed on a NovaSeq
                      6000. From the same model we also used 20-months old mice
                      with the Visium Spatial V1 platform (10x Genomics).
                      Sequencing was performed on a NovaSeq 6000. Both were PE150.
                      snRNA pipeline: For the alignment of reads, a custom
                      reference was created by adding the sequences of the V1S/SV2
                      transgene and the Camk2a promoter to the mm10 mouse
                      reference genome. Count matrices generated by cellranger
                      count 7.1 were loaded into an AnnData object and processed
                      using the Python-based framework Scanpy 1.10.2. Integration
                      with R, where needed, was facilitated through the rpy2
                      package. Raw count matrices were corrected for ambient RNA
                      contamination using the SoupX 1.6.2. To remove potential
                      doublets, scDblFinder 1.18.0 was employed with a fixed seed
                      (123). Nuclei with nUMI and nGenes values exceeding three
                      median absolute deviations (MADs) from the median were
                      excluded. Genes detected in fewer than five nuclei across
                      the dataset were excluded. The resulting dataset was
                      normalized via $scanpy.pp.normalize_total$ and
                      scanpy.pp.log1p. Highly variable genes were identified using
                      the function $scanpy.pp.highly_variable_genes$ with the
                      Seurat v3 flavor, selecting the top 4,000 genes.
                      Dimensionality reduction was performed using principal
                      component analysis (PCA) and batch effects were corrected
                      using the python-implemented version of Harmony via the
                      function $scanpy.external.pp.harmony_integrate.$ Harmony
                      embeddings were then used to construct a k-nearest neighbor
                      (kNN) graph with scanpy.pp.neighbors. Clustering was
                      performed using Leiden clustering with standard parameters
                      via the function scanpy.tl.leiden. Clusters were annotated
                      using literature, the mousebrain.org, and markers identified
                      via the FindConservedMarkers function in Seurat. First,
                      neurons and non-neuronal cells were distinguished using
                      mainly canonical markers, such as but not limited to Rbfox3
                      (neurons), Mbp (oligodendrocytes), Acsbg1 (astrocytes),
                      Pdgfra (oligodendrocyte precursor cells), Inpp5d
                      (microglia), Colec12 (vascular cells), and Ttr (choroid
                      plexus cells). Neurons were further classified into Vglut1
                      (Slc17a7), Vglut2 (Slc17a6), GABA (Gad2), cholinergic
                      (Scube1), and dopaminergic (Th) neurons. Vglut1 and GABA
                      neurons were further annotated into subtypes based on
                      subclustering and FindConservedMarkers markers. visium
                      spatial pipeline: Sequences were fiducially aligned to spots
                      using Loupe Browser ver. 8. All aligned sequences were
                      mapped using spaceranger count 3.0.1 with a custom refence,
                      which included sequences for the promotor and transgene
                      (Camk2aTTA, V1S/SV2) to the mouse genome mm39. We filtered
                      each sample of the Visium Spatial dataset based on the MAD
                      filtering of number of reads (nUMI), number of genes
                      (nGene), and percentage of mitochondrial genes (percent.mt).
                      A spot was filtered out if it was outside of 3x MAD value in
                      at least two metrics. Filtered samples were merged into one
                      Seurat 5.1.0 object and we obtained normalized counts by the
                      SCTransform function of Seurat. Integration was performed
                      using Harmony 1.2.0 on 50 PCA embeddings and clustering was
                      done using Leiden clustering based on 30 harmony embeddings.
                      Integrated clusters were visualized using the UMAP method.
                      Samples that were not successfully integrated (based on
                      similarity measures of the harmony embeddings) and showed
                      high percentage.mt or low nUMI levels compared to other
                      samples, were removed from subsequent analysis. A final
                      integration and clustering were performed after filtering.
                      Regions were first annotated based on a 0.1 resolution
                      clustering to get high level region annotation (Cortex,
                      Hippocampus, Subcortex). Each high-level region was further
                      annotated based on either more granular resolutions or
                      subclustering. Marker genes from mousebrain.org and
                      literature were used in combination with the Allen mouse
                      brain atlas to obtain anatomically relevant annotations.},
      cin          = {AG Danzer},
      cid          = {I:(DE-2719)5000072},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)32},
      doi          = {10.5281/zenodo.14988055},
      url          = {https://pub.dzne.de/record/279461},
}