DOI: 10.6084/m9.figshare.27643794
title: snRNA-seq, WT vs. Gpnmb-perturbed GL261 tumors (Savage et al. 2025)

description: <b>Summary.</b><br>Single nucleus RNA sequencing (snRNA-seq; sci-RNA-seq3 protocol) was used to profile the in vivo brains from mice engrafted with parental or Gpnmb-perturbed GL261 tumors.<br>Data were preprocessed as described below and only myeloid and tumor cells were retained in the resulting Seurat object. <b>Data Format.</b><br>Data is provided as preprocessed dataset, stored in Seurat Object.<br><br><b>Sample processing, sci-RNA-seq3 library generation, and sequencing</b><br>Cells were harvested with 0.25% typsin-EDTA and neuron dissociation solution (Tian et al., 2019), respectively. Cell pellets were immediately snap-frozen in liquid nitrogen and then stored at -80°C for sci-RNA-Seq3-based single-nucleus RNA-Seq processing.<br><br>Samples from all conditions were processed together to minimize batch effects. Nuclei extraction and fixation were performed as previously described (Cao et al., 2019), except for the use of a modified CST lysis buffer (Slyper et al., 2020) plus 1% SUPERase In RNase Inhibitor (AM2696). Nuclei quality was checked with DAPI and Wheat Germ Agglutinin (WGA) staining. Sci-RNA-Seq3 libraries were generated as previously described (Cao et al., 2019) using three-level combinatorial indexing. The final libraries were sequenced on an Illumina NovaSeq 6000 using the following protocol: read 1: 34bp, read 2: 69bp, index 1: 10bp, index 2: 10bp.<br><br>Raw sequencing reads were first demultiplexed based on i5/i7 PCR barcodes. FASTQ files were then processed using the sci-RNA-Seq3 pipeline (Cao et al., 2019). After barcodes and UMIs were extracted from the read1 FASTQ files, read alignment was performed using the STAR short-read aligner (v2.5.2b) with the mouse genome (hg38) and Gencode v25 gene annotations. After removing duplicate reads based on UMI, barcode, chromosome and alignment position, reads are summarized into a count matrix of M genes x N nuclei.<br><br><b>Filtering</b><br>Raw single-cell gene count matrices were loaded into a Seurat object (version 4.0.4) (Butler et al., 2018; Hao et al., 2021; Satija et al., 2015; Stuart et al., 2019) and filtered to retain cells with (i) 200 – 9000 recovered genes per cell, (ii) less than 60% mitochondrial content, and (iii) unmatched rate within 3 median absolute deviations of the median.<br><br><b>Normalization</b><br>To normalize expression values, we adopted the modeling framework previously described and implemented in the sctransform R Package (version 0.3.2) (Hafemeister & Satija, 2019). In brief, count data were modelled by regularized negative binomial regression, using sequencing depth as a model covariate to regress out the influence of technical effects, and Pearson residuals were used as the normalized and variance stabilized biological signal for downstream analysis.<br><br><b>Integration</b><br>Cells from each treatment condition and differentiation day ere integrated in Seurat using the reciprocal principal component analysis-based approach, using the top 3000 variable features.<br><br><b>Dimensional reduction</b><br>PCA was applied to normalized and scaled data, and the top components (accounting for 90% of variance observed in the first 50 PCs) were used for UMAP embedding using RunUMAP(max_components = 2, n_neighbours = 50, min_dist = 01, metric = cosine) in Seurat.<br><br><b>Clustering</b><br>To identify clusters, we performed Louvain clustering in Seurat using the FindClusters function.<br><b>Contact</b><br>Contact Dr. Nicholas Mikolajewicz regarding any questions about the data or analysis (n.mikolajewicz@utoronto.ca)