snRNAseq- Neuroectodermal IL-12R signaling in neuroinflammation/EAE
We performed single-nucleus RNA sequencing (Chromium Next GEM Single Cell 3’ Reagent Kits v3.1 protocol, 10X Genomics) of FANS-isolated Hoechst+ nuclei from the cerebellum, brainstem and cervical spinal cord of NestinCre/+Il12rb2fl/fl mice and their Il12rb2fl/fl littermates at the steady-state and onset of clinical EAE symptoms (10 dpi) . We performed two independent sequencing experiments and successfully sequenced 5 samples. The first experiment included two samples: Il12rb2fl_naive, NestinCre_Il12rb2fl_naive. The second experiment included three samples: Il12rb2fl_EAE, NestinCre_Il12rb2fl_EAE and Il12rb2_naive_AUG. Sample Il12rb2_naive_AUG was only an internal control to assess reproducibility and batch effects between the two sequencing experiments and is excluded from the final visualizations displayed in our manuscript. After quality control and doublet exclusion, snRNA-seq yielded a total of 87,076 single-nucleus transcriptomic profiles, among which 22,018 distinct genes were detected. Unsupervised clustering of these data identified 16 clusters that were assigned to diverse neuronal, glial and other cell types on the basis of known lineage marker genes. This allowed us to assess how the transcriptional profile of the neuroectoderm differed in the presence or absence of IL-12 receptor signaling by interrogating differentially expressed genes (DEGs). This revealed pronounced alterations particularly in excitatory neurons, granule cells, mature oligodendrocytes (MOLs) and myelin forming oligodendrocytes (MFOLs1) suggesting a specific/concentrated action of IL-12 on these populations in the inflamed CNS. Our data suggest IL-12 to be critically involved in neuroprotection and shaping the trophic factor milieu within the inflamed CNS. Conversely, dysregulation or loss of trophic factor release by neurons in the absence of IL-12 may further propagate the degeneration of neurons, oligodendrocytes and possibly other cell types. Sequencing raw data and processed gene expression data will be deposited into the GEO repository upon publication. Code for analyses will be available upon contacting the corresponding authors: firstname.lastname@example.org and email@example.com
Steps to reproduce
CellRanger software (v6.0.2) was implemented for library demultiplexing, barcode processing, fastq file generation, gene alignment to the mouse genome (GENCODE reference build GRCh38.p6 Release M23), and unique molecular identifier (UMI) counts. We implemented the “include-introns” option for counting intronic reads, as the snRNA-seq assay captures unspliced pre-mRNA as well as mature mRNA. For each sample, a CellRanger report was obtained with all the available information regarding sequencing and mapping parameters. All samples were merged into a matrix using CellRanger (cellranger -aggr function). Starting from the filtered gene-cell count matrix produced by CellRanger’s in-built cell calling algorithms, we proceeded with the SCANPY workflow in Python. All downstream analyses, quality control (QC) processing, graph-based clustering, visualizations and differential gene expression analyses of the snRNA-seq data were performed using SCANPY (v1.8.2.), Python. We followed the same analysis workflow and parameters implemented for the snRNA-seq dataset of Schneeberger et al., bioRxiv, 2021. In brief, for each dataset, we filtered out potentially low-quality nuclei as follows; we excluded genes expressed in less than 3 nuclei and nuclei expressing less than 200 genes or less than 500 or more than 30,000 UMIs and nuclei with more than 5% mitochondrial reads. All remaining variable genes were used for downstream analyses. Again, we used the snRNAseq analysis pipeline of Schneeberger et al. as a reference. We normalized UMI counts utilizing the “LogNormalize” method and by applying a scale factor of 10,000. We found variable genes using “FindVariableFeatures” with the selection method “vst”. In addition, data regression was performed using the ScaleData function with nUMI, percent mitochondrial counts, and sample origin as confounding factors. Dimensionality reduction was performed using PCA and we selected 40 PCs based on Elbow plot. The FindClusters function, which implements shared nearest neighbor (SNN) modularity optimization-based clustering algorithm was applied with a resolution of 0.8 and identified in 32 initial clusters. A further dimensionality reduction step was carried out, using the UMAP algorithm to visualize the data. The UMAP parameters were: n.neighbors = 20, min.dist = 0.35, n.epochs = 500, spread = 2. For assigning clusters to cell types, we used, the “FindAllMarkers” function with default parameters was used, identifying negative and positive markers for that cluster. The assignment of cell type identity to clusters was based on known linage markers in line with previously published snRNAseq (Kozareva et al, Nature, 2021) studies and atlases (Zeisel et al, Cell 2018; Saunders et al, Cell, 2018). For cell doublet identification Scrublet was applied with default parameters.