Amodio_Infertility_2023
Amodio G, Giacomini G, Boeri L, et al. Specific types of male infertility are correlated with T cell exhaustion or senescence signatures
Single-Cell RNA Sequencing Analysis
scRNAseq (from 10X Genomics) analysis of CD3+ T cells purified from the peripheral blood of men diagnosed with oligo-astheno-teratozoospermia (OAT, n=4), idiopathic non-obstructive azoospermia (iNOA, n=6), and a control group (FER, n=5).
scRNAseq analysis was performed using a standard Seurat pipeline that includes the following steps starting from a minimal object after loading of 10X data to markers identification:
- Preprocessing and cell filtering
- Each sample was pre-processed and cells with mitochondrial RNA percentages higher than 10 and a number of features <1200 or >6000, were filtered out. Samples were merged into a single Seurat dataset
- Normalization
- Default Seurat settings (NormalizeData function)
- Scaling:
- Data was regressed out by passing UMI count, the percentage of mitochondrial genes, the difference between the cell cycle phases scores, as described in the Seurat vignette.
- Dimensionality reduction and Harmony batch removal:
- A principal component analysis (PCA) with 100 principal components (PCs) was performed and a UMAP-representation as well as clusters were computed on the top 55 components (orig.ident as batch variable)
- Clustering:
- K-nearest neighbor (KNN) graph was first constructed based on the Euclidean distance using the FindNeighbors function, with the KNN algorithm set to 20.
- The modularity optimization technique was applied using the Louvain algorithm through the FindCluster function, with resolution parameters set to 1.2.
- Markers identification:
- Marker genes for each cluster were identified using the FindAllMarkers function with the logfc.threshold argument set to 0.25. Only genes expressed in at least 25% of cells in one of the compared clusters were considered (min.pct = 0.25). Genes with pvalues < 1e10 -5 from the Wilcoxon Rank Sum test were considered as markers for a specific cluster.
- Cluster annotation
- Gene enrichment analysis (GSEA):
- Intra-cluster comparisons: Intra-cluster comparisons among the experimental conditions were conducted using the FindMarkers function, setting test.use = wilcox, a logFC threshold = 0, min.cells.group = 5 and return.thresh parameter equal to 1.
- GSEA function of ClusterProfiler R package was applied, using the full marker gene list ranked by decreasing logFC and the hallmarks gene set. Gene sets were considered enriched if their adjusted pvalue was <0.1.
Directories and Files
- sampleSheet.csv: names of samples and corresponding conditions