Dynamic Transcriptome Profiles within Spermatogonial and Spermatocyte Populations During Postnatal Testis Maturation Revealed by Single-Cell Sequencing
The scripts developed for the single-cell RNA-seq study in Grive, K. J., Y. Hu, E. Shu, A. Grimson, O. Elemento, J. K. Grenier, and P. E. Cohen. Dynamic Transcriptome Profiles within Spermatogonial and Spermatocyte Populations During Postnatal Testis Maturation Revealed by Single-Cell Sequencing. PLoS Genet (2019)
INTRODUCTION
METHOD DETAILS
Single-cell transcriptome analysis
Data normalization, unsupervised cell clustering, and differential expression were carried out using the Seurat R package. Batch effect and cell-cycle effect were removed by Combat and Seurat together.
The Cells with less than 500 genes or 2000 UMIs or more than 15% of mitochondria genes were excluded from the analysis. Gene expression raw counts were normalized following a global-scaling normalization method with a scale factor of 10,000 and a log 2 transformation, using the Seurat NormalizeData function. The top 4000 highly variable genes were selected using the expression and dispersion (variance/mean) of genes. Combat removed the batch effect. Seurat regressed the difference between the G2M and S phase, then followed by principal component analysis (PCA). The most significant principal components (1-30) were used for unsupervised clustering and t-Distributed Stochastic Neighbor Embedding (tSNE).
Cell types were manually identified by marker genes, and confirmed by SingleR (Single-cell Recognition) package. Differential expression analysis was performed based on the MAST (Model-based Analysis of Single Cell Transcriptomics). Gene Set Enrichment Analysis was performed based on GSEA software and Molecular Signature Database(MSigDB). Pathways were visualized by EnrichmentMap in Cytoscape.
How to use this repository
Software Setup
R version 3.5.1
dplyr_0.7.6
Seurat_2.3.4
SingleR_0.2.0
After pulling this repository, create folders data and output in the top working folder. Move Cell Ranger analysis results into data folder.
1. Seurat_setup.R
Seurat_setup.R
The Cells with less than 500 genes or 2000 UMIs or more than 15% of mitochondria genes were excluded from the analysis. Gene expression raw counts were normalized following a global-scaling normalization method with a scale factor of 10,000 and a log 2 transformation, using the Seurat NormalizeData function. The top 4000 highly variable genes were selected using the expression and dispersion (variance/mean) of genes. Combat removed the batch effect. Seurat regressed the difference between the G2M and S phase, then followed by principal component analysis (PCA). The most significant principal components (1-30) were used for unsupervised clustering with 0.8 resolution and t-Distributed Stochastic Neighbor Embedding (tSNE).
After running this script, a SSCs_(date).Rda file will be generated inside data folder.
2. Identify_Cell_Types_Manually.R
Identify_Cell_Types_Manually.R
All clusters are tested against marker genes.
All cell types are predicted by at least two marker genes.
Smooth muscle were identified by Acta2 and Col1a2.
Endothelial & Hematopoietic cells were identified by Laptm5, Ptprc, Vcam1, Insl3, Lyz2 and Hbb-bt.
Sertoli cells were identified by Ptgds, Adgra3 and Wt1.
Spermatogonia were identified by Gfra1, Zbtb16, Sall4, Dmrt1 and Dazl.
Early Spermatocytes were identified by Kit and Dazl.
Spermatocytes were identified by Id4, Sycp3, Mtl5, Nxt1 and Shcbp1l.
Round Spermatids were identified by Lyzl1, Acrv1 and Hemgn.
Spermatids were identified by Txndc8, Tssk6, Oaz3, Prm1, and Prm2.
3. SingleR.R (optional)
SingleR.R
Cell types were identified by SingleR (Single-cell Recognition) package. SingleR is a novel computational method for unbiased cell type recognition of scRNA-seq. SingleR leverages reference transcriptomic datasets of pure cell types to infer the cell of origin of each of the single cells independently.
We processed and annotated three reference mouse datasets:
Immunological Genome Project (ImmGen): a collection of 830 microarray samples, which we classified to 20 main cell types and further annotated to 253 subtypes.
A dataset of 358 mouse RNA-seq samples annotated to 28 cell types. This dataset was collected, processed and shared, courtesy of Bérénice Benayoun. This data set is especially useful for brain-related samples.
GSE43717: Cellular source and mechanisms of high transcriptome complexity in the mammalian testis (RNA-Seq cells). This dataset contains five mouse testis cell types including Sertoli cells, Spermatogonia, Spermatocytes, Spermatids, and Spermatozoa.
After running this script, a singler_labels.RData file will be generated inside output folder.
4. Differential_analysis.R
Differential_analysis.R
Modified FindAllMarkers() FindAllMarkers.UMI() will generate similar dataframe plus two extra columns UMI.1 and UMI.2 to record nUMI. UMI.1 is average nUMI of current cluster, UMI.2 is average nUMI of all rest of clusters.
FindAllMarkers(object, test.use = "MAST") : MAST (Model-based Analysis of Single Cell Transcriptomics), a GLM-framework that treates cellular detection rate as a covariate (Finak et al, Genome Biology, 2015)
Below is an example of a Differential analysis output file.
| gene | p_val | avg_logFC | pct.1 | pct.2 | p_val_adj | UMI.1 | UMI.2 | cluster |
|---|---|---|---|---|---|---|---|---|
| Acta2 | 0 | 3.9340 | 0.939 | 0.055 | 0 | 3.5565 | 0.0339 | Smooth muscle |
| Myl9 | 0 | 2.9163 | 0.99 | 0.161 | 0 | 3.0622 | 0.1834 | Smooth muscle |
| H19 | 0 | 2.9105 | 0.959 | 0.042 | 0 | 2.6070 | 0.0365 | Smooth muscle |
| Meg3 | 0 | 2.7729 | 0.965 | 0.072 | 0 | 2.6652 | 0.0931 | Smooth muscle |
| Col1a2 | 0 | 2.7221 | 0.995 | 0.035 | 0 | 2.7625 | 0.0496 | Smooth muscle |
The results data frame has the following columns :
gene: gene name.
p_val: p_val (unadjusted) is calculated using MAST (Model-based Analysis of Single Cell Transcriptomics, Finak et al., Genome Biology, 2015)
avg_logFC: log fold-change of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group.
pct.1: The percentage of cells where the gene is detected in the first group.
pct.2: The percentage of cells where the gene is detected in the second group.
p_val_adj: Adjusted p-value, based on bonferroni
UMI.1 is average nUMI of the current cluster.
UMI.2 is average nUMI of rest of clusters.
cluster : either cell type or corresponding cluster.
5. GSEA.R
GSEA.R After running this script, a expression txt and label cls file will be generated inside output folder, for Gene Set Enrichment Analysis.
6. Main_figures.R
Main_figures.R Source code to produce all figures.