Citation

If you use our work, please cite:

Daugelaite K, Lacour P, Winkler I, Koch M, Schneider A, Schneider N, Tolkachov A, Nguyen XP, Vilkaite A, Rehnitz J, Odom DT, Goncalves A. (2025)
Granulosa cell transcription is similarly impacted by superovulation and aging and predicts early embryonic trajectories
Nat Commun 16, 3658 (2025)
doi: https://doi.org/10.1038/s41467-025-58451-9

From raw counts to R objects

create_seurat_age_ov.R - for natural and superovulated, young and old oocytes and granulosa cells Smart-Seq2 data (E-MTAB-13479)
create_seurat_totalrna.R - for natural and superovulated oocytes total-RNA seq data (E-MTAB-13474)
create_seurat_ivf_mouse.R - for IVF-derived mouse embryos (morula or blastocyst) and corresponding granulosa cells, Smart-seq2 data (E-MTAB-13480)

These scripts create the Seurat objects used by the other scripts from the raw count tables.

Scnorm.R - normalizes count data using the SCnorm method to take into account gene length (used for cell communication and classifier scripts).

Differential gene expression and overrepresentation

dge.R - differential expression analysis using DESeq2 for aging and superovulation dataset
ora.R - over-representation analysis of genes found by DESeq2

dge_SNvS.R - differential expression analysis using DESeq2 between S and SN granulosa cells (as identified by cell-to-cell communication analysis and transcription factor activities)

Total-RNA analysis (repolyadenylation, deadenylation, and degradation)

totalrna_vs_smartseq.R - compares the expression of known genes between natural and superovulated oocytes in a polyA-biased technology (Smart-Seq2) and a non-biased one (total RNA)

Cell-to-cell communication

cell_communication.R - computes ligand-receptor interaction score based on gene expression level and CellChatDB annotation

SCENIC and AUCell

scenic.R - runs SCENIC analysis on oocytes and granulosa cells from the aging and superovulation dataset
scenic_post.R - tests for significant differentially active pathways between conditions
tf_scenic_pathway.R - computes the overlap between the TFs targets and the pathways, plots the results in a heatmap

aucell.R - computes pathway activity scores

Granulosa cells classifier

data_preparation_genes.R - selects genes that will be used in the gene classifier (based on differentially expressed genes (DEG) between S and SN granulosa cells)
data_preparation_tfs.R - selects genes that will be used in the TF classifier (based on SCENIC results)
auc_classifier.R - trains different granulosa classifiers using TF activity scores
genes_classifier.R - trains different granulosa classifiers using DEG
gc_scenic_scoring_classifier.R - computes TF activity scores of new samples using the same regulons as the ones in the training dataset (results from the SCENIC analysis)
classifier_combined.R - predicts the class of new granulosa cells using the two classifiers

Embryo development and copy number variation

pseudotime_embryos.R - creates a reference developmental trajectory and calculates a developmental pseudotime for each embryo to assess link between granulosa cell classification and developmental transcriptional trajectory CNV_prep.R - prepares embryo data for inferCNV run
CNV_runner.R - runs inferCNV on embryo data

Validation experiments (HCR and qPCR)

hcr_analysis_and_plots.R - validation of Esr2 expression in natural and superovulated young granulosa cells using HCR fluorescence
qPCR_analysis_and_plots.R - qPCR quantification of genes used in the granulosa cells classifier

Shannon entropy

Shannon_entropy.R - computes differential Shannon entropy for the aging and superovulation dataset

Human data analysis

human_dge_gsea.R - computes differential gene expression on human granulosa cells (E-MTAB-13496) and compares the enriched pathways identified using fgsea to the ones found in mouse

Interaction between aging and superovulation

pca_projection.R - uses a PCA projection approach to summarize the non-linearity between aging and superovulation effects

Old scripts used in previous versions of the manuscript

pseudotime_oc_gc.R - performs pseudotime analysis based on highly variable genes or pathways of interest (e.g. meiosis)