Gregorio Alanis-Lobato

We integrated single cell RNA-sequencing (scRNA-seq) time course data from two different studies, which interrogate the transcriptome of human pre-implantation embryos at different stages. The dataset from Yan et al. (ENA study ID: PRJNA153427) measures gene expression of individual cells from the Oocyte to the Late Blastocyst stage, whereas the dataset from Petropoulos et al. (ENA study ID: PRJEB11202) does it from the 8-cell to the Late Blastocyst stage. Also, we considered the scRNA-seq data from Blakeley et al. (ENA study ID: PRJNA277181), which assayed the transcriptome of the three cell lineages of the human blastocyst (epiblast, primitive endoderm and trophectoderm).

SingleCellExperiment objects with the raw counts from each one of these datasets are provided in the data folder of this repository.

We focused on trophectoderm cells with gene expressions of the GATA3 gene above the median in Blakeley’s dataset. Next, we used package MGFR v1.8.1 to detect genes whose expression is high in all the selected trophectoderm samples compared to the epiblast and primitive endoderm samples. Then, we looked at the expression of the resulting list of genes at the Morula stage using the integrated scRNA-seq data from Yan and Petropoulos. Finally, genes with average expression above 0 at the Morula stage were selected to study the Pearson and Spearman correlation of their expression profile against the profile of GATA3 at the same developmental stage. Functional enrichment analysis of the selected genes was performed with enrichR v1.0.

To identify genes with the opposite trend, we centered our attention on the 10% of the Morula samples with the lowest gene expression levels of GATA3. Genes with average expression at least two-fold higher than that of GATA3 in these samples were shortlisted for correlation analysis against the expression profile of GATA3 at the Morula stage as above.

The bioinformatic pipeline that we prepared requires the following software packages:

• R v3.5.2
  • Bioconductor v3.8
    • SummarizedExperiment v1.12.0
    • SingleCellExperiment v1.4.1
    • scran v1.10.2
    • scater v1.10.1
    • MGFR v1.8.1
  • dplyr v0.8.0.1
  • readr v1.3.1
  • cowplot v0.9.4
  • ggplot2 v3.1.1
  • ggrepel v0.8.0
  • DrImpute v1.1
  • enrichR v1.0

To use this pipeline, knit the provided R Markdown files in the following order:

• 00a_data_processing.Rmd
• 00b_data_processing.Rmd
• 01_gata3_in_TE_YP.Rmd
• 02_enrichment_YP.Rmd
• 03_gata3neg_YP.Rmd

The R script named 04_corr_analysis.R can be used with any list of genes to generate correlation plots between genes of interest and GATA3 at the morula stage.