Identification of epigenetic regulators of fibrotic transformation in cardiac fibroblasts through bulk and single-cell CRISPR screens.

This code supplements the upcoming publication by Aguado Alvaro, Garitano, Esser-Skala et al.

Folders

(Not all of these folders appear in the git repository.)

• data_generated: output files generated by the scripts in this repository
  • python_figure_data: data exported from Python scripts for generating source data
• data_raw: raw input data
• doc: project documentation
• geo_upload_scripts: scripts for uploading raw data to Gene Expression Omnibus
• metadata: additional required data
• plots: generated plots
• renv: R environment data
• scripts_atacseq: scripts for the ATAC-seq analysis
• scripts_chipseq: scripts for the ChIP-seq analysis
• scripts_python: Python scripts
• scripts_r: R scripts
• tables: tables exported from scripts

Download data

Create a folder data_raw that will contain raw data in the following subfolders:

• rna: Download GSE261783_RAW.tar from GEO Series GSE261783 and extract all files.
• rna-seq: Download GSE280438_raw_counts.txt.gz from GEO Series GSE280438 and extract the file.
• signatures:
  • Amrute.xlsx: Table S10 from Amrute et al (https://doi.org/10.1038/s44161-023-00260-8)
  • Buechler.xlsx: Table S5 from Buechler et al (https://doi.org/10.1038/s41586-021-03549-5)
  • Chaffin.xlsx: Table S11 from Chaffin et al (https://doi.org/10.1038/s41586-022-04817-8)
  • Forte.xlsx: Table S3 from Forte et al (https://doi.org/10.1016/j.celrep.2020.02.008)
  • Fu.xlsx: Additional File 6 from Fu et al (https://doi.org/10.1186/s12916-023-03232-8)
  • Koenig.xlsx: Table S27 from Koenig et al (https://doi.org/10.1038/s44161-022-00028-6)
  • Kuppe.xlsx: Table S13 from Kuppe et al (https://doi.org/10.1038/s41586-022-05060-x)

Optionally, obtain intermediary data: Extract the contents of data.tgz from Zenodo repository https://doi.org/10.5281/zenodo.14794723 to folder data_generated.

Analysis workflow

Python

Run Python scripts in order.

R

Run the following scripts in the folder scripts_r in order to run the R analysis pipeline. utils.R contains auxiliary functions and definitions required by several other scripts.

1. create_sce.R: interface to Python scripts; creates a SingleCellObject with the same data and metadata as the AnnData object
2. prepare_signatures.R: assemble fibroblast signatures and gene sets for enrichment analysis
3. run_gsea.R: perform gene set enrichment analysis
4. plot_signature_scores.R: fibroblast signature scores as heatmaps (figure 6b, S2d, S2e)
5. plot_signature_enrichments.R: fibroblast enrichments as dotplots (S10a)
6. plot_ko_distribution.R: UMAPs with knockout distribution (2f, S5)
7. plot_ko_enrichment.R: summary of knockout enrichment (2g, S6a)
8. plot_gsea.R: plot GSEA results (S6b)
9. plot_egr2ko_genes.R: Volcano plot of Egr2-KO vs NTC (S8h)
10. plot_bulk_rnaseq.R: bulk RNA-seq analysis of patient-derived fibroblasts (6d, e, S10c, d, e)
11. plot_fap_expression.R: expression of Fab (R15)