# Single-cell RNA-seq Analysis

This repository contains the R script used to generate the figures based on single-cell RNA-seq data presented in:

**Conti et al., 2025**  
*Senescence and inflammation are unintended adverse consequences of CRISPR-Cas9/AAV6 mediated gene editing in hematopoietic stem cells*.  
Published in **Cell Reports Medicine**.

## 🔬 Study Context

Single-cell RNA sequencing (scRNA-seq) was used to characterize the transcriptional landscape and cell state dynamics in human hematopoietic stem and progenitor cells (HSPCs) following CRISPR-Cas9/AAV6-mediated gene editing. The aim was to identify specific subpopulations enriched for senescence and inflammatory signatures.

## 📂 Repository Content

- `Figures_scRNA_seq.R`  
  This R script includes the code used to generate UMAPs, cluster-specific gene expression plots, and pathway enrichment visualizations included in the manuscript. Analyses were performed using Seurat and clusterProfiler, with annotations based on known hematopoietic markers and senescence-associated gene sets.

## 📊 Data Availability

The scRNA-seq dataset is available on GEO under the accession **[GSE244256](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE244256)**.

## 🧰 Tools and Packages Used

- `Seurat` for normalization, dimensionality reduction, clustering and visualization  
- `clusterProfiler` for pathway enrichment  
- `ggplot2` for figure rendering

## 📌 Citation

If you use this dataset or code, please cite:

> Conti A., Giannetti K., Midena F., et al. (2025).  
> *Senescence and inflammation are unintended adverse consequences of CRISPR-Cas9/AAV6 mediated gene editing in hematopoietic stem cells*.  
> **Cell Reports Medicine**.  
> GEO accession: GSE244256