Integrated single-cell RNA-sequencing data of unwounded and wounded mouse skin and fibroblasts.

This repository contains the .h5ad files that store the integrated scRNA-seq data we generated for the work, Almet et al. (2023), "Fibroblasts evolve in single-cell state to drive extracellular matrix and signaling changes across wound healing", to be published in the Journal of Investigative Dermatology.
The integrated* files contain both raw counts, normalized counts, as well as unspliced and spliced count estimates that were obtained using kallisto|bustools and velocyto. We integrated the data from the following published datasets:

Phan et al. (2021): Unwounded P21 mice and small wound P21 + 7 mice
Haensel et al. (2020): Unwounded P49 mice and small wound P49 + 4 mice
Guerrero-Juarez et al. (2019): Large wound day 12 mice
Abbasi et al. (2020): Large wound day 14 mice
Gay et al. (2020): Large wound fibrotic (hairless) and regenerative (hair follicle neogenesis) day 18 mice
The unwounded_* files were used to briefly integrated unwounded skin scRNA-seq from mouse models of different ages that have been used to analyze wound healing in Haensel et al. (2020), Phan et al. (2021), and Vu et al. (2022), which generated scRNA-seq for unwounded skin from mice aged P21, P49, and P616, respectively. 
The data can be loaded using the Python package Scanpy or AnnData, but you can also load it in R if you use zellkonverter.