GSE175889
A single-cell atlas of the microenvironment of implanted biomaterials and computational analysis of the transcriptional signalling networks [single-cell RNA-seq]
The understanding of the foreign-body responses to implanted biomaterials would benefit from the reconstruction of intracellular and intercellular signalling networks in the microenvironment surrounding the implant. Here, by leveraging single-cell RNA-sequencing data from 42,156 cells harvested from the site of implantation of either polycaprolactone or an extracellular-matrix-derived scaffold in a mouse model of volumetric muscle loss, we report a computational analysis of intercellular-signalling networks reconstructed from data of transcription-factor activation. We found that intercellular signalling networks can be clustered into modules associated with specific cell subsets, and that biomaterial-specific responses can be characterized by interactions between signalling modules for immune, fibroblast and tissue-specific cells. In a Il17ra–/¬– knockout mouse model, we validated that predicted IL-17-linked transcriptional targets led to concomitant changes in gene expression. Moreover, we identified cell subsets that had not been implicated in the responses to implanted biomaterials. Single-cell atlases of the cellular responses to implanted biomaterials will facilitate the design of implantable biomaterials and the understanding of the ensuing cellular responses. Overall design: To generate the scRNAseq data sets we created single cell suspensions from the muscle tissue with and without biomaterials for application to 10X and Drop-seq. Single cell suspensions from whole tissue preparations were enriched for CD45+ cells to enable capture of less frequent cell populations and processed with Drop-seq. Fluorescence activated cell sorting (FACS) was used to apply mesenchymal/fibroblasts (CD45-CD19-CD31-CD29+) to Drop-seq. Finally, a previously published data set of sorted macrophages (CD45+F4/80hi+Ly6c+CD64+) was included. Most samples collected were from young mice (6 week old at time of surgery) during the acute phase of injury one week after surgery. A detailed description of age, time of harvest, treatment, and sorting methodology for each of the samples in the atlas is provided in Supplementary Table 2. The resulting dataset includes 42,156 cells with an average of 198,000 reads and 1,167 genes per cell after filtering low-quality cells and genes across multiple time points and ages.