A single-cell atlas of the microenvironment of implanted biomaterials and computational analysis of the transcriptional signalling networks [single-cell RNA-seq]. 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.

对植入式生物材料引发的异物应答的理解，可通过重构植入物周围微环境中的细胞内与细胞间信号传导网络得到深化。本研究依托从体积性肌肉损伤小鼠模型中，分别植入聚己内酯（polycaprolactone）或细胞外基质衍生支架（extracellular-matrix-derived scaffold）的植入部位获取的42156个细胞的单细胞RNA测序（single-cell RNA-sequencing）数据，开展了基于转录因子激活数据重构细胞间信号传导网络的计算分析，并报道了相关研究结果。研究发现，细胞间信号传导网络可聚类为与特定细胞亚群相关的模块，且不同生物材料引发的特异性应答，可通过免疫细胞、成纤维细胞及组织特异性细胞的信号模块间相互作用进行表征。在IL-17受体A敲除（Il17ra–/–）小鼠模型中，本研究验证了预测得到的IL-17相关转录靶标可引发伴随性的基因表达变化。此外，本研究还鉴定出此前未被关联到植入式生物材料应答过程的细胞亚群。

植入式生物材料细胞应答的单细胞图谱，将助力可植入生物材料的设计以及对后续细胞应答的深入理解。

实验整体设计：为获取单细胞RNA测序数据集，本研究从有无植入生物材料的肌肉组织中制备单细胞悬液，分别用于10X和Drop-seq测序。对完整组织制备的单细胞悬液进行CD45阳性细胞富集，以捕获占比较低的细胞群，随后通过Drop-seq进行测序。采用荧光激活细胞分选（fluorescence activated cell sorting, FACS）分离间充质/成纤维细胞（CD45⁻CD19⁻CD31⁻CD29⁺），并通过Drop-seq进行测序。最后，本研究纳入了已发表的分选巨噬细胞（CD45⁺F4/80hiLy6c⁺CD64⁺）数据集。大部分采集的样本来自手术时为6周龄的年轻小鼠，收集时间为术后1周的损伤急性期。本图谱中所有样本的年龄、收集时间、处理方式及分选方法的详细信息见补充表2。经过对多个时间点与年龄组的低质量细胞和基因进行过滤后，最终得到的数据集包含42156个细胞，每个细胞平均拥有198000条测序读段，检测到1167个基因。