Induction of fibroblast senescence generates a non-fibrogenic myofibroblast phenotype that differentially impacts on cancer prognosis

Cancer-associated fibroblasts (CAF) remain a poorly characterized, heterogeneous cell population. Here we characterized two previously described tumor-promoting CAF sub-types, smooth muscle actin (SMA)-positive myofibroblasts and senescent fibroblasts, identifying a novel link between the two. Analysis of CAF cultured ex vivo, showed that senescent CAF are predominantly SMA-positive; this was confirmed by immunochemistry in head & neck (HNSCC) and esophageal (EAC) cancers. In vitro, we found that fibroblasts induced to senesce develop molecular, ultrastructural and contractile features typical of myofibroblasts and this is dependent on canonical TGF-? signaling. Similar to TGF-?1-generated myofibroblasts, these cells secrete soluble factors that promote tumor cell motility. However, RNA-sequencing revealed significant transcriptomic differences between the two SMA-positive CAF groups, particularly in genes associated with extracellular matrix (ECM) deposition and organization, which differentially promote tumor cell invasion. Notably, second harmonic generation imaging and bioinformatic analysis of SMA-positive human HNSCC and EAC showed that collagen fiber organization correlates with poor prognosis, indicating that heterogeneity within the SMA-positive CAF population differentially impacts on survival. These results show that non-fibrogenic, SMA-positive myofibroblasts can be directly generated through induction of fibroblast senescence and suggest that senescence and myofibroblast differentiation are closely linked processes

Total RNA extraction from tissues for microRNA and target gene expression analysis: not all kits are created equal

Abstract Background microRNAs (miRNAs) are short non-coding RNAs that fine-tune gene expression. The aberrant expression of miRNAs is associated with many diseases and they have both therapeutic and biomarker potential. However, our understanding of their usefulness is dependent on the tools we have to study them. Previous studies have identified the need to optimise and standardise RNA extraction methods in order to avoid biased results. Herein, we extracted RNA from murine lung, liver and brain tissues using five commercially available total RNA extraction methods. These included either: phenol: chloroform extraction followed by alcohol precipitation (TRIzol), phenol:chloroform followed by solid-phase extraction (column-based; miRVana and miRNeasy) and solid-phase separation with/without affinity resin (Norgen total and Isolate II). We then evaluated each extraction method for the quality and quantity of RNA recovered, and the expression of miRNAs and target genes. Results We identified differences between each of the RNA extraction methods in the quantity and quality of RNA samples, and in the analysis of miRNA and target gene expression. For the purposes of consistency in quantity, quality and high recovery of miRNAs from tissues, we identified that Phenol:chloroform phase separation combined with silica column-based solid extraction method was preferable (miRVana microRNA isolation). We also identified a method that is not appropriate for miRNA analysis from tissue samples (Bioline Isolate II). For target gene expression any of the kits could be used to analyse mRNA, but if interested in analysing mRNA and miRNA from the same RNA samples some methods should be avoided. Conclusions Different methods used to isolate miRNAs will yield different results and therefore a robust RNA isolation method is required for reproducibility. Researchers should optimise these methods for their specific application and keep in mind that “total RNA” extraction methods do not isolate all types of RNA equally