Microarray profiling to analyse adult cardiac fibroblast identity

AbstractHeart failure is one of the leading causes of death worldwide [1–4]. Current therapeutic strategies are inefficient and cannot cure this chronic and debilitating condition [5]. Ultimately, heart transplants are required for patient survival, but donor organs are scarce in availability and only prolong the life-span of patients for a limited time. Fibrosis is one of the main pathological features of heart failure [6,7], caused by inappropriate stimulation of fibroblasts and excessive extracellular matrix production. Therefore, an in-depth understanding of the cardiac fibroblast is essential to underpin effective therapeutic treatments for heart failure [5]. Fibroblasts in general have been an underappreciated cell type, regarded as relatively inert and providing only basic functionality; they are usually referred to as the ‘biological glue’ of all tissues in the body. However, more recent literature suggests that they actively participate in organ homeostasis and disease [7,8].We have recently uncovered a unique molecular identity for fibroblasts isolated from the heart [9], expressing a set of cardiogenic transcription factors that have been previously associated with cardiomyocyte ontogenesis. This signature suggests that cardiac fibroblasts may be ideal for use in stem cell replacement therapies, as they may retain the memory of where they derive from embryologically. Our data also revealed that about 90% of fibroblasts from both tail and heart origins share a cell surface signature that has previously been described for mesenchymal stem cells (MSCs), raising the possibility that fibroblasts and MSCs may in fact be the same cell type. Thus, our findings carry profound implications for the field of regenerative medicine. Here, we describe detailed methodology and quality controls related to the gene expression profiling of cardiac fibroblasts, deposited at the Gene Expression Omnibus (GEO) under the accession number GSE50531. We also provide the R code to easily reproduce the data quantification and analysis processes