RESULTS: Several ascending and descending monotonic key genes were identified by Monotonic Feature Selector. The identified descending monotonic key genes are related to stemness or regulation of cell cycle while ascending monotonic key genes are associated with the functions of mesangial cells. The TFs were arranged in a co-expression network in order of time by Time-Ordered Gene Co-expression Network (TO-GCN) analysis. TO-GCN analysis can classify the differentiation process into three stages: differentiation preparation, differentiation initiation and maturation. Furthermore, it can also explore TF-TF-key genes regulatory relationships in the muscle contraction process.
CONCLUSIONS: A systematic analysis for transcriptomic profiling of MSC differentiation into mesangial cells has been established. Key genes or biomarkers, TFs and pathways involved in differentiation of MSC-mesangial cells have been identified and the related biological implications have been discussed. Finally, we further elucidated for the first time the three main stages of mesangial cell differentiation, and the regulatory relationships between TF-TF-key genes involved in the muscle contraction process. Through this study, we have increased fundamental understanding of the gene transcripts during the differentiation of MSC into mesangial cells.
METHODS: This review was performed following the PRISMA guidelines. A systematic search of the study was conducted by retrieving articles from the electronic databases PubMed and Web of Science to identify articles focussed on gene expression and approaches for osteoblast and osteoclast differentiation.
RESULTS: Six articles were included in this review; there were original articles of in vitro human stem cell differentiation into osteoblasts and osteoclasts that involved gene expression profiling. Quantitative polymerase chain reaction (qPCR) was the most used technique for gene expression to detect differentiated human osteoblasts and osteoclasts. A total of 16 genes were found to be related to differentiating osteoblast and osteoclast differentiation.
CONCLUSION: Qualitative information of gene expression provided by qPCR could become a standard technique to analyse the differentiation of human stem cells into osteoblasts and osteoclasts rather than evaluating relative gene expression. RUNX2 and CTSK could be applied to detect osteoblasts and osteoclasts, respectively, while RANKL could be applied to detect both osteoblasts and osteoclasts. This review provides future researchers with a central source of relevant information on the vast variety of gene expression approaches in analysing the differentiation of human osteoblast and osteoclast cells. In addition, these findings should enable researchers to conduct accurately and efficiently studies involving isolated human stem cell differentiation into osteoblasts and osteoclasts.