Gene expression profiles for in vitro human stem cell differentiation into osteoblasts and osteoclasts: a systematic review

• Published in: PeerJ
• Main Author: Zainal Ariffin S.H.; Lim K.W.; Abdul Wahab R.M.; Ariffin Z.Z.; Rus Din R.D.; Shahidan M.A.; Johari A.N.; Zainol Abidin I.Z.
• Format: Article
• Language: English
• Published: PeerJ Inc. 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140310470&doi=10.7717%2fpeerj.14174&partnerID=40&md5=2cc477dddfe89f312095c42489aa02e2

Background. There have been promising results published regarding the potential of stem cells in regenerative medicine. However, the vast variety of choices of techniques and the lack of a standard approach to analyse human osteoblast and osteoclast differentiation may reduce the utility of stem cells as a tool in medical applications. Therefore, this review aims to systematically evaluate the findings based on stem cell differentiation to define a standard gene expression profile approach. 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. Subjects Biochemistry, Biotechnology, Cell Biology, Developmental Biology, Molecular Biology Copyright 2022 Zainal Ariffin et al.