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

Data search

The studies included in this systematic review were retrieved from the PubMed and Web of Science databases. Independent keywords and their combinations were applied to the search engines of these databases. A detailed customised search strategy was established for each electronic database (Table 1). The title, abstract, authors’ names and affiliations, journal name and year of publication were exported to a Microsoft Excel spreadsheet. K.W.L. and A.N.J. then independently screened the titles and abstracts to assess each article’s eligibility for inclusion. During this phase, disagreements between the two observers were discussed and resolved by consensus. If no agreement could be reached, a third observer (S.H.Z.A.) was involved.

Selection criteria

Only original articles that were published in the English language between 2016 and 2022 were included; review articles and duplicate articles were excluded. In vitro studies involving the potential of only human stem cells to differentiate into osteoblasts and osteoclasts were included; studies using any cell lines or primary cultures from animals were excluded. In vivo studies were not included. Studies that had combinations of both animal and human cultures were included, but only the section on human cell cultures was considered.

Data extraction and screening process

Screening involved the following process. First, review articles and articles published in a language other than English were removed. Next, studies performed without utilising human stem cells and that did not match the parameters of osteoblastic and osteoclastic differentiation were removed. Techniques involved in screening profiles were also excluded in this review. All the remaining articles were screened for their eligibility. Data were extracted from each included article by following the PRISMA guidelines (Page et al., 2021). The data extracted included: study characteristics (first author, year of publication, language and study design), organism and cell lines and the methods used to analyse gene expression profiles.

Risk of bias assessment

The quality of methodology of the included studies was evaluated by K.W.L. following the ‘Modified CONSORT checklist of items for reporting in vitro studies’, with slight modifications to fit the study. The main domains are listed as follows: (1) structured summary in abstract, (2) specific objectives or hypothesis, (3) study population, (4) further description of interventions, (5) primary and secondary outcomes, (6) results, (7) limitations, (8) sources of funding and (9) availability of protocol. Any domains that were related to clinical trials or cell lines and primary cultures from animal samples were excluded, while the domains involving human stem cells were included. Disagreements between reviewers were resolved after discussion. Each criterion was marked as follows: present (+), absent (-), unclear (?), not stated (/) or not applicable (NA).

Data extraction results

Searches with keywords in the electronic databases related to stem cell osteogenic differentiation (Table 1) produced a total of 52 articles, 17 results from PubMed and 35 results from Web of Science. Every potential article was assessed independently based on the inclusion and exclusion criteria. After removing five duplicates between the two databases, 47 articles remained. A review article was also excluded. In addition, a single article in Japanese and a single article in German were excluded, followed by 12 articles not relevant to human stem cells and 26 articles that did not match the parameters of interest. Hence, there are six articles published between 2016 and 2022 eligible for qualitative synthesis. Figure 1 shows the flowchart of the article selection process.

Study design

Of the six included studies, four focussed on the differentiation of osteoblasts, and another two focussed on the differentiation of both osteoblasts and osteoclasts. However, no studies focussed specifically on the differentiation of osteoclasts. All six studies were based on in vitro studies. The cultured cells included CD34+ peripheral blood stem cells (HSC) (Srikanth et al., 2016), and human mesenchymal stem cells (hMSC) (Bradamante et al., 2018; Höner et al., 2018; Xie et al., 2021; Xu et al., 2018). One study included multiple cultured cells, namely hMSC and human blood peripheral mononuclear cells (hBPMC) (Höner et al., 2018). The results from the risk of bias assessment are shown in Table 2. The included studies presented an abstract with a brief rationale and clear objective or hypotheses and introduction. The studies also provided information on the populations and results. Three studies did not state the limitations while four studies did not provide information regarding funding.

Gene expression profiles of osteoblasts and osteoclasts

The methods used to analyse osteoblast and osteoclast differentiation included gene expression evaluated with quantitative polymerase chain reaction (qPCR) (Hashimoto et al., 2018; Höner et al., 2018; Srikanth et al., 2016; Xie et al., 2021; Xu et al., 2018) or microRNA sequencing (miRNA-seq) analysis (Bradamante et al., 2018). CD34+ HSC express runt-related transcription factor 2 (RUNX2), osterix (OSX), RANKL and osteonectin (SPARC) during osteoblast differentiation. Meanwhile, when using human bone marrow-derived mesenchymal stem cells (hBMSC), miR-142-5p is the only gene expressed during osteoblast differentiation. hMSC, the most commonly used cells in studies included in this systematic review, express COL1A, BSP, OPN, OCN, miR-139-5p, ALP, OPG, miR-940, four and a half LIM domains 2 (FHL2) and RUNX2 during osteoblast differentiation. hMSCs were also used for osteoclast differentiation; they express cathepsin K (CTSK), NOTCH1, HES1 and HEY1.

Irrespective of the cell types and methodologies, there are certain gene expression profiles for osteoblast and osteoclast differentiation. These genes are specifically expressed during either osteoblast or osteoclast differentiation, except RANKL, which is expressed during both. RUNX2, OSX, SPARC, miR-142-5p, COL1A, BSP, OPN, miR-139-5p, ALP, OPG, miR-940 and FHL2 are only expressed during osteoblast differentiation. CTSK, NOTCH1, HES1 and HEY1 are expressed specifically during osteoclast differentiation. Table 3 presents the gene expression levels, techniques and the type of stem cells applied to investigate osteoblast and osteoclast differentiation, while Table 4 includes the gene markers used in the included studies. Based on Table 4, RUNX2 is the most commonly expressed gene during osteoblast differentiation, while CTSK is the most commonly expressed gene during osteoclast differentiation.

Upregulation of RUNX2, OSX, RANKL, SPARC, BSP, COL1A, OPN, OPG, miR-940, ALP, and/or FHL2 indicates osteoblast differentiation. OPG is highly expressed during osteoblast differentiation and halts osteoclast differentiation. CTSK is highly upregulated during osteoclast differentiation. Increased expression of Notch signalling pathway genes, including NOTCH1, HEY1 and HES1, ultimately suppress osteoblast differentiation.

Types of stem cells used in osteoblast and osteoclast analysis

Of the six included studies, hMSC were the most commonly used cell type (Bradamante et al., 2018; Hashimoto et al., 2018; Höner et al., 2018; Srikanth et al., 2016; Xu et al., 2018). On the other hand, hMSC isolated from bone marrow, namely hBMSC (Bradamante et al., 2018; Xie et al., 2021), were the most used cell source, followed by a hMSC cell line from the human umbilical cord (Hashimoto et al., 2018), primary culture of hMSC from the femoral head (Höner et al., 2018) and primary culture of hMSC from blood peripheral monocytes (Xu et al., 2018). The properties of hBMSC such as ease of isolation from bone marrow without causing an immunological problem and the ability to reach confluence in a short period make them the most popular model for in vitro osteogenic differentiation studies (Bhat et al., 2021; Ouryazdanpanah et al., 2018). Ansari, Ito & Hofmann (2021) showed that rapid osteogenic differentiation under biochemical and/or mechanical stimuli significantly increase gene expression specific to osteoblast differentiation. The other variant of adult stem cells in the included studies are HSC (Srikanth et al., 2016). HSC are the most thoroughly characterised tissue-specific stem cells and possess potential in regenerative medicine (Zakrzewski et al., 2019). Monocytes derived from HSC, which comprise 10%–20% of peripheral blood, have been used during in vitro studies as osteoclast precursor cells. HSC and monocytes can be isolated and purified based on the expression of their specific surface markers such as CD34 and CD14. However, unlike MSC, the HSC isolation procedures are time-consuming and might lead to a low number of cells obtained, resulting in a larger volume of peripheral blood needed (Ansari, Ito & Hofmann , 2021).

Gene expression profiling techniques

Genes are upregulated and downregulated during cell-specific differentiation. Changes in gene expression can be detected by qPCR and miRNA-seq analysis. qPCR can detect the expression of a single gene while miRNA-seq analysis provides a profile of predefined transcripts or genes via hybridisation. Most of the studies included in this systematic review used qPCR rather than miRNA-seq to evaluate osteoblast and osteoclast differentiation because qPCR provides quantitative information about relative gene expression. In addition, miRNA-seq lacks an optimised standard protocol despite its computational infrastructure and bioinformatic analyses (Rao et al., 2019). Therefore, qPCR is the most commonly chosen technique.

Markers for both osteoblast and osteoclast differentiation

RANKL stimulates osteoclast formation and activity, which induces the expression of RANKL by osteoblastic stromal cells (Konukoğlu, 2019; Tobeiha et al., 2020). RANKL together with its receptor, RANK, is essential for bone remodelling. RANKL is highly expressed in osteoblasts while it is also important in osteoclastogenesis: dysregulation of RANKL signalling may impair bone resorption (Ono et al., 2020). Osteoblasts regulate bone resorption through RANKL expression (Konukoğlu, 2019). RANKL, part of the RANKL/RANK/OPG signalling pathway, is secreted by osteoblasts. It then binds to its receptor (RANK) on osteoclasts and increases osteoclastic differentiation, resulting in bone resorption and bone loss (Fig. 2) (Roumeliotis et al., 2020). On the other hand, OPG could bind to RANKL to inhibit osteoclastogenesis (Tobeiha et al., 2020).

Gene expression profile of osteoblast differentiation

The most frequently used gene markers among the included articles to detect osteoblastic differentiation are RUNX2 (Höner et al., 2018; Srikanth et al., 2016; Xie et al., 2021; Xu et al., 2018) and COL1A (Höner et al., 2018; Xie et al., 2021; Xu et al., 2018). Early osteoblastic genes such as RUNX2 and OSX showed high expression on the seventh day of culture, and enhanced expression was the key factor of osteogenesis (Xu et al., 2018; Srikanth et al., 2016). RUNX2 is a member of the Runt-related transcription factor family. It is a master transcription factor and communicates with target gene promoters via its Runt domain. RUNX2 facilitates bone remodelling through interaction with proteins and DNA sequences (Narayanan et al., 2019). Positive and negative regulation of RUNX2 is crucial for bone formation (Narayanan et al., 2019). RUNX2 is initially detected in pre-osteoblasts and later upregulated in immature osteoblasts but downregulated in mature osteoblasts (Fig. 3). RUNX2 is required for the determination of the osteoblast lineage during multipotent MSC differentiation into immature osteoblasts (Komori, 2009). RUNX2 encodes multiple transcripts that are derived from two promoters (P1 and P2) and alternative splicing. P1 (distal) and P2 (proximal) initiate the expression of the major RUNX2 isoforms, type II (RUNX2-II) and type I (RUNX2-I), respectively. The structure of the promoter has been conserved in both human and murine RUNX2 genes. RUNX2-I is expressed by osteoblasts at consistent levels throughout osteoblast differentiation while RUNX2-II expression is increased during osteoblast differentiation under the induction BMP (Schroeder, Jensen & Westendorf, 2005). Therefore, RUNX2 is the master transcription factor, and no bone is formed in the absence of RUNX2; making RUNX2 the preferred standard marker for osteoblast differentiation.

COL1A is a bone matrix protein that facilitates morphological changes and transformation of pre-osteoblasts into mature osteoblasts; it also serves as an early marker for osteoblasts (Fig. 3) (Narayanan et al., 2019). Collagen is a triple helical structure in which procollagen forms the first helical structure during collagen synthesis. Protease removes the amino and carboxyl ends of the molecule, forming tropocollagen followed by cross-linking. PYD and DYP cross-link collagen polypeptides, providing mechanical support to maintain and stabilise collagen. These cross-linkages affect the differentiation of osteoblasts. DYP is a more specific and sensitive marker as it is found specifically in bones and dentin (Konukoğlu, 2019). Similarly to RUNX2, MSC transfected with a miRNA mimic of miR-139-5p significantly enhances COL1A expression (Xu et al., 2018).

Some osteoblastic markers such as RANKL and SPARC (Roumeliotis et al., 2020) show high expression only at the later stages of osteoblast differentiation. SPARC regulates extracellular matrix assembly and the formation of matrix metalloproteinases and collagen that is needed for fibronectin-induced, integrin-linked kinase activation as extracellular matrix development needs an organised fibronectin matrix (Purnachandra Nagaraju et al., 2014).

FHL2 interacts with integrins and transcription factors to control osteoblast differentiation. FHL2 overexpression leads to rapid differentiation of stem cells into osteoblasts and increases the expression of osteoblast markers. On the other hand, knocking out FHL2 downregulates osteoblast markers (Lai et al., 2006; Xie et al., 2021).

OSX is also upregulated during osteoblast differentiation. This gene encodes an osteoblast-specific transcription factor required for osteoblast differentiation and bone formation (Fig. 3). OSX, one of the early osteoblastic genes, shows high expression in the early days of differentiation (Srikanth et al., 2016). OSX is considered a major effector in skeletal formation. OSX interacts with the nuclear factor of activated T cells (NFAT), which then forms a complex that improves osteoblast-mediated bone formation via activation of the COL1A1 promoter (Han et al., 2016).

ALP (Hashimoto et al., 2018; Höner et al., 2018) is an early marker for osteoblastic differentiation produced by osteoblasts; its elevated level is positively correlated with bone formation rate (Fig. 3). ALP increases the local rate of inorganic phosphate release and aids in mineralisation while reducing extracellular pyrophosphate, which is an inhibitor of mineral formation (Vimalraj, 2020).

OPN and BSP are co-expressed in osteoblasts and osteoclasts. These genes encode proteins that promote adhesion of the cells to the bone matrix through the RGD (Arg-Glu-Asp) cell adhesion sequence. OPN is an acidic molecule; its central section consists of sequences that communicate and interact with seven integrins. OPN is a crucial factor in bone remodelling and settling osteoclasts in the bone matrix (Zhao et al., 2018). On the other hand, BSP, which is highly negatively charged, can isolate calcium ions while conserving polyglutamate regions with hydroxyapatite crystal nucleation potential. BSP allows the attachment and activation of osteoclasts through the RGD motif (Huang et al., 2005). qPCR analysis has revealed that elevated OPN and BSP expression is an osteogenic signature (Xu et al., 2018).

OPG (Iaquinta et al., 2019) is a decoy receptor for RANKL. It is secreted by osteoblasts to inhibit osteoclast differentiation: OPG binds to RANKL to block the interaction between RANK and RANKL (Kenkre & Bassett, 2018). Kang et al. (2014) reported very low OPG expression during osteoclastogenesis in osteoclasts involved in alveolar bone resorption. OPG expression plays a role in autoregulation in the later phase of osteoclastogenesis (Kang et al., 2014).

Several studies have shown the role of miRNAs in bone turnover, such as miR-940, which promotes in vitro osteoblast differentiation from hMSC (Hashimoto et al., 2018; Konukoğlu, 2019). However, the roles of miRNAs are very complicated, and additional studies are needed to understand them better.

Gene expression profile of osteoclast differentiation

The Notch signalling pathway is highly conserved; it regulates cell proliferation and differentiation, determines cell fate and is involved in cellular processes in adult tissues, including skeletal tissue development and regeneration (Luo et al., 2019). The Notch pathway regulates bone marrow mesenchymal progenitors by suppressing osteoblast differentiation and NOTCH1 overexpression inhibits osteoblastogenesis in stromal cells. Hence, activation of Notch signalling has a negative effect on osteoblast differentiation. When exposed to an osteogenic induction medium, MSC are forced to undergo epigenetic modifications, resulting from the upregulation of miR-139-5p, a phenomenon that inhibits NOTCH1 signalling activity, triggering osteoclast differentiation (Xu et al., 2018). However, NOTCH1 deletion indirectly promotes osteoclast differentiation through the enhancement of osteoblast-lineage-cell-mediated stimulation of osteoclastogenesis (Konukoğlu, 2019).

Osteoclast Associated Ig-Like Receptor (OSCAR), RANK, NFATC and CTSK are predominantly expressed by active osteoclasts (Konukoğlu, 2019; Srikanth et al., 2016). CTSK expression is regulated by the RANKL/RANK signalling pathway, which is one of the important pathways for osteoclastogenesis. Activation of this signalling pathway in osteoclast precursors enhances the pro-osteoclastogenesis transcriptional factor NFATC1, which allows the initiation of CTSK transcription to occur (Fig. 2) (Dai et al., 2020).