Discussion:
In this study we highlighted the potential of bioinformatic, and systems biology tools and approaches to provide a better insight into the interaction of biomaterials with stem cells. Bioinformatic analyses can unravel subtle molecular interactions between biomaterials and stem cells. Using such information will allow for the optimization of stem cell differentiation in regard to time, expense, and quantity of properly differentiated cells. Here, we briefly discuss the impact of biomaterials and of cells of origin on stem cell differentiation.
Recent studies have shown that the microenvironment in which stem cells grow has a dramatic effect on the efficiency of generated cells (Tibbitt & Anseth, 2012). For example, it has been shown that hyaluronic acid-based 3D hydrogels can successfully be used to culture hippocampal neural progenitor cells (Tarus et al., 2016). However, use of a 3D system has limitations, as this dimensionality can negatively impact the size and shape of 3D spheres. For instance, Hydrogel films of 3-hydroxybutyrate and 3-hydroxyhexanoate copolymers has been used in 2D form to drive NSCs toward neurons (Xu et al., 2010). This is one example in which the ideal culturing techniques have yet to be completely elucidated, stressing the importance to better understand 2D versus 3D culturing systems. In our study, we delved deeper into the importance of biomaterial dimensionality for NPC differentiation and found that NPCs cultured on 2D porous polystyrene scaffolds appear to produce more transcriptionally mature neuronal cells than those cultured on 3D biomaterials based on their transcriptome profile. Our data also indicates the importance of specific biomaterial use, as NPCs were differentiated more efficiently on a 3D hyaluronic acid compared to 2D, but both of these results were overshadowed by the efficiency of NPC differentiation on 2D porous polystyrene scaffolds.
In addition to the effect of biomaterials on stem cell differentiation, the cell of origin has been shown to be an important factor in determining the quality of the differentiated cell. For example, Wu et al. compared biological characteristics of different adult multipotent stem cells, including stem cells isolated from human bone marrow, placental decidua basalis and urine, based on their ability in colony formation, morphology, proliferation and differentiation into different cell types (Wu et al., 2018). They showed that BM-MSCs have a higher potential to differentiate into chondrogenic and osteogenic cell lineages than other multipotent stem cells (Wu et al., 2018). In another study, Rim et al. took a similar approach, but they aimed to determine the effect of the cell of origin on iPSC differentiation. iPSCs were generated from four different cell of origins, including dermal fibroblasts (DF), peripheral blood mononuclear cells (PBMC), cord blood mononuclear cells (CBMC), and osteoarthritis fibroblast-like synoviocytes (OAFLS) (Rim et al., 2018). Differentiation of these cells toward chondrogenic fate showed iPSCs derived from CBMC had the highest differentiation potential (Rim et al., 2018). These groups found that cell of origin played an important role in the functionality of differentiated cells. However, we believed that there are more subtle differences between cells differentiated from separate cells of origin that may be difficult to interpret with some functional assays. Therefore, we investigated differences at the transcriptomic level that would give a more precise similarity/difference readout between the different cells of origin as they were differentiated.
Therefore, we attempted to determine whether MSCs generated from different cell sources, including hiPSCs, hESCs, and BM, have different capacities during osteogenesis. Our results show that osteoblasts generated from hiPSC-MSCs and hESC-MSCs clustered together, while BM-MSC derived osteoblasts displayed a greater variability in their transcriptomic profile. Furthermore, our results clearly showed more genes involved in cell cycle and extracellular matrix organization in differentiated osteoblasts from MSCs originated from ESCs and iPSCs than BM-MSCs. The transcriptomic profile of each separately derived osteoblast lineage was mapped on the regulatory network specific for ESC-MSCs, and large differences between each lineage were evidently observed. In this regulatory network, MYC was significantly decreased exclusively in the ESC-MSC generated osteoblast and was the main regulator of the entire network in this lineage. Ontology analysis of MYC target genes also showed that cell cycle and mitochondrial terms were the most significant affected biological terms. This finding is of interest because Hanna et al and Persson et al showed that the proliferative capacity of osteogenic differentiating cells reduces in later stages of this process (Hanna, Mir, & Andre, 2018; Persson et al., 2018). Therefore, according to the expression pattern of MYC in osteoblasts generated across the three cells of origin, the ESC-MSC derived osteoblasts may be the most biologically relevant differentiated cell. Although more analysis is required to get the full picture, our analysis shows the importance of looking at the transcriptomic level of different cells of origin to understand which lineage may be the best for a particular differentiation.