Cell seeding into scaffolds plays a crucial role in the development of efficient bone tissue engineering constructs. Hence, it becomes imperative to identify the key factors that quantitatively predict reproducible and efficient seeding protocols. In this study, the optimization of a cell seeding process was investigated using design of experiments (DOE) statistical methods. Five seeding factors (cell type, scaffold type, seeding volume, seeding density, and seeding time) were selected and investigated by means of two response parameters, critically related to the cell seeding process: cell seeding efficiency (CSE) and cell-specific viability (CSV). In addition, cell spatial distribution (CSD) was analyzed by Live/Dead staining assays. Analysis identified a number of statistically significant main factor effects and interactions. Among the five seeding factors, only seeding volume and seeding time significantly affected CSE and CSV. Also, cell and scaffold type were involved in the interactions with other seeding factors. Within the investigated ranges, optimal conditions in terms of CSV and CSD were obtained when seeding cells in a regular scaffold with an excess of medium. The results of this case study contribute to a better understanding and definition of optimal process parameters for cell seeding. A DOE strategy can identify and optimize critical process variables to reduce the variability and assists in determining which variables should be carefully controlled during good manufacturing practice production to enable a clinically relevant implant.

The repair of large and complex bone defects could be helped by a cell-based bone tissue engineering strategy. A reliable and consistent cell-seeding methodology is a mandatory step in bringing bone tissue engineering into the clinic. However, optimization of the cell-seeding step is only relevant when it can be reliably evaluated. The cell seeding efficiency (CSE) plays a fundamental role herein. Results showed that cell lysis and the definition used to determine the CSE played a key role in quantifying the CSE. The definition of CSE should therefore be consistent and unambiguous. The study of the influence of five drop-seeding-related parameters within the studied test conditions showed that (i) the cell density and (ii) the seeding vessel did not significantly affect the CSE, whereas (iii) the volume of seeding medium-to-free scaffold volume ratio (MFR), (iv) the seeding time, and (v) the scaffold morphology did. Prolonging the incubation time increased the CSE up to a plateau value at 4 h. Increasing the MFR or permeability by changing the morphology of the scaffolds significantly reduced the CSE. These results confirm that cell seeding optimization is needed and that an evidence-based selection of the seeding conditions is favored.

The osteogenic differentiation of progenitor populations allows analysis of cell functionality as well as creating a platform for investigating stem cells for bone tissue engineering. Protocols used for osteogenic differentiation of progenitor cells are often identical to those detailed for bone marrow mesenchymal stem cells, however this may be flawed due to cell populations residing in different niches and being in distinct stages of differentiation. We herein describe the individual and combined effects of known osteo-inductive agents; dexamethasone (Dex), 1,25-dihydroxyvitamin D3 (VitD3), all trans-retinoic acid (atRA), cyclic AMP (cAMP) and bone morphogenic protein 2 (BMP2) in combination with fetal bovine serum (FBS) on osteogenesis of human periosteal derived cells (hPDCs). The addition of Dex&FBS was essential for the transition of hPDCs to an ALP positive cell population. Subsequently, atRA, Dex&FBS and BMP2 were required for the expression of transcription factors governing osteogenesis and hence differentiation towards a mature osteoblast. It is also hypothesized that Dex has no direct effect on the differentiation of hPDCs, instead its effect is to augment differentiation in combination with other factors. These data provide a comprehensive assessment of known osteogenic factors, in a novel multiplex system, to evaluate their effect on progenitor cell differentiation.

DNA measurement and RNA extraction are two frequently used methods for cell characterization. In the conventional protocols, they require similar, but separate samples and in most cases, different pretreatments. The few combined protocols that exist still include time-consuming steps. Hence, to establish an efficient combined RNA extraction and DNA measurement protocol for two-dimensional (2D) and three-dimensional (3D) cell cultures, a PicoGreen-based DNA measurement was integrated in an existing RNA extraction protocol. It was validated by analysis of the influence of different lysis buffers, RLT, RA1, or Trizol, used for RNA extraction on the measured DNA concentration. The DNA cell yield was evaluated both in cell suspensions (2D) and on 3D cell-seeded scaffolds. Results showed that the different RNA lysis buffers caused a concentration-dependent perturbation of the PicoGreen signal. The measured DNA concentrations in 2D and 3D using RLT and RA1 buffer were comparable, also to the positive control. We, therefore, concluded that RNA extraction protocols using RA1 or RLT buffer allow the integration of a DNA quantification step without the buffer influencing the results. Hence, the combined DNA measurement and RNA extraction offer an alternative for DNA measurement techniques that is time and sample saving, for both 2D cell cultures and specific 3D constructs.