Rationalisations: 143

The phenotypic change that was been observed in the experiments could be attributed to cell differentiation. This a possible effect of the culture conditions, the literature regarding embryonic and pluripotent cells suggests that seeding at low densities causes differentiation, much like the conditions that the cells were cultured under256–258_{. The gene expression fold changes observed for key pluripotency}

markers DNTM3B and SOX2 suggest that the cells are differentiating. Furthermore, the experimental data shows evidence of higher metabolite rate and phenotypic change, which is often concomitant with cell differentiation and/or the presence of a different cell population. However, there is evidence within the experiments that is contrary to the cells differentiating, suggesting it is the protocol parameters i.e. medium exchange which cause variation. For instance, no morphological change was observed under light microscopy throughout the ten passages; additionally, NucleoCounter cell analysis showed no significant change in the measured cell diameters between the two routes (Figure 33C) and even cultures seeded at higher densities exhibit levels of phenotypic change.

Another proposed explanation of the observed phenotypic variation is that the change is linked to cell death. This rational is reinforced by the fact that the phenotypic change is not cumulative over time, suggesting that the expression presents just before the cells start to die, resulting in a non-cumulative expression of SSEA-1. However, cell viability and SGR data show evidence that is contrary to this, as cells in route A2 have on average, higher cell viability and growth rate which would not be expected with cell death (Figure 33A and B). Furthermore, at passage cycle 7 there is a drastic decrease in OCT3/4 yet in the next passage there is no significant decrease in cell number or cell viability.

Density had been reported to be a crucial factor in some cell culture protocols, particularly those using embryonic cell lines, as it is reported to have an influence on metabolic behaviour and both directed and spontaneous differentiation87,125,257,259,260_{. In experiment 4 the observed differences in behaviour}

could be attributed to the seeding densities used, since this was the main parameter that was altered between the two routes. Nonetheless, density is unlikely to be the only driving force, since the initial experiments showed no drastic difference in SMR and growth based on density (Figure 29 and Figure 30). Furthermore, the results from experiment 5 demonstrated that growth medium exchange has a significant effect on the SMR on the cells regardless of density, implying that cell metabolism based on feeding regime had a notable impact on SMR and marker expression as well as seeding density.

An alternative rationalisation that has been considered is that the phenotypic change is linked to a cell ‘selection’ or ‘survival’ mode, which has no observed instances of support in the literature. It can be suggested that the cells are conditioned to metabolise nutrients differently under the excess nutrient

conditions which results in the marker expression change. Route A2 appears to be a favourable condition as the cells had higher cell viability and better growth performance. Therefore, it is unclear as to why the cells would default to selection or survival mode as they are in an optimal cell time range; thus, they should not experience any starvation or stress. Moreover, there is no observed response to excess nutrients in the metabolite data in experiments 3.1 to 3.4. Furthermore, if phenotypic change is a response to nutrient levels it would be expected that route A1 would also exhibit drastic changes in marker expression and/or SMR rate as fewer nutrients per cell were available in comparison to route A2 (due to a higher cell density in route A1). However, it is unclear if the feed/no feed regime is a potential driving force causing the observed change as no metabolic data is available until the passage day, resulting in unknown behaviour trends on the intermediate days of route A2. Furthermore, if the phenotypic change is linked to a cell ‘selection’ or ‘survival’ mode the most drastic change in marker expression during, E.P7 48.6 % (OCT3/4) and 6.5 % (SSEA-1), it would be expected to have occurred when the cells were performing at their best. This is not the case as the drastic change does not occur when the cells are at their highest rate of metabolism or growth rate.

The above rationalisations highlight that even in a cell line that is considered to be a stable reference point; differences can be observed due to changes in parameters and conditions, over time. These different conditions such as when to perform medium exchanges and the use of seeding densities that vary i.e. through the use non-standardised seeding densities or split ratios, are often left to the user’s discretion in many protocols. Evidently, this results in noteworthy effects on cell behaviour and characterisation outcomes. Split ratios are not best practice as innately cells will grow differently from passage to passage. For example, a split ratio of 1:3 can be drastically different from passage to passage, particularly if the cells grow at significantly different rates at different passages, which is a concomitant feature of cells that are revived from cryopreservation. In addition, split ratios that are based on observed confluency, are also likely to cause variation in cell growth dynamics. This is due to the subjective nature of observed confluency and its lack of accurate representation of cell number, therefore potentially resulting in non-ideal metabolite profiles that can influence cell growth behaviour, producing further cell system inconsistency. Differing levels of variability and non-conformity are detrimental to the successful use of reference cell lines, especially for those intended to be a standardised QC reference247,261_{. This is of relevant importance as the use of reference cell line is likely}

to see prominence due to the increase in assays and product validation. The advent of chimeric antigen receptor T-cell (CAR-T) therapies, could potentially use reference cell lines as QC reference standards for flow cytometry. Interestingly, flow cytometry analysis revealed marker profile differences; even when the cells were at the prescribed density of 66,667 cells/cm2_{, showing that even when cultured in}

This further emphasises the need to standardise parameters within protocols to minimise and control variation. This can be achieved by obtaining cell counts, ensuring that the input and output cells numbers are used to maintain optimal culture conditions for cell growth, without entering regions of metabolic instability. Consequently, this allows for greater control and consistency of the culture system as the seeding density and nutrient levels are predefined. The use of cell time, which is a concept that can be used to quantify the capability of a given volume of medium to sustain the growth of a given number of cells for a specific period, ensures that the culture system given the set density and nutrient availability will not enter a region of metabolic strain. This is important since imbalances in glucose and lactate levels have previously been shown to result in limitation of cell growth241,260 _{and importantly}

for reference lines, metabolite imbalances have been shown to impact the stability of marker expression. Metabolic strain is potentially problematic if not controlled, as reference lines are used for QC of CTPs need to demonstrate their stability over time, using gene and phenotype marker expression.