Mechanobiological Understanding of Internal and External Variability-Generated Uncertainty in Stem Cell Bioprocesses
Understanding the complex interplay between internal and external forces affecting cellular mechanotransduction under correctly combined conditions could be of benefit for optimal bioprocess design.Figure 2 illustrates the intrinsic disorder characteristics of stem cell bioprocesses dominated by external forcing and internal variability. Cellular functions are influenced not only by internal cellular machinery but also by external mechanical cues from the surrounding microenvironment. Forces that arise in biological systems can be classified into two main categories: internal and external (Chen et al., 2019; Mao and Baum, 2015; Vining and Mooney, 2017). Internal forces are defined as forces generated by cells themselves. The ability of cells to adhere, move, and divide requires a cytoskeleton that can reversibly assemble into a wide range of structures. The spatiotemporally organized forces produced between cells and their extracellular environment, as well as intercellular forces within cell colonies, play pivotal roles in driving these migration steps (Shuzui et al., 2019). In this way, the regulated re-modeling of cell-substrate and cell-cell interactions during growth aids the maintenance of cellular homeostasis. Conversely, external forces are defined as forces, such as tensile, compressive, or shear stresses, which are applied to cells from their environment and culture operation (Goodwin and Nelson, 2021; Ingber, 2018; Wall et al., 2018). These mechanical forces resulting from both intracellularly-generated and externally-applied forces can alter the internal equilibrium state, thereby affecting cellular mechanobiological responses. Experimental and theoretical investigations have determined the architecture of external noise and its causal factors, though substantial uncertainty surrounds the importance of mechanical forces (Charras and Yap, 2018; Ivanovska et al., 2015; Jégou and Romet-Lemonne, 2021). When subjected to mechanical forces, cells adopt a mechanoprotective and adaptative behavior, mechanically explained by a strain-stiffening process, to control membrane integrity, cell shape, and structural integrity (Doss et al., 2020; Stricker et al., 2010; Walker et al., 2020b). The situation is fundamentally different in signaling and regulatory networks. Interactions embedded in such signaling networks need to be extremely dynamic and versatile to be able to respond quickly to specific stimuli and to adapt to this response over time (Kinney et al., 2019; Mack et al., 2004; Mendez and Janmey, 2012). If the response time is too short, these forces are not transmitted sufficiently, and if it is too long, these forces are transmitted across the cytoskeleton to the nucleus, where they result in substantial deformation. These forces and deformations can regulate chromatin structure and transcriptional activity through a number of mechanisms (Miroshnikova et al., 2017; Tajik et al., 2016). Both magnitude and frequency of an external force affect the consequence (Freund et al., 2012; Kaazempur Mofrad et al., 2005; Mack et al., 2004). Many questions concerning the generation of internal and external mechanical forces in gene expression and their effects on cellular behavior remain unanswered. Since all cellular processes, including transcription and translation, are basic to stochasticity in biochemical processes, the fluctuations in the amount and state of mRNA and corresponding protein levels will influence their cellular functions (Raser and O’shea, 2005; Thomase et al., 2018). The mechanisms by which externally applied mechanical forces control stem cells during culture do not involve the direct and spontaneous triggering of differentiation per se, but rather the formation of a particular behavioral memory under dynamic microenvironment that can generate several mechanical stimuli. Importantly, there is the added complexity of demonstrating that the key process input variables are critical for the resultant product quality in a combinatorial process. In process in relation to a combinatorial operation, it is very difficult to determine cause-and-effect interactions and their relationship to the final product quality, since inputs may act singly, agonistically, or cumulatively on process outputs and final process output is a viable cell that cannot currently be comprehensively defined.