Identifying Criteria and Indicators for Assessing External Forcing and Internal Variability-Generated Uncertainly in Stem Cell Bioprocesses
Understanding the prevalence of pre-existing intrinsic and extrinsic variability over external noise is important for developing key indicators to identify and predict the bioprocess variability. The phenomenon of “intrinsic disorder” has been widely studied in bioprocess conditions, and understanding and controlling the sources of variability are anticipated to play important roles in improving bioprocess control and optimization (Kim and Kino-oka, 2020b; Misener et al., 2018). Indeed, it has been shown that the phenotypic variability among stem cells can contribute to the inherent differences resulting from donor-dependent variability as well as changes induced during cellular reprogramming and expansion and cell handling processes such as passaging, freezing, or thawing (Kim and Kino-oka, 2018). The causes of cell variability occurring in a series of bioprocessing steps and the related mechanisms are often still a “black box.”
While the impact of forces and mechanical environment on the structure and function of stem cells has long been appreciated (Ivanovska et al., 2015; Liu et al., 2010; Maul et al., 2011; Mendelson and Frenette, 2014; Naqvi and McNamara, 2020), the importance of mechanical forces in bioprocessing has only recently been investigated. The potential sources of cell-to-cell variability to bioprocesses—described here as intrinsic (intra-cellular) noise, extrinsic (inter-cellular) noise, or external (environmental) noise—have been difficult to investigate because the evidence assessing the effect of external environmental fluctuations on a system is limited (Hilfinger and Paulsson, 2011). The intrinsic noise often refers to the inherent stochasticity of biochemical processes, such as transcription and translation (Raser and O’shea, 2005; Lei, 2009; Soltani et al., 2016; Swain et al. 2002; Thomas, 2019). In contrast, the extrinsic noise and external noise are generated form cellular processes, such as cell-cell and cell-matrix contacts, and cell migration or form environmental fluctuation, respectively (Lei, 2009; Hilfinger and Paulsson, 2011; Swain et al. 2002). Thus, it is essential to identify all factors causing intrinsic disorder within a bioprocess, based on the noise sources associated with a process in the locations and interactions among different sources, both within and outside the cells.
The adaptation mechanisms involve a multistep cellular mechanotransduction process, including: (i) mechanosensing — conversion of mechanical forces into local mechanical signals, such as fluid shear stresses, which initiate a cellular response and (ii) mechanosignaling transduction of an intracellular signaling event occurring in response to a mechanical force and gene expression or protein activation, which ultimately alters cell phenotype and function (Argentati et al., 2019; Ingber, 2018; Mammoto et al., 2013; Sun et al., 2012) (Figure 3 ). Cells probe and respond to the forces of their surroundings through cytoskeletal networks composed of actin filaments (F-actin), myosin motors (Jégou and Romet-Lemonne, 2021; Lenormand et al., 2007; Matthews et al., 2006). F-actin filaments are assembled with the assistance of actin nucleators, such as formins and Arp2/3, which are involved in generating long unbranched and branched actin filaments, respectively (Rotty et al., 2013; Zalevsky et al., 2001). Furthermore, the myosin-driven contraction of the actin cytoskeleton plays a central role in the ability of stress fibers to sense matrix mechanics and generate contractile forces inside cells (Weirich et al., 2021). Both externally-applied and cell-generated mechanical forces are transmitted across the cytoskeleton to the nucleus, where they alter the epigenetic state and expression of genes relating to cellular homeostasis to maintain self-renewal and pluripotency (Li et al., 2020; Vining and Mooney, 2017). Based on these mechanisms, the intracellular processes favoring the interpretation of mechanical cues can be classified here. In this context, the first step is universally recognized as the change in cell behaviors, such as cell-cell interaction, cell-substrate interaction, and cell migration, which serves as the central trigger, converting these input signals into cellular outputs, which in turn manifest as changes to the actin cytoskeleton of the cells. In addition, the actin-myosin cytoskeleton is a major “integrator” and “organizer” of mechanical and biochemical signaling inputs that continuously senses, organizes, and integrates these signals using its control “effector” protein belonging to the Rho family of GTPases (Arnold et al., 2017; Kimura et al., 1996). Specially, the antagonistic activities of RhoA and Rac1 GTPases play a role in the dynamics of myosin-mediated contraction and relaxation during cell migration in several cellular settings (Thanuthanakhun et al., 2021). This confirms that the cells adapt to culture environment through the alteration of the Rho-Rho kinase-phospho-myosin pathway, influencing the epigenetic modifications and the transcription factors that contribute to their inherent states (Thanuthanakhun et al., 2021). Studies using ESCs and iPSC cultures revealed that the time-dependent regulation of two key bivalent epigenetic marks, histone H3 trimethylation at lysine 4 (H3K4me3) and histone H3 trimethylation at lysine 27 (H3K27me3), could contribute to the maintenance of the stem cell state and differentiation potential (Harikumar and Meshorer, 2015; Li et al., 2018; Thanuthanakhun et al., 2021). This regulation can take place through epigenetic changes and may be manifested as a “memorizer.” Memories acquired during culture are initialized by the disruption of the actin cytoskeleton during enzymatic digestion-based passage culture while maintaining cell homeostasis.
Thus, by adopting these definitions, we can describe the intracellular processes favoring the interpretation of mechanical cues and concentric groups acting to deliver the signals from cell surface receptors in the plasma membrane to the nucleus via cytoskeleton (Figure 4 ). This chain reaction can eventually lead to epigenetic changes and may be manifested as “memorizer.” These multiple correlations can be described using the key factors “trigger,” “effector,” and “memorizer,” and the impact of mechanical force application to the cells may be best described using the terms “sensory,” “short-term,” and “long-term” memory, implying that any of the various definitions or various components can be used to describe different coordinating systems. The short-term memory induced during growth processes is transient and repeatedly undergoes initialization and re-formation during the passage culture. However, the acquired long-term memory is permanent and is stored or consolidated after passage culture. This simple conceptual framework and the comprehensive classification approach described here provides potential mechanistic insights into the formation and maintenance of epigenetic memory for lasting changes in cell behavior during different growth phases, and indicates that the appropriate seeding density and time required to maintain cellular homeostasis are important from a technological perspective for developing and optimizing bioprocess design and operation.
Mechanobiological Conceptual Framework for Assessment and Prediction of Stem Cell Bioprocess: A Case Study
To understand how cells control and exploit the extent of intrinsic disorder in bioprocesses, we must develop a conceptual framework to define the objective of the assessment. Here, we illustrate a conceptual framework with example case studies and discuss about future outlooks and challenges on the use of data-driven approaches (Figure 5 ). The comparison between intrinsic disorder sources highlights how the consensus on biological indicators of intrinsic disorder captures the main features of intrinsic disorder and correlates well with growth kinetic profiles. Growth kinetics studies are often used to optimize bioprocesses because cells respond to external disturbances by maintaining a homeostatic level of mechanical tension, and these studies lead to the establishment of specific criteria for cell and tissue production (Ehrig et al., 2019; Kato et al., 2018; Nath et al., 2018). In many stem cell studies, it has been reported that the ability to adjust growth kinetics in response to unpredictable environmental fluctuations is associated with the impacts of mechanical force changes (Borys et al., 2021; Kato et al., 2018; Nath et al., 2018). Four basic shapes are hypothesized as plausible to represent the dominant growth curve shape experienced by bioengineers during the culture period. There are four different types of growth: time-dependent, time-delay, tardive, and uncertainty. The identification of phenomena related to the quality of the growth kinetics analysis depends directly on the processing applied to the initial and subsequent data. The use of growth kinetics analysis in the estimation of the impact of study interventions not only improves the statistical quality of the estimates but also enables rational process optimization alongside the dynamic changes of cell requirements throughout the process progression.
For successful development and optimization of stem cell bioprocesses, we must study the fluctuations in current bioprocesses and the variability in stem cells and derived products allowed by regulators and their applications to obtain an understanding of improvement areas (Kim and Kino-oka, 2020a; Kim and Kino-oka, 2020b). The main problem is the lack of biological indicators that use formulas to facilitate their assessment and comparability of bioprocess effectiveness for various external stimuli. Thus, the designed classifier based on mechanical signals will help understand the relationship between externally forced and internally generated variations and identify the time-varying noise generation within and between processes. Based on mechanobiological conceptual frameworks, identifying and quantifying the relationship between inputs, outputs, and changes in bioprocess properties can provide deep insights into bioprocess interactions and would allow the opportunity to balance the costs and benefits of cell production while explicitly acknowledging and internalizing the unintended outcomes of bioprocess strategies. The proposed indicators for assessing a fluctuating interaction network and time-varying stability and consistency are used together with target or threshold values of the metrics employed for determining the degree of stability of a bioprocess responding to externally applied forces. The discussion on which biological indicators are more suitable for describing the cause of intrinsic disorders in stem cell bioprocesses and predicting the impact of mechanical forces in nature and engineered systems is still ongoing. The processes should fit within an overall stem cell-based bioprocess, and where current automation is capable of replicating manual processes, if we consider future production systems there must be a move towards machine tools that are more advanced than a human manual process (Ratcliffe et al., 2011). The determination of growth kinetics, thus, paved a way for the optimal design of the operating conditions and process for optimal product formation. Acceptable ranges for a subset of these indicators were established based on a combination of mechanical, biological, and clinical studies and prior knowledge. The determination of the boundary conditions of applied mechanical forces is important to ascertain robust and reproducible cell culture processes that result in the right product quality and suitable product yields. The proposed conceptual framework here will help reduce process variability and increase within- and between-process reproducibility by understanding bioprocess forces and cellular responses of interest and will ensure that the calibrated apparatus performance checks are carried out and operation limits are set adequately.