Abstract
Organoids are three-dimensional multicellular tissue constructs. When cultured in vitro, they recapitulate the structure, heterogeneity, and function of their in vivo counterparts. As awareness of the multiple uses of organoids has grown, e.g. in drug discovery and personalised medicine, demand has increased for low-cost and efficient methods of producing them in a reproducible manner and at scale. Here we focus on a bioreactor technology for organoid production, which exploits fluid flow to enhance mass transport to and from the organoids. To ensure large numbers of organoids can be grown within the bioreactor in a reproducible manner, nutrient delivery to, and waste product removal from, the organoids must be carefully controlled. We develop a continuum mathematical model to investigate how mass transport within the bioreactor depends on the inlet flow rate and cell seeding density, focusing on the transport of two key metabolites: glucose and lactate. We exploit the thin geometry of the bioreactor to systematically simplify our model. This significantly reduces the computational cost of generating model solutions, and provides insight into the dominant mass transport mechanisms. We test the validity of the reduced models by comparison with simulations of the full model. We then exploit our reduced mathematical model to determine, for a given inlet flow rate and cell seeding density, the evolution of the spatial metabolite distributions throughout the bioreactor. To assess the bioreactor transport characteristics, we introduce metrics quantifying glucose conversion (the ratio between the total amounts of consumed and supplied glucose), the maximum lactate concentration, the proportion of the bioreactor with intolerable lactate concentrations, and the time when intolerable lactate concentrations are first experienced within the bioreactor. We determine the dependence of these metrics on organoid-line characteristics such as proliferation rate and rate of glucose consumption per cell. Finally, for a given organoid line, we determine how the distribution of metabolites and the associated metrics depend on the inlet flow rate. Insights from this study can be used to inform bioreactor operating conditions, ultimately improving the quality and number of bioreactor-expanded organoids.
Highlights
Organoid technology is becoming increasingly prominent as a biomedical tool, with applications in drug discovery and personalised medicine
We develop a mathematical model of the Cellesce Expansion 1 (CXP1) system, with the goal of determining how glucose and lactate levels within the CXP1 bioreactor change as the operating conditions, and organoid growth characteristics, vary
For the sublimit approaximation model, Equations (2.49)-(2.52), we obtain an analytical expression for the glucose concentration, and the lactate concentration is numerically computed from Equation (2.50) subject to Equation (2.51) with a Runge-Kutta method using the in-built ordinary differential equation (ODE) solver ode45 in MATLAB
Summary
Organoid technology is becoming increasingly prominent as a biomedical tool, with applications in drug discovery and personalised medicine. Brain, kidney, and liver organoids are used to understand the underlying biological mechanisms in tissue development and tissue-drug interactions (Eisenstein, 2018; Kondo and Inoue, 2019; Tuveson and Clevers, 2019; Bock et al, 2020). Organoids are three-dimensional, multicellular structures which, when grown in vitro, recapitulate the structure, function, and heterogeneous cellular composition of in vivo tissues (Drost and Clevers, 2018). Their three-dimensional geometry means they are more representative of in vivo tissues than 2D cell cultures (Young and Reed, 2016). The stem cells are embedded in a supporting extra-cellular matrix (ECM) and cultured in carefully-controlled conditions designed to promote organoid growth. The surrounding ECM provides the biochemical and biomechanical cues needed for the cells to proliferate and differentiate into specialised cells, as happens in vivo (Huang et al, 2012; Eisenstein, 2018)
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