Process control and in silico modeling strategies for enabling high density culture of human pluripotent stem cells in stirred tank bioreactors

Abstract

Summary The routine therapeutic and industrial applications of human pluripotent stem cells (hPSCs) require their constant mass supply by robust, efficient, and economically viable bioprocesses. Our protocol describes the fully controlled expansion of hPSCs in stirred tank bioreactors (STBRs) enabling cell densities of 35 × 10 6 cells/mL while reducing culture medium consumption by 75%. This is achieved by in silico process modeling and computable upscaling. We provide a detailed blueprint for systematic process development of hPSCs and their progenies. For complete details on the use and execution of this protocol, please refer to Manstein et al. (2021) .

Matrix-free human pluripotent stem cell manufacturing by seed train approach and intermediate cryopreservation

“…The hiPS cell line GMPDU_8 (CD34 + hPBHSC_GMPDU_SeV-iPS8; MHHi008-A) [ 22 ] was used for validation of the multiple passage suspension culture approach. For the initial bioreactor inoculation, conventional 2D cell culture was performed as described [ 14 ]. In brief, cells were cultivated on Geltrex (Thermo Fisher Scientific)-coated flasks at feeder-free conditions.…”

“…For media perfusion a porous outflow filter (20- to 40-mm pore size) was incorporated as a cell retention device similar to that described by Weegman et al [ 23 ]. Calibration of pumps and sensors including other basic culture conditions for stirred suspension culture was performed as described elsewhere [ 14 ]. In brief, bioreactors were inoculated at a viable cell density of 5 × 10 5 cells/mL in E8 + RI + Pluronic F-68 (Thermo Fisher Scientific) in a total volume of 150 mL (DASbox Mini Bioreactor System) or 500 mL (DASGIP Bioblock) and cells were cultivated at 37 °C.…”

“…The STBR technology enables a homogeneous cultivation environment while key process parameters such as pH, dissolved oxygen concentration (DO), the rate of media perfusion and other conditions can be monitored and controlled [ 4 ]. Applying and optimizing these parameters recently enabled the advanced high density bioprocessing of hPSCs by metabolic control and in silico modelling, yielding up to 35 × 10 6 cells/mL in a culture scale of 150 mL, which is equivalent to a 70-fold expansion of the inoculated hPSCs within 7 days [ 13 , 14 ]. However, the applicability of this protocol to clinically relevant process dimensions and to specific GMP requirements is currently limited by the necessity to pre-expand the hPSCs in 2D adherent culture for subsequent bioreactor inoculation in 3D suspension culture.…”

A review of protocols for human iPSC culture, cardiac differentiation, subtype-specification, maturation, and direct reprogramming

“…However, the proposed albumin-free protocols have not been optimized or explored in depth, and CDM3- and RPMI/B27-based protocols dominate the landscape of hiPSC-derived cardiomyocyte (hiPSC-CM) differentiation due to their reliability, yield, and cost. The majority of recent developments have focused on small improvements to these protocols, such as with the addition of transferrin to the CDM3 basal medium ( Zhang et al., 2021a ), and on increased scalability via the use of bioreactors either with CDM3 ( Manstein et al., 2021a , 2021b ) or RPMI/B27 ( Laco et al., 2020 ) systems. Similarly, the re-modulation of the Wnt/β-catenin pathway at different time points has been proposed to increase the yield of cardiomyocytes in an RPMI/B27-based differentiation ( Buikema et al., 2020 ; Maas et al., 2021 ; Sharma et al., 2018 ).…”

Bioprocess Economic Modeling: Decision Support Tools for the Development of Stem Cell Therapy Products

“…Interestingly, computational models should undergo iterative refinement alongside their respective bioprocesses, since, as more becomes known about an underlying bioprocess, the robustness and fidelity of its respective model can be progressively increased. Recent work by Manstein et al presents an excellent example of this [ 23 , 24 ]. In their work, after establishing an initial mechanistic model based on experimental data, they cyclically challenged the model by altering input parameters and verifying if the model’s outputs corresponded with reality.…”