Manstein, F.; Ullmann, K.; Kropp, C.; Halloin, C.; Triebert, W. et al.: High Density Bioprocessing of Human Pluripotent Stem Cells by Metabolic Control and in Silico Modeling. In: Stem Cells Translational Medicine 10 (2021), Nr. 7, S. 1063-1080. DOI: https://doi.org/10.1002/sctm.20-0453
Zusammenfassung: | |
To harness the full potential of human pluripotent stem cells (hPSCs) we combined instrumented stirred tank bioreactor (STBR) technology with the power of in silico process modeling to overcome substantial, hPSC-specific hurdles toward their mass production. Perfused suspension culture (3D) of matrix-free hPSC aggregates in STBRs was applied to identify and control process-limiting parameters including pH, dissolved oxygen, glucose and lactate levels, and the obviation of osmolality peaks provoked by high density culture. Media supplements promoted single cell-based process inoculation and hydrodynamic aggregate size control. Wet lab-derived process characteristics enabled predictive in silico modeling as a new rational for hPSC cultivation. Consequently, hPSC line-independent maintenance of exponential cell proliferation was achieved. The strategy yielded 70-fold cell expansion in 7 days achieving an unmatched density of 35 × 106 cells/mL equivalent to 5.25 billion hPSC in 150 mL scale while pluripotency, differentiation potential, and karyotype stability was maintained. In parallel, media requirements were reduced by 75% demonstrating the outstanding increase in efficiency. Minimal input to our in silico model accurately predicts all main process parameters; combined with calculation-controlled hPSC aggregation kinetics, linear process upscaling is also enabled and demonstrated for up to 500 mL scale in an independent bioreactor system. Thus, by merging applied stem cell research with recent knowhow from industrial cell fermentation, a new level of hPSC bioprocessing is revealed fueling their automated production for industrial and therapeutic applications. | |
Lizenzbestimmungen: | CC BY-NC 4.0 Unported |
Publikationstyp: | Article |
Publikationsstatus: | publishedVersion |
Erstveröffentlichung: | 2021 |
Die Publikation erscheint in Sammlung(en): | Fakultät für Mathematik und Physik |
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