Online characterization of surrogate metrics for metabolic phenotype in human induced pluripotent stem cell bioprocessing

Abstract Human induced pluripotent stem cells (hiPSCs) are the most accessible source material for derivation of stem-cell-based therapies at scale. However, a disconnect exists between quality characteristics of phenotype in the pluripotent state, and downstream metrics for efficacy and safety. Bridging this gap is a major challenge. Given hiPSC plasticity, environmental conditioning plays a crucial role in guiding phenotype. This work presents a parallelizable scale-down approach, acquiring real-time data to inform hiPSC phenotype throughout biomanufacturing. We developed an optoelectronic instrumentation suite capable of measuring pH, dissolved oxygen, and cell density as important surrogates for phenotype in a scale-down expansion bioprocess. We were successful in obtaining continuous, integrated parametric data throughout cultivation and estimating metabolic characteristics of hiPSC phenotype. This system functions as a proof-of-concept tool for development of predictive models and monitoring strategies around the elucidation of phenotypic dynamics within hiPSC biomanufacturing. We have demonstrated a feasible open-source multivariate continuous monitoring approach at research scale that combines common process parameters with a scattering measurement against aggregate density. The combination of these parameters enables surrogate measurement of a metric for metabolic phenotype. This contribution emphasizes monitoring how the bioprocess influences variables important in the context of cell state, in broader pursuit of better understanding the link to downstream functionality and global optima in hiPSC biomanufacturing for regenerative medicine. Competing Interest Statement The authors have declared no competing interest. Footnotes mskallos{at}ucalgary.ca, kmurari{at}ucalgary.ca https://github.com/JxColter/Optoelectronics-Colter-J.-et-al.-2025