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Modelling the Osmotic Behaviour of Human Mesenchymal Stem Cells

Author:
Casula, Elisa, Traversari, Gabriele, Fadda, Sarah, Klymenko, Oleksiy V., Kontoravdi, Cleo, Cincotti, Alberto
Source:
Biochemical engineering journal 2019 pp. 107296
ISSN:
1369-703X
Subject:
blood, cell membranes, cryopreservation, cryoprotectants, dimethyl sulfoxide, humans, ions, mathematical models, mechanics, mesenchymal stromal cells, model validation, osmolality, osmosis, permeability, prediction, solutes, sucrose, surface area, umbilical cord
Abstract:
In this work, a novel mathematical model for the description of the osmotic behavior during the cryopreservation of human Mesenchymal Stem Cells (hMSCs) from Umbilical Cord Blood (UCB) is proposed. In cryopreservation, the two-parameter formalism of perfect osmometer behavior is typically adopted and preferred due to its simplicity: cell volume osmotic excursions are described as due only to the passive trans-membrane transport of water and permeant solutes such as cryoprotectant agents (CPAs); intracellular solutes, responsible of the isotonic osmolality, are assumed to be impermeant. The application of the two-parameter model fails to capture the osmotic response of hMSCs whenever a swelling phase is involved, as demonstrated by the authors. To overcome this limitation, the imperfect osmometer behavior of hMSCs is successfully modelled herein by improving the two-parameter formalism through the coupling of osmosis with cell mechanics and cell membrane Surface Area Regulation (SAR): now the transmembrane permeation of solutes (ions/salt) during the swelling phase through the temporary opening of mechanosensitive channels is allowed. This way cells can reach an equilibrium volume different from the initial isotonic one, when isotonic conditions are re-established after contact with impermeant or permeant solutes, such as sucrose or dimethyl-sulfoxide (DMSO), respectively. The sequential best-fit procedure adopted to adjust model parameters is reported herein along with model validation through full predictions.
Agid:
6545146