Microplate-based impedance and thermal sensing system for concurrent cell viability and counting analysis

Cell count and viability are critical parameters in biological research, drug discovery, and bioprocessing. Traditional methods for assessing these metrics often rely on destructive, end-point analyses. This research presents a novel multi-parameter sensing platform that enables concurrent analysis of cell viability and count in a microplate format. The platform combines thermal-based and impedance-based sensing to harness the distinct responses of these methods to variations in cell number and viability. Crucially, both techniques are influenced by cell viability and count, but to different degrees. This difference in sensitivity allows for the exploitation of both methods to independently assess these parameters. Thermal sensing primarily quantifies cell biomass, while impedance measurements are more sensitive to membrane integrity changes associated with cell viability. The integration of these sensing elements into a standard microwell format facilitates real-time and label-free measurements. Experiments with Saccharomyces cerevisiae cultures at various concentrations and viability states demonstrated the platform's capabilities. Multivariate regression models were developed to independently predict cell number and viability, achieving root mean square errors of 0.106 ×10⁷ cells and 19.67% viability respectively. Notably, performance improved at higher cell concentrations, with viability prediction error reduced to 5.02%. This integrated approach shows promise for continuous, non-destructive monitoring of cell cultures, offering a cost-effective alternative to traditional end-point analysis methods. The platform's ability to provide real-time insights into cell population dynamics could significantly enhance various applications in biotechnology, including bioprocess optimization, drug screening, and toxicity testing. Furthermore, its compatibility with standard microplate formats facilitates easy integration into existing laboratory workflows.