Despite the fact that yeast is a widely used microorganism in the food, beverage, and pharmaceutical industries, the impact of viability and age distribution on cultivation performance has yet to be fully understood. For a detailed analysis of fermentation performance and physiological state, we introduced a method of magnetic batch separation to isolate daughter and mother cells from a heterogeneous culture. By binding functionalised iron oxide nanoparticles, it is possible to separate the chitin‐enriched bud scars by way of a linker protein. This reveals that low viability cultures with a high daughter cell content perform similarly to a high viability culture with a low daughter cell content. Magnetic separation results in the daughter cell fraction (>95%) showing a 21% higher growth rate in aerobic conditions than mother cells and a 52% higher rate under anaerobic conditions. These findings emphasise the importance of viability and age during cultivation and are the first step towards improving the efficiency of yeast‐based processes.

Magnetic nanoparticles (MNPs) are used for magnetophoresis-based separation processes in various biomedical and engineering applications. Essential requirements are the colloidal stability of the MNPs and the ability to be separated even in low magnetic field gradients. Bare iron oxide nanoparticles (BIONs) with a diameter of 9.2 nm are synthesized via coprecipitation, exhibiting a high saturation magnetization of 70.84 Am2 kg−1 and no remanence. In our study, zeta potential, dynamic light scattering (DLS), and sedimentation analysis show that the aggregation behavior of BIONs is influenced by pH and viscosity. Small aggregate clusters are formed with either low or high pH values or increased viscosity. Regarding magnetophoresis-based separation, a higher viscosity leads to lower magnetophoretic velocities, similar to how small aggregates do. Additionally, cooperative magnetophoresis, the joint motion of strongly interacting particles, affects the separation of the BIONs, too. Our study emphasizes the effect of pH and viscosity on the physicochemical characteristics of MNPs, resulting in different aggregation behavior. Particularly, for high viscous working media in downstream processing and medicine, respectively, the viscosity should be taken into account, as it will affect particle migration.

For industrial processes, a fast, precise, and reliable method of determining the physiological state of yeast cells, especially viability, is essential. However, an increasing number of processes use magnetic nanoparticles (MNPs) for yeast cell manipulation, but their impact on yeast cell viability and the assay itself is unclear. This study tested the viability of Saccharomyces pastorianus ssp. carlsbergensis and Pichia pastoris by comparing traditional colourimetric, high-throughput, and growth assays with membrane fluidity. Results showed that methylene blue staining is only reliable for S. pastorianus cells with good viability, being erroneous in low viability (R2 = 0.945; $$\widehat{\sigma }$$ σ ^ = 5.78%). In comparison, the fluorescence microscopy–based assay of S. pastorianus demonstrated a coefficient of determination of R2 = 0.991 at $$\alpha =0$$ α = 0 ($$\widehat{\sigma }$$ σ ^ = 2.50%) and flow cytometric viability determination using carboxyfluorescein diacetate (CFDA), enabling high-throughput analysis of representative cell numbers; R2 = 0.972 ($$\alpha =0$$ α = 0 ; $$\widehat{\sigma }$$ σ ^ = 3.89%). Membrane fluidity resulted in a non-linear relationship with the viability of the yeast cells ($$\alpha \ne 0$$ α ≠ 0 ). We also determined similar results using P. pastoris yeast. In addition, we demonstrated that MNPs affected methylene blue staining by overestimating viability. The random forest model has been shown to be a precise method for classifying nanoparticles and yeast cells and viability differentiation in flow cytometry by using CFDA. Moreover, CFDA and membrane fluidity revealed precise results for both yeasts, also in the presence of nanoparticles, enabling fast and reliable determination of viability in many experiments using MNPs for yeast cell manipulation or separation.

No abstract

No abstract