In yeast, aging is widely understood as the decline of physiological function and the decreasing ability to adapt to environmental changes. Saccharomyces cerevisiae has become an important model organism for the investigation of these processes. Yeast is used in industrial processes (beer and wine production), and several stress conditions can influence its intracellular aging processes. The aim of this review is to summarize the current knowledge on applied stress conditions, such as osmotic pressure, primary metabolites (e.g., ethanol), low pH, oxidative stress, heat on aging indicators, age-related physiological changes, and yeast longevity. There is clear evidence that yeast cells are exposed to many stressors influencing viability and vitality, leading to an age-related shift in age distribution. Currently, there is a lack of rapid, non-invasive methods allowing the investigation of aspects of yeast aging in real time on a single-cell basis using the high-throughput approach. Methods such as micromanipulation, centrifugal elutriator, or biotinylation do not provide real-time information on age distributions in industrial processes. In contrast, innovative approaches, such as non-invasive fluorescence coupled flow cytometry intended for high-throughput measurements, could be promising for determining the replicative age of yeast cells in fermentation and its impact on industrial stress conditions.

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.

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.

Background: Flow cytometry is a powerful tool for identifying and quantifying various cell markers, such as viability, vitality, and individual cell age, at single‐cell stages. However, cell autofluorescence and marker fluorophore signals overlap at low fluorescence intensities. Thus, these signals must be unmixed before determining the age fraction.Methods and Results: A comparison was made between principal component regression (PCR) and random forest (RF) to predict autofluorescence signals of Saccharomyces pastorianus var. carlsbergensis in a flow cytometer. RF provided better prediction results than the PCR and was therefore determined to be better suited for unmixing signals. In the subsequent application for unmixing the autofluorescence signal from the marker fluorophore signal, the Gaussian mixture analysis based on RF was in better agreement with the microscopy‐determined replicative age distribution than the PCR‐based method.Conclusion: The proposed approach of single‐laser spectral unmixing and subsequent Gaussian mixture analysis showed that the microscopy data was consistent with the unmixed fluorescence spectra. The demonstrated approach enables fast and reliable unmixing of flow cytometric spectral data using a single‐laser spectral unmixing method. This analysis method enables age determination of cells in industrial processes. This age determination allows for quantifying the yeast cell's age fractions, providing a detailed view of age‐related changes. Additionally, the bud scar labeling technique can be used to determine age‐related changes in Pichia pastoris yeast for biotechnological applications or recombinant protein expression.

The physiological and metabolic diversity of a yeast culture is the sum of individual cell phenotypes. As well as environmental conditions, genetics, and numbers of cell divisions, a major factor influencing cell characteristics is cell age. A postcytokinesis bud scar on the mother cell, a benchmark in the replicative life span, is a quantifiable indicator of cell age, characterized by significant amounts of chitin. We developed a binding process for visualizing the bud scars of Saccharomyces pastorianus var. carlsbergensis using a protein linker containing a polyhistidine tag, a superfolder green fluorescent protein (sfGFP), and a chitin-binding domain (His6-SUMO-sfGFP-ChBD). The binding did not affect yeast viability; thus, our method provides the basis for non-invasive cell age determination using flow cytometry. The His6-SUMO-sfGFP-ChBD protein was synthesized in Escherichia coli, purified using two-stage chromatography, and checked for monodispersity and purity. Linker-cell binding and the characteristics of the bound complex were determined using flow cytometry and confocal laser scanning microscopy (CLSM). Flow cytometry showed that protein binding increased to 60 455 ± 2706 fluorescence units per cell. The specific coupling of the linker to yeast cells was additionally verified by CLSM and adsorption isotherms using yeast cells, E. coli cells, and chitin resin. We found a relationship between the median bud scar number, the median of the fluorescence units, and the chitin content of yeast cells. A fast measurement of yeast population dynamics by flow cytometry is possible, using this protein binding technique. Rapid qualitative determination of yeast cell age distribution can therefore be performed.

Cellular heterogeneity plays a crucial role in biotechnological processes, significantly influencing metabolic activity, product yield, and process consistency. This review explores the different dimensions of cellular heterogeneity, focusing on its manifestation at both single-cell and population levels. The study examines how factors such as asymmetric cell division, age, and environmental conditions contribute to functional diversity within cell populations, with an emphasis on microorganisms like yeast. Age-related cellular heterogeneity, in particular, is highlighted for its impact on metabolic pathways, mitochondrial function, and secondary metabolite production, which directly affect bioprocess outcomes. Furthermore, the review discusses advanced techniques for detecting and managing heterogeneity, including surface marker-based approaches, which utilize proteins, polysaccharides, and lipids, and label-free methods that leverage cellular volume and physical properties for separation. Understanding and controlling cellular heterogeneity is essential for optimizing industrial bioprocesses, improving yield, and ensuring product quality. The review also underscores the potential of emerging biotechnological tools, such as real-time single-cell analysis and microfluidic devices, in enhancing separation techniques and managing cellular diversity for better process efficiency and robustness.