Autofluorescence prediction model for fluorescence unmixing and age determination

Abstract

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.

Studying the impact of cell age on the yeast growth behaviour of Saccharomyces pastorianus var. carlsbergensis by magnetic separation

“…These contrast with the traditional assumption that age fractions are constant in yeast cell populations but support the results of variations in cell age compositions, especially regarding daughter cell content, as previously demonstrated by the authors. [ 32 ] It is assumed that under optimal conditions, the daughter cell content is 50%, the mother cell content is 25%, the second‐generation content is 12.5%, and so on. [ 49 ] The deviation from this assumption can be explained by the difference in the G1 phase, which depends on cell age.…”

“…Outliers in calibration curves were eliminated by applying Cook's distance, as described by Dennis Cook, [46] (A) (B) represents the total number of data points. [47] The yeast cell age distribution was determined as described by Eigenfeld et al [32] Briefly, the replicative cell age distribution was determined by measuring the fluorescence intensity of yeast cells using stained bud scars, followed by autofluorescence prediction using the random forest model. Subsequent autofluorescence subtraction resulted in a fluorescence intensity distribution, allowing the determination of age fraction content by Gaussian mixture analysis.…”

“…[48] These contrast with the traditional assumption that age fractions are constant in yeast cell populations but support the results of variations in cell age compositions, especially regarding daughter cell content, as previously demonstrated by the authors. [32] It is assumed that under optimal conditions, the daughter cell content is 50%, the mother cell content is 25%, the second-generation content F I G U R E 3 (A) Microscopic image of the specific binding of ethylenediaminetetraacetic acid (EDTA)-functionalised iron oxide nanoparticles, agglomerated with the linker protein, to the yeasts' bud scar. (B) Age distribution of a heterogeneous yeast culture, labelled only with the linker protein and with adsorbed EDTA-functionalised iron oxide nanoparticles derived from cytometric analysis (n min = 20,000 counts, n = 3, error bars representing the standard deviation of the triplicate measurement).…”

“…[ 31 ] This approach cannot be used in our case, since the fluorescence intensities determined have to be processed by autofluorescence calculation and subtraction. [ 32 ] As flow cytometry cannot perform this offline data processing, another method of cell sorting needs to be developed.…”

Quantification methods of determining brewer’s and pharmaceutical yeast cell viability: accuracy and impact of nanoparticles

“…The cytocompatibility of the BIONs can be increased by embedding them in biocompatible shells such as bovine serum albumin (BSA) [42] or dextran [43]. Nevertheless, the application of BIONs with or without functionalisation is of interest in the future separation of yeast cells using specific protein-binding proteins [8,44].…”