Bioethanol is an important renewable and clean energy source. Much effort has been put into selecting and constructing ethanol-tolerant yeast strains to improve ethanol yields. However, previous studies have often been limited to screening and obtaining tolerant mutants, with a little in-depth investigation of the phenotypic changes during fermentation. Most studies have been conducted at the population level, which masks intercellular heterogeneity. Microbial cells in a population may exhibit heterogeneity, i.e., individual cells respond differently, under stress conditions. Therefore, developing a phenotypic analysis strategy based on single-cell techniques is imperative for better understanding the stress resistance mechanisms of yeast cells. In this study, Raman tweezers are used to collect single-cell spectra of different yeast strains at different times of ethanol fermentation. Data mining technique using the multivariate curve resolution-alternating least squares (MCR-ALS) method is performed to extract spectra and spectral intensity profiles associated with specific biomolecules and gain insights into the metabolic fermentation processes and adaptation mechanisms of yeast cells.
Three Saccharomyces cerevisiae strains, Bp1, INVSc1, and W303a, are used. After activation in a solid YEPD medium, the yeast strains are transferred to a liquid YEPD medium and incubated overnight at 30 ℃ and 220 r/min. Then, they transfer to a fermentation medium (i.e., 300 g of glucose, 5.0 g of peptone, 0.06 g of CaCl2,0.06 g of MgSO4·7H2O, 1.5 g of KH2PO4,1000 mL of distilled water, and pH 4.5) at 5% inoculum for ethanol fermentation. The optical density at 600 nm (D600) of the culture is measured to estimate the growth of yeast cells. The content of glucose and ethanol in the fermentation broth are determined using Raman spectroscopy.
The Raman tweezers are used to acquire the Raman spectra of individual yeast cells. The laser tweezers randomly capture individual cells. The Raman signals of the cells are collected with an acquisition time of 30 s. A self-programmed MATLAB program is used to preprocess the data. The raw spectra are subtracted from the background spectra and then smoothed using the 17-point Savitzky Golay method and baseline corrected using the alternating least squares (ALS) algorithm. MCR-ALS, a chemometric method for resolving the individual, pure components within an unknown mixture, is run using the MATLAB toolbox MCR-ALS GUI 2.0. First, multiple single-cell Raman spectra from different periods are formed into column-increasing matrices. Then, the spectral intensity profiles of the components are initially estimated using evolving factor analysis. To reduce the ambiguity of the resolution results, non-negativity constraints are used for the concentrations and spectra.
The Bp1 strain shows the best fermentation performance, followed by the INVSc1 strain and the worst by the W303a strain. Five or three different biomacromolecules’ spectra and spectral intensity profiles are resolved for each strain, mainly lipid-related substances (phospholipids, triglycerides, and lipoproteins), polysaccharides, or proteins. The content of lipids in W303a strain does not increase significantly during the fermentation. In contrast, INVSc1 and Bp1 strains, which have a higher ethanol production capacity, increase with ethanol volume fraction, showing the prominent role of lipids in the resistance of yeast cells to ethanol toxicity.
The Bp1 and INVSc1 strains have high phospholipid content in the late fermentation stage, showing that yeast cells increase ergosterol content to adapt to the accumulating ethanol. It is hypothesized that in strains with high ethanol-producing capacity, yeast cells may increase the synthesis of triacylglycerol, consequently increasing the fluidity of the cell membrane to mitigate the damage by ethanol, thus making the endogenous ethanol flow out more easily. In addition, the cells will also increase the synthesis of ergosterol to maintain the integrity of the cell membrane and therefore have greater stress tolerance and better fermentation performance.
The content of major biomacromolecules is relatively homogeneous between cells in the Bp1 strain under the same fermentation conditions (Fig. 3). Simultaneously, cellular heterogeneity is high in the less ethanol-tolerant strains, INVSc1 and W303a (Figs. 4 and 5). This indicates that cell heterogeneity affects the strains’ ethanol fermentation performance and fermentation efficiency.
We apply single-cell Raman spectroscopy combined with MCR-ALS to rapidly obtain information on the variations in the spectral intensity of major biomacromolecules (i.e., phospholipids, proteins, polysaccharides, and triglycerides) in yeast cells during ethanol fermentation at the single-cell level without any a priori information. We find that lipids (i.e., phospholipids, triglycerides, and lipoproteins) and cell heterogeneity have an imperative role in the strain’s fermentation performance and efficiency. Therefore, the method is consistently outstanding in exploring resistance mechanisms in yeast cells and has wide application prospects.
