Visualizing wax ester fermentation in single Euglena gracilis cells by Raman microspectroscopy and multivariate curve resolution analysis

Abstract

Background

Global demand for energy is on the rise at a time when limited natural resources are fast depleting. To address this issue, microalgal biofuels are being recommended as a renewable and eco-friendly substitute for fossil fuels. Euglena gracilis is one such candidate that has received special interest due to their ability to synthesize wax esters that serve as precursors for production of drop-in jet fuel. However, to realize economic viability and achieve industrial-scale production, development of novel methods to characterize algal cells, evaluate its culture conditions, and construct appropriate genetically modified strains is necessary. Here, we report a Raman microspectroscopy-based method to visualize important metabolites such as paramylon and ester during wax ester fermentation in single Euglena gracilis cells in a label-free manner.

Results

We measured Raman spectra to obtain intracellular biomolecular information in Euglena under anaerobic condition. First, by univariate approach, we identified Raman markers corresponding to paramylon/esters and constructed their time-lapse chemical images. However, univariate analysis is severely limited in its ability to obtain detailed information as several molecules can contribute to a Raman band. Therefore, we further employed multivariate curve resolution analysis to obtain chain length-specific information and their abundance images of the produced esters. Accumulated esters in Euglena were particularly identified to be myristyl myristate (C28), a wax ester candidate suitable to prepare drop-in jet fuel. Interestingly, we found accumulation of two different forms of myristyl myristate for the first time in Euglena through our exploratory multivariate analysis.

Conclusions

We succeeded in visualizing molecular-specific information in Euglena during wax ester fermentation by Raman microspectroscopy. It is obvious from our results that simple univariate approach is insufficient and that multivariate curve resolution analysis is crucial to extract hidden information from Raman spectra. Even though we have not measured any mutants in this study, our approach is directly applicable to other systems and is expected to deepen the knowledge on lipid metabolism in microalgae, which eventually leads to new strategies that will help to enhance biofuel production efficiency in the future.