Multiscale spectroscopic analysis of lipids in dimorphic and oleaginous Mucor circinelloides accommodate sustainable targeted lipid production

Abstract Background Oleaginous fungi have versatile metabolism and able to transform a wide range of substrates into lipids, accounting up to 20–70% of their total cell mass. Therefore, oleaginous fungi are considered as an alternative source of lipids. Oleaginous fungi can accumulate mainly acyl glycerides and free fatty acids which are localized in lipid droplets. Some of the oleaginous fungi possessing promising lipid productivity are dimorphic and can exhibit three cell forms, flat hyphae, swollen hyphae and yeast-like cells. To develop sustainable targeted fungal lipid production, deep understanding of lipogenesis and lipid droplet chemistry in these cell forms is needed at multiscale level. In this study, we explored the potential of infrared spectroscopy techniques for examining lipid droplet formation and accumulation in different cell forms of the dimorphic and oleaginous fungus Mucor circinelloides. Results Both transmission- and reflectance-based spectroscopy techniques are shown to be well suited for studying bulk fungal biomass. Exploring single cells with infrared microspectroscopy reveals differences in chemical profiles and, consequently, lipogenesis process, for different cell forms. Yeast-like cells of M. circinelloides exhibited the highest absorbance intensities for lipid-associated peaks in comparison to hyphae-like cell forms. Lipid-to-protein ratio, which is commonly used in IR spectroscopy to estimate lipid yield was the lowest in flat hyphae. Swollen hyphae are mainly composed of lipids and characterized by more uniform distribution of lipid-to-protein concentration. Yeast-like cells seem to be comprised mostly of lipids having the largest lipid-to-protein ratio among all studied cell forms. With infrared nanospectroscopy, variations in the ratios between lipid fractions triglycerides and free fatty acids and clear evidence of heterogeneity within and between lipid droplets are illustrated for the first time. Conclusions Vibrational spectroscopy techniques can provide comprehensive information on lipogenesis in dimorphic and oleaginous fungi at the levels of the bulk of cells, single cells and single lipid droplets. Unicellular spectra showed that various cell forms of M. circinelloides differs in the total lipid content and profile of the accumulated lipids, where yeast-like cells are the fatty ones and, therefore, could be considered as preferable cell form for producing lipid-rich biomass. Spectra of single lipid droplets showed an indication of possible droplet-to-droplet and within-droplet heterogeneity

Deep convolutional neural network recovers pure absorbance spectra from highly scatter‐distorted spectra of cells

Infrared spectroscopy of cells and tissues is prone to Mie scattering distortions, which grossly obscure the relevant chemical signals. The state‐of‐the‐art Mie extinction extended multiplicative signal correction (ME‐EMSC) algorithm is a powerful tool for the recovery of pure absorbance spectra from highly scatter‐distorted spectra. However, the algorithm is computationally expensive and the correction of large infrared imaging datasets requires weeks of computations. In this paper, we present a deep convolutional descattering autoencoder (DSAE) which was trained on a set of ME‐EMSC corrected infrared spectra and which can massively reduce the computation time for scatter correction. Since the raw spectra showed large variability in chemical features, different reference spectra matching the chemical signals of the spectra were used to initialize the ME‐EMSC algorithm, which is beneficial for the quality of the correction and the speed of the algorithm. One DSAE was trained on the spectra, which were corrected with different reference spectra and validated on independent test data. The DSAE outperformed the ME‐EMSC correction in terms of speed, robustness, and noise levels. We confirm that the same chemical information is contained in the DSAE corrected spectra as in the spectra corrected with ME‐EMSC.imag