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

Preclassification of broadband and sparse infrared data by multiplicative signal correction approach

Abstract Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied

Preprocessing strategies for sparse infrared spectroscopy:a case study on cartilage diagnostics

Abstract The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC

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