Detection and imaging of lipids of Scenedesmus obliquus based on confocal Raman microspectroscopy
© The Author(s) 2017
Received: 17 August 2017
Accepted: 26 November 2017
Published: 13 December 2017
In this study, confocal Raman microspectroscopy was used to detect lipids in microalgae rapidly and non-destructively. Microalgae cells were cultured under nitrogen deficiency. The accumulation of lipids in Scenedesmus obliquus was observed by Nile red staining, and the total amount of lipids accumulated in the cells was measured by gravimetric method. The signals from different microalgae cells were collected by confocal Raman microspectroscopy to establish a prediction model of intracellular lipid content, and surface scanning signals for drawing pseudo color images of lipids distribution. The images can show the location of pyrenoid and lipid accumulation in cells. Analyze Raman spectrum data and build PCA-LDA model using four different bands (full bands, pigments, lipids, and mixed features). Models of full bands or pigment characteristic bands were capable of identifying S. obliquus cells under different nitrogen stress culture time. The prediction accuracy of model of lipid characteristic bands is relatively low. The correlation between the fatty acid content measured by the gravimetric method and the integral Raman intensity of the oil characteristic peak (1445 cm−1) measured by Raman spectroscopy was analyzed. There was significant correlation (R 2 = 0.83), which means that Raman spectroscopy is applicable to semi-quantitative detection of microalgal lipid content.
The world energy crisis is intensified continuously nowadays, which raises interest in looking for renewable energy resources. Among the existing renewable energy sources, biodiesel is the most widely used variety which also have the fastest speed of development. Microalgae have good potential for fuel production because of their promising biomass feedstock and ability to synthesize large amounts of certain chemical compounds from sunlight and carbon dioxide [1, 2]. Under the environment of nitrogen stress, some kind of microalgae (e.g., Chlorella pyrenoidosa, Scenedesmus obliquus…) significantly synthesize and accumulate lipids, mainly in the form of triacylglycerol (TAG) [3, 4].
Several methodologies used for quantitative analysis of microalgae lipids, such as gas chromatography–mass spectrometry (GC–MS), are not just time-consuming and ruinous, but also requires sophisticated sample preparation process which produces hazardous chemical waste. Although GC–MS provide precise analysis of lipid composition, it cannot provide the detailed information of lipid metabolism. Vital staining methods using Nile Red or BODIPY 505/515 can show lipid distribution in a single cell but cannot provide some desired lipid characteristics such as chain length and degree of unsaturation [5, 6]. Studies showed that the lipid content of one cell was significantly correlated with the fluorescence intensity of Nile Red combined with the lipid inside the cell [7, 8].
Confocal Raman microscopy is a powerful tool for physicochemical characterization of biological samples, which directly detects vibrations of biochemical bonds through the inelastic scattering by a laser light . It enables single-cell, in vivo monitoring of various cellular components in a rapid, non-destructive, label-free and quantitative manner [10–13]. With the assistance of chemometric methods, Raman spectroscopy can be applied to wide research area like microalgae species identification , water pollution identification, and nutritional status identification [15–17]. A study showed that the water bodies under different nitrogen nutrition conditions can be effectively identified by the LDA classification model established based on lipid-related Raman shift . Heraud described a in vivo method for predicting the nutrient status of individual algae cells using Raman microscopy and partial least squares discriminant analysis . Samek employed the characteristic peaks in the Raman scattering spectra at 1656 and 1445 cm−1 as the markers defining the ratio of unsaturated-to-saturated carbon–carbon bonds of the fatty acids in the algal lipids . In situ and in vivo chemical compounds distribution and concentration can be shown by Raman spectrum data processing [21–24].
At the present time, the research of microalgae lipid production in China is mostly focused on promoting the ability of lipid production of microalgae through cultivation environment modifying or genetic engineering as well as optimization of lipid conversion technology. Development of rapid and non-destructive Raman spectrum testing process toward microalgae lipid will accelerate the research on biodiesel production. Problems still remain in the direct quantitative and qualitative detection research of microalgae lipid using Raman spectrum. In our preliminary experiments, we found that the characteristic peaks of lipids are not obvious due to the concealment effect of pigments (e.g., chlorophyll, carotenoids). The object of the preliminary experiment was Chlorella sp. Although this kind of microalgae can accumulate large amounts of lipids, its cellular volume is so small that obstructs the acquisition of spectrum and the process of data. The object of this study was a different kind of microalgae, S. obliquus. Scenedesmus obliquus is a Chlorophyta that the dry weight of its cellular lipid content can be accounted for more than 50% of the weight of cells. The large cell individual is suitable for the detection research of Raman point and surface scan. Under normal growth conditions, the cells of S. obliquus are fusiform. Combined living form of four cells is common, which can be changed under the stress environment.
This study, using S. obliquus as the experimental subject, verified the ability of Raman spectroscopy to classify cells collected from different days cultivated under nitrogen stress, and discussed the optimization of detection process according to the Raman mapping measurement imaging results of single cells.
We first list the materials and methods of the experiment, including the cultivation of microalgae, Nile Red staining and observing process, gravimetric analysis of total lipid content, GC–MS analysis of lipid composition and Raman spectral acquisition. We next build a PCA-LDA model to classify microalgae cells collected from different days, and its correct classification rate reached to 100%. We then make visual analysis of cellular lipids and pigments and cell growth pattern based on the Raman mapping data. The paper also attempts to discuss the correlations between the lipid content measured by gravimetric analysis and Raman mapping measurement.
Algae species and culture conditions
Scenedesmus obliquus, FACHB-276, was purchased in Freshwater Algae Culture Collection at the Institute of Hydrobiology, FACHB-collection. The SE basal medium was configured according to the standards provided by the Institute of Hydrobiology. Scenedesmus obliquus was expanded in the SE medium for 30 days and cultured to a stable growth stage. 2 l BG11 nitrogen-deficient (BG11-N) medium was configured with NaNO3 concentration of 0.5 g/l. 1 l uniform algae fluid was centrifugated and then liquid supernatant was discarded. The washed algal mud was quickly added to the BG11-N medium, placed at a constant temperature (25–27 °C), under uniform illumination (2000–3000 lx), and under periodic illumination conditions (12 l:12 days) cultured continuously for 9 days. Before each experiment, the microalgae cell concentration was estimated by the cell count plate method to ensure that the microalgae were in stable growth during the whole experiment.
Intracellular lipid accumulation observation
The intracellular lipid of S. obliquus was observed by Nile red staining. 1.5 ml uniform algae fluid and 0.5 ml DMSO were mixed, and oscillated 10 min for breaking cytoderm. 20 μl NR liquor (0.1 g/l) was then added, and oscillated 10 min for dyeing evenly. A 2 μl stained algae fluid was taken for the observation by fluorescence microscope (Nikon Eclipse 90i, 20× objective lens). The excitation wavelength range is 505–566 nm. Pictures of the stained algae cells were taken using corresponding image processing software.
Determination of total lipid content
Determination of lipid composition
The oil composition of algae cells was directly treated with methyl esterification. Fatty acid methyl ester (FAMEs) was analyzed by gas chromatograph-mass spectrometer (GC–MS). At ninth day, 240 ml uniform algae fluid was centrifugated and the supernatant was discarded. The algae mud was transferred to a 50-ml round-bottomed flask and crashed it using ultrasonication for 10 min. 10 ml reactant mixture (methanol:chloroform: HCL = 10:1:1) was added into the algae mud, mixed, and then refluxed in 90 °C water baths for 4 h. After the reaction is complete, the flask was removed and cooled it to room temperature. 3 ml hexane was added into the mixture for extracting the FAMEs, and repeated three times. The extract was dried with nitrogen and then added with 1 ml hexane. 37 FAMEs mixture standard heating process was used for GC–MS detection.
Raman spectrum acquisition
1.5 g powdered agar and 50 ml distilled water were mixed, boiled for 2 min, and then cooled to 40 °C. 2 ml algae fluid was transferred to a clean centrifuge tube, and 2 ml prepared agar solution was added. The mixture was cooled and solidified The solidified sample was cut into thin slices and was placed it on the slide.
The Renishaw laser confocal micro Raman spectrometer (Renishaw PLC, United Kingdom/InVia–Reflex 532/XYZ) was the main instruments for this study. Data acquisition software WiRE3.3 was used to adjust the acquisition parameters. Specific parameters were as follows: the excitation wavelength is 532 nm; the spectral collection range is 633–1813 Raman shift/cm−1; the laser intensity is 1.5 W; and the exposure time is 2 s. 15 single data were acquired . S. obliquus cells were selected with different growth forms and mapping data were acquired, after the target area was selected; acquisition step is 0.5 μm. 1–2 sets of mapping data were acquired for each growth morphology of the cells. The experiment was performed every other day; 75 sets of single data and 20 sets of mapping data were acquired altogether.
Spectral data processing
Single data processing and modeling
In this paper, we use Baseline correction, Savitzky–Golay smoothing (SG) and data normalization to preprocess single data. The purpose of the baseline correction is to eliminate the baseline drift caused by the instrument or other interference and reduce the data error through polynomial fitting and other mathematical algorithms . Through the least square fitting of spectral data, SG can reduce data noise level and retain the characteristics of spectral distribution, such as characteristic peak height, relative maximum, minimum and spectral peak width to a great extent . Different spectral data processings apply different data normalization formulas. In this paper, the relative content of oil and chlorophyll is used to describe the accumulation of oil over time.
In order to reduce modeling variables and computation, principal components analysis (PCA) was used to reduce the dimension of spectral data after pretreatment . Linear discriminant analysis (LDA) was used for modeling. LDA is a classical algorithm for pattern recognition. The purpose is to obtain the best separability of the model in the space by vector projection . Data processing softwares were MATLAB R2015b and The Unscrambler X®.
Mapping data processing
Baseline correction and SG smoothing were used for preprocessing the mapping data. After selecting the peak of target characteristic, the pseudo color image of lipids distribution is drawn by interpolation method.
Results and discussion
Content and composition of lipids in S. obliquus
Fatty acid composition of S. obliquus
NR microscopic examination of S. obliquus
Visual analysis of intracellular constituents in S. obliquus
PCA-LDA modeling based on Raman spectroscopy
The prediction accuracy of PCA-LDA models
The response of lipid characteristic peak was significantly lower than that of pigment characteristic peak, which directly resulted in the low discriminant accuracy of modeling scheme L. The prediction accuracy of modeling scheme P can reach 100%, which indicated that the nitrogen deficiency caused the significant change of pigments in S. obliquus cells of stable stage. Although the main purpose of nitrogen stress in this experiment was to induce the accumulation of intracellular lipids, this growth condition also affected the pigments in cells. The effect of pigment change on the model establishment was significant, and the discriminant accuracy of modeling using pigment characteristic bands was better than that of lipid characteristic bands.
Correlation of Raman mapping and gravimetric method for lipid determination
Under the nitrogen stress, significant accumulation of lipid was observed in S. obliquus cells. This microalgae species has the ability to synthesize large quantities of long-chain fatty acids and can be used as potential algae for biodiesel production. UFA synthesized by these algae has nutrient value; OCFA has industrial and health care value. The accumulation of lipid in S. obliquus was observed by Nile red staining: light yellow spots appeared in cells over time, which were the lipid particles produced by S. obliquus.
Raman mapping data of algae cells were visualized. The pyrenoid and intracellular lipid could be located in the pseudo color graph. Raman response high value was dotted in single algae cells, corresponding to the accumulation of lipid particles in the cells. The distribution of SFA and UFA was slightly different. Lipid characteristic peaks were obvious in Raman spectrum, but pigment peaks were more significant.
The Raman spectra of the cells in different culture days showed obvious clustering in PC-1 (56%) and PC-2 (26%) directions. In the PC-1 loading plot, the characteristic peaks with greater contribution rate were basically attributed to β-carotene and chlorophyll a. During the process of nitrogen stress culture, the contents of β-carotene and chlorophyll a in the microalgae cells in the stable stage were changed greatly. Four different models (F—full band, P—characteristic band of pigments, L—characteristic band of lipids, PL—mixed characteristic band) were build and analyzed: the model constructed by Raman spectrum of full band or pigment characteristic band can discriminate S. obliquus cells under different nitrogen stress culture times effectively, but accuracy of the model based on lipid characteristic band was relatively low. On this basis, we combined partial Raman bands of pigment and lipid to model (modeling scheme PL) and obtained better results, which means that this prediction method was effective.
Result of correlation analysis of total lipid contents measured by gravimetric method and Raman intensity of characteristic peak of lipid (1445 cm−1) showed that there was significant correlation (R 2 = 0.83), indicating that Raman spectroscopy can be applied to the semi-quantitative detection of microalgal lipid content.
From the above experimental results, it is possible to apply Raman spectroscopy to identify different algal cells with different nitrogen stress days and analyze the intracellular lipid content of algae qualitatively and quantitatively.
The work presented here was carried out in collaboration between all authors. YS and HF conceived the idea. HF, HZ, QW co-worked on associated data collection and carried out the experimental work. HF drafted the manuscript, YS helped to renew the original paper. YZ have provided experience on paper revision. YH, in which institute, supported the instrument and technology. All authors have contributed, reviewed and improved the manuscript. All authors read and approved the final manuscript.
The research presented in this paper was partially supported by The National Natural Science Foundation of China (31,402,318, 61,605,173), the Public welfare project in Zhejiang Province (2016C32055), and the Ph.D. Programs Foundation of Ministry of Education of China (20133120110007).
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