In Vivo Study of Lipid Accumulation in the Microalgae Marine Diatom Thalassiosira pseudonana Using Raman Spectroscopy Phiranuphon Meksiarun,a Nicolas Spegazzini,b Hiroaki Matsui,a Kensuke Nakajima,a Yusuke Matsuda,a Hidetoshi Satoa,* a Department of Bioscience, School of Science and Technology, Kwansei Gakuin University, Gakuen, Sanda, Hyogo 669-1337, Japan b Department of Chemistry, School of Science and Technology, Kwansei Gakuin University, Gakuen, Sanda, Hyogo 669-1337, Japan
An in vivo non-invasive quantitative analysis technique was introduced for evaluating the fat composition of living marine diatoms by using Raman spectroscopy in conjunction with a chemometric method. This technique enabled the observation of real-time variations in individual lipids in diatom cells without specific treatment or fat extraction. A confocal Raman spectroscope was used to measure the marine centric diatom Thalassiosira (T.) pseudonana that was cultured under six stress conditions, and the spectral data of accumulated fatty acids were obtained. A model-based chemometrics technique, ordinary least square was then used to extract specific signals from Raman spectra obtained for a mixture of fatty acids. The levels of four major lipid moieties from diatoms were extracted simultaneously, including myristic acid, palmitic acid, palmitoleic acid, and eicosapentaenoic acid from the Raman spectra. These results indicate that Raman spectroscopy in conjunction with a chemometrics method is reliable for the quantitative determination of the lipid composition accumulated in the cells of marine diatoms. Index Headings: Diatom; Fatty acid; Ordinary least square; OLS; Raman spectroscopy; In vivo analysis.
INTRODUCTION The energy crisis has become a global concern as the demand for crude oil increases while supplies continually decrease. To solve this problem, biofuel production by microalgae has been examined.1 As an alternative source of energy, microalgae have many advantages over the use of fossil fuels. Biofuel produced from microalgae has been recognized as a feasible source of energy as the diatom lipid production in the form of triacylglycerol (TAG) can be increased up to 60% of the total dry mass when it is cultured under optimal conditions.2 To achieve an optimal growth rate, diatoms must be cultivated under nutrient-replete conditions. In contrast, nutrient exhaustion leads to a decrease in the cell growth; however, it increases lipid accumulation dramatically. The cellular factors contributing to lipid accumulation include silica, nitrogen, phosphorus, and CO2, which are associated with specific biosynthesis mechanisms.3,4 Raman spectroscopy can be potentially used to analyze fat composition in living single cells. This Received 15 May 2014; accepted 7 July 2014. * Author to whom correspondence should be sent. E-mail: hidesato@ kwansei.ac.jp. DOI: 10.1366/14-07598
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method enables identification of live cells and transfer of single cells for further cloning of the selective phenotype. Selective production of fat (e.g., chain length and number of double bonds) will contribute to the application of algal biofuel production, material, and food industries, and medical production. However, typical auxotrophic selection methods cannot be used because the production or the absence of the particular fat should not be fatal to the algae.5 Additionally, conventional methods, such as liquid or gas chromatography, require sufficient amounts of sample and lysis of cells, which is not feasible for mass screening of a particular phenotype from enormous number of potential mutants. Another difficulty of chromatographic analyses arises from the large variation in the patterns of three acyl chains in the TAG. Triacylglycerol must be decomposed into fatty acid chains through a methylation process, which requires additional operations to obtain reliable results. This type of conditional setting for the sample preparation is not necessary in Raman analysis. Raman spectroscopy is based on the light scattering phenomenon. In the biological samples, water is the major component that may disturb the resolution of the spectroscopic methods, and this problem is particularly evident in infrared and near-infrared spectroscopy. Additionally, while information regarding lipid components cannot be obtained using fluorescence-based experiments, Raman spectroscopy can be used to acquire this data.6 Briefly, chemometrics is a method for interpreting or extracting information from spectra obtained using various spectrometers. Partial least squares regression (PLSR) and principle component analysis (PCA) are quantitative and qualitative analytical methods that have been used for various applications, including the study of nutrient status of microalgae,7 fishoil quantity,8 and monitoring of cancer cells.9 Ordinary least square (OLS) analysis was used to determine the composition of materials in a mixed sample, which is based on the linear combination of pure component spectra.10 Ordinary least squares was employed for extracting the pure component in skin tissue from biochemical Raman spectra.11 Lipid droplets in cells treated with oleic and palmitic acid were investigated by Raman spectroscopy and OLS to determine the location of cellular peroxisomes.12 In situ analysis of lipids accumulated in microalgae by Raman spectroscopy was performed for the lipid globules in green algae Botryococcus sudeticus, Chlamydomonas sp., and the
0003-7028/15/6901-0045/0 Q 2015 Society for Applied Spectroscopy
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Xanthophyceae, Trachydiscus minutus.13 Laser-trapping Raman spectroscopy was used to immobilize diatom cells during measurements in the green algae Neochloris oleoabundans.14 Both studies employed the ratio between 1656 cm1 (C=C) and 1445 cm1 (C-H deformation) to construct a calibration model in order to determine the composition of fatty acids by constructing calibration models based on C=C and CH2 together with melting point and differential scanning calorimetry data. The green algae Chlorella sorokiniana and N. oleoabundans were cultured under nitrogen-starved conditions, and then carotenoids and triglycerides were investigated by Stokes Raman spectroscopy.15 In the present study, model-based OLS was employed to determine the relative composition of different types of fatty acid chains in a mixture of TAG in cells. The conventional method based on the ratio between two bands (e.g., I1656/I1445) is not reliable when the diatom had three or more fatty acid chain types in one sample. The reliability of OLS was validated by gas chromatography. The absolute concentration in the cell was not obtained in the present study.
Materials and methods Preparation of Diatom Samples. Thalassiosira (T.) pseudonana was grown in artificial seawater supplemented with half-strength of Gulland’s solution (F/ 2ASW). Cells were first grown in normal F/2ASW and then transferred and acclimated to one control and six different conditions for 3 days at 20 8C with a photosynthetic photon flux density (PPFD) of 60 lEm2 s1. Culture conditions were as follows: media with high nutrient content; (1) þFe (F/2ASW with 0.036 mM FeCl36H2O), (2) þN (F/2ASW containing 5.4 mM NaNO3), and (3) þCO2 (F/ 2ASW under constant aeration with 5% CO2) and media without particular nutrients; (4) –Fe (iron depleted), (5) – N (nitrogen depleted), and (6) -CO2 cultured under ambient aeration. Confocal Raman Spectroscopy. Raman measurements of T. pseudonana were carried out using a confocal Raman spectroscope, Nanofinder 30 (Tokyo Instruments, Inc., Tokyo, Japan), equipped with CWelectronically tuned Ti: sapphire laser (ETL), Peltier cooled CCD detector (BR-DD410, Andor Technology, Belfast, Ireland), and water-immersed objective lens (360, 1.10 NA, Olympus, Tokyo, Japan). The cells were placed in a dish with a quartz bottom for Raman measurements. The excitation wavelength was 785 nm with 50 mW at the sampling point. The spectra from 30 cells from each cultured condition were collected over a range of 730–1800 cm1. The exposure time was 60 s (30 s 3 2 times). Raw spectra were pretreated by removing the background (e.g., quartz, cultivation medium) and were normalized with a band at 1440 cm1 (C-H deformation mode). Gas Chromatography. Lipid was extracted from cells according to the Bligh and Dyer protocol,16 which is suitable for a wet algal sample. Each 300 mL of diatom culture was centrifuged at 15003 g for 10 min. The collected diatom pellet (1.2 mL) was mixed with methanol (1.5 mL)/chloroform (1.5 mL)/distilled water (1.5 mL) and sonicated for 1 min. The chloroform layer
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FIG. 1. Scheme of mathematical expression of OLS.
including the total extracted lipid was collected, and the solvent was vaporized using nitrogen stream. The residue was then treated with a fatty acid methylation kit (Nacalai Tesque, Kyoto, Japan). Squalene was added to reach an internal standard concentration of 0.82 nm. Next, 1 lL of sample was injected and analyzed using a gas chromatographic (GC) system (GC-2010, Shimadzu Co., Ltd., Kyoto, Japan). The peak area of each lipid species was normalized based on an internal standard. All chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA). Selection of Major Fat Species in Diatoms Using Principle Component Analysis. Principle component analysis (PCA) with full-cross validation was used to interpret the Raman spectra of the major lipids in diatom cells (The Unscrambler 10.1, CAMO Software AS., Oslo, Norway). To construct a prediction model, eight lipid species (myristic acid (MA), palmitic acid (PA), palmitoleic acid (POA), oleic acid (OA), linoleic acid (LA), linolenic acid (NA), arachidonic acid (AA), and eicosapentaenoic acid (EPA)) were purchased from SigmaAldrich and used without further purification. Ordinary Least Square. Ordinary least square analysis was used to decompose the mixed components into pure components (Fig. 1). Ordinary least square is a chemometrics technique based on least square analysis (Eq. 1). Here, D is data matrix r 3 c, where r is the spectra obtained from diatoms, and c is corresponding wavenumber of each sample. Also, C (r 3 n) is the concentrations of n components, which are major components composing the samples. Further, ST (n 3 c) is the corresponding spectra of each mixture component. Finally, E (r6c) is an unexplained residual matrix. D ¼ CST þ E
ð1Þ
C ¼ DðST Þþ
ð2Þ
Prior to OLS, initial estimations were calculated to be roughly estimated to minimize the lipid library by PCA. In the first step, the spectral profile (ST) is the lipid library spectra, which is then used to estimate the sample concentration (C) in Eq. 2. To validate the OLS results, correlations between lipid libraries and diatom samples concentration were performed by using R (correlation coefficient) as follows n X ðxi x Þðyi x Þ i¼1 ffisffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n X X 2 ðxi x Þ ðyi y Þ2 i¼1
i¼1
ð3Þ
FIG. 2. Ratiometric curve calculated for diatom lipids and pure fatty acids; palmitic acid (PA), oleic acid (OA), glycerol trioleate (GTO), palmitoleic acid (POA), linoleic acid (LA), linolenic acid (LnA), and arachidonic acid (AA).
where x and y are lipid libraries and calculated sample concentration, respectively. The number of the sample is assigned with ith from 1 to n sample. Ordinary least square, correlation coefficient, and standard deviation were performed by using Matlab (The Mathworks Inc., Matlab version 7.1 R2010a).
RESULTS AND DISCUSSION In the present study, chain lengths and the number of double bonds of fatty acid chains were analyzed. Although various types of fatty acid chains are found in diatoms, exhaustive analysis of all these chains was not possible. Only major types of fatty acid chains could be analyzed in this study. It was extremely difficult to precisely estimate the spectral features from the raw spectral data since spectral profiles of each fatty acid resembled each other. In order to identify the major types of hydrocarbon chains, a conventional ratiometric method was first applied. Ratiometrics have been used to identify lipid species with respect to the ratio of two bands at 1660 and 1440 cm1.13,14 Figure 2 shows the ratiometric plot of fatty acids and diatom samples. Diatom lipids are mostly plotted between palmitic and linoleic acid, suggesting that their fatty acid chains contain more than one double bond. One sample plotted between palmitic and oleic acids had a fatty acid chain with less than one double bond. Because diatoms contain TAGs with varied compositions in their fatty acid chains, ratiometric analysis is not sufficient to reflect their characteristics. Because the band intensity at 1660 cm1 corresponds to the total number of C=C bonds in this method, the ratio of 1660/1440 is only valid when the sample contains only two fatty acid species. Additionally, the band at 1660 cm1 was sensitive to lipid oxidation as the band intensity changed depending on the level of conjugation.17,18 To overcome these limitations, multivariate analysis was necessary. Principle component analysis was applied to determine major fatty acid chains in diatoms. When diatoms
FIG. 3. Score plot of PCA for diatom lipids and pure fatty acids.
have particular variations in several major fatty acid chains, they will appear as major PCs in PCA, if the concentration of these fatty acid chains changes independently. The data from several pure fatty acids were added into the dataset of diatoms for PCA. These data for pure fatty acids were used as indices to assign the origin of each PC. Figure 3 depicts a score plot of PC 1 and 2 of the PCA analysis, which was carried out for the dataset of diatoms and six fatty acids. The plot shows that the data for myristic acid (MA: 14-carbon, 0-double bond), palmitic acid (PA: 16-carbon, 0-double bond), palmitoleic acid (POA: 16-carbon, 1-double bond), and eicosapentaenoic acid (EPA: 20-carbon, 5-double bond) were close to the data of diatoms, with data for two other fatty acids plotted relatively far from these diatoms. According to analysis of the score plots constructed for PCs 15, these four fatty acids were always observed close to the diatom dataset. This result is consistent with the results of GC, suggesting that the diatoms examined in this study have these four major fatty acid chains in their TAGs. Raman spectra for the four major fatty acids and an averaged spectrum of normal diatoms are shown in Fig. 4. The diatom cell includes protein and carotenoid in addition to TAG but the contribution of these components is relatively small according to the spectral features of diatoms. Major bands at 1660, 1440, 1304, and 1265 cm1 were assigned to C=C stretching, C–H deformation, CH2 bending, and =C–H bending vibrational modes of the fat species. Band assignments in the 8001200 cm1 region are listed in Table 1. A band at 1735 cm1, which was observed only in the diatom spectrum, was assigned to the C=O stretching mode of ester bonding in TAG, as the fat in the diatom was in the form of TAG. Bands at 1660 and 1265 cm1 were particularly strong and sharp in the spectra of unsaturated fatty acids POA and EPA, which are good markers for these fatty acid chains. The spectral region from 8001200 cm1 (Fig. 4) was useful for decomposing the spectra because it represented the characteristic pattern of each lipid species. This region
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FIG. 4. (a) Raman spectra of EPA, (b) POA, (c) PA, (d) MA, and (e) the fat rich region of the diatom.
contains the C–C backbone structure of each fatty acid which is contributed to the chain length and conformation from rotation of C–C bonds. The band at 1068 cm1 was assigned to trans–C–C rotamers, and those at 1080 and 1120 cm1 were due to gauche C–C rotamers. The alkyl chains in solid fatty acid are typically arranged in the trans form, while the gauche rotamers are typically observed in the liquid phase fatty acid.19 Significant bands at 865, 931, and 974 cm1 assigned to d C–C, m C–C, and d =C–H vibrational modes were observed on the spectra of polyunsaturated fatty acids, EPA, and NA, but they were not observed on the spectrum of POA with a single double-bond. These bands were observed also in the spectra of the diatoms, suggesting the existence of polyunsaturated fatty acid chains. Diatoms showed distinct types of Raman spectra compared to the pure fatty acid sample, with spectral features that were totally different when an excitation wavelength of 633 nm was used. Figure 5 shows the Raman spectra of the diatom measured at (Fig. 5a) 633 nm excitation and (Fig. 5b) 785 nm excitation wavelengths. Broad bands near 1200 and 1650 cm1 appeared on the spectrum (Fig. 5a), which were attributed to the fluorescence of chlorophyll-a and b. TABLE I. Assignment of major bands. Peak position (cm1)
Assignment d m d m m m d d d m m
865 931 974 1068 1080 1120 1265 1304 1440 1660 1748
C-C lipids C-C =C-H out-of-plane C-C trans C-C gauche C-C gauche =C-H in-plane CH2 twisting CH2 scissor C=C cis C=O
* Abbreviations: m, Stretching; d, bending; q, rocking.13,14,19
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FIG. 5. Raman spectra of diatom measured at an excitation wavelength of (a) 633 and (b) 785 nm.
The 633 nm light appeared to excite absorption bands due to Qx and Qy electronic modes, with corresponding fluorescent bands observed. In contrast, the spectrum measured at an excitation wavelength of 785 nm showed several strong bands due to the presence of carotenoids. Because the molecular concentration of carotenoids was much lower than that of lipid or protein, the spectrum was attributed to pre-resonance enhanced by the excitation at 785 nm. Because the absorption bands of carotenoid appear near 450 and 490 nm, the excitation light was resonant with the bands, although they were about 300 nm apart from the excitation wavelength. The bands at 1170, 1525, and 1630 cm1 were only minimally observed in the 633 nm excited spectrum over the strong fluorescent background, suggesting that the carotenoids are also pre-resonant enhanced at this excitation wavelength. In contrast, there were no bands due to carotenoids in the fat-rich Raman spectrum of diatom (Fig. 4e). Carotenoid species show high affinity to fat and have suppressive effects for singlet oxygen and lipid oxidation.20 However, the present result indicates that no carotenoid exists near the fat drop in the diatom. Thus, the fat drop is not a long-term energy storage form, but is included in a dynamic physiological regulation under changing environment. The diatom appears to actively synthesize, modify, and consume fat. The recent studies have also stated the role of lipid droplets rather than energy depots; lipid droplets play many active roles in the cell regulation.21,22 Diatoms cultured under control and six different conditions were analyzed by Raman-OLS analysis and GC (Figs. 6 and 7). For OLS analysis, the spectral region from 800–1200 cm1 was used because the intensity of the band of the C=C stretching mode near 1660 cm1 was too sensitive to the conjugation of double bonding. Raman analysis of each diatom cell showed three patterns of variations. These three groups were defined according to the composition of saturation of the fatty acid chains. Classifications were made based on the
FIG. 6. Composition of MA, PA, POA, and EPA analyzed using the Raman MCR-ALS method for three groups of diatoms; SFD (long dashed lines), UFD (short dashed lines), and SUFT (dotted-dashed lines) cultured under the absence () and excess (þ) of Fe, CO2, and N, as well as control conditions. The error bars shows standard deviation.
ratio between the total amounts of saturated fatty acid chains (SFA; myristic and palmitic acids) and unsaturated fatty acid chains (UFA; palmitoleic and eicosapentaenoic acids). The first group was for the saturated-fatdominated (SFD) group, in which the SFA/UFA ratio was higher than 1.5. The other refers to the unsaturated-fatdominated (UFD) group in which the SFA/UFA ratio was lower than 0.67. Another group included similar amounts of saturated and unsaturated fat chains, with an SFA/UFA ratio between 0.67 and 1.5. This group was referred to as the saturated-unsaturated fat transformation (SUFT) group. Guschina et al.23 suggested that the unsaturated fatty chain is derived from the saturated fatty chain by desaturase during its biosynthesis. The results suggested that the SUFT diatom underwent transformation from SFD to UFD, and vice versa. The GC result showed a high PA chain content, but the three different types (SFD, UFD, and SUFT) in diatoms were unable to be identified in the GC results. The correlation coefficient of OLS analysis was typically more than 0.9, and the averaged Raman data agreed with that of GC, strongly suggesting that Raman spectroscopy with OLS analysis is reliable for total fat analysis in diatoms. Notably, diatoms show large varieties in the particular components of fat chains, even under the same culture conditions which were not differentiated by the GC analysis.
FIG. 7. Composition of MA, PA, POA, and EPA analyzed with Raman (long dashed lines) and GC (short dashed lines) analyses cultured under poverty () and excess (þ) of Fe, CO2, and N, as well as control conditions. The Raman data shows averaged values including SFD, UFD, and SUFT types. The error bar shows the standard deviation.
According to the GC analysis, the contents of total fatty acids increased in diatoms cultured under Fe, þCO2, and N conditions (Fig. 7). Palmitoleic acid content was relatively high under control, Fe, and CO2 conditions, and that of EPA was high under control, Fe, and CO2 conditions. In contrast, Raman analysis suggested slightly different results. The relative populations of SFD, SUFT, and UFD groups are shown in Table 2. Unsaturated-fat-dominated rich diatoms were the primary components in diatoms cultured under control and Fe conditions. The population of the SUFT group was remarkably reduced under the Fe condition. This indicates that the iron starvation simultaneously stimulates fat production together with the activities of desaturase and elongase. Lipid accumulation response to iron deficiency in Thalassiosira oceanica was found to be used as supplementary energy sources when the photosynthetic system is ruined by iron starvation.24 In addition, polyunsaturated fatty acid elongase (protein ID: 3741) was up-regulated in T. pseudonana grown in irondepleted condition. This protein family is involved in the elongation of long-chain fatty acids.25,26 The accumulation of fatty acids in Pavlova lutheri, Chlorella vulgaris, Chlorella kessleri, and Dunaliella tertiolectra diatoms supplied with high CO2 levels were found to contain more SFA and MUFA.27,28 Sato et al. found that a reduction in UFA could result from the balance mechanism of CO2 concentration and fatty acid desaturation.29 The effects of lipid accumulation in nitrogen limitation
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TABLE II. Populations of diatom cells in the SFD, SFA, and UFA groups cultured under control, poverty (), and excess (þ) of Fe, CO2, and N conditions. Diatom population (%) Condition Control Fe þFe CO2 þCO2 N þN
SFD
SUFT
UFD
23 33 55 68 39 72 68
15 9 33 22 56 22 15
61 52 5 4 4 5 10
from S. minutulus showed that total TAGs were increased.30 Fatty acid compositions in the diatoms cultured under N were found to cause high accumulation of SFA in the present study. As previously reported by Liang et al., saturated fatty acids in P. tricornutum and C. muelleri were increased by nitrogen deprivation.31 The diatoms cultured under þFe and þCO2 conditions showed a high population in the SUFT group. This cannot be determined using GC analysis, which suggests a decrease in unsaturated fats. It is unlikely that the fat storage ability is a housekeeping function for the UFD diatom because the SUFT diatom also contains some unsaturated fat. The diatom initially showed a UFD-rich pattern that was similar to the control condition, but then the UFD population reduced with the addition of Fe or CO2 to the mediums. Ordinary least square results also indicated that, for UFD of þFe and þCO2, the relative concentration of POA was high while EPA remained low. This result indicates that the activity of D9 desaturase contributes to form C=C to POA; however, another mechanism involved in the production of polyunsaturated fat was nonfunctional. Fatty acid elongase adds hydrocarbons to the fatty acid chains to synthesize EPA from shorter fatty acids.23 Fatty acid elongase appeared to be suppressed under þFe and þCO2 conditions, resulting in a reduced concentration of unsaturated fat and classification of these diatoms into the UFD group. The pattern of fat composition in the SFD group remained similar, with PA always high and MA, POA, and PA always low. These components remain steady in T. pseudonana. In contrast, the diatoms cultured under CO2, N, and þN showed remarkable property in the fat composition of the UFD group. Because the concentration of EPA was relatively high, the concentration of POA was similar or less than that of PA. Despite this relative amount of EPA, these conditions showed the same population pattern in which only approximately 6% belonged to the UFD group, while more than 60% were categorized as SFD. It is assumed that the present simple classification was not sufficient to elucidate the dynamics of the fat production and degradations in the diatom, although obtained data were much more detailed information for each diatom cell by comparing the results with those of conventional GC analysis.
CONCLUSION In this study, Raman spectroscopy combined with OLS was demonstrated to analyze the detailed composition of
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fatty acid chains in live diatoms. The consistency between GC and Raman results confirmed the feasibility of this method as a non-invasive/non-preprocessing tool that can be used to decompose fat composition and quantify the concentration from a mixed sample used for biological applications. These results provide insight into the dynamics of variations in fat synthesis that could not be observed using any other methods. High-quality Raman spectra of fat content was obtained by focusing on a single live diatom. Some spectra revealed strong pre-resonant Raman bands of carotenoids, which are components of photosynthetic systems. This suggests that localization of the fat storage (fat drop) is isolated from photosynthetic systems. Despite the complicated bands in the 8001200 cm1 region, OLS can provide reliable quantitative information with a correlation coefficient of more than 0.9. The spectra for each condition were classified into three groups: SFD, SUFT, and UFD. Sufficient pretreatment is required to improve the spectrum and prediction quality. 1. D.R. Georgianna, S.P. Mayfield. ‘‘Exploiting Diversity and Synthetic Biology for the Production of Algal Biofuels’’. Nature. 2012. 488(7411): 329-335. 2. E.T. Yu, F.J. Zendejas, P.D. Lane, S. Gaucher, B.A. Simmons, T.W. Lane. ‘‘Triacylglycerol Accumulation and Profiling in the Model Diatoms Thalassiosira pseudonana and Phaeodactylum tricornutum (Baccilariophyceae) During Starvation’’. J. Appl. Phycol. 2009. 21(6): 669-681. 3. M. Hildebrand, A.K. Davis, S.R. Smith, J.C. Traller, R. Abbriano. ‘‘The Place of Diatoms in the Biofuels Industry’’. Biofuels. 2012. 3(2): 221-240. 4. K.K. Sharma, H. Schuhmann, P.M. Schenk. ‘‘High Lipid Induction in Microalgae for Biodiesel Production’’. Energies. 2012. 5(5): 15321553. 5. D. O¨zc¸imen, M.O¨. Gu¨lyurt, B. Inan. ‘‘Algal Biorefinery for Biodiesel Production’’. In: Z. Fang, editor. Biodiesel—Feedstocks, Production and Applications: InTech, 2012. 6. E. Smith, G. Dent. ‘‘The Raman Experiment—Raman Instrumentation, Sample Presentation, Data Handling and Practical Aspects of Interpretation’’. In: Modern Raman Spectroscopy—A Practical Approach. Chichester, West Sussex: John Wiley and Sons, 2005. 7. P. Heraud, J. Beardall, D. McNaughton, B.R. Wood. ‘‘In vivo Prediction of the Nutrient Status of Individual Microalgal Cells Using Raman Microspectroscopy’’. FEMS. 2007. 275(1): 24-30. 8. J. Vongsvivut, P. Heraud, W. Zhang, J.A. Kralovec, D. McNaughton, C.J. Barrow. ‘‘Rapid Determination of Protein Contents in Microencapsulated Fish Oil Supplements by ATR-FTIR Spectroscopy and Partial Least Square Regression (PLSR) Analysis’’. Food Bioprocess. Tech. 2014. 7(1): 265-277. 9. S. Duraipandian, W. Zheng, J. Ng, J.J. Low, A. Ilancheran, Z. Huang. ‘‘Simultaneous Fingerprint and High-Wavenumber Confocal Raman Spectroscopy Enhances Early Detection of Cervical Precancer in vivo’’. Anal. Chem. 2012. 84(14): 5913-5919. 10. M.J. Pelletier. ‘‘Quantitative Analysis Using Raman Spectrometry’’. Appl. Spectrosc. 2003. 57(1): 20A-42A. 11. L. Silveira Jr., F.L. Silveira, B. Bodanese, R.A. Zaˆngaro, M.T. Pacheco. ‘‘Discriminating Model for Diagnosis of Basal Cell Carcinoma and Melanoma In Vitro Based on the Raman Spectra of Selected Biochemicals’’. J. Biomed. Opt. 2012. 17(7): 077003. 12. I.W. Schie, L. Nolte, T.L. Pedersen, Z. Smith, J. Wu, I. Yahiate`ne, J.W. Newman, T. Huser. ‘‘Direct Comparison of Fatty Acid Ratios in Single Cellular Lipid Droplets as Determined by Comparative Raman Spectroscopy and Gas Chromatography’’. Analyst. 2013. 138(21): 6662-70. 13. O. Samek, A. Jona´sˇ, Z. Pila´t, P. Zema´nek, L. Nedbal, J. Trˇ ı´ ska, P. Kotas, M. Trtı´ lek. ‘‘Raman Microspectroscopy of Individual Algal Cells: Sensing Unsaturation of Storage Lipids in vivo’’. Sensors. 2010. 10(9): 8635-8651. 14. H. Wu, J.V. Volponi, A.E. Oliver, A.N. Parikh, B.A. Simmons, S. Singh. ‘‘In vivo Lipidomics Using Single-Cell Raman Spectroscopy’’. Proc. Natl. Acad. Sci. U. S. A. 2011. 108(9): 3809-3814.
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and Low-Iron Response of an Oceanic Diatom Adapted to Chronic Iron Limitation’’. Genome Biol. 2012. 13(7): R66. K. Thamatrakoln, O. Korenovska, A.K. Niheu, K.D. Bidle. ‘‘WholeGenome Expression Analysis Reveals a Role for Death-Related Genes in Stress Acclimation of the Diatom Thalassiosira pseudonana’’. Environ. Microbiol. 2012. 14(1): 67-81. L.M. Obeid, C.M. Linardic, L.A. Karolak, Y.A. Hannun. ‘‘Programmed Cell Death Induced by Ceramide’’. Science. 1993. 259(5102): 1769-1771. A.P. Carvalho, F.X. Malcata. ‘‘Optimization of x-3 Fatty Acid Production by Microalgae: Crossover Effects of CO2 and Light Intensity Under Batch and Continuous Cultivation Modes’’. Mar. Biotechnol. 2004. 7(4): 381-388. M. Tsuzuki, E. Ohnuma, N. Sato, T. Takaku, A. Kawaguchi. ‘‘Effects of CO2 Concentration During Growth on Fatty Acid Composition in Microalgae’’. Plant Physiol. 1990. 93(3): 851-856. N. Sato, M. Tsuzuki, A. Kawaguchi. ‘‘Glycerolipid Synthesis in Chlorella kessleri 11 h: II. Effect of the CO2 Concentration During Growth’’. Biochim. Biophys. Acta. 2003. 1633(1): 35-42. S.G. Lynn, S.S. Kilham, D.A. Kreeger. ‘‘Effect of Nutrient Availability on the Biochemical and Elemental Stoichiometry in Freshwater Diatom Stephanodiscus minutulus (Bacillariophyceae)’’. J. Phycol. 2000. 36(3): 510-522. Y. Liang, J. Beardall, P. Heraud. ‘‘Effects of Nitrogen Source and UV Radiation on the Growth, Chlorophyll Fluorescence and Fatty Acid Composition of Phaeodactylum tricornutum and Chaetoceros muelleri (Bacillariophyceae)’’. J. Photochem. Photobiol. B. 2006. 82(3): 161-172.
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An in vivo non-invasive quantitative analysis technique was introduced for evaluating the fat composition of living marine diatoms by using Raman spectroscopy in conjunction with a chemometric method. This technique enabled the observation of real-time variations in individual lipids in diatom cells without specific treatment or fat extraction. A confocal Raman spectroscope was used to measure the marine centric diatom Thalassiosira (T.) pseudonana that was cultured under six stress conditions, and the spectral data of accumulated fatty acids were obtained. A model-based chemometrics technique, ordinary least square was then used to extract specific signals from Raman spectra obtained for a mixture of fatty acids. The levels of four major lipid moieties from diatoms were extracted simultaneously, including myristic acid, palmitic acid, palmitoleic acid, and eicosapentaenoic acid from the Raman spectra. These results indicate that Raman spectroscopy in conjunction with a chemometrics method is reliable for the quantitative determination of the lipid composition accumulated in the cells of marine diatoms. Index Headings: Diatom; Fatty acid; Ordinary least square; OLS; Raman spectroscopy; In vivo analysis.
INTRODUCTION The energy crisis has become a global concern as the demand for crude oil increases while supplies continually decrease. To solve this problem, biofuel production by microalgae has been examined.1 As an alternative source of energy, microalgae have many advantages over the use of fossil fuels. Biofuel produced from microalgae has been recognized as a feasible source of energy as the diatom lipid production in the form of triacylglycerol (TAG) can be increased up to 60% of the total dry mass when it is cultured under optimal conditions.2 To achieve an optimal growth rate, diatoms must be cultivated under nutrient-replete conditions. In contrast, nutrient exhaustion leads to a decrease in the cell growth; however, it increases lipid accumulation dramatically. The cellular factors contributing to lipid accumulation include silica, nitrogen, phosphorus, and CO2, which are associated with specific biosynthesis mechanisms.3,4 Raman spectroscopy can be potentially used to analyze fat composition in living single cells. This Received 15 May 2014; accepted 7 July 2014. * Author to whom correspondence should be sent. E-mail: hidesato@ kwansei.ac.jp. DOI: 10.1366/14-07598
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method enables identification of live cells and transfer of single cells for further cloning of the selective phenotype. Selective production of fat (e.g., chain length and number of double bonds) will contribute to the application of algal biofuel production, material, and food industries, and medical production. However, typical auxotrophic selection methods cannot be used because the production or the absence of the particular fat should not be fatal to the algae.5 Additionally, conventional methods, such as liquid or gas chromatography, require sufficient amounts of sample and lysis of cells, which is not feasible for mass screening of a particular phenotype from enormous number of potential mutants. Another difficulty of chromatographic analyses arises from the large variation in the patterns of three acyl chains in the TAG. Triacylglycerol must be decomposed into fatty acid chains through a methylation process, which requires additional operations to obtain reliable results. This type of conditional setting for the sample preparation is not necessary in Raman analysis. Raman spectroscopy is based on the light scattering phenomenon. In the biological samples, water is the major component that may disturb the resolution of the spectroscopic methods, and this problem is particularly evident in infrared and near-infrared spectroscopy. Additionally, while information regarding lipid components cannot be obtained using fluorescence-based experiments, Raman spectroscopy can be used to acquire this data.6 Briefly, chemometrics is a method for interpreting or extracting information from spectra obtained using various spectrometers. Partial least squares regression (PLSR) and principle component analysis (PCA) are quantitative and qualitative analytical methods that have been used for various applications, including the study of nutrient status of microalgae,7 fishoil quantity,8 and monitoring of cancer cells.9 Ordinary least square (OLS) analysis was used to determine the composition of materials in a mixed sample, which is based on the linear combination of pure component spectra.10 Ordinary least squares was employed for extracting the pure component in skin tissue from biochemical Raman spectra.11 Lipid droplets in cells treated with oleic and palmitic acid were investigated by Raman spectroscopy and OLS to determine the location of cellular peroxisomes.12 In situ analysis of lipids accumulated in microalgae by Raman spectroscopy was performed for the lipid globules in green algae Botryococcus sudeticus, Chlamydomonas sp., and the
0003-7028/15/6901-0045/0 Q 2015 Society for Applied Spectroscopy
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Xanthophyceae, Trachydiscus minutus.13 Laser-trapping Raman spectroscopy was used to immobilize diatom cells during measurements in the green algae Neochloris oleoabundans.14 Both studies employed the ratio between 1656 cm1 (C=C) and 1445 cm1 (C-H deformation) to construct a calibration model in order to determine the composition of fatty acids by constructing calibration models based on C=C and CH2 together with melting point and differential scanning calorimetry data. The green algae Chlorella sorokiniana and N. oleoabundans were cultured under nitrogen-starved conditions, and then carotenoids and triglycerides were investigated by Stokes Raman spectroscopy.15 In the present study, model-based OLS was employed to determine the relative composition of different types of fatty acid chains in a mixture of TAG in cells. The conventional method based on the ratio between two bands (e.g., I1656/I1445) is not reliable when the diatom had three or more fatty acid chain types in one sample. The reliability of OLS was validated by gas chromatography. The absolute concentration in the cell was not obtained in the present study.
Materials and methods Preparation of Diatom Samples. Thalassiosira (T.) pseudonana was grown in artificial seawater supplemented with half-strength of Gulland’s solution (F/ 2ASW). Cells were first grown in normal F/2ASW and then transferred and acclimated to one control and six different conditions for 3 days at 20 8C with a photosynthetic photon flux density (PPFD) of 60 lEm2 s1. Culture conditions were as follows: media with high nutrient content; (1) þFe (F/2ASW with 0.036 mM FeCl36H2O), (2) þN (F/2ASW containing 5.4 mM NaNO3), and (3) þCO2 (F/ 2ASW under constant aeration with 5% CO2) and media without particular nutrients; (4) –Fe (iron depleted), (5) – N (nitrogen depleted), and (6) -CO2 cultured under ambient aeration. Confocal Raman Spectroscopy. Raman measurements of T. pseudonana were carried out using a confocal Raman spectroscope, Nanofinder 30 (Tokyo Instruments, Inc., Tokyo, Japan), equipped with CWelectronically tuned Ti: sapphire laser (ETL), Peltier cooled CCD detector (BR-DD410, Andor Technology, Belfast, Ireland), and water-immersed objective lens (360, 1.10 NA, Olympus, Tokyo, Japan). The cells were placed in a dish with a quartz bottom for Raman measurements. The excitation wavelength was 785 nm with 50 mW at the sampling point. The spectra from 30 cells from each cultured condition were collected over a range of 730–1800 cm1. The exposure time was 60 s (30 s 3 2 times). Raw spectra were pretreated by removing the background (e.g., quartz, cultivation medium) and were normalized with a band at 1440 cm1 (C-H deformation mode). Gas Chromatography. Lipid was extracted from cells according to the Bligh and Dyer protocol,16 which is suitable for a wet algal sample. Each 300 mL of diatom culture was centrifuged at 15003 g for 10 min. The collected diatom pellet (1.2 mL) was mixed with methanol (1.5 mL)/chloroform (1.5 mL)/distilled water (1.5 mL) and sonicated for 1 min. The chloroform layer
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FIG. 1. Scheme of mathematical expression of OLS.
including the total extracted lipid was collected, and the solvent was vaporized using nitrogen stream. The residue was then treated with a fatty acid methylation kit (Nacalai Tesque, Kyoto, Japan). Squalene was added to reach an internal standard concentration of 0.82 nm. Next, 1 lL of sample was injected and analyzed using a gas chromatographic (GC) system (GC-2010, Shimadzu Co., Ltd., Kyoto, Japan). The peak area of each lipid species was normalized based on an internal standard. All chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA). Selection of Major Fat Species in Diatoms Using Principle Component Analysis. Principle component analysis (PCA) with full-cross validation was used to interpret the Raman spectra of the major lipids in diatom cells (The Unscrambler 10.1, CAMO Software AS., Oslo, Norway). To construct a prediction model, eight lipid species (myristic acid (MA), palmitic acid (PA), palmitoleic acid (POA), oleic acid (OA), linoleic acid (LA), linolenic acid (NA), arachidonic acid (AA), and eicosapentaenoic acid (EPA)) were purchased from SigmaAldrich and used without further purification. Ordinary Least Square. Ordinary least square analysis was used to decompose the mixed components into pure components (Fig. 1). Ordinary least square is a chemometrics technique based on least square analysis (Eq. 1). Here, D is data matrix r 3 c, where r is the spectra obtained from diatoms, and c is corresponding wavenumber of each sample. Also, C (r 3 n) is the concentrations of n components, which are major components composing the samples. Further, ST (n 3 c) is the corresponding spectra of each mixture component. Finally, E (r6c) is an unexplained residual matrix. D ¼ CST þ E
ð1Þ
C ¼ DðST Þþ
ð2Þ
Prior to OLS, initial estimations were calculated to be roughly estimated to minimize the lipid library by PCA. In the first step, the spectral profile (ST) is the lipid library spectra, which is then used to estimate the sample concentration (C) in Eq. 2. To validate the OLS results, correlations between lipid libraries and diatom samples concentration were performed by using R (correlation coefficient) as follows n X ðxi x Þðyi x Þ i¼1 ffisffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n X X 2 ðxi x Þ ðyi y Þ2 i¼1
i¼1
ð3Þ
FIG. 2. Ratiometric curve calculated for diatom lipids and pure fatty acids; palmitic acid (PA), oleic acid (OA), glycerol trioleate (GTO), palmitoleic acid (POA), linoleic acid (LA), linolenic acid (LnA), and arachidonic acid (AA).
where x and y are lipid libraries and calculated sample concentration, respectively. The number of the sample is assigned with ith from 1 to n sample. Ordinary least square, correlation coefficient, and standard deviation were performed by using Matlab (The Mathworks Inc., Matlab version 7.1 R2010a).
RESULTS AND DISCUSSION In the present study, chain lengths and the number of double bonds of fatty acid chains were analyzed. Although various types of fatty acid chains are found in diatoms, exhaustive analysis of all these chains was not possible. Only major types of fatty acid chains could be analyzed in this study. It was extremely difficult to precisely estimate the spectral features from the raw spectral data since spectral profiles of each fatty acid resembled each other. In order to identify the major types of hydrocarbon chains, a conventional ratiometric method was first applied. Ratiometrics have been used to identify lipid species with respect to the ratio of two bands at 1660 and 1440 cm1.13,14 Figure 2 shows the ratiometric plot of fatty acids and diatom samples. Diatom lipids are mostly plotted between palmitic and linoleic acid, suggesting that their fatty acid chains contain more than one double bond. One sample plotted between palmitic and oleic acids had a fatty acid chain with less than one double bond. Because diatoms contain TAGs with varied compositions in their fatty acid chains, ratiometric analysis is not sufficient to reflect their characteristics. Because the band intensity at 1660 cm1 corresponds to the total number of C=C bonds in this method, the ratio of 1660/1440 is only valid when the sample contains only two fatty acid species. Additionally, the band at 1660 cm1 was sensitive to lipid oxidation as the band intensity changed depending on the level of conjugation.17,18 To overcome these limitations, multivariate analysis was necessary. Principle component analysis was applied to determine major fatty acid chains in diatoms. When diatoms
FIG. 3. Score plot of PCA for diatom lipids and pure fatty acids.
have particular variations in several major fatty acid chains, they will appear as major PCs in PCA, if the concentration of these fatty acid chains changes independently. The data from several pure fatty acids were added into the dataset of diatoms for PCA. These data for pure fatty acids were used as indices to assign the origin of each PC. Figure 3 depicts a score plot of PC 1 and 2 of the PCA analysis, which was carried out for the dataset of diatoms and six fatty acids. The plot shows that the data for myristic acid (MA: 14-carbon, 0-double bond), palmitic acid (PA: 16-carbon, 0-double bond), palmitoleic acid (POA: 16-carbon, 1-double bond), and eicosapentaenoic acid (EPA: 20-carbon, 5-double bond) were close to the data of diatoms, with data for two other fatty acids plotted relatively far from these diatoms. According to analysis of the score plots constructed for PCs 15, these four fatty acids were always observed close to the diatom dataset. This result is consistent with the results of GC, suggesting that the diatoms examined in this study have these four major fatty acid chains in their TAGs. Raman spectra for the four major fatty acids and an averaged spectrum of normal diatoms are shown in Fig. 4. The diatom cell includes protein and carotenoid in addition to TAG but the contribution of these components is relatively small according to the spectral features of diatoms. Major bands at 1660, 1440, 1304, and 1265 cm1 were assigned to C=C stretching, C–H deformation, CH2 bending, and =C–H bending vibrational modes of the fat species. Band assignments in the 8001200 cm1 region are listed in Table 1. A band at 1735 cm1, which was observed only in the diatom spectrum, was assigned to the C=O stretching mode of ester bonding in TAG, as the fat in the diatom was in the form of TAG. Bands at 1660 and 1265 cm1 were particularly strong and sharp in the spectra of unsaturated fatty acids POA and EPA, which are good markers for these fatty acid chains. The spectral region from 8001200 cm1 (Fig. 4) was useful for decomposing the spectra because it represented the characteristic pattern of each lipid species. This region
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FIG. 4. (a) Raman spectra of EPA, (b) POA, (c) PA, (d) MA, and (e) the fat rich region of the diatom.
contains the C–C backbone structure of each fatty acid which is contributed to the chain length and conformation from rotation of C–C bonds. The band at 1068 cm1 was assigned to trans–C–C rotamers, and those at 1080 and 1120 cm1 were due to gauche C–C rotamers. The alkyl chains in solid fatty acid are typically arranged in the trans form, while the gauche rotamers are typically observed in the liquid phase fatty acid.19 Significant bands at 865, 931, and 974 cm1 assigned to d C–C, m C–C, and d =C–H vibrational modes were observed on the spectra of polyunsaturated fatty acids, EPA, and NA, but they were not observed on the spectrum of POA with a single double-bond. These bands were observed also in the spectra of the diatoms, suggesting the existence of polyunsaturated fatty acid chains. Diatoms showed distinct types of Raman spectra compared to the pure fatty acid sample, with spectral features that were totally different when an excitation wavelength of 633 nm was used. Figure 5 shows the Raman spectra of the diatom measured at (Fig. 5a) 633 nm excitation and (Fig. 5b) 785 nm excitation wavelengths. Broad bands near 1200 and 1650 cm1 appeared on the spectrum (Fig. 5a), which were attributed to the fluorescence of chlorophyll-a and b. TABLE I. Assignment of major bands. Peak position (cm1)
Assignment d m d m m m d d d m m
865 931 974 1068 1080 1120 1265 1304 1440 1660 1748
C-C lipids C-C =C-H out-of-plane C-C trans C-C gauche C-C gauche =C-H in-plane CH2 twisting CH2 scissor C=C cis C=O
* Abbreviations: m, Stretching; d, bending; q, rocking.13,14,19
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FIG. 5. Raman spectra of diatom measured at an excitation wavelength of (a) 633 and (b) 785 nm.
The 633 nm light appeared to excite absorption bands due to Qx and Qy electronic modes, with corresponding fluorescent bands observed. In contrast, the spectrum measured at an excitation wavelength of 785 nm showed several strong bands due to the presence of carotenoids. Because the molecular concentration of carotenoids was much lower than that of lipid or protein, the spectrum was attributed to pre-resonance enhanced by the excitation at 785 nm. Because the absorption bands of carotenoid appear near 450 and 490 nm, the excitation light was resonant with the bands, although they were about 300 nm apart from the excitation wavelength. The bands at 1170, 1525, and 1630 cm1 were only minimally observed in the 633 nm excited spectrum over the strong fluorescent background, suggesting that the carotenoids are also pre-resonant enhanced at this excitation wavelength. In contrast, there were no bands due to carotenoids in the fat-rich Raman spectrum of diatom (Fig. 4e). Carotenoid species show high affinity to fat and have suppressive effects for singlet oxygen and lipid oxidation.20 However, the present result indicates that no carotenoid exists near the fat drop in the diatom. Thus, the fat drop is not a long-term energy storage form, but is included in a dynamic physiological regulation under changing environment. The diatom appears to actively synthesize, modify, and consume fat. The recent studies have also stated the role of lipid droplets rather than energy depots; lipid droplets play many active roles in the cell regulation.21,22 Diatoms cultured under control and six different conditions were analyzed by Raman-OLS analysis and GC (Figs. 6 and 7). For OLS analysis, the spectral region from 800–1200 cm1 was used because the intensity of the band of the C=C stretching mode near 1660 cm1 was too sensitive to the conjugation of double bonding. Raman analysis of each diatom cell showed three patterns of variations. These three groups were defined according to the composition of saturation of the fatty acid chains. Classifications were made based on the
FIG. 6. Composition of MA, PA, POA, and EPA analyzed using the Raman MCR-ALS method for three groups of diatoms; SFD (long dashed lines), UFD (short dashed lines), and SUFT (dotted-dashed lines) cultured under the absence () and excess (þ) of Fe, CO2, and N, as well as control conditions. The error bars shows standard deviation.
ratio between the total amounts of saturated fatty acid chains (SFA; myristic and palmitic acids) and unsaturated fatty acid chains (UFA; palmitoleic and eicosapentaenoic acids). The first group was for the saturated-fatdominated (SFD) group, in which the SFA/UFA ratio was higher than 1.5. The other refers to the unsaturated-fatdominated (UFD) group in which the SFA/UFA ratio was lower than 0.67. Another group included similar amounts of saturated and unsaturated fat chains, with an SFA/UFA ratio between 0.67 and 1.5. This group was referred to as the saturated-unsaturated fat transformation (SUFT) group. Guschina et al.23 suggested that the unsaturated fatty chain is derived from the saturated fatty chain by desaturase during its biosynthesis. The results suggested that the SUFT diatom underwent transformation from SFD to UFD, and vice versa. The GC result showed a high PA chain content, but the three different types (SFD, UFD, and SUFT) in diatoms were unable to be identified in the GC results. The correlation coefficient of OLS analysis was typically more than 0.9, and the averaged Raman data agreed with that of GC, strongly suggesting that Raman spectroscopy with OLS analysis is reliable for total fat analysis in diatoms. Notably, diatoms show large varieties in the particular components of fat chains, even under the same culture conditions which were not differentiated by the GC analysis.
FIG. 7. Composition of MA, PA, POA, and EPA analyzed with Raman (long dashed lines) and GC (short dashed lines) analyses cultured under poverty () and excess (þ) of Fe, CO2, and N, as well as control conditions. The Raman data shows averaged values including SFD, UFD, and SUFT types. The error bar shows the standard deviation.
According to the GC analysis, the contents of total fatty acids increased in diatoms cultured under Fe, þCO2, and N conditions (Fig. 7). Palmitoleic acid content was relatively high under control, Fe, and CO2 conditions, and that of EPA was high under control, Fe, and CO2 conditions. In contrast, Raman analysis suggested slightly different results. The relative populations of SFD, SUFT, and UFD groups are shown in Table 2. Unsaturated-fat-dominated rich diatoms were the primary components in diatoms cultured under control and Fe conditions. The population of the SUFT group was remarkably reduced under the Fe condition. This indicates that the iron starvation simultaneously stimulates fat production together with the activities of desaturase and elongase. Lipid accumulation response to iron deficiency in Thalassiosira oceanica was found to be used as supplementary energy sources when the photosynthetic system is ruined by iron starvation.24 In addition, polyunsaturated fatty acid elongase (protein ID: 3741) was up-regulated in T. pseudonana grown in irondepleted condition. This protein family is involved in the elongation of long-chain fatty acids.25,26 The accumulation of fatty acids in Pavlova lutheri, Chlorella vulgaris, Chlorella kessleri, and Dunaliella tertiolectra diatoms supplied with high CO2 levels were found to contain more SFA and MUFA.27,28 Sato et al. found that a reduction in UFA could result from the balance mechanism of CO2 concentration and fatty acid desaturation.29 The effects of lipid accumulation in nitrogen limitation
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TABLE II. Populations of diatom cells in the SFD, SFA, and UFA groups cultured under control, poverty (), and excess (þ) of Fe, CO2, and N conditions. Diatom population (%) Condition Control Fe þFe CO2 þCO2 N þN
SFD
SUFT
UFD
23 33 55 68 39 72 68
15 9 33 22 56 22 15
61 52 5 4 4 5 10
from S. minutulus showed that total TAGs were increased.30 Fatty acid compositions in the diatoms cultured under N were found to cause high accumulation of SFA in the present study. As previously reported by Liang et al., saturated fatty acids in P. tricornutum and C. muelleri were increased by nitrogen deprivation.31 The diatoms cultured under þFe and þCO2 conditions showed a high population in the SUFT group. This cannot be determined using GC analysis, which suggests a decrease in unsaturated fats. It is unlikely that the fat storage ability is a housekeeping function for the UFD diatom because the SUFT diatom also contains some unsaturated fat. The diatom initially showed a UFD-rich pattern that was similar to the control condition, but then the UFD population reduced with the addition of Fe or CO2 to the mediums. Ordinary least square results also indicated that, for UFD of þFe and þCO2, the relative concentration of POA was high while EPA remained low. This result indicates that the activity of D9 desaturase contributes to form C=C to POA; however, another mechanism involved in the production of polyunsaturated fat was nonfunctional. Fatty acid elongase adds hydrocarbons to the fatty acid chains to synthesize EPA from shorter fatty acids.23 Fatty acid elongase appeared to be suppressed under þFe and þCO2 conditions, resulting in a reduced concentration of unsaturated fat and classification of these diatoms into the UFD group. The pattern of fat composition in the SFD group remained similar, with PA always high and MA, POA, and PA always low. These components remain steady in T. pseudonana. In contrast, the diatoms cultured under CO2, N, and þN showed remarkable property in the fat composition of the UFD group. Because the concentration of EPA was relatively high, the concentration of POA was similar or less than that of PA. Despite this relative amount of EPA, these conditions showed the same population pattern in which only approximately 6% belonged to the UFD group, while more than 60% were categorized as SFD. It is assumed that the present simple classification was not sufficient to elucidate the dynamics of the fat production and degradations in the diatom, although obtained data were much more detailed information for each diatom cell by comparing the results with those of conventional GC analysis.
CONCLUSION In this study, Raman spectroscopy combined with OLS was demonstrated to analyze the detailed composition of
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Volume 69, Number 1, 2015
fatty acid chains in live diatoms. The consistency between GC and Raman results confirmed the feasibility of this method as a non-invasive/non-preprocessing tool that can be used to decompose fat composition and quantify the concentration from a mixed sample used for biological applications. These results provide insight into the dynamics of variations in fat synthesis that could not be observed using any other methods. High-quality Raman spectra of fat content was obtained by focusing on a single live diatom. Some spectra revealed strong pre-resonant Raman bands of carotenoids, which are components of photosynthetic systems. This suggests that localization of the fat storage (fat drop) is isolated from photosynthetic systems. Despite the complicated bands in the 8001200 cm1 region, OLS can provide reliable quantitative information with a correlation coefficient of more than 0.9. The spectra for each condition were classified into three groups: SFD, SUFT, and UFD. Sufficient pretreatment is required to improve the spectrum and prediction quality. 1. D.R. Georgianna, S.P. Mayfield. ‘‘Exploiting Diversity and Synthetic Biology for the Production of Algal Biofuels’’. Nature. 2012. 488(7411): 329-335. 2. E.T. Yu, F.J. Zendejas, P.D. Lane, S. Gaucher, B.A. Simmons, T.W. Lane. ‘‘Triacylglycerol Accumulation and Profiling in the Model Diatoms Thalassiosira pseudonana and Phaeodactylum tricornutum (Baccilariophyceae) During Starvation’’. J. Appl. Phycol. 2009. 21(6): 669-681. 3. M. Hildebrand, A.K. Davis, S.R. Smith, J.C. Traller, R. Abbriano. ‘‘The Place of Diatoms in the Biofuels Industry’’. Biofuels. 2012. 3(2): 221-240. 4. K.K. Sharma, H. Schuhmann, P.M. Schenk. ‘‘High Lipid Induction in Microalgae for Biodiesel Production’’. Energies. 2012. 5(5): 15321553. 5. D. O¨zc¸imen, M.O¨. Gu¨lyurt, B. Inan. ‘‘Algal Biorefinery for Biodiesel Production’’. In: Z. Fang, editor. Biodiesel—Feedstocks, Production and Applications: InTech, 2012. 6. E. Smith, G. Dent. ‘‘The Raman Experiment—Raman Instrumentation, Sample Presentation, Data Handling and Practical Aspects of Interpretation’’. In: Modern Raman Spectroscopy—A Practical Approach. Chichester, West Sussex: John Wiley and Sons, 2005. 7. P. Heraud, J. Beardall, D. McNaughton, B.R. Wood. ‘‘In vivo Prediction of the Nutrient Status of Individual Microalgal Cells Using Raman Microspectroscopy’’. FEMS. 2007. 275(1): 24-30. 8. J. Vongsvivut, P. Heraud, W. Zhang, J.A. Kralovec, D. McNaughton, C.J. Barrow. ‘‘Rapid Determination of Protein Contents in Microencapsulated Fish Oil Supplements by ATR-FTIR Spectroscopy and Partial Least Square Regression (PLSR) Analysis’’. Food Bioprocess. Tech. 2014. 7(1): 265-277. 9. S. Duraipandian, W. Zheng, J. Ng, J.J. Low, A. Ilancheran, Z. Huang. ‘‘Simultaneous Fingerprint and High-Wavenumber Confocal Raman Spectroscopy Enhances Early Detection of Cervical Precancer in vivo’’. Anal. Chem. 2012. 84(14): 5913-5919. 10. M.J. Pelletier. ‘‘Quantitative Analysis Using Raman Spectrometry’’. Appl. Spectrosc. 2003. 57(1): 20A-42A. 11. L. Silveira Jr., F.L. Silveira, B. Bodanese, R.A. Zaˆngaro, M.T. Pacheco. ‘‘Discriminating Model for Diagnosis of Basal Cell Carcinoma and Melanoma In Vitro Based on the Raman Spectra of Selected Biochemicals’’. J. Biomed. Opt. 2012. 17(7): 077003. 12. I.W. Schie, L. Nolte, T.L. Pedersen, Z. Smith, J. Wu, I. Yahiate`ne, J.W. Newman, T. Huser. ‘‘Direct Comparison of Fatty Acid Ratios in Single Cellular Lipid Droplets as Determined by Comparative Raman Spectroscopy and Gas Chromatography’’. Analyst. 2013. 138(21): 6662-70. 13. O. Samek, A. Jona´sˇ, Z. Pila´t, P. Zema´nek, L. Nedbal, J. Trˇ ı´ ska, P. Kotas, M. Trtı´ lek. ‘‘Raman Microspectroscopy of Individual Algal Cells: Sensing Unsaturation of Storage Lipids in vivo’’. Sensors. 2010. 10(9): 8635-8651. 14. H. Wu, J.V. Volponi, A.E. Oliver, A.N. Parikh, B.A. Simmons, S. Singh. ‘‘In vivo Lipidomics Using Single-Cell Raman Spectroscopy’’. Proc. Natl. Acad. Sci. U. S. A. 2011. 108(9): 3809-3814.
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