Fourier transform infrared spectroscopy as a method to study lipid accumulation in oleaginous yeasts
- Diletta Ami†1, 2, 3,
- Riccardo Posteri†1,
- Paolo Mereghetti4,
- Danilo Porro1,
- Silvia Maria Doglia1, 2, 3Email author and
- Paola Branduardi1
© Ami et al.; licensee BioMed Central Ltd. 2014
Received: 11 September 2013
Accepted: 10 January 2014
Published: 23 January 2014
Oleaginous microorganisms, such as different yeast and algal species, can represent a sustainable alternative to plant oil for the production of biodiesel. They can accumulate fatty acids (FA) up to 70% of their dry weight with a predominance of (mono)unsaturated species, similarly to what plants do, but differently from animals. In addition, their growth is not in competition either with food, feed crops, or with agricultural land.
Despite these advantages, the exploitation of the single cell oil system is still at an early developmental stage. Cultivation mode and conditions, as well as lipid extraction technologies, represent the main limitations. The monitoring of lipid accumulation in oleaginous microorganisms is consequently crucial to develop and validate new approaches, but at present the majority of the available techniques is time consuming, invasive and, when relying on lipid extraction, can be affected by FA degradation.
In this work the fatty acid accumulation of the oleaginous yeasts Cryptococcus curvatus and Rhodosporidium toruloides and of the non-oleaginous yeast Saccharomyces cerevisiae (as a negative control) was monitored in situ by Fourier Transform Infrared Spectroscopy (FTIR). Indeed, this spectroscopic tool can provide complementary information to those obtained by classical techniques, such as microscopy, flow cytometry and gas chromatography. As shown in this work, through the analysis of the absorption spectra of intact oleaginous microorganisms it is possible not only to monitor the progression of FA accumulation but also to identify the most represented classes of the produced lipids.
Here we propose FTIR microspectroscopy - supported by multivariate analysis - as a fast, reliable and non invasive method to monitor and analyze FA accumulation in intact oleaginous yeasts. The results obtained by the FTIR approach were in agreement with those obtained by the other classical methods like flow cytometry and gas chromatography. Moreover, the possibility to track lipid production in real time is highly desirable to support the initial screening of strains and media as well as to optimize the scaling up experiments, which are essential for a viable and successful development of an industrial production process.
KeywordsFourier transform infrared spectroscopy (FTIR) Principal component analysis (PCA) Cryptococcus curvatus Rhodosporidium toruloides Saccharomyces cerevisiae Oleaginous yeasts Fatty acids (FA) Biodiesel
During the last two decades there has been increasing interest in biodiesel as an alternative biofuel, relying on a considerable number of research projects. In the perspective of a viable production, the use of edible vegetable oils (such as soybean and rapeseed) as well as of non-edible oils (such as Jatropha curcas) needs to be improved using new technologies and alternative oil sources, which are currently being explored and developed. An emerging potential alternative for biodiesel production is represented by microbial lipids (also referred to as single-cell oils (SCOs), ) which oleaginous microorganisms can accumulate up to 70% or more of their biomass when growing in media with a high carbon/nitrogen (C/N) ratio .
The applications of oleaginous fungi for biodiesel production are still few, even if they offer several advantages over conventional plant and algal resources. In particular, in comparison to open-pond grown algae and to plants, yeasts can be easily grown in bioreactors, display rapid growth rates, are unaffected by space limitations, light or climatic variations, and are also easier to scale up . Moreover, oleaginous yeasts have the ability to utilize a wide range of inexpensive renewable carbon sources and while the first investigations commonly employed glucose as a carbon source, nowadays raw materials, by-products and surplus are increasingly used, leading to cost reduction and waste cutback. In particular, xylose [4, 5], lactose , mannose, mannitol , and ethanol  have been reported as possible substrates. More recently, carbon sources obtained from lignocellulosic material have been also successfully used [9, 10]. This metabolic versatility combines well with the demand for cheap production of biofuels, since the feeding substrate (and its availability) represents a relevant fraction of the overall costs. In view of developing a biorefinery-based bioprocess, nowadays most of the investigations for lipid production are focused on the selection and the development of oleaginous yeasts able to utilise glycerol - which is the major side-product of the trans-esterification of oils into biodiesel - as a carbon source for fatty acid accumulation (as examples see [11, 12]).
Together with the selection of the best cell factory and of the best and cheapest medium, the development of a robust and effective production process is a primary requirement. It is consequently essential to develop a reliable and quick method for monitoring fatty acid accumulation in yeasts. It will be necessary to i) support the initial screening phases for strains and media, which are affected by many variables like substrate composition, carbon source, C/N ratio and temperature; ii) track the effective production and productivity during the first tests as well as during the initial scaling up of the process; and iii) further screen for improved cell factories after possible iterative trials of engineering and/or mutagenesis processes.
Different techniques for lipid quantification are now available. In particular, fluorescent microscopy, spectrophotometry or flow cytometry after staining the producing cells with specific fluorescent probes (that is, Nile red, ) can be used essentially for qualitative analysis. These approaches are relatively quick and do not require lipid extraction. Furthermore, thin-layer chromatography (TLC) and - more importantly - gas chromatography (GC) are qualitative and quantitative methods that can also provide information about the composition of the lipids produced by the cells, a highly desirable issue [14, 15]. However, these methods are time-consuming and invasive, as they require the lipid extraction from the yeast cells, which can cause lipid losses, also due to lipase activity. Moreover, these approaches do not allow quick screening of numerous samples, or the real-time monitoring of the production process.
On the contrary, Fourier transform infrared (FTIR) spectroscopy is a non-invasive and label-free technique that allows rapid acquisition of a biochemical fingerprint of the sample under investigation, giving information on its main biomolecule content. Indeed, this spectroscopic tool is successfully applied not only to the characterization of the structural properties of isolated biomolecules, such as proteins, lipids, nucleic acids, and carbohydrates [16–21], but also to the in situ investigation of complex biological systems, including intact cells, tissues and whole-model organisms [22–25]. Interestingly, the FTIR method was recently used to determine the lipid accumulation in microalgae and in marine yeasts and protists [26, 27]. Moreover, the use of an infrared microscope enables measurement of infrared (IR) absorption spectra from a micro-volume within the sample. In particular, adjusting the variable aperture of the microscope it is possible to select an area of interest in the sample from 250 μm × 250 μm down to approximately 20 μm × 20 μm.
We should add that the FTIR spectra of biological systems are very complex, being due to the overlapping absorption of the main biomolecules. For this reason, it is necessary to apply an appropriate multivariate analysis, able to process very high-dimensional data, to pull out the significant and non-redundant information contained in the spectra [28–30].
As previously mentioned, yeasts can be an alternative source of SCOs, and a number of different species are now under investigation. Among them, the basidiomycetes Cryptococcus curvatus and Rhodosporidium toruloides were chosen as two of the most promising cell factories [31–34]. Here we propose FTIR microspectroscopy supported by multivariate analysis as an alternative method for lipid detection in situ that provides not only a snapshot of FA production, but also information about the relative abundance of the most represented species. Therefore, FTIR can be proposed as a powerful tool for the initial screening and optimization and in the perspective of the bioreactor scale-up, which are all required steps for the development of a viable biodiesel production based on oleaginous microorganisms.
Results and Discussion
Monitoring lipid production in oleaginous yeasts by Nile red staining
The strains were grown for 72 h in batch cultivation with shaking flasks in a rich medium containing malt extract and soytone (MS) as nutrients. This medium was chosen since it is known to favour lipid production in different oleaginous yeasts (see Methods for details). Samples were collected from the culture over time to monitor cellular growth, which was comparable for all the examined strains (see Figure 1A). At 0 and 72 h of growth, samples were stained with Nile red and the lipid content of the culture was evaluated by measuring the sample fluorescence by fluorescence microscopy and flow cytometry. Indeed, Nile red (9-diethylamino-5H-benzo(a)phenoxazine-5-one) is a red phenoxazine dye, present as a minor component of commercial preparations of the non-fluorescent stain Nile blue, which selectively stains lipophilic substances. Over the years, this dye has been extensively used as a lipid probe for the in vivo detection of intracellular lipids in intact cells by fluorescence microscopy and flow cytometry [13, 35].
In this work, through dichroic optical microscopy images (Figure 1B, left panels) a profound difference was observed in the cytoplasm of the three examined yeasts. In C. curvatus and R. toruloides refractive droplets were visible, and their lipid nature was confirmed by fluorescence images (Figure 1B, right panels). On the contrary, a very faint fluorescence signal was observed in S. cerevisiae cells, as expected. In particular, these images enable detection of the progression and the morphology of lipid accumulation: small droplets were seen to accumulate and to collapse into bigger structures. At longer growth times most of the cells were found to be completely filled with one to two very big lipid droplets (data not shown).
The same staining procedure was applied for flow cytometric analyses. The Nile red fluorescence signals collected at 72 h from the inoculum of C. curvatus and R. toruloides were found to be higher compared to the fluorescence signals collected at time 0 h. On the contrary, this increase was found to be completely negligible in the non-oleaginous yeast S. cerevisiae (Figure 1C, upper panels). The mean fluorescence values for each strain at time 0 and at 72 h are reported in the table of Figure 1C. These parameters were providing a useful semiquantitative evaluation of the lipid accumulation over time for every examined yeast strain.
Fatty acid quantification by gas chromatography
FTIR microspectroscopy to monitor lipid production in intact oleaginous yeast cells
When examining the spectrum time dependence of S. cerevisiae from 0 to 168 h (Figure 3), a few changes of the main absorption bands were observed. In particular, the intensity of the CH2/CH3 bands between 3,000 and 2,800 cm-1 and of the C = O absorption around 1,740 cm-1 was found to slightly increase, while the complex absorption between 1,250 and 1,000 cm-1 significantly changed. Overall, these results might reflect expected variations due to cell metabolism.
Second derivative analysis of the FTIR yeast spectra in the lipid absorption regions
To better evaluate the spectral changes occurring during the growth of the different studied strains, we analyzed the second derivatives of the FTIR absorption spectra, as they enable to resolve the overlapping components of the IR absorption bands . In particular, we analyzed the second derivative spectra of the cells at time 0 h and we compared them with those taken at 72 h, when the oleaginous yeasts have stored an appreciable amount of FA.
Principal component analysis (PCA)
With the aim to assign the spectroscopic changes observed during the growth of C. curvatus and R. toruloides to specific lipid molecules, we compared their IR response with that of standard fatty acids, chosen among the most representative products of the oleaginous yeasts . To this goal, we performed the PCA that also allowed verification of the reproducibility of the data. PCA was first performed including all samples taken at 0, 24, 48 and 72 h from the inoculum. In particular, looking at the PCA score plots (Additional files 1, 2, 3 and 4), we observed a clear distinction between the time 0 h samples and the group of samples taken after 24, 48, 72 h of growth for both C. curvatus and R. toruloides. We therefore decided to proceed with our analysis, including only the samples taken at 0 and 72 h as representative of the yeast time-dependent behaviour.
Interestingly, as shown in Figure 8A and B, the producer (red plus-sign) and the control (green star) strains at time 0 h almost overlap. Moreover, they are also close to the data belonging to the control strain at 72 h (blue square). On the contrary, the producer at 72 h (blue cross) is well separated from the samples at time 0 h, indicating that during growth the lipid spectral features of C. curvatus changed more significantly compared to the other samples.
Mean squared distances (MSD) (3,050 to 2,800 cm -1 ) for S. cerevisiae versus C. curvatus
Fatty acid standard
Δ% (C.c.- S.c.)
To better evaluate the lipid changes occurring during yeast growth, we extended the PCA to the spectral range between 1,500 and 1,350 cm-1 (data not shown), where several vibrational modes due to both lipid acyl chains and head group also occur.
Mean squared distances (MSD) (1,500 to 1,350 cm -1 ) for S. cerevisiae versus C. curvatus
Fatty acid standard
Δ% (C.c.- S.c.)
Mean squared distances (MSD) (3,050 and 2,800 cm -1 ) S. cerevisiae versus R. toruloides
Fatty acid standard
Δ% (R.t.- S.c.)
Mean squared distances (MSD) (1,500 and 1,350 cm -1 ) S. cerevisiae versus R. toruloides
Fatty acid standard
Δ% (R.t.- S.c.)
In this work we have reported the use of FTIR microspectroscopy as a powerful tool to monitor in situ fatty acid accumulation over time in oleaginous yeasts. In particular, we monitored the lipid production in two oleaginous yeasts taken as model systems, using the FTIR approach. Moreover, with the support of PCA we were able to obtain information about the FA profile of the oleaginous yeasts. Interestingly, our results were in excellent agreement with those reported in the literature [33, 41]. In particular, we should underline that the FTIR analysis is a fast and non-invasive technique that does not require cell disruption and/or lipid extraction. This is indeed an important asset, as not only does it decrease the time and costs associated with the currently used methods (that is, GC), but it also reduces the risk of lipid loss and degradation. Of note, this method, supported by multivariate analysis, can provide not only a qualitative output of lipids, but it can also discriminate among the different classes of the produced FA, enabling the optimization of the production process for matching the FA profile with the requirements of the applications of interest. Finally, a further outcome of great general and applicative interest of our FTIR study has been the detection of cell-wall modifications occurring during FA accumulation. Indeed, it is well-known that one of the main drawbacks of biodiesel production from oleaginous yeasts is represented by the downstream process, lipid extraction being one of the determining steps. Product extraction from cells is generally detrimental in terms of yields and costs, but in this case it is particularly tedious, as the cell wall of oleaginous yeasts becomes more and more difficult to break while cells are accumulating FA. Considering the limited information on the genome of these yeasts and the even more limited tools for their manipulation, FTIR could be a useful support in the screening of possible cell-wall mutants obtained by indirect engineering approaches.
Strains and growth conditions
C. curvatus DSMZ 70022 and R. toruloides DSMZ 4444 were purchased from DSMZ . Strains were stored at −80°C in 20% glycerol. The S. cerevisiae strain used in this study was GRF18U (MATa; ura3; leu2-3,112; his3-11,15; cir + , ). Yeasts were cultivated either in YPD (10 g/l yeast extract, 20 g/l Tryptone, 20 g/l D-glucose) or in MS (30 g/l malt extract, 3 g/l soytone) media, the second being preferred for lipid accumulation, according to the provider’s indication (http://www.dsmz.de/microorganisms/medium/pdf/DSMZ_Medium90.pdf). YPD medium was utilised only for control experiments (not shown). Cultivations were performed in 250-ml shake flasks with 50 ml of media, at 25°C and 220 rpm. Growth was monitored by measuring the optical density at 660 nm (OD660) in a Shimadzu UV-1800 UV spectrophotometer (Shimadzu Corporation).
Yeast extract and soytone were provided by Biolife Italiana S.r.l., Milano, Italia. Tryptone and malt extract were provided by Dyfco, NJ, USA. D-glucose and glycerol were provided by Aldrich Co., St Louis, MO, USA.
Fluorescence microscopy and citofluorimetry
Cell staining for lipids analysis was performed by using Nile red (Sigma Aldrich Co., St Louis, MO, USA) at a final concentration of 31.4 μM in PBS buffer (0.05 M, pH 7.0). A Nile red stock solution (314 μM) was prepared by dissolving Nile red powder in acetone. Before measurements, cells were incubated for 5 minutes in the dark at room temperature.
Fluorescence microscopy studies were carried out with a Nikon Eclypse E600 (Nikon Instruments, Inc.). Nile red fluorescence was registered with two spectral settings: yellow-gold fluorescence, using a 450- to 500-nm band-pass exciter filter and red fluorescence using a 515- to 560-nm band-pass exciter filter. Images of stained cells were acquired both in dichroic and fluorescence mode.
Flow cytometry was conducted using a Beckman Coulter Cytomics FC500 (Beckman Coulter, Inc). A total of 20,000 cells were measured for each sample (FL4 channel 575 nm +/− 15 nm). Data analysis was performed afterwards with WinMDI 2.8 software, build #13 01-19-2000 (Purdue University, Cytometry Laboratories http://facs.scripps.edu/software.html).
Gas chromatography analysis
To determine the lipid content in yeast cells, lipids were extracted, based on the method of Bligh and Dyer  with modifications and then analyzed through GC. Briefly, 10 OD (about 5 × 108 cells) of samples was centrifuged at 4,000 rpm for 10 minutes; cells were then washed twice with 1 ml of distilled water. Pellets were then resuspended in 5 ml of MeOH/CHCl3 (2:1) and mechanically disrupted twice using French Press at 28,000 psi (Constant Cell Disruption System, Constant System Ltd). Then, 2 ml of citric acid and 3 ml of CHCl3 were added to the samples. After mixing, the samples were centrifuged at 4,000 rpm for 2 minutes and the upper phase was discarded. Derivation of methyl esters from fatty acids was as previously described . Fatty-acid methyl esters were analyzed with a GC-mass spectrometer (GC-MS) (Agilent 7920A. Agilent J&W column, 30 m in length with an internal diameter of 0.25 mm and a film thickness of 0.25 mm, J&W Scientific, Rancho Cordova; Waters mass spectrometer 4 m). Decanoic acid was used as an internal standard.
Yeast cells from S. cerevisiae, C. curvatus, and R. toruloides, at 0, 24, 48, 72, 144, and 168 h of growth were washed three times in distilled water to eliminate medium contamination. Approximately 3 μl of the cell suspensions were then deposited onto an IR transparent BaF2 support, and dried at room temperature for about 30 minutes to eliminate the excess water.
FTIR absorption spectra were acquired in transmission mode, between 4,000 and 700 cm-1, by means of a Varian 610-IR infrared microscope coupled to the Varian 670-IR FTIR spectrometer (both from Varian Australia Pty Ltd), equipped with a mercury cadmium telluride (MCT) nitrogen-cooled detector. The variable microscope aperture was adjusted from approximately 60 μm × 60 μm to 100 μm × 100 μm). Measurements were performed at 2 cm-1 spectral resolution; 25 KHz scan speed, triangular apodization, and by the accumulation of 512 scan co-additions. When necessary, spectra were corrected for residual water vapour absorption [46, 47].
Spectral analysis was conducted in the spectral range between 4,000 and 800 cm−1. To this aim, second-derivative spectra were obtained following the Savitsky-Golay method (third-grade polynomial, 9 smoothing points), after a binomial 13 smoothing points of the measured spectra, using the GRAMS/32 software (Galactic Industries Corporation, USA).
To verify the reproducibility and reliability of the spectral results, more than three independent preparations were analyzed.
Principal component analysis of FTIR data
where K is the sample (C. curvatus, R. toruloides, S. cerevisiae), t corresponds to the time of growth (0 h, 72 h), S indicates the standard lipids, pc is the principal component index and C pc is the centroid of the cluster. The centroid has been computed as the median value among the replicas within the same set of data.
A positive value indicates that a given lipid standard contributes to the spectral profile changes of the sample at 72 h compared to the time 0 h, indicating that it is accumulated during the yeast growth.
Fourier transform infrared
mercury cadmium telluride
malt extract, soytone
principal component analysis
mean squared distance
single cell oil
yeast extract, peptone, dextrose.
This work was partly supported by a grant of the Regione Lombardia ASTIL- Cooperazione scientifica e tecnologica internazionale, Project “Diesel-Biotech” and partially by Regione Lombardia, Fondo per la Promozione di Accordi Istituzionali, Project BIOGESTECA 15083/RCC. PB, SMD, and DP acknowledge the support by FAR (Fondo di Ateneo per la Ricerca) of the University of Milano-Bicocca. DA acknowledges the post doctoral fellowship of the University of Milano-Bicocca. The authors gratefully acknowledge Andrea Giuzzi for his technical contribution.
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