Algal Raman F E M S Heraud Et Al. 2007


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First time that the nutrient status of individual algal cells in a cell population has been predicted using IR microspectroscopy.

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Algal Raman F E M S Heraud Et Al. 2007

  1. 1. RESEARCH LETTER In vivo prediction of the nutrient status of individual microalgal cells using Raman microspectroscopy Philip Heraud1,2, John Beardall1,2, Don McNaughton1 & Bayden R. Wood1 1 Centre for Biospectroscopy and School of Chemistry and 2School of Biological Sciences, Monash University, Clayton, Victoria, Australia Correspondence: Philip Heraud, School of Abstract Biological Sciences, Monash University, Wellington Road, Clayton, Vic. 3800, An in vivo method for predicting the nutrient status of individual algal cells using Australia. Tel.: 161399060765; Raman microspectroscopy is described. Raman spectra of cells using 780 nm laser fax: 161399055613; e-mail: excitation show enhanced bands mainly attributable to chlorophyll a and b- carotene. The relative intensities of chlorophyll a and b-carotene bands changed under nitrogen limitation, with chlorophyll a bands becoming less intense and b- Received 27 April 2007; revised 7 June 2007; carotene bands more prominent. Although spectra from N-replete and N-starved accepted 8 June 2007. cell populations varied, each distribution was distinct enough such that multi- variate classification methods, such as partial least squares discriminant analysis, DOI:10.1111/j.1574-6968.2007.00861.x could accurately predict the nutrient status of the cells from the Raman spectral data. Editor: Geoffrey Gadd Keywords Raman spectroscopy; nutrient status; microspectroscopy; chlorophyll; b-carotene. ents (NIFTs; Beardall et al., 2001b; Holland et al., 2004), Introduction observed when nutrient-deficient cells are resupplied with Nutrient availability is a major factor determining phyto- the missing nutrient. The fluorescence-based techniques plankton primary productivity (Beardall & Raven, 2004). face a number of serious problems: measurements of Nutrients also play a major role in algal bloom formation photosynthetic competence based on chlorophyll fluores- (Henson et al., 2006). Furthermore, manipulation of nu- cence can be affected by many factors other than nutrients, trient availability is often used in algal nutrient-pharmaceu- such as previous light history or prior UVexposure (Beardall tical and bio-fuel production to optimize yields (Liang et al., et al., 2001a, b), whereas the NIFT response is not observed 2005). Hence, the ability to determine the nutrient status of in some microalgal species (Beardall et al., 2001a). FTIR algal samples quickly and accurately is of significant scien- spectroscopy has also been used to predict the nutrient tific importance. status of algal cells (Beardall et al., 2001a; Giordano et al., The traditional approach to assessing the nutrient status 2001; Liang et al., 2005), which infers nutrient status via of microalgal samples has been to use bioassays, measuring macromolecular composition. the growth rate of cells inoculated into a range of different It has been demonstrated recently that nitrogen limita- nutrient media, each enriched with one of the nutrients tion in phytoplankton could also be determined using suspected of being in limitation. This approach suffers from Raman spectroscopy (Wood et al., 2005). It was shown that both a lack of specificity and sensitivity, because, among Raman spectroscopy was sensitive to the levels of chloro- other reasons, the artificial culture conditions are often phyll a and b-carotene in small masses of phytoplankton subject to a range of uncontrolled environmental factors cells. The concentration of chlorophyll a in algal cells (Beardall et al., 2001b; Holland et al., 2004). Chlorophyll declines under N limitation, because nitrogen is a major fluorescence has been used to predict the nutrient status of constituent of this pigment and its apoprotein, whereas the algal cells: via measurement of photosynthetic efficiency, levels of b-carotene (a pigment with no N, although in which is affected by nutrient availability (Beardall et al., thylakoid membranes it is associated with pigment protein 2001a), and through nutrient-induced fluorescence transi- complexes) have been shown to be stable or to increase FEMS Microbiol Lett ]] (2007) 1–7 c 2007 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
  2. 2. 2 P. Heraud et al. under these conditions (Geider et al., 1998). Raman spectro- (equipped with a Peltier cooled CCD detector) using a scopy was able to differentiate populations of N-starved 782 nm excitation line from a diode laser and a modified cells from those of N-replete microalgal cells based on the BH2-UMA Olympus optical microscope with a Zeiss  60 different levels of chlorophyll a and b-carotene in the water immersion objective. The maximum power at the samples. sample was $2–3 mW for a 1–2 mm laser spot size. Spectra Heraud et al. (2006) extended the use of Raman micro- were recorded between 1800 and 200 cmÀ1 at a spectral spectroscopy for the determination of nutrient limitation resolution of 1 cmÀ1. Spectra (1800–200 cmÀ1) were ac- to single algal cells. Variabilities in chlorophyll a and b- quired from 30 cells in each nutrient condition. Spectra carotene within cells drawn from N-replete and N-starved were acquired at 2 mm intervals along the length of each cell populations were described using multivariate data analysis and the resultant spectra were averaged. About a 0.3 mW methods, which are used to classify reliably the nutrient power was applied to the cells and each spectrum was status of the cells. The work by Heraud et al. (2006) acquired during a 10-s exposure. The laser power employed concentrated exclusively on the chemometric-based meth- in the main experiment was selected after preliminary trials ods, in particular preprocessing, required to improve the indicated that 10-s exposures to a range of powers from prediction of the nutrient status of microalgal cells using 0.03 to 3 mW did not significantly change the signal from Raman spectral data. In this paper, the authors refine and b-carotene, a component that is known to be very sensitive give a complete description of biological methods for to photo-degradation. Spectra from chlorophyll a and obtaining in vivo Raman spectra from single living algal b-dissolved in MilliQ water were obtained according to the cells, provide a complete band assignment for the in vivo method described in Wood et al. (2005). single-cell spectra, give comparisons with Raman studies on extracted algal photosystems and test the effects of different Spectral preprocessing laser powers on the sample for the first time. A quality test was applied to ensure that only spectra with a good signal-to-noise ratio were used in the analysis; hence, Materials and methods only spectra where the maximum of the most intense band in the spectrum (at 1525 cmÀ1) was 4 5000 counts were Organisms and growth conditions selected. Spectra were transformed using extended multi- Axenic batch cultures of the unicellular eukaryotic chlor- plicative signal correction (EMSC; Martens et al., 2003) in ophyte alga Dunaliella tertiolecta were grown in artificial the UNSCRAMBLER 9.2 software (Camo, Oslo, Norway), mod- seawater medium (D medium; Provasoli et al., 1957) at a eling and subtracting for channel number and squared photon flux of 150 mmol quanta mÀ2 sÀ1 and a constant channel number. temperature of 18 1C under nutrient-replete conditions in 250 mL Erlenmeyer flasks. Cells were harvested at the mid- Principal component analysis (PCA) and spectral exponential growth phase and resuspended at a final cell classification density of 5  105 cells mLÀ1 into 250 mL Erlenmeyer flasks PCA with full cross-validation was used as an initial method containing either standard D media or into D media devoid to examine general trends in the dataset. PCA score plots of nitrogen-containing compounds, and the cultures were were used to visualize any clustering of the samples, and maintained for a further 4 days under the same growth loading plots were used to determine which spectral region conditions described above to induce N-starvation. most contributed to the variance in the dataset. Partial least squares discriminant analysis (PLS-DA; Ge- Measurement of chlorophyll a ladi, 1988), which was determined to be the optimum The concentration of chlorophyll a in the starved and N- method in an earlier study (Heraud et al., 2006), was used replete samples was determined using a UV-visible spectro- as a classification method. The data were first split into meter using the method of Lorenzen (1967). calibration and validation sets, comprising two-thirds of the samples in the calibration set and one-third of the samples in the validation set for each of the N-replete and N-starved Raman spectroscopy conditions. The Y variables were 11 for the N-replete and 0 Replete and N-starved cells were adhered to the surface of for the N-starved condition. PLS models were generated by aluminium-coated, quartz-glass Petri dishes overcoated full cross-validation, after the elimination of outliers. Cor- with poly-L-lysine and maintained in either replete or rect classification of the N-replete condition was arbitrarily nitrogen-free artificial seawater medium at 4 1C. Raman assigned to samples with predicted Y 4 0.5, and correct spectra of in vivo viable algal cells were recorded on a classification of the N-starved state was assigned when Renishaw (Renishaw plc, Gloucestershire, UK) system 2000 Y o 0.5. The first two principal components (PCs), which c 2007 Federation of European Microbiological Societies FEMS Microbiol Lett ]] (2007) 1–7 Published by Blackwell Publishing Ltd. All rights reserved
  3. 3. Prediction of nutrient status of microalgae Raman spectroscopy 3 accounted for 77% of the variance, were used in all PLS Table 1. Assignments of bands identified in the spectra of Dunaliella classification. tertiolecta cells shown in Fig. 1 (Williams, 1983; Fujiwara et al., 1987; Overman Thomas, 1999; Wood et al., 2005) Wave number (cmÀ1) Band assignmentsà Results and discussion 1670 nC = O chla and/or amide I of protein 1605 nCC chla Assignment of Raman spectra of D. tertiolecta 1525 n(C = C) b-car (1524 cmÀ1); cells nCC chla (1535 cmÀ1) 1495 nCC, dCH3 chla In vivo Raman spectra obtained from D. tertiolecta using 1438 nCC, dCH3 chla; dCH3 b-car near-IR excitation wavelengths (780 or 785 nm) in this and 1389 dCH3, dCH, nC–N chla the two previous studies (Wood et al., 2005; Heraud et al., dCH3 b-car 2006) contained enhanced bands that can be assigned to 1348 dCH3, dCH, nC–N chla either chlorophyll a or b-carotene (Fig. 1 and Table 1). The 1325 nC–N, dCH chla strong enhancement of b-carotene bands from NIR excita- 1308 dCH3, dCH chla 1289 dCH3, dCH, nC–N chla tion, well away from major UV-visible absorbance bands, 1265 dCH3 b-car and/or amide III of protein has been explained by a p-electron/phonon coupling me- 1230 dCH, nCC, nN–C chla chanism (Castiglioni et al., 1993; Parker et al., 1999). 1187 dCH, nN–C chla (1186 cmÀ1) Similarly, chlorophyll a enhancement at NIR wavelengths dCH b-car (1191 cmÀ1) cannot be explained by resonant enhancement involving the 1157 nCC, dCH b-car Qy band, which has a maximum intensity in the 672–679 nm 1008 r CH3, nCC b-car range in algal photosystems (Marquardt Ried, 1992). No 988 dCH3 chla 915 dN–C–C, dC–C–C chla explanation to date seems to exist for the NIR enhancement 744 dH–C–O, dN–C–C chla Ãn, stretch; d, deformation (bend). of chlorophyll a bands in living algal cells (Wood et al., 2005). However, recent circular dichroism studies with algal photosystem I (PSI) particles demonstrate excitonic cou- pling in the reaction centre and antenna chlorophylls (Witt et al., 2003), which may suggest a possible mechanism. The band at around 1670 cmÀ1 in the spectra of the algal cells can be assigned to keto-carbonyl stretching in chlor- ophyll a. This band was not identified in the two previous Raman studies on living D. tertiolecta cells (Wood et al., 2005; Heraud et al., 2006); however, it is observed in resonance Raman spectra of chlorophyll a dissolved in various polar and nonpolar solvents (Koyama et al., 1986) and in Raman spectra of algal photosystems, where it is assigned to chlorophyll a (Fujiwara et al., 1987). Alterna- tively, the band at 1670 cmÀ1 could be assigned to the amide I band from proteins (Williams, 1983). This is supported by the absence of a band at this position in the spectra of chlorophyll a dissolved in water using 780 nm excitation (Fig. 1). However, the small band at about 1640 cmÀ1 in the spectrum of pure chlorophyll a (Fig. 1) may be due to chlorophyll a carbonyl stretching, as the keto-carbonyl band is often red shifted when chlorophyll a is dissolved in aqueous solutions (Koyama et al., 1986). Bands from C = C Fig. 1. Raman spectra of N-replete and N-starved Dunaliella tertiolecta cells compared with spectra from purified b-carotene and chlorophyll a. stretching vibrations in chlorophyll a occur between 1620 The spectra from the cells are the average of 30 N-starved and 30 N- and 1500 cmÀ1, together with the prominent band at replete cells, respectively. The assignments for the highlighted bands are 1525 cmÀ1 assigned to C = C stretching vibrations in b- shown in Table 1. carotene. The positions of the bands from chlorophyll a at FEMS Microbiol Lett ]] (2007) 1–7 c2007 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
  4. 4. 4 P. Heraud et al. 1605 and 1553 cmÀ1 are sensitive to the coordination number of the Mg atom (Fujiwara Tasumi, 1986). The band positions are similar to that observed in intact photosystems (Fujiwara et al., 1987), which indicates a five- coordinate species with one axial ligand, rather than a six- coordinated species with two axial ligands, in which case the bands would be red shifted to 1599–96 and 1548–45 cmÀ1, respectively. Bands in the 1500–1400 cmÀ1 spectral range arise mainly from CC stretching in chlorophyll a as well as CH2 and CH3 deformation modes from b-carotene and chlorophyll a. Bands between 1400 and 1200 cmÀ1 arise from the N–C stretching and methyl and methine bending in chlorophyll a as well as methyl bending in b-carotene. The band at 1265 cmÀ1 may also be contributed to by the amide III mode from proteins (Overman Thomas, 1999), Fig. 2. Average of 10 spectra acquired from N-replete Dunaliella tertio- which is usually strong in the Raman spectrum of biological lecta cells at different laser powers. The maximum laser power (100%) was 3 mW at the sample. cells. Bands observed in the 1200–1000 cmÀ1 range are mainly due to C–O stretching vibrations in chlorophyll a, together with the very strong band at 1155 cmÀ1 and the weaker band at 1006 cmÀ1 assigned to stretching vibrations CC bonds in b-carotene. Bands below 1000 cmÀ1 are as- signed to vibrations from NCC and CCC in-plane bending in chlorophyll a, while OCO vibrations and CH deforma- tions from chlorophyll a result in bands below 800 cmÀ1 (Wood et al., 2005). Effects of laser power on Raman spectra An important consideration tested in this study was whether laser power could lead to deterioration of the pigments in the algal cells. Exposure to laser light inevitably involves some heating of the cell as well as possible photo-degradation of chromophores. Carotenoids and chlorophylls are known to be particularly sensitive to degradation by light (Bumann Fig. 3. Distribution of ratios of intensities (counts) of the b-carotene band at 1157 cmÀ1 to the chlorophyll band at 1325 cmÀ1 for the N- Oesterhelt, 1995; He et al., 2000); hence, it was important replete and N-starved cell populations. to determine whether the Raman-based measurement could lead to deterioration of these analytes. There appeared to be little difference in spectra obtained using a range of laser Effects of nitrogen starvation on Raman spectra powers from 0.1 to 3 mW applied to the cells for 10 s (Fig. 2), of D. tertiolecta cells implying that there was little or no photo-degradation of chlorophyll a or b-carotene caused by laser exposure during Nitrogen starvation caused a decline in the intensity of measurement. A laser power of 0.3 mW was chosen for the bands attributable to chlorophyll a and a relative increase experimental trials because it was reasoned that exposure of in the intensity of bands from b-carotene compared with the the sample to this flux was unlikely to result in changes N-replete condition. These differences are clearly observed to chlorophyll a or b-carotene, but at the same time the in the mean spectra (Fig. 1), particularly from the strong laser power was sufficient to produce spectra with a good bands that can be unambiguously assigned to b-carotene, signal-to-noise ratio. such as that at 1157 cmÀ1, which is more intense in the Exposure to the laser light did not appear to affect cell spectra from N-starved cells, and from chlorophyll a, such as viability. This was judged in terms of cell motility of the the band at 744 cmÀ1, which is more intense in spectra from flagellated cells. Cells remained motile even after repeated N-replete cells. The spectral differences between the two measurements (data not shown). Evidently, heating or other nutrient populations are also demonstrated in Fig. 3, which changes that may have been induced in cells to the cells due to shows the distribution of the ratio of the intensities of a exposure to the laser was insufficient to affect cell survival. prominent b-carotene band (1157 cmÀ1) to a chlorophyll a c 2007 Federation of European Microbiological Societies FEMS Microbiol Lett ]] (2007) 1–7 Published by Blackwell Publishing Ltd. All rights reserved
  5. 5. Prediction of nutrient status of microalgae Raman spectroscopy 5 band (1325 cmÀ1). The b-carotene to chlorophyll a spectral Raman spectra of cells drawn from N-replete and N-starved intensity ratio is generally higher in the N-starved popula- cultures show distinct clustering along PC1, with only one tion, with a statistically significant difference between sample out of 60 showing an overlap (Fig. 4). Loading plots the N-starved and N-replete ratio means (1.54 vs. 1.36; can explain which regions of the spectrum contribute to P o 0.001 by t-test). These spectral differences corroborate variation in the dataset. PC1 loadings plots show that there the previous findings with masses of cells (Wood et al., 2005) is an opposite correlation between spectral regions attribu- and from spectra recorded from individual spatial locations table to chlorophyll a (e.g. bands at 752, 921, 1190, 1231 and within single N-replete and N-starved cells (Heraud et al., 1329 cmÀ1) and those from b-carotene (e.g. bands at 1158 2006). Differences in the signal from chlorophyll a are also and 1521 cmÀ1), supporting the view that the spectral corroborated in this study by spectrophotometric measure- differences between the N-replete and N-starved cells are ments of chlorophyll a content of the N-starved (0.21 Æ due to differences in the relative levels of chlorophyll a and 0.03 pg cellÀ1) and N-replete (0.95 Æ 0.02 pg cellÀ1) cultures b-carotene in cells drawn from the two nutrient conditions. in this study. b-carotene concentrations were not measured In line with the findings of Heraud et al. (2006), the first two spectrophotometrically in this investigation. However, an- PCs in PLS models described the chemical information in other study (Geider et al., 1998) has shown that b-carotene the Raman spectra (Fig. 4). to chlorophyll a ratios increase when D. tertiolecta is PLS-DA was very accurate in predicting the nutrient subjected to N-starvation, supporting the Raman spectro- status of cells in the independent validation set. All the cells scopic findings reported here. drawn from the N-starved condition were correctly as- signed, whereas 90% of the N-replete cells were correctly classified (Fig. 5). The results for classification of replete cells Population variability of Raman spectra and in this study were better than in Heraud et al. (2006), which classification of nutrient status may represent the reduction in intra-cellular variability in Raman spectra from single, living D. tertiolecta cells show spectral intensity using an average of several spectra from broad features such as baseline offset, resulting from changes in laser power in the instrument, sloping baseline from fluorescence and a range of spectral intensity (data not shown). Heraud et al. (2006) showed that multivariate statistical models could be developed to predict the nutrient status of the single living algal cells based on Raman spectral data. However, the accuracy of the prediction was dependent on the type of spectral preprocessing techniques used to correct for these broad baseline features. EMSC(Martens et al., 2003) was shown to be a superior method, as it corrected for baseline offset, linear and nonlinear baseline sloping, as well as normalizing for differences in spectral intensity (Heraud et al., 2006). In line with these findings, comparisons between PCA plots of raw data and EMSC processed data in this study showed a more defined cluster- ing in the EMSC data set, which was confirmed by soft independent modeling by class analogy (SIMCA) model distances (data not shown). Variability in Raman spectral intensity from algal cells was partly explained by Heraud et al. (2006) by differences in pigment concentration in different intracellular locations. Accordingly, in this study mean spectra were obtained from at least four individual spectra acquired at 2 mm intervals across the longitudinal axis of each cell, in an attempt to minimize intracellular variation and improve classification accuracy. For classification, PLS-DA was used, found by Heraud et al. (2006) to be superior, in terms of correct prediction rates, compared with PCA-based classification methods such as SIMCA. Score plots from the PLS-DA allow Fig. 4. PC1 vs. PC2 scores plots and corresponding loading plots for the visualization of any clustering in the dataset. The mean PLS analysis. FEMS Microbiol Lett ]] (2007) 1–7 c 2007 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
  6. 6. 6 P. Heraud et al. individual differences in gene expression (Handa et al., 1983), or is the same set of genes expressed in all individuals to each environmental stimulus, with the timing of expres- sion differing (Levsky Singer, 2003)? The method presented in this study can be added to the small list of single-cell measurements (see Brehm-Stecher Johnson, 2004 for a review) able to probe these questions. Raman spectroscopy might prove especially useful in conjunction with some of these alternative techniques, including chlor- ophyll fluorescence imaging (Underwood et al., 2005), to obtain a clearer picture of nutrient limitation and the general physiological status of cells within populations. Knowledge of variability of responses on the individual Fig. 5. Predicted vs. measure values for the validation set obtained using cell level provides the possibility of obtaining a deeper discriminant PLS classification. The dotted line indicates the boundary between classes. understanding of the nutrient response in microalgae. The existence and nature of nutrient micro-environments could the same cell. The classification of the N-starved cells was be probed, or correlations between responses to nutrient equivalent to the best classification of N-starved cells limitation and other cell parameters like cell size or age reported in Heraud et al. (2006). Of course, it might be could be determined, for instance. It is possible that changes possible that individual cells could indeed be nutrient- in population distribution in response to nutrient change starved, even when drawn from replete cultures, due to may be more predictive of the primary productivity of cellular ageing or pathogenesis, for example. Conversely, natural assemblages of phytoplankton or bloom formation cells from nutrient-starved cultures could be nutrient- than simply a mean response. replete if they had previously experienced some nutrient enrichment in their local micro-environment, caused by close proximity to decaying dead cells, for instance. It must References also be recognized that smaller changes in chl : carotenoids Beardall J Raven J (2004) The potential effects of global climate ratios (associated with minor changes in chlorophyll con- change on microalgal photosynthesis, growth and ecology. tent) are found under P-limitation (Geider et al., 1998) and Phycologia 43: 26–40. Fe-limitation (Young Beardall, 2005), and Raman-based Beardall J, Berman T, Heraud P et al. (2001a) A comparison of approaches for natural populations, especially if based on methods for detection of phosphate limitation in microalgae. single-cell measurements, may have to be taken in conjunc- Aquat Sci 63: 107–121. tion with indicators of possible nutrient limitations. Need- Beardall J, Young E Roberts S (2001b) Approaches for less to say, further work is required, with larger sample determining phytoplankton nutrient limitation. Aquat Sci 63: numbers drawn from a range of nutrient conditions, before 44–69. the variability of responses of cell populations to nutrient Brehm-Stecher BF Johnson EA (2004) Single-cell change can be comprehensively defined. microbiology: tools, technologies, and applications. Microbiol Mol Biol Rev 68: 538–559. Conclusions and future directions Bumann D Oesterhelt D (1995) Destruction of a single chlorophyll is correlated with the photoinhibition of Raman spectroscopy enables a new direct in vivo measure- photosystem II with transiently inactive donor side. Proc Natl ment method of the nutrient status of single microalgal cells. Acad Sci 92: 12195–12199. The advantage of single-cell analysis is that population Castiglioni C, Del Zoppo M Zerbi G (1993) Vibrational Raman variability can be assessed. It has been shown that cell spectroscopy of polyconjugated organic oligomers and populations respond to the environment in ways often not polymers. J Raman Spectrosc 24: 485–494. predicted by measurement of the mean response, dispelling Dressel R Gunther E (1999) Heat-induced expression of MHC- the myth of the ‘average cell’ (Zhao, 1997; Dressel linked HSP70 genes in lymphocytes varies at the single cell Gunther, 1999; Levsky Singer, 2003). In vivo single-cell level. J Cell Biochem 72: 558–569. measurement allows one to probe fundamental questions Elowitz M, Levine A, Siggia E Swain P (2002) Stochastic gene about intercellular variability and what governs it. Is there a expression in a single cell. Science 297: 1183–1186. ‘normal response’ to environmental stimuli with each Fujiwara M Tasumi M (1986) Resonance Raman and infrared individual varying stochastically around the mean response studies on axial coordination to chlorophylls a and b in vitro. (Elowitz et al., 2002), or is intercellular variability due to J Phys Chem 90: 250–255. c 2007 Federation of European Microbiological Societies FEMS Microbiol Lett ]] (2007) 1–7 Published by Blackwell Publishing Ltd. All rights reserved
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