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

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-
phil.heraud@sci.monash.edu.au 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 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 Â 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. 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 assignmentsÃ
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
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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
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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
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Published by Blackwell Publishing Ltd. All rights reserved

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
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