Refractive index of planktonic cells as a measure of cellular carbon and chlorophyll a content

Transcription

1 Deep-Sea Research I 46 (1999) Refractive index of planktonic cells as a measure of cellular carbon and chlorophyll a content Dariusz Stramski* Marine Physical Laboratory, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA , USA Received 14 July 1997; received in revised form 27 October 1997; accepted 21 April 1998 Abstract Current methods for determining carbon content in individual planktonic cells from particle volume alone may involve large errors, and no routine technique exists for determining chlorophyll a content in individual phytoplankters. In this study the concept of using the refractive index of cells as a measure of intracellular concentrations of carbon and chlorophyll a is discussed. Specifically, the real part of the refractive index n (at light wavelengths where absorption is negligible or very small) is shown to correlate well with the intracellular concentration of carbon, C. The imaginary part of the refractive index n (in the red band of chlorophyll a) correlates well with the intracellular chlorophyll concentration, Chl. These relationships were found to be nearly identical for two species, a cyanobacterium Synechococcus and a diatom ¹halassiosira pseudonana, over a two-fold range in C and Chl. This range was associated with interspecies differences and intraspecies variations in the cell properties over a day night cycle. These observations and the underlying theoretical considerations suggest that the relationships C vs. n and Chl vs. n may be robust and hold for a variety of planktonic species regardless of interspecies and intraspecies variability in cellular carbon content, Chl a content, and cell size. In addition, these relationships may be applicable to single-particle analysis of natural water samples, which promises a unique capability to acquire information about the distribution of carbon and chlorophyll a among individual cells, different size classes, and taxonomic groups of planktonic microorganisms in the ocean. Further research with various planktonic species is needed to examine the generality of the relationships C vs. n and Chl vs. n before the approach can be implemented in field studies Elsevier Science Ltd. All rights reserved. * Fax: ; stramski@mpl.ucsd.edu /99/$ see front matter 1999 Elsevier Science Ltd. All rights reserved. PII: S ( 9 8 ) X

2 336 D. Stramski / Deep-Sea Research I 46 (1999) Introduction Accurate information about the fluxes and sizes of various carbon reservoirs in the ocean is prerequisite to a good understanding of the carbon cycle. This cycle is largely controlled by biologically mediated processes and provides the main long-term control of atmospheric CO and the Earth s climate. In order to develop better understanding and modeling capabilities of carbon dynamics, it is essential to know not only the total concentrations of various carbon pools, such as the total dissolved organic carbon (DOC) and total particulate organic carbon (POC), but also the distribution of POC among various particulate components (phytoplankton, zooplankton, bacteria, other organisms, detritus), including the distribution among various size classes and taxa of microorganisms. In addition to the carbon cycling, such information is important to a number of research areas including ecosystem modeling, in which photosynthesis and respiration of phytoplankton, as well as zooplanktonic feeding, growth and respiration are all quantified as carbon rates. The sizes of various reservoirs of POC in the ocean are difficult to determine with high accuracy, primarily because the existing measurement techniques are subject to considerable uncertainties and limitations. The co-occurrence of variable and significant fractions of POC associated with phytoplankton, heterotrophic organisms, and nonliving particulate matter prevents accurate determinations of any of those components from analysis of total POC. Estimates of phytoplankton POC from measurements of related biochemical variables, such as chlorophyll a or ATP concentrations, are uncertain, because the conversion factors undergo significant and difficult to predict changes under varying environmental conditions (Banse, 1977; Sinclair et al., 1979). It is also difficult to accurately determine the carbon content in individual planktonic cells and hence the distribution of carbon among different size classes or taxa of microorganisms. Such information is now acquired from microscopic analysis of sea water samples, which involves enumeration and sizing of cells, followed by the conversion of cell volume into carbon content (e.g. Verity and Sieracki, 1993). The mass of carbon associated with a single cell (C ) is calculated from published data on the cell carbon per unit cell volume conversion factor C, which is typically derived from laboratory experiments with cultures. Subsequently, carbon associated with a planktonic population or taxonomic group of interest can be estimated from the concentration of cells and cellular carbon content. The conversion factor C is hereafter referred to as the intracellular carbon concentration. As we see, the fundamental assumption of this approach is that the cellular content of carbon C can be predicted from the cell volume (» ) alone. This assumption along with large intraspecies and interspecies variability in C may lead to significant error in cellular carbon estimates. Another important biochemical variable in the ocean is the concentration of chlorophyll a (Chl a), which is the most important single pigment present in phytoplankton. Knowledge of Chl a concentration and distribution in the ocean is critical to the study of phytoplankton productivity and ecophysiology. Bulk chlorophyll measurements have become a routine component of oceanographic studies during the

3 D. Stramski / Deep-Sea Research I 46 (1999) past decades. The spectrophotometric, fluorometric, and chromatographic techniques (see Gieskes, 1991 for a review) have been applied in conjunction with a variety of in situ sampling strategies. Methods to estimate Chl a from satellite ocean color imagery have also been developed (e.g. Gordon and Morel, 1983). Because such bulk determinations of Chl a apply to a large number of cells, they provide no information about the heterogeneous nature of the phytoplankton population. With the introduction of new techniques for single-particle analysis in oceanography such as flow cytometry (Yentsch et al., 1983; Chisholm et al., 1986), microspectrophotometry (Iturriaga et al., 1988), and optical tweezers (Sonek et al., 1995), scientists have begun to measure fluorescence and other optical properties of individual cells, and to address the problem of heterogeneous natural samples. However, no satisfactory approach exists for determining the cellular chlorophyll from single-cell analyses. In particular, it has been recognized that no simple generalizations about the relationship between fluorescence intensity measured in flow cytometry and cellular Chl a content can be made (Sosik et al., 1989). Variations in the relative abundance of Chl a and accessory pigments, variations in the effect of pigment packaging, and variations in fluorescence yield are the major obstacles to estimating cellular Chl a content from single-cell fluorescence measurements. In this paper we discuss the concept of relating the intracellular concentration of carbon and chlorophyll a to the refractive index of planktonic cells. In support of this concept we show data acquired with two phytoplankton species, a cyanobacterium and a diatom, grown in cultures under a day night cycle in irradiance. The data suggest that such relationships may provide a basis for developing new techniques for estimating cellular carbon and chlorophyll a content in individual planktonic microorganisms from optical determinations of the refractive index. 2. Cellular carbon vs. cell volume Before embarking on a discussion of the relationships that involve the refractive index of cells, we will briefly review the technique for determining carbon content in individual cells that relies upon the conversion of cell volume into mass of carbon. In the past, there has been considerable effort to establish empirical relationships between the cellular carbon content C and cell volume» for a range of marine phytoplankton species representing different taxa (Mullin et al., 1966; Strathmann, 1967; Taguchi, 1976; Moal et al., 1987; Verity et al., 1992; Montagnes et al., 1994). The nonlinear regression model C "A» was usually found to be most appropriate. These results are summarized in Fig. 1, which shows significant differences among the derived relationships. In consequence, C estimated from» may differ by as much as 5-fold depending on the choice of the regression model. Such difference occurs, for example, in the small size range between recent models proposed by Verity et al. (1992) and Montagnes et al. (1994) (see lines 6 and 7 in Fig. 1A). Similar differences occur if the regression models, C vs.», are converted into the relationship between the intracellular carbon concentration C and cell volume, which has the form C " A» (Fig. 1B).

4 338 D. Stramski / Deep-Sea Research I 46 (1999) Fig. 1. Comparison of the relationships between cell carbon and cell volume for phytoplankton obtained by several investigators. Panel A shows the carbon content per cell C vs. cell volume», and panel B shows the intracellular carbon concentration C vs. cell volume». The lines represent the following studies: 1 Mullin (1966), 14 species from 4 taxonomic classes, fixed cells; 2 Strathmann (1967), 5 diatom species, live cells; 3 Taguchi (1967), 7 diatom species, fixed cells; 4 Moal et al. (1987), 11 species from 5 classes, probably fixed cells; 5 Verity et al. (1992), 13 species from 7 classes, fixed cells; 6 Verity et al. (1992), 9 species from 6 classes, live cells; 7 Montagnes et al. (1994), 30 species from 8 classes, live cells. Each line is plotted over the appropriate range of cell volume that was used in a particular study. Note that the use of different lines will result in significantly different prediction of carbon from cell volume. Much effort in the past focused on determining representative values of C for a specific group of microorganisms such as heterotrophic bacteria (Bratbak, 1985; Lee and Fuhrman, 1987; Nagata and Watanabe, 1990), heterotrophic flagellates (Borsheim and Bratbak, 1987), and ciliates (Verity and Langdon, 1984; Putt and Stoecker, 1989). The resultant values vary widely; for example, the recommended C for bacteria vary between 0.1 and 0.56 pgc μm (Nagata and Watanabe, 1990). These large differences in cellular carbon vs. cell volume relationships (or equivalently cell volume to carbon conversion factor) in previous studies can be attributed to two major sources. First, various measurement methodologies were used to estimate cell volume and carbon, which may influence the derived relationships and conversion factors (see Verity et al., 1992; Montagnes et al., 1994). Second, and most importantly, there exists large interspecies and intraspecies variability in the intracellular carbon concentration of planktonic organisms (e.g. Moal et al., 1987; Thompson et al., 1991, 1992). This variability places limitations upon the ability to accurately estimate carbon content of microorganisms from the measurement of cell volume alone. The problem is that we never know an exact value of C which should be applied to convert cell volume to cell carbon for any given microorganism whose volume is measured, so there is potential for significant error in the estimated cellular carbon.

5 3. Carbon vs. real part of refractive index 3.1. Conceptual framework D. Stramski / Deep-Sea Research I 46 (1999) We here present a concept that has its roots in the classic empirical relationship of Gladstone and Dale (1863), who studied a variety of liquids at various temperatures. This relationship implies that the refractive increment n!1 (where n is the real part of the refractive index of a given substance in vacuum) is approximately proportional to the density of the substance. The Gladstone Dale approximation was later given a sounder theoretical support by the Lorentz Lorenz formula, which involved the relation between (n!1) (n #1)/(n #2) and the density of the substance (e.g. Barer and Joseph, 1954; Aas, 1996). Note that for the range of n that is of interest to our study (from about ) the expression (n #1)/(n #2) varies weakly with n, and the Lorentz Lorenz formula approximates to the simpler Gladstone Dale relationship. Let us now consider dilute aqueous solutions or suspensions of biological macromolecules, cells, or microorganisms. The density of such media can be assumed to be linearly related to the concentration of the dispersed substance, C, which is expressed in terms of mass of the substance per unit volume of the medium. According to the Gladstone Dale approximation, the refractive index of the solution or suspension, n, will increase linearly with the concentration C (Barer and Joseph, 1954) n "A#αC (1) where A and α are constants. Although the refractive index is generally a complex number, n refers here to the real part of the index. Eq. (1) is thus applicable to spectral regions with no absorption, or very weak absorption, where the imaginary part of the refractive index can be neglected. In such regions the refractive index n changes comparatively slowly with light wavelength and is unaffected by anomalous dispersion that occurs within absorption bands. Our further discussion in this section will thus pertain to the spectral region where the biological substance dispersed in water exhibits sufficiently weak absorption. Note that such regions are easily identified for planktonic microorganisms, including pigmented phytoplankton; for example, in the visible spectral region it will usually suffice to avoid the major absorption bands of chlorophylls, carotenoids and phycobiliproteins, if present. In addition, the absorption by phytoplankton becomes negligible at near-infrared wavelengths, which can also be used for the present purposes. Note also that in the visible spectral region the complex refractive index of pure water or pure sea water (that is the suspending medium in our considerations) can be represented just by the real part of the index, n, whose value is approximately 1.34 (the imaginary part is negligibly small). We now return to Eq. (1) and set C "0. We see that A"n and Eq. (1) can be rewritten as n!n "αc (2a) or in terms of the relative refractive index, n (i.e. relative to water rather than vacuum) n"α C #1 (2b)

6 340 D. Stramski / Deep-Sea Research I 46 (1999) where n"n /n and α "α/n. Equation (2a) shows that α"(n!n )/C, which describes the increase in the refractive index of the solution (suspension) per unit increase in the concentration of the dispersed substance. The parameter α is referred to as the specific refraction increment, and it connects the refractive index with the concentration of the substance. Similarly, α "(n!1)/c is the relative specific refraction increment of the substance dispersed in water (note that while α corresponds to the absolute refractive index n in vacuum, α corresponds to the relative refractive index n in a given medium). Experiments with various gases and liquids showed that the specific refraction increment depends very weakly on the density of the substance (e.g. Akhmanov and Nikitin, 1997). Also, it has long been recognized that refractometry can serve as a method for estimating protein concentrations, in particular various protein constituents of the blood (Adair and Robinson, 1930). Further work in that area led to the application of the immersion refractometry in cell biology (Barer, 1952; Barer and Ross, 1952; Davies and Wilkins, 1952; Barer et al., 1953; Barer and Joseph, 1954; Ross and Billing, 1957). It was shown that the real part of the refractive index of a living cell can be taken as a measure of the water and solid contents. Given Eqs. (1) and (2), this postulate becomes clear when it is realized that any biological cell can be regarded as an aqueous solution of substances with a certain average specific refraction increment. It is remarkable that equal concentrations of aqueous solutions of different proteins were all found to have approximately the same refraction increment. The values of α for many proteins do not differ by more than 5% from a mean value of 0.18 cm g or 0.18 ) 10 m kg (Barer et al., 1953; Barer and Joseph, 1954). It is also important that α was found to be constant over a wide range of protein concentration. This indicated that protein concentration in a biological cell could be estimated from measurements of refractive index, assuming that the cell can be regarded as an aqueous solution of protein. Such an assumption is only a first approximation, because there are other substances present besides proteins. The three other major classes of organic substances that are particularly important are carbohydrates, lipids, and nucleic acids. Carbohydrates and lipids usually have relatively low values of α, in the range cm g. For nucleic acids, α is between 0.16 and 0.2 cm g (Barer et al., 1953; Barer and Joseph, 1954). These investigators also concluded that typical variations in the chemical composition of biological cells will induce only small changes in α. This is because the main solid constituent of nearly all cells is protein, and some lipids and carbohydrates are present in the form of complexes with protein, so that the refraction increments of such complexes do not differ markedly from those of other proteins. In addition, the refractive increments of many organic substances, and certainly those present in living cells including nucleic acids, amino acids, and other nonprotein nitrogenous constituents, are nearly the same as for proteins. This is understandable as most cellular organic constituents are fairly large but composed of relatively few elements (primarily carbon, hydrogen, oxygen, and nitrogen), so one does not expect great differences in their refractive properties. Only in exceptional cases, as perhaps in some cells of unusual chemical composition or in cells heavily loaded with pigments (within the

7 spectral bands of strong absorption), is any considerable variation in these refractive properties likely to occur. The above considerations suggest that α"0.18 cm g represents the average refraction increment of cells, meaning that the refractive index increases approximately by 0.18 for every 1 g cm rise in the intracellular concentration of solid constituents. Barer and Joseph (1954, p. 422) stated that For the majority of cells it seems unlikely that this figure would be in error by more than perhaps 5%. Although the α values determined by Barer and his associates were mostly for eukaryotic cells, similar values were also obtained for prokaryotic cells, specifically for various types of vegetative and dormant bacteria (Bateman et al., 1966; Coles et al., 1975; Gerhardt et al., 1982). The conclusion is that variations in the real part of refractive index of biological cells are induced primarily by changes in the relative amounts of cellular water and solids rather than chemical composition of solids. Importantly, this notion has received further support in a recent analysis of the refractive index of marine phytoplankton, which involved the estimation of the index from metabolite composition of different phytoplankton taxa (Aas, 1996). Thus those previous studies suggest that the refractive index could be used as a measure of the intracellular concentration of solid consituents. We will now present the experimental evidence that this concept can also be applied to intracellular concentration of carbon instead of total solids. The conceptual transition from total solids to carbon appears to be warranted, because carbon is one of the relatively few major elements that make up cellular materials, and it is the skeleton of carbon atoms that forms the basic framework of all classes of biologically important organic molecules (e.g. Avers, 1981) Experimental data D. Stramski / Deep-Sea Research I 46 (1999) Determinations of the refractive index were used in the past to estimate the water and solid content of living bacterial cells and spores (e.g. Ross and Billing, 1957; Wyatt, 1970; Gerhardt et al., 1982). It was not until recently, however, that the relationship between the refractive index and the intracellular carbon concentration in marine microorganisms was shown. The significant correlation between the real part of the refractive index of marine microorganisms, n, and the intracellular carbon concentration, C, was first observed for the cyanobacterium Synechocystis (Stramski and Morel, 1990). The slope of the relationship n(440) vs. C for those data was ) 10 m (kg C) and the correlation coefficient was Note that this relationship was determined at a light wavelength λ"440 nm, where absorption by chlorophyll a and some accessory pigments is significant. Based on some simplifying assumptions about the cellular material in planktonic cells, Morel and Ahn (1990) estimated the value of the carbon-specific refraction increment for cells suspended in water, α "0.233 ) 10 m (kg C). This estimation was made without any specific reference to light wavelength, and was then shown to be consistent with data for hetrotrophic bacteria and flagellates (Morel and Ahn, 1991). Our recent experiments with the marine diatom ¹halassiosira pseudonana and the cyanobacterium Synechococcus (clone WH8103) grown under a day night cycle in natural irradiance support a close correlation between n and C. Detailed descriptions

8 342 D. Stramski / Deep-Sea Research I 46 (1999) of these experiments can be found elsewhere (Stramski and Reynolds, 1993; Stramski et al., 1995). Briefly, the refractive index of the cells in these experiments was determined from an inverse scattering model using experimental data on the spectral beam attenuation coefficient, absorption coefficient, size distribution of the cell suspensions, and concentration of cells. The beam attenuation and absorption spectra were measured on cell suspensions over a 1 cm pathlength with a dual beam spectrophotometer equipped with appropriate geometric configuration. The size distribution was determined with a Coulter counter and the actual concentration of cells in the suspension was obtained by microscopic enumeration. The inverse model for deriving the refractive index based on anomalous diffraction approximation for homogeneous spheres and its first applications to marine phytoplankton are described in Bricaud and Morel (1986) and Stramski et al. (1988). Our calculations for ¹. pseudonana and Synechococcus were based on a similar inverse model with the exception that Mie scattering theory rather than anomalous diffraction approximation was used. The derived values for the real part of the refractive index represent the best match between the experimental estimate of the spectral attenuation efficiency of the cells and its theoretical counterpart for homogeneous spheres. For the imaginary part of the refractive index this match is obtained for the absorption efficiency. Because the inverse model uses the measurements of the bulk properties of cell suspensions, the estimated refractive index represents an average value (or an average cell) derived from the entire population of cells. Note that such estimation should be regarded as a practical way of characterizing refractive index of cells rather than providing the actual true value of the index (see also Zaneveld and Pak, 1973). Similarly, C in our experiments represents an average value for the cell population because it was determined as the ratio of the bulk concentration of particulate organic carbon to the total volume of cells in the cultures. Twenty-five measurements at intervals of 2 4 h were taken for ¹. pseudonana during three successive day night cycles, and 16 measurements were taken for Synechococcus during two day night cycles. The regression analysis of n at λ" 660 nm (where absorption by phytoplankton pigments is comparatively weak) on C yielded n(660)" C # for ¹. pseudonana (r"0.892) (3a) n(660)" C # for Synechococcus (r"0.646) (3b) where C is in (kg m ) and r is the correlation coefficient. It is remarkable that the two regression formulas are very similar even though they represent different types of cells: small prokaryotic cyanobacteria (&1 μm in size) and eukaryotic diatoms (&4 μm). These formulas agree with the expectation that n must approach 1 with decreasing C, which is consistent with Eq. (2b). Note, however, that the intercept values are slightly greater than 1 for both species, with the standard error being for ¹. pseudonana and for Synechococcus. Because the best-fit values for the intercept differ between the two species, the regression parameters for the pooled data (the two species considered together) are different from those in Eqs. (3a) and (3b). Specifically, the best fit values of the slope and intercept for the pooled data

9 D. Stramski / Deep-Sea Research I 46 (1999) are ) 10 m (kg C) and , and the correlation coefficient is Note that the intercept is now essentially 1, and the slope is very close to the values obtained by Stramski and Morel (1990) and Morel and Ahn (1990). In the first approximation the slope parameter can be interpreted as the carbonspecific refraction increment α of the cells suspended in sea water. This increment represents the increase in n for every 1 kg m rise in C. As shown above, depending on whether our data for the two species are considered separately or together, the estimate of α at 660 nm varies; it is 28% lower for each species considered separately than for the pooled data. The uncertainties in the data, including statistical errors, are likely an important source of this variation, and the actual values of α do not necessarily differ significantly between the two species. Although we must assume that α may vary to some extent with different types of planktonic cells, and possibly also for the same species under extremely different growth conditions, our present data suggest that these variations are likely contrained to a narrow range. It is the estimation of C from n that is of special interest to this study; therefore, in Fig. 2 our data for ¹. pseudonana and Synechococcus are combined and plotted as C vs. n(660). These data cover a fairly broad range in n(660) and C because of interspecies differences and intraspecies variations in the cell properties over a day night cycle during the experiments. The scatter in the data points is not surprising given uncertainties associated with measurements and computational procedure for deriving n(660) as well as the determination of C. Nevertheless, the correlation coefficient is high, r"0.915 (41 data points). The best fit regression is C " n(660)! (4) Fig. 2. The intracellular carbon concentration C vs. the relative real part of the refractive index of cells n(660). The graph includes 25 points for ¹halassiosira pseudonana (from Stramski and Reynolds, 1993) and 16 data points for Synechococcus (from Stramski et al., 1995). The best linear fit is also plotted.

10 344 D. Stramski / Deep-Sea Research I 46 (1999) with standard errors of for the slope and kgc m for the intercept. This result serves as an illustration of the simple empirical relationship that can potentially be used as a means for determining the intracellular carbon concentration from the real part of refractive index of cells. Note that if cell volume» is known in addition to the refractive index, then carbon content per cell, C, can be easily calculated as a product of C and». Figure 3 illustrates the potential errors in estimating cellular carbon content from cell volume alone. The cell carbon content C estimated from cell volume is compared with true C determined from measurements made in our two experiments: one with ¹. pseudonana and the other with Synechococcus. The estimated C was obtained from two different relationships from the literature (Verity et al., 1992; Montagnes et al., 1994) using our measurements of the mean cell volume (Stramski and Reynolds, 1993; Stramski et al., 1995). The measured C was obtained as a ratio of the measured bulk POC concentration to the cell number concentration within the cultures. In these two examples, the relationships based on cell volume fail to predict cellular carbon content with a reasonable degree of accuracy. The estimated values differ significantly from the measured values, for many data points more than 2-fold. We have here chosen to apply the models of Verity et al. (1992) and Montagnes et al. (1994) because these models represent the most comprehensive recent work on the relationship between cellular carbon and cell volume, and they are based on various species including the size range of Synechococcus and ¹. pseudonana (these cells have volumes of approximately 1 and 35 μm, respectively). If other models shown in Fig. 1 were extrapolated Fig. 3. The illustration of possible errors in estimating cell carbon from cell volume alone. The estimates derived from the relationships of Verity et al. (1992) and Montagnes et al. (1994) are plotted against the measured values for ¹halassiosira pseudonana (panel A) and Synechococcus (panel B). The measured data for the two species are from Stramski and Reynolds (1993) and Stramski et al. (1995), respectively. The solid lines represent 1 : 1 relationship (perfect prediction). The points denoted as Verity et al. (1992) represent their relationship for fixed cells (the estimates based on their relationship for live cells would deviate even more from the 1 : 1 line).

11 D. Stramski / Deep-Sea Research I 46 (1999) to this small size range and used in Fig. 3, the discrepancies between the estimated carbon and measured carbon would also be significant. The purpose of Fig. 3 is not to evaluate the performance of the two specific regression models that are based on cell volume but merely to illustrate potential errors associated with general application of this type of model to planktonic organisms that exhibit variability in the intracellular carbon concentration. The relationship involving the refractive index, such as that expressed by Eq. (4), offers an attractive alternative method, which may improve the accuracy of the estimates of cellular carbon content and may have more general applicability to microorganisms regardless of variability among species and physiological state of the cells. 4. Chlorophyll vs. imaginary part of refractive index 4.1. Conceptual framework A concept for determining chlorophyll a content in individual cells is based upon the relationship between the imaginary part of the refractive index of cells and the absorption coefficient of cellular substance. For a phytoplankton cell suspended in sea water, the imaginary part of the refractive index n at any wavelength λ can be related to the absorption coefficient of cellular substance a (λ) (e.g. Morel and Bricaud, 1981) n (λ)"[a (λ) λ]/[4πn ]. (5) The coefficient a (λ) can be regarded as a product of the chlorophyll-specific absorption coefficient of cellular substance in a hypothetical aqueous solution, a* (λ), and the intracellular chlorophyll concentration Chl a (λ)"a* (λ) Chl. (6) If we consider the red band of Chl a (&675 nm), the contribution to absorption by other pigments present in phytoplankton is typically very small. It can therefore be assumed that Chl a is the dominant absorbing compound in this spectral band, and thus a* (λ) is (approximately) constant. This constancy follows from the fact that a* (λ) is free from the package effect because it represents the absorption per unit amount of Chl a in a hypothetical situation that pigments are in dissolved phase rather than being present as discrete particulates (e.g. Duysens, 1956; Morel and Bricaud, 1981). As a result, we can expect that n (in the vicinity of λ"675 nm) is linearly related to the intracellular chlorophyll a concentration according to n (λ)"[chl a* (λ)λ]/[4πn ]. (7) This relationship forms the basis for estimating Chl from n Experimental data In our recent studies of ¹. pseudonana and Synechococcus we reported the relationships between the imaginary part of the refractive index n (at the Chl a maximum in

12 346 D. Stramski / Deep-Sea Research I 46 (1999) the red) and Chl (Stramski and Reynolds, 1993; Stramski et al., 1995) n (673)" Chl # n (678)" Chl # for ¹. pseudonana (r"0.608) (8a) for Synechococcus (r"0.986) (8b) where Chl is in (kg m ). The imaginary part of the refractive index was derived from the inverse optical model (Bricaud and Morel, 1986) using the measured absorption coefficient and size distribution of cell suspensions as well as the concentration of cells. The intracellular concentration Chl was determined as the ratio of the bulk concentration of Chl a to the total volume of cells in the cultures. Therefore, these variables, just like n(660) and C, represent the average cell derived from the entire population. The regression formulas n vs. Chl are very similar for the two species. The intercept is only slightly different from zero, supporting the assertion that pigments other than Chl a have very little effect on absorption in this spectral band. In analogy to the carbon-specific refraction increment α, we can introduce the chlorophyll a-specific increment for the imaginary index of refraction, which equals the slope parameter of the relationship n vs. Chl [units for this quantity are m (kg Chl a) ]. From Eqs. (8a) and (8b) we see that n in the red band of Chl a increases approximately by for every 1 kg m increase in the intracellular concentration of Chl a. Ignoring the intercept, we can also estimate a* from Eq. (7) using the slope parameter of the derived regressions. This estimate is and m (mg Chl a) for Fig. 4. The intracellular chlorophyll a concentration Chl vs. the imaginary part of the refractive index of cells in the red absorption band n (675). The graph includes 25 points for ¹halassiosira pseudonana (from Stramski and Reynolds, 1993) and 16 data points for Synechococcus (from Stramski et al., 1995). The best linear fit is also plotted.

13 D. Stramski / Deep-Sea Research I 46 (1999) Synechococcusand ¹. pseudonana,respectively.the fact that these estimates are very close to the value of m (mg Chl a) for the Chl a peak at 663 nm in 90% acetone solution (Jeffrey and Humphrey, 1975) lends confidence to the derived relationships. Again, because it is the estimation of Chl from n that is of interest to this study, Fig. 4 shows Chl plotted vs. n (675) for both species together. A good linear fit is obtained for these data (r"0.928, 41 data points) Chl " n (675) (9) with the standard errors of 64.1 and 0.42 ((kg Chl a) m ) for the slope and intercept, respectively. This type of relationship can serve as a means for estimating the intracellular chlorophyll aconcentration from the imaginary part of the refractive index of cells. If cell volume» is known in addition to n, then Chl a content per cell can be determined as a product of Chl and». 5. Conclusions The existing methods for determining carbon content in individual planktonic cells from particle volume alone are subject to potentially large errors. In addition, no routine technique exists for determining chlorophyll a content in individual phytoplankters. We have here proposed that the intracellular carbon and Chl a concentrations in planktonic cells can be estimated from their refractive index. Both theoretical and empirical facts that support this concept have been discussed. The first experimental results are encouraging as they show that the relationship between the intracellular carbon (C ) and the real part of the refractive index (n at 660 nm) can be very similar for different planktonic species. Also, we observed that the relationship between the intracellular chlorophyll a concentration (Chl ) and the imaginary part of the refractive index (n at 675 nm) can be similar for different species. These similarities were observed for a cyanobacterium Synechococcus and a diatom ¹halassiosira pseudonana (Figs. 2 and 4). In addition, the relationships were not affected by intraspecies variations in cell properties over a day night cycle in irradiance. These results suggest that the general relationships C vs. n and Chl vs. n may hold for a variety of planktonic species regardless of interspecies and intraspecies variability in cellular carbon content, Chl a content, and cell size. We also note that while the relationship Chl vs. n is naturally limited to Chl a-bearing species, the relationship C vs. n may prove to be applicable to microorganisms regardless of whether they are photoautotrophs, heterotrophs, or mixotrophs. The likely existence of robust relationship C vs. n is based on the assumption that the changes in the composition of cellular solids for most species and typical growth conditions will not be great enough to induce significant variations in the carbonspecific refraction increment. Similarly, the changes in the cellular composition are expected to be of little importance to the determination of Chl from n, because such determinations are based upon the red spectral band of Chl a (&675 nm) where absorption by other pigments is typically negligible. Nevertheless, further studies

14 348 D. Stramski / Deep-Sea Research I 46 (1999) including a variety of planktonic species grown under various conditions are needed to validate these hypotheses and establish improved relationships, if warranted. One of the important questions is to examine whether there might exist situations when cells exhibit sufficiently unusual chemical composition so that the carbon-specific and/or Chl a-specific refraction increments differ considerably from the average values representing the majority of species. We cannot exclude the possibility that some species or unusual cases will have to be characterized by separate relationships. As an example, calcareous phytoplankters, coccolithophorids, may require separate relationships because such cells have the outer covering of small calcareous plates or coccoliths. The coccoliths consist of calcium carbonate, primarily in the form of crystalline calcite or aragonite whose real part of refractive index is high, in the range , relative to water. Because some variations in the refractive increments will undoubtedly occur (e.g. Reynolds et al., 1997), the accurate quantification of the extent of these variations is of primary importance to the application of the presented concept. The relationships C vs. n and Chl vs. n discussed in this paper were determined from bulk measurements of monospecific phytoplankton cultures, and thus they represent an average cell from the populations. However, one can expect that the carbon-specific and Chl a-specific refraction increments will vary weakly among individual cells, and therefore such average relationships will be applicable with good accuracy to any single cell within the population. Thus the concept of linking the intracellular carbon and Chl a to refractive index of cells appears to be applicable to single-cell analyses of natural sea water samples. Flow cytometry (Yentsch et al., 1983), microspectrophotometry (Iturriaga et al., 1988), and optical tweezers (Sonek et al., 1995) are among available techniques for single-cell analysis, which have the potential for the application of the proposed concept. These single-particle techniques provide the capability for measuring cell size as well as light scatter and absorption from single cells, which are all required to derive the refractive index. The determinations of the real part of the refractive index from flow cytometric measurements have already been demonstrated (Spinrad and Brown, 1986; Ackleson and Spinrad, 1988). Similarly, the microspectrophotometric measurements of absorption by single cells can provide information necessary for deriving the imaginary part of the refractive index. Thus there is prospect for determining the intracellular concentrations of carbon and Chl a for each analyzed single cell from its refractive index using relationships such as Eqs. (4) and (9), and eventually the carbon and Chl a contents per cell using additional information about the cell size. In addition, the single-particle techniques offer the capability to distinguish between living, non-living, autotrophic and heterotrophic particles, primarily by means of the fluorescence signatures of particles (e.g. Olson et al., 1991). This capability would help overcome problems associated with diversity of particles within natural assemblages, and it would allow one to analyze just microorganisms for which the relationships involving the refractive index hold. In order to ensure the best possible implementation of the proposed concept to single-particle analysis, the present-day instrumentation, especially commercially available flow cytometers, will likely need to be refined or modified. For example, it is important that the measurement of particle size not be based on any inverse optical

15 D. Stramski / Deep-Sea Research I 46 (1999) method requiring a priori knowledge of the refractive index. This is because the particle size is needed to determine the refractive index from optical measurements of light scatter and absorption, so the particle size should be determined in an independent fashion. Although certain limitations may occur related, for example, to the range of particle sizes, it seems possible to resolve this problem as there exists a number of principles to size single particles without the necessity of knowing the refractive index, such as far-field diffraction, light blocking, the time-of-flight or impedance pulse principle (Allen, 1990). An example of a specific design of flow cytometer for sizing marine microorganisms based on light diffraction was described by Cunningham and Buonnacorsi (1992). We conclude that the proposed concept involving the refractive index and its implementation into single-particle techniques requires further work as it promises a unique capability of acquiring information about the distribution of carbon and Chl a among individual cells, different size classes, and taxonomic groups of planktonic microorganisms in the ocean. Acknowledgements This work was supported by the Environmental Optics Program of the Office of Naval Research (grants N , N , and N ) and the Ocean Biology/Biogeochemistry Program of NASA (grant NAGW- 3574). I thank R. Reynolds for helpful discussions and his comments on the manuscript. Thanks are also due to A. Bricaud and an anonymous reviewer for their comments. References Aas, E., Refractive index of phytoplankton derived from its metabolite composition. Journal of Plankton Research 18, Ackleson, S.G., Spinrad, R.W., Size and refractive index of individual marine particulates: a flow cytometric approach. Applied Optics 27, Adair, G.S., Robinson, M.E., The specific refraction increment of serum-albumin and serum-globulin. The Biochemical Journal 24, Akhmanov, S.A., Nikitin, S. Yu., Physical optics. Clarendon Press, Oxford, 488 pp. Allen, T., Particle Size Measurement. Chapman & Hall, London, 806 pp. Avers, C.J., Cell Biology. D. Van Nostrand Company, New York, 485 pp. Banse, K., Determining the carbon-to-chlorophyll ratio of natural phytoplankton. Marine Biology 41, Barer, R., Interference microscopy and mass determination. Nature 169, Barer, R., Joseph, S., Refractometry of living cells. Part I. basic principles. Quarterly Journal of Microscopical Science 95, Barer, R., Ross, K.F.A., Refractometry of living cells. The Journal of Physiology 118, 38. Barer, R., Ross, K.F.A., Tkaczyk, S., Refractometry of living cells. Nature 171, Bateman, J.B., Wagman, J., Carstensen, E.L., Refraction and absorption of light by bacterial suspensions. Kolloid-Zeitschrift und Zeitschrift fur Polymere, Band 208, Heft 1, Borsheim, K.Y., Bratbak, G., Cell volume to cell carbon conversion factors for a bacterivorous Monas sp. enriched from seawater. Marine Ecology Progress Series 36,

16 350 D. Stramski / Deep-Sea Research I 46 (1999) Bratbak, G., Bacterial biovolume and biomass estimations. Applied and Environmental Microbiology 49, Bricaud, A., Morel, A., Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling. Applied Optics 25, Chisholm, S.W., Armburst, E.W., Olson, R.J., The individual cell in phytoplankton ecology: Cell cycles and flow cytometry. In: Photosynthetic Picoplankton. Platt, T., Li, W.K.W. (Eds.), Canadian Bulletin of Fisheries and Aquatic Sciences, 214, Department of Fisheries and Oceans, Ottawa, pp Coles, H.J., Jennings, B.R., Morris, V.J., Refractive index increment measurement for bacterial suspensions. Physics in Medicine and Biology 20, Cunningham, A., Buonnacorsi, G., Narrow angle forward light scattering from individual algal cells: implications for size and shape discrimination in flow cytometry. Journal of Plankton Research 14, Davies, H.G., Wilkins, M.H.F., Interference microscopy and mass determination. Nature 169, 541. Duysens, L.M., The flattening of the absorption spectra of suspensions as compared to that of solutions. Biochimica et Biophysica Acta 19, Gerhardt, P., Beaman, T.C., Corner, T.R., Greenamyre, J.T., Tisa, L.S., Photometric immersion refractometry of bacterial spores. Journal of Bacteriology 150, Gieskes, W.W.C., Algal pigment fingerprints: Clue to taxon-specific abundance, productivity and degradation of phytoplankton in seas and oceans. In: Demers, S. (Ed.), Particle analysis in oceanography. NATO ASI Series. Springer, Berlin, pp Gladstone, J.H., Dale, J., Researches on the refraction, dispersion, and sensitiveness of liquids. Philosophical Transactions of the Royal Society of London 153, Gordon, H.R., Morel, A.Y., Remote assessment of ocean color for interpretation of satellite visible imagery, a review. In: Lecture Notes on Coastal and Estuarine Studies. Barber, R.T., Mooers, C.N.K., Bowman, M.J., Zeitschel, B. (Eds.), Springer, New York, 114 pp. Iturriaga, R., Mitchell, B.G., Kiefer, D.A., Microphotometric analysis of individual particle absorption spectra. Limnology and Oceanography 33, Jeffrey, S.W., Humphrey, G.F., New spectrophotometric equations for chlorophylls a, b, c1, and c2 in higher plants, algae and natural phytoplankton. Biochemie Physiologie Pflanzen 167, Lee, S., Fuhrman, F.A., Relationships between biovolume and biomass of naturally derived marine bacterioplankton. Applied and Environmental Microbiology 53, Moal, J., Martin-Jezequel, V., Harris, R.P., Samain, J.-F., Poulet, S.A., Interspecific and intraspecific variability of the chemical composition of marine phytoplankton. Oceanologica Acta 10, Morel, A., Ahn, Y.-H., Optical efficiency factors of free living marine bacteria: influence of bacterioplankton upon the optical properties and particulate organic carbon in oceanic waters. Journal of Marine Research 48, Morel, A., Ahn, Y.-H., Optics of heterotrophic nanoflagellates and ciliates. A tentative assessment of their scattering role in oceanic waters compared to those of bacterial and algal cells. Journal of Marine Research 49, Morel, A., Bricaud, A., Theoretical results concerning light absorption in a discrete medium, and application to specific absorption by phytoplankton. Deep-Sea Research 28A, Montagnes, D.J., Berges, J.A., Harrison, P.J., Taylor, F.J.R., Estimation of carbon, nitrogen, protein, and chlorophyll a from volume in marine phytoplankton. Limnology and Oceanography 39, Mullin, M.M., Sloan, P.R., Eppley, R.W., Relationship between carbon content, cell volume, and area in phytoplankton. Limnology and Oceanography 11, Nagata, T., Watanabe, Y., Carbon- and nitrogen-to-volume ratios for bacterioplankton grown under different nutritional conditions. Applied and Environmental Microbiology 56, Olson, R.J., Zettler, E.R., Chisholm, S.W., Dusenberry, J.A., Advances in oceanography through flow cytometry. In: Demers, S. (Ed.), Particle Analysis in Oceanography. NATO ASI Series, Springer, Berlin, pp

17 D. Stramski / Deep-Sea Research I 46 (1999) Putt, M., Stoecker, D.K., An experimentally determined carbon:volume ratio for marine oligotrichous ciliates from estuarine and coastal waters. Limnology and Oceanography 34, Reynolds, R.A., Stramski, D., Kiefer, D.A., The effect of nitrogen limitation on the absorption and scattering properties of the marine diatom Thalassiosira pseudonana. Limnology and Oceanography 42, Ross, K.F.A., Billing, E., The water and solid content of living bacterial spores and vegetative cells as indicated by refractive index measurements. Journal of General Microbiology 16, Sinclair, M., Keighan, E., Jones, J., ATP as a measure of living phytoplankton carbon in estuaries. Journal of the Fisheries Research Board of Canada 36, Sonek, G.J., Liu, Y., Iturriaga, R.H., In situ microparticle analysis of marine phytoplankton cells with infrared laser-based optical tweezers, Applied Optics 34, Sosik, H.M., Chisholm, S.W., Olson, R.L., Chlorophyll fluorescence from single cells: Interpretation of flow cytometric signals. Limnology and Oceanography 34, Spinrad, R.W., Brown, J.F., Relative real refractive index of marine microorganisms: a technique for flow cytometric estimation. Applied Optics 25, Stramski, D., Morel, A., Optical properties of photosynthetic picoplankton in different physiological states as affected by growth irradiance. Deep-Sea Research 37A, Stramski, D., Morel, A., Bricaud, A., Modeling the light attenuation and scattering by spherical phytoplanktonic cells: a retrieval of the bulk refractive index. Applied Optics 27, Stramski, D., Reynolds, R.A., Diel variations in the optical properties of a marine diatom. Limnology and Oceanography 38, Stramski, D., Shalapyonok, A., Reynolds, R.A., Optical characterization of the oceanic unicellular cyanobacterium Synechococcus grown under a day night cycle in natural irradiance. Journal of Geophysical Research 100(C7), Strathmann, R.R., Estimating the organic carbon content of phytoplankton from cell volume or plasma volume. Limnology and Oceanography 12, Taguchi, S., Relationship between photosynthesis and cell size of marine diatoms. Journal of Phycology 12, Thompson, P.A., Guo, M., Harrison, P.J., Effects of variation in temperature. 1. on the biochemical composition of eight species of marine phytoplankton. Journal of Phycology 28, Thompson, P.A., Harrison, P.J., Parslow, J.S., Influence of irradiance on cell volume and carbon quota for ten species of marine phytoplankton. Journal of Phycology 27, Verity, P.G., Langdon, C., Relationships between lorica volume, carbon, nitrogen, and ATP content of tintinnids in Narragansett Bay. Journal of Plankton Research 66, Verity, P.G., Robertson, C.Y., Tronzo, C.R., Andrews, M.G., Nelson, J.R., Sieracki, M.E., Relationships between cell volume and the carbon and nitrogen content of marine photosynthetic nanoplankton. Limnology and Oceanography 37, Verity, P.G., Sieracki, M.E., Use of color image analysis and epifluorescence microscopy to measure plankton biomass. In: Kemp, P.F., Sherr, B.F., Sherr, E.B., Cole, J.J. (Eds.), Handbook of Methods in Aquatic Microbial Ecology. Lewis Publishers, Boca Raton, pp Wyatt, P.J., Cell wall thickness, size distribution, refractive index ratio and dry weight content of living bacteria (Staphylococcus aureus). Nature 226, Yentsch, C.M., Horan, P.K., Muirhead, K., Dortch, Q., Haugen, E., Legendre, L., Murphy, L.S., Perry, M.J., Phinney, D.A., Pomponi, S.A., Spinrad, R.W., Wood, M., Yentsch, C.S., Zahuranec, B.J., Flow cytometry and cell sorting: a technique for analysis and sorting of aquatic particles. Limnology and Oceanography 28, Zaneveld, J.R.V., Pak, H., Method for the determination of the index of refraction of particles suspended in the ocean. Journal of the Optical Society of America 63,