Raman spectroscopy provides a rapid, non-invasive method for quantitation of starch in live, unicellular microalgae.

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Biotechnology Journal

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Technical Report Raman spectroscopy provides a rapid, non-invasive method for quantitation of starch in live, unicellular microalgae

Yuetong Ji1,§, Yuehui He1, 2,§, Yanbin Cui3, Tingting Wang1, Yun Wang1, Yuanguang Li3, Wei E. Huang4 and Jian Xu1,#

1

Single-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of

Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China

2

University of Chinese Academy of Sciences, Beijing, China

3

State Key Laboratory of Bioreactor Engineering, East China University of Science and

Technology, Shanghai, China

4

Kroto Institute, University of Sheffield, Sheffield, UK

Keywords: starch, microalgae, single cell analysis, Raman spectroscopy, non-invasive analysis

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/biot.201400165. Submitted: Revised: Accepted:

26-Mar-2014 04-May-2014 04-Jun-2014

This article is protected by copyright. All rights reserved.


532-80662653, e-mail: [email protected] §


Corresponding author: Prof. Jian Xu; Fax: (86) 532-80662654, Phone: (86)

Accepted Article

#

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These authors contributed equally to this work.

Abbreviations: SCRS: Single-cell Raman Spectroscopy TAP medium: Tris acetate phosphate medium Group N+: the group in which the nitrogen was replete Group N-: the group in which the nitrogen was depleted Group G+: the group which is rich in glucose AGPase: ADP-glucose pyrophosphorylase Wt %: percentage of dry weight of biomass

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Abstract Conventional methods for quantitation of starch content in cells generally involve

starch extraction steps and are usually labor intensive, thus a rapid and noninvasive method

will

be

valuable.

Using

the

starch-producing

unicellular

microalga

Chlamydomonas reinhardtii as a model, we employed a customized Raman Spectrometer to capture the Raman spectra of individual single-cells under distinct culture conditions and along various growth stages. The results revealed a nearly linear correlation (R2=0.9893) between the signal intensity at 478 cm−1 and the starch content of the cells.

We validated the specific correlation by showing that the starch-associated Raman peaks were eliminated in a mutant strain where the AGPase gene was disrupted and consequentially the biosynthesis of starch blocked. Furthermore, the method was validated in an industrial algal strain of Chlorella pyrenoidosa. This is the first demonstration of starch quantitation at individual live cells. Compared to existing cellular-starch quantitation methods, this single-cell Raman spectra based approach is rapid, label-free, noninvasive, culture-independent, low-cost and potentially able to simultaneously track multiple metabolites in individual live cells, therefore should enable many new applications.

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1 Introduction Starch is the most abundant and important staple food for both animals and humans

[1] and is used in numerous additional applications that include paper, adhesives, textiles, cosmetics and medicines [2]. Moreover, residual starch-rich biomass can serve as source for biofuels such as bioethanol and biodiesel [3, 4]. Microalgae are promising feedstock of starch for producing biofuels [5]. These unicellular photosynthetic eukaryotes are able to convert solar energy and carbon dioxide to energy storage compounds such as starch and lipids, without necessarily competing for land and water with food crops [6]. For many microalgae, starch is one major component of cellular biomass under optimal growth conditions [7] or the preferred energy storage form under stressed conditions (e.g., nitrogen starvation) [8]. Therefore screening and engineering microalgal strains for higher starch productivity have been of interest over the past decades [7, 9, 10]. Multiple methods have been developed to measure the starch content in tissues and

cellular biomass, however they usually require multiple steps that include extraction of starch from cells and thus are labor intensive [11, 12]. For example, starch content can be estimated by solubilizing starch from ethanol-treated tissues by boiling or chemical treatments (e.g. using 45–52% perchloric acid or 90% dimethyl sulfoxide), mixing with iodine solution and then comparing colorimetrically (OD620) with standards of known starch content [13]. However this method is only semi-quantitative [11]. In quantitative methods for cellular starch contents, the starch is first extracted from the sacrificed cells and then hydrolyzed by either acids or enzymes to glucose, which is subsequently


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quantified colorimetrically via enzymatic reactions [11, 12]. These methods are invasive, limited in throughput and demanding in terms of reagents and consumables. Moreover, their accuracy and reliability are dependent on efficiency of starch extraction and solubilization. These disadvantages have significantly limited the throughput, accuracy

and scale in real-time tracking and control of starch production processes. On the other hand, as these methods require the cumulative biomass from a large number of cells and thus are culture-dependent, wild-type or mutant cells that are not yet cultured or difficult to cultivate have been beyond the reach of these approaches. Single-cell Raman Spectroscopy (SCRS) is a label-free technique for analyzing the

in vivo chemical profiles of single cells [14, 15]. It detects the emitted light from light-excited molecules, with each wave number shift representing a distinct mode of vibration from a specific molecular structure. In contrast to infrared spectroscopy, Raman spectrometry is not compromised by water molecules thus is suitable for analyzing live biological samples. Here we exploited these features of SCRS to establish a new approach for quantitation of starch content in live single cells without any external labeling. Employing the starch-producing unicellular microalga Chlamydomonas reinhardtii as a model, we used SCRS to sample individual cells under distinct culture conditions and along various growth stages., which revealed a nearly linear correlation

(R2=0.9893) between the signal intensity at 478 cm−1 and starch content. The starch-associated Raman peaks were eliminated in a starchless mutant strain of C.

reinhardtii. Furthermore, the method was validated in Chlorella pyrenoidosa, a


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starch-producing industrial microalga that has been cultivated worldwide as food supplements. This new approach is rapid, label-free, noninvasive, culture-independent, low-cost and potentially able to simultaneously track multiple metabolites in individual live cells, therefore should enable many new applications.

2 Materials and Methods 2.1 Strains and growth conditions Chlamydomonas reinhardtii CC4324, CC4333 and CC124 were obtained from the

Chlamydomonas Resource Center (http://chlamycollection.org/). Both CC4324 and CC124 were starch-producing strains, with CC4324 deficient in cell wall, while CC124

maintaining normal cell wall. CC4333 is a starch-deficient mutant generated from CC4324 by random integration of cassette pARG7 in the nuclear genome [10, 16]. Cells

were inoculated in TAP (Tris acetate phosphate) liquid medium [17] under continuous lighting (approximate 150 µmol photons m-2 s-1) at 25˚C and were bubbled with air to ensure mixing and prevent settling. Arginine (100 μg mL-1) was added to the culture medium of CC4324. Cultures were grown to late log phase in nitrogen-replete TAP

medium and reinoculated in triplicate at 2.5×105 to 4.0×105cells mL-1 in parallel in either

nitrogen-depleted TAP medium (Group N-; in which NH4Cl was omitted) or nitrogen-replete TAP medium (control or Group N+). Aliquots of cultures were collected for analyses of cellular starch content, just before reinoculation (i.e. 0 h) and at five time


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points afterwards (12, 24, 48, 72 and 96 h), from each of the triplicate cultures of Group N+ and Group N- respectively. Chlorella pyrenoidosa strain FACHB-9 (Freshwater Algae Culture Collection of the

Institute of Hydrobiology, Chinese Academy of Sciences) was inoculated into a 500 mL Erlenmeyer flask containing 200 mL Endo Medium (Group G+, which was rich in glucose) [18] with 100 µmol m-2s-1 continuous fluorescent illumination at 25˚C. Samples were collected for analyses of cellular starch content at 0, 8, 24, 48, and 72 h. 2.2 Quantitation of starch content in algal biomass using conventional

approaches The algae cells were harvested by centrifugation and subjected to lyophilization.

Cellular starch content was determined using an enzymatic starch assay kit (Amyloglucosidase/α-amylase Method, Megazyme K-TSTA 07/11). Briefly, lyophilized microalgal biomass samples of about 30 mg were treated with 80% ethanol to remove sugars, digested with thermal stable α-amylase in boiling water bath for 12 minutes and then further digested with amyloglucosidase at 50℃ for 30 minutes. The generated glucose was treated with a reagent containing glucose oxidase, peroxidase and 4-aminoantipyrine, and starch content was determined spectrophotometrically at a wavelength of 510 nm. The weight of free glucose was converted to anhydroglucose using a multiplication factor of 0.9.

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2.3 Quantitation of starch content in individual microalgal cells via single-cell

Raman spectra The algae cells were harvested by centrifugation, washed for three times,

resuspended in ddH2O to remove residual culture media and then loaded into a capillary

tube (50 mm length × 1 mm width × 0.1 mm height, Camlab, UK). Raman spectra of individual microalgal cells were acquired using our customized Raman Activated Cell Sorting system [14, 19], which was equipped with a confocal microscope with a 50 × PL magnifying dry objective (NA=0.55, BX41, Olympus, UK) and a 532 nm Nd:YAG laser (Ventus, Laser Quantum Ltd, UK). A number of components have been modified in this system, which mainly include shortening the Raman light path, employing a low noise and sensitive EMCCD for the Raman signal detection, and increasing incident laser power. The power output of the objective was 25 mW. The scattered photons were collected by a Newton EMCCD (Andor, UK) using a 1600 × 200 array of 16 µm pixels with thermoelectric cooling down to -70˚C for negligible dark current. Each Raman

spectra was acquired between 3340.9 cm-1 and 394.1 cm-1 with a average CCD resolution

of 1.8 cm-1/pixel (2946 cm-1/1581 pixels) achieved by a 300 grooves mm-1 in the spectrograph for Chlamydomonas reinhardtii, and a 600 grooves mm-1 for Chlorella pyrenoidosa. With each measurement (including the chlorophyll and carotenoids fluorescence quenching and Raman signal acquisition) completed within 2 sec, the RACS system was optimized to achieve high-throughput Raman spectrum acquisitions [19]. Sixty individual cells were respectively measured for the sample at 0 h, and 20 were


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measured at all other time points for each of the triplicate cultures. For each individual cell, a background spectrum was generated from the average of five spectra acquired from the liquid around the cell. Pre-processing of the raw single-cell Raman spectra was performed with LabSpec 5

(HORIBA Scientific), including background subtraction and second-order derivative

(Savitzky-Golay algorithm with 15 points and a second-order polynomial function), which was used to minimize artifacts caused by the potential shifts of baseline among the various samples. Raman signal intensity was measured via the height of the spectrum.

3 Results and Discussion 3.1 Measuring and tracking starch content in Chlamydomonas reinhardtii

CC4324, CC4333 and CC124 cells via SCRS The laboratory model alga Chlamydomonas reinhardtii CC4324 was grown in TAP

medium with (Group N+) or without NH4Cl (Group N-) in three biological replicates in

parallel. Single cells were first immobilized by the laser trap, and the fluorescence from the chlorophyll and carotenoids were photobleached immediately and simultaneously. SCRS acquisition time per cell was less than 2 sec. Single-cell Raman spectra of 60 independent single cells (20 from each biological replicate) were collected at each time point (12, 24, 48, 72 and 96 h respectively) under each culture condition. Raman spectra of Group N+ exhibited no obvious alteration during the whole duration of cultivation (Figure 1A), while in Group N-, a number of Raman bands (e.g. 478, 866, 940, 1083,


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1127, 1340, 1459 and 2911) grew significantly in intensity (Figure 1B). The wave

numbers of these bands were consistent with the typical Raman spectra for the standard starch samples (from corn; Figure 1B). These bands were annotated with their tentative

assignments of chemical bonds based on literature [20-23] (Table 1). Two of these Raman bands, at 478 and 2911 cm-1, respectively, were the most intense

in signal (Figure 1B) and thus chosen as candidate markers for quantitation of total cellular starch. The band at 2911 cm-1 was related to the symmetrical and antisymmetrical

CH stretching [24], but many organic compounds (including the cellular intrinsic components such as lipids) were also linked to this band [25]. The other band, at 478 cm-1,

depicted the degree of polymerization in polysaccharides [26] and was one of the dominating skeletal vibration modes of the pyranose ring [27]. Therefore, to avoid interference by other organic compounds, the Raman band of 478 cm-1 was chosen as the marker for quantifying total cellular starch. To test whether the single-cell Raman spectra can predict cellular starch content,

starch content was determined from 30 mg algal biomass at the corresponding time-series samples under the nitrogen depletion conditions using the enzyme method (Methods). For C. reinhardtii CC4324, the starch content increased from 2.5 to 46.3 wt % (dry weight of biomass) (Figure 2A). A nearly linear correlation (R2=0.9893) between the

signal intensity at 478 cm−1 and the starch content of algal biomass was observed (Figure 2B).


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To probe whether the presence or absence of cell walls affects the SCRS-based

modeling of cellular starch content, we next applied the SCRS-based and the enzyme-based approaches on C. reinhardtii CC124 which processes a cell wall (Figure

1D). The correlation between the Raman signal intensity at 478 cm−1 of individual cells

and the starch content of algal biomass remained high (R2=0.8925), suggesting that the ability of SCRS to model cellular starch content is largely independent of the presence or absence of cell wall (Figure 2C, D). Collectively, these results showed that signal intensity at 478 cm−1 was highly correlated with cellular starch content, and thus can be effective in quantifying cellular starch content in individual microalgae cells. To validate the link between the Raman spectra and the starch content, we further

tested our approach on C. reinhardtii CC4333. This strain was different from the control strain CC4324 in that its AGPase (ADP-glucose pyrophosphorylase) gene was disrupted and consequentially the biosynthesis of starch was blocked [16]. Cultivation of the CC4333 strain under nitrogen-depleted conditions confirmed the absence of starch production along the full cultivation process. SCRS-based tracking of the strain along the process revealed that the characteristic peaks of starch were all absent (Figure 1C). These results further validated the SCRS-based approach for quantification of cellular starch. 3.2 Measuring and tracking starch content in Chlorella pyrenoidosa cells via

SCRS To probe its applicability to other microalgae, the SCRS-based approach was further

tested on Chlorella spp., which with an annual net production of about 4,000 tons


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biomass worldwide are among the most widely cultivated microalgae [28]. Chlorella pyrenoidosa FACHB-9, an industrial algal strain for protein, starch and lipid production [8, 29] was employed as a model. Chlorella pyrenoidosa FACHB-9 cells were subjected to the SCRS analysis and the

enzyme method respectively at 0, 8, 24, 48 and 72 h during cultivation. Starch content of the biomass as measured by the enzyme method started from 5.47 wt % at 0 h, peaked at 37.21 wt % at 24 h and then declined to 3.95 wt % at 72 h in the Group G+. This dynamic pattern was highly consistent with that from the SCRS analysis (Figure 2E): the

correlation coefficient of linear regression function (R2) between the two was 0.9645 (Figure 2F).

4 Concluding Remarks In this work we introduced a new approach to quantify starch content in live cells.

Compared to existing methods that measure the starch content in tissues and bulk cellular biomass, this SCRS-based approach is rapid, label-free, noninvasive, culture-independent, low-cost and potentially able to simultaneously track multiple metabolites in individual live cells. As throughput, ease of deployment, cost and multiplexing are all important considerations in design of starch assays, such advantages can be exploited for development of many novel applications such as screening of microbial cell factories and optimization of bioprocesses.


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There are several limitations of our approaches. Although our results on C.

reinhardtii supported that the accuracy of our approach is not affected by the presence or absence of the cell wall, it is possible that other glycosyl polymers might interfere with the signal for starch. Therefore it is advised that a calibration curve based on population-level measurement of starch content and SCRS are produced for each new type of cells. However, once established, the calibration curve can be employed as reference for all subsequent measurements for the type of cells. On the other hand, commercial starch analysis kits for bulk measurement of starch content in cellular populations are readily available on the market, whereas wide adoption of our approach can be limited by the availability of Raman spectroscopes. On the other hand, as SCRS provides a wild spectrum of chemical profiles at single-cell resolution (e.g., [30] ), our approach can be highly valuable to many applications where the amount of cellular biomass is a limiting factor or simultaneous profiling of multiple types of chemicals is required. Finally, to the best of our knowledge, this was the first demonstration of in vivo

quantitation of starch content at the level of individual live cells. Moreover, the cells remain alive after the SCRS measurement (e.g., [14, 31-34] ). In our study, the cells were suspended in water which reduced the strength of laser, possibly by absorbing the energy released from vibrating structures to prevent the cells from damage. Therefore the cells of targeted starch content can then be isolated, followed by cultivation or characterization of genome, transcriptome and proteome [14, 19, 35]. As the vast majority of cells in nature


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have yet to be cultured, our method should enable explorations into the diversity and dynamics of starch and other carbon storage forms in environmental microbiota, and pave the way for probing the molecular mechanisms underlying the heterogeneity of such phenotypes in natural ecosystems and industrial bioprocesses. Acknowledgements This work was supported by the National High-Tech Development Program

(2012AA02A707) and National Basic Research Program of China (2012CB721101 and 2011CB200902) from Ministry of Science and Technology of China and by International Partnership Innovation Program from Chinese Academy of Sciences.

Competing interests The authors declare that they have no competing interests.

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5 References [1] Englyst, H. N., Kingman, S. M., Cummings, J. H., Classification and Measurement of Nutritionally Important Starch Fractions. Eur J Clin Nutr 1992, 46, 33-50. [2] Ellis, R. P., Cochrane, M. P., Dale, M. F. B., Duffus, C. M., et al., Starch production and industrial use. J.

Sci. Food Agric. 1998, 77, 289-311. [3] Chen, C. Y., Yeh, K. L., Aisyah, R., Lee, D. J., Chang, J. S., Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: a critical review. Bioresource Technology 2011, 102,

71-81. [4] Nigam, P. S., Singh, A., Production of liquid biofuels from renewable resources. Prog. Energy Combust. Sci. 2011, 37, 52-68. [5] Mussatto, S. I., Dragone, G., Guimarães, P. M. R., Silva, J. P. A., et al., Technological trends, global market, and challenges of bio-ethanol production. Biotechnol. Adv. 2010, 28, 817-830. [6] Wijffels, R. H., Barbosa, M. J., An Outlook on Microalgal Biofuels. Science 2010, 329, 796-799. [7] Branyikova, I., Marsalkova, B., Doucha, J., Branyik, T., et al., Microalgae--novel highly efficient starch producers. Biotechnology and Bioengineering 2011, 108, 766-776. [8] Dragone, G., Fernandes, B. D., Abreu, A. P., Vicente, A. A., Teixeira, J. A., Nutrient limitation as a strategy for increasing starch accumulation in microalgae. Appl. Energy 2011, 88, 3331-3335. [9] Li, Y. T., Han, D. X., Hu, G. R., Dauvillee, D., et al., Chlamydomonas starchless mutant defective in

ADP-glucose pyrophosphorylase hyper-accumulates triacylglycerol. Metab. Eng. 2010, 12, 387-391.

[10] Li, Y. T., Han, D. X., Hu, G. R., Sommerfeld, M., Hu, Q. A., Inhibition of Starch Synthesis Results in Overproduction of Lipids in Chlamydomonas reinhardtii. Biotechnol. Bioeng. 2010, 107, 258-268. [11] Smith, A. M., Zeeman, S. C., Quantification of starch in plant tissues. Nature Protocols 2006, 1,

1342-1345. [12] Rose, R., Rose, C. L., Omi, S. K., Forry, K. R., et al., Starch Determination by Perchloric-Acid Vs Enzymes - Evaluating the Accuracy and Precision of 6 Colorimetric Methods. J. Agric. Food Chem. 1991, 39, 2-11. [13] Kaplan, F., Guy, C. L., RNA interference of Arabidopsis beta-amylase8 prevents maltose accumulation upon cold shock and increases sensitivity of PSII photochemical efficiency to freezing stress. Plant J. 2005,

44, 730-743. [14] Li, M., Xu, J., Romero-Gonzalez, M., Banwart, S. A., Huang, W. E., Single cell Raman spectroscopy for cell sorting and imaging. Curr. Opin. Biotechnol. 2012, 23, 56-63. [15] Huang, W. E., Li, M. Q., Jarvis, R. M., Goodacre, R., Banwart, S. A., Shining Light on the Microbial World: The Application of Raman Microspectroscopy. Adv. Appl. Microbiol. 2010, 70, 153-186. [16] Zabawinski, C., Van den Koornhuyse, N., D'Hulst, C., Schlichting, R., et al., Starchless mutants of Chlamydomonas reinhardtii lack the small subunit of a heterotetrameric ADP-glucose pyrophosphorylase. J. Bacteriol. 2001, 183, 1069-1077. [17] Harris, E. H., The Chlamydomonas sourcebook: introduction to Chlamydomonas and its laboratory use, Academic Press 2009. [18] Endo, H., Nakajima, K., Chino, R., Shirota, M., Growth characteristics and cellular components of Chlorella regularis, heterotrophic fast growing strain. Agricultural and Biological Chemistry 1974, 38,


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9-18.

[19] Wang, Y., Ji, Y., Wharfe, E. S., Meadows, R. S., et al., Raman activated cell ejection for isolation of single cells. Anal. Chem. 2013, 85, 10697-10701. [20] Mahdad-Benzerdjeb, A., Taleb-Mokhtari, I. N., Sekkal-Rahal, M., Normal coordinates analyses of disaccharides constituted by D-glucose, D-galactose and D-fructose units. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy 2007, 68, 284-299. [21] De Gussem, K., Vandenabeele, P., Verbeken, A., Moens, L., Raman spectroscopic study of Lactarius spores (Russulales, Fungi). Spectrochim Acta A 2005, 61, 2896-2908. [22] Mokrane, A., Friant-Michel, P., Cartier, A., Rivail, J.-L., Scaled semiempirical method for the calculation of vibrational spectra Molecular vibrational frequencies of monosaccharides and disaccharides by PM3 method. J. Mol. Struc-theochem 1997, 395–396, 71-80.

[23] Synytsya, A., Čopıḱ ová, J., Matějka, P., Machovič, V., Fourier transform Raman and infrared spectroscopy of pectins. Carbohydr. Polym. 2003, 54, 97-106. [24] Almeida, M. R., Alves, R. S., Nascimbem, L. B. L. R., Stephani, R., et al., Determination of amylose content in starch using Raman spectroscopy and multivariate calibration analysis. Anal. Bioanal. Chem. 2010, 397, 2693-2701. [25] Maquelin, K., Kirschner, C., Choo-Smith, L. P., van den Braak, N., et al., Identification of medically relevant microorganisms by vibrational spectroscopy. Journal of Microbiological Methods 2002, 51,

255-271. [26] Bulkin, B. J., Kwak, Y., Dea, I. C. M., Retrogradation Kinetics of Waxy-Corn and Potato Starches - a Rapid, Raman-Spectroscopic Study. Carbohydr. Res. 1987, 160, 95-112.

[27] Kizil, R., Irudayaraj, J., Seetharaman, K., Characterization of irradiated starches by using FT-Raman and FTIR spectroscopy. J Agr Food Chem 2002, 50, 3912-3918. [28] Klein-Marcuschamer, D., Chisti, Y., Benemann, J. R., Lewis, D., A matter of detail: Assessing the true potential of microalgal biofuels. Biotechnol. Bioeng. 2013, 110, 2317-2322. [29] Fan, J., Huang, J., Li, Y., Han, F., et al., Sequential heterotrophy-dilution-photoinduction cultivation for efficient microalgal biomass and lipid production. Bioresource Technology 2012, 112, 206-211. [30] Wang, T., Ji, Y., Wang, Y., Jia, J., et al., Quantitative dynamics of triacylglycerol accumulation in microalgae populations at single-cell resolution revealed by Raman microspectroscopy. Biotechnology for biofuels 2014, 7, 58. [31] Palonpon, A. F., Ando, J., Yamakoshi, H., Dodo, K., et al., Raman and SERS microscopy for molecular imaging of live cells. Nat Protoc 2013, 8, 677-692. [32] Kakita, M., Okuno, M., Hamaguchi, H. O., Quantitative analysis of the redox states of cytochromes in a living L929 (NCTC) cell by resonance Raman microspectroscopy. J Biophotonics 2013, 6, 256-259.

[33] Zhang, X., Roeffaers, M. B., Basu, S., Daniele, J. R., et al., Label-Free Live-Cell Imaging of Nucleic Acids Using Stimulated Raman Scattering Microscopy. ChemPhysChem 2012, 13, 1054-1059. [34] Wu, H. W., Volponi, J. V., Oliver, A. E., Parikh, A. N., et al., In vivo lipidomics using single-cell

Raman spectroscopy. Proc. Natl. Acad. Sci. USA. 2011, 108, 3809-3814. [35] Wang, Y., Song, Y., Zhu, D., Ji, Y., et al., Probing and sorting single cells-the application of a Raman-activated cell sorter. Spectroscopy Europe 2013, 25, 16-20.

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Table 1. Major Raman peaks that are linearly correlated with starch content in Chlamydomonas reinhardtii CC4324 under nitrogen-depleted conditions. Wave number (cm−1)

Assignments

2911

C-H stretching

1459

CH, CH2 and C-O-H deformations

1340

C-O stretching; C-O-H deformation

1127

C-O and C-C stretching; C-O-H deformation

1083

C-O and C-C stretching; C-O-H deformation

940

C-O-C and C-O-H deformations; C-O stretching

866

C-C-H and C-O-C deformations

478

C-C-C deformation; C-O stretching

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Figure legends: Figure 1. Dynamics of single-cell

A

Raman spectra for Chlamydomonas

48 h

12 h

1083

1 500

2 000

(A) C. reinhardtii CC4324 cells under

reinhardtii

conditions.

CC4324

(B)

cells

C.

under

nitrogen-depleted conditions; the top Raman spectrum is from the standard 2 500

Raman Shift (cm-1)

3 000

sample of corn starch. (C) C. reinhardtii CC4333 cells (the mutant strain in which starch biosynthesis is blocked) under nitrogen-depleted conditions. (D)

CC4333, Group N-

96 h 72 h

C.

48 h 24 h 0h 1 000

1 500

2 000

2 500

3 000

2 500

3 000

Raman Shift (cm-1)

CC124, Group N-

96 h 72 h 48 h 24 h 12 h 0h 1 000

1 500

2 000

reinhardtii

CC124

cells

(the

wild-type strain which has the cell wall)

12 h

D

500

curve

biological replicates) at each time points.

C

500

Each

3 000

Corn starch 96 h 72 h 48 h 24 h 12 h 0h 1 000

various

2911

2 500

Raman Shift (cm-1)

1127 1340 1459

866 940

2 000

conditions.

nitrogen-replete

CC4324, Group N-

478

1 500

during

(20 cells from each of the three

0h 1 000

strains

represents the average spectra of 60 cells

24 h

B

500

culture

72 h

CC4324, Group N+

500

reinhardtii

96 h

Raman Shift (cm-1)

under nitrogen-depleted conditions.


Raman intensity Total starch content

30

40000

20

30000

10

20000

0

10000 0

24

48

72

20000 y = 719.96x + 6579.9 R2= 0.9893 10000

0 0

Time (h)

D


1000

60

40

500 20 0

24

48

72

20

10

1000 0

0

-10 0

12

24

36

Time (h)

48

60

72

F

40

Total starch content (wt %)

2000

50

y = 47.151x + 203.66 R2 = 0.8925

0

30

40

1000

Time (h)

3000

30

0

96


20


500

0 0

10

2000

1500

Raman intensity

2000


Raman intensity

30000

96

-500

Raman intensity

50000

-10

1500

E


40000

5

10

15

20


25

30

2500

2000

Raman intensity

Raman intensity

40


50000

C

B

50

Raman intensity

60000

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A

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y = 69.195x - 80.854 2 R = 0.9645

1500

1000

500

0 0

8

16

24

32

40


Figure 2. Quantitation of starch content in Chlamydomonas reinhardtii CC4324, CC124 and the industrial microalga Chlorella pyrenoidosa FACHB-9 by

enzyme-based or SCRS-based methods. (A) Analysis of starch content for C. reinhardtii CC4324 during nitrogen-depleted cultivation by the enzyme-based (circle) or the SCRS-based method (triangle). (B) Correlation between Raman signal intensity from individual cells and starch content of microalgal biomass as measured by the


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enzyme-based method in C. reinhardtii CC4324 under nitrogen-depleted conditions. (C)

Analysis of starch content for C. reinhardtii CC124 during nitrogen-depleted cultivation by the enzyme-based (circle) or the SCRS-based method (triangle). (D) Correlation

between Raman signal intensity from individual cells and starch content of microalgal biomass as measured by the enzyme-based method in C. reinhardtii CC124 under nitrogen-depleted conditions. (E) Analysis of starch content for C. pyrenoidosa FACHB-9

during glucose-rich cultivation by the enzyme-based (circle) or the SCRS-based (triangle) methods. (F) Correlation between Raman signal from individual cells and starch content of microalgal biomass as measured by the enzyme-based method in C. pyrenoidosa

FACHB-9 under glucose-rich conditions. The Y-axe values represent the absolute value

of the intensity of the Raman peak. Raman intensity was calculated from the average of twenty cells sampled from each of the three biological replicates (Methods), with the

error bars of Raman intensity representing standard deviation among the triplicate cultures.

Raman spectroscopy provides a noninvasive approach for determining biochemical composition of the pregnant cervix in vivo.

Noninvasive glucose sensing by transcutaneous Raman spectroscopy.

Noninvasive Scanning Raman Spectroscopy and Tomography for Graphene Membrane Characterization.

A rapid chemiluminescent method for quantitation of human DNA.

Rapid and nondestructive method for evaluation of embryo culture media using drop coating deposition Raman spectroscopy.

Rapid biotin-avidin method for quantitation of antiviral antibody isotypes.

In vivo study of lipid accumulation in the microalgae marine diatom Thalassiosira pseudonana using Raman spectroscopy.

Mechanism behind autoflocculation of unicellular green microalgae Ettlia texensis.

Rapid surface enhanced Raman scattering detection method for chloramphenicol residues.

Rapid Identification of Legionella Pathogenicity by Surface-Enhanced Raman Spectroscopy.

Rapid-flow resonance Raman spectroscopy of bacterial photosynthetic reaction centers.

A single-step method for rapid extraction of total lipids from green microalgae.

A rapid method for the quantitation of estrogen receptors in small amounts of tissue.

Potential and limitations of microscopy and Raman spectroscopy for live-cell analysis of 3D cell cultures.

A state of the art of metabolic networks of unicellular microalgae and cyanobacteria for biofuel production.

A new method for determining the relative crystallinity of chickpea starch by Fourier-transform infrared spectroscopy.

Rapid and accurate peripheral nerve imaging by multipoint Raman spectroscopy.

Rapid monitoring of benzylpenicillin sodium using Raman and surface enhanced Raman spectroscopy.

A rapid method for quantitation of immunofluorescence on human lymphoblastoid cell suspensions.

A novel method for discrimination of beef and horsemeat using Raman spectroscopy.

Noninvasive measurement of glucose in artificial plasma with near-infrared and Raman spectroscopy.

A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders.

Rapid, noninvasive quantitation of skin disease in systemic sclerosis using optical coherence elastography.

Rapid Characterization of Fatty Acids in Oleaginous Microalgae by Near-Infrared Spectroscopy.