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. 2013 Mar;79(6):1850–1858. doi: 10.1128/AEM.03399-12

Monitoring the Single-Cell Stress Response of the Diatom Thalassiosira pseudonana by Quantitative Real-Time Reverse Transcription-PCR

Xu Shi 1, Weimin Gao 1, Shih-hui Chao 1, Weiwen Zhang 1,*,✉, Deirdre R Meldrum 1,✉
PMCID: PMC3592218  PMID: 23315741

Abstract

Directly monitoring the stress response of microbes to their environments could be one way to inspect the health of microorganisms themselves, as well as the environments in which the microorganisms live. The ultimate resolution for such an endeavor could be down to a single-cell level. In this study, using the diatom Thalassiosira pseudonana as a model species, we aimed to measure gene expression responses of this organism to various stresses at a single-cell level. We developed a single-cell quantitative real-time reverse transcription-PCR (RT-qPCR) protocol and applied it to determine the expression levels of multiple selected genes under nitrogen, phosphate, and iron depletion stress conditions. The results, for the first time, provided a quantitative measurement of gene expression at single-cell levels in T. pseudonana and demonstrated that significant gene expression heterogeneity was present within the cell population. In addition, different expression patterns between single-cell- and bulk-cell-based analyses were also observed for all genes assayed in this study, suggesting that cell response heterogeneity needs to be taken into consideration in order to obtain accurate information that indicates the environmental stress condition.

INTRODUCTION

It was generally assumed in the field of microbiology that microbial cells growing under the same conditions have a uniform population (1). Based on this assumption, microbiologists in the past decades have been analyzing average values at the population level to describe microbial behaviors. However, recent studies showed that even isogenic cells exhibit notable diversity and a significant cell-to-cell difference that is an order of magnitude greater than previously thought for any microbial population (2). For isogenic populations, gene expression heterogeneity could arise from a stochastic process in the expression of individual genes. The amplitude of such stochasticity in gene expression is further complicated by many factors, such as regulatory dynamics, transcription rate, and genetic factors of the cells (3–15). These stochasticities, once amplified, could offer the opportunity to generate long-term heterogeneity at the cellular level in a microbial population. Therefore, more attention has recently been paid to exploring the heterogeneity of a small number of cells, even at the single-cell level (16). For example, Lenz et al. dissected and captured subsets of cells from vertical strata within Pseudomonas aeruginosa biofilms and quantified mRNA transcripts as well as 16S rRNA using quantitative real-time reverse transcription-PCR (RT-qPCR) (17). By dissecting the heterogeneity, cells with abnormal gene expression patterns can be identified. These atypical expression patterns may indicate potential environmental problems earlier than conventional population-level analysis and improve regulation efficiency, since the stress response always starts from a small number of cells, including even a single cell.

Under adverse conditions such as nutrient deficiency or other environmental stresses, microorganisms can trigger protective response mechanisms for survival. Concurrently, many regular physiological activities such as photosynthesis may be repressed under these stresses. Directly monitoring the stress response of microorganisms to their environments could be one way to inspect the health of microorganisms themselves, as well as the environments in which they live. Under laboratory conditions or biotechnological industry settings, direct monitoring of the stress response of cultured microorganisms can be carried out easily. However, the same endeavor could pose a large challenge with many environmental microorganisms. This is because many environmental microbial species/phylotypes often live together as consortia, the cell density of individual species/phylotypes can be very low, and over 99% of them cannot be cultivated under laboratory conditions (18). Thus, many of them are not accessible to conventional methods, which typically require large numbers (∼105 to 106) of cells. In such situations, the pursuit of analysis methods targeting a few or single microbial cells, which are directly recovered from environments without further cultivation, is necessary. Diatoms are a group of unicellular phytoplankton (19, 20) that are present in widespread niches, from inland lakes to open oceans (21, 22). It was reported that diatoms contribute up to 30 to 40% of the primary productivity in the oceans (23, 24). They play such significant roles in the global carbon cycle that it is essential to understand what environmental stresses they are susceptible to and how they respond in order to maintain the primary productivity in oceans. Furthermore, we can monitor the stress conditions of the oceans and other water bodies by direct using native diatoms as biosensors or bioreportors. This is in contrast to the case for other similar research which requires introducing foreign species to achieve a similar objective (25).

Nitrogen is an essential element for living organisms and is required for the biosynthesis of macromolecules such as amino acids. It has been reported that the availability of nitrogen in oceans varies drastically on spatial and temporal scales due to physical and biological processes, and nitrogen has been considered a major limiting nutrient for primary production in the oceans (19, 26). Phosphate is another important element involved in many aspects of cellular metabolism, such as ATP synthesis. It was reported that photosynthesis was disrupted by low-level phosphorus (27, 28). Iron is a key component of ferredoxin, an iron-sulfur protein that controls electron transfer (29), and its limitation and restriction of primary productivity have been reported for some ocean regions (30, 31). Because of the short residence time of bioavailable iron (32) and the extremely low concentration of iron in the surface water, which is only 0.07 nM/kg (33), phytoplankton growth and primary productivity are restricted in vast high-nutrient, low-chlorophyll (HNLC) regions of the Southern Ocean, the equatorial Pacific, and the North Pacific (30, 34). Nevertheless, due to their key roles in the ecology and biogeochemistry of the oceans, it is important to further understand the mechanism that diatoms use to deal with various environmental stresses. In this study, Thalassiosira pseudonana (35), a typical centric diatom, was applied as a model system to measure the stress response of microorganisms to their environment at the single-cell level using single-cell RT-qPCR, based on our previous efforts (36, 37). In contrast to previously published single-cell analyses on mammalian or prokaryotic cells, working with diatoms has its own particular challenges due to their small size (∼5-μm diameter) and protective frustules. We quantitatively measured the expression of six genes in single T. pseudonana cells, each with three technical replicates. The single-cell results revealed significant heterogeneity in terms of stress responses within the T. pseudonana population. The study provides the first quantitative gene expression evidence for the response heterogeneity of the diatom T. pseudonana to environmental stresses. Our work also demonstrates the possibility of applying native habitants as biosensors to monitor environmental stress conditions.

MATERIALS AND METHODS

Cell culture.

Thalassiosira pseudonana (CCMP1335) cells were obtained from the National Center for Marine Algae and Microbiota (NCAM), and were grown in f/2 medium at 24 ± 1°C (38, 39) under a constant-light condition (30 μmol photons m−2 s−1 irradiance measured using a LiCor [Lincoln, NE] instrument) without mixing. The total volume for each growth condition was 20 ml within a 125-ml flask. Cells at mid-exponential phase were harvested by centrifugation at 1,500 × g for 5 min at 4°C and used to inoculate f/2 medium with or without nitrogen (NaNO3, 8.82 × 10−4 M), phosphate (NaH2PO4, 3.62 × 10−5 M), and iron (FeCl3 · 6H2O, 1.17 × 10−5 M) depending on the condition of starvation. Artificial seawater was prepared using chemicals of analytical purity and based on the formula of Kester et al. (40) and used instead of filtered nature seawater for f/2 medium.

Sampling and RNA extraction.

For bulk-cell-based analysis, 1 ml cell culture was collected by centrifugation at 1,500 × g for 5 min at 4°C. A 3900 hemocytometer (Hausser Scientific, Horsham, PA) was used to count the cell number directly. An RNeasy Minikit (Qiagen, Valencia, CA) was used to extract RNA from the bulk cells. For single-cell-based analysis, a micromanipulator developed in our center (41, 42) was used to pick cells from the diluted cell population and load them into individual Eppendorf microtubes. This micromanipulator uses a piezoelectric actuated diaphragm to dispense/aspirate picoliter-level liquid through a 30-μm capillary. Owing to the low flow rates, single cells suffer very little shear stress, which will minimize the effects on their gene expression profile. Thirty individual cells from each growth condition were picked. A ZR Fungal/Bacterial RNA MicroPrep kit (Zymo Research, Irvine, CA) was used to extract RNA from single cells, and the total RNA was eluted into a final volume of 6 μl in Eppendorf microtubes.

cDNA synthesis.

A SuperScript VILO cDNA synthesis kit (Invitrogen, Carlsbad, CA) was used to synthesize cDNA. For cDNA synthesis from bulk-cell RNA, the total reaction volume was 20 μl containing 2 μl 10× SuperScript enzyme mix, 4 μl 5× VILO reaction mix, and 14 μl of eluted RNA. To increase the relative concentration of single-cell mRNA for cDNA synthesis preparation, the total reaction volume was decreased to 10 μl, which contained 1 μl of specific primer mixture, 1 μl 10× SuperScript enzyme mix, 2 μl 5× VILO reaction mix, and 6 μl of eluted RNA. After cDNA synthesis, 10 μl diethyl pyrocarbonate (DEPC)-treated water (Ambion, Austin, TX) was added to make the final volume of 20 μl before the mixture was used as the template for quantitative PCR analysis.

Quantitative PCR.

Primers for RT-qPCR were designed using Primer-BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome). To differentiate PCR products from primer dimers, we selected primers which will generate amplicons with sizes around 170 to 220 bp (37). qPCR was performed using Express SYBR GreenER qPCR SuperMix kits (Invitrogen, Carlsbad, CA) on an ABI StepOne real-time PCR system for bulk-cell analysis and an ABI 7900HT real-time PCR system for single-cell analysis (Applied Biosystems, Foster, CA). The temperature for qPCR was 10 min at 95°C for an initial hot start, and this was followed by 40 cycles of 15 s at 95°C for denaturing, 50 s at 60°C for annealing and extension, and 10 s at 75°C for signal detection. There was also another melting curve analysis step which was set to be the default condition based on the real-time PCR system. For PCRs, 1 μl of each primer at 4 μM, 5 μl of master mixture, 0.1 μl ROX reference dye, 0.9 μl DEPC-treated water, and 2 μl cDNA were combined. Technical triplicates of PCR analysis were performed for each gene. Reaction mixtures without cDNA templates served as negative controls. Expression levels of target genes were normalized against an internal control actin gene.

Data analysis.

To describe the distribution variation of single-cell gene expression levels among cells, nonparametric statistic tests which do not require normal distribution of data sets were applied (43). Kolmogorov-Smirnov and Kruskal-Wallis analysis of variance (ANOVA) tests were used to analyze the relationship between four different groups of RT-qPCR measurements using the OriginPro 8.1 software (OriginLab Corporation, Northampton, MA). Principal-component analysis (PCA) was conducted using the SPSS Statistics 20 package (IBM, Armonk, NY) to determine the possible control variances.

RESULTS

Growth of T. pseudonana under stress conditions.

T. pseudonana growth was determined by counting the cell number with a hemocytometer directly. Figure 1 showed the growth-time curves of T. pseudonana under control and three stress conditions. The results showed that the initial increases in cell numbers over days 1 to 4 were roughly exponential for all conditions, although the growth under the nitrogen and phosphate depletion conditions was at a relatively low rate. After day 4, the cultures under control and iron depletion conditions still maintained exponential growth for another 24 h. After day 5, both cultures of the control and iron depletion conditions reached stationary phase, while the cell numbers under the nitrogen and phosphate depletion conditions declined. The results showed that all three depletion conditions caused significant decreases in cell growth, with phosphate and nitrogen affected the most. In these cases, the cell number reached only 10 to 28% of the peak cell numbers. The slow growth of T. pseudonana under these stress conditions was consistent with previous reports (27, 31). Cells at mid-exponential phase were collected for RT-qPCR analysis (Fig. 1).

Fig 1.

Fig 1

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Growth of T. pseudonana cells under various conditions. The arrow indicates the sampling time for gene expression analysis.

Primer evaluation.

A total of 82 pairs of PCR primers were designed and evaluated for 39 different target genes. Since the major goals of this study were (i) to evaluate the possibility of using single cells as biosensors, (ii) to determine the response heterogeneity of T. pseudonana to various important environmental factors (i.e., nitrogen, phosphate, and iron limitation), and (iii) also to compare the results with those previously obtained at the bulk-cell level, the target genes included some of the genes with demonstrated functions in photosynthesis, iron transportation, and stress responses. Although most of the primers (78 out of 82) functioned well with bulk-cell RNA, only one pair of primers each was obtained for nine genes after the evaluation process (see Fig. S1 and Data S1 in the supplemental material). The relatively low success rate for primer selection reflected the different performance between bulk-cell-based and single-cell-based RT-qPCR analyses and also the difficulty of measuring gene expression at the single-cell level. The successful primer sets and their corresponding gene targets were as follows: psaA, photosystem I (PS I) P700 chlorophyll a apoprotein A1 (forward primer, CGGTTCTGCATCTTCAGCATACGGC; reverse primer, GTGCTAAACCAACGGCACGACCT); psaF, photosystem I reaction center subunit (forward primer, TGTGGCGCAGATGGCTTACCTC; reverse primer, TGCACTCGTACTTACTGCGCGTA); psbA, photosystem II protein D1 (forward primer, CCACATGGCTGGTGTTGCTGGT; reverse primer, CGACCAAAGTAACCGTGTGCAGCT); psbC (forward primer, TCATCTGCACAAGGTCCAACTGGT; reverse primer, AGCAGCACGACGTTCTTGCCA); psbC, photosystem II reaction center protein (forward primer, TCATCTGCACAAGGTCCAACTGGT; reverse primer, AGCAGCACGACGTTCTTGCCA); hsp90, heat shock protein (forward primer, AGGCTCTTACGGCCGGGGCGGA; reverse primer, AAGACCCGCCAGCCTCGGAAGCC); rbcL, ribulose-bisphosphate carboxylase (forward primer, AGGCTCTTACGGCCGGGGCGGA; reverse primer, TGTAGATAACTTGACGACCTGCGCC); actin gene (forward primer, CCGTAGTGAACGCCTATCGTGGC; reverse primer, CCATCGTCTCGCTGCGGCTG); tubulin gene (forward primer, GGACGCTACGTTCCTCGTGCC; reverse primer, GCTCTCGGCCTCCTTCCTCACA); and 18S rRNA gene (forward primer, TGCCAGTAGTCATACGCTCGTCTCA; reverse primer, CCTTCCGCGAACAGTCGGGTAT). The primers that functioned well at the bulk-cell level but not at the single-cell level are also provided in Table S1 in the supplemental material.

Enhanced cDNA synthesis by addition of target-specific primers.

cDNA synthesis typically employs random primers which generate the least bias in the resulting cDNA (44). However, since we were using total RNA rather than purified mRNA as the starting template, most of the cDNA synthesized through the random primers will be rRNA-derived cDNA, which could further complicate the single-cell gene expression (44). To address this issue and to enhance the yield of cDNA derived from target mRNA, primers specific to the target genes were added to the reverse transcription reaction mixture so that more mRNA of the target genes would be converted to cDNA (45). To ensure detection sensitivity and reproducibility of single-cell qPCR, cDNA from each T. pseudonana cell was used to detect a maximum of three different genes, each with three technical replicates. In the cDNA synthesis step, 1 μl of primer mixture containing three target gene-specific primers (reverse primers which are complementary to the mRNA sequence) was added. The final concentration of each target-specific reverse primer in the qPCR mixtures was 4 nM. To demonstrate the effects of adding target gene-specific primers on single-cell analysis, we evaluated the single-cell RT-qPCR of three genes, psbC, the actin gene, and the 18S rRNA gene. In this experiment, we diluted the RNA isolated from bulk cells (∼106 cells/ml) to the level of a single cell, which is approximately 50 fg/μl (46). During cDNA synthesis, a primer mixture containing target gene-specific primers was added to 6 replicates, while another 6 replicates contain only random cDNA synthesis primers. The results showed that except for the 18S rRNA gene, addition of the target-specific primers could significantly decrease the quantification cycle (Cq) values by 2 to 4 cycles, which is a 4- to 16-times-higher yield of target cDNA than for control samples for the psbC and actin genes, suggesting the target-specific primers in the cDNA synthesis reaction were able to improve the yield of target cDNA significantly (Fig. 2). No effect was observed for the 18S rRNA gene, probably because it is one of the most abundant genes in the total RNA (47). However, even for the 18S rRNA gene, our results showed that addition of target-specific primers can improve the qPCR reproducibility by decreasing the standard deviation of Cq values from 0.2 to 0.1 cycle (Fig. 2). The results demonstrated that adding target-specific primers to the cDNA synthesis reaction mixture was a useful approach which can improve the performance of qPCR, especially for the genes with larger Cq values.

Fig 2.

Fig 2

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Effects of adding target-specific primers. Cq is the qPCR quantification cycle, the fractional cycle number where fluorescence increases above the threshold. Six in-parallel reactions (with and without addition of specific primers) were run.

Selection of internal reference gene.

In order to ensure that the gene expression across different conditions or analytical platforms is quantitatively comparable, expression measurements need to be normalized against an internal reference gene (48). While several internal reference genes have been demonstrated in the bulk-cell-based RT-qPCR analysis, so far limited information is available regarding the constant expression of these internal reference genes across individual cells (49–51). For single-cell-based analysis, relative activities of each target gene against the reference gene were acquired by the ΔΔCq method (52, 53). Based on previous studies, we selected three genes, i.e., the tubulin gene, the 18S rRNA gene, and the actin gene (54–56), as candidate reference genes for further evaluation. To simplify the selection process, only control and iron depletion growth condition were used. A total of 12 cells from control and iron depletion conditions were picked and subject to expression determination for the tubulin gene, the 18S rRNA gene, and the actin gene. The Cq measurements for a total of 24 cells (i.e., 12 from control conditions and 12 from iron depletion conditions) are presented in Fig. 3. The results showed that the standard deviations (SD) of the Cq values for the tubulin gene, the 18S rRNA gene, and the actin gene among all 24 cells were 0.89, 2.9, and 0.39 cycles, respectively. The actin gene had the smallest variance among cells and was thus selected as an internal control for our further analysis. The result was also consistent with that of Kustka et al. that the expression of the actin gene was constitutive under all iron concentration conditions (57). The results also showed that even for the 18S rRNA and tubulin genes, which were widely used as internal controls in various bulk-cell-based RT-qPCR analyses, significant cell-cell heterogeneity existed.

Fig 3.

Fig 3

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Evaluation of three internal control candidates under control and iron depletion conditions. Cq is the qPCR quantification cycle, the fractional cycle number where fluorescence increases above the threshold. Twelve cells from the control condition and 12 cells from the iron depletion condition were used to evaluate the consistency of the internal control genes.

Single-cell gene expression under stress conditions.

To establish a baseline for single-cell-based analysis, we first performed a bulk-cell-based RT-qPCR for the selected target genes under three stress conditions. The relative activity of each gene was derived from the Cq value, which was normalized first by cell number and then by the activity of the control growth condition. The results showed that except for the hsp90 gene under iron depletion condition, all other genes were downregulated by the stresses (see Fig. S2 in the supplemental material). Upregulation of the hsp90 gene under iron depletion condition was also reported by Thamatrakoln et al., who applied a combined genome-wide and targeted comparative transcriptomic analysis with diagnostic biochemistry and in vivo cell staining as a platform to identify the suite of genes involved in acclimation to iron and associated oxidative stress in T. pseudonana (20). In another study, Allen et al. also found that the hsp90 gene was upregulated under iron starvation stress condition in a pennate diatom, Phaeodactylum tricornutum (58). Both the psaA gene, encoding photosystem I P700 chlorophyll II a apoprotein A1, and the psaF gene, encoding a photosystem I reaction center subunit, were downregulated under three nutrient deficiency conditions. Similar results of a PS I decrease under iron limitation were also reported by Allen et al. (58). Compared with the psaA and psaF genes of PS I, the psbA and psbC genes of PS II were downregulated more under all nutrient depletion conditions, suggesting that photosystem II may be more vulnerable to nutrient depletion conditions than photosystem I; this is consistent with the results of Mock et al., who analyzed whole-genome expression profiling under several different growth conditions, such as Fe depletion, N depletion, Si depletion, and high temperature (59). The rbcL gene was downregulated significantly under nitrogen starvation and phosphate depletion conditions but was downregulated only slightly under iron depletion condition. In a recent study, Allen et al. reported that downregulation of several proteins, such as phosphoribulokinase (PRK) and two enzymes supplying substrate for RubisCO, will lead to a decrease of carbon fluxes toward RubisCO under Fe stress in P. tricornutum (58). In addition, comparison of gene expression patterns showed that although T. pseudonana and P. tricornutum were divergent ∼90 million years ago and had vast differences in genome structure (60), they may still share a similar fundamental response mechanism to iron starvation. Other than these results, Pearson correlation coefficients under different conditions (see Tables S2 to S5 in the supplemental material) indicated that psaA and psaF were always negatively correlated under different nutrient depletion conditions, suggesting that the two genes were regulated by a similar mechanism but in opposite directions under different nutrient depletion conditions, which was rational since both of them belong to photosystem I. For psbA and psbC, no such correlation was found, which suggested that possibly the regulation mechanisms were different for photosystem II and photosystem I.

For single-cell level analysis, the ΔΔCq method was used to calculate the relative expression of each gene against the reference actin gene. Figure 4 and Fig. S3 in the supplemental material show the result of qPCR analysis of 6 genes under control and three stress conditions. For each condition, 30 individual cells were picked and analyzed. Reactions with large variations between technical replicates and/or with multiple peaks observed in the melting curves were considered failed reactions and were excluded from further analysis. Overall, the success rate for qPCRs was approximately 93%. The reproducibility of the qPCR was derived from the SD of the technical replicates of each cell. Based on our results, the hsp90, psbA, psbC, and actin genes all had small average SD values among all samples, which were 0.2041 cycle (0.75% of average Cq values), 0.2109 (0.75%), 0.2116 (0.72%), and 0.2148 (0.74%), respectively. For the genes with larger Cq values, although the average SD values were almost doubled, to 0.3847 (1.2%), 0.4048 (1.2%), and 0.422 (1.3%) for the psaF, psaA, and rbcL genes, respectively, they were still in the relatively low-variation range. In general, our single-cell-level qPCR protocol was robust and able to generate reproducible data.

Fig 4.

Fig 4

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Gene expression distributions of selected genes under four different growth conditions. P values were determined by using the nonparametric two-sample Kolmogorov-Smirnov test between each depletion and control conditions (α = 0.05). The x axis shows the relative activity of a specific gene compared with the activity of the control actin gene in the same cell, and the y axis shows the number of cells that have the same relative activity.

The RT-qPCR results showed that gene expression varied significantly between individual cells, suggesting significant cell-cell heterogeneity in the T. pseudonana population (Fig. 4), consistent with the previous conclusions that stochasticity of transcription contributed significantly to the level of heterogeneity within a clonal population and that this heterogeneity cannot be revealed by snap-shot measurements of bulk cells (4, 5, 61). Comparison of the distribution patterns between conditions can be achieved by using the Kruskal-Wallis ANOVA test (62). The results showed that 4 genes, not including the psaF gene, exhibited independent expression distribution patterns under four growth conditions (Table 1). The P value for the psaF gene was 0.06841, which was close to the cutoff (i.e., < 0.05), indicating that there were still some differences for the psaF gene under the four conditions.

Table 1.

P values of Kruskal-Wallis tests at the 95% confidence level

Gene P value
psaA 8.23229E−15
psaF 0.06841
psbA 0.00255
psbC 9.47652E−5
hsp90 7.70247E−4
rbcL 4.40972E−8
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Bulk-cell-based analysis showed that the psaA gene had a higher expression level under the phosphate depletion condition than under the nitrogen depletion condition (see Fig. S2 in the supplemental material). However, a reverse pattern was observed from the single-cell-based analysis. A similar pattern between bulk- and single-cell analyses was also observed for the psaF gene. For psbA genes, the nitrogen depletion condition had the lowest activity among four growth conditions, which was only 10% of that under the control condition. This may be due to the insufficient supply of inorganic nitrogen as found by Kolber et al. (63), who showed that nitrogen limitation could lead to substantial decreases in photosynthetic energy conversion efficiency and loss of PS II protein D1, which is encoded by the psbA gene. For psbC genes, the results from the single-cell level were consistent with the results from bulk-cell analysis. Although the bulk-cell results indicated that phosphate depletion had about 2-times-higher activity than under the nitrogen depletion condition for the hsp90 gene, the single-cell-level results indicated that the activities were similar to each other, while the upregulation of the hsp90 gene under the iron depletion condition at the bulk cell level was confirmed by single-cell-level results which indicated that low Fe availability indeed triggered stress on T. pseudonana. For the rbcL gene, the results showed that iron depletion had no effect on rbcL expression while nitrogen depletion affected rbcL expression, based on both single-cell- and bulk-cell-based analyses, consistent with previous work with the marine diatom P. tricornutum (64).

PCA of single-cell RT-qPCR data.

With the aid of powerful statistical tools, more intrinsic information can be extracted from single-cell-based data sets. For instance, besides the independence test based on results of response distributions, principal-component analysis (PCA) also can be applied to analyze the relationship between different growth conditions (Fig. 5). PCA can provide a simple plot that shows the most important two factors that affect the samples of each growth condition. PCA analysis of psaF showed that the nitrogen depletion condition had no significant effect on gene expression in single cells compared with the control condition. For psbA and rbcL, the PCA results showed that the four growth conditions were distinguished from each other. These results agreed well with the distribution analysis. For the psaA and hsp90 genes, the P value generated from the Kolmogorov-Smirnov test suggested that there were no significant differences between iron depletion and control conditions (Fig. 4). However, based on the PCA analysis, they had a similar score for component 1 but a slightly different score for component 2 for different nutrient depletion conditions, which indicated that the expression of these two genes under nutrient depletion conditions was similar but distinct from that under the control condition. In addition, for psbC, the distribution analysis showed that iron depletion and control conditions were similar to each other, but the PCA results showed that expression of the psbC gene under iron depletion and control conditions was not controlled in the same way.

Fig 5.

Fig 5

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Principal-component analysis (PCA) of single-cell-based analysis of selected genes. Component 1 and component 2 are the top two indicators that could explain the pattern of the gene expression data set. Dots represent each of the conditions we tested in this study (C, control; N, nitrogen depletion; Fe, iron depletion; P, phosphate depletion). The distances between dots represent similarity in terms of gene expression pattern between various growth conditions. A longer distance indicates less similarity.

DISCUSSION

The responses of diatoms to various nutrient deficiencies have been evaluated at the population level (20, 59). However, because they are planktonic microorganisms, the cell-cell heterogeneity of diatoms in terms of responses to environmental factors could be significant and have never been documented. In this study, we made the first attempt to measure the expression of selected genes of the model diatom T. pseudonana when they were subject to nitrogen, phosphate, and iron depletion conditions. The results showed significant heterogeneity, which shed light on potential environmental problems. Opposite expression patterns were found for the psaA, psaF, psbA, and hsp90 genes between single-cell-based and bulk-cell-based analyses. The abnormal cells may be an indicator of potential environmental problems and suggest that further investigations would be possibly buried under the average value without single-cell-level analysis.

In order to use T. pseudonana as a sensor by using single-cell RT-qPCR, several issues need to be addressed. The first is the sensitivity of the sensor, which is equivalent to single-cell RT-qPCR sensitivity. Since the sensitivity of our technology can go down to a single-cell level, it has the capability to analyze some important and/or uncultured environmental samples.

Our results showed that as the copy number of transcripts of a gene decreased, the SD of RT-PCR technical replicates increased accordingly. To overcome the issue, small reaction volumes, which increase the local template concentration, are preferred. In the study, we used 10-μl reaction volumes instead of conventional 20-μl reaction volumes for qPCR. Currently, 10 μl is the smallest volume with which we can obtain consistent and reliable results in the tube/microtiter plate-based qPCRs. In order to further decrease the reaction volume, chip-level devices which can decrease the volume to several microliters (65) or even to the picoliter level (66–68) will be more attractive. In addition, we also addressed the issue of low levels of starting material by increasing the cDNA yields of specific targets through adding target-specific primers. Ståhlberg et al. evaluated 4 different primer strategies, i.e., random hexamers, oligo(dT), gene-specific primers, and gene-specific primer mixtures, on five different genes, and the results showed that gene-specific primer mixtures had an overall advantage based on the yield and SD of qPCR results for several different genes (45). In order to simplify the whole process, considering that the reverse primer of qPCR is complementary to the mRNA sequence and may bind to specific mRNA during the cDNA synthesis step, which will increase the cDNA yield of specific targets, we added the reverse primer directly rather than using the specially designed specific primers that are complementary to the mRNA sequence as described by Ståhlberg et al. (45). The results showed that adding target-specific primers in the cDNA synthesis step could increase the quality and yield of target cDNA by about 10-fold on average.

The second issue is how to interpret RT-qPCR results in a quantitative way so that the result can be used as an indicator of environmental stress conditions. The use of reference genes is important in order to normalize qPCR results, and much research has been done on the selection of reference genes for various bulk-cell-based analysis (51, 69). However, considering gene expression stochasticity in single cells, the reliability of employing these genes for single-cell gene expression is still unclear. In this study, we selected and validated the actin gene as an internal reference based on its better performance than other candidate genes, and expression heterogeneity of the actin gene was still observed between individual T. pseudonana cells. To fully address the heterogeneity issue, an alternative internal control strategy, such as using a molecule that is artificially incorporated into the sample as an RNA spike (44, 51, 70), may be necessary and worth further development.

There are still technical challenges for using microbial gene expression at the level of a small number of cells as an environmental sensor. For example, the targeted microbe is in the mixture with other microbes, yet further manipulation such as cell sorting or cultivation will alter gene expression levels. How to successfully determine the atypical gene expression patterns from a few cells among a larger number of background normal cells is another big challenge. To overcome this, a feasible approach is to perform high-throughput single-cell level analysis on the microbiota and then extract the targeted information using postprocessing on the acquired data.

Finally, although our results demonstrated that with proper selection of gene targets and optimization of RT-PCR condition, gene expression measurements at single-cell resolution will allow monitoring of the marine environmental health, possibly at an early stage of potential environmental problems to minimize the cost of environmental remediation, currently the technology works well only with highly expressed genes, which limits the selection of gene targets. In the future, further development and optimization of the molecular biology protocol and integration with chip-level real-time PCR devices (65) will generate a chip-level sensor instrument for monitoring marine environmental health in a fast and effective way to overcome the remaining technical challenges. At the same time, fundamental microbiology questions about heterogeneity within an isogenic population will be answered as well.

ACKNOWLEDGMENTS

We thank ASU's NEPTUNE fund for funding to Deirdre Meldrum for the support of this research. Weiwen Zhang is currently funded by a grant from the National Natural Science Foundation of China (project no. 31170043).

Footnotes

Published ahead of print 11 January 2013

Supplemental material for this article may be found at http://dx.doi.org/10.1128/AEM.03399-12.

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