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Use of flow cytometric sorting to better assess the diversity of small photosynthetic eukaryotes in the English Channel

Dominique Marie , Xiao Li Shi , Fabienne Rigaut-Jalabert , Daniel Vaulot
DOI: http://dx.doi.org/10.1111/j.1574-6941.2010.00842.x 165-178 First published online: 1 May 2010

Abstract

Small photosynthetic eukaryotes are key primary producers in marine waters. In recent years, their diversity has been studied by the analysis of 18S rRNA gene sequences directly amplified and cloned from filtered natural samples. However, these clone libraries are often dominated by nonphotosynthetic organisms and few sequences from autotrophs are recovered. In the present paper, we developed a new approach based on flow cytometry. Photosynthetic pico-, nano- and phycoerythrin-containing (PE-) eukaryotes from the coastal English Channel were sorted based on their size and pigment fluorescence. 18S rRNA gene libraries were constructed from the DNA of sorted cells. We addressed methodological issues linked to the relatively low concentration of these cells. This novel approach confirmed that, in the English Channel, pico-eukaryotes are dominated by three genera Micromonas, Ostreococcus and Bathycoccus, while PE-eukaryotes are mainly cryptophytes from clade 4. It also revealed that nano-eukaryotes are dominated by haptophytes with important contributions from small diatoms and Prasinophyceae. It should be emphasized that haptophytes were nearly absent from clone libraries constructed from filtered samples, which explains why they have been overlooked in previous studies. The new strategy should be very useful to conduct similar studies on other specific populations that can be discriminated by flow cytometry (e.g. red tide organisms or uncultivated protists).

Keywords
  • flow cytometry
  • diversity
  • photosynthetic eukaryotes

Introduction

In recent years, there has been a growing recognition of the importance of small-sized eukaryotes in both marine and freshwater environments. The abundance of pico-eukaryotes (defined as cells smaller than 2–3 μm) ranges from 1000 to 5000 cells mL−1 in oceanic and coastal waters, respectively. Despite these relatively low concentrations compared with photosynthetic prokaryotes such as Prochlorococcus or Synechococcus (Partensky et al., 1999), recent studies have pointed out that marine photosynthetic pico-eukaryotes are major primary producers (Worden & Not, 2008). Nano-eukaryotes (2–20 μm) are in general 10 times less abundant than pico-eukaryotes in the plankton (e.g. Moran, 2007), but many nanoplanktonic species such as Emiliania huxleyi can sporadically form dense blooms. Species of small marine photosynthetic eukaryotes belong to a variety of algal classes (Vaulot et al., 2008), in particular within green (Prasinophyceae) and brown (Stramenopiles) algae for picoplankton, and within Haptophyta and Dinophyceae (dinoflagellates) for nanoplankton (Vaulot et al., 2008).

Several strategies have been applied to analyze the diversity of small photosynthetic eukaryotes in marine systems. The culturing approach led to the isolation and description of many important species (Vaulot et al., 2008), but is limited by medium selectivity and poor knowledge of actual growth requirements (Le Gall et al., 2008). Molecular techniques, especially the cloning of the 18S rRNA gene from natural populations, have established that small-eukaryote assemblages are highly diverse in marine systems (Vaulot et al., 2008).

One major limitation of current 18S rRNA gene studies is that environmental libraries generated from filtered samples with universal PCR primers are dominated by heterotrophic organisms (Vaulot et al., 2002). For example, 18S clone libraries constructed from samples off Roscoff (Brittany, France) contain only few representative sequences from photosynthetic lineages such as the Prasinophyceae, with most sequences belonging to nonphotosynthetic lineages such as marine stramenopiles (MAST, see Massana et al., 2004), Syndiniales (alveolate groups I and II, Guillou et al., 2008), ciliates, Cercozoa, or choanoflagellates (Romari & Vaulot, 2004). This is surprising since microscopy counts with probes detected by FISH suggest that in these waters an average on an annual basis of 85% of eukaryotic cells belong to the Chlorophyta with one Prasinophyceae sp., Micromonas pusilla, being dominant most of the year (Not et al., 2004).

Several approaches have recently been applied to target more specifically the photosynthetic fraction of small-eukaryote assemblages. For example, clone libraries have been constructed using 18S rRNA gene primers biased toward photosynthetic groups such as Chlorophyta (Viprey et al., 2008) or primers targeting the 16S rRNA plastid gene, only present in photosynthetic organisms (Fuller et al., 2006). These approaches have revealed that some important groups, such as Chrysophyceae (Fuller et al., 2006) had escaped detection. Even within classes thought to be well known, such as the Prasinophyceae, major novel clades have been detected (Viprey et al., 2008). However, these two approaches are somewhat biased because they rely on specific primers that can only be designed based on available sequences.

Flow cytometry has been used extensively in the last 25 years to characterize both auto- and heterotrophic plankton populations in natural samples. It allows discriminating and enumerating specific groups of cells (e.g. cyanobacteria) based on their size and fluorescence characteristics either due to natural cell pigments or staining, for example of DNA (Veldhuis & Kraay, 2000; Czechowska et al., 2008). However, the capacity of flow cytometry to sort specific subpopulations, which allows characterizing them subsequently using molecular techniques, has been little used in marine systems, and to the best of our knowledge only for prokaryotes (Wallner et al., 1997; Fuchs et al., 2005).

In the present paper, we develop a novel approach to assess the diversity of small photosynthetic eukaryotes by sorting specific populations of photosynthetic cells by flow cytometry, extracting DNA from the sorted cells, and constructing clone libraries with universal eukaryotic primers of the 18S rRNA gene. This approach was tested on samples from the English Channel collected during each season of the year and proved to be very efficient in minimizing the contribution of heterotrophic groups.

Materials and methods

Sample collection and preparation

Surface seawater samples were collected using a Niskin bottle, once each season (Table 1), at the SOMLIT-Astan site (48.46°N, 3.56°W) off Roscoff (Brittany, France). An extra sample was taken on August 30, 2007 to conduct tests. In order to sort enough cells in a reasonable period of time and better visualize the different cell populations, 3 L of seawater were concentrated to a volume of 30 mL by tangential flow filtration using a Vivaflow 200 cartridge equipped with a 100 000 MWCO RC membrane (Sartorius Biotechnologie SAS, France). Concentration factors varied between 50 and 80 times (Table 1). Cell recovery was lowest for Synechococcus cyanobacteria (49% recovery, mean value), and increased for larger cells (57%, 74% and 82% for pico-, nano- and PE-eukaryotes, respectively). Five milliliters of the concentrated sample were reserved for flow cytometry and prefiltered through a 50-μm nylon mesh in order to avoid clogging of the flow cell. The remaining volume (not prefiltered and therefore possibly containing microplanktonic cells) was filtered using a syringe through a 0.22-μm pore size Sterivex unit (Millipore, Billerica, MA). Seawater was completely removed from the Sterivex unit that was then filled with 2 mL of lysis buffer (0.75 M sucrose, 50 mM Tris-HCl, 40 mM EDTA), and stored at −80 °C until extraction.

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1

Sampling dates, population abundance, percentages of recovery after tangential flow filtration, number of cells sorted by flow cytometry, and PCR primers used (see Table 2)

Date SynechococcusPico-eukNano-eukPE-eukPCR primers
Cells mL−1Recovery %Cells mL−1Recovery %SortedCells mL−1Recovery %SortedCells mL−1Recovery %Sorted
April 11, 200754477010 88972200 000106411350 00055705000Euk328f/Euk329r
June 25, 200744104418 67740205 00017585450 0002497015 000Euk328f/Euk329r and nested PCR Euk1Af/1492rE
October 04, 2007379547792460250 0005166050 0006011115 000373Cf/Euk329r
February 15, 2008158235384755250 0007917150 00084755500373Cf/Euk329r
  • Note that some percentages of recovery are higher than 100, due to the difficulty in clearly finding the limits of the populations on nonconcentrated samples.

Flow cytometry

Samples were analyzed as described previously (Marie et al., 2000) using a FACSAria flow cytometer (Becton Dickinson, San Jose, CA) equipped with a laser emitting at 488 nm and a 70-μm nozzle. Emitted light was collected through the following set of filters: 488/10 band pass (BP) for side scatter, 576/26 BP for orange fluorescence, and 655 long pass for red fluorescence. Signal detection was triggered on the chlorophyll fluorescence. Samples were run for 2 min at a flow rate of 40 μL min−1 to estimate cell abundance. Three populations of pico-eukaryotes (Pico-euk), nano-eukaryotes (Nano-euk) and phycoerythrin-containing eukaryotes (PE-euk) were selected for sorting based on light scatter, orange phycoerythrin fluorescence, and red chlorophyll fluorescence (Fig. 1). Between 5000 and 250 000 eukaryotic cells were sorted into Eppendorf tubes containing 180 μL of lysis buffer (Tris-HCl, pH8; EDTA-Na2 2 mM; Triton X-100, 1.2%). Sorting was performed in high-purity sorting mode at a frequency of 90 000 Hz and with a deflection voltage of 6000 V. For sorting, the flow rate was adjusted such that the analysis rate remained below 12 000 events per second.

1

Flow cytometric distributions obtained for Astan samples taken at four different seasons, before (a) and after (b) concentration by tangential filtration. Cytograms correspond to phycoerythrin orange fluorescence vs. chlorophyll red fluorescence, both in arbitrary units. The inset in (a) corresponds to the side scatter (a proxy of cell size) vs. chlorophyll fluorescence cytogram. Colored dots correspond to the limits of the different populations based on multiparameter gating: pico-eukaryotes (Pico-euk), nano-eukaryotes (Nano-euk), PE-eukaryotes (PE-euk), and Synechococcus (Syn) cyanobacteria. Ellipses emphasize the three eukaryote populations that were sorted from concentrated samples.

In order to estimate the number of cells that have to be sorted for constructing 18S rRNA gene clone libraries and to determine whether the mode of cell collection influences DNA recovery, series of 10–200 000 pico-eukaryotes and 10–50 000 nano-eukaryotes from the Astan sample taken in August 2007 were sorted either in Eppendorf tubes containing 180 μL of lysis buffer or onto 0.2-μm Supor membrane filters (Pall Life Sciences, France). In the latter case, following sorting, filters were placed into cryovials containing 1 mL of lysis buffer. All samples were stored at −80 °C until extraction.

DNA extraction

Samples were thawed at room temperature. Either 20 or 100 μL of lysozyme (20 mg mL−1) was added to populations sorted into Eppendorf tubes or onto filters, respectively. Incubation at 37 °C was performed for 30 min. One hundred and eighty microliters and 1 mL of lysis buffer AL from DNeasy Blood and Tissue Extraction Kit (Qiagen) was added to populations sorted into lysis buffer and onto filters, respectively. Proteinase K (50 μM final concentration) and 5 μL glycogen (5 mg mL−1), which helps to recover low amounts of DNA, were added, followed by incubation at 55 °C for 30 min. Proteinase K was inactivated by 10-min incubation at 70 °C. DNA was precipitated by addition of pure ethanol (30% final concentration). Samples were then transferred to Qiagen Kit columns, and washed following the manufacturer's instruction. Finally, purified DNA was eluted with 80 μL of sterile water.

The DNA of whole samples collected on Sterivex filters was extracted following the same procedure. Two milliliters of solution was recovered from the Sterivex unit and the different incubation steps were performed with 200 μL of lysozyme, 2 mL of lysis buffer AL, and 50 μM proteinase K, but without glycogen in contrast to the sorted populations. Finally, DNA was precipitated with 2 mL of ethanol and transferred to columns from the Qiagen Kit. Washing and elution of DNA was performed following the manufacturer's instruction.

PCR amplification

A 30 μL PCR was performed using 10 μL of extracted DNA and Hot Star Taq Master Mix (Qiagen) for sorted populations from April and June and Go Taq (Promega) for all other samples. Universal primers Euk328f and Euk329r targeting the 18S rRNA gene were used for sorted populations from April and June, while 373Cf and Euk329r were used for the two other dates (Table 2). The change of primer set during the study was motivated by the fact that, for unknown reasons, we observed a higher cloning efficiency using the combination of 373Cf and Euk329r. Amplification conditions included 5 min at 95 °C to activate the Taq polymerase, followed by 10 cycles at 95 °C for 45 s, touchdown from 64 to 54 °C for 45 s and 72 °C for 75 s. Then, 25 cycles of 95 °C for 45 s, 57 °C for 45 s and 72 °C for 75 s were performed, followed by a final extension step for 10 min at 72 °C. The quality and amount of the PCR products were evaluated after migrating 10 μL of PCR reactions onto a 1% agarose gel and comparing it with 5 μL of Smart Ladder (Eurogentec).

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2

Sequences of primers targeting the 18S rRNA gene used in this study

Primer nameSequence from 5′ to 3′PositionGC%T m (°C)Reference
Euk328fACC TGG TTG ATC CTG CCA G15851Romari & Vaulot (2004)
Euk329rTGA TCC TTC YGC AGG TTC AC17475350Romari & Vaulot (2004)
Euk1AfCTG GTT GAT CCT GCC AG35948Sogin & Gunderson (1987)
1492rEACC TTG TTA CGR CTT17244337Dawson & Pace (2002)
373CfGAT TCC GGA GAG GGA GCC TGA3616255Weekers (1994)
  • * Relative to Ostreococcus tauri 18S rRNA gene.

For most populations from the sorting test, PCR products were not concentrated enough to be cloned and a second nested PCR with the internal primers Euk1Af and 1492rE was performed using 0.5 μL of the first PCR diluted 10-fold. Amplification conditions included 5 min at 95 °C followed by 35 cycles of 95 °C for 45 s, 45 °C for 45 s and 72 °C for 75 s with a final extension for 10 min at 72 °C.

Cloning and sequencing

One microliter of fresh PCR product was cloned using the TA vector pCR2.1-TOPO (Invitrogen) following the manufacturer's protocol. Cloning reactions were dispersed on Petri dishes containing 30 mL of Luria–Bertani agar (Difco), 30 μL ampicillin (50 mg mL−1), and 70 μL of both X-Gal (40 mg mL−1) and IPTG (200 mg mL−1). Plates were incubated overnight at 37 °C and then transferred to 4 °C for approximately 2 h. White colonies were resuspended directly into 30 μL of PCR mix containing 0.25 μL Go Taq polymerase (Qiagen), 3 μL of reaction buffer, 1.5 μL of MgCl2, 1 μL of dNTP (Eurogentec), 0.25 μL of primers Euk1Af and 1492rE (10 pM) for sorted populations from April and June and 373Cf and Euk329r for the other samples, and 23.75 μL of sterile water. PCR amplification was performed with one step of 5 min at 95 °C followed by 35 cycles of 95 °C for 45 s, 45 °C for 45 s and 72 °C for 75 s with a final extension for 10 min at 72 °C.

For each clone, the presence of a PCR product was evaluated after migration of 5 μL of the PCR reactions onto a 1% agarose gel by comparison with Smart Ladder. Selected PCR products were transferred to a sequencing reaction cleanup plate (Montage SEQ96, Millipore) and washed twice with 100 μL of sterile water.

PCR sequencing reactions were performed using 0.5 μL of the fluorescent Big Dye Terminator DNA sequencing kit V3.1 (Applied Biosystems, Foster City, CA), 0.75 μL of Buffer (400 mM Tris-HCl, 10 mM MgCl2, pH 9), 0.5 μL of 10 μM forward primer Euk1A for sorted populations from April and June and 373Cf for the other samples and 0.5–1 μL of the purified PCR product. Sterile water was used to bring the reaction volume to 5 μL. The PCR amplification involved an initial denaturation step at 96 °C for 5 min, followed by 30 cycles including 30 s of denaturation at 94 °C, 30 s of annealing at 56 °C and 2-min extension at 60 °C. A final extension of 7 min at 60 °C followed by cooling at 4 °C terminated the PCR program. The PCR product was then purified into 20 μL of injecting solution to eliminate the remaining fluorescent nucleotides and the sequencing was then performed using an ABI Prism 3100 (Applied Biosystems).

Sequence analysis

Sequences were analyzed for close relatives using blast (http://www.ncbi.nih.gov/BLAST/, June 2008). Sequences were also aligned using the silva aligner (http://www.arb-silva.de, Pruesse et al., 2007) and merged into an arb (Ludwig et al., 2004) database containing >30 000 complete or partial SSU rDNA sequences from eukaryotes. Finally, sequences were analyzed with keydnatools (http://keydnatools.com/), which provides taxonomic affiliation and chimera detection based on sequence motifs (Guillou et al., 2008). Phylogenetic assignation of each sequence was finalized based on these three analyses. Sequences that presented >98% identity to a given genus were assigned to this genus. Rarefaction curves and the Chao1 index, a statistical indicator of richness, were estimated with the fastgroupII software (http://biome.sdsu.edu/fastgroup/) using a sequence match similarity threshold of 80% (roughly equivalent to a 98% sequence identity threshold).

Sequences have been deposited to GenBank under accession numbers FJ431283FJ432001.

Results

Flow cytometric analysis of photosynthetic plankton in the English Channel

At least four populations of photosynthetic organisms could be detected by flow cytometry based on scatter and fluorescence signals (Fig. 1a) in English Channel coastal waters sampled off Roscoff. A population of Synechococcus was clearly identified by its small size and presence of both red and orange fluorescences, with cell concentrations ranging from 1600 cells mL−1 in February up to 5500 cells mL−1 in April (Table 1). A population of larger cells displaying both chlorophyll and phycoerythrin fluorescences, labeled PE-eukaryotes, was clearly seen in June with concentration of about 250 cells mL−1. Its flow cytometric signature was more difficult to detect for samples from October and February and seemed to be absent in April. The presence of this population was clearer in samples concentrated by tangential filtration with evidence of two groups of organisms that differed in their pigment content and scatter in all samples except that of October (Fig. 1b). The two other populations lacked orange phycoerythrin fluorescence and corresponded roughly to the pico- (<2 μm) and nanophytoplankton (2–20 μm) size ranges (Sieburth et al., 1978). Therefore, they are labeled in the rest of the article as pico- and nano-eukaryotes. These two populations reached maximum abundance in June with cell densities at about 19 000 and 1800 cells mL−1, respectively (Table 1).

Methodological considerations

We tested the effect of the collection mode of sorted cells: either directly into Eppendorf tubes filled with lysis buffer or onto filters. We also tried to determine the number of cells necessary to obtain enough DNA to build reliable 18 rRNA gene clone libraries. These tests were achieved on pico- and nano-eukaryote populations sorted from a sample collected at the Astan Station in August 2007. No differences were observed between PCR products obtained on populations directly sorted into lysis buffer and those sorted onto filters (Fig. 2). Using Euk328f and Euk329r primers, large amounts of PCR product were obtained for 100 000 and 200 000 pico-eukaryotes and 50 000 nano-eukaryotes. For cell concentrations below these values, only a faint PCR product was observed. Therefore, a second nested PCR was performed before cloning for populations sorted into lysis buffer.

2

PCR products obtained using the universal primers Euk328f and Euk329r on different numbers of pico-eukaryotes (10, 100, 1000, 10 000, 100 000, 200 000) and nano-eukaryotes (10, 100, 1000, 10 000, 50 000) sorted into Eppendorf tubes (top) and onto filters (bottom).

Clone library composition varied sharply as a function of the number of cells sorted. In particular, the percentage of sequences matching that of photosynthetic organisms increased with the number of sorted cells (Tables 3 and 4). For 10 sorted pico-eukaryote cells (Table 3, Supporting Information, Table S1), only two sequences of photosynthetic eukaryotes were obtained. However, they corresponded to cells (Mantoniella, Chaetoceros) with a size larger than a typical picoplankton. When 100 cells were sorted, all photosynthetic sequences belonged to the pico-eukaryotic genus Ostreococcus. With 1000 pico-eukaryotes sorted, Ostreococcus still dominated (66% of the photosynthetic sequences) with the rest corresponding to another pico-eukaryotic species, Bathycoccus prasinos. For all samples with pico-eukaryote cells in excess of 10 000, clone composition was quite similar. Sequences matching M. pusilla became dominant with up to 80% of the photosynthetic sequences recovered, Ostreococcus was still present while Bathycoccus was much less represented or even absent. Sequences of nano-eukaryotes were dominated by diatoms (Table 4), especially Chaetoceros socialis, a small chain-forming species (around 10 μm). When higher numbers of cells were sorted (10 000 and 50 000), some haptophyte sequences were also obtained (Chrysochromulina).

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3

Sequences obtained after cloning populations containing different numbers of sorted pico-eukaryotes (Astan sample from August 30, 2007)

Cells sortedSequences obtainedMicromonasOstreococcusBathycoccusOther photosynthetic groupsFungiMetazoaOthers
105221
10020164
1000171052
10 000161231
100 0002010811
200 00019123121
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4

Sequences obtained after cloning populations containing different numbers of sorted nano-eukaryotes (Astan sample from August 30, 2007)

Cells sortedSequences obtainedDiatomsHaptophytaFungiMetazoaOthers
1014113
10011362
10001055
10 0001441135
50 000104132

Sequences of nonphotosynthetic protists were also recovered in these populations including in particular Cercozoa in the nano-eukaryote population (Table S1). Fungal sequences were especially abundant in populations with low numbers of sorted cells (10 for pico-eukaryotes and 10–100 for nano-eukaryotes), but could also be found in populations with larger numbers of sorted cells. These sequences probably originated from laboratory contamination since some of them (Table S1) corresponded to common house molds such as Penicillium or to human-associated fungi such as Trichosporon (Pfaller & Diekema, 2004). Other sequences not originating from protists included some from metazoans and land plants. The former were affiliated to Muggiaea atlantica, a siphonophore whose gametes might be found within the picoplanktonic population, while the latter matched Musa basjoo, a banana tree commonly found in gardens along the coast of Roscoff.

Seasonal study: 18S rRNA gene clone libraries from sorted populations

Three photosynthetic populations (pico-, nano- and PE-eukaryotes) were sorted by flow cytometry at each season (Table 1) and their 18S rRNA gene was amplified, cloned and sequenced. Using the software keydnatools (Guillou et al., 2008), we were able to detect that at least 19 sequences (6%) were likely chimeras (Table S3). All potential chimeras were manually checked by aligning the 5′ and 3′ ends of the sequences to their respective targets. In many cases, chimeras were formed by sequences from two very closely related taxa. For example, chimerical combinations of Micromonas and Bathycoccus were recovered from pico-eukaryotes sorted in April, and in June, the dominating Ostreococcus formed chimeras with Micromonas or Bathycoccus. For nano-eukaryotes sorted in June, two chimeras combined the two dominant genera Chrysochromulina and Phaeocystis (Table S3). For PE-eukaryotes, the four detected chimeras were combinations of cryptophyte nuclear and nucleomorph 18S rRNA genes. All these chimeras are not considered further in the rest of Results.

Among the 313 nonchimerical partial sequences obtained from sorted populations, 93% were most similar to those from photosynthetic organisms (Table 5 and Table S2). More than 88% of the sequences had at least 98% similarity with known sequences based on blast analysis. This proportion was higher among pico-eukaryotes and PE-eukaryotes (96% and 91%, respectively), but lower (79%) for nano-eukaryotes for which 8% of the sequences had <96% of similarity with known organisms. Rarefaction curves and Chao1 estimators (Fig. 3) demonstrate that pico- and PE-eukaryotes were less diverse than nano-eukaryotes.

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5

Summary of phylogenetic assignments for sequences obtained at different times of the year for the three sorted populations and for filtered samples. ‘All’ corresponds to the total of the four seasonal samples

DivisionClassClone LibraryRA070411SRA070625SRA071004SRA080215SRA070411BRA070625BRA071004BRA080215BRA070411CRA070625CRA071004CRA080215CRA070411TRA070625TRA071004TRA080215TTotal
FractionPico-euksNano-euksPE-euksFiltered
MonthAprJunOctFebAllAprJunOctFebAllAprJunOctFebAllAprJunOctFebAll
Genus
Photosynthetic
ChlorophytaPrasinophyceaeBathycoccus223182546151641
ChlorophytaPrasinophyceaeMicromonas191212341313111010122371
ChlorophytaPrasinophyceaeOstreococcus21019312212512760
ChlorophytaPrasinophyceaeOthers118331411217
CryptophytaCryptophyceaeGeminigera41310532212537
CryptophytaCryptophyceaeTeleaulax11651610372212745
CryptophytaCryptophyceaeOthers52212211122
Picobiliphytes111345
HaptophytaPrymnesiophyceaePhaeocystis213731211435
HaptophytaPrymnesiophyceaeChrysochromulina62221211214
HaptophytaPrymnesiophyceaeOthers23166
StramenopilesBacillariophyceae1154132269112649
StramenopilesBolidophyceae111
StramenopilesChrysophyceae11112
StramenopilesRaphidophyceae222
StramenopilesPelagophyceaePelagomonas111
StramenopilesPhaeophyceaeSargassum111
CercozoaChlorarachniophyceae111
AlveolataDinophyceae111113321185557
Heterotrophic
AlveolataCiliophora115471617
AlveolataSyndiniales Group I61521414
AlveolataSyndiniales Group II323975151
AlveolataSyndiniales Other222
Cercozoa312668141925
Choanoflagellida111
Fungi11331237
RadiolariaAcantharea222
StramenopilesMAST and others112263321417
Telonemia555
Unidentified111
Multicellular
Metazoa11462142627
StreptophytaEmbryophytaMusa555
Sum2413313098233037251151620323210079828582328641
Chimera76132212143181231
3

Rarefaction curves of number of unique sequences recovered vs. number of clones sequenced, for sorted pico-eukaryotes, nano-eukaryotes, and PE-eukaryotes and for filtered samples. Libraries maintained at the four seasons were pooled together for each population. The Chao1 index is an estimate of the richness (total number of phylotypes) for each sample type. Rarefaction curves and Chao1 indices were computed with fastgroupII (Yu et al., 2006), using a sequence match similarity threshold of 80% (roughly equivalent to a 98% sequence identity threshold).

Among the 98 pico-eukaryote sequences, Prasinophyceae were dominant (Fig. 4) with 92% belonging to the three genera Micromonas, Ostreococcus and Bathycoccus (Mamiellales). If we exclude sequences of Metazoa and fungi, pico-eukaryote populations sorted in April, June and February only yielded prasinophyte sequences, while in October we also recovered four sequences of photosynthetic stramenopiles, and one cryptophyte (Table 5). nano-eukaryotes were dominated by haptophytes (Fig. 4), especially the genera Phaeocystis and Chrysochromulina, but diatoms and nanosized Prasinophyceae sp. were also important contributors (Fig. 4). In October, Micromonas sequences were abundant within the nano-eukaryotes (Table 5).

4

Overall phylogenetic composition of 18S clone libraries constructed from sorted populations and filtered samples.

Seasonal study: 18S rRNA gene clone libraries from filtered samples

For comparison with previous studies, 340 sequences were obtained from clone libraries constructed from filtered samples. Less than 4% of the sequences (12) were identified as chimeras. In contrast to chimerical sequences obtained from sorted populations that were composed of pieces from closely related phylogenetic groups, those from filtered samples resulted from the assemblage of widely different groups.

Alveolates, prasinophytes and stramenopiles contributed most to the 328 nonchimerical sequences recovered (42%, 20%, and 9% of the sequences, respectively, Table 5). Metazoa, Cercozoa, and cryptophytes represented approximately 8%, 6% and 4% of the clone libraries, respectively. Prymnesiophytes represented <2% of the sequences and were not recovered in February. Four sequences of picobiliphytes were obtained in samples from April and February. Four sequences of Telonemia and two of Radiolaria were also obtained.

Seasonal study: major groups

Chlorophyta

Sequences matching that of the three major genera Micromonas, Ostreococcus and Bathycoccus belonging to the Mamiellales (Prasinophyceae) were similarly distributed in sorted populations and filtered samples with a dominance of Bathycoccus in February, Micromonas in April, and Ostreococcus in June and October (Table 5). Sequences of larger prasinophytes belonging to the genera Pyramimonas and Mantoniella were found among nano-eukaryotes except in February (Table S2). In October, one sequence matching the genus Pycnococcus was also found. Sequences of Prasinophyceae besides the three major Mamiellales genera were poorly represented in filtered samples with only one sequence of Pyramimonas in April and one of Pycnococcus in June.

Cryptophyta

Although cryptophytes were poorly represented in filtered samples, they were very well recovered in sorted PE-eukaryotes (Table 5). Among the 104 partial sequences obtained for cryptophytes (91 for sorted and 13 for filtered samples), 79% are affiliated to clade 4 (Hoef-Emden, 2008), mostly related to the genera Geminigera and Teleaulax (37% and 35%, respectively). Plagioselmis, also from clade 4, represented 9% of the sequences and was only recovered in sorted populations from October.

Haptophyta

Sequences of haptophytes were poorly represented in clone libraries from filtered samples with a maximum of 4% in April and they were completely absent in February. In contrast, haptophytes constituted an important fraction of the sequences recovered for sorted nano-eukaryotes representing 26%, 83%, 21%, and 40% in April, June, October, and February, respectively. Many sequences were affiliated to two major genera; Chrysochromulina in April and Phaeocystis in June and February. Some sequences were related to uncultivated haptophytes.

Stramenopiles

Sequences of photosynthetic stramenopiles were not recovered for nano-eukaryotes in June, while they represented 22%, 11%, and 52% of the sequences obtained in April, October and February, respectively. They were also absent from the October clone library from filtered samples. A wide variety of diatom genera, including Thalassiosira, Minidiscus, Minutocellus, Guinardia, Chaetoceros, and Corethron (Table S2) represented 87% of the sequences. Bolidophyceae, Chrysophyceae, Pelagophyceae, and Phaeophyceae were more sporadically found. Four photosynthetic stramenopiles (diatoms, Chrysophyceae, Raphidophyceae) sequences were found within the pico-eukaryote population sorted in October. Sequences of heterotrophic stramenopiles (MAST, Massana et al., 2004) were recovered in filtered samples (4% of all sequences), but also in nano- and PE-eukaryotes sorted in October.

Cercozoa

Sequences of Cercozoa were retrieved in all clone libraries from filtered samples with a maximum contribution of 10% in June. Cercozoa contributed to 13% and 8% of the clones analyzed from nano-eukaryotes sorted in April and February, respectively, much less in June, and they were absent in October.

Alveolata

Sequences of dinoflagellates (Dinophyceae) were recovered in all filtered samples making up from 16% to 25% of the sequences, except in June where they only represented <4% of the sequences. At least nine genera were represented, including Gymnodinium, Heterocapsa, Pheopolykrikos, Gyrodinium, and Karlodinium. In contrast, they were virtually absent from the sorted populations with only one sequence in the nano-eukaryotes and one in the PE-eukaryotes. Heterotrophic alveolates were major contributors in filtered samples, with three major groups: Ciliophora (5%), Syndiniales group I (4%) and group II (15%). With the exception of one ciliate sequence recovered in the PE-eukaryote population sorted in October, heterotrophic alveolates were completely excluded from clone libraries originating from sorted populations.

Other protist sequences

A few sequences from the recently discovered picobiliphytes (Not et al., 2007) were retrieved, one from the nano-eukaryotes sorted in June and four from filtered samples, one in April and three in February. Other sequences from heterotrophic protists (Radiolaria, choanoflagellates, Telonemia) were only obtained from filtered samples and completely excluded by sorting (Table 5).

Metazoa, Fungi, and land plants

Sequences from nonprotist groups were found within clone libraries both from sorted populations and filtered samples. With the exception of one metazoan sequence within the pico-eukaryotes in April, Metazoa were only recovered from filtered samples, reaching a maximum of 15% of the sequences in February. Crustacea (15) and Polychaeta (nine) dominated as observed previously (Romari & Vaulot, 2004), probably originating from larval stages that are particularly abundant in coastal waters in spring. In contrast, fungal sequences were not recovered from filtered samples, but only in sorted populations, representing up to 8% of nano-eukaryote sequences in October. As in the case of the test sample, sequences of the banana tree, M. basjoo, were found (13% of sequences) in the October nano-eukaryote population.

Discussion

Diversity of photosynthetic eukaryotes

Our aim was to develop an approach that would allow a better assessment of the diversity of small photosynthetic eukaryotes from 18S rRNA gene clone libraries. The composition of clone libraries obtained from filtered samples during the course of the present work was very similar to that observed in a previous study at the same location (Romari & Vaulot, 2004). In contrast, sorting autotrophic subpopulations based on chlorophyll fluorescence (Fig. 1) allowed better targeting of photosynthetic groups (Fig. 4). Their overall contribution jumped from 54% for filtered samples up to 92% for the sum of the three sorted populations. For pico-eukaryotes, the share of photosynthetic sequences reached even 98% (Table 5). These photosynthetic populations are clearly less diverse than the whole eukaryotic microbial community as evidenced by the rarefaction curves and estimation of the Chao1 index (Fig. 3). Pico- and PE-eukaryote populations were 10 times less diverse than the total community and nano-eukaryote populations five times less. This supports an earlier claim that, indeed, photosynthetic pico-eukaryotes are less diverse than heterotrophic ones (Vaulot et al., 2002). One advantage of sorting by flow cytometry lies in the reduction of the sequencing effort needed to assess the composition of the photosynthetic community. For example, when the PE-eukaryote concentration was very low in October (60 cells mL−1), only a single sequence was obtained from filtered samples, while a much more detailed view of this population was achieved following sorting.

The value of cell sorting is clearly illustrated by the fact that several sequences were only recovered in the sorted populations and not in the filtered samples. Sequences related to Mantoniella (Mamiellales) were only found in sorted nano-eukaryotes (Table S2) and one of these presented a 100% similarity to an environmental sequence recovered with Chlorophyta-biased primers from the Mediterranean Sea (Viprey et al., 2008). In the same group, sequences related to Pyramimonas were found in the filtered sample only in April, but were present in sorted nano-eukaryote populations in April, June and October. Among Haptophyta, we detected novel sequences unrelated to any known genus in the October nano-eukaryotes (Table S2). Sequences related to Chrysochromulina were observed only in April and October in the filtered samples, but at all seasons in the sorted nano-eukaryotes.

Pico-eukaryote sequences were dominated by the three genera of Micromonas, Ostreococcus, and Bathycoccus (Mamiellales, Prasinophyceae) that are very typical of coastal waters (Vaulot et al., 2008). If the four seasonal samples are grouped together, these three genera contributed almost equally. However, at a given time, either one (spring, summer) or two (fall, winter) genera appeared to dominate. Quite surprisingly in summer, Ostreococcus was the most abundant in clone libraries. In contrast, a previous quantitative study using FISH has shown that Ostreococcus was always low in abundance off Roscoff (at most 18% of Mamiellales, Not et al., 2004). Other groups contributed very little to pico-eukaryotes and contaminants were quite low. Two identical stramenopile clones somewhat related to Raphidophyceae (precise assignment would require obtaining the full 18S rRNA gene) could belong to a yet undescribed class. The PE-eukaryote community was also quite simple, showing little seasonal variation with cryptophytes of clade 4 dominating, especially two closely related genera (Geminigera and Teleaulax). Cryptophytes are characteristic of coastal waters where their pigment alloxanthin is often detected (Breton et al., 2000). Curiously, we did not recover any picobiliphyte sequences among the PE-eukaryotes despite the fact that sequences from this group were detected in the filtered samples from late winter and early spring as reported previously (Romari & Vaulot, 2004) and picobiliphytes have been shown to contain PE (Not et al., 2007; Cuvelier et al., 2008). This absence could be explained by the fact that the window chosen to sort PE-eukaryotes was inadequate for picobiliphytes, either because of a larger size (Cuvelier et al., 2008) or a different pigmentation. Finally, nano-eukaryotes consisted mainly of haptophytes, diatoms, and Mamiellales (Prasinophyceae). Besides the bloom-forming genus Phaeocystis, which appears to be present throughout the year except in spring, and the highly diversified genus Chrysochromulina, some other haptophytes were only distantly related to known species. Among diatoms, genera containing small-sized species such as Minutocellus or Minidiscus were quite well represented. Diatoms were especially important in the nano-eukaryote population in winter, i.e. much before the diatom bloom that develops in Western Channel waters in late spring. Interestingly during the bloom (June), diatom sequences were only found in the filtered sample, but not in nano-eukaryotes, suggesting that they corresponded to large cells. These diatom sequences matched very closely to that of Guinardia delicatula, which is quite large (typically 50 μm long). Guinardia delicatula is one of the major blooming species off Roscoff (Sournia et al., 1987) and was observed in a very high concentration in the Lugol's counts on that date (F. Rigaut-Jalabert & F. Jouenne, unpublished data). Sequences of the nanoplanktonic genus Mantoniella (3–5 μm) from the order Mamiellales (prasinophytes) were observed at all seasons except in winter.

Methodological considerations

One of the major drawbacks of flow cytometric sorting is that, while purity is in general very high, the amount of material recovered is quite low and downstream analysis requires adaptation of existing protocols. We tried to solve this methodological bottleneck in several ways.

First, we preconcentrated the samples in order to better visualize the different subpopulations and to speed up sorting. As an example, sorting 5000 cells from a PE-eukaryote population at 84 cells mL−1 as observed in February would require to run a 60-mL sample and would take about 12 h, while after preconcentration it took <10 min. One potential problem with preconcentration is that it may induce differential cell loss. Indeed, the percentages of recovery of the different populations discriminated by flow cytometry after tangential flow filtration are lower for the small cells (Synechococcus and pico-eukaryotes) than for the larger cells (Table 1). However, the real extent of cell loss is very hard to evaluate since some rare and fragile taxa may be lost, but sequences from these taxa may not be recovered in unconcentrated samples because of their scarcity. Still the major groups are probably quite well recovered such as the Mamiellales or the cryptophytes from clade 4 that are found to be dominant in unconcentrated samples (Romari & Vaulot, 2004) and are very well recovered after tangential flow filtration (this work).

Second, we developed a cell collection and DNA extraction protocol that was efficient at recovering small quantities of DNA. In particular, cells were sorted directly into a lysis buffer to minimize cell loss. One problem stemming from the low number of collected cells is the potential for contamination, in particular by fungi often found on laboratory benches. For all practical purposes, it seems that sorted populations of 100 000 cells for pico-eukaryotes and 50 000 cells for nano-eukaryotes provide enough material for reliable clone library construction with minimal contamination and without relying on nested PCR.

If one is willing to use nested PCR, the number of sorted cells can be decreased by an order of magnitude. However, for populations below 1000 pico-eukaryotes or 100 nano-eukaryotes, contamination issues appear more drastic. More surprisingly, clone library composition seems to depend somewhat on the number of sorted cells even for quite large numbers. For example in our tests, Micromonas is only observed in pico-eukaryote populations for 10 000 sorted cells or more (Table 3). Moreover, Bathycoccus represent almost 30% of the sequences for 1000 sorted cells and disappears for larger numbers of sorted cells. Possible explanations are that for some DNA templates, once it reaches a certain concentration it can be better amplified, or alternatively that at low cell numbers (1000 and below), there is some stochastic effect either during sorting (rare cells have a low chance to be sorted) or during PCR (low concentration templates have little chance to be amplified). In order to test this stochastic effect, it would be necessary to perform separate PCR and cloning reactions on replicate populations with low numbers of sorted cells (e.g. 100). Our data suggest that at least 10 000 cells need to be sorted in order to build clone libraries exempt of stochastic biases.

Another potential problem is cross-contamination between populations. For example in October, pico-eukaryote populations yielded sequences of photosynthetic stramenopiles and cryptophytes while the PE-eukaryotes contained noncryptophyte sequences. This may suggest that some technical problems during sorting in October led to cross-contamination between the different populations. Alternatively the presence of heterotrophic sequences among the PE-eukaryotes could be due to the recent ingestion of cryptophytes by these organisms, providing them with a fluorescent signal triggering sorting.

Sorting clearly allows a better assessment of the diversity of some groups that are virtually absent in clone libraries constructed from filtered samples. This is the case for Haptophyta. While their representative pigment 19′ hexanoyl-oxyfucoxanthin is very abundant in marine waters, they are under-represented in clone libraries (Moon-van der Staay et al., 2000). This under-representation could be linked to the fact that their 18S rRNA genes have a slightly higher GC% than for other groups. In our samples, the GC% of haptophytes was significantly higher (49.2% on average) in comparison with Chlorophyta (45.9%) and Stramenopiles (44.2%). In sorted populations, haptophyte templates have probably much less competition from other groups and can be readily amplified as demonstrated here.

The existence of chimeras among sequences recovered from environmental 18S clone libraries has received some attention lately (Berney et al., 2004). The formation of such artefactual sequences often occurs within the last PCR cycles, when the concentration of primers decreases (Kanagawa, 2003). When working on sorted material, the amount of starting DNA is low and therefore the number of PCR cycles has to be increased (up to 40 here) to be able to clone the amplified material, increasing the potential for chimera formation. The use of the new software keydnatools (Guillou et al., 2008) proved to be very efficient to detect chimerical sequences (5% of all sequences from the seasonal study), even those occurring between very closely related organisms, such as the Mamiellales Micromonas, Ostreococcus, and Bathycoccus (Table S3) that are usually hard to spot. Interestingly, the highest percentages of chimerical sequences recovered were obtained for pico-eukaryotes, a population of quite low diversity. Chimeras were quite abundant for populations sorted in April and June (29% and 46% of the total sequences, respectively) for which the universal primers Euk328f/Euk329r were used. In contrast, no chimera was observed for pico-eukaryotes sorted in October and February for which amplification was performed with the primer set 373Cf/Euk329r. For nano-eukaryotes, chimeras were only observed between closely related organisms (Chrysochromulina and Phaeocystis). These data suggest (1) that chimera formation could be higher for low diversity populations containing closely related organisms and (2) that primer sets may have an influence. It was however, surprising to observe chimeras between nuclear and nucleomorph 18S rRNA genes for cryptophytes since their sequences are phylogenetically quite different.

Conclusions

Molecular analysis of flow cytometrically sorted populations appears to have many advantages over that of conventional filtered samples. It reduces the sequencing effort needed to target specific groups and provides access to deeper diversity (Shi et al., 2009). The use of dyes, such as nucleic acid stains, would certainly help discriminating more specific populations, such as dinoflagellates, which have a large genome size compared with their cell size. Despite potential biases, linked for example to the necessary preconcentration, we anticipate that, coupled with whole genome amplification as recently demonstrated for cyanobacteria (Palenik et al., 2009), our approach permits environmental metagenomic studies of marine eukaryotic microorganisms, a feat not yet possible when starting from conventional filtered samples, because the metagenomic signal is overwhelmed by bacterial sequences.

Supporting Information

Additional Supporting Information may be found in the online version of this article:

Table S1. Phylogenetic affiliation for sequences from samples used to test the effect of the number of cells sorted.

Table S2. Phylogenetic affiliation for sequences from the seasonal samples.

Table S3. List of chimerical sequences obtained for the seasonal samples.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

Acknowledgements

This work has been funded in part by PICOFUNPAC (ANR Biodiversité 06-BDIV-013). X.L.S. benefited from fellowships from the Université Pierre et Marie Curie (Paris 6) and from the China Scholarship Council (CSC) managed by the Fondation Franco-Chinoise pour la Science et ses Applications (FFCSA). We are indebted to Peter von Dassow for helping us to improve the final version.

Footnotes

  • Editor: Riks Laanbroek

References

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