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Keywords:

  • Fluorescence in situ hybridization;
  • EUB338;
  • Sensitivity;
  • Quantitative review

Abstract

  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Materials and methods
  5. 3Results
  6. 4Discussion
  7. 5Conclusion and future research directions
  8. Acknowledgements
  9. References

Fluorescence in situ hybridization (FISH) is widely used to describe bacterial community composition and, to a lesser extent, to describe the physiological state of cells. One of the limitations of the technique is that the effectiveness of the detection of target cells appears to vary widely. Here, we present a quantitative review of published reports on the percentage of cells detected using the common EUB338 probe (%Eub) in aquatic ecosystems. The %Eub varies from 1 to 100% in the different published reports, with an average of 56%. There is a methodological component in this variation, with a significant effect of the fluorochrome type and the stringency conditions of the reaction. But there is also a strong environmental component, and the type of ecosystem and dominant phylogenetic group significantly influence %Eub. We argue that the optimization of the FISH protocol to describe the phylogenetic composition of bacterial assemblages will probably lead to techniques that are not effective to describe the physiological state of cells.


1Introduction

  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Materials and methods
  5. 3Results
  6. 4Discussion
  7. 5Conclusion and future research directions
  8. Acknowledgements
  9. References

The development of fluorescence in situ hybridization (FISH) over the last decade has had a large impact on the way environmental microbiologists approach their research. This methodology is now widely used by many laboratories worldwide to describe the temporal and spatial distribution of aquatic bacteria, and the specific roles of microbes in biogeochemical cycles and food web dynamics [1–3]. Although it has gained widespread acceptance in the scientific community, FISH has technical and conceptual problems, the most evident being its effectiveness in detecting the specific cells that are targeted with oligonucleotide sequences.

As an example, most studies employ at least one probe targeting the domain Bacteria, in addition to more specific probes. In theory, the number of cells detected with the probe specific for Bacteria should be roughly equal to the total bacterial count in samples that contain a low proportion of Archaea, such as most lake and coastal ecosystems. However, this expectation is not always supported by the data in literature. Cells detected with the eubacterial probe EUB338, perhaps the most commonly used probe in aquatic studies, vary from 1% to 100% of the total bacterial counts (see Section 3.1). The sources of this large variability in the proportion of cells detected have never been investigated in depth. It is clear from the published literature that the various protocols used to perform FISH often yield wide-spreading results (e.g. [4]), so the variation in the proportion of cells detected using FISH may simply be the result of methodological artifacts with little or no ecological significance.

On the other hand, it is also possible that the proportion of cells that can be detected with oligonucleotide probes may be linked to variations in the physiological condition of the cells [5]. Several papers have shown that, at least in bacterial cultures, there is a link between the growth rate and the physiological condition of cells, their rRNA contents, and the detection of these cells using oligonucleotide probes [6–8]. The hypothesis that detection using the FISH protocol is directly related to the metabolic state of the cells, and that therefore FISH may yield useful information on their physiology (e.g. in sediment [9]), has seldom been assessed for natural communities, but may explain some of the observed variability in the proportion of cells detected with this method. Another aspect that has not been well investigated, is whether different phylogenetic groups have intrinsically different detection thresholds and therefore react differently to the same FISH protocol. If there are indeed links between detection by FISH and bacterial activity or composition, it is also conceivable that, using FISH for detection, there may be systematic differences in the proportion of cells between ecosystems that differ in trophic state or community composition.

A large body of literature has accumulated in journals on aquatic environmental sciences since the inception of FISH in microbial ecological studies, approximately a decade ago. Here we have quantitatively put together the existing data on the use and application of FISH in aquatic science. Our aims were, first, to quantify the variability in the detection of target cells using FISH in published reports, and second, to explore some of the factors that underlie this variability and more specifically, to partition the variability of FISH between environmental and methodological factors. We explore whether there are systematic differences in the performance of FISH between ecosystems, and whether there is any evidence that cell activity or the phylogenetic composition plays a role in the performance of the protocol. The latter both have profound implications on the effectiveness of FISH as a descriptor of either bacterial phylogenetic composition or physiological state.

2Materials and methods

  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Materials and methods
  5. 3Results
  6. 4Discussion
  7. 5Conclusion and future research directions
  8. Acknowledgements
  9. References

2.1The probe EUB338 to assess the sensitivity of FISH

A wide variety of probes is currently being used to examine natural bacterial communities, all of which target different phylogenetic levels. It is virtually impossible to analyze the effectiveness of hybridization of most specific probes within complex bacterial assemblages, because the actual number of target cells is unknown. Perhaps the only exception are the universal or bacterial probes: it is possible to scale the results obtained with the bacterial probes to the total bacterial counts, provided that the relative abundance of Archaea in the sample remains low. It was originally thought that Archaea were confined to specialized habitats, characterized by very high or low temperatures, salinities and pH, and to strictly anaerobic niches [10]. However, more recent studies based on the comparison of 16S rRNA genes and FISH have revealed the widespread occurrence of Archaea in habitats that range from freshwaters to marine sediments (e.g. [11–13]). Archaea tend to be numerically important in deep ocean waters [14] and occasionally in coastal waters as well [4], but in most surface water samples they represent less than 2% of the total cell count (see below). It is reasonable to assume that in most surface water samples, counts obtained using the bacterial probe should approximate those obtained with DAPI or other general bacterial stains.

We have thus focused on published reports of bacterial counts performed with the common bacterial oligonucleotide probe EUB338, in order to assess the factors affecting the performance of the FISH protocol. It is possible, however, that other probes behave differently. For instance, the position of the probe's target has a strong influence on fluorescence and hence detection [15], and therefore, EUB338 should be regarded only as an index to assess the performance of FISH.

2.2Data collection and statistical analysis

A comprehensive literature search on aquatic science sources was performed, specifically targeted to papers that utilize FISH and report on the proportion of cells hybridized with the probe EUB338, and that also report complementary data on factors that may affect the variability of the hybridization. Although researchers do acknowledge that environmental and methodological factors influence the FISH performance (see [16,17] for review), the relevant information is often not reported in detail, and it is also difficult to gather sufficient ancillary data to perform statistical analysis. We have chosen to perform statistical analyses on factors for which we could gather data from at least three separate papers. As a result, we had to restrict our analysis to three environmental factors (type of ecosystem, bacterial growth rate and dominant phylogenetic group, i.e. the subdivision of Proteobacteria, the Cytophaga-Flavobacterium group and the Actinobacteria) and six methodological factors (fixative, fluorochrome, hybridization temperature, formamide concentration in hybridization buffer, NaCl concentration in wash solution and counting method), for which we could collect sufficient data (Table 1). Of the 151 reviewed publications reporting FISH data, 51 contained complementary data that could be used in our analysis. From the chosen manuscripts, we extracted 105 observations of the percentage of Bacteria that are reported in Table 1, together with ancillary data. Note that for each data point of %Eub, we could seldom collect a full complement of data for the eight variables that we analyzed. The number of data points thus varies with the different combinations of variables that were analyzed. There are other important factors not analyzed in this study, due to lack of data. For instance, the probe sequence, the nature of the probe or the number of different probes used have been reported to influence the detection of hybridized cells [16,17].

Table 1.  Proportion of Bacteria (mean (S.D.)) hybridized with EUB338 in different ecosystems, and methodological and environmental factors that may affect FISH efficiency
  1. na: Not applicable (method not performed; parameter not measured or not communicated); IA=image analysis; DO=direct observation; PF: paraformaldehyde; F: formaldehyde; AL+F=alkaline Lugol's solution+formaldehyde; ETH=ethanol; DIG: digoxigenin; TRITC: tetramethylrhodamine-5-isothiocyanate; FLUOS: 5(6)-carboxyfluorescein-N-hydroxysuccinimide-ester; BODIPY: 8-chloromethyl-4,4-difluoro-1,3,5,7-tetramethyl-4-bora-3a,4a-diaza-s-indacene; CY3: indocarbocyanine; FITC: fluorescein isothiocyanate; ALEXA: Alexa Fluor®488 carboxylic acid, succinimidyl ester, dilithium salt; CT: carboxytetramethylrhodamine-5-isothiocyanate; α: α-proteobacteria detected with the probe Alf968; β: β-proteobacteria detected with the probe Bet42a; γ: γ-proteobacteria detected with the probe Gam42a; Cf: cytophaga-flavobacterium cluster detected with the probe CF319a; δ: δ-proteobacteria detected with the probe SRB385; Act: actinobacteria detected with the probe HGC69a. N: Number of data; Fix: fixative employed; Fd and NaCl: formamide and NaCl concentration in the hybridization buffer and wash solution, respectively; θ: hybridization temperature; Cm: counting method; Dom: dominant phylogenetic bacterial group in the ecosystem; Arch: proportion of the Archaea detected with the probe Arch915 (mean (S.D.)).ud=unpublished data.

  2. aCorrected by subtracting the fraction of autofluorescent and non-specifically stained cells as determined with the negative control probe NON338. When not specified, data are percent of total cell counts determined by DAPI staining.

  3. bCalculated from their data.

  4. cAverage of four seasonal means.

  5. dPercent detection compared to nucleic acid stain SYTO-15.

  6. eAuthors did not find any difference between stains used.

  7. fPercent detection compared to nucleic acid stain Ethidium bromide -EtBr-.

  8. gPersonal communication.

Ecosystem typeEubacteriaa (%)NFixFd (%)NaCl (mM)θ (°C)FluorochromeCmDomArch (%)References
Culture
Rhodopseudomonas palustris95 (1)4PF0050FITCIAnana[7]
Pseudomonas fluorescens100naPF090045TRITCDOnana[43]
Escherichia coli100naPF090045TRITCDOnana[43]
North Sea off shore water82 (7)4FnananaCY3DOγna[18]
Continuous flow cultureb85 (12)27AL+F090046CY3DOαna[55]
Continuous flow culture (interdidal sediment)b821ETH354048CY3DOCFna[65]
Continuous flow culture (interdidal sediment)b411ETH354048CY3DOγna[65]
            
Drinking water
Biofilm (water distribution system, Berlin)b67 (17)20PF202546FLUOSDOβna[66]
Biofilm (water distribution system, Stockholm)68 (8)naPF394044TRITCDOna0[29]
Planktonic cells (water distribution system, Stockholm)37 (8)naPF394044TRITC, FITCDOna0[29]
Planktonic cells (water distribution system, Berlin)23 (4)naPF202546FLUOSDOβna[66]
            
Activated sludge
Wastewater treatment (Germany)b79 (14)2PF2090046TRITC, FLUOS, DIGIAβna[30]
Wastewater treatment (Germany)b76 (6)4PF2090046TRITC, FLUOS, DIGIAβna[31]
Wastewater treatment (Germany)81naETH0146CT, FLUOSIAβ0.5[32]
Wastewater treatment, aerobic basin (Germany)83 (8)naPF2090046TRITC, FLUOS, DIGnaβna[67]
Wastewater treatment, anaerobic basin (Germany)78 (10)naPF2090046TRITC, FLUOS, DIGnaβna[67]
            
Sediment
Artic (Svalbard)43naF10146CY3IACF2[68]
Rotmoos glacial streamb49 (9)2PF3590046CY3DOβ4.7(1)[11]
Refinery soil (Germany)b22 (12)23PF3010242CY3DOδna[56]
Wadden Sea (Beach)b44 (6)8F35na46CY3DOCF1 (0.4)[53]
Wadden Sea (Mud)b48 (13)10F35na46CY3DOCF0.5 (0.5)[53]
Pristine forest Hau (silty clay)37naETH3010242CY3DOα<1[57]
Pristine forest Hau (silty clay)41naPF3010242CY3DOα<1[57]
Soil (sand and clay)1naPFna90045TRITCDOnana[69]
Wastewater treatment (sand filter)b75naPF0na46FLUOS, CT, CY3DOβ<1[33]
Wastewater treatment (sand filter)b50naPF0na46FLUOS, CT, CY3DOβ<1[33]
            
Suspended aggregates
Constance Lakeb49 (18)45PF2090046TRITC, FLUOSeDOβna[36]
Constance Lakeb76 (12)31PF2090046TRITC, FLUOSe,gDOβ0[35]
Constance lakeb78 (15)6PF2090046TRITC, FLUOSeDOβna[34]
Elbe Riverc75 (3)4na2025046CY3DOβ<1[70]
            
Ice and snow
Glacier ice561PF3590046CY3DOβ4[11]
Snowb46 (6)2PF090046CY3DOβna[21]
lake iceb75 (2)2PF090046CY3DOβna[21]
            
Freshwater (lake, pond and river)
Gossenköllesee lake51 (4)1PF35546CY3DOβ6[2]
Gossenköllesee lake55 (12)13PF0146CY3DOβ1 (3)[1]
Gossenköllesee lake708F090046CY3IAβna[71]
Gossenköllesee lake79 (13)13PF35546CY3DOActna[72]
Gossenköllesee lakeb63 (1)2PF090046CY3DOβna[21]
Gossenköllesee lake641PF357046CY3DOβna[73]
Piburger See lake521PF357046CY3DOβna[73]
Herrensee lake291PF357046CY3DOβna[73]
Baikal lake44 (5)1PF35546CY3DOβ0[2]
Baikal lake (central basin)741PF35546CY3DOCFna[72]
Baikal lake (south basin)711PF35546CY3DOActna[72]
Großer Ostersee lake46 (10)1PF35546CY3DOβ0[2]
Großer Ostersee lake601PF357046CY3DOβna[73]
Großer Schwaigsee lake461PF357046CY3DOβna[73]
Cadagno lake46 (9)1PF35546CY3DOβ0[2]
Cadagno lake53 (7)2PF357046CY3DOβna[73]
Cadagno lake53–61nananana46CY3DOγna[74]
Fuchskuhle lake692PF35546CY3DOActna[72]
Sau reservoir431AL+F090046CY3DOCFna[20]
Ŕímov reservoirb47 (4)9PF090046CY3DOβna[23]
Ŕímov reservoirb77 (2)6PF090046CY3DOβna[19]
Aquaculture pond67 (14)3PF090045CY3DOnana[43]
Aquaculture pond45 (14)3PF090045CY3DOnana[43]
Aquaculture pondb66 (7)2F0146CY3IACFna[75]
Natural pond70 (5)6F0146CY3DOβ and CF<1[76]
Greenhouse pond35 (2)3PF090045CY3DOnana[43]
Acide mine drainage streamb92 (8)15PF201537CY3DOna<5[77]
Glacial Stream (Biofilm)b38 (9)15PF3590046CY3DOβ2.5(2.2)[11]
Choptank River29 (2)3F301046ALEXADOβ1ud
Choptank River33 (19)12F301046BODIPYDOβ1[25]
Pocomoke River40 (21)4F301046BODIPYDOβ2[25]
Ter River591AL+F090046CY3DOCFna[20]
Saskatchewan River (biofilm)d7920F2025046CY3IAna0[78]
Kitahashi River20naPFnana37FITCDOCFna[26]
            
Estuary, bays and tidal marshes
Dennis township marsh52 (14)7F301046BODIPYDOvary1ud
Horn Point marsh77(30)52F301046CY3DOvary4ud
Choptank estuary25(29)7F301046ALEXADOCF5ud
Choptank estuary31 (22)36F301046BODIPYDOCF0.5[25]
Pocomoke estuary17 (10)13F301046BODIPYDOCF1[25]
Monie bay35 (21)32F301046BODIPYDOvary2ud
Delaware Bay80 (9)4Fna10242CY3IAnana[3]
Delaware Bay34 (2)2F301046BODIPYDOα1ud
Chesapeake Bay25 (13)13F301046BODIPYDOα1ud
Chesapeake Bay20 (10)5F301046ALEXADOα4ud
            
Nearshore seawater (<5 miles from the shoreline)
Pacific (Playa del Rey)75 (5)3F03043TRITCIAnana[5]
Pacific (Playa del Rey)22 (4)3F03043TRITCIAnana[5]
Pacific (Santamonica)78 (4)3F03043CY3IAnana[5]
Pacific (del Rey Harbor)53 (3)3F03043CY3IAnana[5]
Pacific (Palya del Rey)42 (3)1PF35546CY3DOCF2[2]
Pacific (Point Sur)b80 (12)11F3010246CY3DOCFna[79]
Pacific (Monteray bay, Spring)70naF357046CY3DOnana[4]
Pacific (Monteray bay, Winter)38naF357046CY3DOnana[4]
Atlantic (Indian River inlet)80 (9)4Fna10242CY3IACFna[3]
Antarctic Peninsulab54 (9)16FnananaFITCDOna9 (3)[54]
English Chanel261PF357046CY3DOCFna[80]
Plymouth Soundb701PF357046CY3DOCFna[80]
Fjord (Mariager)f50naPF351537FLUOSIAnana[81]
Fjord (Mariager)f20naPF351537FLUOSDOnana[81]
Golfo Dulce (Costa Rica)15naPF351537FLUOSIAnanaud
Mediterranean Sea92 (1)2PF090046CY3DOγna[22]
North sea (Helgoland Roads, Summer)66naF357046CY3DOnana[4]
North sea (Helgoland Roads, Winter)43naF357046CY3DOnana[4]
Pacific (san Pedro Chanel)36 (3)3F03043CY3IAnana[5]
            
Continental shelf (5–50 miles from the shoreline)
North Sea39 (3)1PF35546CY3DOCF3[2]
North Seab73 (8)4FnananaCY3DOCFna[18]
North Sea (February)591F03046CY3DOαna[82]
North Sea (August)55 (15)1F03046CY3DOCFna[82]
North Sea (September)55 (15)1F03046CY3DOCFna[82]
North Sea (November)55 (15)1F03046CY3DOγna[82]
Antarctic96 (3)1PF35546CY3DOCF0[2]
Antarcticb81 (14)3PF357046CY3DOCF0[83]
Baltic Sea72 (8)1PF35546CY3DOβ0[2]

When incubations or experimental treatments were used to address ecological issues (e.g. nutrient enrichment or grazing effect on the phylogenetic structure [18,19]), only the first time point was recorded except when the effect of bacterial growth rate on FISH performance was adressed. In this case, we used all the data reported, because both bacterial activity and FISH are rarely measured simultaneously. For instance, we used data from Gasol et al. [20], who performed transplant experiments in an eutrophic reservoir and followed bacterial activity and FISH during 50 h. We found and used six studies that used different protocols to estimate bacterial metabolism and composition and targeted very different ecosystems [11,19–23], in order to address the effect of the growth rate on the %Eub. They used isotope incorporation rates to estimate the bacterial activity with either [3H]leucine or [3H]thymidine. An index of bacterial growth rates was obtained by dividing isotope incorporation rates by bacterial abundance. This index avoids problems in converting isotope incorporation rates and cell abundance into biomass units [24].

Statistical tests were performed with JMP® statistical software (SAS Institute Inc.) to explore which environmental and methodological factors best explained the variability in the effectiveness of FISH to detect target Bacterial cells, i.e. the variability in the %Eub. ANCOVA was used to simultaneously examine the effects of categorical (type of ecosystem, dominant phylogenetic group, fluorochrome, fixative, counting method) and continuous (hybridization temperature, formamide concentration in hybridization buffer, NaCl concentration in wash solution) variables on the %Eub. Therefore, the reported least-squares means for each level of the categorical variables correspond to the estimated %Eub, holding the continuous variable constant at their mean value. The arcsin transformation of the %Eub (often useful when dealing with fractions) did not make any difference in the statistical results and was therefore not employed in this study. When variables were categorical or continuous, the analysis corresponded to a one-way or a regression analysis, respectively. Collinearity between factors has been tested before analysis. Analyzed alone, the formamide and salt concentrations were significantly correlated (R2=0.29, P<0.001, N=87) as well as the ecosystem type and the dominant phylogenetic group (ChiSquare Pearson test, P<0.0001, N=73). Bacterial growth rate was not included in the overall test with the other factors but rather analyzed independently, because the data set was very limited and would have biased the entire analysis.

3Results

  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Materials and methods
  5. 3Results
  6. 4Discussion
  7. 5Conclusion and future research directions
  8. Acknowledgements
  9. References

3.1Variability in the percentage of EUB observed

The number of target cells detected with the oligonucleotide probe EUB338, ranged from 1% in soil to 100% of the total bacterial count in enriched culture, with an overall median of 56% (S.D.: 22%; N=105) (Table 1). This large range implies that FISH is extremely sensitive to variations in either the methodological aspects of the protocol or the environmental conditions. There is some evidence to suggest that this variation cannot be attributed to the contribution of Archaea. From over 50 studies selected for analysis, [41] used the probe targeting Archaea and reported, on average, that less than 2% of total cells were Archaea (S.D.: 1.8%).

3.2Factors influencing the sensitivity of FISH

Standard parametric statistic showed that when considered together, the eight variables mentioned above (not including specific growth rate) explained 72% of the variability observed in the detection of Bacteria with the EUB338 oligonucleotide probe (N=60, P<0.001). In this overall model, four factors appeared to be significant, and two of these alone (ecosystem type and fluorochrome) explained most (47%) of the variability in the %Eub (P=0.001 and P=0.009, respectively). The formamide and salt concentration (in the hybridization buffer and wash solution, respectively) were significant in this model (P=0.02 and 0.01, respectively), and the dominant phylogenetic group was only marginally not significant (P=0.06). The other factors were clearly not significant (P>0.1).

Among the ecosystem type, estimated mean %Eub higher than 60% detection was found in culture, activated sludge and marine snow (Fig. 1A). Conversely, hybridization carried out in ice, snow, drinking waters, estuaries, marshes and the sediments gave an estimated mean of %Eub lower than 40%. In nearshore and continental shelf samples, the estimated mean of %Eub was around 50%. Among the different fluorochromes used, Cy3 gave the highest estimated mean of %Eub, being 68% (Fig. 1B). The lowest percentages were found associated with TRITC and ALEXA (<40%), while FLUOS, FITC and BODIPY provided similar estimated means of %Eub, being 55%, 53% and 48%, respectively.

image

Figure 1. Estimated proportion of natural bacterioplankton cells hybridized with the oligonucleotide probe EUB338 as a function of (A) ecosystem type and (B) fluorochrome bound to the probe. Cult: Culture. Slud: Activated sludge. Shelf: Continental shelf. Nearshore: Nearshore seawaters. Aggregate: Suspended aggregates. Fresh: Freshwater (river, pond, lake). Ice: Ice and snow. Drink: Drinking water. Estuary: Estuary, bays, and tidal marshes. Sed: Sediment. Error bars: Estimated standard error.

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As explained earlier in the text, bacterial growth rate was not included in the overall model but tested independently, so it is only possible to infer the importance of the physiological state of the cells relative to methodological aspects. Data from each individual study did not show any relationship between the specific incorporation rates and the %Eub, regardless of the isotope used. When all [3H]leucine incorporation data were combined, cell activity did not explain any of the variance in %Eub (Fig. 2A). In this data set, there was a striking difference between bacteria from a sediment biofilm habitat and from bacterioplankton. The sediment biofilm samples appeared to be characterized by extremely high incorporation rates relative to all other samples, but a relatively low %Eub (Fig. 2A). Beside the type of sample (benthic vs. pelagic), the major difference between Battin et al. [11] and the other studies used in Fig. 2A was the hybridization stringency employed. Battin et al. [11] used 35% v/v formamide in their hybridization buffer, whereas the other studies did not use any formamide. If only bacterioplankton are considered, the data do suggest a positive relationship between the specific incorporation rates and %Eub (Fig. 2A). The relationship between cell-specific incorporation rate and %Eub was stronger when [3H]thymidine incorporation rates were considered (R2=0.74, P<0.0001, N=43; Fig. 2B). Both plots suggest a leveling off at the 95%Eub for high growth rates, but there was still a large range of %Eub at low bacterial growth rates.

image

Figure 2. Relationship between the proportion of bacterioplankton cells hybridized with the oligonucleotide probe EUB338 and the specific rates of isotope incorporation of the bacterial assemblage. A: Cell-specific rate by [3H]leucine uptake ([11,21,22], Gasol pers. comm.). B: Cell-specific rate by [3H]thymidine uptake [19,22,23].

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4Discussion

  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Materials and methods
  5. 3Results
  6. 4Discussion
  7. 5Conclusion and future research directions
  8. Acknowledgements
  9. References

Although the use of FISH is now widespread and its application varied, the method has proven limitations. Bacterial counts obtained by FISH with a universal probe seldomly equal the counts obtained with nucleic acid labeling. Our literature review shows that on average, 56% of the all cells present in a sample is detected using the probe EUB338. Furthermore, the detection of target cells with FISH varies enormously, from 1% to 100%. Although this range reflects the variability across 51 studies covering a wide range of study sites and methodological differences, substantial variability has also been reported within individual studies (e.g. [1,4,25]). This variability is often reported, but not fully investigated. In the sections below, we discuss the main factors that seem to contribute to this variability, as well as other factors that were not significant in the statistical analysis, but are nevertheless relevant.

4.1Methodological factors linked to FISH performance

The type of fluorochrome bound to the probe explained a significant portion of the variation in %Eub cells detected using FISH. In the early nineties, the most commonly used dyes for FISH in microbiology were fluorescein and rhodamine-derivates (e.g. FITC, FLUOS and TRITC; expending formula in Table 1). Their low fluorescence intensity per mole fluorochrome, and their sensitivity to pH and bleaching limits their application, especially in the case of cells with low abundance of rRNA targets [26]. Fluorescent dyes with high quantum yields and extinction coefficients such as the cyanine series Cy (expending formula in Table 1) are increasingly being used to overcome these problems. The latter are superior to the classical fluorescein dyes, because they result in significantly brighter staining and are very stable to photobleaching [27,28]. These differences are reflected in our analysis, because the choice of fluorochrome significantly affected the performance of the FISH protocol, with Cy3 yielding the highest average %Eub.

Our results strongly suggest that the use of different fluorochromes will yield widely different results in the same samples, but the actual patterns obtained with a given fluorochrome within a study will probably still be robust. We also caution against the use of probes conjugated with different fluorochromes (e.g. [29,31–36]) within the same study. There seems to be a general agreement on the benefit of fluorochrome with high quantum yield, since 61% of the studies used Cy3, against 27% that used FITC, FLUOS or TRITC. However, the use of high-performance fluorochrome such as Cy3 has a tendency to increase the background signal caused by cross-reaction with other specimens, inorganic particles and detritus (T.B. and P.d.G., pers. obs.). Improving the hybridization and wash conditions and the adjustment of stringency can reduce this drawback.

Both formamide and sodium chloride (in the hybridization buffer and the wash solution, respectively), significantly influenced the performance of FISH. These two chemicals are used to adjust the stringency conditions of the hybridization and post-hybridization steps for proper annealing of the oligonucleotide probes to the target sequence [37]. Formamide decreases the melting temperature by weakening the hydrogen bonds, thus enabling lower temperatures to be used with high stringency. The salt immobilizes hybrid molecules and is used instead of formamide in order to reduce the amount of toxic waste [38]. Optimizing the hybridization stringency conditions is a trade-off between an optimal probe sensitivity and specificity. Increasing the formamide concentration increases the specificity of the hybridization, but further addition results in a drastic drop of bound probe and signal intensity [39,40]. In theory, the optimal formamide concentration depends mostly on the structure of the oligonucleotide sequence and the hybridization temperature, and this should not vary greatly among studies that use the same sequence and hybridization conditions. Yet, this is not the case at all. For example, many of the studies reviewed here used a hybridization temperature of 46°C, but the formamide concentration used ranged from 0 to 35%. Likewise, there was a range from 0 to 900 mM NaCl in the wash solution used in these same studies. The results presented here suggest that these methodological differences among studies result in significant differences in the detection of target cells and render the results less comparable among studies.

The other methodological factors such as the hybridization temperature, the fixative and the counting methods, did not have a significant effect on the proportion of target cells hybridized by EUB338. This lack of significant effect must be interpreted with caution, however. Studies that have examined these factors individually have reported important effects on the hybridization efficiency [15–17]. Our analysis suggests that the type of fixative, for example, may be secondary to other methodological aspects, such as formamide concentration, or alternatively, that there may be insufficient data to perform a meaningful comparison, as is the case with the counting method. The lack of a significant effect of these methodological factors in a combined analysis such as the one we carried out, should not be interpreted as a suggestion that these factors are unimportant and therefore be overlooked when developing a FISH protocol.

4.2Environmental factors that influence FISH performance

The ecosystem type was the factor that explained the largest amount of variability in %Eub. We aggregated the data into 10 Ecosystem types, which vary greatly in trophic state, from the hyper-eutrophy of an enriched culture to the oligotrophy of drinking water. It is conceivable that if researchers tended to apply a certain variation of the protocol in specific ecosystems, the strong influence of the latter may reflect differences in the protocol. An inspection of Table 1, however, reveals that there are no systematic patterns in terms of protocols used within a given system.

The influence of ecosystem type becomes obvious when the extremes are analyzed. Systems that have little resource limitation, such as laboratory cultures, biofilms and sewage sludge, tend to yield a high percentage of hybridized cells. Not surprisingly, these are the systems where the first successful applications of FISH in natural aquatic systems were performed [30,42–44]. But other aspects of the patterns in %Eub across different systems are more difficult to explain simply on the basis of productivity or nutrient enrichment. For example, the %Eub in continental shelf waters is, on average, higher than in nearshore marine waters, which are typically more productive. Freshwaters on average appear to yield somewhat lower %Eub than marine samples in general, although there are several eutrophic lakes, reservoirs and ponds included in the data set (Fig. 1A). Finally, coastal bays and marshes, as well as marine and freshwater sediments, tend to yield lower %Eub than all other systems, although the latter also tend to have high levels of metabolism [25]. The low detection in sediments (and to a lesser extent in other samples with high particulate loads, such as some estuarine and marsh samples, Fig. 1A) could be partially attributed to the difficulty in extracting the cells and distinguishing them among the particles [45], and there are reports on problems associated to the detection of cells even in sediments characterized by a high bacterial activity (Fig. 2A) [11]. However, the differences between marine and freshwater systems, for example, are more difficult to explain simply on a methodological basis. The absence of a clear relationship between the perceived productivity of a system and the FISH signal may result from the fact that a system's trophic status and the observed bacterial growth rate and biomass are often relatively uncoupled [20,25,50]. Alternatively, these differences could suggest either an almost complete lack of relationship between the level of bacterial activity and %Eub, or alternatively, that these ecosystem effects may be driven by intrinsic differences in phylogenetic composition across ecosystems [2].

Different molecular approaches are now converging to show that there are major differences in the phylogenetic composition of the bacterial assemblages among different aquatic systems [2,46]. In this regard, the dominant phylogenetic group was marginally not significant in explaining the variability in the %Eub cells detected with FISH. It is possible that intrinsic differences in cell activity or rRNA contents between phylogenetic groups could influence the capacity to detect these cells using FISH, and explain the strong effect of ecosystem type on %Eub. Such differences, for example, have been shown between Sphingomonas sp. and Vibrio sp., two species that are abundant in marine waters [47] and that belong to two different phylogenetic groups, i.e. the α- and γ-subclass of the Proteobacteria[48]. These two species have different numbers of copies of the rRNA operon, leading to intrinsic differences in the number of potential targets [47]. There is also emerging evidence that different phylogenetic groups may be functionally distinct [3,49] and have intrinsically different levels of single-cell activity [11,19,20,21,50], which could theoretically impact their response to FISH. For example, a strong relationship was found between the abundance of β-proteobacteria, the abundance of highly active cells, and bacterial growth rate in a temperate estuary [50], suggesting that this bacterial group may be intrinsically either more active or more responsive to FISH than are other estuarine populations. However, most of the evidence to date linking phylogenetic composition to cellular activity and macromolecular contents is largely circumstantial, and the importance of the phylogenetic dominance as a factor driving the variability in FISH is one aspect that has profound implications and that needs to be further addressed.

Regardless of whether there is a link between phylogenetic composition and the response of cells to FISH, it is likely that the detection sensitivity will depend in part on the physiological state of the target cells. Faster-growing or highly active cells tend to have more ribosomes, and hence bind proportionately more probe molecules, resulting in cells hybridized with a stronger fluorescent signal [6]. This relationship has been demonstrated on bacterial strains in steady-state laboratory cultures (e.g. [8,51,52]), and is often used to explain variation in the proportion of cells detected with FISH in natural water samples [20,23,35,43,50,53–57]. However, Kerkhof and Kemp [58] showed that, in contrast to bacteria grown in steady state, there was no relationship between rRNA content and specific growth rate with bacteria in non-steady-state, and there are virtually no studies that have assessed the relationship between bacterial activity and FISH response in natural bacterioplankton communities. Gasol et al. [20], for example, showed that there was a strong correlation between the number of high-DNA cells (assumed to be more active) and the number of hybridized freshwater riverine and reservoir Bacteria, but could not find any correlation at all with the %Eub.

We attempted to assess the possible relationship between %Eub and the level of cellular activity with data extracted from six papers that reported %Eub and also bacterial growth rate determined from the incorporation of leucine or thymidine [11,19–23]. Individually, none of these data sets showed a correlation between specific rates of leucine or thymidine incorporation and %Eub. When the data sets are combined, however, there is a suggestion of a positive relationship between substrate incorporation rate and %Eub when only bacterioplankton are considered. This relationship seems much stronger for studies that have used thymidine as a tracer of bacterial production (Fig. 2B). What Fig. 2 does show, is that in assemblages characterized by high bacterial growth rates, FISH detection is also high and rapidly approaches 100%, but when community growth is slower, %Eub is not only lower on average, but also much more variable. The studies that focused on bacterioplankton ([19,21–23], Gasol pers. comm.) employed almost identical protocols, so not all the variability observed in Fig. 2 is driven by methodological differences. On the other hand, the large departure of the biofilm data [11] from all others may be an indication of methodological differences, or may suggest that different types of communities, i.e. bacterioplankton versus biofilm, may have intrinsically different relationships between specific bacterial activity and the capacity to detect cells using FISH.

The variability in the effectiveness of FISH for a given level of bacterial activity might be expected if we consider that the bulk of the ribosome pool is not required for protein synthesis, and that the ribosomes are not the limiting factor contributing to a low rate of bacterial growth. Cells with low activity might have rRNA at a sufficient concentration to yield a fluorescent signal detectable with FISH. For example, Fukui et al. [59] and Kramer and Singleton [60] concluded from their experiment on Desulfobacter and Vibrio species that between 20% and 30% of the maximum value of intracellular rRNA still persisted in starved cells, and Fukui et al. [59] showed that the limit of detection of cells (after FISH with EUB338 labeled with FITC) was reached when the rRNA content of the cells was less than 8% of the rRNA content of growing cells. Interestingly, a different pattern of physiological response to starvation has been demonstrated between the γ-proteobacteria and the Cytophaga-Flavobacterium group [61], perhaps with consequences on taxon-specific responses to FISH. In addition, it has been suggested that, besides the actual physiological state of bacteria, the physiological history of the cells appears to have a dramatic effect on the rate at which cells cease to be detected using rRNA-targeted probes [7].

The absence of a strong relationship between %Eub and cell-specific activity could be partly due to the fact that the latter may not be a good reflection of the level of activity of the cells that are actually growing and being detected by FISH. In natural bacterioplankton assemblages there is coexistence of cells in various stages of growth, dormancy and starvation, and bulk measurements of bacterial activity provide no information regarding the distribution of this activity among cells at all. It is conceivable that the relationship between %Eub and bacterial activity would be much stronger if the former were compared to the bulk activity scaled to the fraction of cells responsible for most of the community metabolism, or to the growth rate of the dominant ribotypes, rather than to the entire assemblage.

There is little question that the type of protocol used will also affect the apparent relationship between bacterial growth or metabolic rate and %Eub. For example, Alfreider et al. [21] report a relatively constant proportion of hybridized Bacteria in the slush layers of a mountain lake, in samples where bacterial production or other growth proxies varied by more than an order of magnitude. They hypothesize that this was partially the consequence of improved methodology, especially the superior signal strength of the fluorescent dye Cy3 they used. Another example of how methodology may influence the relationship between cell activity and %Eub is provided by Pernthaler et al. [4]. In this study, the authors compared the performance of the common EUB338 probe conjugated with CY3 with a polynucleotide probe with multiple fluorescein or CY3 conjugates, in samples from the North Sea. The latter probes were shown to be significantly superior in terms of detection of target cells relative to EUB338. Interestingly, the polynucleotide probes appeared to be much less sensitive to changes in the level of bacterial activity (that most likely occurred during the seasonal cycle) than the conventional EUB338 probe, which seems more dependent on seasonal changes in cell activity. The conclusion here is that, the more effective the protocol is to detect target cells, the less effective the protocol may become to assess the physiological state of the cells.

5Conclusion and future research directions

  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Materials and methods
  5. 3Results
  6. 4Discussion
  7. 5Conclusion and future research directions
  8. Acknowledgements
  9. References

The effectiveness of FISH, i.e. the proportion of the target cells that can be detected using FISH, is crucial from both a phylogenetic and a physiological point of view. It is important to the former, because ineffective hybridization may result in an incomplete and biased description of the community composition. It is important to the latter, because the conclusions on the metabolic state of cells that may be drawn from hybridization results may simply be wrong. However, the effectiveness of FISH (at least for the detection of cells with the EUB338 probe) is still very variable and this variability is partially explained by methodological factors. It is important not only to improve the sensitivity of the FISH protocol, but also to attempt to standardize this protocol, so that the increasing volume of published results is broadly comparable and can eventually be pooled to detect large-scale patterns in phylogenetic structure.

Not all the variability in %Eub appears to be the result of methodological artifacts, and there seem to be systematic differences between major ecosystem types, driven perhaps by differences in the level of single-cell bacterial activity among systems and even among phylogenetic groups. Across a broad environmental gradient, there seems to be a positive relationship between bacterial specific metabolic rates and the capacity to detect these cells with FISH, but this relationship disappears when narrower ranges of bacterial activity are considered. All these hypotheses, however, remain to be tested. In this regard, the application of FISH as a physiological index (e.g. [9,50,62]) has potential, but will require substantial empirical validation with natural bacterial assemblages. We argue, however, that the different applications of FISH require different methodological approaches. From the point of view of assessing bacterial phylogenetic structure, it is important to maximize the proportion of cells that can be detected, regardless of their physiological condition. For example, the use of polynucleotide probes [4], multi-labeled probes [49] and RNA amplification [63,64] may all be effective in improving the protocol for the phylogenetic description of the community. But from the point of view of assessing bacterial single-cell activity, it is important to capture the variability in cell physiology, and therefore maximizing cell detection may not necessarily be the most effective approach.

Acknowledgements

  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Materials and methods
  5. 3Results
  6. 4Discussion
  7. 5Conclusion and future research directions
  8. Acknowledgements
  9. References

We thank Yves Prairie and David Bird for their statistical advice, Pep Gasol for constructive criticism on an earlier version of the manuscript and Jude Apple for his help collecting the data. We are grateful to three anonymous reviewers for useful comments and to H.S.C. for encouragement. This work was supported by grants from NSF (USA) and NSERC (Canada).

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  2. Abstract
  3. 1Introduction
  4. 2Materials and methods
  5. 3Results
  6. 4Discussion
  7. 5Conclusion and future research directions
  8. Acknowledgements
  9. References
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