raman spectroscopic detection of nickel impact on single streptomyces cells – possible...

Research Article

Received: 26 August 2011 Revised: 2 November 2011 Accepted: 2 November 2011 Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI 10.1002/jrs.3126

Raman spectroscopic detection of Nickelimpact on single Streptomyces cells – possiblebioindicators for heavy metal contaminationAngela Walter,a Susanne Kuhri,a Martin Reinicke,b Thomas Bocklitz,a

Wilm Schumacher,a Petra Rösch,a Dirk Merten,c Georg Büchel,c

Erika Kotheb and Jürgen Poppa,d*

Heavy metal contamination of soil has an immense impact on the surrounding environment, such as the ground water, andhence, has become an important issue within bioremediation. Therefore, heavy metal contamination has to be determined
preferably cost-efficiently, rapidly, and reliably. Here, soil bacteria of the genus Streptomyces are used as bioindicators forheavy metal contamination investigated via micro-Raman spectroscopy. A single cell approach is studied to avoid time-consuming culturing and plate counting. Bacteria of Streptomyces galilaeus were incubated in Ni2+ enriched media and singlecell spectra were recorded. Supervised statistics linear discriminant analysis was used to evaluate the influence of the cultureage and the anion on bacterial cells, which has been determined to be minor compared with the spectral impact of Ni2+. Theidentification of the Raman spectra according to different Ni2+ concentration ranges is accomplished with a predictionaccuracy of about 88%. Therefore, we conclude that Streptomyces can be used as a bioindicator to predict Ni2+ concentrationsin the micromolar range. Copyright © 2011 John Wiley & Sons, Ltd.
Supporting information may be found in the online version of this article.

Keywords: Raman spectroscopy; Streptomyces; Ni2+ concentration

* Correspondence to: Juergen Popp, Institute for Physical Chemistry, Friedrich-Schiller-Universität Jena, Jena, Germany. E-mail: [email protected]

a Institut für Physikalische Chemie und Abbe-Zentrum für Photonik, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743 Jena, Germany

b Institut für Mikrobiologie, Friedrich-Schiller-Universität Jena, Neugasse 25,07743 Jena, Germany

c Institut für Geowissenschaften, Friedrich-Schiller-Universität Jena, Burgweg 11,07743 Jena, Germany

d Institut für Photonische Technologien, Albert-Einstein-Strasse 9, 07745 Jena,Germany

Introduction

Heavy metal contaminations have an enormous effect on theenvironment, e.g. on soil fertility, because heavy metals cannot bedegraded like organic pollutants.[1] Therefore, the contaminantshave to be extracted or stabilized by, e.g. immobilization or adsorp-tion to reduce mobility and bioavailability of the heavy metals.[2]

The current clean-up technology of contaminated areas includesremoval of the polluted soil and deposition in landfills. This impairsthe microbial diversity in the remaining subsoil and, therefore, theentire ecosystem, and is very cost intensive in addition.[3] Microor-ganisms have been implicated for bioremediation because of theirmetal adsorbing capacity at the cell wall. This, and the formation ofprecipitates, reduces the mobile and bioavailable fraction of heavymetals. Both adaptation mechanisms have been reported forbacteria[4–7] and fungi[8,9] including yeasts. At present, bioremedia-tion approaches are under investigation aimed at reduced costsand simplified clean-up procedures.

Abandoned mining areas suffer from acidification and dissolu-tion of heavy metals, which are bioavailable in their mobilized formfor microbes and plants. The mobile elements are transportedin the water phase, leading to unhampered distribution in thehorizontal and vertical direction into the surrounding, previouslyuncontaminated areas. The short-term effect of heavy metalcontamination on soil is a decrease in microbial biomass, loss ofmetal-sensitivemicrobes and changes in biochemical activities thatare fundamental for soil functions. The long-term effect areassumed to be changes in the microbial community because ofnatural selection and gene exchange to adapt to the exposure to

J. Raman Spectrosc. (2011)

the contaminants.[10,11] These shifts within microbial diversity areof interest for soil remediation, because metal resistant soilmicrobes have established mechanisms to deal with the selectionpressure, which derives from contamination. The mechanisms inplace are under investigation to be potentially implemented forbioremediation. However, remediation of soil does not only includethe removal of the contaminants, but requires in a first step thedetermination of heavy metals and the biologically active concen-trations to adapt the remediation strategy to the specific environ-mental issue. Here again, soil microbes can be envisioned as atarget for bioremediation approaches. Soil microbial populationand activity has already been claimed to be a useful indicator forsoil improvement and organic pollutant degradation, e.g. to detectarsenic contamination.[2,11–14] Gram-positive bacteria with high

Copyright © 2011 John Wiley & Sons, Ltd.

A. Walter et al.

DNA G+C content have especially been pointed out to be used asindicator for metal contaminations in soil.[10]

The genus Streptomyces exhibits a high G+C content of about70% and represents about 20% of soil bacteria populations.Streptomyces are aerobic and spore-forming actinobacteriabelonging to the Gram-positive bacteria, and they produce a widevariety of secondary metabolites. Many Streptomyces are able todegrade cellulose or chitin. Additionally, Streptomyces spp. are knownto be capable of adapting to high heavy metal concentrations.Moreover, Streptomyces spp. have been envisioned to be poten-tially utilized as bioindicators for heavy metal binding compoundsand, therefore, for heavy metal contamination[2,10] and havealready been investigated because of their adsorption abilityconcerning heavy metals like Al, Cu, U, Zn, Pb, Cd and Ni.[15–19]

Some Streptomyces species have even been able to resist up to130mM Ni2+ or 1.5mM Cd2+.[20,21] Adsorption and biomineraliza-tion have been demonstrated as mechanisms to decrease thewater soluble and, thus, bioavailable concentration of toxicheavy metal fractions by converting the metals into harmlessfractions. For Streptomyces acidiscabies E13, biomineralizationof a Ni-containing mineral has been reported.[15] Therefore,Streptomyces spp. fulfill the first requirement, i.e. surviving underabiotic induced stress, to be in the focus of interest for bioreme-diation research concerning both the heavy metal determina-tion and clean-up purpose.To estimate the biological response to heavy metal pollution,

culturing of bacteria is commonly carried out.[22,23] This requirescultivable bacteria and time-consuming plate counting todetermine the impact of contaminations.[12] Relating the recentdevelopment in microbiology to the progress in technology,rapid characterization of microorganisms is enabled.Because of the demand for reliable and fast identification

and characterization of microorganisms, Raman spectroscopy hasreceived much attention during the last few years and has beensuccessfully applied within the fields of medicine, biomaterialsand online-monitoring.[24,25] There are certain advantages wheninvestigating biological materials using Raman spectroscopy. Forexamplewater, which is omnipresent in biological and soil samples,does not contribute significantly to the Raman spectrum.[26] How-ever, when applying Raman spectroscopy in the visible range,the resulting fluorescence generally masks the Raman signal –especially when the spectra are recorded from bulk material. This,and the increased time efficiency when prior cultivation steps areomitted, are the driving forces to establish single cell measure-ments.[27] Single cell measurements have successfully been appliedfor rapid and selective identification of yeasts[28] and bacteria onspecies and strain level.[29–33] Because spectra of different bacterialspecies and strains appear to be very similar, unaided and intuitiveidentification cannot be achieved. Hence, statistical analysis shouldbe incorporated for supporting the calculation of spectral differ-ences in the fingerprint region. The spectral fingerprint involvesdifferences in bacterial composition, depending on different cultiva-tion conditions such as media, media supplements, cell age andgrowth temperature.[34–37] In accordance with this, these para-meters influence the classification. Spectra differences inducedby, e.g. an antibiotic agent[38] or thermal denaturation of bacte-ria,[39] have already been published. However, the impact ofdifferent media compositions influences or even overrides theclassification aiming at bacterial differentiation.[40] Furthermore,Raman spectroscopy showed that a manganese supplementto the medium induces the production of manganese contain-ing endospores.[41] Besides the influence of the inorganic

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supplements, organic medium pollutions can also be detectedvia Raman spectroscopy.[42] From these findings, it can be con-cluded that media composition and its resulting impact on thecell physiology can be determined via Raman spectroscopicmethods. Therefore, the investigation of soil contamination willbenefit from the characterization of microorganisms by utilizingRaman spectroscopy.

Within this study, the potential use of Streptomyces bacteria asindicators for heavy metal contamination in soils by means ofmicro-Raman spectroscopy is discussed. For this purpose, oneStreptomyces species has been chosen, which in general neitheradapts to high Ni2+ concentration nor exhibits other resistancemechanisms. This allows investigating the potential of Streptomyceslacking additional traits for coping with metals and, thus, exploringthe impact of heavy metal stress. Low Ni2+ concentrations wereused, which are relevant for heterogeneously, but lowly contami-nated landscapes – the target area of bioremediation activities. Thisis the first presentation of the application of Raman spectroscopyon single cells to identify heavy metal concentrations. This tech-nique allows to characterize the complex interactions between soilmicroorganisms and environmental parameters. To correlateRaman spectra of single cells to the heavy metal concentrationsin the growth medium, Streptomyces bacteria were cultivated inmedia with different Ni2+ content.

Experimental Section

Sample preparation

For the investigation of the influence of age on the bacteria,Streptomyces galilaeus HKI 22 (S. galilaeus) (Jena Microbial ResourceCollection) was cultured in batch cultures containing minimalmedium (0.5 g asparagine, 11 g glucose monohydrate, 0.2 g MgSO4

� 7 H2O, 0.01g FeSO4 � 7 H2O, 0.66g K2HPO4 � 3 H2O in 1 Ldistilled water) for 24, 48 and 72h, in complexmedium (10 g starch,1 g casamino acids (aMReSCO), 0.5 g K2HPO4 in 1 L distilled water)for 12, 24, 36, 48 and 72h, and in complex medium additionallycontaining 1mM NiCl2 for 12, 24, 36 and 48h. To investigatethe influence of different Ni2+ concentrations on the bacteria,S. galilaeus was incubated in complex medium with varying NiCl2concentrations (between 0.7 and 200mM) for 72h. The cultureswere incubated at 150 rpm and 28 �C. For comparison, two controlcultures – one without any addition, the other as anion control with50mM chloride (KCl) – were incubated under the same conditions.For sample preparation, a volume of 1mL liquid medium contain-ing Streptomyces biomass was collected by centrifugation andwashedwith 1mL distilled water. A single cell smear was generatedon the fused silica objective slide.

Within batch cultures, Ni2+ is immobilized by adsorption tobiomass and forms strong complexes with organic agents that arepresent in the medium. Therefore, the bioavailable Ni2+ concentra-tion differs from the original adjusted Ni2+ concentration. Mediumsamples to determine the bioavailable Ni2+ concentration were an-alyzed by inductively coupled plasma mass spectrometry (ICP-MS)at the times of sampling for the Raman spectroscopy investigation.

Instrumentation

Inductively coupled plasma mass spectrometry

The determination of Ni-concentration was carried out by applyingan inductively coupled plasma mass spectrometer (XSeries II,

1 John Wiley & Sons, Ltd. J. Raman Spectrosc. (2011)

Raman spectroscopic detection of Nickel impact on single Streptomyces cells

Thermo Fisher Scientific, Bremen, Germany). Standard solutionscontaining 0.1, 5, 10, 50 and 100mg/l Ni2+ were used for instrumentcalibration and isotopes 9Be and 101Ru were applied for driftcorrection.

For sample preparation, a volume of 1.5mL medium from thebatch cultures was obtained at the times of sampling for theRaman spectroscopic measurements. The biomass suspensionwas centrifuged and the supernatant filtrated with a pore sizeof 0.2 mm, acidified with HNO3 to a pH value of 2 and Ni2+

concentrations were determined to be: 0.71, 5.0, 6.9, 26, 47, 94,133 and 190mM NiCl2 in the culture supernatant, respectively.

Raman spectroscopy

The micro-Raman spectra were recorded with a micro-Ramansetup (HR Labram invers; Horiba/Jobin Yvon, Bensheim, Germany)combining a spectrometer with a focal length of 800mm anda 300 linesmm–1 grating with an inverse microscope (BX41;Olympus). The laser beam of a frequency doubled Nd:YAG-Laser(Coherent, Dieburg, Germany) with an excitation wavelength of532 nm was focused by a 100� microscope objective (PL FluotarL100x/0.75; Leica) with a working distance of 4.7mm onto thebacteria with an approximate diameter of the laser focus of about1mm. The net power is decreased by a grey filter down to about3–5mW on the sample. The Raman scattered light of the singlebacteria cells was collected by the microscope objective and passedthe entrance slit of the spectrometer, whose width was set to be100mm, and was detected by a Peltier-cooled, back-illuminatedcharge-coupled device camera (1024�512 pixel). The spectralresolution was approximately 10 cm–1.

Data preprocessing and chemometrical methods

The data preprocessing and the chemometrical analysis were pro-cessed in the statistical language R.[43] Within data preprocessingprocedures, baseline correction is indispensable (blue ‘singlespectrum’ and black ‘mean spectrum’ in Fig. S1), which was carriedout with a sensitive nonlinear iterative peak clipping algorithm[44]

applying a clipping filter of 4th (single (blue) and mean spectrum(black) in Fig. S1). For statistical data analysis, the CH stretchingwavenumber region from 3050 to 2830 cm–1 and the fingerprintregion from 1770 to 1145 cm–1 was chosen and vector normalized.For dimension reduction, a principal component analysis (PCA) wasperformed. For the applied statistics, the first 15 principal compo-nents (PCs) were introduced, whereas the exact number of PCswas chosen to achieve the most successful classification and toavoid overfitting. The supervised algorithm, linear discriminantanalysis (LDA),[45] was applied for classification.

Results and Discussion

In the following, the application of single cell analysis will bediscussed concerning the heavy metal stress induced by differentNiCl2 concentrations in the cultivation medium. Single cellspectra were received from one point measurements of thefilamentous growing Streptomyces bacteria. Because the meanhyphal length reaches values from 12.5 up to 35mm for differentStreptomyces species,[46–48] a spectrum derived from a laser focuswith a diameter of about 1mm represents only a small fraction ofthe cell. The procedure of recording representative single cellspectra has been established for bacteria[27,30,31,34,49,50] with sizesin the range of the laser focus. However, this has recently been

J. Raman Spectrosc. (2011) Copyright © 2011 John Wiley

transferred to the filamentous growing bacteria of the genusStreptomyces.[51]

Initially, the impact of Ni2+ on bacterial single cell Raman spectrawere investigated. Two S. galilaeus batch cultures containing bothcomplex medium, one with Ni2+ (1mM) and one without Ni2+, arecompared. The response of Streptomyces to Ni2+ is investigated incomplex medium to ensure sufficient nutrient supply and thus, toavoid the activation of secondary metabolism in the stationaryphase because of a lack of nutrients, which could otherwise possi-bly superpose the impact of Ni2+. Furthermore, the presence ofNi2+ in the bacterial culture medium causes a change in the nutri-ent availability, and the toxicity of Ni2+ also has to be considered.A Streptomyces species that does not exhibit any distinct resistancemechanism to heavy metals has been used as a model system toavoid any specific resistance mechanism that may influence metalstatus within or around the cells.

Bacterial cultures inoculated in an undisturbed system growfaster compared with those exposed to a stress situation. The differ-ent growth states induce variations in the Raman fingerprint andthus, culture age plays an important role for bacteria separationvia Raman spectroscopy.[49] Therefore, the age of the Streptomycescultures has to be investigated for this analysis to ensure that thespectra are rather classified because of Ni2+ content, and notbecause of the resulting aging differences of the cells.

Figure 1(A) comprises the preprocessed and averaged Ramanspectra of single S. galilaeus bacteria incubated in complexmedium(87 spectra, labeled by CM), complex medium with 1mM NiCl2(66 spectra, labeled by 1mMNi2+), 26mM NiCl2 (31 spectra, labeledby 26mMNi2+) and 50mM KCl (91 spectra, labeled by 50mM Cl–).The respective spectra of the bacteria incubated in different mediawithout andwith Ni2+ andwith KCl addition exhibit slight variationsof the band intensities but no additional bands appear. The statis-tical method LDA was applied to evaluate the influence of thedifferent media compositions.

Spectra of two cultures incubated in complex medium with1 mM NiCl2 and without Ni2+ addition were processed to eval-uate the abiotic stress induced by Ni2+ on the bacterial cells.Figure 1(B) comprises the two-dimensional LDA score plotconcerning this two class problem: separating the spectraderived from the medium with 1mM Ni2+ (labeled by complexmedium+NiCl2 (1mM)) and without Ni2+ (labeled by complex me-dium). Single bacterial spectra of different ages (for both culturestime points at 12, 24, 36 and 48h were investigated and addition-ally at 72 h for the culture without Ni2+) are included in the statis-tical evaluation. The LDA evaluation classified 91% of the spectraderived from Ni2+ containing medium correctly to the spectralclass containing the spectra recorded from the culture with Ni2+

addition. This is defined as sensitivity. The specificity, instead,is related to the spectra recorded from the cultures withoutNi2+ addition. In our experiment, 94% of the spectra are cor-rectly classified to the class of complex medium without Ni2+

addition. It can be concluded that the bacteria are separated intwo groups of spectra because of the presence or the absenceof Ni2+, and irrespective of the culture age. As published for otherbacterial genera and species, the changes because of age arereflected in varying ratios of DNA to protein band intensities withadditional lipid bands increasing during the aging process.[52] Theeffect of undisturbed aging (with regard to any medium supple-ment) has been investigated before for both liquid and solidmedia. The cultural aging reflected in the Raman spectrum corre-sponds to an increase of the lipid contents. These spectralchanges mask any other spectral features that are related to the

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Figure 1. (A) Raman spectra of S. galilaeus batch cultures grown in complex medium (CM), with additional 50mM KCl (50 mM Cl–), with additional 26mM(26mM Ni2+) and with additional 1mM NiCl2 (1mM Ni2+) are represented by the averaged Raman spectra for each culture. The individual Raman spectrahave been preprocessed (for details, please see ‘Experimental Section’). For visualization purpose, the spectra have been averaged and depicted with anoffset. (B) LDA score plot of the separation of S. galilaeus incubated in CM with 1mM and without NiCl2. (C) Grouping of S. galilaeus spectra that havebeen originated from cultures incubated in pure CM (Complex Medium, positions of the scores are indicated by blue cycles), in CM with 50mM KCl(+KCl, positions of the scores are indicated by red asterisks), and 26mM NiCl2 (+NiCl2 (26mM), positions of the scores are indicated by green triangles).(D) Separation of S. galilaeus grown in 26mMNiCl2 containing CM from those that have been incubated without Ni supplement (combining the spectrarecorded from the cultures grown in pure medium and those grown in medium containing 50mMKCl).

A. Walter et al.

relative DNA and protein content, which also usually change duringthe aging process.[51] The effect of aging in the spectra is moredominant when more lipids are present, and both can be mini-mized by liquid cultivation of S. galilaeus. These previous findingsmight help to explain the successful classification concerning Ni2+

presence and absence in bacteria of different ages. We reason thatchanges induced by the presence of Ni2+ in the mediumdominate the spectroscopic changes because of aging when thepresented statistic is applied. This is demonstrated in a two-dimensional scores plot, showing the single values of each LDAclass (absence and presence of Ni2+) including different coloursfor each culture age (Fig. S2). However, the spectral scores of theyounger cultures (12 and 24h) are overlapping to some extent.Even so this is minimal, the sampling time has been chosen to be72h after incubation, because the spectra are nicely separated forculture age of more than 48h. Culture age independent investiga-tion procedures bear immense advantages, because the filamen-tous Streptomyces bacteria grow by branching hyphae, formingcomplex and tightly wovenmatrices.[53] Therefore, the growth stateof Streptomyces batch cultures cannot easily be determined byoptical density measurements[54] usually used to follow the growthof single-celled bacteria, and Streptomyces cultures are generally ofextremely heterogeneous character.The addition of 1mM Ni2+ to the medium has a strong decel-

erating impact on growth behaviour of Streptomyces species,and therefore, lower concentrations of Ni2+ have been includedin the investigation as well. For this purpose, a concentration of26mM NiCl2 has been chosen. The changes induced by the


counterions, which are unavoidable when the influences ofheavy metal cations are investigated, have to be considered.The question arises whether the spectral changes are inducedrather by the metabolic changes originating from the chlorideanion, or by the nickel cation. As a reference, S. galilaeus wasgrown in complex medium with the addition of 50mM KCl (spec-tra depicted in Fig. 1(A) and labeled by 50mM Cl–), which containsalmost the same additional amount of Cl– as the batch culturecontaining 26mM NiCl2. The potassium cation was chosen becauseit is physiologically harmless and already present as a mediumcomponent in higher concentrations.

The three spectral groups (complex medium, complexmedium+KCl and complex medium+NiCl2) were introduced inthe statistic evaluation as separated classes. The LDA score plot,depicted in Fig. 1(C) contains grouping according to these threeclasses. The spectra originated from the Ni2+ culture (labeledby+NiCl2 (26mM), position of the scores indicated by green trian-gles) separate clearly from the spectra of both the control culture(labeled by Complex Medium, position of the scores indicated byblue circles) and the one containing KCl (labeled by+KCl, positionof the scores indicated by red asterisks). The group of the controlculture (Complex Medium) overlays the group comprising theCl– enriched culture (+KCl) showing clearly that the counter ionhad no comparable effect as the heavy metal. Concluding fromthe scoreplot (Fig. 1(C)), the spectra of Cl– containing medium aremore similar to the bacterial spectra of the control culture than tothose of the Ni containing culture. Thus, it can be assumed thatthe changes induced by NiCl2 are rather caused by the heavy metal



cation than by the Cl– anion. Therefore, the anion is not responsiblefor the separation according to presence or absence of NiCl2. Forfurther evaluation, the spectra of the KCl culture are combinedwith the spectra of the control medium culture to one class.Figure 1(D) comprises the results of the LDA of the two classes:medium with (labeled by Medium+NiCl2 (26 mM)) and withoutNi2+ (labeled by Medium without NiCl2). The latter spectral classcontains spectra recorded from the control medium culture andthe KCl culture. The score plot depicted in Fig. 1(D) shows a clearseparation of both groups with a sensitivity of 97% and aspecificity of 99% to detect a single bacterium grown in Ni2+

containingmedia. Thus, micro-Raman spectroscopy in combinationwith LDA is capable of differentiating between bacteria sampledfrommediumwith andwithout Ni2+ supplementation independentof the considered (12 to 72h) cultivation time or of the counterionCl–. Additionally, heavy metal and salt stress can be differentiatedby the application of Raman spectroscopy in combination withthe statistical algorithms LDA.

The influence of different Ni2+ concentrations was studied inbatch cultures with complex medium and varying Ni2+ concentra-tions. Because Ni2+ has a high tendency to form strong complexeswith organic agents and is adsorbed at the bacterial cell wall, themobile Ni2+ concentration in the media was determined for eachmeasurement point of time by ICP-MS. The added Ni2+ supplemen-tation led to concentrations of 0.71, 5.0, 6.9, 26, 47, 94, 133 and190mM Ni2+ in the Streptomyces batch cultures. The preprocessedand averaged Raman spectra corresponding to the eight Ni2+

containing cultures are depicted in Fig. 2(A). The bacterial Ramanspectra show slight differences with varying Ni2+ concentration.Within the fingerprint region, relative intensity changes concerningthe protein and DNA signals are observed. The relative band inten-sities of the Ramanmarker bands for proteins (1660 and 1448 cm–1)increase, whereas the relative band intensities of the Raman bandsfor DNA (1574 and 1480 cm–1) decrease with increasing Ni2+

concentration. From the lowest (0.71mM) to the highest Ni2+

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Figure 2. (A) Preprocessed and averaged Raman spectra for each NiCl2 concall spectral classes containing Ni2+ in the medium. The color coding correspoconcentration groups: a, averaged spectra of chigh (94, 133 and 190mM); b, c


concentration (190mM), a shoulder (at 2882 cm–1) of the CH stretch-ing vibration signal emerges, which has already been assignedwithin a protein spectrum.[55]

Within Fig. 2(B), the two-dimensional LDA scores plot of theeight classes for varying Ni2+ impact are contained. For bettervisualization, the ellipses are depicted for all concentration classes.Each ellipse represents the doubled standard deviation of the class,containing all spectra with a probability of at least 87%. It isobvious that three superior classes are formed: one containingthe spectra from the cultures with low Ni2+ concentration: 0.71and 5.0 mMNi2+, one group containing spectra from the culturesof middle Ni2+ concentration: 6.9, 26 and 47 mM Ni2+, and onegroup containing spectra from the cultures of high concentrations:94, 133 and 190mMNi2+. Because a classification based on the data-set containing the eight different concentrations failed, the spectraare combined to three subgroups according to low (clow - c), middle(cmiddle - b) and high (chigh - a) Ni

2+ concentrations, which are clearlyseparated in the two-dimensional scores plot of the LDA (Fig. 2(C)).The single scores are depicted in black when the spectrumbelongs to the group clow (c), in red when the spectrum belongsto the group cmiddle (b) and in green when the spectrum belongsto chigh (a). To validate the LDA model according to the identifi-cation capacity of unknown Ni2+ concentration in the threeconcentration ranges, a leave-one-block-out-cross-validation(LOBO-CV) has been carried out. Each concentration class includingthe control culture without Ni2+ addition is assigned as one block.The LDA model was trained by eight concentrations, and the leftout concentration has been predicted to belong to the clow, cmiddle

or chigh concentration range. The results of the LOBO-CV is summa-rized in Table 1, and for each concentration class, the recognitionaccuracy is given. For all, but the control culture and the cultureof the highest Ni2+ concentration, recognition rates between 78and 97% are reached. The recognition rates are concentration de-pendant, whereas the best results are reached for the highest con-centration range. Concluding from these findings, the combination

c

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entration from batch cultures with complex medium. (B) LDA scoreplot ofnds to the Raman spectra depicted in A. (C) LDA score plot of the three

middle (6.9, 26 and 47mM); and c, clow (0.71 and 5.0mM).


Table 1. LDA prediction for one left out concentration, whereas the LDA model was trained with all other (in sum eight) concentration classes(LOBO-CV). Each concentration class is predicted according to the concentration ranges clow, cmiddle and chigh. The recognition rate is given for eachconcentration class in the last column, representing the identification capacity for each LDA-model

c(group) c(NiCl2)

0 mMa low 0.71 mM low 5.0 mM low 6.9 mMmiddle

26 mMmiddle

47 mMmiddle

94 mMhigh

133 mMhigh

190 mMhigh

c(low)pred 13 42 56 2 2 1 1 0 2

c(middle)pred 3 9 0 43 27 41 6 1 5

c(high)pred 7 3 2 5 2 6 33 33 18

Recognition rate 57% 78% 97% 86% 87% 85% 83% 97% 72%

aControl culture without Ni2+ addition to the medium.

Table 2. Comprising the results for the identification dataset usingLDA prediction. The LDA model is trained with six of the nine concen-tration classes that are summarized in column 2 (titled as Training).The identification accuracy is determined by predicting the threeunknown concentration classes that are noted in column 3 (titled asIdentification). The class sensitivities are summarized in column 4(titled as Class sensitivity), whereas the positively correctly predictedspectra are related to the number of spectra in this class. The accuracyis determined to be 88%, to correctly identify one of the threeunknown concentrations with this presented LDA model

Classes Trainingc(Ni) (mM)

Identificationc(Ni) (mM)

Class sensitivity (%)

c (low) 0 0.71 82

5.0

c (middle) 6.9 26 90

47

c (high) 94 133 97

190

A. Walter et al.

of Raman spectroscopy on single Streptomyces cells and LDA isable to separate between bacteria, which have been exposed todifferent Ni2+ concentrations, within certain concentration ranges.To predict an unknown concentration, the dataset is divided into

a training dataset and an identification dataset. For this purpose,the bacterial spectra of complex medium without Ni2+ are addedto the Raman dataset, to result in a model of three subclasses eachcontaining three original Ni2+ concentrations. The training data-set contains the threshold concentration of each concentrationgroup, i.e. 0 and 5.0 mM for clow, 6.9 and 47 mM for cmiddle, and94 and 190 mM for chigh resulting in a model trained with six ofthe nine concentration classes. The unknown spectra with theconcentration lying between the two training concentrations(0.71, 26 and 133 mM) are predicted using this LDA model. Theaveraged identification accuracy for the unknown concentra-tions is 88%, which are additionally noted for each class inTable 2. A bacterial spectrum recorded from a culture exposedto a Ni2+ concentration between 0 and 5.0 mM is correctly identi-fied to the class of clow with 82% (sensitivity). To predict anunknown spectrum belonging to the cmiddle range between 6.9and 47mM, a sensitivity of 90% is reached. For the concentrationrange between 94 and 190mM (chigh), an identification sensitivityof 97% is accomplished. Thus, a combination of LDA analysis andsingle cell spectroscopy has successfully been used to predict theunknown concentrations of Ni2+ in the media within determinedconcentration ranges.


Conclusions and Outlook

Here, we demonstrated the capability of micro-Raman spectros-copy to discriminate between S. galilaeus bacteria exposed todifferent Ni2+ concentrations. The influence of the counterion isfound to be minor compared with the influence of the heavymetal cation. Therefore, the differentiation between heavy metaland salt stress is possible by the application of this method. Thespectroscopic influence of different growth stages has also beenfound to be negligible, thus, allowing the use of cultures of differ-ent age, which facilitates investigations of environmentalsamples. The classification of spectra deriving from cultures of vary-ing Ni2+ concentrations has been carried out successfully within acertain concentration range applying LDA, and the predictions ofthe spectra from unknown concentrations are possible withinthose concentration ranges. Here, we have presented a first stepto functionalize Streptomyces bacteria as bioindicators for heavymetal contamination without precultivation or plate counting.Future studies should extend the investigation to environmentalsamples, and to different heavy metals for multimetal stress withinone dataset.

Acknowledgements

We gratefully acknowledge financial support from the DeutscheForschungsgemeinschaft (Graduiertenkolleg GK 1257 ‘Alterationand element mobility at the microbe-mineral interface’) in theframe of the Jena School of Microbial Communication and theThüringische Ministerium für Bildung, Wissenschaft und Kultur(TMBWK) under the project code ‘MikroPlex’ (PE113-1).

Supporting Information

Supporting information may be found in the online version ofthis article.

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