Culture Independent Raman Spectroscopic.pdf

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Culture Independent Raman Spectroscopic Identification of UrinaryTract Infection Pathogens: A Proof of Principle StudySandra Klo, Bernd Kampe, Svea Sachse, Petra Rosch, Eberhard Straube, Wolfgang Pfister,

Michael Kiehntopf, and Jurgen Popp*,,

Institute of Physical Chemistry and Abbe Center of Photonics, University of Jena, Helmholtzweg 4, D-07743 Jena, GermanyInstitute of Medical Microbiology, Jena University Hospital, Erlanger Allee 101, D-07747 Jena, GermanyInstitute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, Erlanger Allee 101, D-07747 Jena, GermanyInstitute of Photonic Technology, Albert-Einstein-Strae 9, D-07745 Jena, Germany

*S Supporting Information

ABSTRACT: Urinary tract infection (UTI) is a very commoninfection. Up to every second woman will experience at least

one UTI episode during her lifetime. The gold standard foridentifying the infectious microorganisms is the urine culture.However, culture methods are time-consuming and need atleast 24 h until the results are available. Here, we report abouta culture independent identification procedure by using Ramanmicrospectroscopy in combination with innovative chemo-metrics. We investigated, for the first time directly, urinesamples by Raman microspectroscopy on a single-cell level. Ina first step, a database of eleven important UTI bacterialspecies, which were grown in sterile filtered urine, was built up.A support vector machine (SVM) was used to generate a statistical model, which allows a classification of this data set with anaccuracy of 92% on a species level. This model was afterward used to identify infected urine samples of ten patients directlywithout a preceding culture step. Thereby, we were able to determine the predominant bacterial species (seven Escherichia coliand three Enterococcus faecalis) for all ten patient samples. These results demonstrate that Raman microspectroscopy in

combination with support vector machines allow an identification of important UTI bacteria within two hours without the needof a culture step.

U rinary tract infection (UTI) is considered to be among themost common bacterial infections. Statistically, one out ofthree women has an UTI episode already at the age of 24 years,making an antimicrobial treatment necessary.1 Fourty to fiftypercent of women will experience at least one UTI episodeduring her lifetime.1,2 UTIs account for more than 40% of allnosocomial infections in Germany.3While the majority of theseinfections proceed without any complications, in some cases,serious progress of UTIs like bacteremia, sepsis, and even death

are observed.2,4

In particular, for these severely progressinginfections, a fast and reliable identification of the causingpathogens is of utmost importance. Currently, besides clinicalexamination, urine cultures including resistogram of isolatedbacteria, which needs at least 24 h, are the gold standard todiagnose UTI.5 Until the result of the urine culture is available, aninitial antimicrobial therapy against common UTI-causingmicroorganisms is normally started. However, this procedurecould lead to rising antibiotic resistances due to selectionpressure.6 To shorten the time until results are available,alternative bacterial classification methods like polymerase chainreaction (PCR)-based techniques7,8 and matrix-assisted laserdesorption time-of-flight mass spectrometry (MALDI-TOF

MS)911 have also been explored and have found their wayinto clinical analysis. Recently, bacterial identification approachesusing 1H nuclear magnetic resonance (1H NMR) spectroscopy12

and microcalorimetry13 were also reported.Within the last few years vibrational spectroscopic methods

especially, which are label-free and nondestructive, like infrared(IR)1420 and Raman spectroscopy in combination withpowerful statistical data evaluation procedures, have showntheir great potential to rapidly identify bacteria.2126 Also,epidemiological studies using Raman spectroscopy are possi-ble.2730 Raman microspectroscopy (i.e., the combination ofRaman spectroscopy with conventional light microscopy) incombination with chemometrical methods has been successfullyapplied to classify bacteria on the species level with highaccuracy.21,22,31 For the identification of a certain species, areference database is required.32,33 In particular, Ramanmicrospectroscopy is able to investigate single bacterial cells,

Received: June 17, 2013Accepted: September 6, 2013

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which could be isolated from different matrices, making culturesteps unnecessary.3338

Up to now, Raman spectroscopy has only been employed toidentify UTI pathogens for cultured samples, including E. coli, K.pneumoniae, and Proteus spp.39,40 or additional Enterococcusspp.41 The preceding culture step of these Raman studiesmarginalizes the time benefit of Raman spectroscopy ascompared to classical culture methods. The aim of the study

presented in the following is to build up a reference database ofsingle-cell Raman spectra of in urine-cultured UTI-causingbacteria, which allows a subsequent identification of bacteriadirectly in patient urine samples without a culture step.

MATERIAL AND METHODS

The study was approved by the ethics committee of theFriedrich-Schiller University, Jena, Germany.

Bacterial Culture. For the construction of a referencedatabase, the following bacterial strains were used: Enterococcusfaecalis (E. faecalis) DSM 20478, Enterococcus faecium (E.faecium) DSM 20477, Staphylococcus epidermidis (S. epidermidis)DSM 20044, Staphylococcus haemolyticus (S. haemolyticus) DSM20263, Staphylococcus hominis (S. hominis) DSM 20328,Staphylococcus saprophyticus (S. saprophyticus) DSM 20229,Staphylococcus aureus (S. aureus) ATCC 43300, Escherichia coli(E. coli) DSM 10806, E. coli ATCC 35218, Klebsiella pneumoniae(K. pneumoniae) ATCC 700603, Pseudomonas aeruginosa (P.aeruginosa) ATCC 27853, and Proteus mirabilis (P. mirabilis)DSM 4479. All strains were provided by the Institute of MedicalMicrobiology, University of Jena, and were originally purchasedfrom the German Collection of Microorganisms and CellCultures (DSMZ) and the American Type Culture Collection(ATCC). The bacteria were cultured on Columbia blood agar(CBA, Oxoid) plates at 37 C for 24 h. Five milliliter sterilefiltered urine (syringe filter 0.8/0.2 m pore size, PallCorporation, New York) was inoculated with one colony of

the CBA culture and incubated at 37C for 24 h. One milliliter ofthe urineculture was centrifuged for 5 min at 10000g(Eppendorf

MiniSpin plus, Eppendorf AG, Hamburg, Germany), and thesupernatant was discarded. The resulting pellet was resuspendedin 1 mL of phosphate-buffered saline [PBS, prepared in-house,composition per 1000 mL: 1.44 g Na2HPO4 2 H2O (Roth), 8 gNaCl, 0.2 g KH2PO4, and 0.2 g KCl (all purchased from Merck)]and centrifuged at 10000gfor 5 min. This washing step was donetwice. Afterward the pellet was suspended in 1 mL autoclaveddeionized water, and 4 L of this suspension was spread on anickel foil and allowed to dry at room temperature. For eachstrain, at least five independent batches were prepared.

Direct Patient Urine Preprocessing. Ten urine samplesfrom patients with a UTI were included in this study; the samples

and their origins are listed in Table 3 (columns 1

2). Onlysamples with at least 104 colony forming unit (CFU)/mL wereused for the study because these cell counts are significant forUTI.42Also, samples with less bacterial load (down to 103 CFU/sample) are possible, if they are of interest for some specialcases.34

The containing bacterial species in the patient urine sampleswere also typed according to their biochemical properties byusing Vitek 2 (Biomerieux, Marcy lEtoile, France) (Table 3,column 3). To determine possible growth inhibitors within theurine samples, an agar diffusion test on Columbia agar (Oxoid)inoculated with 106 CFU/mL ofBacillus subtilis ATCC 6633 hasbeen performed. The test plates were inoculated with onedroplet of the urine sample and after 24 h incubation at 37C,the

occurrence of an inhibitory zone was determined (Table 3,column 4).

For the Raman spectroscopic measurements, 1 mL of the urinewas directly centrifuged at 10000g and the supernatant wasdiscarded. Afterward, the pellet was washed twice with PBSsimilar to the cultured samples. The resulting pellet wassuspended in 1 mL of autoclaved deionized water, and 4 L ofthe suspension was spread on the nickel foil and allowed to dry at

room temperature before the Raman microspectroscopicinvestigation took place. Raman spectra were only taken ofparticles which were in the size of single bacterial cells. Anexample of the image processing is given in Figure S1 of theSupporting Information.

Spectroscopic Instrumentation. All Raman spectroscopicmeasurements were performed with the Raman microscopeBioParticleExplorer (MicrobioID 0.5, RapID, Berlin, Germany).A frequency-doubled solid-state Nd:YAG diode pumped laser(LCM-S-111, Laser-Export Company Ltd., Moscow, Russia) at532 nm was used for excitation. The laser beam was focused witha 100 magnification objective (MPLFLN 100, NA: 0.9,Olympus Corporation, Tokyo, Japan) on the sample with a laserpower of approximately 7 mW. The backscattered light wasfocused on a single stage monochromator (HE532, Horiba JobinYvon, Munich, Germany) which is equipped with a 920 lines/mm grating and collected with a thermoelectrically cooled CCDcamera (DV401A-BV, Andor Technology, Belfast, NorthernIreland). The spectral resolution was about 10 cm1. For eachbacterial cell, two consecutive Raman spectra were measured atthe same position, which were afterward combined for spikeremoval (see also Figure S1 of the Supporting Information).Integration times between 6 and 30 s for a single bacterial cell,within a wavenumber range of 331970 cm1, were chosen.

Data Preprocessing and Chemometrical Analysis. Allpreprocessing wasdone using the R software package43with in-house developed scripts.44 The first step consisted in removing

the spectral background, using a method based on the SNIPclipping algorithm.45 For this, we used a fourth-order clippingfilter. Sincewith the BioParticleExplorer, two consecutive spectraof one cell are always measured, spikes were eliminated afterwardby a robust variant of the upper-bound spectrum algorithm.46

The single-cell Raman spectra were then wavenumber-calibratedby using an acetaminophen Raman spectrum measured at thesame dayas the reference.47,48 The wavenumber regions between3100 and 2650 cm1 and 1750450 cm1 were used for thechemometrical analysis. Finally, all spectra were vector-normalized. As has been shown previously, support vectormachines (SVMs) are especially suited to classify and identifydifferent bacterial species.33,49 SVMs belong to the group ofmaximum margin classifiers and will efficientlyfind the optimal

solution for given parameters. A classification model was builtusing the all-pairs approach for SVMs with a linear kernel and acost factor of 2.50 This model was afterward used to predict theindependent test data set and the patient urine samples.

Mean Raman spectra were calculated using the preprocessed,vector-normalized single-cell spectra of all classification batchesof one species.

RESULTS AND DISCUSSION

Raman spectra of single bacterial cells can be seen as acharacteristic spectroscopic fingerprint of the investigated cell.The diameter of the used laser spot is in the same dimension asthe diameter of a single bacterial cell (around 1 m). Therefore,

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the Raman spectrum represents a superposition of the molecularvibrations of all cellular compounds.51

The most importantstep in bacterial identificationby means ofRaman microspectroscopy is the buildup of a database ofreference Raman spectra. In order to establish such a database,we used eleven different UTI relevant bacterial species, namely,E. coli (Ecol), K. pneumoniae (Kpne), P. aeruginosa (Paer), P.

mirabilis (Pmir), E. faecalis (Efca), E. faecium (Efci), S. aureus(Saur), S. epidermidis (Sepi), S. hominis (Shom), S. haemolyticus(Shae), and S. saprophyticus (Ssap). These bacterial species werecultured in sterile filtered urine samples (at least four differentbatches per species, each in different urine) followed byrecording Raman spectra of single bacterial cells. Differentbatches for each species were used to increase the variability ofthe data set. This variability is important because the Ramanspectra of single bacterial cells, even of the same bacterial species,differ according to the growth stage22,52 and the nutritionsituation (composition of culture medium).22,53,54 Since duringsingle-cell measurements no standardization process can takeplace, possible variation has to be included in the database, inorder to be able to identify bacteria grown in urine samples with

unknown bacterial composition and culture conditions. Thevariability of the data set in the form of the standard deviations ofthe mean spectra is available in Figures S2 and S3 of theSupporting Information.

In Figure 1, the mean Raman spectra of all bacterial species inthe CH-stretching (31002650 cm1) and the fingerprintwavenumber region (1750450 cm1) are shown. Each meanspectrum was constructed from at least 200 single-cell Ramanspectra.

The most prominent band at around 2935 cm1 can beassigned to symmetric and asymmetric CH2 and CH3 stretchingvibrations.22,55 Other CH vibration bands can be found at 3059,1451, 1334, and 723 cm1.38 Such CH structures are common for

proteins, lipids, and carbohydrates.Table 1 provides an overview of the observed Raman bands

and their tentative band assignments. Besides the CH-vibrations,additional protein bands can be found at 1665, 1606, 1334, 1241,1099, and 1004 cm1. Typical bands for DNA structures are at1665, 1573, 1334, 1241, 781, and 748 cm1.

The enhanced bands at 1573, 1127, 1310, and 748 cm1, whichcan be found for some species like for example, Staphylococci(cg), are due to a higher cytochrome c content which exhibitsan electronic absorption in the range of the Raman excitationwavelength and therefore experiences a resonance Ramanenhancement.56

Figure 1 shows that the differences between the Raman spectraof the single bacterial cells of different species in spiked urine

Figure 1. Mean spectra of the investigated species: (a) E. faecalis (429spectra), (b) E. faecium (256 spectra), (c) S. epidermidis (227 spectra),(d) S. haemolyticus (225 spectra), (e) S. hominis (207 spectra), (f) S.saprophyticus (237 spectra), (g) S. aureus (285 spectra), (h) E. coli (360spectra), (i) K. pneumoniae (233 spectra), (j) P. aeruginosa (249spectra), and (k) P. mirabilis (244 spectra).

Table 1. Observed Raman Bands and Their TentativeAssignment

wavenumber(cm1) tentative band assignmenta

reference(cm1)

3059 (CH) olefinic 306022

2935 (CH2) asymmetric 293522

(CH3) symmetric 293555

1665 amide I 166638

(CC) 166755

nucleic acids 166355

1606 (CC) ring vibrations of phenylalanine,tyrosine

1603/160555

1573 ring vibrations of guanine and adenine 157522

cytochrome c 158356

1451 (CH2/CH3) 1440146022

145038

1334 (CH2) (in proteins) 133755

ring vibrations of guanine and adenine 133755

tryptophan 1339/133755

1310 CH2/CH3 twisting, wagging, bendingmodes of lipids

1313130755

cytochrome c 131156

1241 amide III 124555

(PO2) asymmetric (DNA bases) 124355

1127 cytochrome c 112856

1099 phenylalanine 110455

(CN) 109955

1004 ring breathing modes of phenylalanine 100455

781 ring breathing modes of cytosine, uraciland thymine

78678055

OPO backbone of DNA 78555

748 DNA 74855

cytochrome c 75056

723 (CH2) 72222

a: stretching vibration, : deformation vibration, : rocking vibration.


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samples are rather subtle and often not visible by the naked eye,making the application of chemometrical methods to distinguishbetween the bacterial species necessary. As described in theprevious section, we used a SVM with a linear kernel and a costfactor of 2 to build a statistical model which is able to classify/

identify the bacterial species. The performance of the SVM wasassessed by a 10-fold cross validation.

The resulting confusion table is shown in Table 2A. In total,2718 out of 2952 spectra were correctly classified, resulting in anaccuracy of 92.1%. The best results could be achieved for E.

Table 2. (A) SVM Results for Classification Model and (B) Identification of an Independent Test Data Set of Samples from SpikedUrine

(A)

true

classified asa Efca Efci Sepi Shae Shom Ssap Saur Ecol Kpne Paer Pmir Sensb Specb

Efca 422 14 1 0 0 2 1 0 1 0 0 98.4 99.2

Efci 5 242 0 0 0 0 0 0 0 0 0 94.5 99.8

Sepi 0 0 208 2 5 10 7 2 0 0 0 91.6 99.0

Shae 0 0 2 208 4 4 3 0 0 0 0 92.4 99.5

Shom 0 0 0 6 177 19 7 0 0 0 0 85.5 98.8

Ssap 0 0 9 2 15 194 3 1 0 0 0 81.9 98.8

Saur 0 0 5 7 4 3 264 0 0 0 0 92.6 99.2

Ecol 2 0 1 0 0 2 0 323 26 2 8 89.7 98.3

Kpne 0 0 0 0 0 0 0 26 203 3 2 87.1 98.8

Paer 0 0 0 0 0 0 0 1 2 244 1 98.0 99.8

Pmir 0 0 1 0 2 3 0 7 1 0 233 95.5 99.4

(B)

true

identified asa Efca Efci Sepi Shae Shom Ssap Saur Ecol Kpne Paer Pmir Sensb Specb

Efca 53 1 0 1 0 0 0 0 0 0 0 100 99.5

Efci 0 49 0 0 0 0 0 0 0 0 0 98.0 100Sepi 0 0 41 0 0 0 0 1 0 0 0 87.2 99.8

Shea 0 0 0 50 0 0 0 0 0 0 0 96.2 100

Shom 0 0 1 0 44 0 0 0 0 0 0 89.8 99.8

Ssap 0 0 1 0 5 29 0 0 0 0 0 96.7 98.7

Saur 0 0 1 1 0 1 50 0 0 0 0 100 99.3

Ecol 0 0 3 0 0 0 0 30 0 0 3 81.1 98.7

Kpne 0 0 0 0 0 0 0 4 40 0 0 100 99.1

Paer 0 0 0 0 0 0 0 0 0 39 0 100 100

Pmir 0 0 0 0 0 0 0 2 0 0 64 95.5 99.5aEfca = E. faecalis, Efci = E. faecium, Sepi = S. epidermidis, Shae = S. haemolyticus, Shom = S. hominis, Ssap = S. saprophyticus, Saur = S. aureus, Ecol =

E. coli, Kpne = K. pneumoniae, Paer = P. aeruginosa, Pmir = P. mirabilis. bSens = sensitivity (%). Spec = specificity (%).

Figure 2. Evaluation of patient urine specimens: assignment of measured single-cell Raman spectra based on the before built SVM model.


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faecalis, where 422 out of 429 single-cell spectra were classifiedcorrectly. This yields a sensitivity of 98.4% together with aspecificity of 99.2%. The specificities for the whole data set rangebetween 98.8% in cases of S. hominis, S. saprophyticus, and K.pneumoniae and 99.8% for E. faecium and P. aeruginosa.Sensitivities vary between 81.9% for S. saprophyticus and 98.4%

for E. faecalis.Most of the misclassified S. saprophyticus spectra were

classified as other coagulase-negative Staphylococci (33 out of43). Incorrect assignments also occurred between E. coli and K.pneumoniae: 26 E. coli spectra out of 350 were classified falsepositive as K. pneumoniae and 26 K. pneumoniae spectra out of233 were false positive as E. coli. This could be explained by theirclose genetic relationship within Enterobacteriaceae.

To confirm the predictive capacity of the classification model,an independent batch for each species was cultured in sterile,filtered urine, and single-cell Raman spectra were measured. Thebefore-built SVM model was used to predict this independenttest data set to check for overfitting.

In Table 2B, the correspondent results are summarized: 95.1%of the spectra were correctly assigned (489 out of 514 spectra)while the sensitivities range between 81.1% for E. coli and 100%for E. faecalis, S. aureus, K. pneumoniae, and P. aeruginosa.Specificities between 98.7% for S. saprophyticus as well as E. coliand 100% for E. faecium, S. haemolyticus, and P. aeruginosa werereached. These results suggest that the built SVM model can beused to identify independent test data.

Evaluation of Direct Urine Specimens. Finally, the above-described SVM model was tested on ten real patient urinesamples to provide a proof of principle that Raman micro-spectroscopy can be applied to directly identify single bacterialcells out of infected urine samples without the need for time-consuming culture steps. Thereby, the challenge consists in

identifying direct patient samples where the following threeconditions are unknown as compared to the reference database:(1) the composition of the patients urine (matrix), (2) the dwelltime in the urine (culture time), and (3) the bacterial strainsmight be different as compared to the ones used to build up thedatabase. To cope with these challenges, it was necessary toimplement as much as possible, variations into the classificationreference data set.

The bacteria were isolated by using the same centrifugationand washing steps as for the bacteria used to build up thereference database described in the preceding section.

Figure 2 displays the SVM identification results of the realurine patient samples by showing the assignments of the single-cell spectra for each urine sample. For each of the urine samples,

one predominant germ could be determined. Seven sampleswere predicted to contain E. coli and three E. faecalis. This agreeswith the microbiological analysis of the samples (see Table 3).

At least 66.7% of the spectra of each specimen were identifiedcorrectly. The abundance ranges from 66.7 to 97.8%, whereby itwas higher for E. faecalis isolates (92.0 to 97.8%) than for E. coli

samples (66.7 to 87.8%). In five out of the ten samples, thebacterial growth was suppressed as proven by the inhibitor test(Table 3); this means that the patients were pretreated withantibiotics. In accordance with our results, substances causingsuppression of bacterial growth in these samples seem to haveonly a little influence on the identification abundance. The twoE.faecalis isolates with a positive inhibitor test were identified withan abundance of 92.0% (EF1) and 92.6% (EF3), respectively.The E. faecalis without an inhibitor in the urine (EF2) wasidentified with an abundance of 97.8%. The chemometricalevaluationforE. coli provides for thethree samples with a positiveinhibitor test, an abundance between 66.7% (EC1) and 81.6%(EC6). For samples without growth inhibitor, the abundanceranges from 72.5% (EC2) to 87.8% (EC7). We set the threshold

for a correct identification of a whole sample to an abundance of65% to account for false positive classifications, which areincluded in the reference database. In addition, possiblecontaminations of the urine samples due to, for example, skinflora are not unusual.

CONCLUSIONS

Here, we report to the best of our knowledge about the firstsingle-cell typing method for a direct identification of UTIpathogens. We accomplished a Raman microspectroscopicidentification of bacterial species in real patient urine sampleswithout any time-consuming culture steps. Prior to thisidentification, a reference database needs to be established. For

that purpose, eleven UTI relevant species were used to build up areference database. The applicability of the reference model toevaluate infected real urine samples was tested for ten patientsurine samples without a preceding culture step. For all tensamples, the correct species could be determined by Ramanmicrospectroscopy. It is very promising for further studies, that itwas also possible to identify the containing species in urinesamples from antibiotic-pretreated patients. Since Ramanspectroscopy can be applied on single bacterial cells, it ispossible also to investigate samples with mixed bacterial species.In addition, the investigation of patient samples is performedwithout prior culture, therefore, even minor sample contami-nations due to sampling might be identified aside the mainpathogens present in the urine. In further studies, the amount

Table 3. Result for the Evaluation of Direct Patient Urine Samples without a Preceding Culture Step

sample origina real speciesb inhibitor test predicted asc spectrad abundance (%)

EC1 urine E. coli positive E. coli 40/60 66.7

EC2 ur. cath. E. coli negative E. coli 37/51 72.5

EC3 urine E. coli positive E. coli 35/51 68.6

EC4 urine E. coli negative E. coli 41/50 82.0

EC5 urine E. coli negative E. coli 25/31 80.6

EC6 ur. cath. E. coli positive E. coli 40/49 81.6EC7 urine E. coli negative E. coli 43/49 87.8

EF1 urine E. faecalis positive E. faecalis 46/50 92.0

EF2 blad. punct. E. faecalis negative E. faecalis 45/46 97.8

EF3 ur. cath. E. faecalis positive E. faecalis 50/54 92.6aUr. cath. = permanent urinary catheder. blad. punct. = bladder punction. bDetermined by Vitek 2. cBy Raman microspectroscopy. dNumber ofcorrect identified spectra/number of measured spectra.


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and species of contaminating bacterial cells has to be investigatedboth by Raman spectroscopy and other single-cell typingmethods, additional to the gold-standard urine culture. Theresults of this study are very encouraging for a fast and culture-independent identification of UTI pathogens directly out ofurine specimen by using Raman microspectroscopy. Restric-tively, it has to be mentioned that the herein presented proof-of-principle was only done with the most important UTI species E.

coli and E. faecalis, since these are the most common UTIpathogens. For other species, the applicability has to be shown infurther studies. Nevertheless, the whole identification can bedone within approximately two hours after the specimencollection, which is an enormous time benefit in comparison tothe gold standard urine culture.

ASSOCIATED CONTENT

*S Supporting Information

Additional information as noted in text. This material is availablefree of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION

Corresponding Author*E-mail: [email protected]. Tel: +49-3641-948320 and+49-3641-206300. Fax: +49-3641-948302.

Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS

Funding of the research project FastDiagnosis (13N11350) fromthe Federal Ministry of Education and Research, Germany(BMBF) is gratefully acknowledged.

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