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LOC-SERS: A promising closed system for the identification of mycobacteria Anna Mühlig, Thomas Wilhelm Bocklitz, Ines Labugger, Stefan Dees, Sandra Henk, Elvira Richter, Sönke Andres, Matthias Merker, Stephan Stöckel, Karina Weber, Dana Cialla-May, and Juergen Popp Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b01152 • Publication Date (Web): 21 Jul 2016 Downloaded from http://pubs.acs.org on July 24, 2016

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Analytical Chemistry

LOC-SERS: A promising closed system for the identification of mycobacteria Anna Mühlig a, b, Thomas Bocklitz b, Ines Labugger c, Stefan Dees c, Sandra Henk c, Elvira Richter d, Sönke Andres e, Matthias Merker f, Stephan Stöckel b, Karina Weber a, b, Dana Cialla-May a, b, Jürgen Popp a, b. a. Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Straße 9, 07745 Jena, Germany b. Institute for Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University Jena, Helmholtzweg 4, 07743 Jena, Germany c. Alere Technologies GmbH, Löbstedter Strasse 103-105, 07743 Jena, Germany d. Laboratory Dr. Limbach, Heidelberg, Germany (MVZ Labor Dr. Limbach & Kollegen GbR, Im Breitspiel 15, 69126 Heidelberg) e. National Reference Center for Mycobacteria, Research Center Borstel, Parkallee 1-40, 23845 Borstel, Germany f. Molecular Mycobacteriology, Research Center Borstel, Parkallee 1-40, 23845 Borstel, Germany ABSTRACT: A closed droplet based lab-on-a-chip (LOC) device has been developed for the differentiation of six species of mycobacteria, i.e., both Mycobacterium tuberculosis complex (MTC) and nontuberculous mycobacteria (NTM) using surfaceenhanced Raman spectroscopy (SERS). The combination of a fast and simple bead-beating module for the disruption of the bacterial cell with the LOC-SERS device enables the application of an easy and reliable system for bacteria discrimination. Without extraction or further treatment of the sample, the obtained SERS spectra are dominated by the cell-wall component mycolic acid. For the differentiation, a robust data set was recorded using a droplet based LOC-SERS device. Thus, more than 2100 individual SERS spectra of the bacteria suspension were obtained in one hour. The differentiation of bacteria using LOC-SERS provides helpful information for physicians to define the conditions for the treatment of individual patients.

INTRODUCTION In 2014, an estimated 9.6 million people worldwide suffered from acute tuberculosis (TB) and 1.5 million individuals died of this disease.1 Tuberculosis is caused by various strains of the Mycobacterium tuberculosis complex (MTC), comprising animal adapted species such as M. bovis and the major human pathogenic species M. tuberculosis. Along with the M. tuberculosis complex strains the nontuberculous mycobacteria (NTM) can also cause serious lung infections, the latter particularly affecting immunocompromised patients.2 Mycobacterial infections are extraordinarily difficult to treat, because the multilayer mycolic acid-rich outer cell membrane gives the organism increased resistance to chemical damage and dehydration. Furthermore, it constrains the activity of hydrophobic antibiotics3-5. Among the MTC, the so-called modern M. tuberculosis strains, e.g., the Beijing family and the Euro-American superfamily (including the H37Rv reference strain), are globally distributed and the main cause for pulmonary TB manifestation. In particular, a Beijing strain infection is highly associated with multidrug-resistant (MDR) TB if an epidemiological link to Eastern Europe, Asia or Russia is assumed.6,7 In the group of NTM M. abscessus has been found to be one of the most resistant mycobacteria to chemotherapeutic antibiotics8,9.

To reduce the overall global TB burden, rapid species identification is crucial for the timely administration of efficient drugs. To this day, culture methods are still the gold standard for the detection of mycobacteria and their drug resistance patterns. However, the diagnosis can take up to 6 weeks. New technologies commonly use molecular diagnostics or nucleic acid amplification tests (NAAT) that require complex laboratory infrastructure, hampering their utility in decentralized settings. Spectroscopy is a promising method for fast and reliable diagnostics in non-laboratory settings.10 Raman spectroscopy has the major advantage of being rather unsusceptible to aqueous environments10,11 because the Raman cross-section of water is relatively small.12 Furthermore, Raman measurements require less sample preparation and no complex laboratory infrastructure. Every molecule has a specific Raman signal, which provides a spectroscopic fingerprint of the investigated substance. One major disadvantage of Raman spectroscopy is the inherently weak signal intensity, which results in low sensitivity. By enhancing the Raman cross-section, using e.g., plasmonic resonances in the close vicinity of metallic nanoparticles, an amplification of the Raman signal intensity of about 6 to 11 orders of magnitude can be achieved.13,14 For surface-enhanced Raman spectroscopy (SERS), the same equipment as in normal Raman spectroscopy can be used. Thus, the recent progress in instrumentation development

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offers benefits both in terms of device size and cost efficiency. The well-known reproducibility problems of single SERS spectra can be overcome using a droplet-based microfluidic system (Lab-on-a-chip, LOC)15, which can analyze a high number of independent samples with fast throughput and a low sample amount. The major advantage of LOC-SERS is the easy generation of a well-defined detection environment.16,17 Thus, a statistical valid data set can be generated using the LOC-SERS device. Additionally, the sample preparation, manipulation and separation steps can be readily implemented into one system. This leads to better reproducibility, equipment miniaturization for portable devices and easy equipment handling. As reported in the literature, Raman spectroscopy is a powerful method for the identification and differentiation of bacteria using single-cell analysis.18-20 Microfluidic devices are applied in a wide range of clinical diagnostics21-25. Walter et al. have demonstrated the use of the LOC-SERS device for the discrimination of E. coli in a suspension26. After ultrasonic treatment, the bacterial suspension was manually transferred into a microfluidic system. This approach was perfectly suitable for a proof-of-principle study. However, if pathogens are investigated a high safety level has to be considered. Thus, the manual transfer of the sample into the syringe after cell disruption suffers from potential contamination problems. In this contribution, an easy and fast approach to disrupt mycobacteria and subsequently, record LOC-SERS spectra in one closed system is introduced. The cell wall of mycobacteria is lysed using a bead-beating module, which is a common strategy for the mechanical lysis of hard-to-lyse samples.27 The bead-beating module was successfully combined with the microfluidic device, leading to an automated closed system for sample preparation and measurement. Using this system, the successful identification of nontuberculous mycobacteria (NTM) and Mycobacteria tuberculosis complex (MTC) species was achieved.

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15 minutes. The bacterial samples were washed twice with Tris EDTA (TE) buffer, centrifuged and resuspended in 1.5 ml of TE buffer. The optical density was determined with extinction measurements at 600 nm (see Figure S4, Supporting information). It is found, that the signal intensity of the SERS spectra is not corresponding to the OD values ranging from 0.15 to 1.54. The bacterial cells were disrupted in the beadbeating module (Alere Technologies, Jena, Germany) using spheres with a diameter of 106 µm. The spheres consist of soda lime glass with a density of 2.5 g/cm2. The size of the bead-beating camber was 785 µl, 600 mg of the glass spheres were included into the chamber. The bead-beating time was 4 min, with a speed of 2400 rpm. The final sample volume was 600 µl. Subsequently, the bacterial suspension was pumped into the LOC device for SERS measurements via an internal storage container (ISC) or stored at -80°C prior to the SERS measurements. The ISC was linked to the syringe of the syringe pump system. Using a T-shape adapter the syringe was connected as well to the ISC as to the LOC device. A scheme of the whole experimental device is presented in Figure 1. The relevant information for the analyzed mycobacterial species is summarized in Table 1, i.e., the species, genotype, ID strain, abbreviation used in the text and figures, and the number of obtained spectra per species.

MATERIALS AND METHODS Chemicals and reagents Silver nitrate (ACS reagent, ≥99%), hydroxylamine hydrochloride (ReagentPlus, 99%) and sodium hydroxide were purchased from Sigma-Aldrich. Mycobacteria were cultivated by the National Reference Laboratory (NRC) for Mycobacteria at the Research Center Borstel (RCB). Sample preparation The silver colloids were prepared according to the protocol published by Leopold and Lendl.28 Briefly, 0.1 mmol silver nitrate was added to a mixture of hydroxylamine hydrochloride (0.15 mmol) and sodium hydroxide (0.3 mmol) under vigorous stirring. The solution turned a grey-yellow color instantaneously. Stirring was continued for 10 min. Mycobacterial strains were pre-cultivated on LoewensteinJensen (LJ) medium and further cultivated thereafter on solid Middlebrook (MB) medium in MB tubes. After a cultivation time of 5-8 weeks, the bacteria from three media tubes were pooled. This was done for nine individual media tubes. Thus, three independent batches of bacterial suspension for each species were obtained. To guaranty a safety handling of the pathogenic MTC species all samples were heated to 99°C for

Figure 1 Scheme of sample preparation, including lysing module (bead-beating system) for the bacterial tion, the internal storage container, the syringe pump the droplet-based microfluidic device mounted to the stage.

the sample cell disrupsystem and microscope

Instrumentation For the Raman spectroscopic measurements, a Raman microscopic device (BioParticleExplorer, rap.ID Particle Systems GmbH, Berlin, Germany) was used, which allowed the automated measurements of single-cell Raman spectra with an excitation wavelength of 532 nm provided by a continuouswave frequency-doubled Nd:YAG laser (LCMS-111-NNP25, Laser-export Co. Ltd., Moscow, Russia). An Olympus MPLFLN 100 BD objective focused the Raman excitation light onto the sample with a spot size smaller than 1 µm and approximately 7 mW laser power. The integration time per Raman spectrum (ranging from 3319 to 70 cm-1) was 10 s. After the Rayleigh scattered light was blocked with two edge filters, the 180° backscattered Raman light was spectrally


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Table 1. List of the analyzed mycobacterial species and their genotypes, ID strains, abbreviations, and number of spectra in the database.

Species

M. tuberculosis M. bovis BCG M. canettii M. abscessus M. szulgai

Genotype

ID Strain

Abbr.

No. of spectra

M. tub. Beijing M. tub. Beijing M. tub. H37Rv M. bovis BCG M. canettii M. abscessus M. szulgai

1934/03 8304/09 SR16b BCG Pasteur 3040/99 5512/11 8895/14

M. tb Beij M. tb Beij M. tb H37Rv M. bov BCG M. can M. abs M. szul

6131 6456 4819 4800 4230 5279 3462

dispersed by a single-stage monochromator (HE 532, Horiba Jobin Yvon, Bensheim, Germany) equipped with a 920 lines/mm grating and collected with a thermoelectrically-cooled charge-coupled device camera (DV401-BV, Andor Technology, Belfast, UK). The spectral resolution was approximately 10 cm-1. The LOC-SERS spectroscopic measurements were carried out with a Raman microscope (WITec GmbH, Ulm, Germany), which allowed continuous time series measurements. The excitation source was a continuous-wave diode-pumped solid-state laser (CoboltTM, Solna, Sweden) with a wavelength of 514 nm and a maximum output power of 100 mW. For focusing the laser beam and collecting the backscattered light, the same objective (Zeiss EC ‘‘Epiplan’’ DIC, 20×, NA=0.4, Oberkochen, Germany) was used. For the measurements, a 600 lines per mm grating was employed with a spectral resolution of 5 cm-1. The detection of the Raman signal was achieved with a thermoelectrically cooled CCD detector (at -70°C) with 1024 × 127 active pixels and a pixel size of 26 mm × 26 mm. To achieve a viable SERS data set of the investigated mycobacterial species, a microfluidic platform was employed. A detailed description of the platform can be found elsewhere15. Briefly, the glass chip has six inlet ports and one outlet. To achieve a constant flow volume, a computer controlled syringe pump system (neMESYS, Cetoni, Korbußen, Germany) was used to pump all reagents into the microfluidic chip. The continuous phase mineral oil was injected into the first port. The mycobacteria suspension prepared by the bead-beating module was pumped through a dropletgenerator unit, generating droplets in the continuous oil phase. At the dosing unit, the silver nanoparticles and the 1 M potassium chloride solution were added into the already assembled droplets. A serpentine channel section assured the mixing of all the components in the droplet. The flow rates of the reagents were kept constant during the measurements and were as follows: mineral oil, 11 nl/s; bacterial suspension, 14 nl/s; silver nanoparticles, 9 nl/s and 1 M KCl, 2 nl/s (a scheme of the chip design is illustrated in Supporting Information Figure S1). To prevent contaminations and memory effects (as previously reported29), the glass walls were rendered by functionalization with octadecyltrichlorosilane.30 The bacteria cell wall was mechanical disrupted using the bead-beating module, as described in the sample preparation section. The chip was mounted on the microscope stage for the LOC-SERS measurements. The SERS spectra were recorded continuously in the third channel with a laser power

of 35 mW and an integration time of 1 s. For every mycobacteria strain a series of 3600 to 6000 spectra was obtained, containing pure droplet SERS spectra, pure mineral oil Raman spectra and mixed spectra. Data analysis and chemometrical methods The preprocessing and the chemometrical treatment of the recorded data set ware carried out using the programming language “GNU R”, which is published as an open source language and statistical software within the GNU-project31. For the Raman data set, the first preprocessing step consisted of a background elimination (based on the SNIP algorithm32). Subsequently, spurious spikes in the Raman spectra were detected and removed by comparing two consecutive spectra of the same cell and employing a robust variant of the upper-bound spectrum algorithm33. The spectra were then subjected to a wavenumber calibration with the Raman spectrum of acetaminophen as a daily standard34. Next, the spectral region under consideration was limited to the region of interest (1700 to 700 cm-1). After a further SNIP background correction, to reduce admittedly minor background information especially on the borders of the truncated spectra, all spectra were scaled by a vector normalization, and the mean was calculated. The preprocessing treatment of the microfluidic data set consisted of a reduction of the wavenumber area (3300 to 330 cm-1), spike removal (by applying a median filter to the data) and a background correction (based on the SNIP algorithm). Subsequently, a spectra separation step, which separates the Raman spectra of the mineral oil from the SERS spectra measured in the droplets, was applied and the SERS spectra were treated with a wavenumber calibration17,35. The preprocessing was applied to all SERS data sets. Subsequently, a principal component analysis (PCA)36 was conducted, reducing the dimensionality of the data set. In addition, the white noise was removed by cutting off the scores after a particular channel in the new spectral space. The exact number of scores used is dependent on the size of the data set. A good choice for the number of scores is beween 2% and 5% of the number of data points. The reduction of the dimensionality of the problem is necessary to avoid overfitting37. For the discrimination, a linear discriminant analysis (LDA)38 was executed. This method has been shown widely capable of bacterial discrimination18,39-41.



RESULTS AND DISCUSSION Implementation of the bead-beating module In this study, the suspension containing the intact bacteria was introduced into a bead-beating module. There, the bacteria were disrupted using glass spheres and washed afterwards. After the washing step, the suspension containing the lysed bacteria was flushed into an ISC. The syringe was filled automatically from the ISC, controlled with the pump system software. The syringe was connected to the ISC as well as to the LOC device via a T-shape adapter. Subsequently, the bacterial suspension was directly injected into the LOC device without leaving the system (see Figure 1). As mentioned in the introduction, a similar LOC-SERS device was used to discriminate E.coli strains by Walter et al.26. Due to the high safety level required for the work with pathogens, an automated closed sample preparation had to be included into the system. Clearly, the advantage of the enhanced safety of the closed system described here, as well as the higher stability and reproducibility is a remarkable improvement of the device.

718

850 830

1004 944

857

1009

1134 1089

1238

1317 1309 1278

1392

1452

1441

1668 1604 1590 1563

Spectroscopic characterization of mycolic acid For the characterization of the obtained SERS spectra of the bacterial suspension reference Raman spectra of nondisrupted mycobacteria were taken into account. Figure 2 illustrates the mean of 100 single spectra of the strain M. tb Beijing (ID 8304/09) obtained with Raman (a) and SERS (b).

Raman Intensity / arb. units

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200 cts.

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Raman spectra show significant differences in the spectroscopic fingerprint region (1700 to 700 cm-1). The detailed band assignment is summarized in Table 2.

Table 2 Band assignment for intact mycobacteria in the Raman measurements and disrupted mycobacteria in the LOC-SERS measurements.

Functional group/ band assignment Amide I Phenylalanine (C=O) -alkylketone; (C=C) str.; (C=O)-β-conjugated; νS(C=O) carboxylic acid (C=C) str. (CH2) def. (C-O-H) bend. (C-O-H) bend.; -(CH2)n-in-phase twist (C-O-H) bend. (C-O-H) bend. (C-C) skel. str. in Alkane Nucleic acid ν(C-C-O) out-of-phase stretch of primary alcohol; phenylalanine ν(C-C-O) in-phase stretch. of primary alcohol; vS(C-O-C); (C-C) skel. str. in Alkane tyrosine cytosine; uracil (CH2) in-phase rock; ν(CHR2, where R≠CH3) (C-C) skel. str.

Raman signal/cm-1 1668 1604 -

SERS signal/cm-1 1590

1441 1309

1565 1452 1392 1317

1089 1004

1278 1238 1134 1009

-

857

850, 830 781 -

718

b)

a)

1600

1400

1200

1000

800

wavenumbers / cm-1 Figure 2 a): Mean of Raman spectra of M. tb Beijing (ID 8304/09); b): Mean of SERS spectra of M. tb Beijing (ID 8304/09); light gray: standard deviations. Contributions from all parts of the bacterial cells can be found in the Raman spectra, while the SERS spectra of the disrupted bacterial cells are strongly dominated by contributions from mycolic acid.

The standard deviations for both mean spectra are indicated in light gray. Furthermore, for the band assignment, the SERS spectra were compared with the obtained Raman spectra. As illustrated in Figure 2 the obtained SERS and

The Raman bands measured for the whole bacterial cells are assigned to Amide I (1668 cm-1), phenylalanine (1604 cm-1, 1004 cm-1), nucleic acid (1089 cm-1), tyrosine (850 cm-1, 830 cm-1), cytosine and uracil (781 cm-1)42,43. However, for the SERS spectra the very prominent peak centered at 1392 cm-1 is ascribed to the symmetric (COO ) vibration44 as 45 well as to the (COH) bending mode . Contributions of the (COH) bending mode are also indicated by the peaks centered at 1317 cm-1, 1278 cm-1 and 1238 cm-1.45 The ν(C-C-O) out-of-phase stretching mode of a secondary alcohol is represented by the peak at 1009 cm-1.45 The ν(C-C-O) in-phase mode of the same group produces the peak centered at 857 cm-1.45 Furthermore, the C-C skeletal stretching mode form alkanes is indicated by the peak at 1134 cm-1.42 The peak centered at 718 cm-1 contains vibrational contributions from the CH2 in-phase rocking and the ν(CHR2; R≠CH3) C-C skeletal stretching. The vibration of (C=C) is confirmed by the peaks appearing at 1565 cm-1 and 1590 cm-1,45 to the latter peak are also assigned contributions of the vibrational mode from the (C=O) functional group. The peak centered at 857 cm-1 is caused by the ν(C-C-O) out-of-phase stretching of a secondary alcohol and ν(C-C) skeleton stretching in alkanes. It is evident that the SERS and the Raman spectra show significantly different spectroscopic information; the

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detailed band assignment is presented in Table 2. While the Raman spectra includes spectral information of the entire bacteria cell, the SERS spectra exhibits the spectral information of the molecules binding to the surface of the silver nanoparticles. Thus, the SERS spectra are dominated by the vibrational signals of the mycolic acid due to their preferential binding to the metal surface in the presented approach. The spectra obtained with

the LOC-SERS device are in excellent agreement with the SERS spectra Rivera-Betancourt et al. reported for mycolic acid extracted from mycobacteria.45 Thus, it can be concluded that the approach presented here enables the recording of SERS spectra strongly dominated by the vibrational contributions of mycolic acid without an extraction procedure. Furthermore, the signal intensity is not depending on the OD in the investigated OD-value range from 0.15 to 1.54 (Figure S4 in the Supporting information). This indicates that the metal surface of the nanoparticle is saturated with mycolic acid molecules in the investigated OD rage. This is very promising for the detection in patient samples, where the concentration of the mycobacteria is lower than the concentration applied in this study. For the discrimination at the species and strain level,

the analysis of mycolic acid is an established method. 46-49 Mycolic acid is a characteristic component of the cell membrane unique to mycobacteria; this family of fatty acids is composed of a long-chain β-hydroxylated fatty acid branched by another shorter α-alkyl side chain. Examples of the structure are depicted in Figure S3 in the supporting information provided online. To obtain a representative spectral data set, three pooled batches of every species were investigated. The means of the three batches for every mycobacterial species are presented in Figure 3.

norm. Raman Intensity / arb. units

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M. tub. H37Rv

tra of the mineral oil, and mixed spectra. As part of the preprocessing, the spectra were sorted into these three groups and a calibration using the mineral oil spectra as a reference was applied to correct for the variation of the laser wavelength. The wavenumber range was reduced to the region of interest, ranging from 1700 cm-1 to 700 cm-1. To proof the batch to batch reproducibility the mean SERS-spectra of M. abs are presented in Figure S2 in the supporting Information. All mean spectra have a similar appearance and visual differentiation was not possible. Thus, a combination of two chemometrical methods was used to achieve the identification. First, a principal component analysis (PCA) was applied, followed by a linear discriminant analysis (LDA). Chemometrical treatment - Differentiation using the PCA and LDA methods For clinical applications two types of information are crucial. First, whether the patient is infected with TB and second, whether the infecting strain is associated with MDR-TB, which would initiate other infection control measures and extended drug susceptibility screens. To address these questions, we developed a chemometrical model to differentiate between MTC and NTM species and identify the clinically important species. Two batches of every species were used to train the model. Subsequently, the third batch was used for the identification. After the obtained spectra were treated with the preprocessing routine, a principal component analysis (PCA) was applied to reduce the dimensions to 30 principal components (PCs). Afterward, a hierarchical model was constructed to identify the six mycobacterial species. A linear discriminant analysis (LDA) of the first 20 PCs was used to find the best parameters for the model, resulting in an optimized model including the first 16 PCs. The first two loadings (LD), representing the spectral variables in the space of the principal components, are illustrated in Figure 4.

M. abs

M. can

M. bov BCG

M. szul

M. tb Beij

1600

1400

1200

1000

800

-1

wavenumbers / cm

Figure 3 Mean spectra recorded from 3 batches of each species, from top to bottom: M. tb H37Rv (SR16b), M. abscessus (5512/11), M. canettii (3040/99), M. bovis BCG (Pasteur), M. szulgai (8895/14), M. tb Beijing (8304/09; 1934/03).

For every batch, a total of approximately 3000 spectra were recorded. The data set of every measurement contains pure SERS spectra of the bacterial suspension, pure Raman spec-

Figure 4 LD1 versus LD2. A PCA-LDA model trained to separate the six species was used for data visualization. A direct modelling was not possible; therefore, a hierarchical model was constructed instead.

The first step in the hierarchical model was to divide the data set into two groups: nontuberculous mycobacteria (NTM) and Mycobacterium tuberculosis complex (MTC). The true positive value, corresponding to the sensitivity (MTC recog-


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nized as MTC), was 98.2%, and the true negative value, correlating with the specificity (NTM recognized as NTM), was 77.8%. An accuracy of 93.3% was achieved. Subsequently, the species in each distinct group were identified. A scheme of the chemometrical model can be found in Figure 5.

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ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Figure S1: Lab-on-a-chip device used to measure the in-droplet SERS spectra of the bacterial cell parts. The channel dimensions are 120 µm × 260 µm (height × width). The chip has six inlet ports and one outlet. (PDF) Figure S2: Mean of the SERS spectra of three batches of M. abscessus (5512/11). Indicated in gray, 2 × standard deviation values. (PDF) Figure S3: Structures of mycolic acids typical for Mycobacterium tuberculosis. The exact structure of the mycolic acid molecules varies by strains, but the basic structure is the same for all mycobacterial species. (PDF)

Figure 5 Structure and parameters of the identification tree of the chemometrical model on the basis of the database construted.

The two NTM species M. abs and M. szul were identified with an accuracy of 100%. In the MTC group, containing the M. bov BCG, the M. tb Beij, the M. can and the M. tb H37Rv species, the identification of the M. tb Beij species and the M. can species was achieved with an accuracy of 100% as well. M. bov BCG and M. tb H37Rv were differentiated with an accuracy of 75.3%. Thus, this method provided an indication of the origin of the analyzed mycobacterial species.

CONCLUSIONS Applying LOC-SERS to the identification of mycobacteria is a highly promising approach. In this contribution, a closed system for sample preparation and analysis using LOCSERS was successfully applied to the identification of NTM and MTC species. Thus, enhanced safety of the closed system, as well as high stability and reproducibility of the achieved data set is a remarkable improvement of the device. The spectral information of the obtained SERS spectra was dominated by the vibrational signals of the cell-wall component mycolic acid. This is the first time that the identification of mycobacteria was realized using the SERS spectral information of mycolic acid without additional extraction steps. The exact structure of mycolic acid differs for the various mycobacterial strains. The identification of the analyzed species was realized with high accuracy and reliability. These results show that LOC-SERS has a high potential as an analytical tool for the differentiation of mycobacteria. Thus, the identification using chemometric methods is a reliable approach. The LOC-SERS system is a promising tool to supplement well established molecular diagnostic methods. A joint approach would accumulate the advantages of both techniques: While employing LOC-SERS, pathogenic mycobacteria are detected and identified based on a reliable database, the existing molecular diagnostic methods might be applied to estimate the resistances against various drugs, which is of utmost importance considering the worldwide spread of multidrug-resistant tuberculosis. A prerequisite of LOC-SERS, however, is that the underlying reference databases are adapted to the task at hand. Thus, real-time molecular approaches and Raman-spectroscopic techniques can perfectly complement each other in the future.

Figure S4: Mean spectra of 3 batches of a) M. tb Beij b) M. tb can and c) M. tb H37Rv. The OD values are indicated for every batch. Standard deviation values are indicated in gray.

AUTHOR INFORMATION Corresponding Author * E-mail: [email protected]

Authors’ Contributions The manuscript was written through contributions of all authors. / All authors have given approval to the final version of the manuscript.

ACKNOWLEDGMENTS Funding of the research projects FastDiagnosis (13N11350), InfectoGnostics (13GW0096F) and JBCI 2.0 (03IPT513Y— Unternehmen Region, InnoProfile Transfer) from the Federal Ministry of Education and Research, Germany (BMBF) and FastTB (2013FE9057, 2013FE9058) from Free State of Thuringia and the European Union (EFRE) are gratefully acknowledged. The authors thank Dr. Thomas Henkel for providing the microfluidic chips, Konstanze Olschewski and Evelyn Kämmer for support in the chemometric analysis and Dr. Christian Matthäus for constructive discussions and support in the present manuscript.

REFERENCES (1) WHO. global tuberculosis report 2015; WHO Press: Geneva, 2015. (2) Griffith, D. E.; Aksamit, T.; Brown-Elliott, B. A.; Catanzaro, A.; Daley, C.; Gordin, F.; Holland, S. M.; Horsburgh, R.; Huitt, G.; Iademarco, M. F.; et al. Am J Resp Crit Care 2007, 175, 367-416. (3) Brennan, P. J. Tuberculosis 2003, 83, 91-97. (4) Jarlier, V.; Nikaido, H. Fems Microbiol Lett 1994, 123, 11-18. (5) Bhamidi, S.; Shi, L. B.; Chatterjee, D.; Belisle, J. T.; Crick, D. C.; McNeil, M. R. Anal Biochem 2012, 421, 240249. (6) Merker, M.; Blin, C.; Mona, S.; Duforet-Frebourg, N.; Lecher, S.; Willery, E.; Blum, M. G.; Rusch-Gerdes, S.; Mokrousov, I.; Aleksic, E.; et al. Nat Genet 2015, 47, 242249.


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