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FT-IR Hyperspectral Imaging and Artificial Neural Network Analysis for Rapid Identification of Pathogenic Bacteria Peter Lasch, Maren Stämmler, Miao Zhang, Malgorzata Baranska, Alejandra Bosch, and Katarzyna Majzner Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b01024 • Publication Date (Web): 26 Jun 2018 Downloaded from http://pubs.acs.org on June 26, 2018

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Analytical Chemistry

FT-IR Hyperspectral Imaging and Artificial Neural Network Analysis for Rapid Identification of Pathogenic Bacteria

Peter Lasch 1*, Maren Stämmler 1, Miao Zhang 1,#, Malgorzata Baranska 2, Alejandra Bosch 3 and Katarzyna Majzner 1,2

1

Robert Koch-Institute, ZBS6 - Proteomics and Spectroscopy, Seestraße 10, Berlin, D-13353, Germany

2

Jagiellonian University, Faculty of Chemistry, Gronostajowa 2, 30-060 Krakow, Poland

3

CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina

#

current address: Department of Biology, Humboldt University Berlin, Philippstraße 13, 10115 Berlin, Germany

* Corresponding author. Mailing address: Robert Koch-Institute, Centre for Biological Threats and Special Pathogens (ZBS), “Proteomics and Spectroscopy” Unit (ZBS6), Seestraße 10, D - 13353 Berlin, Germany. Phone: +49 30 18754-2259. Fax: +49 30 1871054-2259. E-Mail: [email protected]

Running Title: FT-IR Hyperspectral Imaging of Microorganisms Keywords: Infrared Spectroscopy, Hyperspectral Imaging, Identification of Microorganisms, Artificial Neural Networks

Abbreviations: ANN, artificial neural network; CF, Cystic Fibrosis; FPA, focal plane array; FT, Fourier transform; FT-IR, Fourier transform infrared; HSI, hyperspectral imaging; IR, infrared; MCT, mercury cadmium telluride; MLST, multi-locus sequence typing; PHB, poly-β-hydroxybutyrate; RKI, Robert Koch-Institute; ROI, region of interest; SNR, signal-to-noise ratio; UHCA, unsupervised hierarchical cluster analysis; WGS, whole genome sequencing

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ABSTRACT Identification of microorganisms by Fourier transform-infrared (FT-IR) spectroscopy is known as a promising alternative to conventional identification techniques in clinical, food and environmental microbiology. In this study we demonstrate the application of FT-IR hyperspectral imaging for rapid, objective and cost effective diagnosis of pathogenic bacteria. The proposed method involves a relatively short cultivation step under standardized conditions, transfer of the microbial material onto suitable IR windows by a replica method, FT-IR hyperspectral imaging measurements and image segmentation by machine learning classifiers, a hierarchy of specifically optimized artificial neural networks (ANN). For cultivation, aliquots of the initial microbial cell suspension were diluted to guarantee single colony growth on solid agar plates. After a short incubation period when microbial micro-colonies achieved diameters between 50 and 300 µm, micro-colony imprints were produced by using a specifically developed stamping device which allowed spatially accurate transfer of the microcolonies’ upper cell layers onto IR transparent CaF2 windows. Dry micro-colony imprints were subsequently characterized using a mid-IR microspectroscopic imaging system equipped with a focal plane array (FPA) detector. Spectral data analysis involved pre-processing, quality tests and the application of supervised modular ANN classifiers for hyperspectral image segmentation. The resulting easily interpretable segmentation maps suggest a taxonomic resolution below the species level.

INTRODUCTION Phenotypic microbial characterization techniques such as infrared (IR) spectroscopy, Raman spectroscopy, and mass spectrometry are today widely used methods to characterize microorganisms in clinical routine diagnostics,1,2 in food safety,3,4 or in biodefense applications.5,6 Data from an enormous number of studies have demonstrated that these modern analytical spectroscopies allow for the non-destructive, rapid, reliable, and cost-effective differentiation, identification, and classification of pathogenic microorganisms. Among the spectroscopy/spectrometry-based phenotypic characterization techniques IR spectroscopy is considered to be the first method that has been applied for microbial characterization. First reports of application date back to the 1950s, however, it was only in the 1980s that reasonably priced Fourier transform-infrared (FT-IR) spectrometers, modern minicomputers and the progress in information technology allowed recording and analyzing highquality IR spectra from microorganisms quickly, cheaply and with high precision. These years also saw the foundations being laid by Naumann and co-workers for successful application of FT-IR spectroscopy as it is used today in microbiology.7,8 The suggested workflow for IR spectroscopy-based microbial identification involved cultivation under standardized conditions, usually for 24 h, to provide a sufficient amount of microbial material and preparation of the microbial material onto IR-compatible substrates like ZnSe or CaF2. The main goal of microbial sample preparation for (macro) FT-IR transmission measurements was the production of dried homogeneous films which assure sufficiently high absorbance values, high spectral quality and an optimal signal to noise ratio (SNR) for subsequent spectral fingerprint analysis. The data analysis


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strategy proposed by Naumann and colleagues involved approaches based on multivariate interspectral distances (D-values), unsupervised hierarchical cluster analysis (UHCA)7,8 and pattern analysis by machine learning classifiers such as artificial neural networks (ANN). Such methods were found helpful for estimating the degree of similarity between the IR microbial fingerprints under investigation and spectral records contained in validated spectral reference libraries. Library-based approaches for the identification of pathogenic microorganisms have proved to be highly successful, not only in FT-IR spectroscopy but also in Raman spectroscopy1 and particularly in MALDI-TOF mass spectrometry (MS).9 While each of the analytical techniques has their pros and cons, it has been shown numerous times that FT-IR spectral fingerprints allow for differentiation below the species level.7,10,11 The latter fact is particularly important because the question whether MALDI-TOF MS is generally suitable for microbial identification below the species level is currently under discussion.9 Compared with MALDI-TOF MS which has revolutionized microbiological routine diagnostics in the last decade, IR spectroscopy appears to offer a higher taxonomic resolution, however, at the costs of a more complex sample handling and relatively high requirements with regard to standardization of cultivation, sample preparation and data acquisition. Nevertheless, advantages of spectroscopy-based microbial characterization techniques are that vibrational spectroscopies coupled to microscopy enable the recording of high-quality IR or Raman microspectra from low sample amounts. In many cases, the reduced requirements for microbial biomass allow the characterization of micro-colonies (IR microspectroscopy) or even of single microbial cells without the need for microbial cultivation (confocal Raman microspectroscopy, CRM). 12-14 In 2003, for example, Maquelin and coworkers performed a study in which single point FT-IR microspectra were collected from micro-colonies grown for 6 – 8 h.15 It was noted that the reduction of cultivation time from 24 h in the standard approach to 6 – 8 h did not result in a measureable reduction of the taxonomic resolution. It was also found that the level of biochemical heterogeneity, which is known to impair the classification results of 24 h cultures, was nearly absent in the micro-colonies studied. The authors noted high levels of taxonomic resolution, low costs, high throughput, and last but not least a high speed of the diagnostic workflow. Disadvantages of the method stem mainly from the high standardization requirements and the need to compile and validate spectral reference databases. In addition, bacterial storage compounds such as PHF (poly-β-hydroxy fatty acids) can significantly interfere with the diagnostic outputs.16-18 Poly-β-hydroxybutyrate (PHB) as the major representative of PHF is a biological polyester which exhibits a strong band at 1738 cm-1 and a number of characteristic absorption features at 976, 1059, 1101, 1135, 1187, 1304 and 1383 cm-1 in the mid-IR range.17 Due to the very high local concentration and the overlap with spectral features of the cell, storage substances like PHB can significantly impair the accuracy of IR-based identification, or even render the diagnostic approach impossible. In this work we systematically investigate the level of taxonomic resolution achievable through the application of FT-IR microspectroscopy and microspectroscopic imaging. Based on examples of taxonomically distinct microbial strains we demonstrate that FT-IR microspectral patterns of microcolony imprints can be employed for accurate microbial identification.



MATERIALS AND METHODS Microbial Strains and Isolates: All strains used in this work belonged to recognized strain collections such as the American Type Culture Collection (ATCC), the Laboratory of Microbiology Gent Culture Collection (LMG), the Deutsche Sammlung von Mikroorganismen (DSM), CAMPA (Colección Argentina de microorganismos patógenos y ambientales) at Centro de Investigación y Desarrollo de Fermentaciones Industriales (CINDEFI, La Plata/Argentina) and the strain collection at the Robert Koch-Institute (RKI, Berlin/Germany). The complete list of microbial strains used is given in table S1 of the supporting information (SI). In this study we defined for methodological reasons three different sample sets. The so-called RKI sample set has been previously used as the microbial test set by the RKI group in a number of earlier studies: This sample set comprised eight taxonomically distinct strains from three genera of Grampositive bacteria (Bacillus, Enterococcus and Staphylococcus) and three genera of Gram-negative bacteria (Citrobacter, Pseudomonas and Escherichia). The second sample set, the Burkholderia species-level set contained strains from eight different species of this genus covering some pathogenic and some environmental organisms, while the Burkholderia strain-level set comprised eight strains from two different Burkholderia species, Burkholderia thailandensis and Burkholderia cenocepacia (see table S1). Bacterial cultivation: Each bacterial strain or isolate was independently cultivated at least three times (three or more biological replicates). Bacterial starter cultures were obtained by growing the strains under aerobic conditions for 24 hours at 28°C , or 37°C (see table S1), using three-quadrant streak patterns, on casein peptone/soymeal peptone agar (Caso, Merck, now Merck Millipore, Darmstadt, Germany) or tryptic soy agar plates (TSA, Oxoid GmbH, Wesel, Germany). For the preparation of micro-colonies, an equivalent of three platinum loops was taken from confluent colonies in the third quadrant. After suspending in 10 mL pre-warmed LB liquid culture medium (1% Bacto-Trypton, 0.5% Bacto-Yeast-Extract, 1% NaCl, Becton Dickinson GmbH, Heidelberg, Germany), the bacterial suspensions were diluted in a decimal dilution series (1:10 – 1:10,000). Volumes of 100 µL of the 1:1000 and 1:10,000 dilutions were distributed via a Drigalski spatula on pre-warmed agar plates. Cultivation of strains from the RKI sample set was done on Caso agar while TSA agar was used to grow Burkholderia strains. Growth temperatures and growth times are given for each individual strain in table S1. After 6–24 hours of growth, the diameter of the microbial micro-colonies varied between 50 and 300 µm. Sample preparation: Transfer of the microbial material from micro-colonies on solid agar plates onto IR transparent substrates was carried out by means of a custom-designed stamping device. 19,20 This device allowed spatially accurate transfer of the micro-colonies’ upper cell layers onto CaF2 windows (Ø 25 mm, thickness of 2 mm) from Korth (Korth Kristalle GmbH, Altenholz, Germany) which were glued into specially designed Teflon holders (Körber & Körber Präzisionsmechanik, Birkenwerder, Germany). Previous studies in our laboratory did not provide indications for transfer of agar, or medium constituents from the solid agar plates onto IR sample supports by the replica technique. Micro-colony imprint samples were dried for approx. 15 minutes and subsequently spectroscopically characterized.


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FT-IR imaging: FT-IR hyperspectral imaging measurements were carried out by employing an Agilent 620 IR microscope coupled to an Agilent 670 FT-IR spectrometer (Agilent Technologies, Santa Clara, CA). The instrument and a custom-designed microscope box were purged by dry air to reduce spectral contributions from atmospheric water vapor. The IR microscope was equipped with a liquid nitrogen cooled mercury-cadmium-telluride (MCT) 128 × 128 focal plane array (FPA) detector and a 15 × Cassegrain objective with a numerical aperture of 0.62. In this configuration the sample area at the focal plane was 700 × 700 µm giving a pixel size of approx. 5.5 × 5.5 µm2. 21 The effective lateral spatial resolution was established to be smaller than 10 µm at 1600 cm-1 (unpublished data, determined by a protocol given in ref. 22). FT-IR data were collected using Agilent’s Resolutions Pro data acquisition software (ver. 5.0.0.640). Hyperspectral images (HSI) were recorded in transmission mode with 8 cm−1 spectral resolution in the 900-3800 cm-1 range. HSI interferograms were processed using a Blackman-Harris 4-term apodization function and a zero filling factor of four. To optimize the signal-to-noise ratio (SNR), 256 interferograms were co-added for each HSI sample and background measurement (cf. SI-2). Background FT-IR HSIs were collected through clean areas of the CaF2 slides. The number of acquired HSIs per individual sample strain varied between 6 and 12, i.e. between two and four HSI measurements were carried out from various locations of the distinct imprint samples. Data analysis: Spectral data analysis was performed by means of the CytoSpec software package (ver. 2.00.04, CytoSpec, Berlin, Germany), OPUS ver. 5.5 (Bruker, Karlsruhe, Germany) and the NeuroDeveloper software (Synthon GmbH, Heidelberg, Germany). The CytoSpec program was used for spectral pre-processing, including quality testing, for defining regions of interest (ROI), calculating ROI mean spectra and selecting / extracting point spectra from the HSI. Generally, between 15 (RKI data set) and 50 (Burkholderia data sets) individual point spectra were extracted from each HSI. The total number of extracted pixel spectra per microbial strain equaled roughly 150 in case of the RKI data set, and between 200 and 400 in the Burkholderia species and strain level sets, respectively. Classification analysis and ANN based image segmentation: Strategy and principles of ANN classification analysis of FT-IR spectral data were applied as previously described. 23,24 First steps of spectra analysis included extraction of pixel spectra for ANN teaching and calculation of so-called ROI mean spectra from representative numbers of biological / technical replicate HSI which served as inputs for UHCA. UHCA was then conducted using Bruker’s OPUS software by utilizing the information content from specific wavenumber regions. Dendrograms were constructed on the basis of D-values 25 and Ward’s algorithm. To teach and optimize the ANN classifiers, the data set of point spectra extracted from the first and the second cultivation series were first divided in subsets for teaching with roughly ∼80% of the point spectra and for internal validation (20%). To avoid overfitting, particular emphasis was placed on the requirement to keep the external validation data separate from the teaching / internal validation subsets. External validation was performed exclusively by HSIs obtained during the third and in some cases by a fourth biological replicate measurement series. Automated testing for spectral quality (poor SNR, non-linear detector response, baseline artefacts due to scattering, etc.) of the external validation HSI was performed by a top-level ANN. This network was compiled on the basis of three classes of manually selected teaching spectra denoted as high



quality (+), low SNR and quality(-). Furthermore, in order to avoid the interfering spectral contribution from poly-β-hydroxybutyrate (PHB), a so-called PHB network was designed and optimized by means of two classes of IR spectra, designated as PHB(-) and PHB(+). Spectra of both classes were manually selected on the basis of the intensity of the ester carbonyl absorption feature at 1738 cm-1. Absorbance values at 1738 cm-1 larger than 0.01 absorbance units were used as univariate thresholds to group the respective spectra into class PHB(+). Neural network analyses were carried out by the NeuroDeveloper software package from Synthon which combines modules for data pre-processing, feature selection, model development and ANNbased classification. Spectra pre-processing included the calculation of first derivative spectra and vector normalization. Feature selection was done based on the Covar feature selection routine. Training of the individual ANNs was carried out by means of the resilient back-propagation (rprop) learning algorithm.23 Three layer feed-forward networks with varying numbers of neurons in the input, hidden and output layers were employed.

RESULTS RKI data set: Today, modern FT-IR spectroscopic instrumentation allows studying the structure and composition of the biological material, while ensuring high sensitivity and specificity, together with a high level of reproducibility and taxonomic resolution. The data quality and the levels of spectral distinctness as well as the reproducibility of the mid-IR microspectra from different microbial test strains are exemplary illustrated in figure 1. This illustration shows normalized first derivatives of average FT-IR microspectra obtained from micro-colony imprints of the strains of the RKI sample set. Average spectra were obtained from regions of interest (ROI) which were typically defined in the central parts of micro-colony imprints and usually contained between 50 and 150 individual point spectra. From figure 1 it is evident that ROI mean spectra from identical microbial strains exhibit high levels of similarity suggesting a satisfactory degree of reproducibility. Furthermore, the FT-IR spectra from different taxons display only moderate spectral differences which are typically distributed over the entire spectral range. The largest inter-class variances can be found in the CH-stretching region between 2800-3000 cm-1, the ester carbonyl stretching region (1700-1760), and in the 1370-1490 cm1 and 900–1200 cm-1 regions. The ester carbonyl band at 1738 cm-1 is known as a suitable indicator of poly-β-hydroxybutyrate (PHB), a biological polymer (polyester) 18,20 whereas the latter IR regions can be associated with deformation modes of the CH2- and CH3-functional groups and contributions from various vibrations of polysaccharides, respectively. 20 ROI mean spectra obtained from the HSI of the RKI test set were chosen as inputs for unsupervised hierarchical cluster analysis (UHCA). For UHCA altogether 242 mean spectra from Gram-positive and Gram negative strains of the RKI data set (cf. table S1) were utilized. The dendrogram shows speciesspecific clusters whereas the two main clusters are formed by ROI mean spectra from Gram-positive, or Gram-negative bacteria, respectively (see supporting information, SI for details). The clustering scheme was in the following utilized to define the topology of a supervised hierarchical neural network classifier. Hierarchical (modular) systems of ANNs were used to simplify complex classification tasks. It has been widely demonstrated that small two or three class-classification models are much easier to


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train and optimize. Furthermore, within a hierarchical ANN classification scheme different subclassifiers can be established by utilizing different spectral pre-processing strategies, learning rules and network topologies. 15,23 In the modular ANN classification scheme differentiation at the top-level was performed between spectra of the classes quality(+), low SNR and quality(-). FT-IR microspectra classified as quality(+) were then forwarded to a second ANN, called Gram net, which was designed to differentiate between spectra from Gram-positive and Gram-negative bacteria. Members of the class Gram(+) were in the following analyzed by the Gram(+) sublevel ANN, whereas spectra classified as Gram(-) were directed to the Gram(-) subnet. These third-level networks permitted differentiation into the classes B. cereus, B. subtilis, E. faecalis, S. aureus and S. epidermidis by the so-called Gram(+) ANN, and C. freundii, E. coli and P. aeruginosa by the Gram(-) sublevel ANN, respectively. Figure 2 shows the results of neural network image segmentation of FT-IR HSI acquired from microcolony imprints of RKI test set samples (third measurement series, the color coding is consistent with the color scheme used in figure 1). In ANN image segmentation, all spectra assigned to a given class are encoded by the same color. Vice versa, spectra classified as different are plotted by different colors. In figure 2 spectra for which the classification failed, or with ambiguous ANN classification results, are encoded by the black color whereas dark gray regions indicate spectra of the class quality(-). The segmentation data of figure 2 demonstrate that FT-IR microspectral patterns of microcolony imprints can be employed for accurate identification of taxonomically different microbial strains: In all instances, the vast majority of the of mid-IR microspectra was accurately classified. Only some strains (e.g. E. coli and S. epidermidis) exhibited certain level of misclassifications, mainly at location at the edges of the micro-colony imprints. These inaccurate classification results are possibly caused by an only moderate SNR at the edge positions of the micro-colonies (cf. example of E. coli). However, the segmentation images of figure 2 show, that misclassifications are virtually absent at locations containing more biological material, i.e. at the center positions of the micro-colonies. Burkholderia species and strain level data sets: Selected examples of FT-IR ROI mean absorbance spectra from both Burkholderia sets are provided in figure 3. These mean spectra generally demonstrate an excellent SNR, which is of course lower in the raw pixel spectra. Main spectra variations are observable at 1738 cm-1 (ester-carbonyl band), in the C-H stretching region (2800-3050 cm-1) and at selected positions in the 900-1400 wavenumber range (contributions from a storage material poly-β-hydroxybutyric acid, PHB, see introduction). The ester-carbonyl band is rather prominent in the microspectra of B. cenocepacia DSM 16553 and B. glathei LMG 14932 and less pronounced in B. stabilis DSM 16586 and B. gladioli DSM 4285. Spectra from B. cenocepacia LMG 16654 and B. caledonica LMG 19076 exhibit only a very weak shoulder at 1738 cm-1. Burkholderia are known to produce PHB in response to conditions of physiological stress 17 and it has been established that IR absorbance values at 1738 cm-1 and of some other features in the 900-1400 wavenumber range show a high correlation with the concentration of PHB. 16-18 Cluster analysis of data from the Burkholderia data sets provided valuable information to define the architecture of the dedicated modular ANN classifier (see SI for details). The toplevel classifier of the modular Burkholderia classification scheme developed in this work involved a quality test ANN, as it is illustrated in figure 4. The subsequent network layer consisted of the PHB net which was specifically designed for differentiation between IR spectral signatures with severe and no, or only



insignificant spectral contributions of PHB. Within the scope of the present study we found that IR microspectra extracted from samples of B. glathei LMG 14932 and B. cenocepacia DSM 16553 always showed relatively intense PHB-associated signals (see figure 3). This observation prompted us to exclude B. glathei as a separate category from the Burkholderia species level net (see figure 4). In consequence, the third-level Burkholderia species network permitted differentiation and identification of only 7 species of the Burkholderia genus. The lowermost layer of the ANN hierarchy is represented by two three-class strain classifiers, the B. thailandensis and the B. cenocepacia networks, respectively. B. cenocepacia DSM 16553 has been excluded as a distinct class for the reasons outlined above whereas the classes B. thailandensis E 125 and E 131 were merged; it turned out that these strains could not be effectively distinguished by the mid-IR microspectroscopic technique. Selected results of the classification analyses by the multi-level Burkholderia classification scheme are illustrated in figure 5. These segmentation images generally demonstrate a high degree of correspondence between ANN classification and microbial taxonomy. Examples with excellent identification rates are HSI segmentation data from strains like B. caledonica LMG 19076, B. gladioli DSM 4285, or B. cepacia DSM 9241. Other segmentation images like those from B. cenocepacia LMG 16654 and B. stabilis DSM 16586 exhibit regions in which the taxonomic information appears to be superimposed by infrared absorption features of PHB, mainly in the central areas of the micro-colony imprints. However, it must be also noted that the high-PHB producer strains B. cenocepacia DSM 16553 and B. glathei LMG 14932 could not be identified: Both strains were disregarded as separate taxonomic categories; their FT-IR microspectra were thus attributed without exceptions to the category PHB(+). By contrast, identification of bacterial strains with no, or only insignificant production of PHB, was quite successful. This is illustrated by the FT-IR segmentation images of the three remaining strains of B. cenocepacia which suggest fully satisfying identification rates (cf. upper row of figure 5). As in the case of the RKI data set, selected instances of the B. cenocepacia data show diminished identification accuracies at the edges of the micro-colonies, presumably because of a reduced SNR. Similar conclusions can be drawn also from the classification data given for some strains of B. thailandensis: At the centers of the micro-colonies the accuracy of strain identification is generally high, see second row of segmentation images in figure 5 with HSI examples of B. thailandensis LMG 20219 and the combined class of B. thailandensis E125/E131. At the other hand, for B. thailandensis DSM 13276 we found that only ~75% (954 out of 1274 pixel spectra) were correctly identified. It is believed that this level of accuracy would be acceptable in a routine setup, however, measures should be introduced to improve this value (for details see section S3 and figure S3).

DISCUSSION The aim of this proof-of-concept study was to evaluate the applicability of FT-IR microspectroscopic imaging and machine learning techniques for identifying pathogenic microorganisms and for assessing the taxonomic resolution. To this end, bacterial cultures were grown on solid media and the stamping method was adapted for spatially accurate transfer of the microbial biomass onto IRcompatible sample carriers. Modern methods of IR microspectroscopy and FT-IR hyperspectral imaging were then employed to sensitively obtain the IR characteristics from micro-colony imprint


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samples while modular ANN models were used for objective and operator-independent image segmentation. Since cultivation is considered the time-determining step of any current IR-based microbial identification method, testing of micro-colonies by IR microspectroscopy enables to reduce the cultivation time while maintaining an adequate spectral quality. The application of IR microspectroscopy should thus allow reducing the time needed for the diagnostic methodology as a whole. In this study three different data sets of microbial test organisms were compiled: (i) the RKI test set, the (ii) Burkholderia species level and the (iii) Burkholderia strain level set. These sample sets of taxonomically distant (RKI test set), or more closely related bacterial strains (Burkholderia sets) permitted to exemplary examine the accuracy of the microspectroscopic method at the genus, species and the strain level. Furthermore, overfitting of noise, or of other random covariates, with the potential to systematically distort the classification results could be avoided by strictly separating the external validation set comprising spectra from the third independent cultivation series from the training and internal validation sets which contained only spectra from the first two cultivation series. The classification results obtained from the RKI test samples generally demonstrated high levels of accuracy of differentiation and identification. Specifically, unsupervised cluster analysis and supervised classification analysis by a modular ANN classifier suggested an excellent agreement between the taxonomic status of the RKI test strains and the outcomes of the FT-IR hyperspectral imaging technique (cf. figure 2). Furthermore, ANN analysis of spectra from the Burkholderia species and strain level sets showed similarly good results (see figure 5). At the other hand, cluster analysis of the Burkholderia data pointed towards the well-known fact, that completely unsupervised classification of microbial IR data not only monitors the taxonomic position of the microorganisms studied, but is sensitive also to the overall structure and biochemical composition of the microbial cells. In fact, phenotyping techniques like Raman or IR spectroscopy detect the actual biochemistry which is not necessarily identical in all microbial cells carrying the same genotype. 26 Therefore, as opposed to genotyping methods like multi-locus sequence typing (MLST) or whole genome sequencing (WGS), IR spectroscopy can largely interfere with the presence of storage materials, such as PHB, and may be influenced by the presence of different types of pili, flagellar appendages produced by Gram-negative rods, or spores in Bacillus, to mention some examples for phenotypic heterogeneity. 17 Phenotypic heterogeneity of genetically homogeneous cell populations has important practical consequences when studying the taxonomy by vibrational spectroscopy, or mass spectrometry. For example, many microbial genera, including species of the genus Legionella, Bacillus, or Burkholderia, may produce considerable amounts of PHB and it has been stated that varying PHB concentration levels can be considered important sources of interference with phenotype-based characterization techniques. 17,26 In this work, the strategy to overcome the problem of spectral contamination by PHB involved a number of measures. First and foremost, reduced cultivation times aimed at lower expression levels of this particular storage compound. Bosch and coworkers have noted in an earlier FT-IR study that the cultivation time should be as short as possible in order to avoid PHB interference with species discrimination. 17 Secondly, adequate spectral pre-processing which included ANN-based quality testing to identify spectra with inacceptable PHB signal intensities also contributed to separate



between the IR fingerprint features of PHB and the taxonomically relevant information. Thirdly, the application of dedicated spectral feature selection routines has led to underrepresentation of PHB markers bands in the teaching data presented to the ANN. Finally, the matrix of weighting values established in the teaching phase of ANN model development should ideally also reflect the status of PHB features as confounding variables of classification. As a consequence, the data analysis strategy employed in this study allowed, in principle, accurate identification of the microbial species and beyond that, also at the strain level. With the exception of two PHB high-producer strains, B. glathei LMG 14932 and B. cenocepacia DSM 16553, the segmentation maps of figure 5 demonstrate accurate identification at the species level. Moreover, the ANN data suggest successful differentiation of strains from the same microbial species: Apart from two non-differentiable strains of B. thailandensis (E 125 and E 131), it could be established that the FT-IR microspectroscopic absorption patterns are indeed suitable for strain level differentiation. This level is comparable with the reported taxonomic resolution of (macro) FT-IR spectroscopy 20,27 and confirms the main findings of many other FT-IR microspectroscopic studies. 2,15,20 Furthermore, the possibilities for automation and high-throughput analyses make the FT-IR microspectroscopy and imaging a suitable candidate for clinical diagnostic microbiology. Another potential benefit of the IR microspectroscopy-based classification analysis is the speed advantage compared to alternative microbial typing methods with comparable taxonomic resolution. In this study the cultivation times varied between 5.5 h for fast growing bacteria and 30 h in case of B. caledonica (cf. table S1). This allows providing diagnostic results either within one working day, or at the next day, in case of some slow growing Burkholderia strains. Optimization of the instrumentation of the Agilent 620 FT-IR microscope, such as a 25× objective with a numerical aperture of 0.81 in combination with the so-called high magnification mode enable acquisition of high-resolution hyperspectral images with an effective geometric pixel size down to 0.66 × 0.66 µm2.21 This type of optics allows HSI measurements from less sample amount and thus, permits shorter cultivation times. Test measurements in our laboratory have confirmed that such an optical configuration improves the lateral spatial resolution at comparable signal collection times and only slightly reduced noise levels (unpublished data). Reduced biomass requirements would not only enable identification of slow-growing, fastidious microorganisms within one working day, but could be helpful also to reduce interfering effects from PHB which is synthesized at only low levels in the very first hours of cultivation. 17 The need for strictly standardizing the condition of cultivation and sample preparation is considered a major drawback of the proposed microbial identification method. It is well known that culture conditions such as growth time, type of culture medium and others may exert a strong impact on the microbial phenotypes and thus on the resulting IR spectral fingerprints. 28,29 Furthermore, different ways of sample preparation and different measurement regimes may additionally alter the accuracy of classification analysis. Last but not least, library based identification approaches like the proposed one require validated spectral databases which ideally should cover a broad panel of the microbial taxonomy. Such databases are, however, currently not available which constitutes - aside from the problem of phenotypic heterogeneity – the major challenge to transfer the technology into practice. Once these problems are solved, we can look forward to interesting applications of FT-IR hyperspectral imaging in clinical, food and environmental microbiology.


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SUMMARY In the present study we have introduced FT-IR hyperspectral imaging in combination with neural network-based image segmentation for rapid, accurate and cost effective identification of Grampositive and Gram-negative bacteria. The proposed method involves a relatively short cultivation step under standardized conditions, transfer of the micro-colony imprints onto IR transparent CaF2 windows by a replica method and FT-IR hyperspectral imaging measurements. Data analysis involved pre-processing, quality tests, and the application of machine learning techniques for hyperspectral image segmentation. Strict standardization of the cultivation conditions and of the measurement protocols were identified as preconditions for successful application of the method. Experiences gathered from the present study also suggest that high-quality spectral databases and specifically adapted data analysis strategies are critical requirements to achieve satisfactory levels of identification accuracy. The compilation of complete and comprehensive databases is considered to be of paramount importance for reaching accurate and reliable spectral diagnoses. Future efforts to establish FT-IR hyperspectral imaging as a new diagnostic approach should therefore focus on the compilation of validated spectral databases and on the development of strategies to address the problem of interfering spectral contributions of storage compounds like PHB. We are confident that the high level of taxonomic resolution detected by this pilot study can be confirmed for many other microbial strains and species.

ACKNOWLEDGEMENTS K.M. is supported by the Foundation for Polish Science (FNP) and National Science Center (grant DEC2014/12/T/ST4/00686). A.B. is a member of Comisión de Investigaiones Científicas de la Provincia de Buenos Aires (CIC PBA). P.L. recognizes funding from the BMBF, grant number 01DN15016.

SUPPORTING INFORMATION -

Overview on the bacterial strains and species studied Optimizing measurement parameters: Noise as a function of scan time Segmentation analysis of FT-IR hyperspectral images by ANNs Unsupervised hierarchical clustering of ROI mean spectra from the RKI data set Cluster analysis of ROI mean spectra from the Burkholderia data sets

COMPETING FINANCIAL INTERESTS P.L. is the author of CytoSpec, a commercial software package for vibrational hyperspectral imaging which was used throughout this study. M.S., M.Z., M.B., A.B. and K.M: declare no competing financial interests.



FIGURE LEGENDS Figure 1. FT-IR mean spectra of the RKI test data set. Mean spectra were obtained by averaging between 15 - 20 individual FT-IR microspectra from microcolony imprints. Spectral pre-processing: first derivative (Savitzky-Golay filter with 9 smoothing points), vector normalization. Spectra were min-max normalized and shifted along the x-axis for more clarity.

Figure 2. ANN segmentation images obtained from micro-colony imprints of the RKI test data set. The spatial segmentation approach was based on FT-IR hyperspectral images of the external validation data (third cultivation series) collected by means of a FT-IR imaging system equipped with a 128 × 128 focal plane array (FPA) detector. The sampled area size was 700 × 700 µm2. The color scheme is consistent with the color scheme used in figure 1.

Figure 3. FT-IR absorbance spectra of selected species and strains from the Burkholderia species level and the Burkholderia strain level data sets. Spectra of B. cenocepacia DSM 16553 and B. glathei LMG 14932 demonstrate absorption features at 1738 cm-1 (see arrow) due to the storage material poly-βhydroxybutyrate (PHB). PHB can be found also in moderate concentrations in FT-IR spectra of B. stabilis DSM 16586 and B. gladioli DSM 4285, but is almost absent in B. cenocepacia LMG 16654 and B. caledonica LMG 19076.

Figure 4. Topology of neural network modules used for quality testing and ANN-based image segmentation of HSI of the Burkholderia species level and the Burkholderia strain level sets.

Figure 5. Characterization of micro-colony imprints using mid-IR hyperspectral imaging data and spatial segmentation based on machine learning techniques. Pseudo-color images were constructed from HSI of the external validation data (third cultivation series) of the Burkholderia species-level and Burkholderia strain-level sets. The color scheme used is consistent with those of figures 3 and 4; the size of the sample area was in all cases 700 × 700 µm2.


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REFERENCES (1) Maquelin, K.; Choo-Smith, L. P.; van Vreeswijk, T.; Endtz, H. P.; Smith, B.; Bennett, R.; Bruining, H. A.; Puppels, G. J. Analytical chemistry 2000, 72, 12-19. (2) Kirschner, C.; Maquelin, K.; Pina, P.; Ngo Thi, N. A.; Choo-Smith, L. P.; Sockalingum, G. D.; Sandt, C.; Ami, D.; Orsini, F.; Doglia, S. M.; Allouch, P.; Mainfait, M.; Puppels, G. J.; Naumann, D. Journal of clinical microbiology 2001, 39, 1763-1770. (3) Wenning, M.; Breitenwieser, F.; Konrad, R.; Huber, I.; Busch, U.; Scherer, S. Journal of microbiological methods 2014. (4) Fetsch, A.; Contzen, M.; Hartelt, K.; Kleiser, A.; Maassen, S.; Rau, J.; Kraushaar, B.; Layer, F.; Strommenger, B. International journal of food microbiology 2014, 187C, 1-6. (5) Demirev, P. A.; Fenselau, C. Journal of mass spectrometry : JMS 2008, 43, 1441-1457. (6) Lasch, P.; Wahab, T.; Weil, S.; Palyi, B.; Tomaso, H.; Zange, S.; Kiland Granerud, B.; Drevinek, M.; Kokotovic, B.; Wittwer, M.; Pfluger, V.; Di Caro, A.; Stammler, M.; Grunow, R.; Jacob, D. Journal of clinical microbiology 2015, 53, 2632-2640. (7) Helm, D.; Labischinski, H.; Schallehn, G.; Naumann, D. Journal of general microbiology 1991, 137, 69-79. (8) Naumann, D.; Helm, D.; Labischinski, H. Nature 1991, 351, 81-82. (9) Demirev, P.; Sandrin , T. R.; (eds). Springer International Publishing Switzerland 2016 2016. (10) Rebuffo-Scheer, C. A.; Kirschner, C.; Staemmler, M.; Naumann, D. Journal of microbiological methods 2007, 68, 282-290. (11) Sousa, C.; Silva, L.; Grosso, F.; Lopes, J.; Peixe, L. Journal of photochemistry and photobiology. B, Biology 2014, 133, 108-114. (12) Schuster, K. C.; Reese, I.; Urlaub, E.; Gapes, J. R.; Lendl, B. Analytical chemistry 2000, 72, 5529-5534. (13) Rosch, P.; Harz, M.; Schmitt, M.; Peschke, K. D.; Ronneberger, O.; Burkhardt, H.; Motzkus, H. W.; Lankers, M.; Hofer, S.; Thiele, H.; Popp, J. Applied and environmental microbiology 2005, 71, 1626-1637. (14) Lasch, P.; Hermelink, A.; Naumann, D. The Analyst 2009, 134, 1162-1170. (15) Maquelin, K.; Kirschner, C.; Choo-Smith, L. P.; Ngo-Thi, N. A.; van Vreeswijk, T.; Stammler, M.; Endtz, H. P.; Bruining, H. A.; Naumann, D.; Puppels, G. J. Journal of clinical microbiology 2003, 41, 324-329. (16) Kansiz, M.; Billman-Jacobe, H.; McNaughton, D. Applied and environmental microbiology 2000, 66, 34153420. (17) Bosch, A.; Minan, A.; Vescina, C.; Degrossi, J.; Gatti, B.; Montanaro, P.; Messina, M.; Franco, M.; Vay, C.; Schmitt, J.; Naumann, D.; Yantorno, O. Journal of clinical microbiology 2008, 46, 2535-2546. (18) Helm, D.; Naumann, D. FEMS microbiology letters 1995, 126, 75-79. (19) Ngo Thi, N. A.; Kirschner, C.; Naumann, D. Proc SPIE 2000, 3918, 36-44. (20) Lasch, P.; Naumann, D. Encyclopedia of Analytical Chemistry 2015. (21) Nallala, J.; Lloyd, G. R.; Hermes, M.; Shepherd, N.; Stone, N. Vib Spec 2017, 91, 83-91. (22) Lasch, P.; Naumann, D. Biochim Biophys Acta 2006, 1758, 814-829. (23) Udelhoven, T.; Naumann, D.; Schmitt, J. Applied spectroscopy 2000, 54, 1471-1479. (24) Lasch, P.; Diem, M.; Hansch, W.; Naumann, D. Journal of chemometrics 2007, 20, 209-220. (25) Helm, D.; Labischinski, H.; Naumann, D. Journal of microbiological methods 1991, 14, 127-142. (26) Hermelink, A.; Brauer, A.; Lasch, P.; Naumann, D. The Analyst 2009, 134, 1149-1153. (27) Dieckmann, R.; Hammerl, J. A.; Hahmann, H.; Wicke, A.; Kleta, S.; Dabrowski, P. W.; Nitsche, A.; Stammler, M.; Al Dahouk, S.; Lasch, P. Faraday Discuss 2016, 187, 353-375. (28) Sales, K. C.; Rosa, F.; Cunha, B. R.; Sampaio, P. N.; Lopes, M. B.; Calado, C. R. Biotechnology progress 2016. (29) Sandt, C.; Sockalingum, G. D.; Aubert, D.; Lepan, H.; Lepouse, C.; Jaussaud, M.; Leon, A.; Pinon, J. M.; Manfait, M.; Toubas, D. Journal of clinical microbiology 2003, 41, 954-959.



B. cereus ATCC 10987

B. subtilis RKI w2gr

E. faecalis DSM 20371

Absorbance [AU]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

S. aureus DSM 20231

S. epidermidis DSM 1798

C. freundii DSM 30039

P. aeruginosa ATCC 27853

E. coli RKI A139

3000

2500 1500 2000 Wavenumber [cm-1]

1000

Lasch et al, 2018, Figure 1, vers. February 28, 2018 ACS Paragon Plus Environment

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Entercoccus faecalis DSM 20371

Bacillus subtilis RKI w2gr

Staphylococcus epidermidis DSM 1798

Citrobacter freundii DSM 30039

Staphylococcus aureus DSM 20231

Escherichia coli RKI A139

neg quality test (toplevel)

Staphylococcus aureus DSM 20231

ANN classification failed

Bacillus cereus ATCC 10987

Entercoccus faecalis DSM 20371

Bacillus subtilis RKI w2gr

Pseudomonas aeruginosa ATCC 27853

Citrobacter freundii DSM 30039

Staphylococcus epidermidis DSM 1798

Escherichia coli RKI A139

Lasch et al, 2018, Figure 2, vers. January 02, 2018 ACS Paragon Plus Environment

1738

B. cenocepacia DSM 16553

B. glathei LMG 14932 Absorbance [AU]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1738


B. stabilis DSM 16586

B. gladioli DSM 4285

B. cenocepacia LMG 16654

B. caledonica LMG 19076

3000

2500

2000

1500

1000

Wavenumber [cm-1]


Lasch et al, 2018, Figure 3, vers. February 28, 2018

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all classes

quality test net

quality tests positive

PHB net

No PHB

Burkholderia species net

B. cenocepacia

B. thailandensis

thailandensis cenocepacia net net


quality tests negative (i): low signal, low SNR

Lasch et al, 2018, Figure 4, vers. March 02, 2018

quality tests negative (ii): bad baseline, saturation PHB contamination B. glathei LMG 14932 B. cenocepacia DSM 16553 B. caledonica LMG 19076 B. cepacia DSM 9241 B. gladioli DSM 4285 B. vietnamiensis LMG 10929 B. cenocepacia Fq 6593 B. cenocepacia LMG 16654 B. cenocepacia LMG 18863 B. stabilis DSM 16586 B. thailandensis DSM 13276 B. thailandensis LMG 20219 B. thailandensis E 125/E 131

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry Page 17 of 19


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B. cenocepacia DSM 16553

B. cenocepacia Fq 6593



B. thailandensis DSM 13276

B. thailandensis LMG 20219

B. thailandensis E 125

B. thailandensis E 131


B. cepacia DSM 9241


B. vietnamiensis LMG 10929


B. glathei LMG 14932 neg quality test (i)/(ii)

B. cenocepacia Fq 6593

classification failed


PHB




B. cepacia DSM 9241

B. thailandensis DSM 13276


B. thailandensis LMG 20219

B. vietnamiensis LMG 10929

B. thailandensis E 125/E 131

Lasch et al, 2018, Figure 5, vers. March 02, 2018 ACS Paragon Plus Environment

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TOC file 44x24mm (300 x 300 DPI)


FT-IR Hyperspectral Imaging and Artificial Neural Network Analysis for

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