Article pubs.acs.org/ac
Label-Free Imaging and Spectroscopic Analysis of Intracellular Bacterial Infections Christina Große,†,‡ Norbert Bergner,‡ Jan Dellith,‡ Regine Heller,†,§ Michael Bauer,† Alexander Mellmann,⊥ Jürgen Popp,†,‡,# and Ute Neugebauer*,†,‡ †
Center for Sepsis Control and Care, Jena University Hospital, Erlanger Allee 101, D-07747 Jena, Thuringia, Germany Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Thuringia, Germany § Institute for Molecular Cell Biology, Jena University Hospital, Hans-Knöll-Straße 2, D-07745 Jena, Thuringia, Germany ⊥ Institute of Hygiene, University of Münster, Robert-Koch-Straße 41, D-48149 Münster, North Rhine-Westphalia, Germany # Institute of Physical Chemistry and Abbe School of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, D-07743 Jena, Thuringia, Germany ‡
S Supporting Information *
ABSTRACT: Staphylococcus aureus is one of the most frequent human pathogens that can also act as a facultative intracellular pathogen causing infections that are extremely difficult to treat. Only little is known about the pathogen’s intracellular adaptation strategies to escape the host’s response. Here, we present an advanced Raman-based imaging approach providing high quality false-color images to specifically identify intracellular S. aureus and to localize them exactly in three dimensions within endothelial cells. At the same time unprecedented insights into the metabolic characteristics of the pathogen are provided in a label-free and nondestructive manner. The spectral information reveals that the intracellular bacteria are in the exponential growth phase with a reduced replication rate and biochemically different from extracellular bacteria proving their adaptation to the host’s conditions. This powerful biophotonic analysis tool paves the way for further mechanistic studies of difficult-to-investigate infection processes.
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appropriate noninvasive analysis methods that enable studying intracellular bacteria in their natural host cell environment. Most available methods depend on purification of the bacteria from the host cell, genetic modification or specific labeling to discriminate bacterial from cellular components. Such invasive methods readily introduce small disturbances that can largely influence gene expression and biochemical composition of the bacteria resulting in misleading information gained from the data output. Thus, there is an urgent need for label-free methods that can be applied to unperturbed infection models to shed more light on pathogenesis mechanisms and associated host−pathogen interactions. So far, there are only a few studies that have dealt with this difficult task, such as fluorescence lifetime imaging of NAD(P)H autofluorescence of intracellular bacteria6 or tracking of living bacteria on host cells with digital holographic microscopy to image the infection process.7 Raman spectroscopy-based techniques hold a high potential as they probe the molecular composition of a sample in a label-free, contact-less manner by analyzing inelastically scattered
he common, well adapted human pathogen that causes invasive, life-threatening diseases such as endocarditis, metastatic infections, and sepsis which lead to high costs in health care is Staphylococcus aureus.1 The bacteria’s pathogenesis comprises versatile adaptation strategies, a multitude of virulence factors, as well as the ability to invade different types of host cells, e.g. endothelial cells, epithelial cells, and osteoblasts, and persist within the intracellular locations as a facultative intracellular pathogen.2 The detailed mechanisms how S. aureus can escape the host response inside the cells are still unclear. However, such intracellular bacterial infections constitute a severe clinical problem as they are extremely difficult to treat. Bacteria inside the host cell often appear resistant to antimicrobial therapy, even though they have been tested susceptible in in vitro assays. There is accumulating evidence that the intracellular state of S. aureus infection represents a highly adapted, alternative state of the pathogenesis process, in which the bacteria can persist for longer time after phenotype-switching to small colony variants3−5 or by possible formation of intracellular biofilm-like reservoirs. A better understanding of intracellular infection processes is urgently needed to develop preventive and therapeutic strategies. Until now, progress is hampered by the lack of © 2015 American Chemical Society
Received: September 3, 2014 Accepted: January 11, 2015 Published: January 12, 2015 2137
DOI: 10.1021/ac503316s Anal. Chem. 2015, 87, 2137−2142
Article
Analytical Chemistry
grating prior to detection on a back illuminated CCD camera (DV401-BV-352, Andor, UK). The pinhole size was reduced to 25 μm by the diameter of the collection fiber assuring high lateral and axial resolution. Overview Raman spectroscopic images of the whole cell were recorded with a step size of 1 μm and 0.5 s integration time per spectrum. For high-resolution Raman maps with high quality spectra, the step size was reduced to 0.25 μm, and the integration time increased to 1 s per spectrum. Z-stacks were recorded with a step size of 1 μm along the z-axis between each image. N-FINDR Analysis of the Raman Image Scans. Data analysis was performed using Matlab software (MathWorks). The following preprocessing steps were applied before statistical analysis: cutting the spectral region from 250 to 3,100 cm−1, automated cosmic spike removal, removal of spectra with spikes that have been not removed by the automated function, and PCA based noise reduction using 10 principal components (PCs). Identification of bacteria and cellular components was carried out with the N-FINDR unmixing algorithm19,20 on the spectral region of 400 to 1,850 cm−1 and 2,770 to 3,025 cm−1 and included vector normalization. Endmember spectra of the pure components were revealed as original spectra. The algorithm assigned similarity to the individual endmember for each spectrum in the data set. Statistical Analysis of the Raman Spectra. All raw spectra of a scan that had more than 60% similarity to the bacteria endmember spectrum after N-FINDR analysis were extracted from Raman scans and further used for statistical analysis within GNU R21 (used packages are listed in the Supporting Information). The spectra were baseline corrected with a third order polynomial, vector normalized, and PCA was applied in the wavenumber range of 600 to 1,800 cm−1 and 2,800 to 3,025 cm−1. The first six PCs were used to build the linear discriminant analysis (LDA) classification model. The training data set consisted of 24 spectra from 12 different bacteria for each time point. Two different batches were used as independent data sets for training and testing the LDA model. The nucleic acid/protein ratio was calculated for each spectrum as integrated intensity of the nucleic acid band (783 cm−1) divided by the integrated intensity of the amide I band. The used integration limits were 775 to 790 cm−1 and 1,640 to 1,700 cm−1, respectively. Immunofluorescence Detection of Bacteria. After Raman measurement the cells were permeabilized with 0.1% saponin (Sigma-Aldrich, Germany) followed by labeling with a mouse anti-Staphylococcus aureus monoclonal IgM antibody (clone STAPH 11-248.2; Millipore) and subsequently an Alexa Fluor 488-conjugated goat antimouse antibody (Jackson Immuno Research Laboratories) in PBS with 3% bovine serum albumin (BSA, Albumin fraction V from bovine serum, Merck) and 0.1% saponin. Thereafter, cells were mounted in Mounting Medium with 4′,6-diamidino-2-phenylindole (DAPI; Vector Laboratories). Fluorescence images were generated with a confocal laser scanning microscope (LSM 510 META, Zeiss).
photons. This powerful vibrational spectroscopic method was already successfully applied to reliably identify different bacterial species and strains,8−10 even on the single bacterial cell level. 11 Information-rich chemical images can be reconstructed with the help of statistical analysis methods from the Raman spectra of eukaryotic cells revealing cellular compartments such as the cytoplasm, nucleus, mitochondria, and lipid bodies.12−16 However, up to now, the potential of confocal Raman microscopy-based methods to noninvasively study intracellular bacteria in their natural surrounding host cells has not been exploited and evaluated yet. In this contribution, an advanced Raman-based imaging approach is presented which provides not only high quality false-color images to specifically identify and localize intracellular S. aureus in human endothelial host cells but also valuable information about the metabolic state of the intracellular bacteria in a label-free and nondestructive manner. The spectroscopic findings are benchmarked against established biochemical analysis methods such as classical growth curves and the analysis of gene expression profiles of the intracellular bacteria. The presented method is a valuable tool to shed more light on the complex intracellular mechanisms of the pathogen in order to pave the way for the development of novel preventive and therapeutic strategies against intracellular hiding bacteria.
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MATERIAL AND METHODS Cells and Bacteria Strains. The human endothelial cell line EA.hy926 from Edgell et al.17 was cultured in Dulbecco’s Modified Eagle Medium (DMEM) containing 10% fetal calf serum (FCS; Biochrom AG) at 37 °C and 5% CO2. Staphylococcus aureus subsp. aureus ATCC 6538 (American Type Culture Collection) was cultured in CASO broth (Carl Roth, Germany) at 37 °C and 100 rpm before use for cell infection and growth curve measurement. Infection Procedure. The infection protocol was adapted from Haslinger-Löffler et al.18 EA.hy926 cells were grown on calcium fluoride slides (Crystal GmbH, Germany) at a density of 5 × 104 cells per cm2. An overnight culture of S. aureus was used to inoculate a new culture that was grown to an optical density of 0.6 at 600 nm, which corresponds to a concentration of 6 × 108 colony forming units (CFU) per ml in the exponential growth phase. After centrifugation at 3,400g for 5 min the bacteria were resuspended in DMEM with 10% FCS. The endothelial cells were then incubated with 1 × 106 CFU S. aureus per mL at 37 °C and 5% CO2 for infection. Uninfected control cells were incubated with medium without bacteria. After 1.5 h extracellular bacteria were removed by treatment with 20 μg/mL lysostaphin (Sigma-Aldrich, Germany) for 10 min and thereafter further cultivated in fresh medium for different time periods to allow intracellular bacterial growth. The determination of the number of intraand extracellular bacteria is described in the Supporting Information. Raman Spectroscopic Imaging. Raman spectra were obtained with the confocal Raman microscope alpha300R (WITec, Germany). Cells were chemically fixed with 4% formaldehyde solution (Roti-Histofix, Carl Roth, Germany) for 10 min and measured in phosphate buffered saline (PBS) with a 60x water immersion objective (apochromat, NA 1.0; Nikon, Japan) and a 532 nm Nd:YAG solid laser giving a power of 15 mW in the focal plane. The backscattered light was transmitted through a multimode fiber onto a 600 lines/mm spectral
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RESULTS AND DISCUSSION Label-Free Imaging of S. aureus Infected Endothelial Cells. An intracellular infection model using S. aureus and the EA.hy926 endothelial cell line was established in our laboratory based on known protocols.18 Endothelial cells were chosen as they are regarded to play an important role in the escape of the pathogen from the bloodstream into tissues.22 Figure 1A shows 2138
DOI: 10.1021/ac503316s Anal. Chem. 2015, 87, 2137−2142
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Analytical Chemistry
Figure 1. Label-free imaging and identification of S. aureus in an infected EA.hy926 endothelial cell. (A) White light image revealing intracellular spherical particles of approximately 1 μm size (pointed out with arrows). They appear either as single forms or grape-like groupings, which are characteristic morphological features of staphylococci. (B) False color Raman image (pixel size: 1 × 1 μm2) showing the intensity distribution of the CH stretching vibration centered at 2,938 cm−1. Band intensity in arbitrary units is indicated in the gray scale bar. (C) False color N-FINDR Raman image of a high resolution scan (pixel size: 0.25 × 0.25 μm2) of the section marked in part A. The relative RGB color contributions indicate the grade of similarity to the endmember spectra which could be assigned to bacteria (green), cellular nucleus (blue), perinuclear region (red), and background (black). (D) Fluorescence image of the same cell after specific staining with an antibody binding S. aureus (Alexa Fluor 488, green) and DAPI binding DNA (blue), which is present in the nucleus and the bacteria. (E) Detailed Raman z-stack with composite N-FINDR images recorded with a distance of 1 μm (part C is identical to the image in the 2 μm plane).
Locating the Bacteria in the Cell in Three Dimensions. This imaging algorithm not only is limited to the visualization of intracellular bacteria in one plane but also can be carried out in the full three-dimensional space occupied by the host cell. Figure 1E shows the same cellular region as depicted in Figure 1C but in five different horizontal planes with spacing of 1 μm in the z-direction starting at the CaF2 surface to which the cell is attached. High confocality of the system assures a spatial resolution close to 1 μm in the z-direction allowing for exact location of staphylococci in the cell. Clearly, the bacterial distribution in different planes along the z-axis can be seen, as some bacteria are located closer to the bottom and some closer to the top of the cell. It should be noted that Raman spectra from the nucleus can be found in all five planes, while the bacteria that reside in the cytoplasm further away from the nucleus are only found in the bottom images (Figure 1E). This can be explained with a fried egg (sunny-side up) shape of the cell where the nucleus sticks out from an otherwise flat cytoplasmic area providing only in its close vicinity enough space in the cell to host the bacteria in different z-regions. With supporting scanning electron microscopic pictures (Figure S-3) this fact could be clearly visualized as well. Combination with Fluorescence Microscopy for Verification of S. aureus Infection. Since the described Raman-based imaging approach is nondestructive, label-free, and noninvasive, the same sample can be used for other analysis methods afterward, such as fluorescence imaging using specific antibodies. Here, the sample was subsequently used for immunofluorescence staining targeting specifically the S. aureus antigen on the bacterial cell surface (Figure 1D). Furthermore, DAPI staining could visualize the cell nucleus as well as bacterial DNA. The fluorescence image (Figure 1D) shows high similarity to the Raman false color image (Figure 1C) giving proof that both bacteria as well as cellular organelles
the white light image of such an infected endothelial cell at 3 h post infection (p.i.). To ensure that only intracellular bacteria are detected, extracellular bacteria were removed by lysostaphin treatment. Real-time, univariate Raman spectroscopic images using the intensity of the CH stretching vibration centered at 2,938 cm−1 reveal the contours of the cell and an abundance map of organic material (Figure 1B). The cellular nucleus and the areas that contain bacteria have a much higher CH stretching band intensity representing a higher density of organic material as is expected from the tightly packed nucleus and compact bacteria. Figure 1C shows the corresponding high resolution false color Raman image of the area marked in Figure 1A where bacteria as well as part of the nucleus and cytoplasm are present. Each pixel of the image represents a full Raman spectrum which contains important biochemical information. With the help of multivariate statistical methods this information can be extracted and visualized. N-FINDR as an unsupervised unmixing algorithm was chosen as it yields realistic false color images while retaining the original spectra as endmember for further analyses and requires only minimal user input for the unmixing algorithm.19 The overlay of the spatial distribution of the different endmembers yields a high quality, realistic illustration of the contour and shape of the roughly 1 μm-sized intracellular bacteria in the false color image (Figure 1C). Importantly, in this image (Figure 1C) the cellular nucleus, perinuclear region, and background can be also identified based on the biochemical characteristics reflected in the respective Raman spectra (Figure S-1). Furthermore, other particles in the cells that have a size and morphology similar to those of bacteria (e.g., lipid droplets or vesicles) could be identified with this algorithm as well and readily distinguished from the bacteria by their very specific Raman signature (Figure S-2). 2139
DOI: 10.1021/ac503316s Anal. Chem. 2015, 87, 2137−2142
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Analytical Chemistry
assign the spectral features and gain insight into the biochemical changes responsible for the separation of intraand extracellular bacteria, a classification model based on principal component analysis and linear discriminant analysis (PCA-LDA) was built (Figure 3). A training data set was well separated by the linear discriminant (LD1) according to the two defined classes with only a few misclassifications (Table 1, 97% correct prediction). The model was applied to an independent test data set. Based on their Raman spectra, the bacteria were predicted to be in an intracellular or extracellular state with high identification accuracy of 85% (Figure 3A and Table 1). This reliable differentiation of intra- and extracellular bacteria was based on important associated biochemical changes which are present in the Raman spectra. The linear coefficient of the PCA-LDA model (Figure 3B) can help to identify those spectral changes. Spectra of intracellular bacteria were found to have higher intensities in the vibrational bands at 1,303, 1,444 and 2,846 cm−1 which are in close vicinity to the CH2 vibrational bands and at the amide I band at 1,658 cm−1. This suggests a different protein and lipid composition, maybe some extra proteins in the intracellular bacteria. Higher intensities at 797 and 950 cm−1 as well as at 2,951 cm−1 near to the CH3 band were found in the spectra of extracellular bacteria. To confirm biochemical differences between intra- and extracellular bacteria, the gene expression of the important S. aureus virulence factor α-toxin was determined by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). A strongly increased expression of this gene was revealed in the intracellular bacteria (3 and 9 h p.i.) compared to the extracellular bacteria (0 and 24h p.i.; Figure S-5). This
(such as the nucleus) in the host cell had been reliably identified by the Raman-based algorithm. Slight differences between the two imaging approaches might be due to incomplete fluorescence labeling of the secondary antibodies as well as varying antibody binding efficiencies. The latter ones can be caused either by varying antigen expression or hindered antibody diffusion within the dense cellular structures in the host cell. The general procedure of subsequent fluorescence staining can be further extended to combine, e.g., single protein detection with bacteria and compartment visualization for further mechanistic studies. Biochemical Differences of Intra- and Extracellular S. aureus. S. aureus is known to undergo significant biochemical changes upon invasion of its host cell, e.g., expression of virulence factors. In order to examine such biochemical changes in the bacteria, Raman spectra of intra- and extracellular bacteria have been extracted from the Raman image scans of infected cells at different time points post infection (p.i.) as well as from scans of extracellular bacteria from the start of the experiment (0 h p.i.) (Figure 2). With the help of powerful statistics it is possible to extract small but meaningful spectral alterations from the very similar appearing spectra. Unsupervised classification algorithms (principal component analysis) revealed a separation of the spectra of bacteria at 0 and 24 h p.i. from the spectra of bacteria at 3 and 9 h p.i. That means extracellular S. aureus, before entering the host cells (0 h p.i.) and after exiting the host cell at 24 h p.i. when the endothelial cells had burst and released a huge amount of bacteria (Figure S-4), can be differentiated from the intracellular bacteria. To
Figure 2. Raman spectroscopic analysis of different infection states of S. aureus in endothelial cells. (A) Schematics of the infection process. (B) Representative N-FINDR composite images showing bacteria (green) and perinuclear region (red) and (C) corresponding Raman mean spectra with standard deviations of the bacteria from different time points p.i. For band assignments see Table S-1. The 0 h p.i. (black spectrum) sample represents extracellular grown bacteria from the start of the experiment. At 3 h p.i. (green spectrum) and 9 h p.i. (yellow spectrum) the bacteria were only detected inside the cells. At 24 h p.i. (red spectrum) most of the bacteria were in an extracellular state again (see also Figure S-4).
Figure 3. Classification model to distinguish extra- and intracellular bacteria. (A) PCA-LDA score plot of an independent test data set resulting in good separation of the 0 h p.i. (black ▲) and 24 h p.i. (red ●) data from the 3 h p.i. (green ■) and 9 h p.i. (yellow ▼) data by the linear discriminant (LD1). (B) The linear coefficient of LD1 reveals the main variances in the extra- and intracellular bacterial spectra. 2140
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Analytical Chemistry Table 1. Confusion Matrix of the PCA-LDA Model prediction classification
identification
reference
extracellular
intracellular
extracellular
intracellular
extracellular intracellular
47 2
1 46
78 18
11 87
correlates well with the above-described spectral distinction of these two states and confirms that major biochemical changes are occurring during the switch between bacteria present outside and inside host cells. Biochemical Changes during Bacterial Growth States. To further characterize the bacterial growth inside the endothelial cells, spectral as well as microbiological information was analyzed. Raman spectra of bacteria have been successfully used to differentiate the growth phases making use of the relative increase of the amount of nucleic acids in the exponential phase compared to the lag and stationary phases.23 This relative nucleic acid increase is due to increased DNA synthesis and transcription related to the faster multiplication in this growth phase. Figure 4A depicts the nucleic acid/protein ratio of the bacteria during four different time points of the endothelial cell infection experiment computed from the intensity of the Raman bands at 783 cm−1 (nucleic acid) and at 1,640 to 1,700 cm−1 (amide I band representative for protein). A slight, but significant decrease of the nucleic acid/ protein ratio is observed during the course of the intracellular infection from 3 to 9 h p.i. The observed change in the first 3 h of infection is the largest and reflects the drastic changes from bacteria extracellular grown in nutrition-rich CASO broth which were used as inoculum at 0 h p.i. to bacteria in the more delicate and very different host cell environment at 3 h p.i. The decreased nucleic acid/protein ratio indicates a reduction in DNA synthesis and thus reduced growth of S. aureus after entry into the host cells. At 24 h p.i., when the bacteria had caused lysis of the host cell and are again in an extracellular state, the nucleic acid/protein ratio showed a higher heterogeneity. Those bacteria could be in different growth phases, and also the time that has passed after the cell burst could be different. For comparison and exact assignment of the growth phases, control experiments were carried out with S. aureus grown under optimal growth conditions in standard nutrition medium. A typical growth curve with the characteristic bacterial growth phases (lag phase, exponential phase and stationary phase) is depicted in Figure 4D. Figure 4B shows the corresponding nucleic acid/protein ratios for the lag phase (30 min), the exponential phase (2.5 h), and the stationary phase (7 h). As expected the ratio is highest in the exponential growth phase indicating that the DNA synthesis increases from lag to exponential phase and then decreases during the stationary phase due to the reduced growth of the bacteria. There is an agreement of the DNA/protein ratio of the extracellular bacteria in the exponential growth phase (2.5 h in Figure 4B) and of the bacteria at the start of the infection experiment (0 h in Figure 4A), as bacteria have been added to the endothelial cells in their exponential growth phase. In order to prove the reduced growth rate observed in the Raman spectra of intracellular bacteria, colony forming units (CFU) were counted from lysed infected cells at different time points (Figure 4C). The growth of intracellular bacteria had a linear slope from 3 to 9 h p.i. indicating that the bacteria are in an exponential phase during this time period. However, the
Figure 4. Differentiation of growth states of intracellular and extracellular grown bacteria. (A) Changes with time p.i. in the nucleic acid (783 cm−1)/protein (amide I band) ratio of S. aureus during the infection experiment. Depicted are median ratios of the Raman band intensities, the whisker marks the 1.5 interquartile range, asterisks show values of 99th and 1st percentile. Statistical significance between the data of the different time points was determined using a Wilcoxon Signed Rank Test giving p values
