Raman microspectroscopy differentiates perinatal pathogens on ex vivo infected human fetal membrane tissues

Abstract

Streptococcus agalactiae, also known as Group B Streptococcus (GBS), is a major cause of chorioamnionitis and neonatal sepsis. This study evaluates Raman spectroscopy (RS) to identify spectral characteristics of infection and differentiate GBS from Escherichia coli and Staphylococcus aureus during ex vivo infection of human fetal membrane tissues. Unique spectral features were identified from colonies grown on agar and infected fetal membrane tissues. Multinomial logistic regression analysis accurately identified GBS infected tissues with 100.0% sensitivity and 88.9% specificity. Together, these findings support further investigation into the use of RS as an emerging microbiologic diagnostic tool and intrapartum screening test for GBS carriage.

Graphical Abstract

Current methods to screen Group B Streptococcus (GBS), a major cause of chorioamnionitis and neonatal sepsis, do not provide accurate, sensitive readings. Raman microspectroscopy combined with logistic regression were utilized to investigate a GBS infection model of human fetal membranes ex vivo. Tissue infected with GBS was successfully distinguished from non-infected, Escherichia coli infected, and Staphylococcus aureus infected tissue. These findings motivate the development of Raman spectroscopy as a diagnostic tool for intrapartum screening of GBS.

INTRODUCTION

Streptococcus agalactiae, also known as Group B Streptococcus (GBS), colonizes 10-40% of women during pregnancy, and GBS vaginal colonization is an important risk factor for chorioamnionitis, or infection of the fetal membranes, and neonatal sepsis [1]. The Centers for Disease Control and Prevention recommends culture-based rectovaginal GBS screening during the third trimester followed by intrapartum antibiotic prophylaxis for women testing positive [2]. Although this strategy has reduced the incidence of early-onset sepsis by 80%, 15% of full-term and 50% of preterm births do not receive screening prior to delivery [3]. Additionally, prior studies of women delivering neonates with early-onset GBS sepsis found that 75-82% were screened, but tested negative [4,5], indicating the need for a more sensitive method. Traditional culture-based screening requires 24-72 hours to provide results; PCR testing could reduce this time to a few hours, but this technology is not available in all settings [6]. A rapid GBS diagnostic test could provide opportunities to identify GBS colonized women at the time of labor and focus the use of antibiotic therapy.

Raman spectroscopy (RS) is an inelastic light scattering technique that provides a biochemical “fingerprint” with sensitivity to features such as nucleic acids, carbohydrates, lipids, and proteins. Raman microspectroscopy (RμS), which provides higher spectral resolution, has been used to characterize bacteria and provide discrimination at the genus and species levels in vitro [7,8] and identify bacteria directly from clinical samples culture-free [9]. This technique could provide opportunities to identify GBS or other bacteria as a rapid diagnostic test, minimizing sample preparation and streamlining diagnostic information.

Due to the pressing need to accurately and rapidly determine the intrapartum GBS status of women, the ability of RμS to discriminate bacteria cultured on agar and in an ex vivo human tissue model of chorioamnionitis was investigated, comparing GBS with other pathogens implicated in perinatal infections and chorioamnionitis [10]. Here, we demonstrate that GBS has unique Raman spectral features that can be observed whether RμS is used to interrogate bacterial colonies on agar or ex vivo infected fetal membrane tissues. Detecting characteristic GBS spectral patterns suggests that this technology might inform new lab-based or point-of-care diagnostic tests to identify GBS colonization or infection.

METHODS

Bacterial Culture

For RμS colony measurements, diverse capsular serotype isolates of Streptococcus agalactiae (Table 1), an invasive clinical isolate of Escherichia coli [11], and methicillin-resistant Staphylococcus aureus (MRSA) strain USA300, (ATCC #BAA-1717, Manassas, VA) were cultured on Mueller-Hinton (MH) agar (BD, Franklin Lakes, NJ) to minimize signal contribution from media.

Table 1.

GBS strains used in this study.

GBS Strain	Molecular Serotype	Multi-Locus Sequence Type	Setting of Isolation	Source
GB00037	V	ST-1	Neonatal sepsis	S. D. Manning, A. C. Springman, E. Lehotzky, M. A. Lewis, T. S. Whittam, and H. D. Davies, Journal of Clinical Microbiology 2009, 47, 1143.
GB00590	III	ST-19	Vaginal/rectal Colonization	S. D. Manning, A. C. Springman, E. Lehotzky, M. A. Lewis, T. S. Whittam, and H. D. Davies, Clinical Infectious Diseases 2008, 46, 1829.
GB00002	Ia	ST-23	Vaginal/rectal Colonization	S. D. Manning, A. C. Springman, E. Lehotzky, M. A. Lewis, T. S. Whittam, and H. D. Davies, Clinical Infectious Diseases 2008, 46, 1829.
GB01084 (CNCTC 10/84)	V	ST-26	Unknown	ATCC #49447 H. W. Wilkinson, Journal of Clinical Microbiology 1977, 6, 183.
GB2603 V/R	V	Unknown	Unknown	ATCC #BAA-611 H. Tettelin, V. Masignani, M. J. Cieslewicz, J. A. Eisen, S. Peterson, M. R. Wessels, I. T. Paulsen, K. E. Nelson, I. Margarit, T. D. Read, L. C. Madoff, A. M. Wolf, M. J. Beanan, L. M. Brinkac, S. C. Daugherty, R. T. DeBoy, A. S. Durkin, J. F. Kolonay, R. Madupu, M. R. Lewis, D. Radune, N. B. Fedorova, D. Scanlan, H. Khouri, S. Mulligan, H. A. Carty, R. T. Cline, S. E. Van Aken, J. Gill, M. Scarselli, M. Mora, E. T. Iacobini, C. Brettoni, G. Galli, M. Mariani, F. Vegni, D. Maione, D. Rinaudo, R. Rappuoli, J. L. Telford, D. L. Kasper, G. Grandi, and C. M. Fraser, Proceedings of the National Academy of Sciences of the United States of America, 2002, 99, 12391.

For human fetal membrane infection, three GBS strains, E. coli, and MRSA were cultured on tryptic soy agar supplemented with 5% sheep blood at 37°C in ambient air overnight. Bacteria were sub-cultured from blood agar plates into Todd-Hewitt broth (BD) and incubated (shaking at 200 RPM) at 37°C in ambient air overnight. Cells were then washed, suspended in phosphate buffered saline (pH 7.4), and bacterial density was measured spectrophotometrically at an optical density of 600 nm (OD₆₀₀).

Human Fetal Membrane Co-Culture

The Vanderbilt Institutional Review Board approved (approval 131607) isolation of de-identified human fetal membrane tissues was conducted as previously described [9]. Bacteria were added to the fetal membrane choriodecidual surface at a multiplicity of infection of 1×10⁶ cells per 12 mm diameter membrane, using a predetermined coefficient of bacterial density of 1 OD₆₀₀= 1×10⁹ cells. Uninfected membrane samples were also maintained. Co-cultures were incubated at 37°C in ambient air containing 5% CO₂ for 48-72 hours prior to RμS evaluation (Supp. Fig. 1).

Raman Microspectroscopy

A Raman microscope (inVia Raman Microscope, Renishaw plc, Gloucestershire, UK) with an 830 nm laser diode was used for spectral measurements [12]. For bacterial colonies, a 100× objective (N PLAN EPI, NA=0.85, Leica, Weltzlar, Germany) was used to focus the laser at ~12 mW. Fetal membrane tissue spectra were measured using a 50× objective (N PLAN EPI, NA=0.75, Leica) to focus a 40 μm laser line on the sample at ~23 mW. Raman scattered light was detected as previously described with a spectral resolution of ~1 cm⁻¹ [12].

Spectral measurements for bacterial colonies included one spot per colony and three colonies per bacteria from a single culture plate using a 15-second exposure with 9 accumulations from 800-1700 cm⁻¹. Raman measurements from three different locations were performed on each punch biopsy tissue (total of 34). These included control (uninfected, n=5), GB00037 (n=6), GB00590 (n=5), GB01084 (n=6), E. coli (n=5), and MRSA (n=7) representing at least three separate placental samples with 1-3 technical replicates (Supp. Table 1). Acquisition parameters for fetal membrane tissues included a 15-second exposure with 3 accumulations.

Raman Data Processing & Spectral Analysis

Spectral data processing prior to analysis including fluorescence background subtraction and noise smoothing was performed as previously described [12]. A 9^th degree modified polynomial fit was used for spectral measurements from GBS colonies and tissue model. Post-processed, non-normalized Raman spectra were z-scored for subsequent analysis. Principal component analysis (PCA), a non-supervised data reduction statistical approach, was performed on z-scored bacterial colony spectra using singular value decomposition (SVD). The scores output from PCA-SVD were then used to calculate the distance between each data point in orthogonal vector space using the Euclidean distance measure. A hierarchical cluster analysis (HCA) was designed based on the PCA-SVD score distances calculated and an agglomerative clustering approach with single linkage.

A machine learning algorithm, sparse multinomial logistic regression (SMLR) [13], was utilized to discriminate across the different tissues [12]. Briefly, training data was compiled based on RμS measurements of the fetal membrane tissues. For this analysis, a value called SMLR feature importance (SMLR-FI), a linear combination of importance (weight) and frequency of features, was used to determine biomarkers critical for successful classification of infected biofilm tissues [14]. A posterior probability of class membership was plotted for infected membrane tissues. Evaluation of this algorithm was performed using a k-fold cross validation (leave-one-tissue-out).

Scanning Electron Microscopy

Following RμS analysis, human fetal membrane samples were prepared for scanning electron microscopy as previously described [10]. Samples were imaged with a FEI Quanta 250 field-emission gun scanning electron microscope (FEG-SEM). Images are representative of three replicates from three different subjects.

RESULTS

Raman microspectroscopy (RμS) differentiates bacterial species and strains on agar.

A diverse set of GBS strains were selected based on capsular type, multilocus sequence types (MLST), and beta-hemolysin pigment production (Table 1). Visual differences in colony pigmentation are evident across strains (Fig. 1A). Corresponding Raman spectra of GBS, MRSA, and E. coli bacterial colonies are shown in Figure 1B. Each of the strains presents familiar Raman peaks at 1004 cm⁻¹ (C-C skeletal stretching of aromatic ring related to phenylalanine), 1033 cm⁻¹ (C-H in plane deformation related to phenylalanine), and 1340 cm⁻¹ (CH₂ and CH₃ related to fatty acids and protein deformation) to name a few. Major strain biochemical variations are highlighted in the gray bands of Raman spectra. For example, GB01084 contains two Raman peaks at 1121 cm⁻¹ and 1506 cm⁻¹ that are higher in intensity compared to other GBS, E. coli, and MRSA strains. Similarly, MRSA contains unique Raman peaks at 1159 cm⁻¹ and 1525 cm⁻¹ that are not present in any other strain evaluated. The HCA dendrogram presents clusters of MRSA, GB01084, E. coli, and the remaining GBS strains studied based on the dissimilarity of the pairwise distances of observations (PCA-SVD scores from principal components 1 and 2) from their respective Raman spectra (Fig. 1C).

Figure 1: Raman spectra of GBS, MRSA, and E. coli bacterial colonies present distinct biochemical features.

A: Bacterial cells from five Group B Streptococcus (GBS) strains, S. aureus strain USA300 (MRSA), and E. coli serotype O75:H5:K1 were grown on Mueller-Hinton (MH) agar to demonstrate pigmentation differences of the strains. B: Mean ± standard deviation Raman spectra of bacterial colonies. C: Hierarchical cluster analysis (HCA) of bacterial colony measurements based on principal component analysis scores.

RμS distinguishes bacterial infection in explanted fetal membrane tissues.

Given similarities of the spectra collected from GBS on agar, ex vivo infection of human fetal membrane tissues were investigated as a biologically relevant model to determine if RμS was sensitive enough to distinguish GBS spectral features within infected tissues. As a first step, mean-normalized Raman spectra ± standard deviation (shaded color region) of uninfected and infected tissues were compared (Fig. 2A). Vertical gray bands represent important biochemical features for classification of infected tissues based on a SMLR-FI of at least 25%. A probability of class membership plot highlights correctly classified spectral measurements compared to incorrectly classified measurements with respect to control and infected tissues (Fig. 2B). A sensitivity of 97.7% and specificity of 66.7% for detection of infection on fetal membrane tissues was determined from the output confusion matrix (Fig. 2C). The ability of RμS to differentiate GBS strains from E. coli and MRSA was further evaluated in these tissues. Mean-normalized Raman spectra of GBS, E. coli, and MRSA infected tissues are shown along with vertical gray bands indicating SMLR-FI features of at least 25% (Fig. 3A). A probability of class membership plot shows correctly classified measurements compared to those incorrectly classified of GBS versus E. coli or MRSA (Fig. 3B). Membrane tissues infected with GBS were detected with 100.0% sensitivity and 88.9% specificity (Fig. 3C). Scanning electron microscopy imaging identifies bacterial cells present in biofilm structures at the area of Raman measurements (denoted by a small cut into membrane tissues seen at low magnification) as demonstrated by the multilayered bacterial cells embedded in extracellular polymeric substances (Fig. 3D).

Figure 2: Raman microspectroscopy distinguishes infected versus uninfected fetal membrane tissues.

A: Mean ± standard deviation Raman spectra of infected tissues compared to control (uninfected) specimens. Gray vertical bands represent biochemical features important for classification based on sparse multinomial logistic regression (SMLR). B: Posterior probability of class membership plot of Raman spectra with respect to infected and uninfected tissues. Filled markers represent correctly classified Raman spectra and unfilled markers represent incorrectly classified spectra. C: Confusion matrix showing the performance of the SMLR classifier.

Figure 3: Raman microspectroscopy of ex vivo infected fetal membrane tissues identifies and differentiates bacterial cells within tissues.

A: Mean ± standard deviation Raman spectra for Group B Streptococcus strains (GB01084, GB00037, and GB00590), E. coli, and S. aureus strain USA300 (MRSA) infected tissues. Gray vertical bands represent biochemical features important for classification based on sparse multinomial logistic regression (SMLR). B: Posterior probability of class membership plot for each tissue type. Filled markers represent correctly classified Raman spectra and unfilled markers represented incorrectly classified spectra. C: Confusion matrix representing the performance of the SMLR classifier for each tissue type. D: Scanning electron microscopy images of fetal membrane tissues used for Raman analysis to verify the presence of bacteria at the location of Raman measurements. A small cut was made into the membrane tissues to denote the relative location of Raman evaluation. Inserts demonstrate bacterial cells and extracellular polymeric substances, suggestive of biofilms, seen in these locations.

DISCUSSION

GBS remains an important perinatal pathogen despite recommendations to screen and prophylactically treat colonized women during pregnancy. Here, RμS was investigated as a means to characterize and distinguish GBS on agar plates and human fetal membrane tissues infected ex vivo. Using RμS, GBS was found to have unique spectral features compared to another Gram-positive bacteria, S. aureus, and the Gram-negative perinatal pathogen, E. coli. Additionally, spectral patterns of GBS strains varied, suggesting that each strain has a unique spectral signature, while maintaining GBS-common identifiable markers. Qualitative analysis of Raman spectra from bacterial colonies presents differences in the 1121 cm⁻¹ (C-C stretching), 1159 cm⁻¹ (C-C stretching), 1506 cm⁻¹ (C=C stretching), and 1525 cm⁻¹ (C=C stretching) Raman bands, which correspond to pigmentation caused by a carotenoid [15]. This pigmentation of GBS cells results from production of beta-hemolysin, a carotenoid pigment and virulence factor. Past studies have shown that GBS pigment demonstrates absorption spectrum strongly resembling carotenoids [16]. GBS beta-hemolysin was originally thought to be a protein and potentially separate from the GBS pigment, but more recent reports indicate that beta-hemolysin is not a protein but a rather a ornithine rhamnolipid identical or very closely related molecules to the GBS pigment [17]. Nonetheless, non-pigmented strains are also capable of causing clinical disease, thus using RμS to screen for pigment alone would be insufficient [18]. Given that RμS can differentiate colonies on agar plates, it could be used to expedite bacterial identification in microbiology labs once adequate spectral libraries of bacterial Raman spectra are constructed.

More importantly, RμS is able to discriminate fetal membrane tissues infected with GBS and distinguish these from uninfected tissues or those infected with E. coli or MRSA. Spectra from infected and uninfected membrane tissue specimens highlight major differences in peak intensity and width for the two groups in Raman bands 880-955 cm⁻¹, 1110-1128 cm⁻¹, 1492-1530 cm⁻¹, and 1645-1672 cm⁻¹ (Fig. 2A). The Raman band 880-955 cm⁻¹ is mainly related to carbohydrates and proteins as seen at 920 cm⁻¹ due to C-C stretch of proline and 938 cm⁻¹ due to C-C stretch of alpha helix and C-O-C glycosidic linkages. In the second Raman band of 1110-1128 cm⁻¹, the peak at 1126 cm⁻¹ is due to C-O stretching in carbohydrates, which appear to be related to differences seen in the Raman spectra when comparing uninfected versus infected fetal membrane tissues. The third Raman band 1492-1530 cm⁻¹ includes peaks at 1504 cm⁻¹ and 1526 cm⁻¹ both due to C=C stretching related to carotenoids. The Raman band 1645-1672 cm⁻¹ mainly features the peak at 1662 cm⁻¹ due to C=O stretching related to amide I. Furthermore, a higher standard deviation is present in Raman spectra of GBS-infected tissues at 1121 cm⁻¹ (C-C stretching) and 1506 cm⁻¹ (C=C stretching) since these features are more intense in GB01084 compared to GB00037 and GB00590. To identify more subtle spectral differences, SMLR-FI was implemented across tissue types. Features important for characterizing uninfected fetal membrane tissue include 889 cm⁻¹ (SMLR-FI=0.56) related to biological protein structures in tissue, 910 cm⁻¹ (SMLR-FI=0.87) related to fatty acids, and 1661 cm⁻¹ (SMLR-FI=0.56) as part of amide I (C=O stretch). Twelve features above a 25% SMLR-FI were found to be important for distinguishing GBS-infected tissue, including 853 cm⁻¹ (SMLR-FI=0.50) as part of tyrosine and 1406 cm⁻¹ (SMLR-FI=0.76) related to lipids. Tissues infected with E. coli presented ten features above the SMLR-FI threshold and include 858 cm⁻¹ (C-C stretch, SMLR-FI=0.58) and 1157 cm⁻¹ (C-C and C-N stretching in proteins, SMLR-FI=0.53). For MRSA-infected tissue, the Raman peak at 1157 cm⁻¹ (C-C related to carotenoid, SMLR-FI=1.0) was most important for distinguishing this infection in tissue relative to nine other features above 25% SMLR-FI. Here, the 1157 cm⁻¹ Raman peak is due to C-C stretching of carotenoids since the Raman spectra of MRSA-infected tissue also includes a peak at 1526 cm⁻¹ due to C=C stretching of carotenoids.

When compared against E. coli or MRSA infected tissue, RμS is able to distinguish GBS infected tissues with 100.0% sensitivity and 88.9% specificity using SEM imaging to confirm bacterial presence at the site of Raman measurements. SEM analysis of these ex vivo infected tissues demonstrated bacterial growth in biofilm structures. The biofilm structures seen in our model are similar to bacterial biofilms identified in human amniotic fluid and on fetal membrane tissues taken from women with confirmed intra-amniotic infection [19,20].

In conclusion, Raman spectroscopy has the ability to detect bacterial infection of human fetal membrane tissue and distinguish between GBS versus MRSA or E. coli. As this technology progresses it holds promise to identify GBS and other bacteria on different tissues, thereby providing more rapid assessment than traditional diagnostic microbiology. More work is needed to reach this goal including construction of bacterial spectral libraries to compare biochemical features between strains, further engineering to allow in vivo spectral measurements on various tissues, and evaluating polymicrobial infections to determine if spectral signatures of pathogenic bacteria can be isolated in the presence of normal bacterial communities or microbiota. Future studies will need to examine human tissues obtained from women with intra-amniotic infection to further demonstrate the relevance of our ex vivo models and the capabilities of this emerging technology. This study takes the first step to expand research in this area.

Supplementary Material

Supp info

Supplemental Figure 1: Flowchart of experimental approach divided into tissue sample preparation (orange), Raman data collection and processing (green), and spectral characterization and analysis (yellow).