Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms.

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Feasibility studies of multimodal nonlinear endoscopy using multicore fiber bundles for remote scanning from tissue sections to bulk organs.

Here, we report on the development and application of a compact multi-core fiber optical probe for multimodal non-linear imaging, combining the label-free modalities of Coherent Anti-Stokes Raman Scattering, Second Harmonic Generation, and Two-Photon Excited Fluorescence. Probes of this multi-core fiber design avoid moving and voltage-carrying parts at the distal end, thus providing promising improved compatibility with clinical requirements over competing implementations. The performance characteristics of the probe are established using thin cryo-sections and artificial targets before the applicability to clinically relevant samples is evaluated using ex vivo bulk human and porcine intestine tissues. After image reconstruction to counteract the data's inherently pixelated nature, the recorded images show high image quality and morpho-chemical conformity on the tissue level compared to multimodal non-linear images obtained with a laser-scanning microscope using a standard microscope objective. Furthermore, a simple yet effective reconstruction procedure is presented and demonstrated to yield satisfactory results. Finally, a clear pathway for further developments to facilitate a translation of the multimodal fiber probe into real-world clinical evaluation and application is outlined.

Identification of bacteria in mixed infection from urinary tract of patient's samples using Raman analysis of dried droplets.

Urinary tract infections (UTI) are among the most frequent nosocomial infections. A fast identification of the pathogen and assignment of Gram type could help to prescribe most suitable treatments. Raman spectroscopy holds high potential for fast and reliable bacterial pathogens identification. While most studies so far have focused on individual pathogens or artificial mixtures, this contribution aims to translate the analysis to primary urine samples from patients with suspected UTIs. For this, we have included 59 primary urine samples out of which 29 were diagnosed as mixed infections. For Raman analysis, we first trained two classification models based on principal component analysis - linear discriminant analysis (PCA-LDA) with more than 3500 Raman spectra of 85 clinical isolates from 23 species in order to (1) identify the Gram type of the bacteria and (2) assign family membership to one of the six most abundant bacterial families in urinary tract infections (Enterobacteriaceae, Morganellaceae, Pseudomonadaceae, Enterococcaceae, Staphylococcaceae and Streptococcaceae). The classification models were applied to artificial mixtures of Gram positive and Gram negative bacteria to correctly predict mixed infections with an accuracy of 75%. Raman scans of dried droplets did not yet yield optimal classification results on family level. When translating the method to primary urine samples, we observed a strong bias towards Gram negative bacteria, on family level towards Morganellaceae, which reduced prediction accuracy. Spectral differences were observed between isolates grown on standard growth medium and bacteria of the same strain when characterized directly from the patient. Thus, improvement of the classification accuracy is expected with a larger data base containing also bacteria measured directly from the urine sample.

Use of polymers as wavenumber calibration standards in deep-UVRR.

Deep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400-1900 cm-1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique.

Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates.

Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation. IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination.

Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo.

Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.

Investigating Origins of FLIm Contrast in Atherosclerotic Lesions Using Combined FLIm-Raman Spectroscopy.

Background: Fluorescence lifetime imaging (FLIm) is a spectroscopic imaging technique able to characterize the composition of luminal surface of arterial vessels. Studies of human coronary samples demonstrated that distinct atherosclerotic lesion types are characterized by FLIm features associate with distinct tissue molecular makeup. While conventional histology has provided indications about potential sources of molecular contrast, specific information about the origin of FLIm signals is lacking. Here we investigate whether Raman spectroscopy, a technique able to evaluate chemical content of biological samples, can provide additional insight into the origin of FLIm contrast. Methods: Six human coronary artery samples were imaged using FLIm (355 nm excitation)-Raman spectroscopy (785 nm excitation) via a multimodal fiber optic probe. The spatial distribution of molecular contrast in FLIm images was analyzed in relationship with histological findings. Raman data was investigated using an endmember technique and compared with histological findings. A descriptive modeling approach based on multivariate regression was used to identify Raman bands related with changes in lifetime in four spectral channels (violet: 387/35 nm, blue: 443/29 nm, green: 546/38 nm, and red: 628/53 nm). Results: Fluorescence lifetime variations in the violet, blue and green spectral bands were observed for distinct areas of each tissue sample associated with distinct pathologies. Analysis of Raman signals from areas associated with normal, pathological intimal thickening, and fibrocalcific regions demonstrated the presence of hydroxyapatite, collagenous proteins, carotene, cholesterol, and triglycerides. The FLIm and Raman descriptive modeling analysis indicated that lifetime increase in the violet spectral band was associated with increased presence of cholesterol and carotenes, a new finding consistent with LDL accumulation in atherosclerotic lesions, and not with collagen proteins, as expected from earlier studies. Conclusions: The systematic, quantitative analysis of the multimodal FLIm-Raman dataset using a descriptive modeling approach led to the identification of LDL accumulation as the primary source of lifetime contrast in atherosclerotic lesions in the violet spectral range. Earlier FLIm validation studies relying on histopathological findings had associated this contrast to increased collagen content, also present in advanced lesions, thus demonstrating the benefits of alternative validation methods.

Deep learning a boon for biophotonics?

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

Towards Raman spectroscopy of urine as screening tool.

For the screening purposes urine is an especially attractive biofluid, since it offers easy and noninvasive sample collection and provides a snapshot of the whole metabolic status of the organism, which may change under different pathological conditions. Raman spectroscopy (RS) has the potential to monitor these changes and utilize them for disease diagnostics. The current study utilizes mouse models aiming to compare the feasibility of the urine based RS combined with chemometrics for diagnosing kidney diseases directly influencing urine composition and respiratory tract diseases having no direct connection to urine formation. The diagnostic models for included diseases were built using principal component analysis with linear discriminant analysis and validated with a leave-one-mouse-out cross-validation approach. Considering kidney disorders, the accuracy of 100% was obtained in discrimination between sick and healthy mice, as well as between two different kidney diseases. For asthma and invasive pulmonary aspergillosis achieved accuracies were noticeably lower, being, respectively, 77.27% and 78.57%. In conclusion, our results suggest that RS of urine samples not only provides a solution for a rapid, sensitive and noninvasive diagnosis of kidney disorders, but also holds some promises for the screening of nonurinary tract diseases.

UV-Raman Spectroscopic Identification of Fungal Spores Important for Respiratory Diseases.

Fungal spores are one of several environmental factors responsible for causing respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), and aspergillosis. These spores also are able to trigger exacerbations during chronic forms of disease. Different fungal spores may contain different allergens and mycotoxins, therefore the health hazards are varying between the species. Thus, it is highly important quickly to identify the composition of fungal spores in the air. In this study, UV-Raman spectroscopy with an excitation wavelength of 244 nm was applied to investigate eight different fungal species implicated in respiratory diseases worldwide. Here, we demonstrate that darkly colored spores can be directly examined, and UV-Raman spectroscopy provides the information sufficient for classifying fungal spores. Classification models on the genus, species, and strain levels were built using a combination of principal component analysis and linear discriminant analysis followed by evaluation with leave-one-batch-out-cross-validation. At the genus level an accuracy of 97.5% was achieved, whereas on the species level four different Aspergillus species were classified with 100% accuracy. Finally, classifying three strains of Aspergillus fumigatus an accuracy of 89.4% was reached. These results demonstrate that UV-Raman spectroscopy in combination with innovative chemometrics allows for fast identification of fungal spores and can be a potential alternative to currently used time-consuming cultivation.

Surface enhanced Raman spectroscopy-detection of the uptake of mannose-modified nanoparticles by macrophages in vitro: A model for detection of vulnerable atherosclerotic plaques.

Atherosclerosis is a process of thickening and stiffening of the arterial walls through the accumulation of lipids and fibrotic material, as a consequence of aging and unhealthy life style. However, not all arterial plaques lead to complications, which can lead to life-threatening events such as stroke and myocardial infarction. Diagnosis of the disease in early stages and identification of unstable atherosclerotic plaques are still challenging. It has been shown that the development of atherosclerotic plaques is an inflammatory process, where the accumulation of macrophages in the arterial walls is immanent in the early as well as late stages of the disease. We present a novel surface enhanced Raman spectroscopy (SERS)-based strategy for the detection of early stage atherosclerosis, based on the uptake of tagged gold nanoparticles by macrophages and subsequent detection by means of SERS. The results presented here provide a basis for future in vivo studies in animal models.The workflow of tracing the SERS-active nanoparticle uptake by macrophages employing confocal Raman imaging.

Do You Get What You See? Understanding Molecular Self-Healing.

The self-healing ability of self-healing materials is often analyzed using morphologic microscopy images. Here it was possible to show that morphologic information alone is not sufficient to judge the status of a self-healing process and molecular information is required as well. When comparing molecular coherent anti-Stokes Raman scattering (CARS) and morphological laser reflection images during a standard scratch healing test of an intrinsic self-healing polymer network, it was found that the morphologic closing of the scratch and the molecular crosslinking of the material do not take place simultaneously. This important observation can be explained by the fact that the self-healing process of the thiol-ene based polymer network is limited by the mobility of alkene-containing compounds, which can only be monitored by molecular CARS microscopy and not by standard morphological imaging. Additionally, the recorded CARS images indicate a mechanochemical activation of the self-healing material by the scratching/damaging process, which leads to an enhanced self-healing behavior in the vicinity of the scratch.

Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool.

BACKGROUND: Due to the steadily increasing number of cancer patients worldwide the early diagnosis and treatment of cancer is a major field of research. The diagnosis of cancer is mostly performed by an experienced pathologist via the visual inspection of histo-pathological stained tissue sections. To save valuable time, low quality cryosections are frequently analyzed with diagnostic accuracies that are below those of high quality embedded tissue sections. Thus, alternative means have to be found that enable for fast and accurate diagnosis as the basis of following clinical decision making. METHODS: In this contribution we will show that the combination of the three label-free non-linear imaging modalities CARS (coherent anti-Stokes Raman-scattering), TPEF (two-photon excited autofluorescence) and SHG (second harmonic generation) yields information that can be translated into computational hematoxylin and eosin (HE) images by multivariate statistics. Thereby, a computational HE stain is generated resulting in pseudo-HE overview images that allow for identification of suspicious regions. The latter are analyzed further by Raman-spectroscopy retrieving the tissue's molecular fingerprint. RESULTS: The results suggest that the combination of non-linear multimodal imaging and Raman-spectroscopy possesses the potential as a precise and fast tool in routine histopathology. CONCLUSIONS: As the key advantage, both optical methods are non-invasive enabling for further pathological investigations of the same tissue section, e.g. a direct comparison with the current pathological gold-standard.

Multimodal Imaging Spectroscopy of Tissue.

Advanced optical imaging technologies have experienced increased visibility in medical research, as they allow for a label-free and nondestructive investigation of tissue in either an excised state or living organisms. In addition to a multitude of ex vivo studies proving the applicability of these optical imaging approaches, a transfer of various modalities toward in vivo diagnosis is currently in progress as well. Furthermore, combining optical imaging techniques, referred to as multimodal imaging, allows for an improved diagnostic reliability due to the complementary nature of retrieved information. In this review, we provide a summary of ongoing multifold efforts in multimodal tissue imaging and focus in particular on in vivo applications for medical diagnosis. We also discuss the advantages and potential limitations of the imaging methods and outline opportunities for future developments.

Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells.

We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohn's disease (CD) and ulcerative colitis (UC). Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. First, support vector machines were applied to highlight the tissue morphology (=Raman spectroscopic histopathology). In a second step, the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy control subjects (n=11), subjects with CD (n=14), and subjects with UC (n=13). These three groups exhibit significantly different molecular specific Raman signatures, allowing establishment of a classifier (support-vector-machine). By utilizing this classifier it was possible to separate between healthy control patients, patients with CD, and patients with UC with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman spectroscopy in combination with chemometric approaches.