Clinical characterization of in vivo inflammatory bowel disease with Raman spectroscopy

Diagnosis and classification of intestinal diseases with urine by surface-enhanced Raman spectroscopy

Intestinal Disease (ID) is often characterized by clinical symptoms such as malabsorption, intestinal dysfunction, and injury. If treatment is not timely, it will increase the risk of cancer. Early diagnosis of ID is the key to cure it. There are certain limitations of the conventional diagnostic methods, such as low sensitivity and specificity. Therefore, development of a highly sensitive, non-invasive diagnostic method for ID is extremely important. Urine samples are easier to collect and more sensitive to changes in biomolecules than other pathological diagnostic samples such as tissue and blood. In this paper, a diagnostic method of ID with urine by surface-enhanced Raman spectroscopy (SERS) is proposed. A classification model between ID patients and healthy controls (HC) and a classification model between different pathological types of ID (i.e., benign intestinal disease (BID) and colorectal cancer (CRC)) are established. Here, 830 urine samples, including 100 HC, 443 BID, and 287 CRC, were investigated by SERS. The ID/HC classification model was developed by analyzing the SERS spectra of 150 ID and 100 HC, while BID/CRC classification model was built with 300 BID and 150 CRC patients by principal component analysis (PCA)-support vector machines (SVM). The two established models were internally verified by leave-one-out-cross-validation (LOOCV). Finally, the BID/CRC classification model was further evaluated by 143 BID and 137 CRC patients as an external test set. It shows that the accuracy of the classification model validated by the LOOCV for ID/HC and BID/CRC is 86.4% and 85.56%, respectively. And the accuracy of the BID/CRC classification model with external test set is 82.14%. It shows that high accuracy can be achieved with these two established classification models. It indicates that ID patients in the general population can be identified and BID and CRC patients can be further classified with measuring urine by SERS. It shows that the proposed diagnostic method and established classification models provide valuable information for clinicians to early diagnose ID patients and analyze different stages of ID.

Recent Trends in Non-Invasive Methods of Diagnosis and Evaluation of Inflammatory Bowel Disease: A Short Review

Inflammatory bowel diseases are a conglomerate of disorders causing inflammation of the gastrointestinal tract, which have gained a significant increase in prevalence in the 21st century. As they present a challenge in the terms of diagnosis as well as treatment, IBDs can present an overwhelming impact on the individual and can take a toll on healthcare costs. Thus, a quick and precise diagnosis is required in order to prevent the high number of complications that can arise from a late diagnosis as well as a misdiagnosis. Although endoscopy remains the primary method of evaluation for IBD, recent trends have highlighted various non-invasive methods of diagnosis as well as reevaluating previous ones. This review focused on the current non-invasive methods in the diagnosis of IBD, exploring their possible implementation in the near future, with the goal of achieving earlier, feasible, and cheap methods of diagnosis as well as prognosis in IBD.

Spectral effects and enhancement quantification in healthy human saliva with surface-enhanced Raman spectroscopy using silver nanopillar substrates

OBJECTIVES

Raman spectroscopy as a diagnostic tool for biofluid applications is limited by low inelastic scattering contributions compared to the fluorescence background from biomolecules. Surface-enhanced Raman spectroscopy (SERS) can increase Raman scattering signals, thereby offering the potential to reduce imaging times. We aimed to evaluate the enhancement related to the plasmonic effect and quantify the improvements in terms of spectral quality associated with SERS measurements in human saliva.

METHODS

Dried human saliva was characterized using spontaneous Raman spectroscopy and SERS. A fabrication protocol was implemented leading to the production of silver (Ag) nanopillar substrates by glancing angle deposition. Two different imaging systems were used to interrogate saliva from 161 healthy donors: a custom single-point macroscopic system and a Raman micro-spectroscopy instrument. Quantitative metrics were established to compare spontaneous RS and SERS measurements: the Raman spectroscopy quality factor (QF), the photonic count rate (PR), the signal-to-background ratio (SBR).

RESULTS

SERS measurements acquired with an excitation energy four times smaller than with spontaneous RS resulted in improved QF, PR values an order of magnitude larger and a SBR twice as large. The SERS enhancement reached 100×, depending on which Raman bands were considered.

CONCLUSIONS

Single-point measurement of dried saliva with silver nanopillars substrates led to reproducible SERS measurements, paving the way to real-time tools of diagnosis in human biofluids.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Review: Emerging Eye-Based Diagnostic Technologies for Traumatic Brain Injury

The study of ocular manifestations of neurodegenerative disorders, Oculomics, is a growing field of investigation for early diagnostics, enabling structural and chemical biomarkers to be monitored overtime to predict prognosis. Traumatic brain injury (TBI) triggers a cascade of events harmful to the brain, which can lead to neurodegeneration. TBI, termed the "silent epidemic" is becoming a leading cause of death and disability worldwide. There is currently no effective diagnostic tool for TBI, and yet, early-intervention is known to considerably shorten hospital stays, improve outcomes, fasten neurological recovery and lower mortality rates, highlighting the unmet need for techniques capable of rapid and accurate point-of-care diagnostics, implemented in the earliest stages. This review focuses on the latest advances in the main neuropathophysiological responses and the achievements and shortfalls of TBI diagnostic methods. Validated and emerging TBI-indicative biomarkers are outlined and linked to ocular neuro-disorders. Methods detecting structural and chemical ocular responses to TBI are categorised along with prospective chemical and physical sensing techniques. Particular attention is drawn to the potential of Raman spectroscopy as a non-invasive sensing of neurological molecular signatures in the ocular projections of the brain, laying the platform for the first tangible path towards alternative point-of-care diagnostic technologies for TBI.

High-sensitivity hyperspectral vibrational imaging of heart tissues by mid-infrared photothermal microscopy

Visualizing the spatial distribution of chemical compositions in biological tissues is of great importance to study fundamental biological processes and origin of diseases. Raman microscopy, one of the label-free vibrational imaging techniques, has been employed for chemical characterization of tissues. However, the low sensitivity of Raman spectroscopy often requires a long acquisition time of Raman measurement or a high laser power, or both, which prevents one from investigating large-area tissues in a nondestructive manner. In this work, we demonstrated chemical imaging of heart tissues using mid-infrared photothermal (MIP) microscopy that simultaneously achieves the high sensitivity benefited from IR absorption of molecules and the high spatial resolution down to a few micrometers. We successfully visualized the distributions of different biomolecules, including proteins, phosphate-including proteins, and lipids/carbohydrates/amino acids. Further, we experimentally compared MIP microscopy with Raman microscopy to evaluate the sensitivity and photodamage to tissues. We proved that MIP microscopy is a highly sensitive technique for obtaining vibrational information of molecules in a broad fingerprint region, thereby it could be employed for biological and diagnostic applications, such as live-tissue imaging.

Hybrid confocal Raman endomicroscopy for morpho-chemical tissue characterization

Confocal laser endomicroscopy (CLE) offers imaging of tissue microarchitecture and has emerged as a promising tool for in vivo clinical diagnosis of cancer across many organs. CLE, however, can show high inter-observer dependency and does not provide information about tissue molecular composition. In contrast, Raman spectroscopy is a label-free optical technique that provides detailed biomolecular compositional information but offers limited or no morphological information. Here we present a novel hybrid fiber-optic confocal Raman endomicroscopy system for morpho-chemical tissue imaging and analysis. The developed confocal endomicroscopy system is based on a novel detection scheme for rejecting Raman silica fiber interference permitting simultaneous CLE imaging and Raman spectral acquisition of tissues through a coherent fiber bundle. We show that this technique enables real-time microscopic visualization of tissue architecture as well as simultaneous pointwise label-free biomolecular characterization and fingerprinting of tissue paving the way for multimodal diagnostics at endoscopy.

The Specific Changes of Urine Raman Spectra Can Serve as Novel Diagnostic Tools for Disease Characteristics in Patients with Crohn’s Disease

Purpose

Crohn’s disease (CD) is a chronic recurrent intestinal inflammatory disease that requires repeated invasive examinations. Convenient and noninvasive diagnostic tools for CD are lacking. Surface-enhanced Raman spectroscopy (SERS) can rapidly provide specific metabolite information in various samples. Our previous study has showed urine Raman spectrum can distinguish CD patients from healthy controls noninvasively. In this study, we further investigated the value of urine Raman spectra on identifying the disease characterizations in patients with CD.

Patients and Methods

Urine samples were analyzed by SERS to acquire specific changes of the spectra from 100 active CD (aCD) patients and 88 inactive CD (iCD) patients. The accuracy of classifier models yielded by SERS was assessed by principal component analysis and support vector machine (PCA-SVM) to investigate spectral differences and disease characterizations.

Results

Given a panel of 16 specific Raman spectra, the classifier model was established to predict disease activity between patients with aCD and iCD and achieved higher efficacy than fecal calprotectin (AUC value, 0.864 vs 0.596, P=0.02). After leave-one-patient-out cross-validation, the classifier model still obtained 75.5% of accuracy. The correlation analysis showed it had negative correlation with endoscopic results (r=−0.616, P<0.0001). We further established the classifier model in identifying disease location to discriminate colonic-type from ileal-type CD with 63.6% of accuracy with the significantly increased intensity of 1643 cm⁻¹ band, and the model to predict the spectra changes of before and after treatment in tumor necrosis factor inhibitor responders with 91.2% of accuracy with a panel of 11 specific spectra. The metabolic changes of amino acids, proteins, lipids, and other compounds in urine levels were noted by SERS in patients with CD.

Conclusion

The specific changes of urine Raman spectra can reflect changes in urine metabolism. It has the potential value on being the promising diagnostic tool for disease characterizations in CD patients by a convenient and noninvasive way.

Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning

SIGNIFICANCE

The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise and reagents may become less specific to the virus.

AIM

We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. The machine learning (ML) models involved could be frequently updated to include spectral information about variants without needing to develop new reagents.

APPROACH

We present a workflow for collecting, preparing, and imaging dried saliva supernatant droplets using a non-invasive, label-free technique-Raman spectroscopy-to detect changes in the molecular profile of saliva associated with COVID-19 infection.

RESULTS

We used an innovative multiple instance learning-based ML approach and droplet segmentation to analyze droplets. Amongst all confounding factors, we discriminated between COVID-positive and COVID-negative individuals yielding receiver operating coefficient curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity and 75% specificity) and females (84% sensitivity and 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%.

CONCLUSION

These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.

Non-invasive diagnosis of Crohn's disease based on SERS combined with PCA-SVM

Crohn's disease (CD) is an idiopathic chronic inflammatory bowel disease without a cure. Most of the CD patients are firstly diagnosed by invasive endoscopy, and clinical and pathological examinations are further required to confirm the diagnosis. Hence, the development of a non-invasive, rapid and accurate diagnosis method for CD patients is essential. In this study, urine samples from 95 CD patients (including 58 active CD (aCD) patients and 37 inactive CD (iCD) patients) and 48 healthy controls (HC) were investigated by surface-enhanced Raman spectroscopy (SERS). The statistical analysis of the three groups (i.e., CD/HC, aCD/HC and iCD/HC) was performed on the measured data. Principal component analysis (PCA)-support vector machine (SVM) and PCA-linear discriminant analysis (LDA) were then employed to establish classification models to distinguish between patients and HC. For the average SERS spectra of patients and HC, the Raman peaks belonging to lipids, proteins and nucleic acids were stronger in patients than those in HC. It showed that the classification accuracy of CD/HC based on PCA-SVM was higher than that of PCA-LDA (82.5% vs. 69.9%). And the classification accuracy of aCD/HC based on PCA-SVM was higher than that of iCD/HC (86.8% vs. 76.5%). The classification model we established distinguished between aCD and HC with 86.2% sensitivity and 87.5% specificity. It indicates that the metabolic change of patients could be identified by measuring urine with SERS, and aCD and HC could be distinguished more effectively. Our findings are helpful for clinicians to diagnose CD patients and monitor the progress and recurrence of the disease.

Evaluation of standardized performance test methods for biomedical Raman spectroscopy

SIGNIFICANCE

Raman spectroscopy has emerged as a promising technique for a variety of biomedical applications. The unique ability to provide molecular specific information offers insight to the underlying biochemical changes that result in disease states such as cancer. However, one of the hurdles to successful clinical translation is a lack of international standards for calibration and performance assessment of modern Raman systems used to interrogate biological tissue.

AIM

To facilitate progress in the clinical translation of Raman-based devices and assist the scientific community in reaching a consensus regarding best practices for performance testing.

APPROACH

We reviewed the current literature and available standards documents to identify methods commonly used for bench testing of Raman devices (e.g., relative intensity correction, wavenumber calibration, noise, resolution, and sensitivity). Additionally, a novel 3D-printed turbid phantom was used to assess depth sensitivity. These approaches were implemented on three fiberoptic-probe-based Raman systems with different technical specifications.

RESULTS

While traditional approaches demonstrated fundamental differences due to detectors, spectrometers, and data processing routines, results from the turbid phantom illustrated the impact of illumination-collection geometry on measurement quality.

CONCLUSIONS

Specifications alone are necessary but not sufficient to predict in vivo performance, highlighting the need for phantom-based test methods in the standardized evaluation of Raman devices.

Raman Spectroscopy Technique: A Non-Invasive Tool in Celiac Disease Diagnosis

Celiac disease (CD) is diagnosed by a combination of specific serology and typical duodenal lesions. The histological confirmation of CD, mandatory in the majority of patients with suspected CD, is based on invasive and poorly tolerated procedures, such as upper gastrointestinal endoscopy. In this study we propose an alternative and non-invasive methodology able to confirm the diagnosis of CD based on the analysis of serum samples using the Raman spectroscopy technique. Three different bands centered at 1650, 1450 and 1003 cm^-1 have been considered and the A₁₄₅₀/A₁₀₀₃ and A₁₆₅₀/A₁₀₀₃ ratios have been computed to discriminate between CD and non-CD subjects. The reliability of the methodology was validated by statistical analysis using receiver operating characteristic (ROC) curves. The Youden index was also determined to obtain optimal cut-off points. The obtained results highlighted that the proposed methodology was able to distinguish between CD and non-CD subjects with 98% accuracy. The optimal cut-off points revealed, for both the A₁₄₅₀/A₁₀₀₃ and A₁₆₅₀/A₁₀₀₃ ratios, high values of sensitivity and specificity (>95.0% and >92.0% respectively), confirming that Raman spectroscopy may be considered a valid alternative to duodenal biopsy and demonstrates spectral changes in the secondary structures of the protein network.

A computationally efficient Monte-Carlo model for biomedical Raman spectroscopy

Monte Carlo (MC) modeling is a valuable tool to gain fundamental understanding of light-tissue interactions, provide guidance and assessment to optical instrument designs, and help analyze experimental data. It has been a major challenge to efficiently extend MC towards modeling of bulk-tissue Raman spectroscopy (RS) due to the wide spectral range, relatively sharp spectral features, and presence of background autofluorescence. Here, we report a computationally efficient MC approach for RS by adapting the massively-parallel Monte Carlo eXtreme (MCX) simulator. Simulation efficiency is achieved through "isoweight," a novel approach that combines the statistical generation of Raman scattered and Fluorescence emission with a lookup-table-based technique well-suited for parallelization. The MC model uses a graphics processor to produce dense Raman and fluorescence spectra over a range of 800 - 2000 cm^-1 with an approximately 100× increase in speed over prior RS Monte Carlo methods. The simulated RS signals are compared against experimentally collected spectra from gelatin phantoms, showing a strong correlation.

Raman spectroscopy accurately differentiates mucosal healing from non-healing and biochemical changes following biological therapy in inflammatory bowel disease

Background

Mucosal healing (MH) is a key treatment target in the management of inflammatory bowel disease (IBD) and is defined in endoscopic terms by the newly published PICaSSO score. Raman Spectroscopy (RS) is based on the scattering of inelastic light giving spectra that are highly specific for individual molecules. We aimed to establish spectral changes before and after treatment and whether Raman Spectroscopy is able to accurately differentiate between inflammation and MH.

Methods

Biopsies were taken for ex vivo RS analysis alongside biopsies for histological analysis from IBD patients undergoing optical diagnosis endoscopic assessment. We compared pre- vs. post-biological treatment in IBD patients and healthy controls and active vs. MH in UC and CD. For spectral analysis, we used supervised self-organising maps for separation and classification.

Results

A total of 23 patients (14 IBD, 9 HC) were recruited for comparison of pre- vs. post-biologic treatment and 74 IBD patients were included for the assessment of MH in IBD, giving 9700 Raman Spectra. Spectral differences were seen between pre- and post-treatment which were observed comparing MH vs. active inflammation. Reductions in intensity at 1003cm^-1 and 1252cm^-1 when a reduction in inflammation was seen post-treatment and when MH was present. MH was associated with an increase in intensity at 1304cm^-1. The trained neural network differentiated MH from active inflammation with a sensitivity, specificity, PPV, NPV and accuracy in UC of 96.29% (sd 0.94), 95.03% (sd 1.52), 94.89% (sd 1.59), 96.33 (sd 0.97) and 95.65 (sd 0.99) and 96.19% (sd 1.46), 88% (sd 4.20), 86.60% (sd 5.39), 96.55% (sd 1.32) and 91.6% (sd 2.75) in CD respectively.

Conclusion

We demonstrated RS can demonstrate biochemical changes following treatment of IBD and accurately differentiates MH from active inflammation in IBD and might be a future tool to personalise therapeutic management in IBD.

Can Coupling Multiple Complementary Methods Improve the Spectroscopic Based Diagnosis of Gastrointestinal Illnesses? A Proof of Principle Ex Vivo Study Using Celiac Disease as the Model Illness

Spectroscopic methods are a promising approach for providing a point-of-care diagnostic method for gastrointestinal mucosa associated illnesses. Such a tool is desired to aid immediate decision making and to provide a faster pathway to appropriate treatment. In this pilot study, Raman, near-infrared, low frequency Raman, and autofluoresence spectroscopic methods were explored alone and in combination for the diagnosis of celiac disease. Duodenal biopsies (n = 72) from 24 participants were measured ex vivo using the full suite of studied spectroscopic methods. Exploratory principal component analysis (PCA) highlighted the origin of spectral differences between celiac and normal tissue with celiac biopsies tending to have higher protein relative to lipid signals and lower carotenoid spectral signals than the samples with normal histology. Classification of the samples based on the histology and overall diagnosis was carried out for all combinations of spectroscopic methods. Diagnosis based classification (majority rule of class per participant) yielded sensitivities of 0.31 to 0.77 for individual techniques, which was increased up to 0.85 when coupling multiple techniques together. Likewise, specificities of 0.50 to 0.67 were obtained for individual techniques, which was increased up to 0.78 when coupling multiple techniques together. It was noted that the use of antidepressants contributed to false positives, which is believed to be associated with increased serotonin levels observed in the gut mucosa in both celiac disease and the use of selective serotonin reuptake inhibitors (SSRIs); however, future work with greater numbers is required to confirm this observation. Inclusion of two additional spectroscopic methods could improve the accuracy of diagnosis (0.78) by 7% over Raman alone (0.73). This demonstrates the potential for further exploration and development of a multispectroscopic system for disease diagnosis.

Endoscopic imaging in inflammatory bowel disease: current developments and emerging strategies

INTRODUCTION

Developments in enhanced and magnified endoscopy have signified major advances in endoscopic imaging of ileocolonic pathology in inflammatory bowel disease (IBD). Artificial intelligence is increasingly being used to augment the benefits of these advanced techniques. Nevertheless, treatment of IBD patients is frustrated by high rates of non-response to therapy, while delayed detection and failures to detect neoplastic lesions impede successful surveillance. A possible solution is offered by molecular imaging, which adds functional imaging data to mucosal morphology assessment through visualizing biological parameters. Other label-free modalities enable visualization beyond the mucosal surface without the need of tracers.

AREAS COVERED

A literature search up to May 2020 was conducted in PubMed/MEDLINE in order to find relevant articles that involve the (pre-)clinical application of high-definition white light endoscopy, chromoendoscopy, artificial intelligence, confocal laser endomicroscopy, endocytoscopy, molecular imaging, optical coherence tomography, and Raman spectroscopy in IBD.

EXPERT OPINION

Enhanced and magnified endoscopy have enabled an improved assessment of the ileocolonic mucosa. Implementing molecular imaging in endoscopy could overcome the remaining clinical challenges by giving practitioners a real-time in vivo view of targeted biomarkers. Label-free modalities could help optimize the endoscopic assessment of mucosal healing and dysplasia detection in IBD patients.

Application driven assessment of probe designs for Raman spectroscopy

In vivo Raman spectroscopy has been utilized for the non-invasive, non-destructive assessment of tissue pathophysiology for a variety of applications largely through the use of fiber optic probes to interface with samples of interest. Fiber optic probes can be designed to optimize the collection of Raman-scattered photons from application-dependent depths, and this critical consideration should be addressed when planning a study. Herein we investigate four distinct probe geometries for sensitivity to superficial and deep signals through a Monte Carlo model that incorporates Raman scattering and fluorescence. Experimental validation using biological tissues was performed to accurately recapitulate in vivo scenarios. Testing in biological tissues agreed with modeled results and revealed that microlens designs had slightly enhanced performance at shallow depths (< 1 mm), whereas all of the beampath-modified designs yielded more signal from deep within tissue. Simulation based on fluence maps generated using ray-tracing in the absence of optical scattering had drastically different results as a function of depth for each probe compared to the biological simulation. The contrast in simulation results between the non-scattering and biological tissue phantoms underscores the importance of considering the optical properties of a given application when designing a fiber optic probe. The model presented here can be easily extended for optimization of entirely novel probe designs prior to fabrication, reducing time and cost while improving data quality.

Temporal In Vitro Raman Spectroscopy for Monitoring Replication Kinetics of Epstein-Barr Virus Infection in Glial Cells

Raman spectroscopy can be used as a tool to study virus entry and pathogen-driven manipulation of the host efficiently. To date, Epstein-Barr virus (EBV) entry and altered biochemistry of the glial cell upon infection are elusive. In this study, we detected biomolecular changes in human glial cells, namely, HMC-3 (microglia) and U-87 MG (astrocytes), at two variable cellular locations (nucleus and periphery) by Raman spectroscopy post-EBV infection at different time points. Two possible phenomena, one attributed to the response of the cell to viral attachment and invasion and the other involved in duplication of the virus followed by egress from the host cell, are investigated. These changes corresponded to unique Raman spectra associated with specific biomolecules in the infected and the uninfected cells. The Raman signals from the nucleus and periphery of the cell also varied, indicating differential biochemistry and signaling processes involved in infection progression at these locations. Molecules such as cholesterol, glucose, hyaluronan, phenylalanine, phosphoinositide, etc. are associated with the alterations in the cellular biochemical homeostasis. These molecules are mainly responsible for cellular processes such as lipid transport, cell proliferation, differentiation, and apoptosis in the cells. Raman signatures of these molecules at distinct time points of infection indicated their periodic involvement, depending on the stage of virus infection. Therefore, it is possible to discern the details of variability in EBV infection progression in glial cells at the biomolecular level using time-dependent in vitro Raman scattering.

Application of Raman Spectroscopy for the Evaluation of Metabolomic Dynamic Analysis in Athletic Horses

Raman spectroscopy is a rapid qualitative and quantitative technique that allows the simultaneous determination of several components, both biomolecules both chemical compounds, in the biological fluids to assess the metabolic status. In this study, the serum composition was evaluated in regularly trained athletic horses using Raman spectroscopy to identify biomarkers of sports performance. Five clinically healthy and regularly trained Italian Saddle horses were subjected to a standardized obstacle course (350 m/minute; eleven 1.25 high jumps) preceded by a warm-up. On the collected sera, at rest, immediately after exercise, 30 minutes, and 1 hour after the end of the exercise Raman measurements were performed using a diode laser with the excitation wavelength of 785 nm. The analysis of the obtained spectra allowed the identification of peaks and bands different in position and intensity among the experimental conditions. The acquired spectra, obtained from horse sera collected during the experimental protocol, were visually similar, except for the large band detected in the 1,250-1,800 cm^-1 range. The spectral intensity of the Raman spectrum decreased after training and 30 minutes after the end of exercise respect to the before exercise value, to come to the basal value after 60 minutes the end of the exercise. In conclusion, we can claim the ability of Raman spectroscopy to reveal the metabolic status of horses after physical exercise.

Surface-enhanced Raman scattering for the diagnosis of ulcerative colitis: will it change the rules of the game?

Ulcerative colitis (UC) is a relapsing-remitting inflammatory bowel disease that requires numerous costly invasive investigations which lead to physical and psychological patient discomfort. We need a non-invasive technological approach that would significantly improve its diagnosis. Surface-enhanced Raman scattering (SERS) is a growing technique that can provide a molecular diagnostic fingerprint in just a few minutes, without the need for prior sample preparation. The aim of this pilot in vivo study was to prove that multivariate analysis of SER spectra collected on plasma samples could be employed for non-invasive diagnosis of UC. Plasma samples were collected from healthy subjects (n = 35) and patients with UC (n = 28). SERS spectra were acquired using a 785-nm excitation laser line and a solid plasmonic substrate developed in our laboratory using an original procedure described in the literature. The classification accuracy yielded by SERS was assessed by principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA). PCA-LDA differentiated UC samples from those of healthy subjects with a sensitivity of 86%, a specificity of 92%, and an accuracy of 89%, the AUC being 0.96. The PLS-DA analysis resulted in a sensitivity of 89%, a specificity of 94%, an accuracy of 92%, and an AUC value of 0.92. Several spectral bands were associated with UC: 376-420, 440-513, 686-715, 919-939, 1035-1062, 1083-1093, 1120-1132, 1148-1156, 1191-1211, 1234-1262, 1275-1294, 1382-1405, 1511-1526, and 1693-1702 cm^-1. Changes in plasma levels of amino acids, proteins, lipids, and other compounds were noted using SERS in patients with UC. Multivariate analysis of SER spectra collected on a solid plasmonic substrate represents a promising alternative to diagnosing UC, as it is non-invasive, easy to use, and fast.

Raman Analysis Reveals Biochemical Differences in Plasma of Crohn's Disease Patients

BACKGROUNDS AND AIMS

There is no accurate and reliable circulating biomarker to diagnose Crohn's disease [CD]. Raman spectroscopy is a relatively new approach that provides information on the biochemical composition of samples in minutes and virtually without any sample preparation. We aimed to test the use of Raman spectroscopy analysis of plasma samples as a potential diagnostic tool for CD.

METHODS

We analysed by Raman spectroscopy dry plasma samples obtained from 77 CD patients [CD] and 45 healthy controls [HC]. In the dataset obtained, we analysed spectra differences between CD and HC, as well as among CD patients with different disease behaviours. We also developed a method, based on principal component analysis followed by a linear discrimination analysis [PCA-LDA], for the automatic classification of individuals based on plasma spectra analysis.

RESULTS

Compared with HC, the CD spectra were characterised by less intense peaks corresponding to carotenoids [p <10-4] and by more intense peaks corresponding to proteins with β-sheet secondary structure [p <10-4]. Differences were also found on Raman peaks relative to lipids [p = 0.0007] and aromatic amino acids [p <10-4]. The predictive model we developed was able to classify CD and HC subjects with 83.6% accuracy [sensitivity 80.0% and specificity 85.7%] and F1-score of 86.8%.

CONCLUSIONS

Our results indicate that Raman spectroscopy of blood plasma can identify metabolic variations associated with CD and it could be a rapid pre-screening tool to use before further specific evaluation.

Towards an Interpretable Classifier for Characterization of Endoscopic Mayo Scores in Ulcerative Colitis Using Raman Spectroscopy

Ulcerative colitis (UC) is one of the main types of chronic inflammatory diseases that affect the bowel, but its pathogenesis is yet to be completely defined. Assessing the disease activity of UC is vital for developing a personalized treatment. Conventionally, the assessment of UC is performed by colonoscopy and histopathology. However, conventional methods fail to retain biomolecular information associated to the severity of UC and are solely based on morphological characteristics of the inflamed colon. Furthermore, assessing endoscopic disease severity is limited by the requirement for experienced human reviewers. Therefore, this work presents a nondestructive biospectroscopic technique, for example, Raman spectroscopy, for assessing endoscopic disease severity according to the four-level Mayo subscore. This contribution utilizes multidimensional Raman spectroscopic data to generate a predictive model for identifying colonic inflammation. The predictive modeling of the Raman spectroscopic data is performed using a one-dimensional deep convolutional neural network (1D-CNN). The classification results of 1D-CNN achieved a mean sensitivity of 78% and a mean specificity of 93% for the four Mayo endoscopic scores. Furthermore, the results of the 1D-CNN are interpreted by a first-order Taylor expansion, which extracts the Raman bands important for classification. Additionally, a regression model of the 1D-CNN model is constructed to study the extent of misclassification and border-line patients. The overall results of Raman spectroscopy with 1D-CNN as a classification and regression model show a good performance, and such a method can serve as a complementary method for UC analysis.

Raman Spectroscopy as Noninvasive Method of Diagnosis of Pediatric Onset Inflammatory Bowel Disease

We propose here a spectroscopic method to diagnose and differentiate inflammatory bowel diseases (IBD), such as ulcerative colitis (UC) and Crohn’s disease (CD) with pediatric onset, in a complete noninvasive way without performing any duodenal biopsy. In particular, the Raman technique was applied to proteic extract from fecal samples in order to achieve information about molecular vibrations that can potentially furnish spectral signatures of cellular modifications occurring as a consequence of specific pathologic conditions. The attention was focused on the investigation of the amide I region, quantitatively accounting the spectral changes in the secondary structures by applying deconvolution and curve-fitting. Inflammation is found to give rise to a significant increasing of the nonreducible (trivalent)/reducible (divalent) cross-linking ratio R of the protein network. This parameter revealed an excellent marker in order to distinguish IBD subjects from non-IBD ones, and, among IBD patients, to differentiate between UC and CD. The proposed methodology was validated by statistical analysis using the receiver operating characteristic (ROC) curve.

Applications of Mass Spectrometry for Clinical Diagnostics: The Influence of Turnaround Time

This critical review discusses how the need for reduced clinical turnaround times has influenced chemical instrumentation. We focus on the development of modern mass spectrometry (MS) and its application in clinical diagnosis. With increased functionality that takes advantage of novel front-end modifications and computational capabilities, MS can now be used for non-traditional clinical analyses, including applications in clinical microbiology for bacteria differentiation and in surgical operation rooms. We summarize here recent developments in the field that have enabled such capabilities, which include miniaturization for point-of-care testing, direct complex mixture analysis via ambient ionization, chemical imaging and profiling, and systems integration.

Raman Spectroscopy: Guiding Light for the Extracellular Matrix

The extracellular matrix (ECM) consists of a complex mesh of proteins, glycoproteins, and glycosaminoglycans, and is essential for maintaining the integrity and function of biological tissues. Imaging and biomolecular characterization of the ECM is critical for understanding disease onset and for the development of novel, disease-modifying therapeutics. Recently, there has been a growing interest in the use of Raman spectroscopy to characterize the ECM. Raman spectroscopy is a label-free vibrational technique that offers unique insights into the structure and composition of tissues and cells at the molecular level. This technique can be applied across a broad range of ECM imaging applications, which encompass in vitro, ex vivo, and in vivo analysis. State-of-the-art confocal Raman microscopy imaging now enables label-free assessments of the ECM structure and composition in tissue sections with a remarkably high degree of biomolecular specificity. Further, novel fiber-optic instrumentation has opened up for clinical in vivo ECM diagnostic measurements across a range of tissue systems. A palette of advanced computational methods based on multivariate statistics, spectral unmixing, and machine learning can be applied to Raman data, allowing for the extraction of specific biochemical information of the ECM. Here, we review Raman spectroscopy techniques for ECM characterizations over a variety of exciting applications and tissue systems, including native tissue assessments (bone, cartilage, cardiovascular), regenerative medicine quality assessments, and diagnostics of disease states. We further discuss the challenges in the widespread adoption of Raman spectroscopy in biomedicine. The results of the latest discovery-driven Raman studies are summarized, illustrating the current and potential future applications of Raman spectroscopy in biomedicine.

Multivariate discrimination of heat shock proteins using a fiber optic Raman setup for in situ analysis of human perilymph

Raman spectroscopy has proven to be an effective tool for molecular analysis in different applications. In clinical diagnostics, its application has enabled nondestructive investigation of biological tissues and liquids. The human perilymph, for example, is an inner ear liquid, essential for the hearing sensation. The composition of this liquid is correlated with pathophysiological parameters and was analyzed by extraction and mass spectrometry so far. In this work, we present a fiber optic probe setup for the Raman spectroscopic sampling of inner ear proteins in solution. Multivariate data analysis is applied for the discrimination of individual proteins (heat shock proteins) linked to a specific type of hearing impairment. This proof-of-principle is a first step toward a system for sensitive and continuous in vivo perilymph investigation in the future.

Diagnosis of immunomarkers in vivo via multiplexed surface enhanced Raman spectroscopy with gold nanostars

In this work, we demonstrate the targeted diagnosis of immunomarker programmed death ligand 1 (PD-L1) and simultaneous detection of epidermal growth factor receptor (EGFR) in breast cancer tumors in vivo using gold nanostars (AuNS) with multiplexed surface enhanced Raman spectroscopy (SERS). Real-time longitudinal tracking with SERS demonstrated maximum accumulation of AuNS occurred 6 h post intravenous (IV) delivery, enabling detection of both biomarkers simultaneously. Raman signal correlating to both PD-L1 and EGFR decreased by ∼30% in control tumors where receptors were pre-blocked prior to AuNS delivery, indicating both the sensitivity and specificity of SERS in distinguishing tumors with different levels of PD-L1 and EGFR expression. Our in vivo study was combined with the first demonstration of ex vivo SERS spatial maps of whole tumor lesions that provided both a qualitative and quantitative assessment of biomarker status with near cellular-level resolution. High resolution SERS maps also provided an overview of AuNS distribution in tumors which correlated well with the vascular density. Mass spectrometry showed AuNS accumulation in tumor and liver, and clearance via spleen, and electron microscopy revealed AuNS were endocytosed in tumors, Kupffer cells in the liver, and macrophages in the spleen. This study demonstrates that SERS-based diagnosis mediated by AuNS provides an accurate measure of multiple biomarkers both in vivo and ex vivo, which will ultimately enable a clinically-translatable platform for patient-tailored immunotherapies and combination treatments.

In-vivo Raman spectroscopy: from basics to applications

For more than two decades, Raman spectroscopy has found widespread use in biological and medical applications. The instrumentation and the statistical evaluation procedures have matured, enabling the lengthy transition from ex-vivo demonstration to in-vivo examinations. This transition goes hand-in-hand with many technological developments and tightly bound requirements for a successful implementation in a clinical environment, which are often difficult to assess for novice scientists in the field. This review outlines the required instrumentation and instrumentation parameters, designs, and developments of fiber optic probes for the in-vivo applications in a clinical setting. It aims at providing an overview of contemporary technology and clinical trials and attempts to identify future developments necessary to bring the emerging technology to the clinical end users. A comprehensive overview of in-vivo applications of fiber optic Raman probes to characterize different tissue and disease types is also given.

Challenges in application of Raman spectroscopy to biology and materials

Raman spectroscopy has become an essential tool for chemists, physicists, biologists and materials scientists. In this article, we present the challenges in unravelling the molecule-specific Raman spectral signatures of different biomolecules like proteins, nucleic acids, lipids and carbohydrates based on the review of our work and the current trends in these areas. We also show how Raman spectroscopy can be used to probe the secondary and tertiary structural changes occurring during thermal denaturation of protein and lysozyme as well as more complex biological systems like bacteria. Complex biological systems like tissues, cells, blood serum etc. are also made up of such biomolecules. Using mice liver and blood serum, it is shown that different tissues yield their unique signature Raman spectra, owing to a difference in the relative composition of the biomolecules. Additionally, recent progress in Raman spectroscopy for diagnosing a multitude of diseases ranging from cancer to infection is also presented. The second part of this article focuses on applications of Raman spectroscopy to materials. As a first example, Raman spectroscopy of a melt cast explosives formulation was carried out to monitor the changes in the peaks which indicates the potential of this technique for remote process monitoring. The second example presents various modern methods of Raman spectroscopy such as spatially offset Raman spectroscopy (SORS), reflection, transmission and universal multiple angle Raman spectroscopy (UMARS) to study layered materials. Studies on chemicals/layered materials hidden in non-metallic containers using the above variants are presented. Using suitable examples, it is shown how a specific excitation or collection geometry can yield different information about the location of materials. Additionally, it is shown that UMARS imaging can also be used as an effective tool to obtain layer specific information of materials located at depths beyond a few centimeters.

This paper reviews various facets of Raman spectroscopy. This encompasses biomolecule fingerprinting and conformational analysis, discrimination of healthy vs. diseased states, depth-specific information of materials and 3D Raman imaging.

Imaging of Mucosal Inflammation: Current Technological Developments, Clinical Implications, and Future Perspectives

In recent years, various technological developments markedly improved imaging of mucosal inflammation in patients with inflammatory bowel diseases. Although technological developments such as high-definition-, chromo-, and autofluorescence-endoscopy led to a more precise and detailed assessment of mucosal inflammation during wide-field endoscopy, probe-based and stationary confocal laser microscopy enabled in vivo real-time microscopic imaging of mucosal surfaces within the gastrointestinal tract. Through the use of fluorochromes with specificity against a defined molecular target combined with endoscopic techniques that allow ultrastructural resolution, molecular imaging enables in vivo visualization of single molecules or receptors during endoscopy. Molecular imaging has therefore greatly expanded the clinical utility and applications of modern innovative endoscopy, which include the diagnosis, surveillance, and treatment of disease as well as the prediction of the therapeutic response of individual patients. Furthermore, non-invasive imaging techniques such as computed tomography, magnetic resonance imaging, scintigraphy, and ultrasound provide helpful information as supplement to invasive endoscopic procedures. In this review, we provide an overview on the current status of advanced imaging technologies for the clinical non-invasive and endoscopic evaluation of mucosal inflammation. Furthermore, the value of novel methods such as multiphoton microscopy, optoacoustics, and optical coherence tomography and their possible future implementation into clinical diagnosis and evaluation of mucosal inflammation will be discussed.

In vivo analysis of mucosal lipids reveals histological disease activity in ulcerative colitis using endoscope-coupled Raman spectroscopy

The goal of this study is to evaluate endoscopic Raman spectroscopy as a noninvasive technique to determine histological inflammatory status of colitis. Colon mucosal composition was investigated in vivo from patients with ulcerative colitis (UC) and from age- and body mass index (BMI) matched controls using endoscope-coupled Raman spectroscopy. The results were co-registered with histological assessment of inflammatory status at the same locations. Substantial decreases (50-60%) in the content of phosphotidylcholines (PCs) and total lipids were observed in inflamed colon tissue (histology grade 1, 2 and 3) compared to those from the quiescent (histology grade 0) and from the controls. No significant difference was observed in lipids or PC contents between control and grade 0, or among grades 1 - 3. The degree of lipid unsaturation increased in the inflamed tissue regardless of disease severity. The inflammation-associated alterations in lipids and PC are observed independent of BMI or the anatomical locations for data collection. Multivariate analysis using support vector machine (SVM) algorithm classified the spectra of the controls or the inactive colitis from those of inflamed tissue with a sensitivity of 83.5% and 97.1% respectively. Our results showed that mucosal lipid content is related to the microscopic disease activity, and thus could serve as a valuable spectral marker to differentiate active colitis from the quiescent.