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

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.

In Vivo Raman Spectroscopy Reveals Biochemical Composition of the Esophageal Tissue in Pediatric Eosinophilic Esophagitis

INTRODUCTION

Biochemical alterations in the esophagus of patients with eosinophilic esophagitis (EoE) are poorly understood. We used Raman spectroscopy through a pediatric endoscope to identify key Raman features reflective of the esophageal biochemical composition to differentiate between children with EoE from non-EoE controls and between children with active (aEoE) and inactive EoE (iEoE).

METHODS

Spectral measurements were obtained using a customized pediatric endoscope-compatible fiber-optic Raman probe in real time during an esophagogastroduodenoscopy. Chemometric analysis was performed to identify key Raman features associated with EoE. Pearson correlation analysis was used to assess relationship between the key Raman features and EoE activity indices. Their diagnostic utility was ascertained using the receiver operator characteristic curve analysis.

RESULTS

Forty-three children were included in the study (EoE = 32 [74%] and non-EoE control = 11 [26%]; aEoE = 20 [63%] and iEoE = 12 [37%]). Raman intensities assigned to lipids, proteins, and glycogen:protein ratio accurately distinguished children with EoE from those without EoE and aEoE from iEoE. They significantly correlated with EoE activity indices. The Raman peak ratio for lipids had 90.6% sensitivity, 100% specificity, and an area under the curve of 0.95 to differentiate children with EoE from non-EoE controls. The glycogen:protein ratio had 70% sensitivity, 91.7% specificity, and an area under the curve of 0.75 to distinguish children with aEoE from iEoE.

DISCUSSION

Real-time intraendoscopy Raman spectroscopy is an effective method for identifying spectral markers reflective of the esophageal biochemical composition in children with EoE. This technique may aid in the diagnosis and monitoring of EoE and help to elucidate EoE pathogenesis.

Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease with an increasing incidence and prevalence worldwide. The diagnosis for UC mainly relies on clinical symptoms and laboratory examinations. As some previous studies have revealed that there is an association between gene expression signature and disease severity, we thereby aim to assess whether genes can help to diagnose UC and predict its correlation with immune regulation. A total of ten eligible microarrays (including 387 UC patients and 139 healthy subjects) were included in this study, specifically with six microarrays (GSE48634, GSE6731, GSE114527, GSE13367, GSE36807, and GSE3629) in the training group and four microarrays (GSE53306, GSE87473, GSE74265, and GSE96665) in the testing group. After the data processing, we found 87 differently expressed genes. Furthermore, a total of six machine learning methods, including support vector machine, least absolute shrinkage and selection operator, random forest, gradient boosting machine, principal component analysis, and neural network were adopted to identify potentially useful genes. The synthetic minority oversampling (SMOTE) was used to adjust the imbalanced sample size for two groups (if any). Consequently, six genes were selected for model establishment. According to the receiver operating characteristic, two genes of OLFM4 and C4BPB were finally identified. The average values of area under curve for these two genes are higher than 0.8, either in the original datasets or SMOTE-adjusted datasets. Besides, these two genes also significantly correlated to six immune cells, namely Macrophages M1, Macrophages M2, Mast cells activated, Mast cells resting, Monocytes, and NK cells activated (P  <  0.05). OLFM4 and C4BPB may be conducive to identifying patients with UC. Further verification studies could be conducted.

Fourier-Transform Infra-Red Microspectroscopy Can Accurately Diagnose Colitis and Assess Severity of Inflammation

The diagnosis and management of inflammatory bowel disease relies on histological assessment, which is costly, subjective, and lacks utility for point-of-care diagnosis. Fourier-transform infra-red spectroscopy provides rapid, non-destructive, reproducible, and automatable label-free biochemical imaging of tissue for diagnostic purposes. This study characterises colitis using spectroscopy, discriminates colitis from healthy tissue, and classifies inflammation severity. Hyperspectral images were obtained from fixed intestinal sections of a murine colitis model treated with cell therapy to improve inflammation. Multivariate analyses and classification modelling were performed using supervised and unsupervised machine-learning algorithms. Quantitative analysis of severe colitis showed increased protein, collagen, and nucleic acids, but reduced glycogen when compared with normal tissue. A partial least squares discriminant analysis model, including spectra from all intestinal layers, classified normal colon and severe colitis with a sensitivity of 91.4% and a specificity of 93.3%. Colitis severity was classified by a stacked ensemble model yielding an average area under the receiver operating characteristic curve of 0.95, 0.88, 0.79, and 0.85 for controls, mild, moderate, and severe colitis, respectively. Infra-red spectroscopy can detect unique biochemical features of intestinal inflammation and accurately classify normal and inflamed tissue and quantify the severity of inflammation. This is a promising alternative to histological assessment.

Screening of characteristic genes in ulcerative colitis by integrating gene expression profiles

Background

This study aimed to screen the feature modules and characteristic genes related to ulcerative colitis (UC) and construct a support vector machine (SVM) classifier to distinguish UC patients.

Methods

Four datasets that contained UC and control samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) with consistency were screened via the MetaDE method. The weighted gene coexpression network (WGCNA) was used to distinguish significant modules based on the four datasets. The protein–protein interaction network was established based on intersection genes. Enrichment analysis of Gene Ontology (GO) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were established based on DAVID. An SVM combined with recursive feature elimination was also applied to construct a disease classifier for the disease diagnosis of UC patients. The efficacy of the SVM classifier was evaluated through receiver operating characteristic curves.

Results

Twelve highly preserved modules were obtained using the WGCNA, and 2009 DEGs with significant consistency were selected using the MetaDE method. Sixteen significantly related GO BPs and 12 KEGG pathways were obtained, such as cytokine-cytokine receptor interaction, cell adhesion molecules, and leukocyte transendothelial migration. Subsequently, 41 genes were used to construct an SVM classifier, such as CXCL1, CCR2, IL1B, and IL1A. The area under the curve (AUC) was 0.999 in the training dataset, whereas the AUC was 0.886, 0.790, and 0.819 in the validation set (GSE65114, GSE37283, and GSE36807, respectively).

Conclusions

An SVM classifier based on feature genes might correctly identify healthy people or UC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01940-0.

Novel Insights Into Tissue-Specific Biochemical Alterations in Pediatric Eosinophilic Esophagitis Using Raman Spectroscopy

INTRODUCTION:

Elucidating esophageal biochemical composition in eosinophilic esophagitis (EoE) can offer novel insights into its pathogenesis, which remains unclear. Using Raman spectroscopy, we profiled and compared the biochemical composition of esophageal samples obtained from children with active (aEoE) and inactive EoE (iEoE) with non-EoE controls, examined the relationship between spectral markers and validated EoE activity indices.

METHODS:

In vitro Raman spectra from children with aEoE (n = 8; spectra = 51) and iEoE (n = 6; spectra = 48) and from non-EoE controls (n = 10; spectra = 75) were acquired. Mann-Whitney test was used to assess the differences in their Raman intensities (median [interquartile range]) and identify spectral markers. Spearman correlation was used to evaluate the relationship between spectral markers and endoscopic and histologic activity indices.

RESULTS:

Raman peaks attributable to glycogen content (936/1,449 cm⁻¹) was lower in children with aEoE (0.20 [0.18–0.21]) compared with that in non-EoE controls (0.24 [0.23–0.29]). Raman intensity of proteins (1,660/1,209 cm⁻¹) was higher in children with aEoE compared with that in non-EoE controls (3.20 [3.07–3.50] vs 2.91 [2.59–3.05]; P = 0.01), whereas that of lipids (1,301/1,260 cm⁻¹) was higher in children with iEoE (1.56 [1.49–1.63]) compared with children with aEoE (1.40 [1.30–1.48]; P = 0.02). Raman peaks attributable to glycogen and lipid inversely correlated with eosinophilic inflammation and basal zone hyperplasia. Raman mapping substantiated our findings.

DISCUSSION:

This is the first study to identify spectral traits of the esophageal samples related to EoE activity and tissue pathology and to profile tissue-level biochemical composition associated with pediatric EoE. Future research to determine the role of these biochemical alterations in development and clinical course of EoE can advance our understanding of EoE pathobiology.

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.

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.

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.

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.

Untargeted Metabolomics and Inflammatory Markers Profiling in Children With Crohn's Disease and Ulcerative Colitis-A Preliminary Study

BACKGROUND

Metabolic profiling might be used to identify disease biomarkers. The aim of our study was to determine the usefulness of untargeted metabolomics analysis to detect differences in serum metabolites between newly diagnosed and untreated pediatric patients with Crohn's disease (CD) or ulcerative colitis (UC) in comparison with a control group (Ctr). Moreover, we investigated the potential of profiling metabolomics and inflammatory markers to improve the noninvasive diagnosis of CD and UC in children.

METHODS

Metabolic fingerprinting of serum samples was estimated with liquid chromatography coupled with mass spectrometry in children with CD (n = 9; median age, 14 years), UC (n = 10; median age, 13.5 years), and controls (n = 10; median age, 12.5 years).

RESULTS

The majority of chemically annotated metabolites belonged to phospholipids and were downregulated in CD and UC compared with the Ctr. Only 1 metabolite, lactosylceramide 18:1/16:0 (LacCer 18:1/16:0), significantly discriminated CD from UC patients. Interestingly, combining LacCer 18:1/16:0 with other inflammatory markers resulted in a significant increase in the area under the curve with the highest specificity and sensitivity.

CONCLUSIONS

Using serum untargeted metabolomics, we have shown that LacCer 18:1/16:0 is a very unique metabolite for CD patients.

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.

High-resolution multimodal flexible coherent Raman endoscope

Coherent Raman scattering microscopy is a fast, label-free, and chemically specific imaging technique that shows high potential for future in vivo optical histology. However, the imaging depth in tissues is limited to the sub-millimeter range because of absorption and scattering. Realization of coherent Raman imaging using a fiber endoscope system is a crucial step towards imaging deep inside living tissues and providing information that is inaccessible with current microscopy tools. Until now, the development of coherent Raman endoscopy has been hampered by several issues, mainly related to the fiber delivery of the excitation pulses and signal collection. Here, we present a flexible, compact, coherent Raman, and multimodal nonlinear endoscope (4.2 mm outer diameter, 71 mm rigid length) based on a resonantly scanned hollow-core Kagomé-lattice double-clad fiber. The fiber design enables distortion-less, background-free delivery of femtosecond excitation pulses and back-collection of nonlinear signals through the same fiber. Sub-micrometer spatial resolution over a large field of view is obtained by combination of a miniature objective lens with a silica microsphere lens inserted into the fiber core. We demonstrate high-resolution, high-contrast coherent anti-Stokes Raman scattering, and second harmonic generation endoscopic imaging of biological tissues over a field of view of 320 µm at a rate of 0.8 frames per second. These results pave the way for intraoperative label-free imaging applied to real-time histopathology diagnosis and surgery guidance.