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

Abstract

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.

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

“…In a similar study by Smith et al RS differentiated inflammation pretreatment from mucosal healing post-treatment in IBD patient tissues. 158 In this work, a supervised ML artificial neural network, specifically a self-optimizing Kohonen index network (SKiNET), was leveraged to distinguish between the two tissue types. They achieved a sensitivity, specificity, and accuracy of 96.29%, 95.03%, and 95.65%, respectively, in UC patients, and 96.19%, 88%, and 91.6% in CD patients.…”

“…Whereas label-free RS has been most prevalent in cancer diagnostics and treatment response, RS has also been used to understand metabolic changes in other disorders, including GI diseases, ,,− cardiac disorders, − and neurodegenerative diseases, ,,− ,, included in this section. We have specifically discussed literature findings, where clinical samples were probed to highlight the clinical relevance of RS in the context of these diseases.…”

“…Further, phospholipids (peaks at 1440 and 2762 cm –1 ), a major component of cell membrane, were downregulated in inflamed tissues, implying that inflammation compromises the integrity of mucosa, and the RS findings were validated with histological studies. In a similar study by Smith et al RS differentiated inflammation pretreatment from mucosal healing post-treatment in IBD patient tissues . In this work, a supervised ML artificial neural network, specifically a self-optimizing Kohonen index network (SKiNET), was leveraged to distinguish between the two tissue types.…”

Review–Vibrational spectroscopy‐aided diagnosis, prognosis and treatment of inflammatory bowel disease

“…Furthermore, at this longer wavelength, the light is capable of penetrating deeper into thick or turbid samples, thus, making it suitable for in vivo or non-destructive analysis of biological tissue, with the lower-energy photons at near-infrared excitation are less likely to cause photodamage to sensitive biological samples. 63 Across the Raman-IBD studies, a range of different laser powers and optics are employed, ranging from 10 mW by Smith et al up to 90 mW by Morasso et al 9,64,72 The choice of laser power in RS requires establishing a balance between maximising the signal intensity and minimising the potential sample damage or interference. It depends upon the nature of the sample, the desired signal strength and the potential for fluorescence.…”

“…9,45,67 Approaches, such as PLS regression, used by Tefas et al, are also capable of establishing quantitative relationships between spectral data and sample properties, valuable for the accurate and rapid quantification of biomarkers in a dataset. 67 Machine learning, a sub-field of artificial intelligence (AI), is another approach applied in studies by Smith et al and Buchan et al 64 In these studies, a novel supervised machine learning algorithm uses an ANNs inspired by the structure and functioning neural networks such as the brain. They consist of interconnected nodes, termed artificial neurons or nodes, organised into layers.…”