A Rheumatoid arthritis study using Raman spectroscopy

Raman micro-spectroscopy as a tool to study immunometabolism

In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.

In Vivo Longitudinal Monitoring of Disease Progression in Inflammatory Arthritis Animal Models Using Raman Spectroscopy

Current techniques for monitoring disease progression and testing drug efficacy in animal models of inflammatory arthritis are either destructive, time-consuming, subjective, or require ionizing radiation. To accommodate this, we have developed a non-invasive and label-free optical system based on Raman spectroscopy for monitoring tissue alterations in rodent models of arthritis at the biomolecular level. To test different sampling geometries, the system was designed to collect both transmission and reflection mode spectra. Mice with collagen antibody-induced arthritis and controls were subject to in vivo Raman spectroscopy at the tibiotarsal joint every 3 days for 14 days. Raman-derived measures of bone content correlated well with micro-computed tomography bone mineral densities. This allowed for time-resolved quantitation of bone densities, which indicated gradual bone erosion in mice with arthritis. Inflammatory pannus formation, bone erosion, and bone marrow inflammation were confirmed by histological analysis. In addition, using library-based spectral decomposition, we quantified the progression of bone and soft tissue components. In general, the tissue components followed significantly different tendencies in mice developing arthritis compared to the control group in line with the histological analysis. In total, this demonstrates Raman spectroscopy as a versatile technique for monitoring alterations to both mineralized and soft tissues simultaneously in rodent models of musculoskeletal disorders. Furthermore, the technique presented herein allows for objective repeated within-animal measurements potentially refining and reducing the use of animals in research while improving the development of novel antiarthritic therapeutics.

Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.

Injectable amnion hydrogel-mediated delivery of adipose-derived stem cells for osteoarthritis treatment

Current treatment strategies for osteoarthritis (OA) predominantly address symptoms with limited disease-modifying potential. There is a growing interest in the use of adipose-derived stem cells (ADSCs) for OA treatment and developing biomimetic injectable hydrogels as cell delivery systems. Biomimetic injectable hydrogels can simulate the native tissue microenvironment by providing appropriate biological and chemical cues for tissue regeneration. A biomimetic injectable hydrogel using amnion membrane (AM) was developed which can self-assemble in situ and retain the stem cells at the target site. In the present study, we evaluated the efficacy of intraarticular injections of AM hydrogels with and without ADSCs in reducing inflammation and cartilage degeneration in a collagenase-induced OA rat model. A week after the induction of OA, rats were treated with control (phosphate-buffered saline), ADSCs, AM gel, and AM-ADSCs. Inflammation and cartilage regeneration was evaluated by joint swelling, analysis of serum by cytokine profiling and Raman spectroscopy, gross appearance, and histology. Both AM and ADSC possess antiinflammatory and chondroprotective properties to target the sites of inflammation in an osteoarthritic joint, thereby reducing the inflammation-mediated damage to the articular cartilage. The present study demonstrated the potential of AM hydrogel to foster cartilage tissue regeneration, a comparable regenerative effect of AM hydrogel and ADSCs, and the synergistic antiinflammatory and chondroprotective effects of AM and ADSC to regenerate cartilage tissue in a rat OA model.

Biomarkers in ANCA-Associated Vasculitis: Potential Pitfalls and Future Prospects

Over the past 3 decades, significant advancements in the understanding of the pathophysiology of ANCA-associated vasculitis has led to the development of a multitude of potential candidate biomarkers. Accompanied by the advent of increasingly effective therapeutic strategies, the need for a dependable biomarker to help determine the extent of disease activity and risk of relapse is ever present. Implementation of such a biomarker would enable tailored therapy, optimizing disease control while helping to mitigate unnecessary exposure to therapy and potential treatment-related damage. Although far from perfect, ANCA serology and B-cell population are the two main staple biomarker tools widely used in practice to help supplement clinical assessment. Over recent years, the application and progress of more novel biomarker tools have arisen in both organ-limited and multisystem disease, including genomics, urinary proteins, degradation products of the alternative complement system, cytokines, metabolomics, and biospectroscopy. Validation studies and clinical translation of these tools are required, with serial assessment of disease activity and determination of therapy according to biomarker status correlated with patient outcomes.

Metabolic fingerprinting for diagnosis of fibromyalgia and other rheumatologic disorders

Diagnosis and treatment of fibromyalgia (FM) remains a challenge owing to the lack of reliable biomarkers. Our objective was to develop a rapid biomarker-based method for diagnosing FM by using vibrational spectroscopy to differentiate patients with FM from those with rheumatoid arthritis (RA), osteoarthritis (OA), or systemic lupus erythematosus (SLE) and to identify metabolites associated with these differences. Blood samples were collected from patients with a diagnosis of FM (n = 50), RA (n = 29), OA (n = 19), or SLE (n = 23). Bloodspot samples were prepared, and spectra collected with portable FT-IR and FT-Raman microspectroscopy and subjected to metabolomics analysis by ultra-HPLC (uHPLC), coupled to a photodiode array (PDA) and tandem MS/MS. Unique IR and Raman spectral signatures were identified by pattern recognition analysis and clustered all study participants into classes (FM, RA, and SLE) with no misclassifications (p < 0.05, and interclass distances > 2.5). Furthermore, the spectra correlated (r = 0.95 and 0.83 for IR and Raman, respectively) with FM pain severity measured with fibromyalgia impact questionnaire revised version (FIQR) assessments. Protein backbones and pyridine-carboxylic acids dominated this discrimination and might serve as biomarkers for syndromes such as FM. uHPLC-PDA-MS/MS provided insights into metabolites significantly differing among the disease groups, not only in molecular m/z+ and m/z- values but also in UV-visible chromatograms. We conclude that vibrational spectroscopy may provide a reliable diagnostic test for differentiating FM from other disorders and for establishing serologic biomarkers of FM-associated pain.

Raman spectroscopic analysis of malaria disease progression via blood and plasma samples

In this study Raman spectroscopy has been used to monitor the changes in erythrocytes and plasma during Plasmodium infection in mice, following malaria disease progression over the course of 7 days. The Raman spectra of both samples are dominated by the spectra of hemoglobin and hemozoin, due to their resonant enhancement. In plasma samples, due to the inherently low heme background, heme-based changes in the Raman spectra could be detected in the very early stages of infection, as little as one day after Plasmodium infection, where parasitemia levels were low, on the order of 0.2%, and typically difficult to detect by existing methods. Further principal component analysis also indicates concurrent erythrocyte membrane changes at around day 4, where parasitemia levels reached 3%. These results show that plasma analysis has significant potential for early, quantitative and automated detection of malaria, and to quantify heme levels in serum which modulate malarial effects on the immune system.