Optical investigation of osteoarthritic human cartilage (ICRS grade) by confocal Raman spectroscopy: a pilot study

Measuring the severity of knee osteoarthritis with an aberration-free fast line scanning Raman imaging system

Osteoarthritis (OA) is a major cause of disability worldwide, with symptoms like joint pain, limited functionality, and decreased quality of life, potentially leading to deformity and irreversible damage. Chemical changes in joint tissues precede imaging alterations, making early diagnosis challenging for conventional methods like X-rays. Although Raman imaging provides detailed chemical information, it is time-consuming. This paper aims to achieve rapid osteoarthritis diagnosis and grading using a self-developed Raman imaging system combined with deep learning denoising and acceleration algorithms. Our self-developed aberration-corrected line-scanning confocal Raman imaging device acquires a line of Raman spectra (hundreds of points) per scan using a galvanometer or displacement stage, achieving spatial and spectral resolutions of 2 μm and 0.2 nm, respectively. Deep learning algorithms enhance the imaging speed by over 4 times through effective spectrum denoising and signal-to-noise ratio (SNR) improvement. By leveraging the denoising capabilities of deep learning, we are able to acquire high-quality Raman spectral data with a reduced integration time, thereby accelerating the imaging process. Experiments on the tibial plateau of osteoarthritis patients compared three excitation wavelengths (532, 671, and 785 nm), with 671 nm chosen for optimal SNR and minimal fluorescence. Machine learning algorithms achieved a 98 % accuracy in distinguishing articular from calcified cartilage and a 97 % accuracy in differentiating osteoarthritis grades I to IV. Our fast Raman imaging system, combining an aberration-corrected line-scanning confocal Raman imager with deep learning denoising, offers improved imaging speed and enhanced spectral and spatial resolutions. It enables rapid, label-free detection of osteoarthritis severity and can identify early compositional changes before clinical imaging, allowing precise grading and tailored treatment, thus advancing orthopedic diagnostics and improving patient outcomes.

Raman spectroscopic probe provides optical biomarkers of cartilage composition predictive of tissue function

OBJECTIVE

The diagnosis of early osteoarthritis when therapeutic interventions may be most effective at reversing cartilage degeneration presents a clinical challenge. We describe a Raman arthroscopic probe and spectral analysis that measures biomarkers reflective of the content of predominant cartilage extracellular matrix (ECM) constituents-glycosaminoglycans (GAG), collagen, water-essential to cartilage function. We compare the capability of Raman-probe-derived biomarkers to predict functional properties of cartilage to quantitative MRI and histopathology assessments.

DESIGN

Osteochondral blocks were sectioned from 6 bovine femoral condyles with no macroscopic injury (n=62 blocks) and 6 condyles with a focal chondral lesion (n=32 blocks), but no macroscopic degeneration of surrounding cartilage (n=34 blocks). Blocks from 10 human knees were further analyzed (age 27-75; n=235 blocks). Using a custom arthroscopic Raman spectroscopy probe, spectra of chondral layers were measured and subjected to multivariate linear decomposition to extract ECM biomarker scores, reflecting the contribution of each ECM constituent to the spectra. Blocks were further analyzed for elastic modulus, T2/T2* MRI relaxation times, and OARSI scores.

RESULTS

For bovine tissues, Raman biomarkers revealed depleted GAG and cartilage softening peripheral to the lesion despite no macroscopic degeneration. Raman biomarkers accounted for 78% of GAG content variation and 71% of modulus variation. For human tissues, Raman biomarkers accounted for 71% of modulus variation. Raman biomarkers accounted for a greater variation of modulus (71%-72%) than OARSI (12-54%), T2* (15%-27%), or T2 (25%-30%).

CONCLUSIONS

These data support the application of Raman-probe-derived biomarkers for molecular assessment of key ECM constituents that define cartilage properties in health and disease.

Harnessing Raman spectroscopy and multimodal imaging of cartilage for osteoarthritis diagnosis

Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need. Label-free techniques such as Raman Spectroscopy (RS), Coherent anti-Stokes Raman scattering (CARS), Second Harmonic Generation (SHG) and Two Photon Fluorescence (TPF) are increasingly being used to characterise cartilage tissue. However, current studies are based on whole tissue analysis and do not consider the different and structurally distinct layers in cartilage. In this work, we use Raman spectroscopy to obtain signatures from the superficial (top) and deep (bottom) layer of healthy and osteoarthritic cartilage samples from 64 patients (19 control and 45 OA). Spectra were acquired both in the 'fingerprint' region from 700 to 1720 cm- 1 and high-frequency stretching region from 2500 to 3300 cm- 1. Principal component and linear discriminant analysis was used to identify the peaks that contributed significantly to classification accuracy of the different samples. The most pronounced differences were observed at the proline (855 cm- 1 and 921 cm- 1) and hydroxyproline (877 cm- 1 and 938 cm- 1), sulphated glycosaminoglycan (sGAG) (1064 cm- 1 and 1380 cm- 1) frequencies for both control and OA as well as the 1245 cm- 1 and 1272 cm- 1, 1320 cm- 1 and 1345 cm- 1, 1451 cm- 1 collagen modes were altered in OA samples, consistent with expected collagen structural changes. Classification accuracy based on Raman fingerprint spectral analysis of superficial and deep layer cartilage for controls was found to be 97% and 93% on using individual/all spectra and, 100% and 95% on using mean spectra per patient, respectively. OA diseased cartilage was classified with an accuracy of 88% and 84% for individual/all spectra, and 96% and 95% for mean spectra per patient based on analysis of the superficial and the deep layers, respectively. Raman spectra from the C-H stretching region (2500-3300 cm- 1) resulted in high classification accuracy for identification of different layers and OA diseased cartilage but low accuracy for controls. Differential changes in superficial and deep layer cartilage signatures were observed with age (under 60 and over 60 years), in contrast, less significant differences were observed with gender. Prominent chemical changes in the different layers of cartilage were preliminarily imaged using CARS, SHG and TPF. Cell clustering was observed in OA together with differences in pericellular matrix and collagen structure in the superficial and the deep layers correlating with the Raman spectral analysis. The current study demonstrates the potential of Raman Spectroscopy and multimodal imaging to interrogate cartilage tissue and provides insight into the chemical and structural composition of its different layers with significant implications for OA diagnosis for an increasing aging demographic.

An update on technical method of cartilage decellularization: A physical-based protocol

Introduction

Despite advances in orthopedic surgery, the lack of effective conventional treatment for cartilage defects has led to research in cartilage tissue engineering. One of the interesting topics is the use of decellularized extracellular matrix (ECM) as a suitable natural scaffold that supports the growth and function of cells cultured in it. A concern with decellularization protocols, especially those with high detergent concentrations, is the disruption of native ECM, which has deleterious effects on subsequent scaffold recellularization. Therefore, this study focused on optimizing cartilage decellularization by physical methods without the use of ionic detergents.

Methods

The bovine tracheal cartilage fragments were decellularized by a combination of 8 cycles of freeze-thaw and ultrasound techniques. Then, the tissues were immersed and shaken in 0.25% trypsin for 24 hours. Efficient cell removal and preservation of ECM were confirmed by histological and cytocompatibility assessments. The in-vivo studies were performed to evaluate the biocompatibility and bioactivity of the scaffold.

Results

The histological assessments indicated the appropriate cytocompatibility and the fibroblast cell culture study demonstrated that cells were able to proliferate and migrate on the decellularized cartilage. In-vivo evaluation also showed a reduced adverse immune response, including leukocyte infiltration into the ECM.

Conclusion

These results suggest that a cartilage scaffold created using a physical decellularization protocol that efficiently removes cells while preserving the native ECM can be a suitable scaffold for cartilage reconstruction. The main advantage of this protocol is the absence of potentially toxic chemicals in the tissues.

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.

Histopathology of the Intervertebral Disc of Nothobranchius furzeri, a Fish Model of Accelerated Aging

INTRODUCTION

Osteoarthritis is a classical age-related disease, which affects millions of patients worldwide. To further understand the pathophysiology and to develop therapeutic strategies for this disease, animal models play a significant role. Nothobranchius furzeri is an established model for accelerated aging that spontaneously develops spinal deformities. Although the bone properties of N. furzeri are well described, characteristics of the intervertebral discs are still unknown. The aim of this study was to investigate the characteristics of the intervertebral discs of healthy and deformed N. furzeri.

MATERIAL AND METHODS

Intervertebral properties of healthy and deformed N. furzeri were investigated in 8-, 12-, 18- and 21.5-week-old male fish of the GRZ strain. For histological evaluations the fish were decalcified, paraffin-embedded and stained with (1) hematoxylin and eosin, (2) toluidine blue and (3) alcian blue/picrosirius red.

RESULTS

8-week-old and deformed N. furzeri showed spongy-like tissue containing vacuolated notochord cells and a beginning formation of fibrous tissue in the central area. Older healthy fish showed fibrous tissue in the central region and a spongy-like tissue in the peripheral region.

CONCLUSION

Our study revealed age- and disease-related alterations of the vertebral discs in N. furzeri. Further studies should investigate the utility of N. furzeri as a model for degenerative spine diseases.

Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties

OBJECTIVE

To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS).

DESIGN

Visually normal bovine patellae (n = 12) were used in this study. Osteochondral plugs (n = 60) were prepared and artificially degraded either enzymatically (via Collagenase D or Trypsin) or mechanically (via impact loading or surface abrasion) to induce mild to severe cartilage damage; additionally, control plugs were prepared (n = 12). Raman spectra were acquired from the samples before and after artificial degradation. Afterwards, reference biomechanical properties, proteoglycan (PG) content, collagen orientation, and zonal (%) thickness of the samples were measured. Machine learning models (classifiers and regressors) were then developed to discriminate healthy from degraded cartilage based on their Raman spectra and to predict the aforementioned reference properties.

RESULTS

The classifiers accurately categorized healthy and degraded samples (accuracy = 86%), and successfully discerned moderate from severely degraded samples (accuracy = 90%). On the other hand, the regression models estimated cartilage biomechanical properties with reasonable error (≤ 24%), with the lowest error observed in the prediction of instantaneous modulus (12%). With zonal properties, the lowest prediction errors were observed in the deep zone, i.e., PG content (14%), collagen orientation (29%), and zonal thickness (9%).

CONCLUSION

RS is capable of discriminating between healthy and damaged cartilage, and can estimate tissue properties with reasonable errors. These findings demonstrate the clinical potential of RS.

Spatial Heterogeneity Directs Energy Dissipation in Condylar Fibrocartilage

Condylar fibrocartilage with structural and compositional heterogeneity can efficiently orchestrate load-bearing and energy dissipation, making the temporomandibular joint (TMJ) survive high occlusion loads for a prolonged lifetime. How the thin condylar fibrocartilage can achieve efficient energy dissipation to cushion enormous stresses remains an open question in biology and tissue engineering. Here, three distinct zones in the condylar fibrocartilage are identified by analyzing the components and structure from the macro-and microscale to the nanoscale. Specific proteins are highly expressed in each zone related to its mechanics. The heterogeneity of condylar fibrocartilage can direct energy dissipation through the nano-micron-macro gradient spatial scale, by atomic force microscope (AFM), nanoindentation, dynamic mechanical analyzer assay (DMA), and the corresponding energy dissipation mechanisms are exclusive for each distinct zone. This study reveals the significance of the heterogeneity of condylar fibrocartilage in mechanical behavior and provides new insights into the research methods for cartilage biomechanics and the design of energy-dissipative materials.

Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy

Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.

Chondrocyte Hypertrophy in Osteoarthritis: Mechanistic Studies and Models for the Identification of New Therapeutic Strategies

Articular cartilage shows limited self-healing ability owing to its low cellularity and avascularity. Untreated cartilage defects display an increased propensity to degenerate, leading to osteoarthritis (OA). During OA progression, articular chondrocytes are subjected to significant alterations in gene expression and phenotype, including a shift towards a hypertrophic-like state (with the expression of collagen type X, matrix metalloproteinases-13, and alkaline phosphatase) analogous to what eventuates during endochondral ossification. Present OA management strategies focus, however, exclusively on cartilage inflammation and degradation. A better understanding of the hypertrophic chondrocyte phenotype in OA might give new insights into its pathogenesis, suggesting potential disease-modifying therapeutic approaches. Recent developments in the field of cellular/molecular biology and tissue engineering proceeded in the direction of contrasting the onset of this hypertrophic phenotype, but knowledge gaps in the cause-effect of these processes are still present. In this review we will highlight the possible advantages and drawbacks of using this approach as a therapeutic strategy while focusing on the experimental models necessary for a better understanding of the phenomenon. Specifically, we will discuss in brief the cellular signaling pathways associated with the onset of a hypertrophic phenotype in chondrocytes during the progression of OA and will analyze in depth the advantages and disadvantages of various models that have been used to mimic it. Afterwards, we will present the strategies developed and proposed to impede chondrocyte hypertrophy and cartilage matrix mineralization/calcification. Finally, we will examine the future perspectives of OA therapeutic strategies.

Prognostic potential and pathological validation of a diagnostic application using Raman spectroscopy in the characterization of degenerative changes in the cartilage of the humeral head

SIGNIFICANCE

Raman spectroscopy is a well-established analytical method in the fields of chemistry, industry, biology, pharmaceutics, and medicine. Previous studies have investigated optical imaging and Raman spectroscopy for osteoarthritis (OA) diagnosis in weight-bearing joints such as hip and knee joints. However, to realize early diagnosis or a curable treatment, it is still challenging to understand the correlations with intrinsic factors or patients’ background.

AIM

To elucidate the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage.

APPROACH

Osteoarthritic cartilage specimens excised from the humeral heads of 14 patients who underwent shoulder arthroplasty were assessed by a confocal Raman microscope and histological staining. The Raman spectroscopic dataset of degenerative cartilage was further analyzed by principal component analysis and hierarchical cluster analysis.

RESULTS

Multivariate association of the Raman spectral data generated three major clusters. The first cluster of patients shows a relatively high Raman intensity of collagen. The second cluster displays relatively low Raman intensities of proteoglycans (PGs) and glycosaminoglycans (GAGs), whereas the third cluster shows relatively high Raman intensities of PGs and GAGs. The reduced PGs and GAGs are typical changes in OA cartilage, which have been confirmed by safranin–O staining. In contrast, the increased Raman intensities of collagen, PGs, and GAGs may reflect the instability of the cartilage matrix structure in OA patients.

CONCLUSIONS

The results obtained confirm the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage. Unsupervised machine learning methods successfully yielded a clinically meaningful classification between the shoulder OA patients. This approach not only has potential to confirm severity of cartilage defects but also to determine the origin of an individual’s OA by evaluating the cartilage quality.

Phosphoproteomics reveals the BRAF-ERK1/2 axis as an important pathogenic signaling node in cartilage degeneration

OBJECTIVE

Osteoarthritis (OA) causes gradual cellular alterations, structural anomalies and joint dysfunction. Progressive decline of chondrocyte function plays a vital role on OA pathogenesis. Although protein phosphorylation controls cartilage metabolism, its regulation mechanism in OA remains unclear. Thus, proteomic methods were used to investigate phosphorylation changes in preserved and OA articular cartilage samples, and to explore the intervention targets of phosphorylated kinase.

METHODS

Preserved (control) and lesioned (OA) cartilage samples from OA cases were assessed by phosphoproteomics. Immobilized metal affinity chromatography was performed for phosphopeptide enrichment. Quantitated phosphosites were comparatively assessed in the cartilage sample pair. Kinase-substrate enrichment analyses were carried out for identifying OA-related kinases. BRAF expression in cartilage tissues was assessed by immunohistochemical staining. The effects of BRAF inhibitor on cartilage degeneration were examined in mouse chondrocytes and OA mouse model.

RESULTS

High-sensitivity mass spectrometry-based proteomics revealed 7,471 peptides and 4,375 phosphorylated peptides differing between preserved and lesioned cartilage samples, which represented the significant alteration of kinase hubs and transduction pathways. Phosphoproteomics identified BRAF may be involved in developing OA. BRAF regulated the downstream ERK signaling pathway. In addition, BRAF was upregulated in human OA cartilage as shown by immunohistochemistry. Remarkably, BRAF inhibition alleviated cartilage degradation in a mouse model of OA through its downstream of ERK pathway activation.

CONCLUSIONS

Jointly, these findings provide an overview of phosphoproteomic alterations occurring during cartilage degeneration, identifying the BRAF-ERK1/2 Axis signaling as a potential signaling pathway involved in OA.

Bone characteristics in condylar hyperplasia of the temporomandibular joint: a microcomputed tomography, histology, and Raman microspectrometry study

Unilateral condylar hyperplasia (UCH) of the temporomandibular joint is a progressive deformation of the mandibular condyle of unknown origin. UCH is characterized by excessive growth of the condylar head and neck, leading to an increase in size and volume. The aim of this study was to investigate the characteristics of the bone in patients with UCH using microcomputed tomography (micro-CT), histology, and Raman microspectroscopy. The mandibular condyles of six patients with UCH were analysed using micro-CT, histology, and Raman microspectrometry and imaging, and the results were compared with those obtained for a normal control subject. Three-dimensional micro-CT models revealed focal abnormalities of the bone microarchitecture, with foci of osteosclerosis. Histological sections showed that these foci included islands of calcified cartilage matrix with live chondrocytes. Raman analysis revealed that the cartilage matrix was more heavily calcified than the bone matrix and that the cartilage could be identified by the phenylalanine (PHE) band of its matrix, as well as by its glycosaminoglycan (GAG) content. The persistence of foci of live and active chondrocytes within the bone matrix is intriguing and appears to be pathognomonic of UCH. These new findings on UCH could help to determine its pathophysiology and thus prevent this disease, which can lead to major facial deformity.

Raman needle arthroscopy for in vivo molecular assessment of cartilage

The development of treatments for osteoarthritis (OA) is burdened by the lack of standardized biomarkers of cartilage health that can be applied in clinical trials. We present a novel arthroscopic Raman probe that can "optically biopsy" cartilage and quantify key extracellular matrix (ECM) biomarkers for determining cartilage composition, structure, and material properties in health and disease. Technological and analytical innovations to optimize Raman analysis include (1) multivariate decomposition of cartilage Raman spectra into ECM-constituent-specific biomarkers (glycosaminoglycan [GAG], collagen [COL], water [H₂ O] scores), and (2) multiplexed polarized Raman spectroscopy to quantify superficial zone (SZ) COL anisotropy via a partial least squares-discriminant analysis-derived Raman collagen alignment factor (RCAF). Raman measurements were performed on a series of ex vivo cartilage models: (1) chemically GAG-depleted bovine cartilage explants (n = 40), (2) mechanically abraded bovine cartilage explants (n = 30), (3) aging human cartilage explants (n = 14), and (4) anatomical-site-varied ovine osteochondral explants (n = 6). Derived Raman GAG score biomarkers predicted 95%, 66%, and 96% of the variation in GAG content of GAG-depleted bovine explants, human explants, and ovine explants, respectively (p < 0.001). RCAF values were significantly different for explants with abrasion-induced SZ COL loss (p < 0.001). The multivariate linear regression of Raman-derived ECM biomarkers (GAG and H₂ O scores) predicted 94% of the variation in elastic modulus of ovine explants (p < 0.001). Finally, we demonstrated the first in vivo Raman arthroscopy assessment of an ovine femoral condyle through intraarticular entry into the synovial capsule. This study advances Raman arthroscopy toward a transformative low-cost, minimally invasive diagnostic platform for objective monitoring of treatment outcomes from emerging OA therapies.

The proton-activated G protein-coupled receptor GPR4 regulates the development of osteoarthritis via modulating CXCL12/CXCR7 signaling

Inflammatory diseases decrease the extracellular environmental pH. However, whether proton-activated G protein-coupled receptors (GPCRs) can regulate the development of osteoarthritis (OA) is largely unknown. In this study, we report that proton-activated GPR4 is essential for OA development. We found a marked increase in expression of the proton-activated GPR4 in human and mouse OA cartilage. Lentivirus-mediated overexpression of GPR4 in mouse joints accelerated the development of OA, including promotion of articular cartilage damage, synovial hyperplasia, and osteophyte formation, while Gpr4 knockout effectively attenuated the development of posttraumatic and aging-associated OA in mice. We also found that inhibition of GPR4 with the antagonist NE52-QQ57 ameliorated OA progression in mice, promoted extracellular matrix (ECM) production, and protected cartilage from degradation in human articular cartilage explants. Moreover, GPR4 overexpression upregulated matrix-degrading enzymes’ expression and inflammation factors under pro-inflammatory and slightly acidic conditions. Mechanistically, GPR4 suppressed chondrocyte differentiation and upregulated cartilage homeostasis through NF-κB/MAPK signaling activation by regulating CXCR7/CXCL12 expression. Together, our results take the lead to illustrate that proton-activated GPCR acts as a key regulator for OA pathogenesis in vivo, and support that GPR4 could be a promising therapeutic target for OA treatment.

Perspective on the integration of optical sensing into orthopedic surgical devices

SIGNIFICANCE

Orthopedic surgery currently comprises over 1.5 million cases annually in the United States alone and is growing rapidly with aging populations. Emerging optical sensing techniques promise fewer side effects with new, more effective approaches aimed at improving patient outcomes following orthopedic surgery.

AIM

The aim of this perspective paper is to outline potential applications where fiberoptic-based approaches can complement ongoing development of minimally invasive surgical procedures for use in orthopedic applications.

APPROACH

Several procedures involving orthopedic and spinal surgery, along with the clinical challenge associated with each, are considered. The current and potential applications of optical sensing within these procedures are discussed and future opportunities, challenges, and competing technologies are presented for each surgical application.

RESULTS

Strong research efforts involving sensor miniaturization and integration of optics into existing surgical devices, including K-wires and cranial perforators, provided the impetus for this perspective analysis. These advances have made it possible to envision a next-generation set of devices that can be rigorously evaluated in controlled clinical trials to become routine tools for orthopedic surgery.

CONCLUSIONS

Integration of optical devices into surgical drills and burrs to discern bone/tissue interfaces could be used to reduce complication rates across a spectrum of orthopedic surgery procedures or to aid less-experienced surgeons in complex techniques, such as laminoplasty or osteotomy. These developments present both opportunities and challenges for the biomedical optics community.

Collagen orientation probed by polarized Raman spectra can serve as differential diagnosis indicator between different grades of meniscus degeneration

The purpose of the present study was to analyze normal and degenerated menisci with Raman methodology on thin sections of formalin fixed paraffin embedding tissues and to correlate the Raman findings with the grade of meniscus degeneration. Menisci (n = 27) were removed from human knee joints after total knee replacement or meniscectomy. Following routine histopathological analysis to determine the grade of meniscal lesions obtained from healthy and degenerated formaline fixed paraffin embedded (FFPE) meniscal sections, Raman polarization approach was applied to evaluate the orientation of collagen fibrils in different levels of the same 5 μm thick FFPE meniscal tissue sections, used for histopathological assessment. We collected Raman spectra in two different polarization geometries, v-HH and v-VV, and calculated the mean value of the v-HH/v-VV intensity ratio of two Raman bands, sensitive and non-sensitive to the molecular orientation. The collagen specific amide I band at 1665 cm^-1, has the higher sensitivity dependence on the Raman polarization. The mean values of ratio v-HH/v-VV of the 1665 cm^-1 peak intensity was significantly higher in healthy, mean ± SD: 2.56 ± 0.46, compared to degenerated menisci, mean ± SD: 1.85 ± 0.42 (p = 0.0014). The mean values of v-HH/v-VV intensity ratio were 2.18 and 1.50 for low and high degenerated menisci, respectively (p < 0.0001). The difference of peak intensities in the two laser polarizations is decreased in the degenerated meniscus; this difference is diminishing as the degeneration increases. The v-HH/v-VV ratio was also of significant difference in low as compared to control and high grade meniscus lesions (p = 0.036 and p < 0.0001, respectively) offering valuable information for the approach of its biology and function. In the present study we showed that the 5 μm thick sections can be used for Raman analysis of meniscal tissue with great reliability, in terms of sensitivity, specificity, false-negative and false-positive results. Our data introduce the interesting hypothesis that compact portable Raman microscopy on tissue sections can be used intra-operatively for fast diagnosis and hence, accurate procedure design in the operating room.

Raman spectroscopy in skeletal tissue disorders and tissue engineering: present and prospective

Musculoskeletal disorders are the most common reason of chronic pain and disability representing worldwide an enormous socio-economic burden. In this review, new biomedical application fields for Raman spectroscopy (RS) technique related to skeletal tissues are discussed showing that it can provide a comprehensive profile of tissue composition in situ, in a rapid, label-free, and non-destructive manner. RS can be used as a tool to study tissue alterations associated to aging, pathologies, and disease treatments. The main advantage with respect to currently applied methods in clinics is its ability to provide specific information on molecular composition, which goes beyond other diagnostic tools. Being compatible with water, RS can be performed without pre-treatment on unfixed, hydrated tissue samples, without any labelling and chemical fixation used in histochemical methods. This review provides first the description of basic principles of RS as a biotechnology tool and introduces into the field of currently available RS based techniques, developed to enhance Raman signal. The main spectral processing statistical tools, fingerprint identification and available databases are mentioned. The recent literature has been analysed for such applications of RS as tendon and ligaments, cartilage, bone, and tissue engineered constructs for regenerative medicine. Several cases of proof-of-concept preclinical studies have been described. Finally, advantages, limitations, future perspectives, and challenges for translation of RS into clinical practice have been also discussed.

Altered collagen chemical compositional structure in osteopenic women with past fractures: A case-control Raman spectroscopic study

Incidences of low-trauma fractures among osteopenic women may be related to changes in bone quality. In this blinded, prospective-controlled study, compositional and heterogeneity contributors of bone quality to fracture risk were examined. We hypothesize that Raman spectroscopy can differentiate between osteopenic women with one or more fractures (cases) from women without fractures (controls). This study involved the Raman spectroscopic analysis of cortical and cancellous bone composition using iliac crest biopsies obtained from 59-cases and 59-controls, matched for age (62.0 ± 7.5 and 61.7 ± 7.3 years, respectively, p = 0.38) and hip bone mineral density (BMD, 0.827 ± 0.083 and 0.823 ± 0.072 g/cm3, respectively, p = 0.57). Based on aggregate univariate case-control and odds ratio based logistic regression analyses, we discovered two Raman ratiometric parameters that were predictive of past fracture risk. Specifically, 1244/1268 and 1044/959 cm-1 ratios, were identified as the most differential aspects of bone quality in cortical cases with odds ratios of 0.617 (0.406-0.938 95% CI, p = 0.024) and 1.656 (1.083-2.534 95% CI, p = 0.020), respectively. Both 1244/1268 and 1044/959 cm-1 ratios exhibited moderate sensitivity (59.3-64.4%) but low specificity (49.2-52.5%). These results suggest that the organization of mineralized collagen fibrils were significantly altered in cortical cases compared to controls. In contrast, compositional and heterogeneity parameters related to mineral/matrix ratios, B-type carbonate substitutions, and mineral crystallinity, were not significantly different between cases and controls. In conclusion, a key outcome of this study is the significant odds ratios obtained for two Raman parameters (1244/1268 and 1044/959 cm-1 ratios), which from a diagnostic perspective, may assist in the screening of osteopenic women with suspected low-trauma fractures. One important implication of these findings includes considering the possibility that changes in the organization of collagen compositional structure plays a far greater role in postmenopausal women with osteopenic fractures.

Vibrational Spectroscopy in Assessment of Early Osteoarthritis-A Narrative Review

Osteoarthritis (OA) is a degenerative disease, and there is currently no effective medicine to cure it. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. Therefore, it is necessary to diagnose OA at an early stage. There are various diagnostic methods for OA, but the methods applied to early diagnosis are limited. Ordinary optical diagnosis is confined to the surface, while laboratory tests, such as rheumatoid factor inspection and physical arthritis checks, are too trivial or time-consuming. Evidently, there is an urgent need to develop a rapid nondestructive detection method for the early diagnosis of OA. Vibrational spectroscopy is a rapid and nondestructive technique that has attracted much attention. In this review, near-infrared (NIR), infrared, (IR) and Raman spectroscopy were introduced to show their potential in early OA diagnosis. The basic principles were discussed first, and then the research progress to date was discussed, as well as its limitations and the direction of development. Finally, all methods were compared, and vibrational spectroscopy was demonstrated that it could be used as a promising tool for early OA diagnosis. This review provides theoretical support for the application and development of vibrational spectroscopy technology in OA diagnosis, providing a new strategy for the nondestructive and rapid diagnosis of arthritis and promoting the development and clinical application of a component-based molecular spectrum detection technology.

Optical Biomarkers for the Diagnosis of Osteoarthritis through Raman Spectroscopy: Radiological and Biochemical Validation Using Ex Vivo Human Cartilage Samples

Osteoarthritis (OA) is the most common rheumatic disease, characterized by progressive articular cartilage degradation. Raman spectroscopy (RS) has been recently proposed as a label-free tool to detect molecular changes in musculoskeletal tissues. We used cartilage samples derived from human femoral heads to perform an ex vivo study of different Raman signals and ratios, related to major and minor molecular components of articular cartilage, hereby proposed as candidate optical biomarkers for OA. Validation was performed against the radiological Kellgren-Lawrence (K-L) grading system, as a gold standard, and cross-validated against sulfated glycosaminoglycans (sGAGs) and total collagens (Hyp) biochemical contents. Our results showed a significant decrease in sGAGs (SGAGs, A1063 cm^-1/A1004 cm^-1) and proteoglycans (PGs, A1375 cm^-1/A1004 cm^-1) and a significant increase in collagen disorganization (ColD/F, A1245 cm^-1/A1270 cm^-1), with OA severity. These were correlated with sGAGs or Hyp contents, respectively. Moreover, the SGAGs/HA ratio (A1063 cm^-1/A960 cm^-1), representing a functional matrix, rich in proteoglycans, to a mineralized matrix-hydroxyapatite (HA), was significantly lower in OA cartilage (K-L I vs. III-IV, p < 0.05), whilst the mineralized to collagenous matrix ratio (HA/Col, A960 cm^-1/A920 cm^-1) increased, being correlated with K-L. OA samples showed signs of tissue mineralization, supported by the presence of calcium crystals-related signals, such as phosphate, carbonate, and calcium pyrophosphate dihydrate (MGP, A960 cm^-1/A1004 cm^-1, MGC, A1070 cm^-1/A1004 cm^-1 and A1050 cm^-1/A1004 cm^-1). Finally, we observed an increase in lipids ratio (IL, A1450 cm^-1/A1670 cm^-1) with OA severity. As a conclusion, we have described the molecular fingerprint of hip cartilage, validating a panel of optical biomarkers and the potential of RS as a complementary diagnostic tool for OA.

Molecular analysis of the destruction of articular joint tissues by Raman spectroscopy

INTRODUCTION

Osteoarthritis (OA) is a highly heterogenous disease influenced by different molecular, anatomic, and physiologic imbalances. Some of the bottlenecks for enhanced diagnosis and therapeutic assessment are the lack of validated biomarkers and early diagnosis tools. In this narrative review, we analyze the potential of Raman spectroscopy (RS) as a label-free optical tool for the characterization of articular joint tissues and its application as a diagnosis tool for OA.

AREAS COVERED

Raman spectra produce a unique 'molecular fingerprint' providing rotational and vibrational molecular information, allowing the identification and follow-up of molecular changes associated with OA pathological mechanisms. Focusing on multiple joint tissues (cartilage, synovium, bone, tendons, ligaments, and meniscus) and their contribution in disease incidence and progression, this review highlights the current knowledge on the application of RS in the characterization of organic and inorganic molecules present at these tissues and alterations that occur in the onset of OA.

EXPERT OPINION

Vibrational spectroscopy techniques, such as RS, are low cost, rapid and minimally invasive approaches that offer high specificity in the assessment of the molecular composition of complex tissues. Combined with multivariate statistical methods, RS offers great potential for optical biomarkers discovery or disease diagnosis applications, and we hereby discuss clinical translational progresses on the field.

Embrittlement of collagen in early-stage human osteoarthritis

Articular cartilage is a remarkable material with mechanical performance that surpasses engineering standards. Collagen, the most abundant protein in cartilage, plays an important role in this performance, and also in disease. Building on observations of network-level collagen changes at the earliest stages of osteoarthritis, this study explores the physical role of the collagen fibril in the disease process. Specifically, we focus on the material properties of collagen fibrils in the cartilage surface. Ten human tibial plateaus were characterised by atomic force microscopy (AFM) and Raman spectroscopy, with histological scoring used to define disease state. Measures of tropocollagen remained stable with disease progression, yet a marked mechanical change was observed. A slight stiffening coupled with a substantial decrease in loss tangent suggests a physical embrittlement caused by increased inter-molecular interactions.

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.

Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to diagnose osteoarthritis in equine serum

BACKGROUND

Reliable and validated biomarkers for osteoarthritis (OA) are currently lacking.

OBJECTIVES

To develop an accurate and minimally invasive method to assess OA-affected horses and provide potential spectral markers indicative of disease.

STUDY DESIGN

Observational, cross-sectional study.

METHODS

Our cohort consisted of 15 horses with OA and 48 without clinical signs of the disease, which were used as controls. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was used to investigate serum samples (50 μL) collected from these horses. Spectral processing and multivariate analysis revealed differences and similarities, allowing for detection of spectral biomarkers that discriminated between the two cohorts. A supervised classification algorithm, namely principal component analysis coupled with quadratic discriminant analysis (PCA-QDA), was applied to evaluate the diagnostic accuracy.

RESULTS

Segregation between the two different cohorts, OA-affected and controls, was achieved with 100% sensitivity and specificity. The six most discriminatory peaks were attributed to proteins and lipids. Four of the spectral peaks were elevated in OA horses, which could be potentially due to an increase in lipids, protein expression levels and collagen, all of which have been previously reported in OA. Two peaks were found decreased and were tentatively assigned to the reduction of proteoglycan content that is observed during OA.

MAIN LIMITATIONS

The control group had a wide range of ages and breeds. Presymptomatic OA cases were not included. Therefore, it remains unknown whether this test could also be used as an early diagnostic tool.

CONCLUSIONS

This spectrochemical approach could provide an accurate and cost-effective blood test, facilitating point-of-care diagnosis of equine OA.

Raman chemical imaging, a new tool in kidney stone structure analysis: Case-study and comparison to Fourier Transform Infrared spectroscopy

BACKGROUND AND OBJECTIVES

The kidney stone's structure might provide clinical information in addition to the stone composition. The Raman chemical imaging is a technology used for the production of two-dimension maps of the constituents' distribution in samples. We aimed at determining the use of Raman chemical imaging in urinary stone analysis.

MATERIAL AND METHODS

Fourteen calculi were analyzed by Raman chemical imaging using a confocal Raman microspectrophotometer. They were selected according to their heterogeneous composition and morphology. Raman chemical imaging was performed on the whole section of stones. Once acquired, the data were baseline corrected and analyzed by MCR-ALS. Results were then compared to the spectra obtained by Fourier Transform Infrared spectroscopy.

RESULTS

Raman chemical imaging succeeded in identifying almost all the chemical components of each sample, including monohydrate and dihydrate calcium oxalate, anhydrous and dihydrate uric acid, apatite, struvite, brushite, and rare chemicals like whitlockite, ammonium urate and drugs. However, proteins couldn't be detected because of the huge autofluorescence background and the small concentration of these poor Raman scatterers. Carbapatite and calcium oxalate were correctly detected even when they represented less than 5 percent of the whole stones. Moreover, Raman chemical imaging provided the distribution of components within the stones: nuclei were accurately identified, as well as thin layers of other components. Conversion of dihydrate to monohydrate calcium oxalate was correctly observed in the centre of one sample. The calcium oxalate monohydrate had different Raman spectra according to its localization.

CONCLUSION

Raman chemical imaging showed a good accuracy in comparison with infrared spectroscopy in identifying components of kidney stones. This analysis was also useful in determining the organization of components within stones, which help locating constituents in low quantity, such as nuclei. However, this analysis is time-consuming, making it more suitable for research studies rather than routine analysis.

Detection of depth-depend changes in porcine cartilage after wear test using Raman spectroscopy

Cartilage damage and wear can lead to severe diseases, such as osteoarthritis, thus, many studies on the cartilage wear process have already been performed to better understand the cartilage wear mechanism. However, most characterization methods focus on the cartilage surface or the total wear extent. With the advantages of high spatial resolution and easy characterization, Raman microspectroscopy was employed for the first time to characterize full-depth changes in the cartilage extracellular matrix (ECM) after wear test. Sections from the cartilage samples after wear were compared with sections from the control group. Univariate and multivariate analyses both indicated that collagen content loss at certain depths (20%-30% relative to the cartilage surface) is possibly the dominating alteration during wear rather than changes in collagen fiber orientation or proteoglycan content. These findings are consistent with the observations obtained by scanning electron microscopy and histological staining. This study successfully used Raman microspectroscopy efficiently assess full-depth changes in cartilage ECM after wear test, thus providing new insight into cartilage damage and wear.

Comparison of Compressive Stress-Relaxation Behavior in Osteoarthritic (ICRS Graded) Human Articular Cartilage

Osteoarthritis (OA) is a common joint disorder found mostly in elderly people. The role of mechanical behavior in the progression of OA is complex and remains unclear. The stress-relaxation behavior of human articular cartilage in clinically defined osteoarthritic stages may have importance in diagnosis and prognosis of OA. In this study we investigated differences in the biomechanical responses among human cartilage of ICRS grades I, II and III using polymer dynamics theory. We collected 24 explants of human articular cartilage (eight each of ICRS grade I, II and III) and acquired stress-relaxation data applying a continuous load on the articular surface of each cartilage explant for 1180 s. We observed a significant decrease in Young’s modulus, stress-relaxation time, and stretching exponent in advanced stages of OA (ICRS grade III). The stretch exponential model speculated that significant loss in hyaluronic acid polymer might be the reason for the loss of proteoglycan in advanced OA. This work encourages further biomechanical modelling of osteoarthritic cartilage utilizing these data as input parameters to enhance the fidelity of computational models aimed at revealing how mechanical behaviors play a role in pathogenesis of OA.

Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure

Conventional arthroscopic evaluation of articular cartilage is subjective and poorly reproducible. Therefore, implementation of quantitative diagnostic techniques, such as near infrared spectroscopy (NIRS) and optical coherence tomography (OCT), is essential. Locations (n = 44) with various cartilage conditions were selected from mature equine fetlock joints (n = 5). These locations and their surroundings were measured with NIRS and OCT (n = 530). As a reference, cartilage proteoglycan (PG) and collagen contents, and collagen network organization were determined using quantitative microscopy. Additionally, lesion severity visualized in OCT images was graded with an automatic algorithm according to International Cartilage Research Society (ICRS) scoring system. Artificial neural network with variable selection was then employed to predict cartilage composition in the superficial and deep zones from NIRS data, and the performance of two models, generalized (including all samples) and condition-specific models (based on ICRS-grades), was compared. Spectral data correlated significantly (p < 0.002) with PG and collagen contents, and collagen orientation in the superficial and deep zones. The combination of NIRS and OCT provided the most reliable outcome, with condition-specific models having lower prediction errors (9.2%) compared to generalized models (10.4%). Therefore, the results highlight the potential of combining both modalities for comprehensive evaluation of cartilage during arthroscopy.