Raman needle arthroscopy for in vivo molecular assessment of cartilage

Label-free 3D molecular imaging of living tissues using Raman spectral projection tomography

The ability to image tissues in three dimensions (3D) with label-free molecular contrast at the mesoscale would be a valuable capability in biology and biomedicine. Here, we introduce Raman spectral projection tomography (RSPT) for volumetric molecular imaging with optical sub-millimeter spatial resolution. We have developed a RSPT imaging instrument capable of providing 3D molecular contrast in transparent and semi-transparent samples. We also created a computational pipeline for multivariate reconstruction to extract label-free spatial molecular information from Raman projection data. Using these tools, we demonstrate imaging and visualization of phantoms of various complex shapes with label-free molecular contrast. Finally, we apply RSPT as a tool for imaging of molecular gradients and extracellular matrix heterogeneities in fixed and living tissue-engineered constructs and explanted native cartilage tissues. We show that there exists a favorable balance wherein employing Raman spectroscopy, with its advantages in live cell imaging and label-free molecular contrast, outweighs the reduction in imaging resolution and blurring caused by diffuse photon propagation. Thus, RSPT imaging opens new possibilities for label-free molecular monitoring of tissues.

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of spatially offset Raman spectroscopy (SORS) for noninvasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Young's modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

Arterial Blood Supply of the Stifle Joint in Horses

The vascularization pattern of the equine stifle joint is insufficiently described in the literature, even though there is a growing need for knowledge of the exact blood supply, as (i) arthroscopy and endoscopic surgery techniques are increasingly performed in horses and (ii) ex vivo models of menisci need nutrient supply that mimic the in vivo situation. The aim of this study was to describe the vessels involved in the stifle joint supply and the exact branching pattern of the popliteal artery. Colored latex was injected into the arteries of nine pelvic limbs of equine cadavers (n = 6) to evaluate the occurrences, variations and approximate diameters of vessels that supplied the stifle joints. Next to a branch of the saphenous and descending genicular arteries, eleven branches of the popliteal artery could be described in horses that feed the vascular network of the stifle joint. With a focus on the blood supply of the menisci, a vascularization map was created to show the main influx to these intra-articular structures in detail. These findings are potentially of great importance to both clinicians in preparation of best-suited incisions for arthroscopy and researchers designing new approaches for meniscal studies and choosing suitable animal models.

Ultraviolet-Visible-Near Infrared Spectroscopy May Aid in the Qualitative Assessment of Early-Stage Cartilage Degradation

Purpose

To assess the potential of ultraviolet-visible near-infrared spectroscopy to provide quantitative information on the cartilage surface at early osteoarthritis.

Methods

We used a similar source and optical path to a standard arthroscope and constraining input to the range available to a standard detector/camera, further capturing and analyzing spectral information quantitatively in terms of specific electronic absorbance bands and scattering from the cartilage surface, with a focus on the early stages of degradation.

Results

The ratio of the 320-nm and longer than 500-nm absorbances produced a distinct change from the normal to diseased states. The slopes between the wavelengths of 600 and 980 nm may show the transition of the single fibril to fibril bundles that occurs during early stages disease.

Conclusions

Ultraviolet-visible near-infrared spectroscopy has good potential for use in integrated arthroscopic assessment.

Clinical Relevance

This raises the possibility of advancing arthroscopy from a qualitative to a quantitative tool, without requiring modification of either the radiation (the light source and path) or instrumentation (the arthroscope itself) delivered to the patient, thus allowing a low-cost yet potentially high-value technology.

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.

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of Spatially Offset Raman Spectroscopy (SORS) for non-invasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Young's modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

An auxiliary diagnostic technology and clinical efficacy evaluation in knee osteoarthritis based on serum surface-enhanced Raman spectroscopy

Knee osteoarthritis (KOA), a progressive joint disease, is a leading source of chronic pain and disability, and its diagnosis mainly depends on medical imaging findings and clinical symptoms. This study aimed to explore an auxiliary diagnostic technology and clinical efficacy evaluation in KOA based on surface-enhanced Raman scattering (SERS). Three sequential experiments were performed: 1) preliminary observation of the therapeutic effects of icariin (ICA); 2) using serum SERS spectra obtained from rat models belonging to sham group, KOA group and icariin treatment group, respectively, to analyze the KOA-related expression profiles; 3) employing partial least squares (PLS) and support vector machines (SVM) algorithms to establish KOA diagnosis model. Pathological changes verified the efficacy of icariin in KOA. Raman peak assignment combined with spectral difference analysis reflected the biochemical changes associated with KOA, including amino acid, carbohydrates and collagen. ICA intervention significantly reversed these changes, although full recovery could not be achieved. Based on PLS-SVM approach, the sensitivity, specificity and accuracy of 100%, 98.33% and 98.89%, respectively, were obtained for screening KOA. This work proves that SERS has great potential to be used as an auxiliary diagnostic technology for KOA, and is also helpful for the exploration of novel KOA treatment agent.

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.

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.