Raman spectroscopy-based water content is a negative predictor of articular human cartilage mechanical function

Biomechanics of the Human Osteochondral Unit: A Systematic Review

The damping system ensured by the osteochondral (OC) unit is essential to deploy the forces generated within load-bearing joints during locomotion, allowing furthermore low-friction sliding motion between bone segments. The OC unit is a multi-layer structure including articular cartilage, as well as subchondral and trabecular bone. The interplay between the OC tissues is essential in maintaining the joint functionality; altered loading patterns can trigger biological processes that could lead to degenerative joint diseases like osteoarthritis. Currently, no effective treatments are available to avoid degeneration beyond tissues' recovery capabilities. A thorough comprehension on the mechanical behaviour of the OC unit is essential to (i) soundly elucidate its overall response to intra-articular loads for developing diagnostic tools capable of detecting non-physiological strain levels, (ii) properly evaluate the efficacy of innovative treatments in restoring physiological strain levels, and (iii) optimize regenerative medicine approaches as potential and less-invasive alternatives to arthroplasty when irreversible damage has occurred. Therefore, the leading aim of this review was to provide an overview of the state-of-the-art-up to 2022-about the mechanical behaviour of the OC unit. A systematic search is performed, according to PRISMA standards, by focusing on studies that experimentally assess the human lower-limb joints' OC tissues. A multi-criteria decision-making method is proposed to quantitatively evaluate eligible studies, in order to highlight only the insights retrieved through sound and robust approaches. This review revealed that studies on human lower limbs are focusing on the knee and articular cartilage, while hip and trabecular bone studies are declining, and the ankle and subchondral bone are poorly investigated. Compression and indentation are the most common experimental techniques studying the mechanical behaviour of the OC tissues, with indentation also being able to provide information at the micro- and nanoscales. While a certain comparability among studies was highlighted, none of the identified testing protocols are currently recognised as standard for any of the OC tissues. The fibril-network-reinforced poro-viscoelastic constitutive model has become common for describing the response of the articular cartilage, while the models describing the mechanical behaviour of mineralised tissues are usually simpler (i.e., linear elastic, elasto-plastic). Most advanced studies have tested and modelled multiple tissues of the same OC unit but have done so individually rather than through integrated approaches. Therefore, efforts should be made in simultaneously evaluating the comprehensive response of the OC unit to intra-articular loads and the interplay between the OC tissues. In this regard, a multidisciplinary approach combining complementary techniques, e.g., full-field imaging, mechanical testing, and computational approaches, should be implemented and validated. Furthermore, the next challenge entails transferring this assessment to a non-invasive approach, allowing its application in vivo, in order to increase its diagnostic and prognostic potential.

Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction

This review presents the changes that the imaging of articular cartilage has undergone throughout the last decades. It highlights that the expectation is no longer to image the structure and associated functions of articular cartilage but, instead, to devise methods for generating non-invasive, function-depicting images with quantitative information that is useful for detecting the early, pre-clinical stage of diseases such as primary or post-traumatic osteoarthritis (OA/PTOA). In this context, this review summarizes (a) the structure and function of articular cartilage as a molecular imaging target, (b) quantitative MRI for non-invasive assessment of articular cartilage composition, microstructure, and function with the current state of medical diagnostic imaging, (c), non-destructive imaging methods, (c) non-destructive quantitative articular cartilage live-imaging methods, (d) artificial intelligence (AI) classification of degeneration and prediction of OA progression, and (e) our contribution to this field, which is an AI-supported, non-destructive quantitative optical biopsy for early disease detection that operates on a digital tissue architectural fingerprint. Collectively, this review shows that articular cartilage imaging has undergone profound changes in the purpose and expectations for which cartilage imaging is used; the image is becoming an AI-usable biomarker with non-invasive quantitative functional information. This may aid in the development of translational diagnostic applications and preventive or early therapeutic interventions that are yet beyond our reach.

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.

Intraoperative Acoustic Evaluation of Living Human Knee Cartilage-Comparison with Respect to Cartilage Degeneration and Aging

OBJECTIVE

The objective of the study was to evaluate the mechanical properties of living human knee cartilage using our ultrasonic device, and to compare the measurements with respect to cartilage degeneration and aging.

DESIGN

A total of 95 knees which had undergone arthroscopic knee surgery, from 88 patients, were included in the study, with informed consent. All procedures were reviewed and approved by the ethical committee of our hospital. In the study group, there were 41 men, 47 women, 39 right knees, and 56 left knees. The conditions primarily included knee osteoarthritis and anterior cruciate ligament rupture. The mean operative age was 44.1 years old (range = 10-83). We compared mechanical properties of the knee cartilage with respect to aging and gender, in comparison with normal cartilage. A P value of <0.05 represented statistical significance.

RESULTS

In the context of the International Cartilage Repair Society (ICRS) classification of cartilage degeneration (grade 0-3), the signal intensity in grade 0 was significantly larger than that in grade 1, 2, or 3. The thickness in grade 0 was significantly higher than that in grade 1, 2, or 3. Normal cartilage in older women had the lowest signal intensity and the least cartilage thickness among all the groups.

CONCLUSION

The ultrasonic system we developed was able to detect early degenerative changes in living cartilage in knees. The lowest signal intensity and least cartilage thickness in normal cartilage among older women were correlated to a large prevalence of knee osteoarthritis in women.

LEVEL OF EVIDENCE

Level IV, case series.

Bone intrinsic material and compositional properties in postmenopausal women diagnosed with long-term Type-1 diabetes

The incidence of diabetes mellitus and the associated complications are growing worldwide, affecting the patients' quality of life and exerting a considerable burden on health systems. Yet, the increase in fracture risk in type 1 diabetes (T1D) patients is not fully captured by bone mineral density (BMD), leading to the hypothesis that alterations in bone quality are responsible for the increased risk. Material/compositional properties are important aspects of bone quality, yet information on human bone material/compositional properties in T1D is rather sparse. The purpose of the present study is to measure both the intrinsic material behaviour by nanoindentation, and material compositional properties by Raman spectroscopy as a function of tissue age and microanatomical location (cement lines) in bone tissue from iliac crest biopsies from postmenopausal women diagnosed with long-term T1D (N = 8), and appropriate sex-, age-, BMD- and clinically-matched controls (postmenopausal women; N = 5). The results suggest elevation of advanced glycation endproducts (AGE) content in the T1D and show significant differences in mineral maturity / crystallinity (MMC) and glycosaminoglycan (GAG) content between the T1D and control groups. Furthermore, both hardness and modulus by nanoindentation are greater in T1D. These data suggest a significant deterioration of material strength properties (toughness) and compositional properties in T1D compared with controls.

Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage.

Cationic Modified PVA Hydrogels Provide Low Friction and Excellent Mechanical Properties for Potential Cartilage and Orthopedic Applications

Poly(vinyl alcohol) (PVA) hydrogel is a promising candidate for articular cartilage repair yet restrained by its mechanical strength and tribological property. Current work reports a newly designed PVA-based hydrogel modified by glycerol (g), bacterial cellulose (BC), and a cationic polymer poly (diallyl dimethylammonium chloride) (PDMDAAC), which is a novel cationic strengthening choice. The resultant PVA-g-BC-PDMDAAC hydrogel proves the effectiveness of this modification scheme, with a confined compressive modulus of 19.56 MPa and a friction coefficient of 0.057 at a joint-equivalent load and low sliding speed. The water content, swelling property, and creep behavior of this hydrogel are also within a cartilage-mimetic range. The properties of PVA-based hydrogels before PDMDAAC addition are likewise studied as a cross-reference. Besides, PDMDAAC-modified PVA hydrogel realizes ideal mechanical and lubrication properties with a relatively low PVA concentration (10 wt.%) and facile fabrication process, which lays a foundation for mass production and marketization in the future.

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.

Raman spectroscopy-based water measurements identify the origin of MRI T2 signal in human articular cartilage zones and predict histopathologic score

We investigated for the first time zonal-dependent water distribution in articular cartilage by Raman spectroscopy (RS). We further investigated the association of histopathologic score with RS- and magnetic resonance imaging (MRI)-based water measurements. Cadaveric human cartilage plugs (N = 16) with different osteoarthritis (OA) severity were used. Water content distribution in cartilage zones was probed using RS- and MRI-based techniques. Histopathologic scoring was performed by two independent observers blindly. Moderate associations existed between RS- and MRI-based water measurements across all cartilage zones. RS-based analysis of different water compartments helped assign the origin of the T2 signal collected from the various cartilage zones. RS-based water parameters significantly correlated with OA-severity score, whereas MRI-based water measurements did not. RS can probe different water compartments in cartilage zones and predict up to 66% of the variation observed in the histopathologic score. RS-based water measurement could be developed further to assess cartilage quality in the clinic.

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.

Vibrational Spectroscopy for In Vitro Monitoring Stem Cell Differentiation

Stem cell technology has attracted considerable attention over recent decades due to its enormous potential in regenerative medicine and disease therapeutics. Studying the underlying mechanisms of stem cell differentiation and tissue generation is critical, and robust methodologies and different technologies are required. Towards establishing improved understanding and optimised triggering and control of differentiation processes, analytical techniques such as flow cytometry, immunohistochemistry, reverse transcription polymerase chain reaction, RNA in situ hybridisation analysis, and fluorescence-activated cell sorting have contributed much. However, progress in the field remains limited because such techniques provide only limited information, as they are only able to address specific, selected aspects of the process, and/or cannot visualise the process at the subcellular level. Additionally, many current analytical techniques involve the disruption of the investigation process (tissue sectioning, immunostaining) and cannot monitor the cellular differentiation process in situ, in real-time. Vibrational spectroscopy, as a label-free, non-invasive and non-destructive analytical technique, appears to be a promising candidate to potentially overcome many of these limitations as it can provide detailed biochemical fingerprint information for analysis of cells, tissues, and body fluids. The technique has been widely used in disease diagnosis and increasingly in stem cell technology. In this work, the efforts regarding the use of vibrational spectroscopy to identify mechanisms of stem cell differentiation at a single cell and tissue level are summarised. Both infrared absorption and Raman spectroscopic investigations are explored, and the relative merits, and future perspectives of the techniques are discussed.

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.