Imaging of Osteoarthritic Human Articular Cartilage using Fourier Transform Infrared Microspectroscopy Combined with Multivariate and Univariate Analysis

Infrared Spectroscopy Can Differentiate Between Cartilage Injury Models: Implication for Assessment of Cartilage Integrity

In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4-13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue's proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.

Chondrocyte deformation during the unloading phase of cyclic compression loading

Cell volume and shape changes play a pivotal role in cellular mechanotransduction, governing cellular responses to external loading. Understanding the dynamics of cell behavior under loading conditions is essential to elucidate cell adaptation mechanisms in physiological and pathological contexts. In this study, we investigated the effects of dynamic cyclic compression loading on cell volume and shape changes, comparing them with static conditions. Using a custom-designed platform which allowed for simultaneous loading and imaging of cartilage tissue, tissues were subjected to 100 cycles of mechanical loading while measuring cell volume and shape alterations during the unloading phase at specific time points. The findings revealed a transient decrease in cell volume (13%) during the early cycles, followed by a gradual recovery to baseline levels after approximately 20 cycles, despite the cartilage tissue not being fully recovered at the unloading phase. This observed pattern indicates a temporal cell volume response that may be associated with cellular adaptation to the mechanical stimulus through mechanisms related to active cell volume regulation. Additionally, this study demonstrated that cell volume and shape responses during dynamic loading were significantly distinct from those observed under static conditions. Such findings suggest that cells in their natural tissue environment perceive and respond differently to dynamic compared to static mechanical cues, highlighting the significance of considering dynamic loading environments in studies related to cellular mechanics. Overall, this research contributes to the broader understanding of cellular behavior under mechanical stimuli, providing valuable insights into their ability to adapt to dynamic mechanical loading.

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms

Introduce

Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in severe cases, they can cause lifelong disability and even death. Studies have shown that early diagnosis and treatment are key to improving patient outcomes. Therefore, a rapid and accurate method for rapid diagnosis of diseases has been established, which is of great clinical significance for realizing early diagnosis of diseases and improving patient prognosis.

Methods

This study was based on Fourier transform infrared spectroscopy (FTIR) combined with a deep learning model to achieve non-invasive, rapid, and accurate differentiation of AS, RA, OA, and healthy control group. In the experiment, 320 serum samples were collected, 80 in each group. AlexNet, ResNet, MSCNN, and MSResNet diagnostic models were established by using a machine learning algorithm.

Result

The range of spectral wave number measured by four sets of Fourier transform infrared spectroscopy is 700-4000 cm-1. Serum spectral characteristic peaks were mainly at 1641 cm-1(amide I), 1542 cm-1(amide II), 3280 cm-1(amide A), 1420 cm-1(proline and tryptophan), 1245 cm-1(amide III), 1078 cm-1(carbohydrate region). And 2940 cm-1 (mainly fatty acids and cholesterol). At the same time, AlexNet, ResNet, MSCNN, and MSResNet diagnostic models are established by using machine learning algorithms. The multi-scale MSResNet classification model combined with residual blocks can use convolution modules of different scales to extract different scale features and use resblocks to solve the problem of network degradation, reduce the interference of spectral measurement noise, and enhance the generalization ability of the network model. By comparing the experimental results of the other three models AlexNet, ResNet, and MSCNN, it is found that the MSResNet model has the best diagnostic performance and the accuracy rate is 0.87.

Conclusion

The results prove the feasibility of serum Fourier transform infrared spectroscopy combined with a deep learning algorithm to distinguish AS, RA, OA, and healthy control group, which can be used as an effective auxiliary diagnostic method for these rheumatic immune diseases.

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

Research progress and future study on physicochemical, nutritional, and structural characteristics of canola and rapeseed feedstocks and co-products from bio-oil processing and nutrient modeling evaluation methods

This article aims to review research progress and provide future study on physicochemical, nutritional, and molecular structural characteristics of canola and rapeseed feedstocks and co-products from bio-oil processing and nutrient modeling evaluation methods. The review includes Canola oil seed production, utilization and features; Rapeseed oil seed production and canola oil seed import in China; Bio-processing, co-products and conventional evaluation methods; Modeling methods for evaluation of truly absorbed protein supply from canola feedstock and co-products. The article provides our current research in feedstocks and co-products from bio-oil processing which include Characterization of chemical and nutrient profiles and ruminal degradation and intestinal digestion; Revealing intrinsic molecular structures and relationship between the molecular structure spectra features and nutrient supply from feedstocks and co-products using advanced vibrational molecular spectroscopy technique. The study focused on advanced vibrational molecular spectroscopy which can be used as a fast tool to study molecular structure features of feedstock and co-products from bio-oil processing. The article also provides future in depth study areas. This review provides an insight as how to use advanced vibrational molecular spectroscopy for in-depth analysis of the relationship between molecular structure spectral feature and nutrition delivery from canola feedstocks and co-products from bio-oil processing.

Comparative Assessment of Primary Osteoarthritis Progression Using Conventional Histopathology, Polarized Light Microscopy, and Immunohistochemistry

OBJECTIVE

Evaluation of collagen orientation and arrangement in articular cartilage can improve our understanding of primary osteoarthritis (OA) progression and targeted therapies. Our goal was to determine if polarized light microscopy (PLM) for collagen organization is useful in identifying early primary OA features in comparison to current standard histopathological methods.

DESIGN

Osteochondral specimens from 90 total knee arthroplasty patients with relatively preserved lateral femoral condyle were scored using (1) histological-histochemical grading system (HHGS); (2) Osteoarthritis Research Society International (OARSI); (3) PLM-Changoor system for repair cartilage, scores ranging between 0 (totally disorganized cartilage) and 5 (healthy adult cartilage); and (4) new PLM system for primary OA cartilage with superficial zone PLM (PLM-SZ) and deep zone PLM (PLM-DZ) scores, each ranging between 0 (healthy adult SZ and DZ collagen organization) and 4 (total loss of collagen organization). Serial sections were stained for collagen I and II antibodies. Spearman correlation coefficients (r_s) were determined.

RESULTS

The associations between: (1) PLM-Changoor and HHGS or OARSI were weak (r_s = -0.36) or moderate (r_s = -0.56); (2) PLM-SZ and HHGS or OARSI were moderate (r_s = 0.46 or r_s = 0.53); and (3) PLM-DZ and HHGS or OARSI were poor (r_s = 0.31 or r_s = 0.21), respectively. Specimens exhibiting early and mild OA (HHGS < 5 and OARSI < 8.6) had PLM-SZ and PLM-DZ scores between 0 and 4 and between 0 and 3, respectively, and indicated new histopathological features not currently considered by HHGS/OARSI.

CONCLUSIONS

PLM was effective at identifying early SZ and DZ collagen alterations that were not evident in the traditional scoring systems. Incorporating PLM scores and/or additional HHGS/OARSI features can help improve characterization of early primary OA cartilage.

Infrared Fiber-Optic Spectroscopy Detects Bovine Articular Cartilage Degeneration

OBJECTIVE

Joint injuries may lead to degeneration of cartilage tissue and initiate development of posttraumatic osteoarthritis. Arthroscopic surgeries can be used to treat joint injuries, but arthroscopic evaluation of articular cartilage quality is subjective. Fourier transform infrared spectroscopy combined with fiber optics and attenuated total reflectance crystal could be used for the assessment of tissue quality during arthroscopy. We hypothesize that fiber-optic mid-infrared spectroscopy can detect enzymatically and mechanically induced damage similar to changes occurring during progression of osteoarthritis.

DESIGN

Bovine patellar cartilage plugs were extracted and degraded enzymatically and mechanically. Adjacent untreated samples were utilized as controls. Enzymatic degradation was done using collagenase and trypsin enzymes. Mechanical damage was induced by (1) dropping a weight impactor on the cartilage plugs and (2) abrading the cartilage surface with a rotating sandpaper. Fiber-optic mid-infrared spectroscopic measurements were conducted before and after treatments, and spectral changes were assessed with random forest, partial least squares discriminant analysis, and support vector machine classifiers.

RESULTS

All models had excellent classification performance for detecting the different enzymatic and mechanical damage on cartilage matrix. Random forest models achieved accuracies between 90.3% and 77.8%, while partial least squares model accuracies ranged from 95.8% to 84.7%, and support vector machine accuracies from 91.7% to 80.6%.

CONCLUSIONS

The results suggest that fiber-optic Fourier transform infrared spectroscopy attenuated total reflectance spectroscopy is a viable way to detect minor and major degeneration of articular cartilage. Objective measures provided by fiber-optic spectroscopic methods could improve arthroscopic evaluation of cartilage damage.

High-resolution infrared microspectroscopic characterization of cartilage cell microenvironment

The lateral resolution of infrared spectroscopy has been inadequate for accurate biochemical characterization of the cell microenvironment, a region regulating biochemical and biomechanical signals to cells. In this study, we demonstrate the capacity of a high-resolution Fourier transform infrared microspectroscopy (HR-FTIR-MS) to characterize the collagen content of this region. Specifically, we focus on the collagen content in the cartilage cell (chondrocyte) microenvironment of healthy and osteoarthritic (OA) cartilage. Human tibial cartilage samples (N = 28) were harvested from 7 cadaveric donors and graded for OA severity (healthy, early OA, advanced OA). HR-FTIR-MS was used to analyze the collagen content of the chondrocyte microenvironment of five distinct zones across the tissue depth. HR-FTIR-MS successfully showed collagen content distribution across chondrocytes and their environment. In zones 2 and 3 (10 - 50% of the tissue thickness), we observed that collagen content was smaller (P < 0.05) in early OA compared to the healthy tissue in the vicinity of cells (pericellular region). The collagen content loss was extended to the extracellular matrix in advanced OA tissue. No significant differences in the collagen content of the chondrocyte microenvironment were observed between the groups in the most superficial (0-10%) and deep zones (50-100%). HR-FTIR-MS revealed collagen loss in the early OA cartilage pericellular region before detectable changes in the extracellular matrix in advanced OA. HR-FTIR-MS-based compositional assessment enables a better understanding of OA-related changes in tissues. This technique can be used to identify new disease mechanisms enabling better intervention strategies. STATEMENT OF SIGNIFICANCE: Osteoarthritis (OA) is the most common degenerative joint disease causing pain and disability. While significant progress has been made in OA research, OA pathogenesis is still poorly understood and current OA treatments are mainly palliative. This study demonstrates that high-resolution FTIR microspectroscopy (HR-FTIR-MS) can characterize OA-induced compositional changes in the cell microenvironment (pericellular matrix) during the early disease stages before tissue changes in the extracellular matrix become apparent. This technique may further enable the identification of new OA mechanisms and improve our current understanding of OA pathogenesis, thus, enabling the development of better treatment methods.

Fourier Transform Infrared Microspectroscopy Combined with Principal Component Analysis and Artificial Neural Networks for the Study of the Effect of β-Hydroxy-β-Methylbutyrate (HMB) Supplementation on Articular Cartilage

The potential of Fourier Transform infrared microspectroscopy (FTIR microspectroscopy) and multivariate analyses were applied for the classification of the frequency ranges responsible for the distribution changes of the main components of articular cartilage (AC) that occur during dietary β-hydroxy-β-methyl butyrate (HMB) supplementation. The FTIR imaging analysis of histological AC sections originating from 35-day old male piglets showed the change in the collagen and proteoglycan contents of the HMB-supplemented group compared to the control. The relative amount of collagen content in the superficial zone increased by more than 23% and in the middle zone by about 17%, while no changes in the deep zone were observed compared to the control group. Considering proteoglycans content, a significant increase was registered in the middle and deep zones, respectively; 62% and 52% compared to the control. AFM nanoindentation measurements collected from animals administered with HMB displayed an increase in AC tissue stiffness by detecting a higher value of Young’s modulus in all investigated AC zones. We demonstrated that principal component analysis and artificial neural networks could be trained with spectral information to distinguish AC histological sections and the group under study accurately. This work may support the use and effectiveness of FTIR imaging combined with multivariate analyses as a quantitative alternative to traditional collagenous tissue-related histology.

To the Question on the Use of Multivariate Analysis and 2D Visualisation of Synchrotron ATR-FTIR Chemical Imaging Spectral Data in the Diagnostics of Biomimetic Sound Dentin/Dental Composite Interface

In this short communication, we provide information on the use of the hierarchical cluster analysis of synchrotron ATR-FTIR 2D chemical imaging spectral data as a useful and powerful approach to the microspectroscopic diagnostics of molecular composition in the hybrid sound dentin/dental composite interfaces and materials, including ones developed with the use of biomimetic strategies. The described diagnostic approach can be successfully transferred to the analysis and visualisation of 2D spectral data, collected using laboratory Raman and FTIR microspectroscopy techniques.

Engineering of a Biomimetic Interface between a Native Dental Tissue and Restorative Composite and Its Study Using Synchrotron FTIR Microscopic Mapping

The aim of this work is to develop a biomimetic interface between the natural tooth tissue and the restorative composite and to study it on the basis of synchrotron micro-FTIR mapping and multidimensional processing of the spectral data array. Using hierarchical cluster analysis of 3D FTIR data revealed marked improvements in the formation of the dentine/adhesive/dental hybrid interface using a biomimetic approach. The use of a biomimetic strategy (application of an amino acid-modified primer, alkaline calcium and a nano-c-HAp-modified adhesive) allowed the formation of a matrix that can be structurally integrated with natural dentine and dental composite. The biomimetic hybrid layer was characterised by homogeneous chemical composition and a higher degree of conversion of the adhesive during polymerisation, which should provide optimal integration of the dental composite with the dentine.

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.

No pressure, no diamonds? - Static vs. dynamic compressive in-situ loading to evaluate human articular cartilage functionality by functional MRI

Biomechanical Magnetic Resonance Imaging (MRI) of articular cartilage, i.e. its imaging under loading, is a promising diagnostic tool to assess the tissue's functionality in health and disease. This study aimed to assess the response to static and dynamic loading of histologically intact cartilage samples by functional MRI and pressure-controlled in-situ loading. To this end, 47 cartilage samples were obtained from the medial femoral condyles of total knee arthroplasties (from 24 patients), prepared to standard thickness, and placed in a standard knee joint in a pressure-controlled whole knee-joint compressive loading device. Cartilage samples' responses to static (i.e. constant), dynamic (i.e. alternating), and no loading, i.e. free-swelling conditions, were assessed before (δ₀), and after 30 min (δ₁) and 60 min (δ₂) of loading using serial T1ρ maps acquired on a 3.0T clinical MRI scanner (Achieva, Philips). Alongside texture features, relative changes in T1ρ (Δ₁, Δ₂) were determined for the upper and lower sample halves and the entire sample, analyzed using appropriate statistical tests, and referenced to histological (Mankin scoring) and biomechanical reference measures (tangent stiffness). Histological, biomechanical, and T1ρ sample characteristics at δ₀ were relatively homogenous in all samples. In response to loading, relative increases in T1ρ were strong and significant after dynamic loading (Δ₁ = 10.3 ± 17.0%, Δ₂ = 21.6 ± 21.8%, p = 0.002), while relative increases in T1ρ after static loading and in controls were moderate and not significant. Generally, texture features did not demonstrate clear loading-related associations underlying the spatial relationships of T1ρ. When realizing the clinical translation, this in-situ study suggests that serial T1ρ mapping is best combined with dynamic loading to assess cartilage functionality in humans based on advanced MRI techniques.

Machine learning-augmented and microspectroscopy-informed multiparametric MRI for the non-invasive prediction of articular cartilage composition

BACKGROUND

Articular cartilage degeneration is the hallmark change of osteoarthritis, a severely disabling disease with high prevalence and considerable socioeconomic and individual burden. Early, potentially reversible cartilage degeneration is characterized by distinct changes in cartilage composition and ultrastructure, while the tissue's morphology remains largely unaltered. Hence, early degenerative changes may not be diagnosed by clinical standard diagnostic tools.

METHODS

Against this background, this study introduces a novel method to determine the tissue composition non-invasively. Our method involves quantitative MRI parameters (i.e., T₁, T_1ρ, T₂ and [Formula: see text] maps), compositional reference measurements (i.e., microspectroscopically determined local proteoglycan [PG] and collagen [CO] contents) and machine learning techniques (i.e., artificial neural networks [ANNs] and multivariate linear models [MLMs]) on 17 histologically grossly intact human cartilage samples.

RESULTS

Accuracy and precision were higher in ANN-based predictions than in MLM-based predictions and moderate-to-strong correlations were found between measured and predicted compositional parameters.

CONCLUSION

Once trained for the clinical setting, advanced machine learning techniques, in particular ANNs, may be used to non-invasively determine compositional features of cartilage based on quantitative MRI parameters with potential implications for the diagnosis of (early) degeneration and for the monitoring of therapeutic outcomes.

Applications of Vibrational Spectroscopy for Analysis of Connective Tissues

Advances in vibrational spectroscopy have propelled new insights into the molecular composition and structure of biological tissues. In this review, we discuss common modalities and techniques of vibrational spectroscopy, and present key examples to illustrate how they have been applied to enrich the assessment of connective tissues. In particular, we focus on applications of Fourier transform infrared (FTIR), near infrared (NIR) and Raman spectroscopy to assess cartilage and bone properties. We present strengths and limitations of each approach and discuss how the combination of spectrometers with microscopes (hyperspectral imaging) and fiber optic probes have greatly advanced their biomedical applications. We show how these modalities may be used to evaluate virtually any type of sample (ex vivo, in situ or in vivo) and how "spectral fingerprints" can be interpreted to quantify outcomes related to tissue composition and quality. We highlight the unparalleled advantage of vibrational spectroscopy as a label-free and often nondestructive approach to assess properties of the extracellular matrix (ECM) associated with normal, developing, aging, pathological and treated tissues. We believe this review will assist readers not only in better understanding applications of FTIR, NIR and Raman spectroscopy, but also in implementing these approaches for their own research projects.

Monitoring of Peripheral Blood Leukocytes and Plasma Samples: A Pilot Study to Examine Treatment Response to Leflunomide in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a painful inflammatory disease of the joints which affects a considerable proportion of the world population, mostly women. If not adequately treated, RA patients can become permanently disabled. Importantly, not all the patients respond to the available anti-rheumatic therapies, which also present diverse side effects. In this context, monitoring of treatment response is pivotal to avoid unnecessary side effects and costs towards an ineffective therapy. Herein, we performed a pilot study to investigate the potential use of flow cytometry and attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy as measures to identify responders and non-responders to leflunomide, a disease-modifying drug used in the treatment of RA patients. The evaluation of peripheral blood CD62L⁺ polymorphonuclear cell numbers and ATR-FTIR vibrational modes in plasma were able to discriminate responders to leflunomide (LFN) three-months after therapy has started. Overall, the results indicate that both flow cytometry and ATR-FTIR can potentially be employed as additional measures to monitor early treatment response to LFN in RA patients.

Slippery when wet: The free surface of the articular cartilage

The free surface of the articular cartilage must withstand compressive and shearing forces, maintain a low friction coefficient and allow oxygen and metabolites to reach the underlying matrix. In many ways it is critical to the physiology of the whole tissue and its disruption always involves deep pathological alterations and loss of the joint integrity. Being very difficult to image with section-based conventional techniques, it was often described by previous research in conflicting terms or entirely overlooked. High-magnification face-on observations with high resolution scanning electron microscopy and with scanning probe microscopy revealed a very thin, delicate superficial layer rich in glycoconjugates, which may explain the very low friction coefficient of the tissue but which was very easily altered and/or dissolved in the preparation. Beneath this superficial sheet lies a thicker coat of thin, highly uniform, slightly wavy collagen fibrils lying parallel to the surface and mutually interconnected by a huge number of interfibrillar glycosaminoglycan bridges. These bridges and the collagen fibrils form an extended reticular structure able to redistribute tensile and compressive stress across a larger area of the surface and hence a greater volume of tissue.

Imaging the local biochemical content of native and injured intervertebral disc using Fourier transform infrared microscopy

Alterations to the biochemical composition of the intervertebral disc (IVD) are hallmarks of aging and degeneration. Methods to assess biochemical content, such as histology, immunohistochemistry, and spectrophotometric assays, are limited in their ability to quantitatively analyze the spatial distribution of biochemical components. Fourier transform infrared (FTIR) microscopy is a biochemical analysis method that can yield both quantitative and high-resolution data about the spatial distribution of biochemical components. This technique has been largely unexplored for use with the IVD, and existing methods use complex analytical techniques that make results difficult to interpret. The objective of the present study is to describe an FTIR microscopy method that has been optimized for imaging the collagen and proteoglycan content of the IVD. The method was performed on intact and discectomized IVDs from the sheep lumbar spine after 6 weeks in vivo in order to validate FTIR microscopy in healthy and degenerated IVDs. FTIR microscopy quantified collagen and proteoglycan content across the entire IVD and showed local changes in biochemical content after discectomy that were not observed with traditional histological methods. Changes in collagen and proteoglycans content were found to have strong correlations with Pfirrmann grades of degeneration. This study demonstrates how FTIR microscopy is a valuable research tool that can be used to quantitatively assess the local biochemical composition of IVDs in development, degeneration, and repair.

The synovial surface of the articular cartilage

The articular cartilage has been the subject of a huge amount of research carried out with a wide array of different techniques. Most of the existing morphological and ultrastructural data on this tissue, however, were obtained either by light microscopy or by transmission electron microscopy. Both techniques rely on thin sections and neither allows a direct, face-on visualization of the free cartilage surface (synovial surface), which is the only portion subject to frictional as well as compressive forces. In the present research, high resolution visualization by scanning electron microscopy and by atomic force microscopy revealed that the collagen fibrils of the articular surface are exclusively represented by thin, uniform, parallel fibrils evocative of the heterotypic type IX-type II fibrils reported by other authors, immersed in an abundant matrix of glycoconjugates, in part regularly arranged in phase with the D-period of collagen. Electrophoresis of fluorophore-labeled saccharides confirmed that the superficial and the deeper layers are quite different in their glycoconjugate content as well, the deeper ones containing more sulfated, more acidic small proteoglycans bound to thicker, more heterogeneous collagen fibrils. The differences found between the synovial surface and the deeper layers are consistent with the different mechanical stresses they must withstand.

Human articular cartilage mechanosensitivity is related to histological degeneration - a functional MRI study

OBJECTIVE

To investigate changes in response to sequential pressure-controlled loading and unloading in human articular cartilage of variable histological degeneration using serial T1ρ mapping.

METHOD

We obtained 42 cartilage samples of variable degeneration from the medial femoral condyles of 42 patients undergoing total knee replacement. Samples were placed in a standardized artificial knee joint within an MRI-compatible whole knee-joint compressive loading device and imaged before (δ₀), during (δ_ld1, δ_ld2, δ_ld3, δ_ld4, δ_ld5) and after (δ_rl1, δ_rl2, δ_rl3, δ_rl4, δ_rl5) pressure-controlled loading to 0.663 ± 0.021 kN (94% body weight) using serial T1ρ mapping (spin-lock multigradient echo sequence; 3.0T MRI system [Achieva, Philips]). Reference assessment included histology (Mankin scoring) and conventional biomechanics (Tangent stiffness). We dichotomized sample into intact (n = 21) and degenerative (n = 21) based on histology and analyzed data using Mann Whitney, Kruskal Wallis, one-way ANOVA tests and Spearman's correlation, respectively.

RESULTS

At δ₀, we found no significant differences between intact and degenerative samples, while the response-to-loading patterns were distinctly different. In intact samples, T1ρ increases were consistent and non-significant, while in degenerative samples, T1ρ increases were significantly higher (P = 0.004, δ0 vs δld1, δ0 vs δld3), yet undulating and variable. With unloading, T1ρ increases subsided, yet were persistently elevated beyond δ0.

CONCLUSION

Cartilage mechanosensitivity is related to histological degeneration and assessable by serial T1ρ mapping. Unloaded, T1ρ characteristics are not significantly different in intact vs degenerative cartilage, while load bearing is organized in intact cartilage and disorganized in degenerative cartilage.

Dataset on equine cartilage near infrared spectra, composition, and functional properties

Near infrared (NIR) spectroscopy is a well-established technique that is widely employed in agriculture, chemometrics, and pharmaceutical engineering. Recently, the technique has shown potential in clinical orthopaedic applications, for example, assisting in the diagnosis of various knee-related diseases (e.g., osteoarthritis) and their pathologies. NIR spectroscopy (NIRS) could be especially useful for determining the integrity and condition of articular cartilage, as the current arthroscopic diagnostics is subjective and unreliable. In this work, we present an extensive dataset of NIRS measurements for evaluating the condition, mechanical properties, structure, and composition of equine articular cartilage. The dataset contains NIRS measurements from 869 different locations across the articular surfaces of five equine fetlock joints. A comprehensive library of reference values for each measurement location is also provided, including results from a mechanical indentation testing, digital densitometry imaging, polarized light microscopy, and Fourier transform infrared spectroscopy. The published data can either be used as a model of human cartilage or to advance equine veterinary research.

Towards Patient-Specific Computational Modelling of Articular Cartilage on the Basis of Advanced Multiparametric MRI Techniques

Cartilage degeneration is associated with tissue softening and represents the hallmark change of osteoarthritis. Advanced quantitative Magnetic Resonance Imaging (qMRI) techniques allow the assessment of subtle tissue changes not only of structure and morphology but also of composition. Yet, the relation between qMRI parameters on the one hand and microstructure, composition and the resulting functional tissue properties on the other hand remain to be defined. To this end, a Finite-Element framework was developed based on an anisotropic constitutive model of cartilage informed by sample-specific multiparametric qMRI maps, obtained for eight osteochondral samples on a clinical 3.0 T MRI scanner. For reference, the same samples were subjected to confined compression tests to evaluate stiffness and compressibility. Moreover, the Mankin score as an indicator of histological tissue degeneration was determined. The constitutive model was optimized against the resulting stress responses and informed solely by the sample-specific qMRI parameter maps. Thereby, the biomechanical properties of individual samples could be captured with good-to-excellent accuracy (mean R² [square of Pearson's correlation coefficient]: 0.966, range [min, max]: 0.904, 0.993; mean Ω [relative approximated error]: 33%, range [min, max]: 20%, 47%). Thus, advanced qMRI techniques may be complemented by the developed computational model of cartilage to comprehensively evaluate the functional dimension of non-invasively obtained imaging biomarkers. Thereby, cartilage degeneration can be perspectively evaluated in the context of imaging and biomechanics.

Non-invasive T1ρ mapping of the human cartilage response to loading and unloading

OBJECTIVE

To define the physiological response to sequential loading and unloading in histologically intact human articular cartilage using serial T1ρ mapping, as T1ρ is considered to indicate the tissue's macromolecular content.

METHOD

18 macroscopically intact cartilage-bone samples were obtained from the central lateral femoral condyles of 18 patients undergoing total knee replacement. Serial T1ρ mapping was performed on a clinical 3.0-T MRI system using a modified prostate coil. Spin-lock multiple gradient-echo sequences prior to, during and after standardized indentation loading (displacement controlled, strain 20%) were used to obtain seven serial T1ρ maps: unloaded (δ₀), quasi-statically loaded (indentation₁-indentation₃) and under subsequent relaxation (relaxation₁-relaxation₃). After manual segmentation, zonal and regional regions-of-interest were defined. ROI-specific relative changes were calculated and statistically assessed using paired t-tests. Histological (Mankin classification) and biomechanical (unconfined compression) evaluations served as references.

RESULTS

All samples were histologically and biomechanically grossly intact (Mankin sum: 1.8 ± 1.2; Young's Modulus: 0.7 ± 0.4 MPa). Upon loading, T1ρ consistently increased throughout the entire sample thickness, primarily subpistonally (indentation₁ [M ± SD]: 9.5 ± 7.8% [sub-pistonal area, SPA] vs 4.2 ± 5.8% [peri-pistonal area, PPA]; P < 0.001). T1ρ further increased with ongoing loading (indentation₃: 14.1 ± 8.1 [SPA] vs 7.7 ± 5.9% [PPA]; P < 0.001). Even upon unloading (i.e., relaxation), T1ρ persistently increased in time.

CONCLUSION

Serial T1ρ-mapping reveals distinct and complex zonal and regional changes in articular cartilage as a function of loading and unloading. Thereby, longitudinal adaptive processes in hyaline cartilage become evident, which may be used for the tissue's non-invasive functional characterization by T1ρ.

Optimal Regression Method for Near-Infrared Spectroscopic Evaluation of Articular Cartilage

Near-infrared (NIR) spectroscopy has been successful in nondestructive assessment of biological tissue properties, such as stiffness of articular cartilage, and is proposed to be used in clinical arthroscopies. Near-infrared spectroscopic data include absorbance values from a broad wavelength region resulting in a large number of contributing factors. This broad spectrum includes information from potentially noisy variables, which may contribute to errors during regression analysis. We hypothesized that partial least squares regression (PLSR) is an optimal multivariate regression technique and requires application of variable selection methods to further improve the performance of NIR spectroscopy-based prediction of cartilage tissue properties, including instantaneous, equilibrium, and dynamic moduli and cartilage thickness. To test this hypothesis, we conducted for the first time a comparative analysis of multivariate regression techniques, which included principal component regression (PCR), PLSR, ridge regression, least absolute shrinkage and selection operator (Lasso), and least squares version of support vector machines (LS-SVM) on NIR spectral data of equine articular cartilage. Additionally, we evaluated the effect of variable selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), backward interval PLS (BiPLS), genetic algorithm (GA), and jackknife, on the performance of the optimal regression technique. The PLSR technique was found as an optimal regression tool (R²_{Tissue thickness} = 75.6%, R²_{Dynamic modulus} = 64.9%) for cartilage NIR data; variable selection methods simplified the prediction models enabling the use of lesser number of regression components. However, the improvements in model performance with variable selection methods were found to be statistically insignificant. Thus, the PLSR technique is recommended as the regression tool for multivariate analysis for prediction of articular cartilage properties from its NIR spectra.

Vibrational spectroscopic techniques to assess bone quality

Although musculoskeletal diseases such as osteoporosis are diagnosed and treatment outcome is evaluated based mainly on routine clinical outcomes of bone mineral density (BMD) by DXA and biochemical markers, it is recognized that these two indicators, as valuable as they have proven to be in the everyday clinical practice, do not fully account for manifested bone strength. Thus, the term bone quality was introduced, to complement considerations based on bone turnover rates and BMD. Bone quality is an "umbrella" term that incorporates the structural and material/compositional characteristics of bone tissue. Vibrational spectroscopic techniques such as Fourier transform infrared microspectroscopy (FTIRM) and imaging (FTIRI), and Raman spectroscopy, are suitable analytical tools for the determination of bone quality as they provide simultaneous, quantitative, and qualitative information on all main bone tissue components (mineral, organic matrix, tissue water), in a spatially resolved manner. Moreover, the results of such analyses may be readily combined with the outcomes of other techniques such as histology/histomorphometry, small angle X-ray scattering, quantitative backscattered electron imaging, and nanoindentation.