Optimal variable selection for Fourier transform infrared spectroscopic analysis of articular cartilage composition

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.

Contrast-enhanced x-ray microscopy of articular cartilage

Osteoarthritis is a common chronic disease of joints characterized by degenerative changes of articular cartilage. An early diagnosis of osteoarthritis may be possible when imaging excised tissue for research in situ at the cellular-molecular scale. Whereas cartilage histopathology is destructive, time-consuming, and limited to 2D views, contrast-enhanced x-ray microscopy (XRM) can image articular cartilage and subchondral bone in 3D. This study evaluates articular cartilage structure ex vivo using both techniques.Osteochondral plugs were excised from non-diseased bovine knees and stained in phosphotungstic acid for 0 to 32 h. XRM imaging revealed an optimal staining time of 16 h and a saturated staining time of 24 h. Histology sections were cut and analyzed by polarized light microscopy (PLM) and second-harmonic-generation dual-photon (SHG-DP) microscopy. Histology photomicrographs were aligned with matching XRM slices and evaluated for features relevant in histopathological scoring of osteoarthritis cartilage, including the tidemark, collagen architecture and chondrocyte morphology.The cartilage collagen network and chondrocytes from the 3D contrast-enhanced XRM were correlated with the 2D histology. This technique has two distinct advantages over routine histopathology: (1) the avoidance of dehydration, demineralization, and deformation of histological sectioning, thereby preserving the intact articular cartilage and subchondral bone plate ex vivo, and (2) the ability to evaluate the entire osteochondral volume in 3D. This work explores several diagnostic features of imaging cartilage, including: visualization of the tidemark in XRM and SHG-DP microscopy, validating the morphology of chondrocytes and nuclei with XRM, SHG-DP and PLM, and correlating collagen birefringence with XRM image intensity.

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.

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.

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.

Non-linear optical microscopy as a novel quantitative and label-free imaging modality to improve the assessment of tissue-engineered cartilage

OBJECTIVE

Current systems to evaluate outcomes from tissue-engineered cartilage (TEC) are sub-optimal. The main purpose of our study was to demonstrate the use of second harmonic generation (SHG) microscopy as a novel quantitative approach to assess collagen deposition in laboratory made cartilage constructs.

METHODS

Scaffold-free cartilage constructs were obtained by condensation of in vitro expanded Hoffa's fat pad derived stromal cells (HFPSCs), incubated in the presence or absence of chondrogenic growth factors (GF) during a period of 21 d. Cartilage-like features in constructs were assessed by Alcian blue staining, transmission electron microscopy (TEM), SHG and two-photon excited fluorescence microscopy. A new scoring system, using second harmonic generation microscopy (SHGM) index for collagen density and distribution, was adapted to the existing "Bern score" in order to evaluate in vitro TEC.

RESULTS

Spheroids with GF gave a relative high Bern score value due to appropriate cell morphology, cell density, tissue-like features and proteoglycan content, whereas spheroids without GF did not. However, both TEM and SHGM revealed striking differences between the collagen framework in the spheroids and native cartilage. Spheroids required a four-fold increase in laser power to visualize the collagen matrix by SHGM compared to native cartilage. Additionally, collagen distribution, determined as the area of tissue generating SHG signal, was higher in spheroids with GF than without GF, but lower than in native cartilage.

CONCLUSION

SHG represents a reliable quantitative approach to assess collagen deposition in laboratory engineered cartilage, and may be applied to improve currently established scoring systems.

Infrared microspectroscopic determination of collagen cross-links in articular cartilage

Collagen forms an organized network in articular cartilage to give tensile stiffness to the tissue. Due to its long half-life, collagen is susceptible to cross-links caused by advanced glycation end-products. The current standard method for determination of cross-link concentrations in tissues is the destructive high-performance liquid chromatography (HPLC). The aim of this study was to analyze the cross-link concentrations nondestructively from standard unstained histological articular cartilage sections by using Fourier transform infrared (FTIR) microspectroscopy. Half of the bovine articular cartilage samples ( n = 27 ) were treated with threose to increase the collagen cross-linking while the other half ( n = 27 ) served as a control group. Partial least squares (PLS) regression with variable selection algorithms was used to predict the cross-link concentrations from the measured average FTIR spectra of the samples, and HPLC was used as the reference method for cross-link concentrations. The correlation coefficients between the PLS regression models and the biochemical reference values were r = 0.84 ( p < 0.001 ), r = 0.87 ( p < 0.001 ) and r = 0.92 ( p < 0.001 ) for hydroxylysyl pyridinoline (HP), lysyl pyridinoline (LP), and pentosidine (Pent) cross-links, respectively. The study demonstrated that FTIR microspectroscopy is a feasible method for investigating cross-link concentrations in articular cartilage.

Investigation of intervertebral disc degeneration using multivariate FTIR spectroscopic imaging

Traditionally tissue samples are analysed using protein or enzyme specific stains on serial sections to build up a picture of the distribution of components contained within them. In this study we investigated the potential of multivariate curve resolution-alternating least squares (MCR-ALS) to deconvolute 2nd derivative spectra of Fourier transform infrared (FTIR) microscopic images measured in transflectance mode of goat and human paraffin embedded intervertebral disc (IVD) tissue sections, to see if this methodology can provide analogous information to that provided by immunohistochemical stains and bioassays but from a single section. MCR-ALS analysis of non-degenerate and enzymatically in vivo degenerated goat IVDs reveals five matrix components displaying distribution maps matching histological stains for collagen, elastin and proteoglycan (PG), as well as immunohistochemical stains for collagen type I and II. Interestingly, two components exhibiting characteristic spectral and distribution profiles of proteoglycans were found, and relative component/tissue maps of these components (labelled PG1 and PG2) showed distinct distributions in non-degenerate versus mildly degenerate goat samples. MCR-ALS analysis of human IVD sections resulted in comparable spectral profiles to those observed in the goat samples, highlighting the inter species transferability of the presented methodology. Multivariate FTIR image analysis of a set of 43 goat IVD sections allowed the extraction of semi-quantitative information from component/tissue gradients taken across the IVD width of collagen type I, collagen type II, PG1 and PG2. Regional component/tissue parameters were calculated and significant correlations were found between histological grades of degeneration and PG parameters (PG1: p = 0.0003, PG2: p < 0.0001); glycosaminoglycan (GAG) content and PGs (PG1: p = 0.0055, PG2: p = 0.0001); and MRI T2* measurements and PGs (PG1: p = 0.0021, PG2: p < 0.0001). Additionally, component/tissue parameters for collagen type I and II showed significant correlations with total collagen content (p = 0.0204, p = 0.0127). In conclusion, the presented findings illustrate, that the described multivariate FTIR imaging approach affords the necessary chemical specificity to be considered an important tool in the study of IVD degeneration in goat and human IVDs.

Determining collagen distribution in articular cartilage using contrast-enhanced micro-computed tomography

OBJECTIVE

Collagen distribution within articular cartilage (AC) is typically evaluated from histological sections, e.g., using collagen staining and light microscopy (LM). Unfortunately, all techniques based on histological sections are time-consuming, destructive, and without extraordinary effort, limited to two dimensions. This study investigates whether phosphotungstic acid (PTA) and phosphomolybdic acid (PMA), two collagen-specific markers and X-ray absorbers, could (1) produce contrast for AC X-ray imaging or (2) be used to detect collagen distribution within AC.

METHOD

We labeled equine AC samples with PTA or PMA and imaged them with micro-computed tomography (micro-CT) at pre-defined time points 0, 18, 36, 54, 72, 90, 180, 270 h during staining. The micro-CT image intensity was compared with collagen distributions obtained with a reference technique, i.e., Fourier-transform infrared imaging (FTIRI). The labeling time and contrast agent producing highest association (Pearson correlation, Bland-Altman analysis) between FTIRI collagen distribution and micro-CT -determined PTA distribution was selected for human AC.

RESULTS

Both, PTA and PMA labeling permitted visualization of AC features using micro-CT in non-calcified cartilage. After labeling the samples for 36 h in PTA, the spatial distribution of X-ray attenuation correlated highly with the collagen distribution determined by FTIRI in both equine (mean ± S.D. of the Pearson correlation coefficients, r = 0.96 ± 0.03, n = 12) and human AC (r = 0.82 ± 0.15, n = 4).

CONCLUSIONS

PTA-induced X-ray attenuation is a potential marker for non-destructive detection of AC collagen distributions in 3D. This approach opens new possibilities in development of non-destructive 3D histopathological techniques for characterization of OA.