Clustering of infrared spectra reveals histological zones in intact articular cartilage

Similarity Metrics for Subcellular Analysis of FRET Microscopy Videos

Understanding the heterogeneity of molecular environments within cells is an outstanding challenge of great fundamental and technological interest. Cells are organized into specialized compartments, each with distinct functions. These compartments exhibit dynamic heterogeneity under high-resolution microscopy, which reflects fluctuations in molecular populations, concentrations, and spatial distributions. To enhance our comprehension of the spatial relationships among molecules within cells, it is crucial to analyze images of high-resolution microscopy by clustering individual pixels according to their visible spatial properties and their temporal evolution. Here, we evaluate the effectiveness of similarity metrics based on their ability to facilitate fast and accurate data analysis in time and space. We discuss the capability of these metrics to differentiate subcellular localization, kinetics, and structures of protein-RNA interactions in Forster resonance energy transfer (FRET) microscopy videos, illustrated by a practical example from recent literature. Our results suggest that using the correlation similarity metric to cluster pixels of high-resolution microscopy data should improve the analysis of high-dimensional microscopy data in a wide range of applications.

Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology

Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.

The cGAS-STING Pathway Affects Vertebral Bone but Does Not Promote Intervertebral Disc Cell Senescence or Degeneration

The DNA-sensing cGAS-STING pathway promotes the senescence-associated secretory phenotype (SASP) and mediates type-I interferon inflammatory responses to foreign viral and bacterial DNA as well as self-DNA. Studies of the intervertebral disc in humans and mice demonstrate associations between aging, increased cell senescence, and disc degeneration. Herein we assessed the role of STING in SASP promotion in STING gain- (N153S) and loss-of-function mouse models. N153S mice evidenced elevated circulating levels of proinflammatory markers including IL-1β, IL-6, and TNF-α, showed elevated monocyte and macrophage abundance in the vertebral marrow, and exhibited a mild trabecular and cortical bone phenotype in caudal vertebrae. Interestingly, despite systemic inflammation, the structural integrity of the disc and knee articular joint remained intact, and cells did not show a loss of their phenotype or elevated SASP. Transcriptomic analysis of N153S tissues demonstrated an upregulated immune response by disc cells, which did not closely resemble inflammatory changes in human tissues. Interestingly, STING^-/- mice also showed a mild vertebral bone phenotype, but the absence of STING did not reduce the abundance of SASP markers or improve the age-associated disc phenotype. Overall, the analyses of N153S and STING^-/- mice suggest that the cGAS-STING pathway is not a major contributor to SASP induction and consequent disc aging and degeneration but may play a minor role in the maintenance of trabecular bone in the vertebrae. This work contributes to a growing body of work demonstrating that systemic inflammation is not a key driver of disc degeneration.

A novel mouse model of intervertebral disc degeneration shows altered cell fate and matrix homeostasis

Intervertebral disc degeneration and associated low back and neck pain is a ubiquitous health condition that affects millions of people world-wide, and causes high incidence of disability and enormous medical/societal costs. However, lack of appropriate small animal models with spontaneous disease onset has impeded our ability to understand the pathogenetic mechanisms that characterize and drive the degenerative process. We report, for the first time, early onset spontaneous disc degeneration in SM/J mice known for their poor regenerative capacities compared to "super-healer" LG/J mice. In SM/J mice, degenerative process was marked by decreased nucleus pulposus (NP) cellularity and changes in matrix composition at P7, 4, and 8 weeks with increased severity by 17 weeks. Distinctions between NP and annulus fibrosus (AF) or endplate cartilage were lost, and NP and AF of SM/J mice showed higher histological grades. There was increased NP cell death in SM/J mice with decreased phenotypic marker expression. Polarized microscopy and FTIR spectroscopy demonstrated replacement of glycosaminoglycan-rich NP matrix with collagenous fibrous tissue. The levels of ARGxx were increased in, indicating higher aggrecan turnover. Furthermore, an aberrant expression of collagen X and MMP13 was observed in the NP of SM/J mice, along with elevated expression of Col10a1, Ctgf, and Runx2, markers of chondrocyte hypertrophy. Likewise, expression of Enpp1 as well as Alpl was higher, suggesting NP cells of SM/J mice promote dystrophic mineralization. There was also a decrease in several pathways necessary for NP cell survival and function including Wnt and VEGF signaling. Importantly, SM/J discs were stiffer, had decreased height, and poor vertebral bone quality, suggesting compromised motion segment mechanical functionality. Taken together, our results clearly demonstrate that SM/J mouse strain recapitulates many salient features of human disc degeneration, and serves as a novel small animal model.

Vibrational Micro-Spectroscopy of Human Tissues Analysis: Review

Vibrational spectroscopy (Infrared (IR) and Raman) and, in particular, micro-spectroscopy and micro-spectroscopic imaging have been used to characterize developmental changes in tissues, to monitor these changes in cell cultures and to detect disease and drug-induced modifications. The conventional methods for biochemical and histophatological tissue characterization necessitate complex and "time-consuming" sample manipulations and the results are rarely quantifiable. The spectroscopy of molecular vibrations using mid-IR or Raman techniques has been applied to samples of human tissue. This article reviews the application of these vibrational spectroscopic techniques for analysis of biological tissue published between 2005 and 2015.

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

The changes in chemical composition of human articular cartilage (AC) caused by osteoarthritis (OA) were investigated using Fourier transform infrared microspectroscopy (FTIR-MS). We demonstrate the sensitivity of FTIR-MS for monitoring compositional changes that occur with OA progression. Twenty-eight AC samples from tibial plateaus were imaged with FTIR-MS. Hyperspectral images of all samples were combined for K-means clustering. Partial least squares regression (PLSR) analysis was used to compare the spectra with the OARSI grade (histopathological grading of OA). Furthermore, the amide I and the carbohydrate regions were used to estimate collagen and proteoglycan contents, respectively. Spectral peak at 1338 cm(-1) was used to estimate the integrity of the collagen network. The layered structure of AC was revealed using the carbohydrate region for clustering. Statistically significant correlation was observed between the OARSI grade and the collagen integrity in the superficial (r = -0.55) and the deep (r = -0.41) zones. Furthermore, PLSR models predicted the OARSI grade from the superficial (r = 0.94) and the deep (r = 0.77) regions of the AC with high accuracy. Obtained results suggest that quantitative and qualitative changes occur in the AC composition during OA progression, and these can be monitored by the use of FTIR-MS.

Biomedical investigations using Fourier transform-infrared microspectroscopy

One of the most exciting recent developments in infrared spectroscopy has been the coupling of the spectrometer to an infrared microscope. The combination of the new infrared spectrometer and a microscope was a natural thought of scientists in these fields. This development has been so rewarding and so useful in solving today's chemical problems that infrared microspectroscopy has quickly become a significant subclassification of infrared spectroscopy. Infrared microspectroscopy has a much longer history than the recent enthusiasm would imply, however. The great interest in the use of infrared spectroscopy to solve biomedical problems that occurred in recent years shortly spread into the medical and biological fields. The aim of this review is to discuss the new developments in applications of FT-IR microspectroscopy in biomedical analysis, covering the period between 2008 and 2013.

FTIR-I compositional mapping of the cartilage-to-bone interface as a function of tissue region and age

Soft tissue-to-bone transitions, such as the osteochondral interface, are complex junctions that connect multiple tissue types and are critical for musculoskeletal function. The osteochondral interface enables pressurization of articular cartilage, facilitates load transfer between cartilage and bone, and serves as a barrier between these two distinct tissues. Presently, there is a lack of quantitative understanding of the matrix and mineral distribution across this multitissue transition. Moreover, age-related changes at the interface with the onset of skeletal maturity are also not well understood. Therefore, the objective of this study is to characterize the cartilage-to-bone transition as a function of age, using Fourier transform infrared spectroscopic imaging (FTIR-I) analysis to map region-dependent changes in collagen, proteoglycan, and mineral distribution, as well as collagen organization. Both tissue-dependent and age-related changes were observed, underscoring the role of postnatal physiological loading in matrix remodeling. It was observed that the relative collagen content increased continuously from cartilage to bone, whereas proteoglycan peaked within the deep zone of cartilage. With age, collagen content across the interface increased, accompanied by a higher degree of collagen alignment in both the surface and deep zone cartilage. Interestingly, regardless of age, mineral content increased exponentially across the calcified cartilage interface. These observations reveal new insights into both region- and age-dependent changes across the cartilage-to-bone junction and will serve as critical benchmark parameters for current efforts in integrative cartilage repair.

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

Articular cartilage (AC) is mainly composed of collagen, proteoglycans, chondrocytes, and water. These constituents are inhomogeneously distributed to provide unique biomechanical properties to the tissue. Characterization of the spatial distribution of these components in AC is important for understanding the function of the tissue and progress of osteoarthritis. Fourier transform infrared (FT-IR) absorption spectra exhibit detailed information about the biochemical composition of AC. However, highly specific FT-IR analysis for collagen and proteoglycans is challenging. In this study, a chemometric approach to predict the biochemical composition of AC from the FT-IR spectra was investigated. Partial least squares (PLS) regression was used to predict the proteoglycan content (n=32) and collagen content (n=28) of bovine cartilage samples from their average FT-IR spectra. The optimal variables for the PLS regression models were selected by using backward interval partial least squares and genetic algorithm. The linear correlation coefficients between the biochemical reference and predicted values of proteoglycan and collagen contents were r=0.923 (p<0.001) and r=0.896 (p<0.001), respectively. The results of the study show that variable selection algorithms can significantly improve the PLS regression models when the biochemical composition of AC is predicted.

Fourier transform infrared imaging and infrared fiber optic probe spectroscopy identify collagen type in connective tissues

Hyaline cartilage and mechanically inferior fibrocartilage consisting of mixed collagen types are frequently found together in repairing articular cartilage. The present study seeks to develop methodology to identify collagen type and other tissue components using Fourier transform infrared (FTIR) spectral evaluation of matrix composition in combination with multivariate analyses. FTIR spectra of the primary molecular components of repair cartilage, types I and II collagen, and aggrecan, were used to develop multivariate spectral models for discrimination of the matrix components of the tissues of interest. Infrared imaging data were collected from bovine bone, tendon, normal cartilage, meniscus and human repair cartilage tissues, and composition predicted using partial least squares analyses. Histology and immunohistochemistry results were used as standards for validation. Infrared fiber optic probe spectral data were also obtained from meniscus (a tissue with mixed collagen types) to evaluate the potential of this method for identification of collagen type in a minimally-invasive clinical application. Concentration profiles of the tissue components obtained from multivariate analysis were in excellent agreement with histology and immunohistochemistry results. Bone and tendon showed a uniform distribution of predominantly type I collagen through the tissue. Normal cartilage showed a distribution of type II collagen and proteoglycan similar to the known composition, while in repair cartilage, the spectral distribution of both types I and II collagen were similar to that observed via immunohistochemistry. Using the probe, the outer and inner regions of the meniscus were shown to be primarily composed of type I and II collagen, respectively, in accordance with immunohistochemistry data. In summary, multivariate analysis of infrared spectra can indeed be used to differentiate collagen type I and type II, even in the presence of proteoglycan, in connective tissues, using both imaging and fiber optic methodology. This has great potential for clinical in situ applications for monitoring tissue repair.

Cluster analysis of infrared spectra can differentiate intact and repaired articular cartilage

OBJECTIVE

Successful repair of articular cartilage (AC) defects would be a major advantage due to the low ability of AC to heal spontaneously. Sensitive methods to determine changes in AC composition and structure are required to monitor the success of repair. This study evaluates the ability of unsupervised cluster analysis applied to Fourier transform infrared (FTIR) microspectroscopy to discriminate between healthy and repaired AC.

METHODS

Osteochondral lesions (3 mm in depth) were surgically created in patellar grooves of rabbit femurs and were either left to heal spontaneously (n = 6) or surgically repaired with autologous chondrocytes in type II collagen gel (n = 6). After 6 months, tissues were harvested, FTIR microspectroscopy was conducted and Fuzzy c-means (FCM) cluster analysis applied to spectra of pairs of intact and repaired AC samples from each rabbit. Two spectral regions [amide I and carbohydrate (CHO)] were analyzed and the results from the two types of repair were compared.

RESULTS

Two separate regions of repair were detected with FCM. The estimated proteoglycan content (from CHO region) in the repaired AC was significantly lower than that in intact AC. The spontaneously repaired AC was better distinguished from the intact AC than the collagen II gel repaired AC. The most distinct clustering was observed for spontaneously repaired samples using CHO region.

CONCLUSIONS

This study revealed that unsupervised cluster analysis applied to FTIR microspectroscopy can detect subtle differences in infrared spectra between normal and repaired AC. The method may help in evaluation and optimization of future AC repair strategies.

Infrared fiber optic probe evaluation of degenerative cartilage correlates to histological grading

BACKGROUND

Osteoarthritis (OA), a degenerative cartilage disease, results in alterations of the chemical and structural properties of tissue. Arthroscopic evaluation of full-depth tissue composition is limited and would require tissue harvesting, which is inappropriate in daily routine. Fourier transform infrared (FT-IR) spectroscopy is a modality based on molecular vibrations of matrix components that can be used in conjunction with fiber optics to acquire quantitative compositional data from the cartilage matrix.

PURPOSE

To develop a model based on infrared spectra of articular cartilage to predict the histological Mankin score as an indicator of tissue quality.

STUDY DESIGN

Comparative laboratory study.

METHODS

Infrared fiber optic probe (IFOP) spectra were collected from nearly normal and more degraded regions of tibial plateau articular cartilage harvested during knee arthroplasty (N = 61). Each region was graded using a modified Mankin score. A multivariate partial least squares algorithm using second-derivative spectra was developed to predict the histological modified Mankin score.

RESULTS

The partial least squares model derived from IFOP spectra predicted the modified Mankin score with a prediction error of approximately 1.4, which resulted in approximately 72% of the Mankin-scored tissues being predicted correctly and 96% being predicted within 1 grade of their true score.

CONCLUSION

These data demonstrate that IFOP spectral parameters correlate with histological tissue grade and can be used to provide information on tissue composition.

CLINICAL RELEVANCE

Infrared fiber optic probe studies have significant potential for the evaluation of cartilage tissue quality without the need for tissue harvest. Combined with arthroscopy, IFOP analysis could facilitate the definition of tissue margins in debridement procedures.