Approaches for In Situ Monitoring of Matrix Development in Hydrogel-Based Engineered Cartilage

Non-neotissue constituents as underestimated confounders in the assessment of tissue engineered constructs by near-infrared spectroscopy

Non-destructive assessments are required for the quality control of tissue-engineered constructs and the optimization of the tissue culture process. Near-infrared (NIR) spectroscopy coupled with machine learning (ML) provides a promising approach for such assessment. However, due to its nonspecific nature, each spectrum incorporates information on both neotissue and non-neotissue constituents of the construct; the effect of these constituents on the NIR-based assessments of tissue-engineered constructs has been overlooked in previous studies. This study investigates the effect of scaffolds, growth factors, and buffers on NIR-based assessments of tissue-engineered constructs. To determine if these non-neotissue constituents have a measurable effect on the NIR spectra of the constructs that can introduce bias in their assessment, nine ML algorithms were evaluated in classifying the NIR spectra of engineered cartilage according to the scaffold used to prepare the constructs, the growth factors added to the culture media, and the buffers used for storing the constructs. The effect of controlling for these constituents was also evaluated using controlled and uncontrolled NIR-based ML models for predicting tissue maturity as an example of neotissue-related properties of interest. Samples used in this study were prepared using norbornene-modified hyaluronic acid scaffolds with or without the conjugation of an N-cadherin mimetic peptide. Selected samples were supplemented with transforming growth factor-beta1 or bone morphogenetic protein-9 growth factor. Some samples were frozen in cell lysis buffer, while the remaining samples were frozen in PBS until required for NIR analysis. The ML models for classifying the spectra of the constructs according to the four constituents exhibited high to fair performances, with F1 scores ranging from 0.9 to 0.52. Moreover, controlling for the four constituents significantly improved the performance of the models for predicting tissue maturity, with improvement in F1 scores ranging from 0.09 to 0.77. In conclusion, non-neotissue constituents have measurable effects on the NIR spectra of tissue-engineered constructs that can be detected by ML algorithms and introduce bias in the assessment of the constructs by NIR spectroscopy. Therefore, controlling for these constituents is necessary for reliable NIR-based assessments of tissue-engineered constructs.

Visible and Near-Infrared Spectroscopy Enables Differentiation of Normal and Early Osteoarthritic Human Knee Joint Articular Cartilage

Osteoarthritis degenerates cartilage and impairs joint function. Early intervention opportunities are missed as current diagnostic methods are insensitive to early tissue degeneration. We investigated the capability of visible light-near-infrared spectroscopy (Vis-NIRS) to differentiate normal human cartilage from early osteoarthritic one. Vis-NIRS spectra, biomechanical properties and the state of osteoarthritis (OARSI grade) were quantified from osteochondral samples harvested from different anatomical sites of human cadaver knees. Two support vector machines (SVM) classifiers were developed based on the Vis-NIRS spectra and OARSI scores. The first classifier was designed to distinguish normal (OARSI: 0-1) from general osteoarthritic cartilage (OARSI: 2-5) to check the general suitability of the approach yielding an average accuracy of 75% (AUC = 0.77). Then, the second classifier was designed to distinguish normal from early osteoarthritic cartilage (OARSI: 2-3) yielding an average accuracy of 71% (AUC = 0.73). Important wavelength regions for differentiating normal from early osteoarthritic cartilage were related to collagen organization (wavelength region: 400-600 nm), collagen content (1000-1300 nm) and proteoglycan content (1600-1850 nm). The findings suggest that Vis-NIRS allows objective differentiation of normal and early osteoarthritic tissue, e.g., during arthroscopic repair surgeries.

In Situ Assessment of Porcine Osteochondral Repair Tissue in the Visible-Near Infrared Spectral Region

Standard assessment of cartilage repair progression by visual arthroscopy can be subjective and may result in suboptimal evaluation. Visible-near infrared (Vis-NIR) fiber optic spectroscopy of joint tissues, including articular cartilage and subchondral bone, provides an objective approach for quantitative assessment of tissue composition. Here, we applied this technique in the 350-2,500 nm spectral region to identify spectral markers of osteochondral tissue during repair with the overarching goal of developing a new approach to monitor repair of cartilage defects in vivo. Full thickness chondral defects were created in Yucatan minipigs using a 5-mm biopsy punch, and microfracture (MFx) was performed as a standard technique to facilitate repair. Tissues were evaluated at 1 month (in adult pigs) and 3 months (in juvenile pigs) post-surgery by spectroscopy and histology. After euthanasia, Vis-NIR spectra were collected in situ from the defect region. Additional spectroscopy experiments were carried out in vitro to aid in spectral interpretation. Osteochondral tissues were dissected from the joint and evaluated using the conventional International Cartilage Repair Society (ICRS) II histological scoring system, which showed lower scores for the 1-month than the 3-month repair tissues. In the visible spectral region, hemoglobin absorbances at 540 and 570 nm were significantly higher in spectra from 1-month repair tissue than 3-month repair tissue, indicating a reduction of blood in the more mature repair tissue. In the NIR region, we observed qualitative differences between the two groups in spectra taken from the defect, but differences did not reach significance. Furthermore, spectral data also indicated that the hydrated environment of the joint tissue may interfere with evaluation of tissue water absorbances in the NIR region. Together, these data provide support for further investigation of the visible spectral region for assessment of longitudinal repair of cartilage defects, which would enable assessment during routine arthroscopy, particularly in a hydrated environment.

Nondestructive assessment of tissue engineered cartilage based on biochemical markers in cell culture media: application of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy

ATR spectral data obtained from cell culture medium discards can be used to assess glucose and lactate content, which are shown here to be a surrogate for matrix development in tissue engineered cartilage.

DMOG Negatively Impacts Tissue Engineered Cartilage Development

OBJECTIVE

Articular cartilage exists in a hypoxic environment, which motivates the use of hypoxia-simulating chemical agents to improve matrix production in cartilage tissue engineering. The aim of this study was to investigate whether dimethyloxalylglycine (DMOG), a HIF-1α stabilizer, would improve matrix production in 3-dimensional (3D) porcine synovial-derived mesenchymal stem cell (SYN-MSC) co-culture with chondrocytes.

DESIGN

Pellet cultures and scaffold-based engineered cartilage were grown in vitro to determine the impact of chemically simulated hypoxia on 2 types of 3D cell culture. DMOG-treated groups were exposed to DMOG from day 14 to day 21 and grown up to 6 weeks with n = 3 per condition and time point.

RESULTS

The addition of DMOG resulted in HIF-1α stabilization in the exterior of the engineered constructs, which resulted in increased regional type II collagen deposition, but the stabilization did not translate to overall increased extracellular matrix deposition. There was no increase in HIF-1α stabilization in the pellet cultures. DMOG treatment also negatively affected the mechanical competency of the engineered cartilage.

CONCLUSIONS

Despite previous studies that demonstrated the efficacy of DMOG, here, short-term treatment with DMOG did not have a uniformly positive impact on the chondrogenic capacity of SYN-MSCs in either pellet culture or in scaffold-based engineered cartilage, as evidenced by reduced matrix production. Such 3D constructs generally have a naturally occurring hypoxic center, which allows for the stabilization of HIF-1α in the interior tissue. Thus, short-term addition of DMOG may not further improve this in cartilage tissue engineered constructs.

Non-Destructive Spatial Mapping of Glycosaminoglycan Loss in Native and Degraded Articular Cartilage Using Confocal Raman Microspectroscopy

Articular cartilage is a collagen-rich tissue that provides a smooth, lubricated surface for joints and is also responsible for load bearing during movements. The major components of cartilage are water, collagen, and proteoglycans. Osteoarthritis is a degenerative disease of articular cartilage, in which an early-stage indicator is the loss of proteoglycans from the collagen matrix. In this study, confocal Raman microspectroscopy was applied to study the degradation of articular cartilage, specifically focused on spatially mapping the loss of glycosaminoglycans (GAGs). Trypsin digestion was used as a model for cartilage degradation. Two different scanning geometries for confocal Raman mapping, cross-sectional and depth scans, were applied. The chondroitin sulfate coefficient maps derived from Raman spectra provide spatial distributions similar to histological staining for glycosaminoglycans. The depth scans, during which subsurface data were collected without sectioning the samples, can also generate spectra and GAG distributions consistent with Raman scans of the surface-to-bone cross sections. In native tissue, both scanning geometries demonstrated higher GAG content at the deeper zone beneath the articular surface and negligible GAG content after trypsin degradation. On partially digested samples, both scanning geometries detected an ∼100 μm layer of GAG depletion. Overall, this research provides a technique with high spatial resolution (25 μm pixel size) to measure cartilage degradation without tissue sections using confocal Raman microspectroscopy, laying a foundation for potential in vivo measurements and osteoarthritis diagnosis.

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.

Characterization of connective tissues using near-infrared spectroscopy and imaging

Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.