Extracellular biomolecular free radical formation during injury.

OBJECTIVE: Determine if oxidative damage increases in articular cartilage as a result of injury and matrix failure and whether modulation of the local redox environment influences this damage. Osteoarthritis is an age associated disease with no current disease modifying approaches available. Mechanisms of cartilage damage in vitro suggest tissue free radical production could be critical to early degeneration, but these mechanisms have not been described in intact tissue. To assess free radical production as a result of traumatic injury, we measured biomolecular free radical generation via immuno-spin trapping (IST) of protein/proteoglycan/lipid free radicals after a 2 J/cm2 impact to swine articular cartilage explants. This technique allows visualization of free radical formation upon a wide variety of molecules using formalin-fixed, paraffin-embedded approaches. Scoring of extracellular staining by trained, blinded scorers demonstrated significant increases with impact injury, particularly at sites of cartilage cracking. Increases remain in the absence of live chondrocytes but are diminished; thus, they appear to be a cell-dependent and -independent feature of injury. We then modulated the extracellular environment with a pulse of heparin to demonstrate the responsiveness of the IST signal to changes in cartilage biology. Addition of heparin caused a distinct change in the distribution of protein/lipid free radicals at sites of failure alongside a variety of pertinent redox changes related to osteoarthritis. This study directly confirms the production of biomolecular free radicals from articular trauma, providing a rigorous characterization of their formation by injury.

Sex-based differences in the severity of radiation-induced arthrofibrosis.

As cancer survivorship increases, so does the number of patients that suffer from the late effects of radiation therapy. This includes arthrofibrosis, the development of stiff joints near the field of radiation. Previous reports have concentrated on skin fibrosis around the joint but largely ignored the deeper tissues of the joint. We hypothesized that fat, muscle, and the joint tissues themselves would play a more significant role in joint contracture after radiation than the skin surrounding the joint. To address this hypothesis, we irradiated the right hind flanks of mice with fractionated and unfractionated dose schedules, then monitored the mice for 3 months postradiotherapy. Mice were euthanized and physiological indications of arthrofibrosis including limb contracture and joint resting position were assessed. Stifle (knee) joints demonstrated significant arthrofibrosis, but none was observed in the hock (ankle) joints. During these studies, we were surprised to find that male and female mice showed a significantly different response to radiation injury. Female mice developed more injuries, had significantly worse contracture, and showed a greater difference in the expression of all markers studied. These results suggest that women undergoing radiation therapy might be at significantly greater risk for developing arthrofibrosis and may require specific adjustments to their care.

Automated quantification of live articular chondrocyte fluorescent staining using a custom image analysis framework.

The goal of this study was to develop, validate, and implement an image analysis framework to automatically analyze chondrocytes in 3D image stacks of cartilage acquired using a fluorescent confocal microscope. Source specimens consist of viable osteochondral tissue co-stained with multiple live-cell dyes. Our framework utilizes a seeded watershed-based algorithm to automatically segment individual chondrocytes in each 2D slice of the confocal image stack. The resulting cell segmentations are colocalized in 3D to eliminate duplicate segmentation of the same cell resulting from the visibility of fluorescence signal in multiple imaging planes, and the 3D cell distribution is used to automatically define the cartilage tissue volume. The algorithm then provides chondrocyte density data, and the associated segmentation can be used as a mask to extract and quantify per cell intensity of a secondary, functional dye co-staining the chondrocytes. The accuracy of the automated chondrocyte segmentation was validated against manual segmentations (average IOU = 0.79). When applied to a cartilage surrogate, this analysis framework estimated chondrocyte density within 10% of the true density and demonstrated a good agreement between framework's counts and manual counts (R2 = 0.99). In a real application, the framework was able to detect the increased dye signal of monochlorobimane (MCB) in chondrocytes treated with N-acetylcysteine (NAC) after mechanical injury, quantifying intracellular biochemical changes in living cells. This new framework allows for fast and accurate quantification of intracellular activities of chondrocytes, and it can be adapted for broader application in many imaging and treatment modalities, including therapeutic OA research.

CD177 modulates the function and homeostasis of tumor-infiltrating regulatory T cells.

Regulatory T (Treg) cells are one of the major immunosuppressive cell types in cancer and a potential target for immunotherapy, but targeting tumor-infiltrating (TI) Treg cells has been challenging. Here, using single-cell RNA sequencing of immune cells from renal clear cell carcinoma (ccRCC) patients, we identify two distinct transcriptional fates for TI Treg cells, Fate-1 and Fate-2. The Fate-1 signature is associated with a poorer prognosis in ccRCC and several other solid cancers. CD177, a cell surface protein normally expressed on neutrophil, is specifically expressed on Fate-1 TI Treg cells in several solid cancer types, but not on other TI or peripheral Treg cells. Mechanistically, blocking CD177 reduces the suppressive activity of Treg cells in vitro, while Treg-specific deletion of Cd177 leads to decreased tumor growth and reduced TI Treg frequency in mice. Our results thus uncover a functional CD177+ TI Treg population that may serve as a target for TI Treg-specific immunotherapy.

Intersections Between Mitochondrial Metabolism and Redox Biology Mediate Posttraumatic Osteoarthritis.

PURPOSE OF REVIEW: This review will cover foundational studies and recent findings that established key concepts for understanding the importance of redox biology to chondrocyte mitochondrial function and osteoarthritis pathophysiology after injury. RECENT FINDINGS: Articular chondrocyte mitochondria can be protected with a wide variety of antioxidants that will be discussed within a framework suggested by classic studies. These agents not only underscore the importance of thiol metabolism and associated redox function for chondrocyte mitochondria but also suggest complex interactions with signal transduction pathways and other molecular features of osteoarthritis that require more thorough investigation. Emerging evidence also indicates that reductive stress could occur alongside oxidative stress. Recent studies have shed new light on historic paradoxes in chondrocyte redox and mitochondrial physiology, leading to the development of promising disease-modifying therapies for posttraumatic osteoarthritis.

Cancer cell-intrinsic function of CD177 in attenuating ß-catenin signaling.

Aiming to identify immune molecules with a novel function in cancer pathogenesis, we found the cluster of differentiation 177 (CD177), a known neutrophil antigen, to be positively correlated with relapse-free, metastasis-free, or overall survival in breast cancer. In addition, CD177 expression is correlated with good prognosis in several other solid cancers including prostate, cervical, and lung. Focusing on breast cancer, we found that CD177 is expressed in normal breast epithelial cells and is significantly reduced in invasive cancers. Loss of CD177 leads to hyperproliferative mammary epithelium and contributes to breast cancer pathogenesis. Mechanistically, we found that CD177-deficiency is associated with an increase in ß-catenin signaling. Here we identified CD177 as a novel regulator of mammary epithelial proliferation and breast cancer pathogenesis likely via the modulation of Wnt/ß-catenin signaling pathway, a key signaling pathway involved in multiple cancer types.

Deep Learning for Chondrocyte Identification in Automated Histological Analysis of Articular Cartilage.

Background: Histology-based methods are commonly used in osteoarthritis (OA) research because they provide detailed information about cartilage health at the cellular and tissue level. Computer-based cartilage scoring systems have previously been developed using standard image analysis techniques to give more objective and reliable evaluations of OA severity. The goal of this work was to develop a deep learning-based method to segment chondrocytes from histological images of cartilage and validate the resulting method via comparison with human segmentation. Methods: The U-Net approach was adapted for the task of chondrocyte segmentation. A training dataset consisting of 235 images and a validation set consisting of 25 images in which individual chondrocytes had been manually segmented, were used for training the U-Net. Chondrocyte count, detection accuracy, and boundary segmentation of the trained U-Net was evaluated by comparing its results with those of human observers. Results: The U-Net chondrocyte counts were not significantly different (p = 0.361 in a paired t-test) than the algorithm trainer counts (Pearson correlation coefficient = 0.92). The five expert observers had good agreement on chondrocyte counts (intraclass correlation coefficient = 0.868), however the resulting U-Net counted a significantly fewer chondrocytes than the average of those expert observers (p < 0.001 in a paired t-test). Chondrocytes were accurately detected by the U-Net (F1 scores = 0.86, 0.90, with respect to the selected expert observer and algorithm trainer). Segmentation accuracy was also high (IOU = 0.828) relative to the algorithm trainer. Conclusions: This work developed a method for chondrocyte segmentation from histological images of arthritic cartilage using a deep learning approach. The resulting method detected chondrocytes and delineated them with high accuracy. The method will continue to be improved through expansion to detect more complex cellular features representative of OA such as cell cloning. Clinical Relevance: The imaging tool developed in this work can be integrated into an automated cartilage health scoring system and helps provide a robust, objective and reliable assessment of OA severity in cartilage.