Assessment of progressive alterations in collagen organization in the postoperative conjunctiva by multiphoton microscopy

Quantitative Stain-Free Conjunctival Collagen Imaging in Cicatrizing Conjunctivitis Using Second Harmonic Generation-Two Photon Excitation Technology

Purpose

We aim to study the structure of collagen in cicatrizing conjunctivitis (CC), a disease with significant morbidity, to find sensitive and quantitative measures of severity of scarring as current progression of conjunctival scarring is reliant only on clinical assessment.

Methods

We used two-photon excitation and second harmonic generation to scan conjunctival tissues from patients with CC from Stevens-Johnson syndrome and its relation to disease severity by correlation with a validated clinical severity assessment tool. Collagen morphometry in region of interest was analyzed with FibroIndex software (HistoIndex Pte Ltd.) in conjunctival biopsies.

Results

Eighteen patients (seven CC and 11 controls) were included. Mean age was 60.7 ± 14.4 years old, with no difference between groups (P = 0.89) Compared to controls, diseased group has significantly smaller collagen area ratio (CAR) (P < 0.01), collagen fiber number (CFN)/mm2 (P < 0.01) and larger collagen fiber density (P = 0.03) In diseased groups, CAR correlated with inflammation (R = -0.55, P = 0.011), scarring (R = 0.61, P = 0.0034), morbidity (R = 0.46, P = 0.035) and overall composite score (R = 0.52, P = 0.017). In all groups, CFN/mm2 negatively correlated with inflammation (R = -0.50, P < 0.01), scarring (R = -0.25, P = 0.07) morbidity (R = -0.37, P < 0.01), and overall composite score (R = -0.33, P = 0.02).

Conclusions

We have further characterized the defining features in CC. CAR has significant correlation to all scores in diseased groups and, hence, may be a reliable marker for diagnosis. CFN/mm2, as the only parameter with significant negative correlation with all scores, can potentially be a predictor for severity.

Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model

Dysregulation of lung tissue collagen level plays a vital role in understanding how lung diseases progress. However, traditional scoring methods rely on manual histopathological examination introducing subjectivity and inconsistency into the assessment process. These methods are further hampered by inter-observer variability, lack of quantification, and their time-consuming nature. To mitigate these drawbacks, we propose a machine learning-driven framework for automated scoring of lung collagen content. Our study begins with the collection of a lung slide image dataset from adult female mice using second harmonic generation (SHG) microscopy. In our proposed approach, first, we manually extracted features based on the 46 statistical parameters of fibrillar collagen. Subsequently, we pre-processed the images and utilized a pre-trained VGG16 model to uncover hidden features from pre-processed images. We then combined both image and statistical features to train various machine learning and deep neural network models for classification tasks. We employed advanced unsupervised techniques like K-means, principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to conduct thorough image analysis for lung collagen content. Also, the evaluation of the trained models using the collagen data includes both binary and multi-label classification to predict lung cancer in a urethane-induced mouse model. Experimental validation of our proposed approach demonstrates promising results. We obtained an average accuracy of 83% and an area under the receiver operating characteristic curve (ROC AUC) values of 0.96 through the use of a support vector machine (SVM) model for binary categorization tasks. For multi-label classification tasks, to quantify the structural alteration of collagen, we attained an average accuracy of 73% and ROC AUC values of 1.0, 0.38, 0.95, and 0.86 for control, baseline, treatment_1, and treatment_2 groups, respectively. Our findings provide significant potential for enhancing diagnostic accuracy, understanding disease mechanisms, and improving clinical practice using machine learning and deep learning models.

Characteristics of Mitomycin C-Loaded Peptide Hydrogel In Vitro and Antiscarring Effects in Rat Ocular Injury Model

Purpose: To investigate the characteristics of sustained drug release systems established by an arginine-glycine-aspartic acid (RGD) peptide hydrogel and mitomycin C (MMC) in vitro, and verify their antiscar effects in rat ocular injury model. Methods: Low, medium, and high loading doses of MMC were added to 5 mL 0.25%, 0.5%, and 1% wt RGD peptide hydrogel, respectively, to prepare 9 ratios of MMC-RGD systems. Drug release characteristics of the systems in phosphate-buffered saline solution were investigated by plotting the drug release curves and fitting them with mathematical models in OriginPro8.0 software. Appropriate ratios of MMC-RGD systems were selected as treatment in rat ocular injury model. Scar formation was observed by Masson staining and immunohistochemical staining with alpha-smooth muscle actin (α-SMA) and fibronectin (FN). Results: Nine ratios of MMC-RGD systems could release drug slowly. The maximum drug release proportions of all systems were >80%, and the time to maximum release proportions statistically prolonged with the increase of drug loading. Fitting with mathematical models indicated that the mechanisms of drug release were mainly Fick diffusion at early stage and Anomalous Transport at later stage. Systems of 1% wt RGD hydrogel were evaluated in animal experiments, which could inhibit hyperplasia of collagen and expression of α-SMA and FN. Conclusions: The RGD peptide hydrogel could be used as the carrier of MMC to establish sustained drug release system, which could inhibit scar formation after rat's ocular injury.

Valproic acid modulates collagen architecture in the postoperative conjunctival scar

Valproic acid (VPA), widely used for the treatment of neurological disorders, has anti-fibrotic activity by reducing collagen production in the postoperative conjunctiva. In this study, we investigated the capacity of VPA to modulate the postoperative collagen architecture. Histochemical examination revealed that VPA treatment was associated with the formation of thinner collagen fibers in the postoperative days 7 and 14 scars. At the micrometer scale, measurements by quantitative multiphoton microscopy indicated that VPA reduced mean collagen fiber thickness by 1.25-fold. At the nanometer scale, collagen fibril thickness and diameter measured by transmission electron microscopy were decreased by 1.08- and 1.20-fold, respectively. Moreover, delicate filamentous structures in random aggregates or closely associated with collagen fibrils were frequently observed in VPA-treated tissue. At the molecular level, VPA reduced Col1a1 but induced Matn2, Matn3, and Matn4 in the postoperative day 7 conjunctival tissue. Elevation of matrilin protein expression induced by VPA was sustained till at least postoperative day 14. In primary conjunctival fibroblasts, Matn2 expression was resistant to both VPA and TGF-β2, Matn3 was sensitive to both VPA and TGF-β2 individually and synergistically, while Matn4 was modulable by VPA but not TGF-β2. MATN2, MATN3, and MATN4 localized in close association with COL1A1 in the postoperative conjunctiva. These data indicate that VPA has the capacity to reduce collagen fiber thickness and potentially collagen assembly, in association with matrilin upregulation. These properties suggest potential VPA application for the prevention of fibrotic progression in the postoperative conjunctiva. KEY MESSAGES: VPA reduces collagen fiber and fibril thickness in the postoperative scar. VPA disrupts collagen fiber assembly in conjunctival wound healing. VPA induces MATN2, MATN3, and MATN4 in the postoperative scar.

Effects of Valproic Acid and Mitomycin C Combination Therapy in a Rabbit Model of Minimally Invasive Glaucoma Surgery

Purpose

This study aimed to compare the effectiveness of combination therapy consisting of low-dose mitomycin C (MMC) and valproic acid (VPA) against high-dose MMC for improving the scar phenotype in minimally invasive glaucoma surgery (MIGS).

Methods

A rabbit model of MIGS incorporating the PreserFlo MicroShunt was treated with high (0.4 mg/mL) or low (0.1 mg/mL) doses of MMC or with combination therapy consisting of low-dose (0.1 mg/mL) MMC and VPA. Operated eyes were examined by live ocular imaging, histochemical evaluation, multiphoton quantitation of collagen characteristics, and molecular analyses.

Results

Although high-dose MMC obliterated the vasculature, combination therapy vastly improved the postoperative tissue morphology by maintaining the vasculature without increased vascularization. Combination therapy also altered collagen morphology and reduced encapsulation of the MicroShunt distal end, which remained at risk with MMC treatment alone. Multiphoton quantitation indicated that the combination therapy significantly reduced collagen density and fiber dimensions compared with monotherapy. At the molecular level, combination therapy significantly reduced Vegfa, Vegfc, and Vegfd expression and inhibited Col1a1 upregulation from baseline levels, all of which low-dose MMC alone was unable to achieve. Notably, COL1A1 protein levels appeared more consistently suppressed by combination therapy compared with high-dose MMC alone.

Conclusions

Compared with high-dose MMC, combination therapy was less toxic by sparing the vasculature and potentially more effective in reducing scarring via the regulation of collagen content and organization.

Translational Relevance

VPA may be combined with low-dose MMC to replace high-dose MMC to deliver safe and effective anti-scarring outcomes.

Inhibited effect of an RGD peptide hydrogel on the expression of β1-integrin, FAK, and Akt in Tenon's capsule fibroblasts

Tenon's capsule fibroblasts are the main cellular components of filtration tract scar that limit the success rate of glaucoma filtration surgery. Scar formation results from infiltration and proliferation of fibroblasts into damaged areas, meanwhile synthesis of extracellular matrix glycoproteins. Integrins are cell surface receptors for extracellular molecules that mediate cell adhesion, spreading, migration, and invasion. They bind their ligands often through recognition of short amino-acid sequences-arginine-glycine-aspartic acid (RGD). Peptides that contain RGD sequence can compete with RGD containing insoluble matrix proteins for binding to the integrin receptor and thus prevent the downstream signaling pathway. Increasing evidence supports that β1-integrin/focal adhesion kinase (FAK)/Akt signal pathway plays an important role in fibrogenesis and scar formation in different tissues. In consideration of advantages of peptide hydrogel, that is well biocompatibility, gel state, degradability, good drug loading, we designed and fabricated an RGD peptide hydrogel, and hypothesized that it could inhibit the expression of β1-integrin, FAK, and Akt in Tenon's capsule fibroblasts. Rheology results showed that 1% wt Fmoc-FFGGRGD peptide solution could self-assemble into hydrogel. Western blot analysis revealed that there were statistical differences between control group and 1% wt group in β1-integrin/β-actin, FAK/β-actin, Akt/β-actin respectively (*p < .05). The relative mRNA expression of β1-integrin, FAK, Akt in control group and 1% wt group were also statistically different respectively (*p < .05). We proved that 1% wt Fmoc-FFGGRGD self-assembly peptide hydrogel could inhibit the expression of β1-integrin, FAK and Akt in Tenon's capsule fibroblasts. It is a promising way to solve scar formation of glaucoma filter channel.

Nanofibrillar Hydrogel Recapitulates Changes Occurring in the Fibrotic Extracellular Matrix

Fibrosis is a pathological condition that leads to excessive deposition of collagen and increased tissue stiffness. Understanding the mechanobiology of fibrotic tissue necessitates the development of effective in vitro models that recapitulate its properties and structure; however, hydrogels that are currently used for this purpose fail to mimic the filamentous structure and mechanical properties of the fibrotic extracellular matrix (ECM). Here, we report a nanofibrillar hydrogel composed of cellulose nanocrystals and gelatin, which addresses this challenge. By altering the composition of the hydrogel, we mimicked the changes in structure, mechanical properties, and chemistry of fibrotic ECM. Furthermore, we decoupled the variations in hydrogel structure, properties, and ligand concentration. We demonstrate that this biocompatible hydrogel supports the three-dimensional culture of cells relevant to fibrotic diseases. This versatile hydrogel can be used for in vitro studies of fibrosis of different tissues, thus enabling the development of novel treatments for fibrotic diseases.