Nonlabeling and quantitative assessment of chondrocyte viability in articular cartilage with intrinsic nonlinear optical signatures

Investigating cartilage-related diseases by polarization-resolved second harmonic generation (P-SHG) imaging

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage.

Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage

Objective

Chondrocyte viability (CV) can be measured with the label-free method using second harmonic generation (SHG) and two-photon excitation autofluorescence (TPAF) imaging. To automate the image processing for the label-free CV measurement, we previously demonstrated a two-step deep-learning method: Step 1 used a U-Net to segment the lacuna area on SHG images; Step 2 used dual CNN networks to count live cells and the total number of cells in extracted cell clusters from TPAF images. This study aims to develop one-step deep learning methods to improve the efficiency of CV measurement.

Method

TPAF/SHG images were acquired simultaneously on cartilage samples from rats and pigs using two-photon microscopes and were merged to form RGB color images with red, green, and blue channels assigned to emission bands of oxidized flavoproteins, reduced forms of nicotinamide adenine dinucleotide, and SHG signals, respectively. Based on the Mask R-CNN, we designed a deep learning network and its denoising version using Wiener deconvolution for CV measurement.

Results

Using training and test datasets from rat and porcine cartilage, we have demonstrated that Mask R-CNN-based networks can segment and classify individual cells with a single-step processing flow. The absolute error (difference between the measured and the ground-truth CV) of the CV measurement using the Mask R-CNN with or without Wiener deconvolution denoising reaches 0.01 or 0.08, respectively; the error of the previous CV networks is 0.18, significantly larger than that of the Mask R-CNN methods.

Conclusions

Mask R-CNN-based deep-learning networks improve efficiency and accuracy of the label-free CV measurement.

Second Harmonic Imaging of Nasal, Auricular, and Costal Cartilage

OBJECTIVE

There is little knowledge about the histological organization of facial and costal cartilages in terms of matrix structure and cell morphology. Second harmonic generation (SHG) imaging is a nonlinear imaging technique that capitalizes on signal generation from highly ordered macromolecules such as collagen fibers. The purpose of this study was to use SHG microscopy to image collagen extracellular matrix (ECM) structure, chondrocyte size, and density of these cartilages.

STUDY DESIGN

Experimental.

METHODS

Surgical remnants of septal, lower lateral, rib, and auricular cartilages were collected following surgery, sectioned into 0.5-1 mm thick samples and fixed to facilitate batch process imaging. A Leica TCS SP8 MP Microscope and multiphoton laser were used to image the specimens. Images were analyzed for cell size, cell density, and collagen fiber directionality patterns using ImageJ.

RESULTS

SHG images of septal specimens show mesh-like structure of the ECM. There appears to be a superficial layer, characterized by flattened lacunae and middle zone, marked by circular lacunae clusters, similar to what is observed in articular cartilage. The structure of the ECM depicts a visible orientation perpendicular to the surface of the perichondrium. Cell size and density analysis through ImageJ suggests variety across cartilage types. Directionality analysis indicates that the collagen in the ECM displays preferred direction.

CONCLUSION

This study establishes clear extracellular models of facial and costal cartilages. Limitations include heterogeneous cartilage thickness due to processing difficulties. Further studies include automating the cutting process to increase uniformity of tissue thickness and increasing sample size to further validate results.

LEVEL OF EVIDENCE

2 Laryngoscope, 133:3370-3377, 2023.

Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction

This review presents the changes that the imaging of articular cartilage has undergone throughout the last decades. It highlights that the expectation is no longer to image the structure and associated functions of articular cartilage but, instead, to devise methods for generating non-invasive, function-depicting images with quantitative information that is useful for detecting the early, pre-clinical stage of diseases such as primary or post-traumatic osteoarthritis (OA/PTOA). In this context, this review summarizes (a) the structure and function of articular cartilage as a molecular imaging target, (b) quantitative MRI for non-invasive assessment of articular cartilage composition, microstructure, and function with the current state of medical diagnostic imaging, (c), non-destructive imaging methods, (c) non-destructive quantitative articular cartilage live-imaging methods, (d) artificial intelligence (AI) classification of degeneration and prediction of OA progression, and (e) our contribution to this field, which is an AI-supported, non-destructive quantitative optical biopsy for early disease detection that operates on a digital tissue architectural fingerprint. Collectively, this review shows that articular cartilage imaging has undergone profound changes in the purpose and expectations for which cartilage imaging is used; the image is becoming an AI-usable biomarker with non-invasive quantitative functional information. This may aid in the development of translational diagnostic applications and preventive or early therapeutic interventions that are yet beyond our reach.

Nanowarming and ice-free cryopreservation of large sized, intact porcine articular cartilage

Successful organ or tissue long-term preservation would revolutionize biomedicine. Cartilage cryopreservation enables prolonged shelf life of articular cartilage, posing the prospect to broaden the implementation of promising osteochondral allograft (OCA) transplantation for cartilage repair. However, cryopreserved large sized cartilage cannot be successfully warmed with the conventional convection warming approach due to its limited warming rate, blocking its clinical potential. Here, we develope a nanowarming and ice-free cryopreservation method for large sized, intact articular cartilage preservation. Our method achieves a heating rate of 76.8 °C min-1, over one order of magnitude higher than convection warming (4.8 °C min-1). Using systematic cell and tissue level tests, we demonstrate the superior performance of our method in preserving large cartilage. A depth-dependent preservation manner is also observed and recapitulated through magnetic resonance imaging and computational modeling. Finally, we show that the delivery of nanoparticles to the OCA bone side could be a feasible direction for further optimization of our method. This study pioneers the application of nanowarming and ice-free cryopreservation for large articular cartilage and provides valuable insights for future technique development, paving the way for clinical applications of cryopreserved cartilage.

Measuring chondrocyte viability of articular cartilage based on label-free two-photon microscopy and deep learning image analysis

Objective

Chondrocyte viability (CV) is an important indicator of articular cartilage health. Two-photon excitation autofluorescence (TPAF) and second harmonic generation (SHG) microscopy provide a label-free method for imaging chondrocytes. In this study, we propose an automated assessment of CV using deep learning cell segmentation and counting based on acquired TPAF/SHG images.

Design

Label-free TPAF/SHG images of cartilage samples from rats and porcine were acquired using both commercial and home-built two-photon microscopes, respectively. TPAF/SHG images were merged to form RGB color images with red, green, and blue channels assigned to TPAF (two channels) and SHG signals, respectively. To make the training datasets for the deep learning networks, individual chondrocyte areas on the RGB color images were manually circled and live or dead chondrocytes were validated by using Calcein-AM and Ethidium homodimer-1 dye labeling. We first built a chondrocyte viability network (MCV-Net) using the Mask R-CNN architecture, which could provide individual segmented cellular areas with live or dead status. Wiener deconvolution preprocessing was added before the input of MCV-Net to improve the accuracy of the CV analysis, forming the Wiener deconvolution CV network (wMCV-Net).

Results

Training (300 images) and test (120 images) datasets were built for rats and porcine cartilage respectively. Wiener deconvolution could improve the Peak Signal-to-Noise Ratio (PSNR) for 30-40%. We demonstrated that both MCV-Net and wMCV-Net significantly improved the accuracy of the CV measurement.

Conclusion

A custom desktop TPAF/SHG microscope was used in collaboration with deep learning algorithm wMCV-Net based label-free method to assess the CV and get 95% accuracy with both rats and porcine samples.

Rapid histological imaging of bone without microtome sectioning using nonlinear microscopy

Tissue preparation for histologic evaluation of bone is particularly lengthy, limiting its use in intraoperative or intraprocedural histological evaluation. Nonlinear microscopy (NLM) is an optical sectioning microscopy method that can visualize pathology in freshly excised tissue without requiring physical microtome sectioning. This study describes a rapid protocol for NLM imaging of bone and associated cartilage. NLM imaging was performed on 71 specimens of normal bone as well as arthritic, malignant and inflammatory bone tissue from 40 patients who underwent joint replacement, amputation, bone marrow biopsy or autopsy. Specimens ranged in size from core needle biopsies to transections of entire femoral heads. Specimens were stained with acridine orange and sulforhodamine 101, nuclear and cytoplasmic/stromal fluorescent dyes, for 5 min, then rinsed for 30 s. NLM fluorescent images were displayed using colors analogous to hematoxylin and eosin (H&E) to facilitate interpretation. Pathologists examined NLM images of the specimens in real time by rapidly translating the specimen to areas of interest, similar to a standard transmission light microscope. By adjusting the NLM focus depth, images from a few-μm-thick layer could be obtained down to ~100 μm below the tissue surface, analogous to serial sectioning. Following real-time NLM imaging, the tissue was processed for conventional paraffin histology, and H&E slides were compared to recorded NLM images. Similarities and differences between NLM and paraffin H&E were assessed. NLM enabled visualization of normal bone architecture, including the lamellar matrix and osteocytes of trabecular bone, articular cartilage, as well as pathological bone features such osteoarthritis, osteomyelitis, and malignancy with an appearance resembling the paraffin H&E. Differences such as changes in cell border sharpness, cellular and nucleolar size, and color patterns were noted, suggesting that training is required for accurate evaluation of bone pathology with NLM. Irregular surface contours and debris generated by gross tissue preparation of bone can make some regions difficult to evaluate with NLM, but the ability to perform rapid three-dimensional translation and sub-surface imaging reduced these problems. NLM is a promising technique for rapid evaluation of bone pathology. Further studies assessing diagnostic performance are warranted.

Deep learning provides high accuracy in automated chondrocyte viability assessment in articular cartilage using nonlinear optical microscopy

Chondrocyte viability is a crucial factor in evaluating cartilage health. Most cell viability assays rely on dyes and are not applicable for in vivo or longitudinal studies. We previously demonstrated that two-photon excited autofluorescence and second harmonic generation microscopy provided high-resolution images of cells and collagen structure; those images allowed us to distinguish live from dead chondrocytes by visual assessment or by the normalized autofluorescence ratio. However, both methods require human involvement and have low throughputs. Methods for automated cell-based image processing can improve throughput. Conventional image processing algorithms do not perform well on autofluorescence images acquired by nonlinear microscopes due to low image contrast. In this study, we compared conventional, machine learning, and deep learning methods in chondrocyte segmentation and classification. We demonstrated that deep learning significantly improved the outcome of the chondrocyte segmentation and classification. With appropriate training, the deep learning method can achieve 90% accuracy in chondrocyte viability measurement. The significance of this work is that automated imaging analysis is possible and should not become a major hurdle for the use of nonlinear optical imaging methods in biological or clinical studies.

Detecting human articular cartilage degeneration in its early stage with polarization-sensitive optical coherence tomography

Detecting articular cartilage (AC) degeneration in its early stage plays a critical role in the diagnosis and treatment of osteoarthritis (OA). Polarization-sensitive optical coherence tomography (PS-OCT) is sensitive to the alteration and disruption of collagen organization that happens during OA progression. This study proposes an effective OA evaluating method based on PS-OCT imaging. A slope-based analysis is applied on the phase retardation images to segment articular cartilage into three zones along the depth direction. The boundaries and birefringence coefficients (BRCs) of each zone are quantified. Two parameters, namely phase homogeneity index (PHI) and zonal distinguishability (Dz), are further developed to quantify the fluctuation within each zone and the zone-to-zone variation of the tissue birefringence properties. The PS-OCT based evaluating method then combines PHI and Dz to provide a G _PS score for the severity of OA. The proposed method is applied to human hip joint samples and the results are compared with the grading by histology images. The G _PS score shows very strong statistical significance in differentiating different stages of OA. Compared to using the BRC of each zone or a single BRC for the entire depth, the G _PS score shows great improvement in differentiating early-stage OA. The proposed method is shown to have great potential to be developed as a clinical tool for detecting OA.