Tong Ye

Understanding musculoskeletal joints from a 3D multiscale perspective, from molecular to anatomical levels, is essential for resolving the confounding relationships between structure and pain, elucidating mechanisms regulating joint health and diseases, and developing new treatment strategies. Here, a musculoskeletal joint immunostaining and clearing (MUSIC) method specifically developed to overcome key challenges of immunostaining and optical clearing of intact joints are introduced. Coupled with large‐field light sheet microscopy, this approach achieves 3D high‐resolution, microscale neurovascular mapping within the context of whole‐joint anatomy without the need for image coregistration across various joints, including temporomandibular joints, knees, and spines, and multiple species, including mouse, rat, and pig. These results reveal 3D heterogeneous neurovascular distributions and previously uncharacterized neurovascular pathways within joints. Using two complementary models of joint disease, degeneration and injury, disease‐specific microscale neurovascular alterations are identified. These findings extend beyond conventional macroscale assessments of joint morphology and provide a framework to link structural changes with pain. Importantly, our results show that the relationship between joint structure and pain is not universal but disease‐dependent, underscoring distinct pain mechanisms in different disease contexts. This platform offers a powerful tool for multiscale 3D analysis, advancing understanding of joint pathophysiology and intricate interplay among joint tissues.

Multicolor microscopy and super-resolution optical microscopy are two widely used techniques that greatly enhance the ability to distinguish and resolve structures in cellular imaging. These methods have individually transformed cellular imaging by allowing detailed visualization of cellular and subcellular structures, as well as organelle interactions. However, integrating multicolor and super-resolution microscopy into a single method remains challenging due to issues like spectral overlap, crosstalk, photobleaching, phototoxicity, and technical complexity. These challenges arise from the conflicting requirements of using different fluorophores for multicolor labeling and fluorophores with specific properties for super-resolution imaging. We propose a novel multicolor super-resolution imaging method called phasor-based fluorescence spatiotemporal modulation (Phasor-FSTM). This method uses time-resolved detection to acquire spatiotemporal data from encoded photons, employs phasor analysis to simultaneously separate multiple components, and applies fluorescence modulation to create super-resolution images. Phasor-FSTM enables the identification of multiple structural components with greater spatial accuracy on an enhanced laser scanning confocal microscope using a single-wavelength laser. To demonstrate the capabilities of Phasor-FSTM, we performed two-color to four-color super-resolution imaging at a resolution of ~λ/5 and observed the interactions of organelles in live cells during continuous imaging for a duration of over 20 min. Our method stands out for its simplicity and adaptability, seamlessly fitting into existing laser scanning microscopes without requiring multiple laser lines for excitation, which also provides a new avenue for other super-resolution imaging technologies based on different principles to build multi-color imaging systems with the requirement of a lower budget.

Decreased myocardial capillary density has been reported as an important histopathological feature associated with various heart disorders. Quantitative assessment of cardiac capillarization typically involves double immunostaining of cardiomyocytes (CMs) and capillaries in myocardial slices. In contrast, single immunostaining of basement membrane protein is a straightforward approach to simultaneously label CMs and capillaries, presenting fewer challenges in background staining. However, subsequent image analysis always requires expertise and laborious manual work to identify and segment CMs/capillaries. Here, we developed an image analysis tool, AutoQC, for automatic identification and segmentation of CMs and capillaries in immunofluorescence images of basement membrane. Commonly used capillarization-related measurements can be derived from segmentation results. By leveraging the power of a pre-trained segmentation model (Segment Anything Model, SAM) via prompt engineering, the training of AutoQC required only a small dataset with bounding box annotations instead of pixel-wise annotations. AutoQC outperformed SAM (without prompt engineering) and YOLOv8-Seg, a state-of-the-art instance segmentation model, in both instance segmentation and capillarization assessment. Thus, AutoQC, featuring a weakly supervised algorithm, enables automatic segmentation and high-throughput, high-accuracy capillarization assessment in basement-membrane-immunostained myocardial slices. This approach reduces the training workload and eliminates the need for manual image analysis once AutoQC is trained.

Within the myocardium, cardiomyocytes reside in a complex and dynamic extracellular matrix (ECM) consisting of a basement membrane (BM) and interstitial matrix. The interactions between cardiomyocytes and the myocardial ECM play a critical role in maintaining cardiac geometry and function throughout cardiac development and in adult hearts. Understanding how the structural changes of the myocardial ECM affect cardiomyocyte function requires knowledge of pericellular structures. These structures are of a size beyond the resolution of conventional optical microscopy. Here, we demonstrated multi-scale and multi-aspect characterization of the cardiomyocyte microenvironment in myocardial tissue sections using multimodal stimulated emission depletion (STED) microscopy. Second harmonic generation and autofluorescence facilitated multiplexed imaging, enabling the interpretation of protein distribution in 3D. STED imaging modality revealed BM structures of cardiomyocytes and myocardial capillaries at the subdiffractional level. Moreover, meaningful measurements retrieved from acquired images, such as sarcomere length and capillary density, enabled quantitative assessment of myocardial structures.

Untrained Physics-based Deep Learning (DL) methods for digital holography have gained significant attention due to their benefits, such as not requiring an annotated training dataset, and providing interpretability since utilizing the governing laws of hologram formation. However, they are sensitive to the hard-to-obtain precise object distance from the imaging plane, posing the Autofocusing challenge. Conventional solutions involve reconstructing image stacks for different potential distances and applying focus metrics to select the best results, which apparently is computationally inefficient. In contrast, recently developed DL-based methods treat it as a supervised task, which again needs annotated data and lacks generalizability. To address this issue, we propose reverse-attention loss, a weighted sum of losses for all possible candidates with learnable weights. This is a pioneering approach to addressing the Autofocusing challenge in untrained deep-learning methods. Both theoretical analysis and experiments demonstrate its superiority in efficiency and accuracy. Interestingly, our method presents a significant reconstruction performance over rival methods (i.e. alternating descent-like optimization, non-weighted loss integration, and random distance assignment) and even is almost equal to that achieved with a precisely known object distance. For example, the difference is less than 1dB in PSNR and 0.002 in SSIM for the target sample in our experiment.

Decreased myocardial capillary density has been reported as an important histopathological feature associated with various heart disorders. Quantitative assessment of cardiac capillarization typically involves double immunostaining of cardiomyocytes (CMs) and capillaries in myocardial slices. In contrast, single immunostaining of basement membrane components is a straightforward approach to simultaneously label CMs and capillaries, presenting fewer challenges in background staining. However, subsequent image analysis always requires manual work in identifying and segmenting CMs and capillaries. Here, we developed an image analysis tool, AutoQC, to automatically identify and segment CMs and capillaries in immunofluorescence images of collagen type IV, a predominant basement membrane protein within the myocardium. In addition, commonly used capillarization-related measurements can be derived from segmentation masks. AutoQC features a weakly supervised instance segmentation algorithm by leveraging the power of a pre-trained segmentation model via prompt engineering. AutoQC outperformed YOLOv8-Seg, a state-of-the-art instance segmentation model, in both instance segmentation and capillarization assessment. Furthermore, the training of AutoQC required only a small dataset with bounding box annotations instead of pixel-wise annotations, leading to a reduced workload during network training. AutoQC provides an automated solution for quantifying cardiac capillarization in basement-membrane-immunostained myocardial slices, eliminating the need for manual image analysis once it is trained.

Autofluorescence (AF) poses challenges for detecting proteins of interest in situ when employing immunofluorescence (IF) microscopy. This interference is particularly pronounced in strongly autofluorescent tissues such as myocardium, where tissue AF can be comparable to IF. Although various histochemical methods have been developed to achieve effective AF suppression in different types of tissue, their applications on myocardial samples have not been well validated. Due to inconsistency across different autofluorescent structures in sometypes of tissue, it is unclear if these methods can effectively suppress AF across all autofluorescent structures within the myocardium. Here, we quantitatively evaluated the performance of several commonly used quenching treatments on formaldehyde-fixed myocardial samples, including 0.3 M glycine, 0.3% Sudan Black B (SBB), 0.1% and 1% sodium borohydride (NaBH4), TrueVIEW® and TrueBlack®. We further assessed their quenching performance by employing the pre-treatment and post-treatment protocols, designed to cover two common IF staining scenarios where buffers contained detergents or not. The results suggest that SBB and TrueBlack® outperform other reagents in AF suppression on formaldehyde-fixed myocardial samples in both protocols. Furthermore, we inspected the quenching performance of SBB and TrueBlack® on major autofluorescent myocardial structures and evaluated their influence on IF imaging. The results suggest that SBB outperforms TrueBlack® in quenching major autofluorescent structures, while TrueBlack® excels in preserving IF labeling signal. Surprisingly, we found the treatment of NaBH4 increased AF signal and enhanced the AF contrast of major autofluorescent structures. This finding suggests that NaBH4 has the potential to act as an AF enhancer and may facilitate the interpretation of myocardial structures without the need for counterstaining.

Maintaining the heart's health is one of the largest challenges in medicine due to the proclivity of life-threatening cardiovascular diseases, such as myocardial infarctions. When the heart experiences an infarction, a scar begins forming within an hour of the event, which will continue to grow and weaken the heart’s ability to contract. The myocardium after an infarction will increase in stiffness as the tissue becomes fibrotic; the influx of collagen dampens the flexion of the ventricles and reduces the cardiac output. The nature of the tissue stiffness is vital to understand not only at the tissue level but also at the mesoscopic domain. It is necessary to specify how the primary structural tissue components at the subcellular level contribute to the mechanical behavior of the muscle. To investigate this, we produced a procedure for mapping the mechanical nature of fresh myocardium: using atomic force microscopy to measure the mechanical properties of each structural component imaging determined by our second harmonic generation (SHG) microscopy. To coregistered AFM and SHG image, which has not been accomplished previously, we developed a convenient means of marking PDMS to be visible in SHG at 830 nm. Our research draws the line between the macroscopic mechanical behavior of the tissue to the nanoscopic structures.

Articular cartilage in the tibiofemoral joint contains unique tissue microstructures that serve specific functions, including reduction of friction and distributing the dynamic and static cyclic loading at the ends of diarthrodial joints. A proficient understanding of these microstructures can lead to significant clinical advances in diagnosing orthopedic diseases such as osteoarthritis and improving cartilage repairs. The surface of tibiofemoral condyles can be roughly separated into loadbearing and meniscus-covered areas. Due to the difference in mechanical loading between the two regions, we hypothesize that their microstructures differ. To test this hypothesis, we used cartilage punches harvested from the tibial condyle of porcine knee joints as an example tissue and a custom nonlinear optical microscope for performing a dye-free imaging study. The custom nonlinear optical microscope could simultaneously acquire Two-Photon excitation Auto-Fluorescence (TPAF) and Second Harmonic Generation (SHG) images. Through the TPAF channel, elastin fibers are visible along with chondrocytes. The SHG channel was utilized for observing the vast collagen network and its evident orientation throughout the tibial condyle. Images were analyzed by ImageJ to reveal alignment angles of the collagen network and elastin fibers. The load-bearing region exhibits a denser uniform collagen network with minimum elastin fibers. In contrast, the meniscuscovered areas have a distinctive collagen orientation with a greater magnitude of co-localized elastin fibers. The biological differences are likely derived from their different biomechanical environments in the tibiofemoral joint.