Three-dimensional imaging of intact porcine cochlea using tissue clearing and custom-built light-sheet microscopy

2D vs. 3D Evaluation of Osteocyte Lacunae - Methodological Approaches, Recommended Parameters, and Challenges: A Narrative Review by the European Calcified Tissue Society (ECTS)

PURPOSE OF REVIEW

Quantification of the morphology of osteocyte lacunae has become a powerful tool to investigate bone metabolism, pathologies and aging. This review will provide a brief overview of 2D and 3D imaging methods for the determination of lacunar shape, orientation, density, and volume. Deviations between 2D-based and 3D-based lacunar volume estimations are often not sufficiently addressed and may give rise to contradictory findings. Thus, the systematic error arising from 2D-based estimations of lacunar volume will be discussed, and an alternative calculation proposed. Further, standardized morphological parameters and best practices for sampling and segmentation are suggested.

RECENT FINDINGS

We quantified the errors in reported estimation methods of lacunar volume based on 2D cross-sections, which increase with variations in lacunar orientation and histological cutting plane. The estimations of lacunar volume based on common practice in 2D imaging methods resulted in an underestimation of lacunar volume of up to 85% compared to actual lacunar volume in an artificial dataset. For a representative estimation of lacunar size and morphology based on 2D images, at least 400 lacunae should be assessed per sample.

Surgical procedure of intratympanic injection and inner ear pharmacokinetics simulation in domestic pigs

Introduction: One major obstacle in validating drugs for the treatment or prevention of hearing loss is the limited data available on the distribution and concentration of drugs in the human inner ear. Although small animal models offer some insights into inner ear pharmacokinetics, their smaller organ size and different barrier (round window membrane) permeabilities compared to humans can complicate study interpretation. Therefore, developing a reliable large animal model for inner ear drug delivery is crucial. The inner and middle ear anatomy of domestic pigs closely resembles that of humans, making them promising candidates for studying inner ear pharmacokinetics. However, unlike humans, the anatomical orientation and tortuosity of the porcine external ear canal frustrates local drug delivery to the inner ear. Methods: In this study, we developed a surgical technique to access the tympanic membrane of pigs. To assess hearing pre- and post-surgery, auditory brainstem responses to click and pure tones were measured. Additionally, we performed 3D segmentation of the porcine inner ear images and used this data to simulate the diffusion of dexamethasone within the inner ear through fluid simulation software (FluidSim). Results: We have successfully delivered dexamethasone and dexamethasone sodium phosphate to the porcine inner ear via the intratympanic injection. The recorded auditory brainstem measurements revealed no adverse effects on hearing thresholds attributable to the surgery. We have also simulated the diffusion rates for dexamethasone and dexamethasone sodium phosphate into the porcine inner ear and confirmed the accuracy of the simulations using in-vivo data. Discussion: We have developed and characterized a method for conducting pharmacokinetic studies of the inner ear using pigs. This animal model closely mirrors the size of the human cochlea and the thickness of its barriers. The diffusion time and drug concentrations we reported align closely with the limited data available from human studies. Therefore, we have demonstrated the potential of using pigs as a large animal model for studying inner ear pharmacokinetics.

Enhancing Light-Sheet Fluorescence Microscopy Illumination Beams through Deep Design Optimization

Light sheet fluorescence microscopy (LSFM) provides the benefit of optical sectioning coupled with rapid acquisition times for imaging of tissue-cleared specimen. This allows for high-resolution 3D imaging of large tissue volumes. Inherently to LSFM, the quality of the imaging heavily relies on the characteristics of the illumination beam, with the notion that the illumination beam only illuminates a thin section that is being imaged. Therefore, substantial efforts are dedicated to identifying slender, non-diffracting beam profiles that can yield uniform and high-contrast images. An ongoing debate concerns the employment of the most optimal illumination beam; Gaussian, Bessel, Airy patterns and/or others. Comparisons among different beam profiles is challenging as their optimization objective is often different. Given that our large imaging datasets (~0.5TB images per sample) is already analyzed using deep learning models, we envisioned a different approach to this problem by hypothesizing that we can tailor the illumination beam to boost the deep learning models performance. We achieve this by integrating the physical LSFM illumination model after passing through a variable phase mask into the training of a cell detection network. Here we report that the joint optimization continuously updates the phase mask, improving the image quality for better cell detection. Our method's efficacy is demonstrated through both simulations and experiments, revealing substantial enhancements in imaging quality compared to traditional Gaussian light sheet. We offer valuable insights for designing microscopy systems through a computational approach that exhibits significant potential for advancing optics design that relies on deep learning models for analysis of imaging datasets.

Three-Dimensional Imaging and Quantitative Analysis of Blood Vessel Distribution in The Meniscus of Transgenic Mouse after Tissue Clearing

OBJECTIVE

Blood supply to the meniscus determines its recovery and is a reference for treatment planning. This study aimed to apply tissue clearing and three-dimensional (3D) imaging in exploring the quantitative distribution of blood vessels in the mouse meniscus.

MATERIALS AND METHODS

In this experimental study, tissue clearing was performed to treat the bilateral knee joints of transgenic mice with fluorescent vascular endothelial cells. Images were acquired using a light sheet microscope and the vascular endothelial cells in the meniscus was analysed using 3D imaging. Quantitative methods were employed to further analyse the blood vessel distribution in the mouse meniscus.

RESULTS

The traditional three-equal-width division of the meniscus is as follows: the outer one-third is the red-red zone (RR), the inner one-third is the white-white zone (WW), and the transition area is the red-white zone (RW). The division revealed significant signal differences between the RW and WW (P<0.05) zones, but no significant differences between the RR and RW zones, which indicated that the division might not accurately reflect the blood supply of the meniscus. According to the modified division (4:2:1) in which significant differences were ensured between the adjacent zones, we observed that the width ratio of each zone was 38 ± 1% (RR), 24 ± 1% (RW), and 38 ± 2% (WW). Furthermore, the blood supply to each region was verified. The anterior region had the most abundant blood supply. The fluorescence count in the anterior region was significantly higher than in the central and posterior regions (P<0.05). The blood supply of the medial meniscus was superior to the lateral meniscus (P<0.05).

CONCLUSION

Analysis of the blood supply to the mouse meniscus under tissue clearing and 3D imaging reflect quantitative blood vessel distribution, which would facilitate future evaluations of the human meniscus and provide more anatomical references for clinicians.

Assessment of drug permeability through an ex vivo porcine round window membrane model

Delivery of pharmaceutical therapeutics to the inner ear to treat and prevent hearing loss is challenging. Systemic delivery is not effective as only a small fraction of the therapeutic agent reaches the inner ear. Invasive surgeries to inject through the round window membrane (RWM) or cochleostomy may cause damage to the inner ear. An alternative approach is to administer drugs into the middle ear using an intratympanic injection, with the drugs primarily passing through the RWM to the inner ear. However, the RWM is a barrier, only permeable to a small number of molecules. To study and enhance the RWM permeability, we developed an ex vivo porcine RWM model, similar in structure and thickness to the human RWM. The model is viable for days, and drug passage can be measured at multiple time points. This model provides a straightforward approach to developing effective and non-invasive delivery methods to the inner ear.

Deep learning-based adaptive optics for light sheet fluorescence microscopy

Light sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that is often used to image intact tissue-cleared specimens with cellular or subcellular resolution. Like other optical imaging systems, LSFM suffers from sample-induced optical aberrations that decrement imaging quality. Optical aberrations become more severe when imaging a few millimeters deep into tissue-cleared specimens, complicating subsequent analyses. Adaptive optics are commonly used to correct sample-induced aberrations using a deformable mirror. However, routinely used sensorless adaptive optics techniques are slow, as they require multiple images of the same region of interest to iteratively estimate the aberrations. In addition to the fading of fluorescent signal, this is a major limitation as thousands of images are required to image a single intact organ even without adaptive optics. Thus, a fast and accurate aberration estimation method is needed. Here, we used deep-learning techniques to estimate sample-induced aberrations from only two images of the same region of interest in cleared tissues. We show that the application of correction using a deformable mirror greatly improves image quality. We also introduce a sampling technique that requires a minimum number of images to train the network. Two conceptually different network architectures are compared; one that shares convolutional features and another that estimates each aberration independently. Overall, we have presented an efficient way to correct aberrations in LSFM and to improve image quality.

COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains

Tissue clearing renders entire organs transparent to accelerate whole-tissue imaging; for example, with light-sheet fluorescence microscopy. Yet, challenges remain in analyzing the large resulting 3D datasets that consist of terabytes of images and information on millions of labeled cells. Previous work has established pipelines for automated analysis of tissue-cleared mouse brains, but the focus there was on single-color channels and/or detection of nuclear localized signals in relatively low-resolution images. Here, we present an automated workflow (COMBINe, Cell detectiOn in Mouse BraIN) to map sparsely labeled neurons and astrocytes in genetically distinct mouse forebrains using mosaic analysis with double markers (MADM). COMBINe blends modules from multiple pipelines with RetinaNet at its core. We quantitatively analyzed the regional and subregional effects of MADM-based deletion of the epidermal growth factor receptor (EGFR) on neuronal and astrocyte populations in the mouse forebrain.

Tissue clearing and three-dimensional imaging of the whole cochlea and vestibular system from multiple large-animal models

The inner ear of humans and large animals is embedded in a thick and dense bone that makes dissection challenging. Here, we present a protocol that enables three-dimensional (3D) characterization of intact inner ears from large-animal models. We describe steps for decalcifying bone, using solvents to remove color and lipids, and imaging tissues in 3D using confocal and light sheet microscopy. We then detail a pipeline to count hair cells in antibody-stained and 3D imaged cochleae using open-source software. For complete details on the use and execution of this protocol, please refer to (Moatti et al., 2022).¹.

The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification

Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into outer hair cells (OHCs) and inner hair cells (IHCs). Light microscopy tools allow for imaging of auditory hair cells along the full length of the cochlea, often yielding more data than feasible to manually analyze. Currently, there are no widely applicable tools for fast, unsupervised, unbiased, and comprehensive image analysis of auditory hair cells that work well either with imaging datasets containing an entire cochlea or smaller sampled regions. Here, we present a highly accurate machine learning-based hair cell analysis toolbox (HCAT) for the comprehensive analysis of whole cochleae (or smaller regions of interest) across light microscopy imaging modalities and species. The HCAT is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs versus OHCs), determining their best frequency based on their location along the cochlea, and generating cochleograms. These automated tools remove a considerable barrier in cochlear image analysis, allowing for faster, unbiased, and more comprehensive data analysis practices. Furthermore, HCAT can serve as a template for deep learning-based detection tasks in other types of biological tissue: With some training data, HCAT's core codebase can be trained to develop a custom deep learning detection model for any object on an image.

Three-dimensional mouse cochlea imaging based on the modified Sca/eS using confocal microscopy

The three-dimensional stria vascularis (SV) and cochlear blood vessel structure is essential for inner ear function. Here, modified Sca/eS, a sorbitol-based optical-clearing method, was reported to visualize SV and vascular structure in the intact mouse cochlea. Cochlear macrophages as well as perivascular-resident macrophage-like melanocytes were detected as GFP-positive cells of the CX3CR1^+/GFP mice. This study's method was effective in elucidating inner ear function under both physiological and pathological conditions.

Toward Personalized Diagnosis and Therapy for Hearing Loss: Insights From Cochlear Implants

ABSTRACT

Sensorineural hearing loss (SNHL) is the most common sensory deficit, disabling nearly half a billion people worldwide. The cochlear implant (CI) has transformed the treatment of patients with SNHL, having restored hearing to more than 800,000 people. The success of CIs has inspired multidisciplinary efforts to address the unmet need for personalized, cellular-level diagnosis, and treatment of patients with SNHL. Current limitations include an inability to safely and accurately image at high resolution and biopsy the inner ear, precluding the use of key structural and molecular information during diagnostic and treatment decisions. Furthermore, there remains a lack of pharmacological therapies for hearing loss, which can partially be attributed to challenges associated with new drug development. We highlight advances in diagnostic and therapeutic strategies for SNHL that will help accelerate the push toward precision medicine. In addition, we discuss technological improvements for the CI that will further enhance its functionality for future patients. This report highlights work that was originally presented by Dr. Stankovic as part of the Dr. John Niparko Memorial Lecture during the 2021 American Cochlear Implant Alliance annual meeting.

Ontogeny of cellular organization and LGR5 expression in porcine cochlea revealed using tissue clearing and 3D imaging

Over 11% of the world's population experience hearing loss. Although there are promising studies to restore hearing in rodent models, the size, ontogeny, genetics, and frequency range of hearing of most rodents' cochlea do not match that of humans. The porcine cochlea can bridge this gap as it shares many anatomical, physiological, and genetic similarities with its human counterpart. Here, we provide a detailed methodology to process and image the porcine cochlea in 3D using tissue clearing and light-sheet microscopy. The resulting 3D images can be employed to compare cochleae across different ages and conditions, investigate the ontogeny of cochlear cytoarchitecture, and produce quantitative expression maps of LGR5, a marker of cochlear progenitors in mice. These data reveal that hair cell organization, inner ear morphology, cellular cartography in the organ of Corti, and spatiotemporal expression of LGR5 are dynamic over developmental stages in a pattern not previously documented.

Lightsheet Microscopy

In this paper, we review lightsheet (selective plane illumination) microscopy for mouse developmental biologists. There are different means of forming the illumination sheet, and we discuss these. We explain how we introduced the lightsheet microscope economically into our core facility and present our results on fixed and living samples. We also describe methods of clearing fixed samples for three-dimensional imaging and discuss the various means of preparing samples with particular reference to mouse cilia, adipose spheroids, and cochleae. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Quantitative analysis of illumination and detection corrections in adaptive light sheet fluorescence microscopy

Light-sheet fluorescence microscopy (LSFM) is a high-speed, high-resolution and minimally phototoxic technique for 3D imaging of in vivo and in vitro specimens. LSFM exhibits optical sectioning and when combined with tissue clearing techniques, it facilitates imaging of centimeter scale specimens with micrometer resolution. Although LSFM is ubiquitous, it still faces two main challenges that effect image quality especially when imaging large volumes with high-resolution. First, the light-sheet illumination plane and detection lens focal plane need to be coplanar, however sample-induced aberrations can violate this requirement and degrade image quality. Second, introduction of sample-induced optical aberrations in the detection path. These challenges intensify when imaging whole organisms or structurally complex specimens like cochleae and bones that exhibit many transitions from soft to hard tissue or when imaging deep (> 2 mm). To resolve these challenges, various illumination and aberration correction methods have been developed, yet no adaptive correction in both the illumination and the detection path have been applied to improve LSFM imaging. Here, we bridge this gap, by implementing the two correction techniques on a custom built adaptive LSFM. The illumination beam angular properties are controlled by two galvanometer scanners, while a deformable mirror is positioned in the detection path to correct for aberrations. By imaging whole porcine cochlea, we compare and contrast these correction methods and their influence on the image quality. This knowledge will greatly contribute to the field of adaptive LSFM, and imaging of large volumes of tissue cleared specimens.

Illumination angle correction during image acquisition in light-sheet fluorescence microscopy using deep learning

Light-sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that provides optical sectioning with reduced photodamage. LSFM is routinely used in life sciences for live cell imaging and for capturing large volumes of cleared tissues. LSFM has a unique configuration, in which the illumination and detection paths are separated and perpendicular to each other. As such, the image quality, especially at high resolution, largely depends on the degree of overlap between the detection focal plane and the illuminating beam. However, spatial heterogeneity within the sample, curved specimen boundaries, and mismatch of refractive index between tissues and immersion media can refract the well-aligned illumination beam. This refraction can cause extensive blur and non-uniform image quality over the imaged field-of-view. To address these issues, we tested a deep learning-based approach to estimate the angular error of the illumination beam relative to the detection focal plane. The illumination beam was then corrected using a pair of galvo scanners, and the correction significantly improved the image quality across the entire field-of-view. The angular estimation was based on calculating the defocus level on a pixel level within the image using two defocused images. Overall, our study provides a framework that can correct the angle of the light-sheet and improve the overall image quality in high-resolution LSFM 3D image acquisition.

Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet

The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.

Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy

Light-sheet fluorescence microscopy (LSFM) is a minimally invasive and high throughput imaging technique ideal for capturing large volumes of tissue with sub-cellular resolution. A fundamental requirement for LSFM is a seamless overlap of the light-sheet that excites a selective plane in the specimen, with the focal plane of the objective lens. However, spatial heterogeneity in the refractive index of the specimen often results in violation of this requirement when imaging deep in the tissue. To address this issue, autofocus methods are commonly used to refocus the focal plane of the objective-lens on the light-sheet. Yet, autofocus techniques are slow since they require capturing a stack of images and tend to fail in the presence of spherical aberrations that dominate volume imaging. To address these issues, we present a deep learning-based autofocus framework that can estimate the position of the objective-lens focal plane relative to the light-sheet, based on two defocused images. This approach outperforms or provides comparable results with the best traditional autofocus method on small and large image patches respectively. When the trained network is integrated with a custom-built LSFM, a certainty measure is used to further refine the network's prediction. The network performance is demonstrated in real-time on cleared genetically labeled mouse forebrain and pig cochleae samples. Our study provides a framework that could improve light-sheet microscopy and its application toward imaging large 3D specimens with high spatial resolution.

Tutorial: practical considerations for tissue clearing and imaging

Tissue clearing has become a powerful technique for studying anatomy and morphology at scales ranging from entire organisms to subcellular features. With the recent proliferation of tissue-clearing methods and imaging options, it can be challenging to determine the best clearing protocol for a particular tissue and experimental question. The fact that so many clearing protocols exist suggests there is no one-size-fits-all approach to tissue clearing and imaging. Even in cases where a basic level of clearing has been achieved, there are many factors to consider, including signal retention, staining (labeling), uniformity of transparency, image acquisition and analysis. Despite reviews citing features of clearing protocols, it is often unknown a priori whether a protocol will work for a given experiment, and thus some optimization is required by the end user. In addition, the capabilities of available imaging setups often dictate how the sample needs to be prepared. After imaging, careful evaluation of volumetric image data is required for each combination of clearing protocol, tissue type, biological marker, imaging modality and biological question. Rather than providing a direct comparison of the many clearing methods and applications available, in this tutorial we address common pitfalls and provide guidelines for designing, optimizing and imaging in a successful tissue-clearing experiment with a focus on light-sheet fluorescence microscopy (LSFM).