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

T-CLEARE: a pilot community-driven tissue clearing protocol repository

Selecting and implementing a tissue clearing protocol is challenging. Established more than 100 years ago, tissue clearing is still a rapidly evolving field of research. There are currently many published protocols to choose from, and each performs better or worse across a range of key evaluation factors (e.g., speed, cost, tissue stability, fluorescence quenching). Additionally, tissue clearing protocols are often optimized for specific experimental contexts, and applying an existing protocol to a new problem can require a lengthy period of adaptation by trial and error. Although the primary literature and review articles provide a useful starting point for optimization, there is growing recognition that results can vary dramatically with changes to tissue type or antibody used. To help address this issue, we have developed a novel, freely available repository of tissue clearing protocols named T-CLEARE (Tissue CLEAring protocol REpository; https://doryworkspace.org/doryviz). T-CLEARE incorporates community responses to an open survey designed to capture details not commonly found in the scientific literature, including modifications to published protocols required for specific use cases and instances when tissue clearing protocols did not perform well (negative results). The goal of T-CLEARE is to help the community share evaluations and modifications of tissue clearing protocols for various tissue types and potentially identify best-in-class methods for a given application.

Intelligent Beam Optimization for Light-Sheet Fluorescence Microscopy through Deep Learning

Light-sheet fluorescence microscopy (LSFM) provides the benefit of optical sectioning coupled with rapid acquisition times, enabling high-resolution 3-dimensional imaging of large tissue-cleared samples. Inherent to LSFM, the quality of the imaging heavily relies on the characteristics of the illumination beam, which only illuminates a thin section of the sample. Therefore, substantial efforts are dedicated to identifying slender, nondiffracting beam profiles that yield uniform and high-contrast images. An ongoing debate concerns the identification of optimal illumination beams for different samples: Gaussian, Bessel, Airy patterns, and/or others. However, comparisons among different beam profiles are challenging as their optimization objectives are often different. Given that our large imaging datasets (approximately 0.5 TB of images per sample) are already analyzed using deep learning models, we envisioned a different approach to the problem by designing an illumination beam tailored to boost the performance of the deep learning model. We hypothesized that integrating the physical LSFM illumination model (after passing it through a variable phase mask) into the training of a cell detection network would achieve this goal. Here, we report that joint optimization continuously updates the phase mask and results in improved image quality for better cell detection. The efficacy of our method is demonstrated through both simulations and experiments that reveal substantial enhancements in imaging quality compared to the traditional Gaussian light sheet. We discuss how designing microscopy systems through a computational approach provides novel insights for advancing optical design that relies on deep learning models for the analysis of imaging datasets.

A gene edited pig model for studying LGR5+ stem cells: implications for future applications in tissue regeneration and biomedical research

Recent advancements in genome editing techniques, notably CRISPR-Cas9 and TALENs, have marked a transformative era in biomedical research, significantly enhancing our understanding of disease mechanisms and helping develop novel therapies. These technologies have been instrumental in creating precise animal models for use in stem cell research and regenerative medicine. For instance, we have developed a transgenic pig model to enable the investigation of LGR5-expressing cells. The model was designed to induce the expression of H2B-GFP under the regulatory control of the LGR5 promoter via CRISPR/Cas9-mediated gene knock-in. Notably, advancements in stem cell research have identified distinct subpopulations of LGR5-expressing cells within adult human, mouse, and pig tissues. LGR5, a leucine-rich repeat-containing G protein-coupled receptor, enhances WNT signaling and these LGR5+ subpopulations demonstrate varied roles and anatomical distributions, underscoring the necessity for suitable translational models. This transgenic pig model facilitates the tracking of LGR5-expressing cells and has provided valuable insights into the roles of these cells across different tissues and species. For instance, in pulmonary tissue, Lgr5+ cells in mice are predominantly located in alveolar compartments, driving alveolar differentiation of epithelial progenitors via Wnt pathway activation. In contrast, in pigs and humans, these cells are situated in a unique sub-basal position adjacent to the airway epithelium. In fetal stages a pattern of LGR5 expression during lung bud tip formation is evident in humans and pigs but is lacking in mice. Species differences with respect to LGR5 expression have also been observed in the skin, intestines, and cochlea further reinforcing the need for careful selection of appropriate translational animal models. This paper discusses the potential utility of the LGR5+ pig model in exploring the role of LGR5+ cells in tissue development and regeneration with the goal of translating these findings into human and animal clinical applications.

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.

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.

T-CLEARE: A Pilot Community-Driven Tissue-Clearing Protocol Repository

Selecting and implementing a tissue-clearing protocol is challenging. Established more than 100 years ago, tissue clearing is still a rapidly evolving field of research. There are currently many published protocols to choose from, and each performs better or worse across a range of key evaluation factors (e.g., speed, cost, tissue stability, fluorescence quenching). Additionally, tissue-clearing protocols are often optimized for specific experimental contexts, and applying an existing protocol to a new problem can require a lengthy period of adaptation by trial and error. Although the primary literature and review articles provide a useful starting point for optimization, there is growing recognition that many articles do not provide sufficient detail to replicate or reproduce experimental results. To help address this issue, we have developed a novel, freely available repository of tissue-clearing protocols named T-CLEARE (Tissue CLEAring protocol REpository; https://doryworkspace.org/doryviz). T-CLEARE incorporates community responses to an open survey designed to capture details not commonly found in the scientific literature, including modifications to published protocols required for specific use cases and instances when tissue-clearing protocols did not perform well (negative results). The goal of T-CLEARE is to provide a forum for the community to share evaluations and modifications of tissue-clearing protocols for various tissue types and potentially identify best-in-class methods for a given application.

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.