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.

Specific Inhibition of Orai1-mediated Calcium Signalling Resolves Inflammation and Clears Bacteria in an Acute Respiratory Distress Syndrome Model.

Rationale: Acute respiratory distress syndrome (ARDS) has an unacceptably high mortality rate (35%) and is without effective therapy. Orai1 is a Ca2+ channel involved in store-operated Ca2+ entry (SOCE), a process that exquisitely regulates inflammation. Orai1 is considered a druggable target, but no Orai1-specific inhibitors exist to date. Objectives: To evaluate whether ELD607, a first-in-class Orai1 antagonist, can treat ARDS caused by bacterial pneumonia in preclinical models. Methods: ELD607 pharmacology was evaluated in HEK293T cells and freshly isolated immune cells from patients with ARDS. A murine acute lung injury model caused by bacterial pneumonia was then used: mice were infected with Pseudomonas aeruginosa, Staphylococcus aureus, methicillin-resistant S. aureus, or multidrug-resistant P. aeruginosa and then treated with ELD607 intranasally. Measurements and Main Results: ELD607 specifically inhibited SOCE in HEK293T cells with a half-maximal inhibitory concentration of 9 nM. ELD607 was stable in ARDS airway secretions and inhibited SOCE in ARDS immune cells. In vivo, inhaled ELD607 significantly reduced neutrophilia and improved survival. Surprisingly, Orai1 inhibition by ELD607 caused a significant reduction in lung bacteria, including methicillin-resistant S. aureus. ELD607 worked as an immunomodulator that reduced cytokine levels, reduced neutrophilia, and promoted macrophage-mediated resolution of inflammation and clearance of bacteria. Indeed, when alveolar macrophages were depleted with inhaled clodronate, ELD607 was no longer able to resolve inflammation or clear bacteria. Conclusions: These data indicate that specific Orai1 inhibition by ELD607 may be a novel approach to reduce multiorgan inflammation and treat antibiotic-resistant bacteria.

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.

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.

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.

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.

Bulk and mosaic deletions of Egfr reveal regionally defined gliogenesis in the developing mouse forebrain.

The epidermal growth factor receptor (EGFR) plays a role in cell proliferation and differentiation during healthy development and tumor growth; however, its requirement for brain development remains unclear. Here we used a conditional mouse allele for Egfr to examine its contributions to perinatal forebrain development at the tissue level. Subtractive bulk ventral and dorsal forebrain deletions of Egfr uncovered significant and permanent decreases in oligodendrogenesis and myelination in the cortex and corpus callosum. Additionally, an increase in astrogenesis or reactive astrocytes in effected regions was evident in response to cortical scarring. Sparse deletion using mosaic analysis with double markers (MADM) surprisingly revealed a regional requirement for EGFR in rostrodorsal, but not ventrocaudal glial lineages including both astrocytes and oligodendrocytes. The EGFR-independent ventral glial progenitors may compensate for the missing EGFR-dependent dorsal glia in the bulk Egfr-deleted forebrain, potentially exposing a regenerative population of gliogenic progenitors in the mouse forebrain.

Maternal organophosphate flame retardant exposure alters the developing mesencephalic dopamine system in fetal rat.

Organophosphate flame retardants (OPFRs) have become the predominant substitution for legacy brominated flame retardants but there is concern about their potential developmental neurotoxicity (DNT). OPFRs readily dissociate from the fireproofed substrate to the environment, and they (or their metabolites) have been detected in diverse matrices including air, water, soil, and biota, including human urine and breastmilk. Given this ubiquitous contamination, it becomes increasingly important to understand the potential effects of OPFRs on the developing nervous system. We have previously shown that maternal exposure to OPFRs results in neuroendocrine disruption, alterations to developmental metabolism of serotonin (5-HT) and axonal extension in male fetal rats, and potentiates adult anxiety-like behaviors. The development of the serotonin and dopamine systems occur in parallel and interact, therefore, we first sought to enhance our prior 5-HT work by first examining the ascending 5-HT system on embryonic day 14 using whole mount clearing of fetal heads and 3-dimensional (3D) brain imaging. We also investigated the effects of maternal OPFR exposure on the development of the mesocortical dopamine system in the same animals through 2-dimensional and 3D analysis following immunohistochemistry for tyrosine hydroxylase (TH). Maternal OPFR exposure induced morphological changes to the putative ventral tegmental area and substantia nigra in both sexes and reduced the overall volume of this structure in males, whereas 5-HT nuclei were unchanged. Additionally, dopaminergic axogenesis was disrupted in OPFR exposed animals, as the dorsoventral spread of ventral telencephalic TH afferents were greater at embryonic day 14, while sparing 5-HT fibers. These results indicate maternal exposure to OPFRs alters the development trajectory of the embryonic dopaminergic system and adds to growing evidence of OPFR DNT.

Inhalable dry powder mRNA vaccines based on extracellular vesicles.

Respiratory diseases are a global burden, with millions of deaths attributed to pulmonary illnesses and dysfunctions. Therapeutics have been developed, but they present major limitations regarding pulmonary bioavailability and product stability. To circumvent such limitations, we developed room-temperature-stable inhalable lung-derived extracellular vesicles or exosomes (Lung-Exos) as mRNA and protein drug carriers. Compared with standard synthetic nanoparticle liposomes (Lipos), Lung-Exos exhibited superior distribution to the bronchioles and parenchyma and are deliverable to the lungs of rodents and nonhuman primates (NHPs) by dry powder inhalation. In a vaccine application, severe acute respiratory coronavirus 2 (SARS-CoV-2) spike (S) protein encoding mRNA-loaded Lung-Exos (S-Exos) elicited greater immunoglobulin G (IgG) and secretory IgA (SIgA) responses than its loaded liposome (S-Lipo) counterpart. Importantly, S-Exos remained functional at room-temperature storage for one month. Our results suggest that extracellular vesicles can serve as an inhaled mRNA drug-delivery system that is superior to synthetic liposomes.

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.

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.

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.

Erratum: Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy: publisher's note.

[This corrects the article on p. 5214 in vol. 12, PMID: 34513252.].

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.

Light-guided sectioning for precise in situ localization and tissue interface analysis for brain-implanted optical fibers and GRIN lenses.

Optical implants to control and monitor neuronal activity in vivo have become foundational tools of neuroscience. Standard two-dimensional histology of the implant location, however, often suffers from distortion and loss during tissue processing. To address that, we developed a three-dimensional post hoc histology method called "light-guided sectioning" (LiGS), which preserves the tissue with its optical implant in place and allows staining and clearing of a volume up to 500 µm in depth. We demonstrate the use of LiGS to determine the precise location of an optical fiber relative to a deep brain target and to investigate the implant-tissue interface. We show accurate cell registration of ex vivo histology with single-cell, two-photon calcium imaging, obtained through gradient refractive index (GRIN) lenses, and identify subpopulations based on immunohistochemistry. LiGS provides spatial information in experimental paradigms that use optical fibers and GRIN lenses and could help increase reproducibility through identification of fiber-to-target localization and molecular profiling.

Enhancement of Bone Regeneration Through the Converse Piezoelectric Effect, A Novel Approach for Applying Mechanical Stimulation.

Serious bone injuries have devastating effects on the lives of patients including limiting working ability and high cost. Orthopedic implants can aid in healing injuries to an extent that exceeds the natural regenerative capabilities of bone to repair fractures or large bone defects. Autografts and allografts are the standard implants used, but disadvantages such as donor site complications, a limited quantity of transplantable bone, and high costs have led to an increased demand for synthetic bone graft substitutes. However, replicating the complex physiological properties of biological bone, much less recapitulating its complex tissue functions, is challenging. Extensive efforts to design biocompatible implants that mimic the natural healing processes in bone have led to the investigation of piezoelectric smart materials because the bone has natural piezoelectric properties. Piezoelectric materials facilitate bone regeneration either by accumulating electric charge in response to mechanical stress, which mimics bioelectric signals through the direct piezoelectric effect or by providing mechanical stimulation in response to electrical stimulation through the converse piezoelectric effect. Although both effects are beneficial, the converse piezoelectric effect can address bone atrophy from stress shielding and immobility by improving the mechanical response of a healing defect. Mechanical stimulation has a positive impact on bone regeneration by activating cellular pathways that increase bone formation and decrease bone resorption. This review will highlight the potential of the converse piezoelectric effect to enhance bone regeneration by discussing the activation of beneficial cellular pathways, the properties of piezoelectric biomaterials, and the potential for the more effective administration of the converse piezoelectric effect using wireless control.

Phenotyping Intact Mouse Bones Using Bone CLARITY.

The bone is typically studied using traditional histology techniques, that is, serial sectioning and staining. While effective, these techniques are laborious and destructive, as the native 3D environment of the bone is not maintained. Presented here is a bone-clearing methodology, termed Bone CLARITY, which combines published techniques for clearing soft tissues, including delipidation for the removal of light-scattering membranes, hydrogel-embedding for the stabilization of fragile epitopes, heme elution for the reduction of blood-based autofluorescence; as well as specialized steps, including decalcification and progressive refractive index matching, for addressing the unique challenges posed by osseous tissue. This method renders the bone transparent and enables the detailed visualization of an intact tissue specimen at multiple spatial scales.

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

Hearing loss is a prevalent disorder that affects people of all ages. On top of the existing hearing aids and cochlear implants, there is a growing effort to regenerate functional tissues and restore hearing. However, studying and evaluating these regenerative medicine approaches in a big animal model (e.g. pigs) whose anatomy, physiology, and organ size are similar to a human is challenging. In big animal models, the cochlea is bulky, intricate, and veiled in a dense and craggy otic capsule. These facts complicate 3D microscopic analysis that is vital in the cochlea, where structure-function relation is time and again manifested. To allow 3D imaging of an intact cochlea of newborn and juvenile pigs with a volume up to ∼ 250 mm3, we adapted the BoneClear tissue clearing technique, which renders the bone transparent. The transparent cochleae were then imaged with cellular resolution and in a timely fashion, which prevented bubble formation and tissue degradation, using an adaptive custom-built light-sheet fluorescence microscope. The adaptive light-sheet microscope compensated for deflections of the illumination beam by changing the angles of the beam and translating the detection objective while acquiring images. Using this combination of techniques, macroscopic and microscopic properties of the cochlea were extracted, including the density of hair cells, frequency maps, and lower frequency limits. Consequently, the proposed platform could support the growing effort to regenerate cochlear tissues and assist with basic research to advance cures for hearing impairments.

Multiplexed Cre-dependent selection yields systemic AAVs for targeting distinct brain cell types.

Recombinant adeno-associated viruses (rAAVs) are efficient gene delivery vectors via intravenous delivery; however, natural serotypes display a finite set of tropisms. To expand their utility, we evolved AAV capsids to efficiently transduce specific cell types in adult mouse brains. Building upon our Cre-recombination-based AAV targeted evolution (CREATE) platform, we developed Multiplexed-CREATE (M-CREATE) to identify variants of interest in a given selection landscape through multiple positive and negative selection criteria. M-CREATE incorporates next-generation sequencing, synthetic library generation and a dedicated analysis pipeline. We have identified capsid variants that can transduce the central nervous system broadly, exhibit bias toward vascular cells and astrocytes, target neurons with greater specificity or cross the blood-brain barrier across diverse murine strains. Collectively, the M-CREATE methodology accelerates the discovery of capsids for use in neuroscience and gene-therapy applications.