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.

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.

Single-molecule RNA detection at depth by hybridization chain reaction and tissue hydrogel embedding and clearing.

Accurate and robust detection of mRNA molecules in thick tissue samples can reveal gene expression patterns in single cells within their native environment. Preserving spatial relationships while accessing the transcriptome of selected cells is a crucial feature for advancing many biological areas - from developmental biology to neuroscience. However, because of the high autofluorescence background of many tissue samples, it is difficult to detect single-molecule fluorescence in situ hybridization (smFISH) signals robustly in opaque thick samples. Here, we draw on principles from the emerging discipline of dynamic nucleic acid nanotechnology to develop a robust method for multi-color, multi-RNA imaging in deep tissues using single-molecule hybridization chain reaction (smHCR). Using this approach, single transcripts can be imaged using epifluorescence, confocal or selective plane illumination microscopy (SPIM) depending on the imaging depth required. We show that smHCR has high sensitivity in detecting mRNAs in cell culture and whole-mount zebrafish embryos, and that combined with SPIM and PACT (passive CLARITY technique) tissue hydrogel embedding and clearing, smHCR can detect single mRNAs deep within thick (0.5 mm) brain slices. By simultaneously achieving ∼20-fold signal amplification and diffraction-limited spatial resolution, smHCR offers a robust and versatile approach for detecting single mRNAs in situ, including in thick tissues where high background undermines the performance of unamplified smFISH.

Q&A: How can advances in tissue clearing and optogenetics contribute to our understanding of normal and diseased biology?

Mammalian organs comprise a variety of cells that interact with each other and have distinct biological roles. Access to evaluate and perturb intact biological systems at the cellular and molecular levels is essential to fully understand their functioning in normal and diseased conditions, yet technical limitations have constrained most research to small pieces of tissue. Tissue clearing and optogenetics can help overcome this hurdle: tissue clearing affords optical interrogation of whole organs at the molecular level, and optogenetics enables the scalable control and measurement of cellular activity with light. In this Q&A, we delineate recent advances and practical challenges associated with these two techniques when applied body-wide.

Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy.

We discuss unique features of lens-free computational imaging tools and report some of their emerging results for wide-field on-chip microscopy, such as the achievement of a numerical aperture (NA) of ∼0.8-0.9 across a field of view (FOV) of more than 20 mm(2) or an NA of ∼0.1 across a FOV of ∼18 cm(2), which corresponds to an image with more than 1.5 gigapixels. We also discuss the current challenges that these computational on-chip microscopes face, shedding light on their future directions and applications.

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.

Wide-field computational color imaging using pixel super-resolved on-chip microscopy.

Lens-free holographic on-chip imaging is an emerging approach that offers both wide field-of-view (FOV) and high spatial resolution in a cost-effective and compact design using source shifting based pixel super-resolution. However, color imaging has remained relatively immature for lens-free on-chip imaging, since a 'rainbow' like color artifact appears in reconstructed holographic images. To provide a solution for pixel super-resolved color imaging on a chip, here we introduce and compare the performances of two computational methods based on (1) YUV color space averaging, and (2) Dijkstra's shortest path, both of which eliminate color artifacts in reconstructed images, without compromising the spatial resolution or the wide FOV of lens-free on-chip microscopes. To demonstrate the potential of this lens-free color microscope we imaged stained Papanicolaou (Pap) smears over a wide FOV of ~14 mm(2) with sub-micron spatial resolution.

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.

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.

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.

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.

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.

Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy.

Lensfree in-line holographic microscopy offers sub-micron resolution over a large field-of-view (e.g., ~24 mm2) with a cost-effective and compact design suitable for field use. However, it is limited to relatively low-density samples. To mitigate this limitation, we demonstrate an on-chip imaging approach based on pixel super-resolution and phase recovery, which iterates among multiple lensfree intensity measurements, each having a slightly different sample-to-sensor distance. By digitally aligning and registering these lensfree intensity measurements, phase and amplitude images of dense and connected specimens can be iteratively reconstructed over a large field-of-view of ~24 mm2 without the use of any spatial masks. We demonstrate the success of this multi-height in-line holographic approach by imaging dense Papanicolaou smears (i.e., Pap smears) and blood samples.

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.

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.].

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.