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.

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.

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.].

Phosphorylation-dependent proteome of Marcks in ependyma during aging and behavioral homeostasis in the mouse forebrain.

Ependymal cells (ECs) line the ventricular surfaces of the mammalian central nervous system (CNS) and their development is indispensable to structural integrity and functions of the CNS. We previously reported that EC-specific genetic deletion of the myristoylated alanine-rich protein kinase C substrate (Marcks) disrupts barrier functions and elevates oxidative stress and lipid droplet accumulation in ECs causing precocious cellular aging. However, little is known regarding the mechanisms that mediate these changes in ECs. To gain insight into Marcks-mediated mechanisms, we performed mass spectrometric analyses on Marcks-associated proteins in young and aged ECs in the mouse forebrain using an integrated approach. Network analysis on annotated proteins revealed that the identified Marcks-associated complexes are in part involved in protein transport mechanisms in young ECs. In fact, we found perturbed intracellular vesicular trafficking in cultured ECs with selective deletion of Marcks (Marcks-cKO mice), or upon pharmacological alteration to phosphorylation status of Marcks. In comparison, Marcks-associated protein complexes in aged ECs appear to be involved in regulation of lipid metabolism and responses to oxidative stress. Confirming this, we found elevated signatures of inflammation in the cerebral cortices and the hippocampi of young Marcks-cKO mice. Interestingly, behavioral testing using a water maze task indicated that spatial learning and memory is diminished in young Marcks-cKO mice similar to aged wildtype mice. Taken together, our study provides first line of evidence for potential mechanisms that may mediate differential Marcks functions in young and old ECs, and their effect on forebrain homeostasis during aging.

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.

Clonal Analysis of Gliogenesis in the Cerebral Cortex Reveals Stochastic Expansion of Glia and Cell Autonomous Responses to Egfr Dosage.

Development of the nervous system undergoes important transitions, including one from neurogenesis to gliogenesis which occurs late during embryonic gestation. Here we report on clonal analysis of gliogenesis in mice using Mosaic Analysis with Double Markers (MADM) with quantitative and computational methods. Results reveal that developmental gliogenesis in the cerebral cortex occurs in a fraction of earlier neurogenic clones, accelerating around E16.5, and giving rise to both astrocytes and oligodendrocytes. Moreover, MADM-based genetic deletion of the epidermal growth factor receptor (Egfr) in gliogenic clones revealed that Egfr is cell autonomously required for gliogenesis in the mouse dorsolateral cortices. A broad range in the proliferation capacity, symmetry of clones, and competitive advantage of MADM cells was evident in clones that contained one cellular lineage with double dosage of Egfr relative to their environment, while their sibling Egfr-null cells failed to generate glia. Remarkably, the total numbers of glia in MADM clones balance out regardless of significant alterations in clonal symmetries. The variability in glial clones shows stochastic patterns that we define mathematically, which are different from the deterministic patterns in neuronal clones. This study sets a foundation for studying the biological significance of stochastic and deterministic clonal principles underlying tissue development, and identifying mechanisms that differentiate between neurogenesis and gliogenesis.

Sp2 regulates late neurogenic but not early expansive divisions of neural stem cells underlying population growth in the mouse cortex.

Cellular and molecular mechanisms underlying the switch from self-amplification of cortical stem cells to neuronal and glial generation are incompletely understood, despite their importance for neural development. Here, we have investigated the role of the transcription factor specificity protein 2 (Sp2) in expansive and neurogenic divisions of the developing cerebral cortex by combining conditional genetic deletion with the mosaic analysis with double markers (MADM) system in mice. We find that loss of Sp2 in progenitors undergoing neurogenic divisions results in prolonged mitosis due to extension of early mitotic stages. This disruption is correlated with depletion of the populations of upper layer neurons in the cortex. In contrast, early cortical neural stem cells proliferate and expand normally in the absence of Sp2. These results indicate a stage-specific requirement for Sp2 in neural stem and progenitor cells, and reveal mechanistic differences between the early expansive and later neurogenic periods of cortical development.This article has an associated 'The people behind the papers' interview.

Ependyma-expressed CCN1 restricts the size of the neural stem cell pool in the adult ventricular-subventricular zone.

Adult neural stem cells (NSCs) reside in specialized niches, which hold a balanced number of NSCs, their progeny, and other cells. How niche capacity is regulated to contain a specific number of NSCs remains unclear. Here, we show that ependyma-derived matricellular protein CCN1 (cellular communication network factor 1) negatively regulates niche capacity and NSC number in the adult ventricular-subventricular zone (V-SVZ). Adult ependyma-specific deletion of Ccn1 transiently enhanced NSC proliferation and reduced neuronal differentiation in mice, increasing the numbers of NSCs and NSC units. Although proliferation of NSCs and neurogenesis seen in Ccn1 knockout mice eventually returned to normal, the expanded NSC pool was maintained in the V-SVZ until old age. Inhibition of EGFR signaling prevented expansion of the NSC population observed in CCN1 deficient mice. Thus, ependyma-derived CCN1 restricts NSC expansion in the adult brain to maintain the proper niche capacity of the V-SVZ.

To scratch an itch: Establishing a mouse model to determine active brain areas involved in acute histaminergic itch.

BACKGROUND: Strategies to efficiently control itch require a deep understanding of the underlying mechanisms. Several areas in the brain involved in itch and scratching responses have been postulated, but the central mechanisms that drive pruritic responses are still unknown. Histamine is recognized as a major mediator of itch in humans, and has been the most frequently used stimulus as an experimental pruritogen for brain imaging of itch. OBJECTIVE: Histaminergic itch via histamine and the selective histamine H4 receptor (H4R) agonist, ST-1006, recruit brain nuclei through c-fos activation and activate specific areas in the brain. METHODS: An acute itch model was established in c-fos-EGFP transgenic mice using ST-1006 and histamine. Coronal brain sections were stained for c-fos immunoreactivity and the forebrain was mapped for density of c-fos + nuclei. RESULTS: Histamine and ST-1006 significantly increased scratching response in c-fos-EGFP mice compared to vehicle controls. Mapping c-fos immunostained brain sections revealed neuronal activity in the cortex, striatum, hypothalamus, thalamus, amygdala, and the midbrain. CONCLUSIONS: Histaminergic itch and selective H4R activation significantly increased the density of c-fos + nuclei in the medial habenula (MHb). Thus, the MHb may be a new target to investigate and subsequently develop novel mechanism-based strategies to treat itch and possibly provide a locus for pharmacological control of pruritus.

MARCKS regulates neuritogenesis and interacts with a CDC42 signaling network.

Through the process of neuronal differentiation, newly born neurons change from simple, spherical cells to complex, sprawling cells with many highly branched processes. One of the first stages in this process is neurite initiation, wherein cytoskeletal modifications facilitate membrane protrusion and extension from the cell body. Hundreds of actin modulators and microtubule-binding proteins are known to be involved in this process, but relatively little is known about how upstream regulators bring these complex networks together at discrete locations to produce neurites. Here, we show that Myristoylated alanine-rich C kinase substrate (MARCKS) participates in this process. Marcks-/- cortical neurons extend fewer neurites and have less complex neurite arborization patterns. We use an in vitro proteomics screen to identify MARCKS interactors in developing neurites and characterize an interaction between MARCKS and a CDC42-centered network. While the presence of MARCKS does not affect whole brain levels of activated or total CDC42, we propose that MARCKS is uniquely positioned to regulate CDC42 localization and interactions within specialized cellular compartments, such as nascent neurites.

Foxj1 expressing ependymal cells do not contribute new cells to sites of injury or stroke in the mouse forebrain.

The stem cell source of neural and glial progenitors in the periventricular regions of the adult forebrain has remained uncertain and controversial. Using a cell specific genetic approach we rule out Foxj1+ ependymal cells as stem cells participating in neurogenesis and gliogenesis in response to acute injury or stroke in the mouse forebrain. Non stem- and progenitor-like responses of Foxj1+ ependymal cells to injury and stroke remain to be defined and investigated.

Regulation of cytokinesis during corticogenesis: focus on the midbody.

Development of the cerebral cortices depends on tight regulation of cell divisions. In this system, stem and progenitor cells undergo symmetric and asymmetric divisions to ultimately produce neurons that establish the layers of the cortex. Cell division culminates with the formation of the midbody, a transient organelle that establishes the site of abscission between nascent daughter cells. During cytokinetic abscission, the final stage of cell division, one daughter cell will inherit the midbody remnant, which can then maintain or expel the remnant, but mechanisms and circumstances influencing this decision are unclear. This review describes the midbody and its constituent proteins, as well as the known consequences of their manipulation during cortical development. The potential functional relevance of midbody mechanisms is discussed.

Stomach curvature is generated by left-right asymmetric gut morphogenesis.

Left-right (LR) asymmetry is a fundamental feature of internal anatomy, yet the emergence of morphological asymmetry remains one of the least understood phases of organogenesis. Asymmetric rotation of the intestine is directed by forces outside the gut, but the morphogenetic events that generate anatomical asymmetry in other regions of the digestive tract remain unknown. Here, we show in mouse and Xenopus that the mechanisms that drive the curvature of the stomach are intrinsic to the gut tube itself. The left wall of the primitive stomach expands more than the right wall, as the left epithelium becomes more polarized and undergoes radial rearrangement. These asymmetries exist across several species, and are dependent on LR patterning genes, including Foxj1, Nodal and Pitx2 Our findings have implications for how LR patterning manifests distinct types of morphological asymmetries in different contexts.

Ependymal cell contribution to scar formation after spinal cord injury is minimal, local and dependent on direct ependymal injury.

Ependyma have been proposed as adult neural stem cells that provide the majority of newly proliferated scar-forming astrocytes that protect tissue and function after spinal cord injury (SCI). This proposal was based on small, midline stab SCI. Here, we tested the generality of this proposal by using a genetic knock-in cell fate mapping strategy in different murine SCI models. After large crush injuries across the entire spinal cord, ependyma-derived progeny remained local, did not migrate and contributed few cells of any kind and less than 2%, if any, of the total newly proliferated and molecularly confirmed scar-forming astrocytes. Stab injuries that were near to but did not directly damage ependyma, contained no ependyma-derived cells. Our findings show that ependymal contribution of progeny after SCI is minimal, local and dependent on direct ependymal injury, indicating that ependyma are not a major source of endogenous neural stem cells or neuroprotective astrocytes after SCI.

Developmentally defined forebrain circuits regulate appetitive and aversive olfactory learning.

Postnatal and adult neurogenesis are region- and modality-specific, but the significance of developmentally distinct neuronal populations remains unclear. We demonstrate that chemogenetic inactivation of a subset of forebrain and olfactory neurons generated at birth disrupts responses to an aversive odor. In contrast, novel appetitive odor learning is sensitive to inactivation of adult-born neurons, revealing that developmentally defined sets of neurons may differentially participate in hedonic aspects of sensory learning.

Neural Stem Cells to Cerebral Cortex: Emerging Mechanisms Regulating Progenitor Behavior and Productivity.

This review accompanies a 2016 SFN mini-symposium presenting examples of current studies that address a central question: How do neural stem cells (NSCs) divide in different ways to produce heterogeneous daughter types at the right time and in proper numbers to build a cerebral cortex with the appropriate size and structure? We will focus on four aspects of corticogenesis: cytokinesis events that follow apical mitoses of NSCs; coordinating abscission with delamination from the apical membrane; timing of neurogenesis and its indirect regulation through emergence of intermediate progenitors; and capacity of single NSCs to generate the correct number and laminar fate of cortical neurons. Defects in these mechanisms can cause microcephaly and other brain malformations, and understanding them is critical to designing diagnostic tools and preventive and corrective therapies.