A practical guide to adaptive light-sheet microscopy

A practical guide to light-sheet microscopy for nanoscale imaging: Looking beyond the cell

We present a comprehensive guide to light-sheet microscopy (LSM) to assist scientists in navigating the practical implementation of this microscopy technique. Emphasizing the applicability of LSM to image both static microscale and nanoscale features, as well as diffusion dynamics, we present the fundamental concepts of microscopy, progressing through beam profile considerations, to image reconstruction. We outline key practical decisions in constructing a home-built system and provide insight into the alignment and calibration processes. We briefly discuss the conditions necessary for constructing a continuous 3D image and introduce our home-built code for data analysis. By providing this guide, we aim to alleviate the challenges associated with designing and constructing LSM systems and offer scientists new to LSM a valuable resource in navigating this complex field.

Applications of Lightsheet Fluorescence Microscopy by High Numerical Aperture Detection Lens

This Review explores the evolution, improvements, and recent applications of Light Sheet Fluorescence Microscopy (LSFM) in biological research using a high numerical aperture detection objective (lens) for imaging subcellular structures. The Review begins with an overview of the development of LSFM, tracing its evolution from its inception to its current state and emphasizing key milestones and technological advancements over the years. Subsequently, we will discuss various improvements of LSFM techniques, covering advancements in hardware such as illumination strategies, optical designs, and sample preparation methods that have enhanced imaging capabilities and resolution. The advancements in data acquisition and processing are also included, which provides a brief overview of the recent development of artificial intelligence. Fluorescence probes that were commonly used in LSFM will be highlighted, together with some insights regarding the selection of potential probe candidates for future LSFM development. Furthermore, we also discuss recent advances in the application of LSFM with a focus on high numerical aperture detection objectives for various biological studies. For sample preparation techniques, there are discussions regarding fluorescence probe selection, tissue clearing protocols, and some insights into expansion microscopy. Integrated setups such as adaptive optics, single objective modification, and microfluidics will also be some of the key discussion points in this Review. We hope that this comprehensive Review will provide a holistic perspective on the historical development, technical enhancements, and cutting-edge applications of LSFM, showcasing its pivotal role and future potential in advancing biological research.

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.

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.

Successful 3D imaging of cleared biological samples with light sheet fluorescence microscopy

In parallel with the development of tissue-clearing methods, over the last decade, light sheet fluorescence microscopy has contributed to major advances in various fields, such as cell and developmental biology and neuroscience. While biologists are increasingly integrating three-dimensional imaging into their research projects, their experience with the technique is not always up to their expectations. In response to a survey of specific challenges associated with sample clearing and labeling, image acquisition, and data analysis, we have critically assessed the recent literature to characterize the difficulties inherent to light sheet fluorescence microscopy applied to cleared biological samples and to propose solutions to overcome them. This review aims to provide biologists interested in light sheet fluorescence microscopy with a primer for the development of their imaging pipeline, from sample preparation to image analysis. Importantly, we believe that issues could be avoided with better anticipation of image analysis requirements, which should be kept in mind while optimizing sample preparation and acquisition parameters.

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.

Armour G, Horman BM, Rock KD, Witchey SK, Greenbaum A, Patisaul HB. 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.

Algorithm for Modern Electron Microscopic Examination of the Golgi Complex

The Golgi complex (GC) is an essential organelle of the eukaryotic exocytic pathway. It has a very complexed structure and thus localization of its resident proteins is not trivial. Fast development of microscopic methods generates a huge difficulty for Golgi researchers to select the best protocol to use. Modern methods of light microscopy, such as super-resolution light microscopy (SRLM) and electron microscopy (EM), open new possibilities in analysis of various biological structures at organelle, cell, and organ levels. Nowadays, new generation of EM methods became available for the study of the GC; these include three-dimensional EM (3DEM), correlative light-EM (CLEM), immune EM, and new estimators within stereology that allow realization of maximal goal of any morphological study, namely, to achieve a three-dimensional model of the sample with optimal level of resolution and quantitative determination of its chemical composition. Methods of 3DEM have partially overlapping capabilities. This requires a careful comparison of these methods, identification of their strengths and weaknesses, and formulation of recommendations for their application to cell or tissue samples. Here, we present an overview of 3DEM methods for the study of the GC and some basics for how the images are formed and how the image quality can be improved.

Practical considerations for quantitative light sheet fluorescence microscopy

Fluorescence microscopy has evolved from a purely observational tool to a platform for quantitative, hypothesis-driven research. As such, the demand for faster and less phototoxic imaging modalities has spurred a rapid growth in light sheet fluorescence microscopy (LSFM). By restricting the excitation to a thin plane, LSFM reduces the overall light dose to a specimen while simultaneously improving image contrast. However, the defining characteristics of light sheet microscopes subsequently warrant unique considerations in their use for quantitative experiments. In this Perspective, we outline many of the pitfalls in LSFM that can compromise analysis and confound interpretation. Moreover, we offer guidance in addressing these caveats when possible. In doing so, we hope to provide a useful resource for life scientists seeking to adopt LSFM to quantitatively address complex biological hypotheses.

Active image optimization for lattice light sheet microscopy in thick samples

Lattice light-sheet microscopy (LLSM) is a very efficient technique for high resolution 3D imaging of dynamic phenomena in living biological samples. However, LLSM imaging remains limited in depth due to optical aberrations caused by sample-based refractive index mismatch. Here, we propose a simple and low-cost active image optimization (AIO) method to recover high resolution imaging inside thick biological samples. AIO is based on (1) a light-sheet autofocus step (AF) followed by (2) an adaptive optics image-based optimization. We determine the optimum AIO parameters to provide a fast, precise and robust aberration correction on biological samples. Finally, we demonstrate the performances of our approach on sub-micrometric structures in brain slices and plant roots.

Challenges and advances in optical 3D mesoscale imaging

Optical mesoscale imaging is a rapidly developing field that allows the visualisation of larger samples than is possible with standard light microscopy, and fills a gap between cell and organism resolution. It spans from advanced fluorescence imaging of micrometric cell clusters to centimetre-size complete organisms. However, with larger volume specimens, new problems arise. Imaging deeper into tissues at high resolution poses challenges ranging from optical distortions to shadowing from opaque structures. This manuscript discusses the latest developments in mesoscale imaging and highlights limitations, namely labelling, clearing, absorption, scattering, and also sample handling. We then focus on approaches that seek to turn mesoscale imaging into a more quantitative technique, analogous to quantitative tomography in medical imaging, highlighting a future role for digital and physical phantoms as well as artificial intelligence.

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.

A Model of Discovery: The Role of Imaging Established and Emerging Non-mammalian Models in Neuroscience

Rodents have been the dominant animal models in neurobiology and neurological disease research over the past 60 years. The prevalent use of rats and mice in neuroscience research has been driven by several key attributes including their organ physiology being more similar to humans, the availability of a broad variety of behavioral tests and genetic tools, and widely accessible reagents. However, despite the many advances in understanding neurobiology that have been achieved using rodent models, there remain key limitations in the questions that can be addressed in these and other mammalian models. In particular, in vivo imaging in mammals at the cell-resolution level remains technically difficult and demands large investments in time and cost. The simpler nervous systems of many non-mammalian models allow for precise mapping of circuits and even the whole brain with impressive subcellular resolution. The types of non-mammalian neuroscience models available spans vertebrates and non-vertebrates, so that an appropriate model for most cell biological questions in neurodegenerative disease likely exists. A push to diversify the models used in neuroscience research could help address current gaps in knowledge, complement existing rodent-based bodies of work, and bring new insight into our understanding of human disease. Moreover, there are inherent aspects of many non-mammalian models such as lifespan and tissue transparency that can make them specifically advantageous for neuroscience studies. Crispr/Cas9 gene editing and decreased cost of genome sequencing combined with advances in optical microscopy enhances the utility of new animal models to address specific questions. This review seeks to synthesize current knowledge of established and emerging non-mammalian model organisms with advances in cellular-resolution in vivo imaging techniques to suggest new approaches to understand neurodegeneration and neurobiological processes. We will summarize current tools and in vivo imaging approaches at the single cell scale that could help lead to increased consideration of non-mammalian models in neuroscience research.

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.

Designs, applications, and limitations of genetically encoded fluorescent sensors to explore plant biology

The understanding of signaling and metabolic processes in multicellular organisms requires knowledge of the spatial dynamics of small molecules and the activities of enzymes, transporters, and other proteins in vivo, as well as biophysical parameters inside cells and across tissues. The cellular distribution of receptors, ligands, and activation state must be integrated with information about the cellular distribution of metabolites in relation to metabolic fluxes and signaling dynamics in order to achieve the promise of in vivo biochemistry. Genetically encoded sensors are engineered fluorescent proteins that have been developed for a wide range of small molecules, such as ions and metabolites, or to report biophysical processes, such as transmembrane voltage or tension. First steps have been taken to monitor the activity of transporters in vivo. Advancements in imaging technologies and specimen handling and stimulation have enabled researchers in plant sciences to implement sensor technologies in intact plants. Here, we provide a brief history of the development of genetically encoded sensors and an overview of the types of sensors available for quantifying and visualizing ion and metabolite distribution and dynamics. We further discuss the pros and cons of specific sensor designs, imaging systems, and sample manipulations, provide advice on the choice of technology, and give an outlook into future developments.

Towards a physical understanding of developmental patterning

The temporal coordination of events at cellular and tissue scales is essential for the proper development of organisms, and involves cell-intrinsic processes that can be coupled by local cellular signalling and instructed by global signalling, thereby creating spatial patterns of cellular states that change over time. The timing and structure of these patterns determine how an organism develops. Traditional developmental genetic methods have revealed the complex molecular circuits regulating these processes but are limited in their ability to predict and understand the emergent spatio-temporal dynamics. Increasingly, approaches from physics are now being used to help capture the dynamics of the system by providing simplified, generic descriptions. Combined with advances in imaging and computational power, such approaches aim to provide insight into timing and patterning in developing systems.

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.

Single-objective high-resolution confocal light sheet fluorescence microscopy for standard biological sample geometries

Three-dimensional fluorescence-based imaging of living cells and organisms requires the sample to be exposed to substantial excitation illumination energy, typically causing phototoxicity and photobleaching. Light sheet fluorescence microscopy dramatically reduces phototoxicity, yet most implementations are limited to objective lenses with low numerical aperture and particular sample geometries that are built for specific biological systems. To overcome these limitations, we developed a single-objective light sheet fluorescence system for biological imaging based on axial plane optical microscopy and digital confocal slit detection, using either Bessel or Gaussian beam shapes. Compared to spinning disk confocal microscopy, this system displays similar optical resolution, but a significantly reduced photobleaching at the same signal level. This single-objective light sheet technique is built as an add-on module for standard research microscopes and the technique is compatible with high-numerical aperture oil immersion objectives and standard samples mounted on coverslips. We demonstrate the performance of this technique by imaging three-dimensional dynamic processes, including bacterial biofilm dispersal, the response of biofilms to osmotic shocks, and macrophage phagocytosis of bacterial cells.

Fluorescence based rapid optical volume screening system (OVSS) for interrogating multicellular organisms

Continuous monitoring of large specimens for long durations requires fast volume imaging. This is essential for understanding the processes occurring during the developmental stages of multicellular organisms. One of the key obstacles of fluorescence based prolonged monitoring and data collection is photobleaching. To capture the biological processes and simultaneously overcome the effect of bleaching, we developed single- and multi-color lightsheet based OVSS imaging technique that enables rapid screening of multiple tissues in an organism. Our approach based on OVSS imaging employs quantized step rotation of the specimen to record 2D angular data that reduces data acquisition time when compared to the existing light sheet imaging system (SPIM). A co-planar multicolor light sheet PSF is introduced to illuminate the tissues labelled with spectrally-separated fluorescent probes. The detection is carried out using a dual-channel sub-system that can simultaneously record spectrally separate volume stacks of the target organ. Arduino-based control systems were employed to automatize and control the volume data acquisition process. To illustrate the advantages of our approach, we have noninvasively imaged the Drosophila larvae and Zebrafish embryo. Dynamic studies of multiple organs (muscle and yolk-sac) in Zebrafish for a prolonged duration (5 days) were carried out to understand muscle structuring (Dystrophin, microfibers), primitive Macrophages (in yolk-sac) and inter-dependent lipid and protein-based metabolism. The volume-based study, intensity line-plots and inter-dependence ratio analysis allowed us to understand the transition from lipid-based metabolism to protein-based metabolism during early development (Pharyngula period with a critical transition time, [Formula: see text] h post-fertilization) in Zebrafish. The advantage of multicolor lightsheet illumination, fast volume scanning, simultaneous visualization of multiple organs and an order-less photobleaching makes OVSS imaging the system of choice for rapid monitoring and real-time assessment of macroscopic biological organisms with microscopic resolution.

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.

Dynamic morphoskeletons in development

Morphogenetic flows in developmental biology are characterized by the coordinated motion of thousands of cells that organize into tissues, naturally raising the question of how this collective organization arises. Using only the kinematics of tissue deformation, which naturally integrates local and global mechanisms along cell paths, we identify the dynamic morphoskeletons behind morphogenesis, i.e., the evolving centerpieces of multicellular trajectory patterns. These features are model- and parameter-free, frame-invariant, and robust to measurement errors and can be computed from unfiltered cell-velocity data. We reveal the spatial attractors and repellers of the embryo by quantifying its Lagrangian deformation, information that is inaccessible to simple trajectory inspection or Eulerian methods that are local and typically frame-dependent. Computing these dynamic morphoskeletons in wild-type and mutant chick and fly embryos, we find that they capture the early footprint of known morphogenetic features, reveal new ones, and quantitatively distinguish between different phenotypes.

Compact and reflective light-sheet microscopy for long-term imaging of living embryos

The development of light-sheet fluorescence microscopy has been a revolution for developmental biology as it allows long-term imaging during embryonic development. An important reason behind the quick adoption has been the availability of open hardware alternatives. In this work, we present a robust and compact version of a light-sheet fluorescence microscope that is easy to assemble and requires little to no maintenance. An important aspect of the design is that the illumination unit consists of reflective elements, thereby reducing chromatic aberrations an order of magnitude as compared to refractive counterparts.

Versatile high-speed confocal microscopy using a single laser beam

We present a new flexible high speed laser scanning confocal microscope and its extension by an astigmatism particle tracking velocimetry (APTV) device. Many standard confocal microscopes use either a single laser beam to scan the sample at a relatively low overall frame rate or many laser beams to simultaneously scan the sample and achieve a high overall frame rate. The single-laser-beam confocal microscope often uses a point detector to acquire the image. To achieve high overall frame rates, we use, next to the standard 2D probe scanning unit, a second 2D scan unit projecting the image directly onto a 2D CCD-sensor (re-scan configuration). Using only a single laser beam eliminates crosstalk and leads to an imaging quality that is independent of the frame rate with a lateral resolution of 0.235 µm. The design described here is suitable for a high frame rate, i.e., for frame rates well above the video rate (full frame) up to a line rate of 32 kHz. The dwell time of the laser focus on any spot in the sample (122 ns) is significantly shorter than those in standard confocal microscopes (in the order of milli- or microseconds). This short dwell time reduces phototoxicity and bleaching of fluorescent molecules. The new design opens up further flexibility and facilitates coupling to other optical methods. The setup can easily be extended by an APTV device to measure three dimensional dynamics while being able to show high resolution confocal structures. Thus, one can use the high resolution confocal information synchronized with an APTV dataset.

Three-Dimensional Light Sheet Fluorescence Microscopy of Lungs To Dissect Local Host Immune-Aspergillus fumigatus Interactions

The use of animal models of infection is essential to advance our understanding of the complex host-pathogen interactions that take place during Aspergillus fumigatus lung infections. As in the case of humans, mice need to suffer an immune imbalance in order to become susceptible to invasive pulmonary aspergillosis (IPA), the most serious infection caused by A. fumigatus. There are several immunosuppressive regimens that are routinely used to investigate fungal growth and/or immune responses in murine models of invasive pulmonary aspergillosis. However, the precise consequences of the use of each immunosuppressive model for the local immune populations and for fungal growth are not completely understood. Here, to pin down the scenarios involving commonly used IPA models, we employed light sheet fluorescence microscopy (LSFM) to analyze whole lungs at cellular resolution. Our results will be valuable to optimize and refine animal models to maximize their use in future research.

Aspergillus fumigatus is an opportunistic fungal pathogen that can cause life-threatening invasive lung infections in immunodeficient patients. The cellular and molecular processes of infection during onset, establishment, and progression of A. fumigatus infections are highly complex and depend on both fungal attributes and the immune status of the host. Therefore, preclinical animal models are of paramount importance to investigate and gain better insight into the infection process. Yet, despite their extensive use, commonly employed murine models of invasive pulmonary aspergillosis are not well understood due to analytical limitations. Here, we present quantitative light sheet fluorescence microscopy (LSFM) to describe fungal growth and the local immune response in whole lungs at cellular resolution within its anatomical context. We analyzed three very common murine models of pulmonary aspergillosis based on immunosuppression with corticosteroids, chemotherapy-induced leukopenia, or myeloablative irradiation. LSFM uncovered distinct architectures of fungal growth and degrees of tissue invasion in each model. Furthermore, LSFM revealed the spatial distribution, interaction, and activation of two key immune cell populations in antifungal defense: alveolar macrophages and polymorphonuclear neutrophils. Interestingly, the patterns of fungal growth correlated with the detected effects of the immunosuppressive regimens on the local immune cell populations. Moreover, LSFM demonstrates that the commonly used intranasal route of spore administration did not result in complete intra-alveolar deposition, as about 80% of fungal growth occurred outside the alveolar space. Hence, characterization by LSFM is more rigorous than by previously used methods employing murine models of invasive pulmonary aspergillosis and pinpoints their strengths and limitations.

Tissue clearing and its applications in neuroscience

State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.

Non-contact characterization of compound optical elements using reflectance confocal microscopy, low-coherence interferometry, and computational ray-tracing

Advances in microscopy have enabled us to see at unprecedented depths and resolutions, even breaking the diffraction-limit by several fold. These improvements have come at the expense of system complexity with microscopes routinely employing multiple objective lenses and custom optical relays. Optimal system design is paramount for imaging performance, but research systems are limited by the use of commercial components because optical prescriptions are often inaccessible. System performance can be further degraded when these components are implemented in nonstandard configurations outside of manufacturer specifications. Here, we describe a method for characterization of compound optical elements including curvatures, material and air-gap thicknesses, and glass types. We present validation data for doublets and a commercial broadband scan lens. Our method is both non-contact and non-destructive, and we believe it addresses a unique gap in optical design that may be extended to broad applications in both research and industrial manufacturing.

Light-Sheet Microscopy and Its Potential for Understanding Developmental Processes

The ability to visualize and quantitatively measure dynamic biological processes in vivo and at high spatiotemporal resolution is of fundamental importance to experimental investigations in developmental biology. Light-sheet microscopy is particularly well suited to providing such data, since it offers exceptionally high imaging speed and good spatial resolution while minimizing light-induced damage to the specimen. We review core principles and recent advances in light-sheet microscopy, with a focus on concepts and implementations relevant for applications in developmental biology. We discuss how light-sheet microcopy has helped advance our understanding of developmental processes from single-molecule to whole-organism studies, assess the potential for synergies with other state-of-the-art technologies, and introduce methods for computational image and data analysis. Finally, we explore the future trajectory of light-sheet microscopy, discuss key efforts to disseminate new light-sheet technology, and identify exciting opportunities for further advances.

Single-Cell Reconstruction of Emerging Population Activity in an Entire Developing Circuit

Animal survival requires a functioning nervous system to develop during embryogenesis. Newborn neurons must assemble into circuits producing activity patterns capable of instructing behaviors. Elucidating how this process is coordinated requires new methods that follow maturation and activity of all cells across a developing circuit. We present an imaging method for comprehensively tracking neuron lineages, movements, molecular identities, and activity in the entire developing zebrafish spinal cord, from neurogenesis until the emergence of patterned activity instructing the earliest spontaneous motor behavior. We found that motoneurons are active first and form local patterned ensembles with neighboring neurons. These ensembles merge, synchronize globally after reaching a threshold size, and finally recruit commissural interneurons to orchestrate the left-right alternating patterns important for locomotion in vertebrates. Individual neurons undergo functional maturation stereotypically based on their birth time and anatomical origin. Our study provides a general strategy for reconstructing how functioning circuits emerge during embryogenesis. VIDEO ABSTRACT.