Light Sheet Fluorescence Microscopy Research Articles

Collagen crosslinking-induced corneal morphological changes: a three-dimensional light sheet Microscopy-based evaluation.

Stiffness-related eye diseases such as keratoconus require comprehensive visualization of the complex morphological matrix changes. The aim of this study was to use three-dimensional (3D) light sheet fluorescence microscopy (LSFM) to analyze unlabeled corneal tissue samples, qualitatively visualizing changes in corneal stiffness. Isolated porcine corneal tissue samples were treated with either NaCl or 0.1% glutaraldehyde (GTA) prior to clearing with benzyl alcohol/benzyl benzoate (BABB) and subsequently scanned with LSFM. After analysis of the LSFM data sets, the samples were embedded in paraffin to validate the results by conventional planar microscopy. In the unlabeled corneal tissue samples the 2D/3D morphology of the entire tissue volume was identified by specific autofluorescence signals. An enhancement of collagen crosslinking was induced by applying GTA to the corneal tissue. Subsequent LSFM scans showed specific morphological changes due to altered autofluorescence signals of the corneal stroma, which were confirmed by conventional histology. Therefore, LSFM analysis of corneal tissue samples allowed label-free 3D autofluorescence assessment of the corneal morphology in its anatomical context. It provides the technical basis for the examination of the pathologically altered cornea and facilitates ophthalmologic examinations of corneal diseases based on the altered tissue stiffness.

Novel application of metabolic imaging of early embryos using a light-sheet on-a-chip device: a proof-of-concept study.

Is it feasible to safely determine metabolic imaging signatures of nicotinamide adenine dinucleotide [NAD(P)H] associated auto-fluorescence in early embryos using a light-sheet on-a-chip approach? We developed an optofluidic device capable of obtaining high-resolution 3D images of the NAD(P)H autofluorescence of live mouse embryos using a light-sheet on-a-chip device as a proof-of-concept. Selecting the most suitable embryos for implantation and subsequent healthy live birth is crucial to the success rate of assisted reproduction and offspring health. Besides morphological evaluation using optical microscopy, a promising alternative is the non-invasive imaging of live embryos to establish metabolic activity performance. Indeed, in recent years, metabolic imaging has been investigated using highly advanced microscopy technologies such as fluorescence-lifetime imaging and hyperspectral microscopy. The potential safety of the system was investigated by assessing the development and viability of live embryos after embryo culture for 67 h post metabolic imaging at the two-cell embryo stage (n = 115), including a control for culture conditions and sham controls (system non-illuminated). Embryo quality of developed blastocysts was assessed by immunocytochemistry to quantify trophectoderm and inner mass cells (n = 75). Furthermore, inhibition of metabolic activity (FK866 inhibitor) during embryo culture was also assessed (n = 18). The microstructures were fabricated following a standard UV-photolithography process integrating light-sheet fluorescence microscopy into a microfluidic system, including on-chip micro-lenses to generate a light-sheet at the centre of a microchannel. Super-ovulated F1 (CBA/C57Bl6) mice were used to produce two-cell embryos and embryo culture experiments. Blastocyst formation rates and embryo quality (immunocytochemistry) were compared between the study groups. A convolutional neural network (ResNet 34) model using metabolic images was also trained. The optofluidic device was capable of obtaining high-resolution 3D images of live mouse embryos that can be linked to their metabolic activity. The system's design allowed continuous tracking of the embryo location, including high control displacement through the light-sheet and fast imaging of the embryos (<2 s), while keeping a low dose of light exposure (16 J · cm-2 and 8 J · cm-2). Optimum settings for keeping sample viability showed that a modest light dosage was capable of obtaining 30 times higher signal-noise-ratio images than images obtained with a confocal system (P < 0.00001; t-test). The results showed no significant differences between the control, illuminated and non-illuminated embryos (sham control) for embryo development as well as embryo quality at the blastocyst stage (P > 0.05; Yate's chi-squared test). Additionally, embryos with inhibited metabolic activity showed a decreased blastocyst formation rate of 22.2% compared to controls, as well as a 47% reduction in metabolic activity measured by metabolic imaging (P < 0.0001; t-test). This indicates that the optofluidic device was capable of producing metabolic images of live embryos by measuring NAD(P)H autofluorescence, allowing a novel and affordable approach. The obtained metabolic images of two-cell embryos predicted blastocyst formation with an AUC of 0.974. N/A. The study was conducted using a mouse model focused on early embryo development assessing illumination at the two-cell stage. Further safety studies are required to assess the safety and use of 405 nm light at the blastocyst stage by investigating any potential negative impact on live birth rates, offspring health, aneuploidy rates, mutational load, changes in gene expression, and/or effects on epigenome stability in newborns. This light-sheet on-a-chip approach is novel and after rigorous safety studies and a roadmap for technology development, potential future applications could be developed for ART. The overall cost-efficient fabrication of the device will facilitate scalability and integration into future devices if full-safety application is demonstrated. This work was partially supported by an Ideas Grant (no 2004126) from the National Health and Medical Research Council (NHMRC), by the Education Program in Reproduction and Development (EPRD), Department Obstetrics and Gynaecology, Monash University, and by the Department of Mechanical and Aerospace Engineering, Faculty of Engineering, Monash University. The authors E.V-O, R.N., V.J.C., A.N., and F.H. have applied for a patent on the topic of this technology (PCT/AU2023/051132). The remaining authors have nothing to disclose.

Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression.

Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We tested male and female mice with a panel of drugs, including psilocybin, ketamine, 5-MeO-DMT, 6-fluoro-DET, MDMA, acute fluoxetine, chronic fluoxetine, and vehicle. In one-versus-rest classification, the exact drug was identified with 67% accuracy, significantly above the chance level of 12.5%. In one-versus-one classifications, psilocybin was discriminated from 5-MeO-DMT, ketamine, MDMA, or acute fluoxetine with >95% accuracy. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results support a novel approach for characterizing and validating psychoactive drugs with psychedelic properties.

Mesoscopic axially swept oblique plane microscope for the imaging of freely moving organisms with near-isotropic resolution

Rapid three-dimensional imaging over extended fields of view (FOVs) is crucial to the study of organism-wide systems and biological processes in vivo. Selective-plane illumination microscopy (SPIM) is a powerful method for high spatio-temporal resolution in toto imaging of such biological specimens. However, typical SPIM implementations preclude conventional sample mounting and have anisotropic imaging performance, in particular when designed for large FOVs over 1 mm diameter. Here, we introduce axial sweeping of the illumination into a non-orthogonal dual-objective oblique plane microscope (OPM) design, thereby enabling the observation of freely moving animals over millimeter-sized FOVs, at close to isotropic, sub-cellular resolution. We apply our mesoscopic axially swept OPM (MASOPM) to image the behavioral dynamics of the sea anemone Nematostella vectensis over 1 × 0.7 × 0.4 mm at 1.7 × 2.6 × 3.7 µm resolution and 0.5 Hz volume rate.

Rapid Whole‐Organ Characterization via Quantitative Light‐Sheet Microscopy

AbstractWhole‐organ imaging and characterization at a submicron level provide abundant information on development and diseases while remaining a big challenge, especially in the context of time load. Herein, a quantitative light‐sheet microscopy platform that enabled highly time‐efficient assessments of fibrous structures within the intact cleared tissue is developed. Dual‐view inverted selective plane illumination microscopy (diSPIM), followed by improved registration and deconvolution, led to submicron isotropic imaging of mouse upper genital tract with one hundred‐fold speed‐ups than previous efforts. Further, optical metrics quantifying 3D local density and structural complexity of targets based on parallel and vectorized convolution in both spatial and frequency domains are developed. Collectively, ≈400–2000 fold increases in time efficiency counting for imaging, postprocessing, and quantitative characterization compared to the traditional method is gained. Using this platform, automatic identification of medulla and cortex within the mouse ovary at over 90% overlap with manual selection by anatomy experts is achieved. Additionally, heterogeneous distributions of immune cells in the mouse ovary and fallopian tube, offering a unique perspective for understanding the immune microenvironment are discovered. This work paves the way for future whole‐organ study, and exhibits potential with promise for offering mechanistic insights into physiological and pathological alterations of biological tissues.

Developmental mouse brain common coordinate framework

3D brain atlases are key resources to understand the brain’s spatial organization and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of developing mouse brain 3D reference atlases hinders advancements in understanding brain development. Here, we present a 3D developmental common coordinate framework (DevCCF) spanning embryonic day (E)11.5, E13.5, E15.5, E18.5, and postnatal day (P)4, P14, and P56, featuring undistorted morphologically averaged atlas templates created from magnetic resonance imaging and co-registered high-resolution light sheet fluorescence microscopy templates. The DevCCF with 3D anatomical segmentations can be downloaded or explored via an interactive 3D web-visualizer. As a use case, we utilize the DevCCF to unveil GABAergic neuron emergence in embryonic brains. Moreover, we map the Allen CCFv3 and spatial transcriptome cell-type data to our stereotaxic P56 atlas. In summary, the DevCCF is an openly accessible resource for multi-study data integration to advance our understanding of brain development.

LiveLattice: Real-time visualisation of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 × 1536 × 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data preprocessing, visualisation, and analysis on standard workstations, thereby revolutionising biological imaging applications for LLSM and similar microscopes.

Unlocking the potential of large-scale 3D imaging with tissue clearing techniques.

The three-dimensional (3D) anatomical structure of living organisms is intrinsically linked to their functions, yet modern life sciences have not fully explored this aspect. Recently, the combination of efficient tissue clearing techniques and light-sheet fluorescence microscopy (LSFM) for rapid 3D imaging has improved access to 3D spatial information in biological systems. This technology has found applications in various fields, including neuroscience, cancer research, and clinical histopathology, leading to significant insights. It allows imaging of entire organs or even whole bodies of animals and humans at multiple scales. Moreover, it enables a form of spatial omics by capturing and analyzing cellome information, which represents the complete spatial organization of cells. While current 3D imaging of cleared tissues has limitations in obtaining sufficient molecular information, emerging technologies such as multi-round tissue staining and super-multicolor imaging are expected to address these constraints. 3D imaging using tissue clearing and light-sheet microscopy thus offers a valuable research tool in the current and future life sciences for acquiring and analyzing large-scale biological spatial information.

Enhancing brain image quality with 3D U-net for stripe removal in light sheet fluorescence microscopy

Light Sheet Fluorescence Microscopy (LSFM) is increasingly popular in neuroimaging for its ability to capture high-resolution 3D neural data. However, the presence of stripe noise significantly degrades image quality, particularly in complex 3D stripes with varying widths and brightness, posing challenges in neuroscience research. Existing stripe removal algorithms excel in suppressing noise and preserving details in 2D images with simple stripes but struggle with the complexity of 3D stripes. To address this, we propose a novel 3D U-net model for Stripe Removal in Light sheet fluorescence microscopy (USRL). This approach directly learns and removes stripes in 3D space across different scales, employing a dual-resolution strategy to effectively handle stripes of varying complexities. Additionally, we integrate a nonlinear mapping technique to normalize high dynamic range and unevenly distributed data before applying the stripe removal algorithm. We validate our method on diverse datasets, demonstrating substantial improvements in peak signal-to-noise ratio (PSNR) compared to existing algorithms. Moreover, our algorithm exhibits robust performance when applied to real LSFM data. Through extensive validation experiments, both on test sets and real-world data, our approach outperforms traditional methods, affirming its effectiveness in enhancing image quality. Furthermore, the adaptability of our algorithm extends beyond LSFM applications to encompass other imaging modalities. This versatility underscores its potential to enhance image usability across various research disciplines.

Model based optimization for refractive index mismatched light sheet imaging

Selective plane illumination microscopy (SPIM) is an optical sectioning imaging approach based on orthogonal light pathways for excitation and detection. The excitation pathway has an inverse relation between the optical sectioning strength and the effective field of view (FOV). Multiple approaches exist to extend the effective FOV, and here we focus on remote focusing to axially scan the light sheet, synchronized with a CMOS camera’s rolling shutter. A typical axially scanned SPIM configuration for imaging large samples utilizes a tunable optic for remote focusing, paired with air objectives focused into higher refractive index media. To quantitatively explore the effect of remote focus choices and sample space refractive index mismatch on light sheet intensity distributions, we developed an open-source computational approach for integrating ray tracing and field propagation. We validate our model’s performance against experimental light sheet profiles for various SPIM configurations. Our findings indicate that optimizing the position of the sample chamber relative to the excitation optics can enhance image quality by balancing aberrations induced by refractive index mismatch. We validate this prediction using a home-built, large sample axially scanned SPIM configuration and calibration samples. Our open-source, extensible modeling software can easily extend to explore optimal imaging configurations in diverse light sheet imaging settings.

Deciphering the spatial distribution of Gli1-lineage cells in dental, oral, and craniofacial regions

Abstract The craniofacial bone, crucial for protecting brain tissue and supporting facial structure, undergoes continuous remodeling through mesenchymal (MSCs) or skeletal stem cells (SSCs) in their niches. Gli1 is an ideal marker for labeling MSCs and osteoprogenitors in this region, and Gli1-lineage cells are identified as pivotal for bone growth, development, repair, and regeneration. Despite its significance, the distribution of Gli1-lineage cells across the dental, oral, and craniofacial (DOC) regions remains to be systematically explored. Utilizing tissue-clearing and light sheet fluorescence microscopy (LSFM) with a Gli1CreER; tdTomatoAi14 mouse model, we mapped the spatial distribution of Gli1-lineage cells throughout the skull, focusing on calvarial bones, sutures, bone marrow, teeth, periodontium, jaw bones, and the temporomandibular joint (TMJ). We found Gli1-lineage cells widespread in these areas, underscoring their significance in DOC regions. Additionally, we observed their role in repairing calvarial bone defects, providing novel insights into craniofacial biology and stem cell niches and enhancing our understanding of stem cells and their progeny’s behavior in vivo.

SARS-CoV-2 infection induces hyaluronan production in vitro and hyaluronan levels in COVID-19 patients relate to morbidity and long-term lung impairment: a prospective cohort study.

We previously demonstrated that the lungs of deceased COVID-19 patients were filled with a clear hydrogel consisting of hyaluronan (HA). In this translational study, we investigated the role of HA at all stages of COVID-19 disease to map the consequences of elevated HA on morbidity and identify the mechanism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced HA production. A reduced alveolar surface area was observed in the lungs of deceased COVID-19 patients compared to healthy controls, as visualized by a 3D rendering of lung morphology using light-sheet fluorescence microscopy. We confirmed the presence of HA in lung biopsies and found large quantities of proinflammatory fragmented HA. The association of systemic HA in blood plasma and disease severity was assessed in patients with mild (WHO Clinical Progression Scale, WHO-CPS, 1-5) and severe COVID-19 (WHO-CPS, 6-9) during the acute and convalescent phases and related to lung function. We found that systemic levels of HA were high during acute COVID-19 disease, remained elevated during convalescence, and were associated with a reduced diffusion capacity. In vitro 3D-lung models, differentiated from primary human bronchial epithelial cells, were used to study the effects of SARS-CoV-2 infection on HA metabolism, and transcriptomic analyses revealed a dysregulation of HA synthases and hyaluronidases, both contributing to increased HA in apical secretions. Furthermore, corticosteroid treatment reduced the inflammation and downregulated HA synthases. Our findings demonstrate that HA plays a role in COVID-19 morbidity and that sustained elevated HA concentrations may contribute to long-term respiratory impairment.IMPORTANCEThis study provides insights into the role of hyaluronan (HA) in the severity and long-term impact of COVID-19 on lung function. Through extensive morphological examination of lung tissues and a multicenter study, we identified that HA levels are significantly elevated in COVID-19 patients, correlating with a reduced lung diffusion capacity during convalescence. Using a 3D-lung model, we further uncovered how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 infection causes a dysregulated HA metabolism, leading to increased HA production. Our findings provide valuable insights into the pathogenesis of SARS-CoV-2 and suggest that targeting HA metabolism could offer new therapeutic avenues for managing COVID-19, particularly to prevent long-term lung impairment. Additionally, HA holds potential as a biomarker for predicting disease severity, which could guide personalized treatment strategies.

Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues.

The geometric shape and arrangement of individual cells play a role in shaping organ functions. However, analyzing multicellular features and exploring their connectomes in centimeter-scale plant organs remain challenging. Here, we established a set of frameworks named Large-Volume Fully Automated Cell Reconstruction (LVACR), enabling the exploration of three-dimensional (3D) cytological features and cellular connectivity in plant tissues. Through benchmark testing, our framework demonstrated superior efficiency in cell segmentation and aggregation, successfully addressing the inherent challenges posed by light sheet fluorescence microscopy (LSFM) imaging. Using LVACR, we successfully established a cell atlas of different plant tissues. Cellular morphology analysis revealed differences of cell clusters and shapes in between different poplar (P. simonii Carr. and P. canadensis Moench.) seeds, whereas topological analysis revealed that they maintained conserved cellular connectivity. Furthermore, LVACR spatiotemporally demonstrated an initial burst of cell proliferation, accompanied by morphological transformations at an early stage in developing the shoot apical meristem. During subsequent development, cell differentiation produced anisotropic features, thereby resulting in various cell shapes. Overall, our findings provided valuable insights into the precise spatial arrangement and cellular behavior of multicellular organisms, thus enhancing our understanding of the complex processes underlying plant growth and differentiation.

Spatial microenvironments tune immune response dynamics in the Drosophila larval fat body.

Immune responses in tissues display intricate patterns of gene expression that vary across space and time. While such patterns have been increasingly linked to disease outcomes, the mechanisms that generate them and the logic behind them remain poorly understood. As a tractable model of spatial immune responses, we investigated heterogeneous expression of antimicrobial peptides in the larval fly fat body, an organ functionally analogous to the liver. To capture the dynamics of immune response across the full tissue at single-cell resolution, we established live light sheet fluorescence microscopy of whole larvae. We discovered that expression of antimicrobial peptides occurs in a reproducible spatial pattern, with enhanced expression in the anterior and posterior lobes of the fat body. This pattern correlates with microbial localization via blood flow but is not caused by it: loss of heartbeat suppresses microbial transport but leaves the expression pattern unchanged. This result suggests that regions of the tissue most likely to encounter microbes via blood flow are primed to produce antimicrobials. Spatial transcriptomics revealed that these immune microenvironments are defined by genes spanning multiple biological processes, including lipid-binding proteins that regulate host cell death by the immune system. In sum, the larval fly fat body exhibits spatial compartmentalization of immune activity that resembles the strategic positioning of immune cells in mammals, such as in the liver, gut, and lymph nodes. This finding suggests that tissues may share a conserved spatial organization that optimizes immune responses for antimicrobial efficacy while preventing excessive self-damage.

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.

Neural Basis of Number Sense in Larval Zebrafish.

Number sense, the ability to discriminate the quantity of objects, is crucial for survival. To understand how neurons work together and develop to mediate number sense, we used two-photon fluorescence light sheet microscopy to capture the activity of individual neurons throughout the brain of larval Danio rerio, while displaying a visual number stimulus to the animal. We identified number-selective neurons as early as 3 days post-fertilization and found a proportional increase of neurons tuned to larger quantities after 3 days. We used machine learning to predict the stimulus from the neuronal activity and observed that the prediction accuracy improves with age. We further tested ethanol's effect on number sense and found a decrease in number-selective neurons in the forebrain, suggesting cognitive impairment. These findings are a significant step towards understanding neural circuits devoted to discrete magnitudes and our methodology to track single-neuron activity across the whole brain is broadly applicable to other fields in neuroscience.

Frontiers in artificial intelligence-directed light-sheet microscopy for uncovering biological phenomena and multi-organ imaging.

Light-sheet fluorescence microscopy (LSFM) introduces fast scanning of biological phenomena with deep photon penetration and minimal phototoxicity. This advancement represents a significant shift in 3-D imaging of large-scale biological tissues and 4-D (space + time) imaging of small live animals. The large data associated with LSFM requires efficient imaging acquisition and analysis with the use of artificial intelligence (AI)/machine learning (ML) algorithms. To this end, AI/ML-directed LSFM is an emerging area for multi-organ imaging and tumor diagnostics. This review will present the development of LSFM and highlight various LSFM configurations and designs for multi-scale imaging. Optical clearance techniques will be compared for effective reduction in light scattering and optimal deep-tissue imaging. This review will further depict a diverse range of research and translational applications, from small live organisms to multi-organ imaging to tumor diagnosis. In addition, this review will address AI/ML-directed imaging reconstruction, including the application of convolutional neural networks (CNNs) and generative adversarial networks (GANs). In summary, the advancements of LSFM have enabled effective and efficient post-imaging reconstruction and data analyses, underscoring LSFM's contribution to advancing fundamental and translational research.

Low-cost and scalable projected light-sheet microscopy for the high-resolution imaging of cleared tissue and living samples.

Light-sheet fluorescence microscopy (LSFM) is a widely used technique for imaging cleared tissue and living samples. However, high-performance LSFM systems are typically expensive and not easily scalable. Here we introduce a low-cost, scalable and versatile LSFM framework, which we named 'projected light-sheet microscopy' (pLSM), with high imaging performance and small device and computational footprints. We characterized the capabilities of pLSM, which repurposes readily available consumer-grade components, optimized optics, over-network control architecture and software-driven light-sheet modulation, by performing high-resolution mapping of cleared mouse brains and of post-mortem pathological human brain samples, and via the molecular phenotyping of brain and blood-vessel organoids derived from human induced pluripotent stem cells. We also report a method that leverages pLSM for the live imaging of the dynamics of sparsely labelled multi-layered bacterial pellicle biofilms at an air-liquid interface. pLSM can make high-resolution LSFM for biomedical applications more accessible, affordable and scalable.

Light sheet fluorescence microscopy: Advancing biological discovery with more dimensions, higher speed, and lower phototoxicity

Light sheet fluorescence microscopy: Advancing biological discovery with more dimensions, higher speed, and lower phototoxicity

Mantis: High-throughput 4D imaging and analysis of the molecular and physical architecture of cells.

High-throughput dynamic imaging of cells and organelles is essential for understanding complex cellular responses. We report Mantis, a high-throughput 4D microscope that integrates two complementary, gentle, live-cell imaging technologies: remote-refocus label-free microscopy and oblique light-sheet fluorescence microscopy. Additionally, we report shrimPy (Smart High-throughput Robust Imaging and Measurement in Python), an open-source software for high-throughput imaging, deconvolution, and single-cell phenotyping of 4D data. Using Mantis and shrimPy, we achieved high-content correlative imaging of molecular dynamics and the physical architecture of 20 cell lines every 15 min over 7.5 h. This platform also facilitated detailed measurements of the impacts of viral infection on the architecture of host cells and host proteins. The Mantis platform can enable high-throughput profiling of intracellular dynamics, long-term imaging and analysis of cellular responses to perturbations, and live-cell optical screens to dissect gene regulatory networks.