A quantitative analysis of various patterns applied in lattice light sheet microscopy

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.

A fluorogenic complementation tool kit for interrogating lipid droplet-organelle interaction

Contact sites between lipid droplets and other organelles are essential for cellular lipid and energy homeostasis upon metabolic demands. Detection of these contact sites at the nanometer scale over time in living cells is challenging. We developed a tool kit for detecting contact sites based on fluorogen-activated bimolecular complementation at CONtact sites, FABCON, using a reversible, low-affinity split fluorescent protein, splitFAST. FABCON labels contact sites with minimal perturbation to organelle interaction. Via FABCON, we quantitatively demonstrated that endoplasmic reticulum (ER)- and mitochondria (mito)-lipid droplet contact sites are dynamic foci in distinct metabolic conditions, such as during lipid droplet biogenesis and consumption. An automated analysis pipeline further classified individual contact sites into distinct subgroups based on size, likely reflecting differential regulation and function. Moreover, FABCON is generalizable to visualize a repertoire of organelle contact sites including ER-mito. Altogether, FABCON reveals insights into the dynamic regulation of lipid droplet-organelle contact sites and generates new hypotheses for further mechanistical interrogation during metabolic regulation.

Posterior approach to correct for focal plane offsets in lattice light-sheet structured illumination microscopy

Significance

Lattice light-sheet structured illumination microscopy (latticeSIM) has proven highly effective in producing three-dimensional images with super resolution rapidly and with minimal photobleaching. However, due to the use of two separate objectives, sample-induced aberrations can result in an offset between the planes of excitation and detection, causing artifacts in the reconstructed images.

Aim

We introduce a posterior approach to detect and correct the axial offset between the excitation and detection focal planes in latticeSIM and provide a method to minimize artifacts in the reconstructed images.

Approach

We utilized the residual phase information within the overlap regions of the laterally shifted structured illumination microscopy information components in frequency space to retrieve the axial offset between the excitation and the detection focal planes in latticeSIM.

Results

We validated our technique through simulations and experiments, encompassing a range of samples from fluorescent beads to subcellular structures of adherent cells. We also show that using transfer functions with the same axial offset as the one present during data acquisition results in reconstructed images with minimal artifacts and salvages otherwise unusable data.

Conclusion

We envision that our method will be a valuable addition to restore image quality in latticeSIM datasets even for those acquired under non-ideal experimental conditions.

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.

Correlative single molecule lattice light sheet imaging reveals the dynamic relationship between nucleosomes and the local chromatin environment

In the nucleus, biological processes are driven by proteins that diffuse through and bind to a meshwork of nucleic acid polymers. To better understand this interplay, we present an imaging platform to simultaneously visualize single protein dynamics together with the local chromatin environment in live cells. Together with super-resolution imaging, new fluorescent probes, and biophysical modeling, we demonstrate that nucleosomes display differential diffusion and packing arrangements as chromatin density increases whereas the viscoelastic properties and accessibility of the interchromatin space remain constant. Perturbing nuclear functions impacts nucleosome diffusive properties in a manner that is dependent both on local chromatin density and on relative location within the nucleus. Our results support a model wherein transcription locally stabilizes nucleosomes while simultaneously allowing for the free exchange of nuclear proteins. Additionally, they reveal that nuclear heterogeneity arises from both active and passive processes and highlight the need to account for different organizational principles when modeling different chromatin environments.

A posterior approach to correct for focal plane offsets in lattice light sheet structured illumination microscopy

Significance

Lattice light sheet structured illumination microscopy (latticeSIM) has proven highly effective in producing 3D images with super resolution rapidly and with minimal photobleaching. However, due to the use of two separate objectives, sample-induced aberrations can result in an offset between the planes of excitation and detection, causing artifacts in the reconstructed images.

Aim

We introduce a posterior approach to detect and correct for the axial offset between the excitation and detection focal planes in latticeSIM and provide a method to minimize artifacts in the reconstructed images.

Approach

We utilized the residual phase information within the overlap regions of the laterally shifted structured illumination microscopy (SIM) information components in frequency space to retrieve the axial offset between the excitation and the detection focal planes in latticeSIM.

Results

We validated our technique through simulations and experiments, encompassing a range of samples from fluorescent beads to subcellular structures of adherent cells. We also show utilizing transfer functions with the same axial offset as that which was present during the data acquisition results in reconstructed images with minimal artifacts and salvages otherwise unusable data.

Conclusion

We envision that our method will be a valuable addition to restore image quality in latticeSIM datasets even for those acquired under non-ideal experimental conditions.

Protein and lipid expansion microscopy with trypsin and tyramide signal amplification for 3D imaging

Expansion microscopy, whereby the relative positions of biomolecules are physically increased via hydrogel expansion, can be used to reveal ultrafine structures of cells under a conventional microscope. Despite its utility for achieving super-resolution imaging, expansion microscopy suffers a major drawback, namely reduced fluorescence signals caused by excessive proteolysis and swelling effects. This caveat results in a lower photon budget and disfavors fluorescence imaging over a large field of view that can cover an entire expanded cell, especially in 3D. In addition, the complex procedures and specialized reagents of expansion microscopy hinder its popularization. Here, we modify expansion microscopy by deploying trypsin digestion to reduce protein loss and tyramide signal amplification to enhance fluorescence signal for point-scanning-based imaging. We name our new methodology TT-ExM to indicate dual trypsin and tyramide treatments. TT-ExM may be applied for both antibody and lipid staining. TT-ExM displayed enhanced protein retention for endoplasmic reticulum and mitochondrial markers in COS-7 cell cultures. Importantly, TT-ExM-based lipid staining clearly revealed the complex 3D membrane structures in entire expanded cells. Through combined lipid and DNA staining, our TT-ExM methodology highlighted mitochondria by revealing their DNA and membrane structures in cytoplasm, as well as the lipid-rich structures formed via phase separation in nuclei at interphase. We also observed lipid-rich chromosome matrices in the mitotic cells. These high-quality 3D images demonstrate the practicality of TT-ExM. Thus, readily available reagents can be deployed in TT-ExM to significantly enhance fluorescence signals and generate high-quality and ultrafine-resolution images under confocal microscopy.

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.

A fluorogenic complementation tool kit for interrogating lipid droplet-organelle interaction

Contact sites between lipid droplets and other organelles are essential for cellular lipid and energy homeostasis. Detection of these contact sites at nanometer scale over time in living cells is challenging. Here, we developed a tool kit for detecting contact sites based on Fluorogen-Activated Bimolecular complementation at CONtact sites, FABCON, using a reversible, low affinity split fluorescent protein, splitFAST. FABCON labels contact sites with minimal perturbation to organelle interaction. Via FABCON, we quantitatively demonstrated that endoplasmic reticulum (ER)- and mitochondria (mito)-lipid droplet contact sites are dynamic foci in distinct metabolic conditions, such as during lipid droplet biogenesis and consumption. An automated analysis pipeline further classified individual contact sites into distinct subgroups based on size, likely reflecting differential regulation and function. Moreover, FABCON is generalizable to visualize a repertoire of organelle contact sites including ER-mito. Altogether, FABCON reveals insights into the dynamic regulation of lipid droplet-organelle contact sites and generates new hypotheses for further mechanistical interrogation during metabolic switch.

Correlative single molecule lattice light sheet imaging reveals the dynamic relationship between nucleosomes and the local chromatin environment

In the nucleus, biological processes are driven by proteins that diffuse through and bind to a meshwork of nucleic acid polymers. To better understand this interplay, we developed an imaging platform to simultaneously visualize single protein dynamics together with the local chromatin environment in live cells. Together with super-resolution imaging, new fluorescent probes, and biophysical modeling, we demonstrated that nucleosomes display differential diffusion and packing arrangements as chromatin density increases whereas the viscoelastic properties and accessibility of the interchromatin space remain constant. Perturbing nuclear functions impacted nucleosome diffusive properties in a manner that was dependent on local chromatin density and supportive of a model wherein transcription locally stabilizes nucleosomes while simultaneously allowing for the free exchange of nuclear proteins. Our results reveal that nuclear heterogeneity arises from both active and passive process and highlights the need to account for different organizational principals when modeling different chromatin environments.

Bridging live-cell imaging and next-generation cancer treatment

By providing spatial, molecular and morphological data over time, live-cell imaging can provide a deeper understanding of the cellular and signalling events that determine cancer response to treatment. Understanding this dynamic response has the potential to enhance clinical outcome by identifying biomarkers or actionable targets to improve therapeutic efficacy. Here, we review recent applications of live-cell imaging for uncovering both tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. Given the increasing uses of T cell therapies, we discuss the unique opportunity of time-lapse imaging for capturing the interactivity and motility of immunotherapies. Although traditionally limited in the number of molecular features captured, novel developments in multidimensional imaging and multi-omics data integration offer strategies to connect single-cell dynamics to molecular phenotypes. We review the effect of these recent technological advances on our understanding of the cellular dynamics of tumour targeting and discuss their implication for next-generation precision medicine.

Formation of multiple complex light structures simultaneously in 3D volume using a single binary phase mask

Complex structure formation inside or through turbid media is a challenging task due to refractive index inhomogeneity, random light scattering, and speckle noise formation. In this article, we have coupled the data regression model in the R-squared metric and used its advantages as a fitness function in the genetic algorithm to advance the resolution and structural uniformity. As a compatible system with the binary genetic algorithm, we have presented a cost-effective iterative wavefront shaping system-design with binary phase modulation using an affordable ferroelectric liquid crystal (FLC) based binary-phase spatial light modulator (SLM). R-squared metric in the genetic algorithm is analyzed to optimize the binary phase mask, and the prototype system based on iterative binary phase modulation has been validated with a 120-grit ground glass diffuser and fresh chicken tissues of thickness 307 [Formula: see text] and 812 [Formula: see text]. The detailed results show that the proposed cost-effective wavefront shaping system with data regression model assisted R-squared fitness function can construct high-resolution multiple complex hetero-structures simultaneously in 3D volume using an optimized single phase-mask.

Nondestructive 3D Pathology with Light-Sheet Fluorescence Microscopy for Translational Research and Clinical Assays

In recent years, there has been a revived appreciation for the importance of spatial context and morphological phenotypes for both understanding disease progression and guiding treatment decisions. Compared with conventional 2D histopathology, which is the current gold standard of medical diagnostics, nondestructive 3D pathology offers researchers and clinicians the ability to visualize orders of magnitude more tissue within their natural volumetric context. This has been enabled by rapid advances in tissue-preparation methods, high-throughput 3D microscopy instrumentation, and computational tools for processing these massive feature-rich data sets. Here, we provide a brief overview of many of these technical advances along with remaining challenges to be overcome. We also speculate on the future of 3D pathology as applied in translational investigations, preclinical drug development, and clinical decision-support assays.

Sidelobe suppression in structured light sheet fluorescence microscopy by the superposition of two light sheets

Light sheet microscopy has emerged as a powerful technique for three-dimensional and long-term vivo imaging within neuroscience and developmental biology. A light sheet illumination with structured light fields allows a better tradeoff between the field of view and axial resolution but suffers from strong side lobes. Here, we propose a method of producing structured light sheet illumination with suppressed side lobes by applying the superposition of two light sheets. The side lobe suppression results from the destructive interference between the side lobes and constructive interference between the main lobe of the two light sheets. In the proposed method, the incident light pattern in the rear pupil plane of the illumination objective is a combination of the incident light line beams required for the generation of the two interfering light sheets. We present a fast and simple calculation method to determine the incident light pattern in the rear pupil plane. Simulation results demonstrate the effectiveness of the proposed sidelobe suppression method for double-line light sheet, four-line light sheet, as well as line Bessel sheet. In particular, an 81% decrease in the relative side lobe energy can be achieved in case of double-line light sheet with an almost nonchanging propagation length. We show a way of using combined incident light patterns to generate structured light sheets with interference-resulted side lobe suppression, which is straightforward in design and with advantages of improved imaging performance.

Large-scale expanded sample imaging with tiling lattice lightsheet microscopy

The ability to observe biological nanostructures forms a vital step in understanding their functions. Thanks to the invention of expansion microscopy (ExM) technology, super-resolution features of biological samples can now be easily visualized with conventional light microscopies. However, when the sample is physically expanded, the demand for deep and precise 3D imaging increases. Lattice lightsheet microscopy (LLSM), which utilizes a planar illumination that is confined within the imaging depth of high numerical aperture (NA=1.1) detection objective, fulfils such requirements. In addition, optical tiling could be implemented to increase the field of view (FoV) by moving the lightsheet without mechanically moving the samples or the objective for high-precision 3D imaging. In this review article, we will explain the principle of the tiling lattice lightsheet microscopy (tLLSM), which combines optical tiling and lattice lightsheet, and discuss the applications of tLLSM in ExM.