Biobeam-Multiplexed wave-optical simulations of light-sheet microscopy

Using high-resolution microscopy data to generate realistic structures for electromagnetic FDTD simulations from complex biological models

Finite-difference time-domain (FDTD) electromagnetic simulations are a computational method that has seen much success in the study of biological optics; however, such simulations are often hindered by the difficulty of faithfully replicating complex biological microstructures in the simulation space. Recently, we designed simulations to calculate the trajectory of electromagnetic light waves through realistically reconstructed retinal photoreceptors and found that cone photoreceptor mitochondria play a substantial role in shaping incoming light. In addition to vision research and ophthalmology, such simulations are broadly applicable to studies of the interaction of electromagnetic radiation with biological tissue. Here, we present our method for discretizing complex 3D models of cellular structures for use in FDTD simulations using MEEP, the MIT Electromagnetic Equation Propagation software, including subpixel smoothing at mesh boundaries. Such models can originate from experimental imaging or be constructed by hand. We also include sample code for use in MEEP. Implementation of this algorithm in new code requires understanding of 3D mathematics and may require several weeks of effort, whereas use of our sample code requires knowledge of MEEP and C++ and may take up to a few hours to prepare a model of interest for 3D FDTD simulation. In all cases, access to a facility supercomputer with parallel processing capabilities is recommended. This protocol offers a practical solution to a significant challenge in the field of computational electrodynamics and paves the way for future advancements in the study of light interaction with biological structures.

Deep learning in mesoscale brain image analysis: A review

Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, their high noise levels, the complex morphology of the brain from the cellular to the regional and anatomical levels, the inhomogeneous distribution of fluorescent labels in the cells and tissues, and imaging artifacts. Due to their impressive ability to extract relevant information from images, deep learning algorithms are widely applied to microscopy images of the brain to address these challenges and they perform superiorly in a wide range of microscopy image processing and analysis tasks. This article reviews the applications of deep learning algorithms in brain mesoscale microscopy image processing and analysis, including image synthesis, image segmentation, object detection, and neuron reconstruction and analysis. We also discuss the difficulties of each task and possible directions for further research.

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.

3D fluorescence microscopy data synthesis for segmentation and benchmarking

Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproducible way. Especially state-of-the-art deep learning-based approaches most often require large amounts of annotated training data to produce accurate and generalist outputs, but they are often compromised by the general lack of those annotated data sets. In this work, we propose how conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy from annotation masks of 3D cellular structures. In combination with mask simulation approaches, we demonstrate the generation of fully-annotated 3D microscopy data sets that we make publicly available for training or benchmarking. An additional positional conditioning of the cellular structures enables the reconstruction of position-dependent intensity characteristics and allows to generate image data of different quality levels. A patch-wise working principle and a subsequent full-size reassemble strategy is used to generate image data of arbitrary size and different organisms. We present this as a proof-of-concept for the automated generation of fully-annotated training data sets requiring only a minimum of manual interaction to alleviate the need of manual annotations.

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.

Ultra-thin boundary layer for high-accuracy simulations of light propagation

The modified Born series method is currently one of the most efficient methods available for simulating light scattering in large inhomogeneous media. However, to achieve high accuracy, the method requires thick gradually absorbing layers around the simulation domain. Here, we introduce new boundary conditions, combining a padding-free acyclic convolution with an ultra-thin boundary layer. Our new boundary conditions minimize the wrap-around and reflection artefacts originating from the edges of the simulation domain, while also greatly reducing the computational costs and the memory requirements of the method. Our GPU-accelerated Matlab implementation is available on GitHub.

How to define and optimize axial resolution in light-sheet microscopy: a simulation-based approach

"How thick is your light sheet?" is a question that has been asked frequently after talks showing impressive renderings of 3D data acquired by a light-sheet microscope. This question is motivated by the fact that most of the time the thickness of the light-sheet is uniquely associated to the axial resolution of the microscope. However, the link between light-sheet thickness and axial resolution has never been systematically assessed and it is still unclear how both are connected. The question is not trivial because commonly employed measures cannot readily be applied or do not lead to easily interpretable results for the many different types of light sheet. Here, we introduce a set of intuitive measures that helps to define the relationship between light sheet thickness and axial resolution by using simulation data. Unexpectedly, our analysis revealed a trade-off between better axial resolution and thinner light-sheet thickness. Our results are surprising because thicker light-sheets that provide lower image contrast have previously not been associated with better axial resolution. We conclude that classical Gaussian illumination beams should be used when image contrast is most important, and more advanced types of illumination represent a way to optimize axial resolution at the expense of image contrast.

Rod nuclear architecture determines contrast transmission of the retina and behavioral sensitivity in mice

Rod photoreceptors of nocturnal mammals display a striking inversion of nuclear architecture, which has been proposed as an evolutionary adaptation to dark environments. However, the nature of visual benefits and the underlying mechanisms remains unclear. It is widely assumed that improvements in nocturnal vision would depend on maximization of photon capture at the expense of image detail. Here, we show that retinal optical quality improves 2-fold during terminal development, and that this enhancement is caused by nuclear inversion. We further demonstrate that improved retinal contrast transmission, rather than photon-budget or resolution, enhances scotopic contrast sensitivity by 18–27%, and improves motion detection capabilities up to 10-fold in dim environments. Our findings therefore add functional significance to a prominent exception of nuclear organization and establish retinal contrast transmission as a decisive determinant of mammalian visual perception.

Development of a beam propagation method to simulate the point spread function degradation in scattering media

Scattering is one of the main issues that limit the imaging depth in deep tissue optical imaging. To characterize the role of scattering, we have developed a forward model based on the beam propagation method and established the link between the macroscopic optical properties of the media and the statistical parameters of the phase masks applied to the wavefront. Using this model, we have analyzed the degradation of the point-spread function of the illumination beam in the transition regime from ballistic to diffusive light transport. Our method provides a wave-optic simulation toolkit to analyze the effects of scattering on image quality degradation in scanning microscopy. Our open-source implementation is available at https://github.com/BUNPC/Beam-Propagation-Method.

BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples

Light-sheet imaging of cleared and expanded samples creates terabyte-sized datasets that consist of many unaligned three-dimensional image tiles, which must be reconstructed before analysis. We developed the BigStitcher software to address this challenge. BigStitcher enables interactive visualization, fast and precise alignment, spatially resolved quality estimation, real-time fusion and deconvolution of dual-illumination, multitile, multiview datasets. The software also compensates for optical effects, thereby improving accuracy and enabling subsequent biological analysis.

Simulation of live-cell imaging system reveals hidden uncertainties in cooperative binding measurements

We propose a computational method to quantitatively evaluate the systematic uncertainties that arise from undetectable sources in biological measurements using live-cell imaging techniques. We then demonstrate this method in measuring the biological cooperativity of molecular binding networks, in particular, ligand molecules binding to cell-surface receptor proteins. Our results show how the nonstatistical uncertainties lead to invalid identifications of the measured cooperativity. Through this computational scheme, the biological interpretation can be more objectively evaluated and understood under a specific experimental configuration of interest.