Cell plate formation during cytokinesis entails multiple stages occurring concurrently and requiring orchestrated vesicle delivery, membrane remodeling, and timely polysaccharide deposition, such as callose. Understanding such a dynamic process requires dissection in time and space; this has been a major hurdle in studying cytokinesis. Using lattice light sheet microscopy (LLSM) we studied cell plate development in four dimensions, through the behavior of the cytokinesis specific GTPase YFP-RABA2a vesicles. We monitored the entire length of cell plate development, from its first emergence, with the aid of YFP-RABA2a, both in the presence and absence of cytokinetic callose. By developing a robust cytokinetic vesicle volume analysis pipeline, we identified distinct behavioral patterns, allowing the identification of three easily trackable, cell plate developmental phases. Notably, the phase transition between phase I and phase II is striking, indicating a switch from membrane accumulation to the recycling of excess membrane material. We interrogated the role of callose using pharmacological inhibition with LLSM and electron microscopy. Loss of callose inhibited the phase transitions, establishing the critical role and timing of the polysaccharide deposition in cell plate expansion and maturation. This study exemplifies the power of combining LLSM with quantitative analysis to decode and untangle such a complex process.

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. While wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement, and slow when serially mapping spatially varying aberrations across large fields of view. Here, we introduce AOViFT (Adaptive Optical Vision Fourier Transformer)---a machine learning-based aberration sensing framework built around a 3D multistage Vision Transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time, and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or post-acquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Differentiable simulations of optical systems can be combined with deep learning-based reconstruction networks to enable high performance computational imaging via end-to-end (E2E) optimization of both the optical encoder and the deep decoder. This has enabled imaging applications such as 3D localization microscopy, depth estimation, and lensless photography via the optimization of local optical encoders. More challenging computational imaging applications, such as 3D snapshot microscopy which compresses 3D volumes into single 2D images, require a highly non-local optical encoder. We show that existing deep network decoders have a locality bias which prevents the optimization of such highly non-local optical encoders. We address this with a decoder based on a shallow neural network architecture using global kernel Fourier convolutional neural networks (FourierNets). We show that FourierNets surpass existing deep network based decoders at reconstructing photographs captured by the highly non-local DiffuserCam optical encoder. Further, we show that FourierNets enable E2E optimization of highly non-local optical encoders for 3D snapshot microscopy. By combining FourierNets with a large-scale multi-GPU differentiable optical simulation, we are able to optimize non-local optical encoders 170× to 7372× larger than prior state of the art, and demonstrate the potential for ROI-type specific optical encoding with a programmable microscope.

In electron crystallography, membrane protein structure is determined from two-dimensional crystals where the protein is embedded in a membrane. Once large and well-ordered 2D crystals are grown, one of the bottlenecks in electron crystallography is the collection of image data to directly provide experimental phases to high resolution. Here, we describe an approach to bypass this bottleneck, eliminating the need for high-resolution imaging. We use the strengths of electron crystallography in rapidly obtaining accurate experimental phase information from low-resolution images and accurate high-resolution amplitude information from electron diffraction. The low-resolution experimental phases were used for the placement of α helix fragments and extended to high resolution using phases from the fragments. Phases were further improved by density modifications followed by fragment expansion and structure refinement against the high-resolution diffraction data. Using this approach, structures of three membrane proteins were determined rapidly and accurately to atomic resolution without high-resolution image data.

SUMMARY: Here, we propose Fourier ring correlation-based quality estimation (FRC-QE) as a new metric for automated image quality estimation in 3D fluorescence microscopy acquisitions of cleared organoids that yields comparable measurements across experimental replicates, clearing protocols and works for different microscopy modalities.

AVAILABILITY AND IMPLEMENTATION: FRC-QE is written in ImgLib2/Java and provided as an easy-to-use and macro-scriptable plugin for Fiji. Code, documentation, sample images and further information can be found under https://github.com/PreibischLab/FRC-QE.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Frealign is a software tool designed to process electron microscope images of single molecules and complexes to obtain reconstructions at the highest possible resolution. It provides a number of refinement parameters and options that allow users to tune their refinement to achieve specific goals, such as masking to classify selected regions within a particle, control over the refinement of specific alignment parameters to accommodate various data collection schemes, refinement of pseudosymmetric particles, and generation of initial maps. This chapter provides a general overview of Frealign functions and a more detailed guide to using Frealign in typical scenarios.

The structures of many helical protein filaments can be derived from electron micrographs of their suspensions in thin films of vitrified aqueous solutions. The most successful and generally-applicable approach treats short segments of these filaments as independent "single particles", yielding near-atomic resolution for rigid and well-ordered filaments. The single-particle approach can also accommodate filament deformations, yielding sub-nanometer resolution for more flexible filaments. However, in the case of thin and flexible filaments, such as some amyloid-β (Aβ) fibrils, the single-particle approach may fail because helical segments can be curved or otherwise distorted and their alignment can be inaccurate due to low contrast in the micrographs. We developed new software called Frealix that allows the use of arbitrarily short filament segments during alignment to approximate even high curvatures. All segments in a filament are aligned simultaneously with constraints that ensure that they connect to each other in space to form a continuous helical structure. In this paper, we describe the algorithm and benchmark it against datasets of Aβ(1-40) fibrils and tobacco mosaic virus (TMV), both analyzed in earlier work. In the case of TMV, our algorithm achieves similar results to single-particle analysis. In the case of Aβ(1-40) fibrils, we match the previously-obtained resolution but we are also able to obtain reliable alignments and \~{}8-Å reconstructions from curved filaments. Our algorithm also offers a detailed characterization of filament deformations in three dimensions and enables a critical evaluation of the worm-like chain model for biological filaments.

The frequencies of transcription initiation of regulated and constitutive genes depend on the concentration of free RNA polymerase holoenzyme [Rf] near their promoters. Although RNA polymerase is largely confined to the nucleoid, it is difficult to determine absolute concentrations of [Rf] at particular locations within the nucleoid structure. However, relative concentrations of free RNA polymerase at different growth rates, [Rf]rel, can be estimated from the activities of constitutive promoters. Previous studies indicated that the rrnB P2 promoter is constitutive and that [Rf]rel in the vicinity of rrnB P2 increases with increasing growth rate. Recently it has become possible to directly visualize Rf in growing Escherichia coli cells. Here we examine some of the important issues relating to gene expression based on these new observations. We conclude that: (i) At a growth rate of 2 doublings/h, there are about 1000 free and 2350 non-specifically DNA-bound RNA polymerase molecules per average cell (12 and 28%, respectively, of 8400 total) which are in rapid equilibrium. (ii) The reversibility of the non-specific binding generates more than 1000 free RNA polymerase molecules every second in the immediate vicinity of the DNA. Of these, most rebind non-specifically to the DNA within a few ms; the frequency of non-specific binding is at least two orders of magnitude greater than specific binding and transcript initiation. (iii) At a given amount of RNA polymerase per cell, [Rf] and the density of non-specifically DNA-bound RNA polymerase molecules along the DNA both vary reciprocally with the amount of DNA in the cell. (iv) At 2 doublings/h an E. coli cell contains, on the average, about 1 non-specifically bound RNA polymerase per 9 kbp of DNA and 1 free RNA polymerase per 20 kbp of DNA. However some DNA regions (i.e. near active rRNA operons) may have significantly higher than average [Rf].

Information within the brain travels from neuron to neuron across synapses. At any given moment, only a few synapses within billions will be active and are thought to transmit key information about the environment, a behavior being executed or memory being recalled. Here we present SynTagMA, which marks active synapses within a ~2 s time window. Upon violet illumination, the genetically expressed tag converts from green to red fluorescence if bound to calcium. Targeted to presynaptic terminals, preSynTagMA allows discrimination between active and silent axons. Targeted to excitatory postsynapses, postSynTagMA creates a snapshot of synapses active just before photoconversion. To analyze large datasets, we developed an analysis program that automatically identifies and tracks the fluorescence of thousands of individual synapses in tissue. Together, these tools provide a high throughput method for repeatedly mapping active synapses in vitro and in vivo.

Mapping mammalian synaptic connectivity has long been an important goal of neuroscience because knowing how neurons and brain areas are connected underpins an understanding of brain function. Meeting this goal requires advanced techniques with single synapse resolution and large-scale capacity, especially at multiple scales tethering the meso- and micro-scale connectome. Among several advanced LM-based connectome technologies, Array Tomography (AT) and mammalian GFP-Reconstitution Across Synaptic Partners (mGRASP) can provide relatively high-throughput mapping synaptic connectivity at multiple scales. AT- and mGRASP-assisted circuit mapping (ATing and mGRASPing), combined with techniques such as retrograde virus, brain clearing techniques, and activity indicators will help unlock the secrets of complex neural circuits. Here, we discuss these useful new tools to enable mapping of brain circuits at multiple scales, some functional implications of spatial synaptic distribution, and future challenges and directions of these endeavors.