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The nematode Caenorhabditis elegans ( C. elegans ) is a model organism used frequently in developmental biology and neurobiology [White, (1986), Sulston, (1983), Chisholm, (2016) and Rapti, (2020)]. The C. elegans embryo can be used for cell tracking studies to understand how cell movement drives the development of specific embryonic tissues. Analyses in late-stage development are complicated by bouts of rapid twitching motions which invalidate traditional cell tracking approaches. However, the embryo possesses a small set of cells which may be identified, thereby defining the coiled embryo’s posture [Christensen, 2015]. The posture serves as a frame of reference, facilitating cell tracking even in the presence of twitching. Posture identification is nevertheless challenging due to the complete repositioning of the embryo between sampled images. Current approaches to posture identification rely on time-consuming manual efforts by trained users which limits the efficiency of subsequent cell tracking. Here, we cast posture identification as a point-set matching task in which coordinates of seam cell nuclei are identified to jointly recover the posture. Most point-set matching methods comprise coherent point transformations that use low order objective functions [Zhou, (2016) and Zhang, (2019)]. Hypergraphs, an extension of traditional graphs, allow more intricate modeling of relationships between objects, yet existing hypergraphical point-set matching methods are limited to heuristic algorithms which do not easily scale to handle higher degree hypergraphs [Duchenne, (2010), Chertok, (2010) and Lee, (2011)]. Our algorithm, Exact Hypergraph Matching ( EHGM ), adapts the classical branch-and-bound paradigm to dynamically identify a globally optimal correspondence between point-sets under an arbitrarily intricate hypergraphical model. EHGM with hypergraphical models inspired by C. elegans embryo shape identified posture more accurately (56%) than established point-set matching methods (27%), correctly identifying twice as many sampled postures as a leading graphical approach. Posterior region seeding empowered EHGM to correctly identify 78% of postures while reducing runtime, demonstrating the efficacy of the method on a cutting-edge problem in developmental biology.

The Caenorhabditis elegans embryo is a powerful model for studying neural development, but conventional imaging methods are either too slow or phototoxic to take full advantage of this system. To solve these problems, we developed an inverted selective plane illumination microscopy (iSPIM) module for noninvasive high-speed volumetric imaging of living samples. iSPIM is designed as a straightforward add-on to an inverted microscope, permitting conventional mounting of specimens and facilitating SPIM use by development and neurobiology laboratories. iSPIM offers a volumetric imaging rate 30x faster than currently used technologies, such as spinning-disk confocal microscopy, at comparable signal-to-noise ratio. This increased imaging speed allows us to continuously monitor the development of C, elegans embryos, scanning volumes every 2 s for the 14-h period of embryogenesis with no detectable phototoxicity. Collecting ∼25,000 volumes over the entirety of embryogenesis enabled in toto visualization of positions and identities of cell nuclei. By merging two-color iSPIM with automated lineaging techniques we realized two goals: (i) identification of neurons expressing the transcription factor CEH-10/Chx10 and (ii) visualization of their neurodevelopmental dynamics. We found that canal-associated neurons use somal translocation and amoeboid movement as they migrate to their final position in the embryo. We also visualized axon guidance and growth cone dynamics as neurons circumnavigate the nerve ring and reach their targets in the embryo. The high-speed volumetric imaging rate of iSPIM effectively eliminates motion blur from embryo movement inside the egg case, allowing characterization of dynamic neurodevelopmental events that were previously inaccessible.

The nematode Caenorhabditis elegans (C. elegans) is a model organism used frequently in developmental biology and neurobiology [White, (1986), Sulston, (1983), Chisholm, (2016) and Rapti, (2020)]. The C. elegans embryo can be used for cell tracking studies to understand how cell movement drives the development of specific embryonic tissues. Analyses in late-stage development are complicated by bouts of rapid twitching motions which invalidate traditional cell tracking approaches. However, the embryo possesses a small set of cells which may be identified, thereby defining the coiled embryo’s posture [Christensen, 2015]. The posture serves as a frame of reference, facilitating cell tracking even in the presence of twitching. Posture identification is nevertheless challenging due to the complete repositioning of the embryo between sampled images. Current approaches to posture identification rely on time-consuming manual efforts by trained users which limits the efficiency of subsequent cell tracking. Here, we cast posture identification as a point-set matching task in which coordinates of seam cell nuclei are identified to jointly recover the posture. Most point-set matching methods comprise coherent point transformations that use low order objective functions [Zhou, (2016) and Zhang, (2019)]. Hypergraphs, an extension of traditional graphs, allow more intricate modeling of relationships between objects, yet existing hypergraphical point-set matching methods are limited to heuristic algorithms which do not easily scale to handle higher degree hypergraphs [Duchenne, (2010), Chertok, (2010) and Lee, (2011)]. Our algorithm, Exact Hypergraph Matching (EHGM), adapts the classical branch-and-bound paradigm to dynamically identify a globally optimal correspondence between point-sets under an arbitrarily intricate hypergraphical model. EHGM with hypergraphical models inspired by C. elegans embryo shape identified posture more accurately (56%) than established point-set matching methods (27%), correctly identifying twice as many sampled postures as a leading graphical approach. Posterior region seeding empowered EHGM to correctly identify 78% of postures while reducing runtime, demonstrating the efficacy of the method on a cutting-edge problem in developmental biology.

Kumar et al. describe how to build a diSPIM from commercially available parts and use it to live-image cultured cells or worm embryogenesis. The inverted setup enables samples to be mounted directly on microscope slides, avoiding agarose embedding. We describe the construction and use of a compact dual-view inverted selective plane illumination microscope (diSPIM) for time-lapse volumetric (4D) imaging of living samples at subcellular resolution. Our protocol enables a biologist with some prior microscopy experience to assemble a diSPIM from commercially available parts, to align optics and test system performance, to prepare samples, and to control hardware and data processing with our software. Unlike existing light sheet microscopy protocols, our method does not require the sample to be embedded in agarose; instead, samples are prepared conventionally on glass coverslips. Tissue culture cells and Caenorhabditis elegans embryos are used as examples in this protocol; successful implementation of the protocol results in isotropic resolution and acquisition speeds up to several volumes per s on these samples. Assembling and verifying diSPIM performance takes ∼6 d, sample preparation and data acquisition take up to 5 d and postprocessing takes 3–8 h, depending on the size of the data.

The range of genomic sequences accessible by zinc-finger nucleases is expanded by this archive of validated two-finger modules. The widespread use of zinc-finger nucleases (ZFNs) for genome engineering is hampered by the fact that only a subset of sequences can be efficiently recognized using published finger archives. We describe a set of validated two-finger modules that complement existing finger archives and expand the range of ZFN-accessible sequences threefold. Using this archive, we introduced lesions at 9 of 11 target sites in the zebrafish genome.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained ‘de-aberration’ networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos. Optical aberrations can hinder fluorescence imaging of thick samples, reducing image signal, contrast, and resolution. Here, the authors introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition or introducing more optics.

Systematic analysis of rich behavioral recordings is being used to uncover how circuits encode complex behaviors. Here, we apply this approach to embryos. What are the first embryonic behaviors and how do they evolve as early neurodevelopment ensues? To address these questions, we present a systematic description of behavioral maturation for Caenorhabditis elegans embryos. Posture libraries were built using a genetically encoded motion capture suit imaged with light-sheet microscopy and annotated using custom tracking software. Analysis of cell trajectories, postures, and behavioral motifs revealed a stereotyped developmental progression. Early movement is dominated by flipping between dorsal and ventral coiling, which gradually slows into a period of reduced motility. Late-stage embryos exhibit sinusoidal waves of dorsoventral bends, prolonged bouts of directed motion, and a rhythmic pattern of pausing, which we designate slow wave twitch (SWT). Synaptic transmission is required for late-stage motion but not for early flipping nor the intervening inactive phase. A high-throughput behavioral assay and calcium imaging revealed that SWT is elicited by the rhythmic activity of a quiescence-promoting neuron (RIS). Similar periodic quiescent states are seen prenatally in diverse animals and may play an important role in promoting normal developmental outcomes.

Light-sheet fluorescence microscopy (LSFM) enables high-speed, high-resolution, and gentle imaging of live specimens over extended periods. Here we describe a technique that improves the spatiotemporal resolution and collection efficiency of LSFM without modifying the underlying microscope. By imaging samples on reflective coverslips, we enable simultaneous collection of four complementary views in 250 ms, doubling speed and improving information content relative to symmetric dual-view LSFM. We also report a modified deconvolution algorithm that removes associated epifluorescence contamination and fuses all views for resolution recovery. Furthermore, we enhance spatial resolution (to <300 nm in all three dimensions) by applying our method to single-view LSFM, permitting simultaneous acquisition of two high-resolution views otherwise difficult to obtain due to steric constraints at high numerical aperture. We demonstrate the broad applicability of our method in a variety of samples, studying mitochondrial, membrane, Golgi, and microtubule dynamics in cells and calcium activity in nematode embryos. Light-sheet fluorescence microscopy enables high resolution imaging of biological samples. Here the authors use reflective coverslips to obtain multiple sample views simultaneously, improving the speed of acquisition and resolution compared to dual-view selective plane illumination microscopy.

The nematode Caenorhabditis elegans possesses a simple embryonic nervous system with few enough neurons that the growth of each cell could be followed to provide a systems-level view of development. However, studies of single cell development have largely been conducted in fixed or pre-twitching live embryos, because of technical difficulties associated with embryo movement in late embryogenesis. We present open-source untwisting and annotation software (http://mipav.cit.nih.gov/plugin_jws/mipav_worm_plugin.php) that allows the investigation of neurodevelopmental events in late embryogenesis and apply it to track the 3D positions of seam cell nuclei, neurons, and neurites in multiple elongating embryos. We also provide a tutorial describing how to use the software (Supplementary file 1 ) and a detailed description of the untwisting algorithm (Appendix). The detailed positional information we obtained enabled us to develop a composite model showing movement of these cells and neurites in an 'average' worm embryo. The untwisting and cell tracking capabilities of our method provide a foundation on which to catalog C. elegans neurodevelopment, allowing interrogation of developmental events in previously inaccessible periods of embryogenesis. Understanding how the brain and nervous system develops from a few cells into complex, interconnected networks is a key goal for neuroscientists. Although researchers have identified many of the genes involved in this process, how these work together to form an entire brain remains unknown. A simple worm called Caenorhabiditis elegans is commonly used to study brain development because it has only about 300 neurons, simplifying the study of its nervous system. The worms are easy to grow in the laboratory and are transparent, allowing scientists to observe how living worms develop using a microscope. Researchers have learned a great deal about the initial growth of the nervous system in C. elegans embryos. However, it has been difficult to study the embryos once their muscles have formed because they constantly twist, fold, and move, making it hard to track the cells. Now, Christensen, Bokinsky, Santella, Wu et al. have developed a computer program that allows scientists to virtually untwist the embryos and follow the development of the nervous system from its beginning to when the embryo hatches. First, images are taken of worm embryos that produce fluorescent proteins marking certain body parts. The program, with user input, labels the fluorescent cells in the images, which indicates how the embryo is bending and allows the program to straighten the worm. The program can also track how cells move around the embryo during development and show the positional relationships between different cells at different stages of development. Christensen et al. have made the program freely available for other researchers to use. The next step is to increase automation, making the software faster and more straightforward for users. Ultimately, the software could help in the challenge to comprehensively examine the development of each neuron in the worm.

Vann McGee has argued against solutions to the liar paradox that simply restrict the scope of the T sentences as little as possible. This argument is often taken to disprove Paul Horwich's preferred solution to the liar paradox for his Minimal Theory of truth (MT). I argue that Horwich's theory is different enough from the theory McGee criticized that these criticisms do not apply to Horwich's theory. On the basis of this, I argue that propositional theories, like MT, cannot be evaluated using the same methods as sentential theories.