During gastrulation, physical forces reshape the simple embryonic tissue to form the complex body plans of multicellular organisms. These forces often cause large-scale asymmetric movements of the embryonic tissue. In many embryos, the gastrulating tissue is surrounded by a rigid protective shell. Although it is well-recognized that gastrulation movements depend on forces that are generated by tissue-intrinsic contractility, it is not known whether interactions between the tissue and the protective shell provide additional forces that affect gastrulation. Here we show that a particular part of the blastoderm tissue of the red flour beetle (Tribolium castaneum) tightly adheres in a temporally coordinated manner to the vitelline envelope that surrounds the embryo. This attachment generates an additional force that counteracts tissue-intrinsic contractile forces to create asymmetric tissue movements. This localized attachment depends on an αPS2 integrin (inflated), and the knockdown of this integrin leads to a gastrulation phenotype that is consistent with complete loss of attachment. Furthermore, analysis of another integrin (the αPS3 integrin, scab) in the fruit fly (Drosophila melanogaster) suggests that gastrulation in this organism also relies on adhesion between the blastoderm and the vitelline envelope. Our findings reveal a conserved mechanism through which the spatiotemporal pattern of tissue adhesion to the vitelline envelope provides controllable, counteracting forces that shape gastrulation movements in insects.

Decisions are held in memory until enacted, which makes them potentially vulnerable to distracting sensory input. Gating of information flow from sensory to motor areas could protect memory from interference during decision-making, but the underlying network mechanisms are not understood. Here, we trained mice to detect optogenetic stimulation of the somatosensory cortex, with a delay separating sensation and action. During the delay, distracting stimuli lost influence on behavior over time, even though distractor-evoked neural activity percolated through the cortex without attenuation. Instead, choice-encoding activity in the motor cortex became progressively less sensitive to the impact of distractors. Reverse engineering of neural networks trained to reproduce motor cortex activity revealed that the reduction in sensitivity to distractors was caused by a growing separation in the neural activity space between attractors that encode alternative decisions. Our results show that communication between brain regions can be gated via attractor dynamics, which control the degree of commitment to an action.

Dividing cells reorganize their microtubule cytoskeleton into a bipolar spindle, which moves one set of sister chromatids to each nascent daughter cell. Early spindle assembly models postulated that spindle pole-derived microtubules search the cytoplasmic space until they randomly encounter a kinetochore to form a stable attachment. More recent work uncovered several additional, centrosome-independent microtubule generation pathways, but the contributions of each pathway to spindle assembly have remained unclear. Here, we combined live microscopy and mathematical modeling to show that most microtubules nucleate at noncentrosomal regions in dividing human cells. Using a live-cell probe that selectively labels aged microtubule lattices, we demonstrate that the distribution of growing microtubule plus ends can be almost entirely explained by Augmin-dependent amplification of long-lived microtubule lattices. By ultrafast 3D lattice light-sheet microscopy, we observed that this mechanism results in a strong directional bias of microtubule growth toward individual kinetochores. Our systematic quantification of spindle dynamics reveals highly coordinated microtubule growth during kinetochore fiber assembly.

While cytokinesis has been intensely studied, the way it is executed during development is not well understood, despite a long-standing appreciation that various aspects of cytokinesis vary across cell and tissue types. To address this, we investigated cytokinesis during the invariant embryonic divisions and found several reproducibly altered parameters at different stages. During early divisions, furrow ingression asymmetry and midbody inheritance is consistent, suggesting specific regulation of these events. During morphogenesis, we found several unexpected alterations to cytokinesis including apical midbody migration in polarizing epithelial cells of the gut, pharynx and sensory neurons. Aurora B kinase, which is essential for several aspects of cytokinesis, remains apically localized in each of these tissues after internalization of midbody ring components. Aurora B inactivation disrupts cytokinesis and causes defects in apical structures, even if inactivated post-mitotically. Therefore, cytokinesis is implemented in a specialized way during epithelial polarization and Aurora B has a new role in the formation of the apical surface.

The most established method of reconstructing neural circuits from animals involves slicing tissue very thin, then taking mosaics of electron microscope (EM) images. To trace neurons across different images and through different sections, these images must be accurately aligned, both with the others in the same section and to the sections above and below. Unfortunately, sectioning and imaging are not ideal processes - some of the problems that make alignment difficult include lens distortion, tissue shrinkage during imaging, tears and folds in the sectioned tissue, and dust and other artifacts. In addition the data sets are large (hundreds of thousands of images) and each image must be aligned with many neighbors, so the process must be automated and reliable. This paper discusses methods of dealing with these problems, with numeric results describing the accuracy of the resulting alignments.

The cerebellum plays an important role in both motor control and cognitive function. Cerebellar function is topographically organized and diseases that affect specific parts of the cerebellum are associated with specific patterns of symptoms. Accordingly, delineation and quantification of cerebellar sub-regions from magnetic resonance images are important in the study of cerebellar atrophy and associated functional losses. This paper describes an automated cerebellar lobule segmentation method based on a graph cut segmentation framework. Results from multi-atlas labeling and tissue classification contribute to the region terms in the graph cut energy function and boundary classification contributes to the boundary term in the energy function. A cerebellar parcellation is achieved by minimizing the energy function using the α-expansion technique. The proposed method was evaluated using a leave-one-out cross-validation on 15 subjects including both healthy controls and patients with cerebellar diseases. Based on reported Dice coefficients, the proposed method outperforms two state-of-the-art methods. The proposed method was then applied to 2(j) 77 subjects to study the region-specific cerebellar structural differences in three spinocerebellar ataxia (SCA) genetic subtypes. Quantitative analysis of the lobule volumes show distinct patterns of volume changes associated with different SCA subtypes consistent with known patterns of atrophy in these genetic subtypes.

Analysis of differential gene expression is crucial for the study of cell fate and behavior during embryonic development. However, automated methods for the sensitive detection and quantification of RNAs at cellular resolution in embryos are lacking. With the advent of single-molecule fluorescence in situ hybridization (smFISH), gene expression can be analyzed at single-molecule resolution. However, the limited availability of protocols for smFISH in embryos and the lack of efficient image analysis pipelines have hampered quantification at the (sub)cellular level in complex samples such as tissues and embryos. Here, we present a protocol for smFISH on zebrafish embryo sections in combination with an image analysis pipeline for automated transcript detection and cell segmentation. We use this strategy to quantify gene expression differences between different cell types and identify differences in subcellular transcript localization between genes. The combination of our smFISH protocol and custom-made, freely available, analysis pipeline will enable researchers to fully exploit the benefits of quantitative transcript analysis at cellular and subcellular resolution in tissues and embryos.

We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197×7351 pixel images (4.5×4.5×45 nm resolution) of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12% false negative, 7% false positive detections) is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem).

High-resolution microscopic imaging of biological samples often produces multiple 3D image tiles to cover a large field of view of specimen. Usually each tile has a large size, in the range of hundreds of megabytes to several gigabytes. For many of our image data sets, existing software tools are often unable to stitch those 3D tiles into a panoramic view, thus impede further data analysis. We propose a simple, but accurate, robust, and automatic method to stitch a group of image tiles without a priori adjacency information of them. We first use a multiscale strategy to register a pair of 3D image tiles rapidly, achieving about 8~10 times faster speed and 10 times less memory requirement compared to previous methods. Then we design a minimum-spanning-tree based method to determine the optimal adjacency of tiles. We have successfully stitched large image stacks of model animals including C. elegans, fruit fly, dragonfly, and mouse, which could not be stitched by several existing methods.

The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.