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.

Social behaviour has a key role in animal survival across species, ranging from insects to primates and humans. However, the biological mechanisms driving natural interactions between multiple animals, over long-term periods, are poorly studied and remain elusive. Rigorous and objective quantification of behavioural parameters within a group poses a major challenge as it requires simultaneous monitoring of the positions of several individuals and comprehensive consideration of many complex factors. Automatic tracking and phenotyping of interacting animals could thus overcome the limitations of manual tracking methods. Here we report a broadly applicable system that automatically tracks the locations of multiple, uniquely identified animals, such as mice, within a semi-natural setting. The system combines video and radio frequency identified tracking data to obtain detailed behavioural profiles of both individuals and groups. We demonstrate the usefulness of these data in characterizing individual phenotypes, interactions between pairs and the collective social organization of groups.

We introduce a method based on machine vision for automatically measuring aggression and courtship in Drosophila melanogaster. The genetic and neural circuit bases of these innate social behaviors are poorly understood. High-throughput behavioral screening in this genetically tractable model organism is a potentially powerful approach, but it is currently very laborious. Our system monitors interacting pairs of flies and computes their location, orientation and wing posture. These features are used for detecting behaviors exhibited during aggression and courtship. Among these, wing threat, lunging and tussling are specific to aggression; circling, wing extension (courtship ’song’) and copulation are specific to courtship; locomotion and chasing are common to both. Ethograms may be constructed automatically from these measurements, saving considerable time and effort. This technology should enable large-scale screens for genes and neural circuits controlling courtship and aggression.

A quantitative description of animal social behaviour is informative for behavioural biologists and clinicians developing drugs to treat social disorders. Social interaction in a group of animals has been difficult to measure because behaviour develops over long periods of time and requires tedious manual scoring, which is subjective and often non-reproducible. Computer-vision systems with the ability to measure complex social behaviour automatically would have a transformative impact on biology. Here, we present a method for tracking group-housed mice individually as they freely interact over multiple days. Each mouse is bleach-marked with a unique fur pattern. The patterns are automatically learned by the tracking software and used to infer identities. Trajectories are analysed to measure behaviour as it develops over days, beyond the range of acute experiments. We demonstrate how our system may be used to study the development of place preferences, associations and social relationships by tracking four mice continuously for five days. Our system enables accurate and reproducible characterisation of wild-type mouse social behaviour and paves the way for high-throughput long-term observation of the effects of genetic, pharmacological and environmental manipulations.