Person:

Lichtman, Jeff

We present a novel semi-automatic method for segmenting neural processes in large, highly anisotropic EM (electron microscopy) image stacks. Our method takes advantage of sparse scribble annotations provided by the user to guide a 3D variational segmentation model, thereby allowing our method to globally optimally enforce 3D geometric constraints on the segmentation. Moreover, we leverage a novel algorithm for propagating segmentation constraints through the image stack via optimal volumetric pathways, thereby allowing our method to compute highly accurate 3D segmentations from very sparse user input. We evaluate our method by reconstructing 16 neural processes in a 1024×1024×50 nanometer-scale EM image stack of a mouse hippocampus. We demonstrate that, on average, our method is 68% more accurate than previous state-of-the-art semi-automatic methods.

The connectivity among neurons holds the key to understanding brain function. Mapping neural connectivity in brain circuits requires imaging techniques with high spatial resolution to facilitate neuron tracing and high molecular specificity to mark different cellular and molecular populations. Here, we tested a three-dimensional (3D), multicolor super-resolution imaging method, stochastic optical reconstruction microscopy (STORM), for tracing neural connectivity using cultured hippocampal neurons obtained from wild-type neonatal rat embryos as a model system. Using a membrane specific labeling approach that improves labeling density compared to cytoplasmic labeling, we imaged neural processes at 44 nm 2D and 116 nm 3D resolution as determined by considering both the localization precision of the fluorescent probes and the Nyquist criterion based on label density. Comparison with confocal images showed that, with the currently achieved resolution, we could distinguish and trace substantially more neuronal processes in the super-resolution images. The accuracy of tracing was further improved by using multicolor super-resolution imaging. The resolution obtained here was largely limited by the label density and not by the localization precision of the fluorescent probes. Therefore, higher image resolution, and thus higher tracing accuracy, can in principle be achieved by further improving the label density.

Recent advances in optical and electron microscopy allow scientists to acquire extremely high-resolution images for neuroscience research. Datasets imaged with modern electron microscopes can range between tens of terabytes to about one petabyte in size. These large data sizes and the high complexity of the underlying neural structures make it very challenging to handle the data at reasonably interactive rates. To provide neuroscientists flexible and interactive tools for their scientific work we introduce SSECRETT and NeuroTrace, two systems that were designed for interactive exploration and analysis of large-scale optical and electron microscope images to reconstruct complex neural circuits of the mammalian nervous system.

During mammalian development, neuromuscular junctions and some other postsynaptic cells transition from multiple- to single-innervation as synaptic sites are exchanged between different axons. It is unclear whether one axon invades synaptic sites to drive off other inputs or alternatively axons expand their territory in response to sites vacated by other axons. Here we show that soon-to-be-eliminated axons rapidly reverse fate and grow to occupy vacant sites at a neuromuscular junction after laser removal of a stronger input. This reversal supports the idea that axons take over sites that were previously vacated. Indeed, during normal development we observed withdrawal followed by takeover. The stimulus for axon growth is not postsynaptic cell inactivity because axons grow into unoccupied sites even when target cells are functionally innervated. These results demonstrate competition at the synaptic level and enable us to provide a conceptual framework for understanding this form of synaptic plasticity.

In the transgenic multicolor labeling strategy called 'Brainbow', Cre-loxP recombination is used to create a stochastic choice of expression among fluorescent proteins, resulting in the indelible marking of mouse neurons with multiple distinct colors. This method has been adapted to non-neuronal cells in mice and to neurons in fish and flies, but its full potential has yet to be realized in the mouse brain. Here we present several lines of mice that overcome limitations of the initial lines, and we report an adaptation of the method for use in adeno-associated viral vectors. We also provide technical advice about how best to image Brainbow-expressing tissue.

Resolving patterns of synaptic connectivity in neural circuits currently requires serial section electron microscopy. However, complete circuit reconstruction is prohibitively slow and may not be necessary for many purposes such as comparing neuronal structure and connectivity among multiple animals. Here, we present an alternative strategy, targeted reconstruction of specific neuronal types. We used viral vectors to deliver peroxidase derivatives, which catalyze production of an electron-dense tracer, to genetically identify neurons, and developed a protocol that enhances the electron-density of the labeled cells while retaining the quality of the ultrastructure. The high contrast of the marked neurons enabled two innovations that speed data acquisition: targeted high-resolution reimaging of regions selected from rapidly-acquired lower resolution reconstruction, and an unsupervised segmentation algorithm. This pipeline reduces imaging and reconstruction times by two orders of magnitude, facilitating directed inquiry of circuit motifs. DOI: http://dx.doi.org/10.7554/eLife.15015.001

Visualizing the precise morphology of closely juxtaposed cells and their interactions can be highly informative, particularly when studying the complex organization of neuronal and glial networks in the nervous system. To this end, one can use optical approaches to image-distinct markers that are differentially distributed among the cells of interest, such as fluorescent proteins of various colors (XFPs). The Brainbow strategies use Cre/lox recombination to stochastically express two to four XFPs in a cellular population from a single promoter. Integration of multiple Brainbow transgene copies results in combinatorial expression of these XFPs, creating a wide range of hues. In the nervous system, the multicolor labeling thus generated can be used to distinguish adjacent neuronal or glial cells and to verify the identity of neuronal processes while tracing circuitry. This article describes the generation of Brainbow transgenes and mice as well as their use to image and digitally reconstruct nerve cells and their interactions in fixed samples. This method also holds potential for studies in other tissues and model organisms as well as live imaging in vivo.

The relation between the structure of the nervous system and its function is more poorly understood than the relation between structure and function in any other organ system. We explore why bridging the structure-function divide is uniquely difficult in the brain. These difficulties also explain the thrust behind the enormous amount of innovation centered on microscopy in neuroscience. We highlight some recent progress and the challenges that remain.

Recent advances in high-resolution microscopy let neuroscientists acquire neural-tissue volume data of extremely large sizes. However, the tremendous resolution and the high complexity of neural structures present big challenges to storage, processing, and visualization at interactive rates. A proposed system provides interactive exploration of petascale (petavoxel) volumes resulting from high-throughput electron microscopy data streams. The system can concurrently handle multiple volumes and can support the simultaneous visualization of high-resolution voxel segmentation data. Its visualization-driven design restricts most computations to a small subset of the data. It employs a multiresolution virtual-memory architecture for better scalability than previous approaches and for handling incomplete data. Researchers have employed it for a 1-teravoxel mouse cortex volume, of which several hundred axons and dendrites as well as synapses have been segmented and labeled.

BACKGROUND: The cleavage-stage mouse embryo is composed of superficially equivalent blastomeres that will generate both the embryonic inner cell mass (ICM) and the supportive trophectoderm (TE). However, it remains unsettled whether the contribution of each blastomere to these two lineages can be accounted for by chance. Addressing the question of blastomere cell fate may be of practical importance, because preimplantation genetic diagnosis requires removal of blastomeres from the early human embryo. To determine whether blastomere allocation to the two earliest lineages is random, we developed and utilized a recombination-mediated, noninvasive combinatorial fluorescent labeling method for embryonic lineage tracing. RESULTS: When we induced recombination at cleavage stages, we observed a statistically significant bias in the contribution of the resulting labeled clones to the trophectoderm or the inner cell mass in a subset of embryos. Surprisingly, we did not find a correlation between localization of clones in the embryonic and abembryonic hemispheres of the late blastocyst and their allocation to the TE and ICM, suggesting that TE-ICM bias arises separately from embryonic-abembryonic bias. Rainbow lineage tracing also allowed us to demonstrate that the bias observed in the blastocyst persists into postimplantation stages and therefore has relevance for subsequent development. CONCLUSIONS: The Rainbow transgenic mice that we describe here have allowed us to detect lineage-dependent bias in early development. They should also enable assessment of the developmental equivalence of mammalian progenitor cells in a variety of tissues.