Annotating Synapses in Large EM Datasets

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders

Tissue remodeling: a mating-induced differentiation program for the Drosophila oviduct

<p>Abstract</p> <p>Background</p> <p>In both vertebrates and invertebrates, the oviduct is an epithelial tube surrounded by visceral muscles that serves as a conduit for gamete transport between the ovary and uterus. While <it>Drosophila </it>is a model system for tubular organ development, few studies have addressed the development of the fly's oviduct. Recent studies in <it>Drosophila </it>have identified mating-responsive genes and proteins whose levels in the oviduct are altered by mating. Since many of these molecules (e.g. Muscle LIM protein 84B, Coracle, Neuroglian) have known roles in the differentiation of muscle and epithelia of other organs, mating may trigger similar differentiation events in the oviduct. This led us to hypothesize that mating mediates the last stages of oviduct differentiation in which organ-specific specializations arise.</p> <p>Results</p> <p>Using electron- and confocal-microscopy we identified tissue-wide post-mating changes in the oviduct including differentiation of cellular junctions, remodeling of extracellular matrix, increased myofibril formation, and increased innervation. Analysis of once- and twice-mated females reveals that some mating-responsive proteins respond only to the first mating, while others respond to both matings.</p> <p>Conclusion</p> <p>We uncovered ultrastructural changes in the mated oviduct that are consistent with the roles that mating-responsive proteins play in muscle and epithelial differentiation elsewhere. This suggests that mating triggers the late differentiation of the oviduct. Furthermore, we suggest that mating-responsive proteins that respond only to the first mating are involved in the final maturation of the oviduct while proteins that remain responsive to later matings are also involved in maintenance and ongoing function of the oviduct. Taken together, our results establish the oviduct as an attractive system to address mechanisms that regulate the late stages of differentiation and maintenance of a tubular organ.</p

Use of osmium tetroxide-potassium ferricyanide in reconstructing cells from serial ultrathin sections

We describe a technique, modified from Langford and Coggeshall [Anat. Rec., 197 (1980) 297-303; J. Comp. Neurol., 203 (1981) 745-750], for enhancing membrane contrast and defining cellular boundaries, that is useful for reconstructing individual cells from serial ultrathin sections. The cells of interest in our study were neuronal germinal cells and their differentiated progeny in the retinas of young goldfish. These cells were labeled by pulse injections of [3H]thymidine, and they were subsequently identified in EM autoradiographs by the presence of silver grains overlying their nuclei. In tissue prepared by traditional procedures (fixation in mixed aldehydes, postfixation in osmium tetroxide) it was difficult to follow the processes of these cells through the complex, dense network of cells in the differentiated retina. However, in tissue postfixed with a mixture of osmium tetroxide and potassium ferricyanide, the contrast of the cell membranes was improved and, in favorable preparations, a dense precipitate was formed in the extracellular spaces, serving to outline individual cells. This greatly faciliated the preparation of reconstructions from serial ultrathin sections.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26703/1/0000253.pd

Germinal cells in the goldfish retina that produce rod photoreceptors

Dividing cells and their progeny in retinae of young goldfish were labeled with [3H]thymidine, and selected cells were reconstructed from serial sections processed for electron microscopic autoradiography. Our goals were to characterize the cells that were identified as rod precursors in previous light microscopic autoradiographical studies and to determine their origin and fate. (In fish the population of rods increases several-fold postembryonically by proliferation of rod precursor cells scattered across the retina.) Over 200 labeled cells taken from 11 retinas were examined, and 20 of these were reconstructed in their entirety. Some retinas were examined at short intervals (1 to 48 hr) after [3H]thymidine injection in order to study mitotically active cells, and others were examined after longer intervals (9 or 14 days) to discover the nature of the progeny of labeled dividing cells. Previous evidence from thymidine studies in larval goldfish suggested that proliferating cells destined to produce rods appear first in the inner nuclear layer and later in the outer nuclear layer, where they continue to divide and generate new rods (P. R. Johns, (1982) J. Neurosci. 2, 179). The present results provide morphological evidence in support of the suggestion that rod precursors migrate from inner to outer nuclear layer and, furthermore, show that the precursors are closely associated with, and perhaps guided by, the radial processes of Muller glial cells. Examination of EM autoradiographs of labeled cells at 9 and 14 days after a pulse label with thymidine confirms that the differentiated progeny of dividing precursor cells are exclusively rods. To our knowledge, rod precursors are the first example of a neuronal germinal cell in the vertebrate central nervous system that under normal conditions produces only one type of neuron.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26652/1/0000196.pd

EM and XRM Connectomics Imaging and Experimental Metadata Standards

High resolution volumetric neuroimaging datasets from electron microscopy (EM) and x-ray micro and holographic-nano tomography (XRM/XHN) are being generated at an increasing rate and by a growing number of research teams. These datasets are derived from an increasing number of species, in an increasing number of brain regions, and with an increasing number of techniques. Each of these large-scale datasets, often surpassing petascale levels, is typically accompanied by a unique and varied set of metadata. These datasets can be used to derive connectomes, or neuron-synapse level connectivity diagrams, to investigate the fundamental organization of neural circuitry, neuronal development, and neurodegenerative disease. Standardization is essential to facilitate comparative connectomics analysis and enhance data utilization. Although the neuroinformatics community has successfully established and adopted data standards for many modalities, this effort has not yet encompassed EM and XRM/ XHN connectomics data. This lack of standardization isolates these datasets, hindering their integration and comparison with other research performed in the field. Towards this end, our team formed a working group consisting of community stakeholders to develop Image and Experimental Metadata Standards for EM and XRM/XHN data to ensure the scientific impact and further motivate the generation and sharing of these data. This document addresses version 1.1 of these standards, aiming to support metadata services and future software designs for community collaboration. Standards for derived annotations are described in a companion document. Standards definitions are available on a community github page. We hope these standards will enable comparative analysis, improve interoperability between connectomics software tools, and continue to be refined and improved by the neuroinformatics community.Comment: 15 Pages, 3 figures, 2 table

En bloc preparation of Drosophila brains enables high-throughput FIB-SEM connectomics

Deriving the detailed synaptic connections of an entire nervous system is the unrealized goal of the nascent field of connectomics. For the fruit fly Drosophila, in particular, we need to dissect the brain, connectives, and ventral nerve cord as a single continuous unit, fix and stain it, and undertake automated segmentation of neuron membranes. To achieve this, we designed a protocol using progressive lowering of temperature dehydration (PLT), a technique routinely used to preserve cellular structure and antigenicity. We combined PLT with low temperature en bloc staining (LTS) and recover fixed neurons as round profiles with darkly stained synapses, suitable for machine segmentation and automatic synapse detection. Here we report three different PLT-LTS methods designed to meet the requirements for FIB-SEM imaging of the Drosophila brain. These requirements include: good preservation of ultrastructural detail, high level of en bloc staining, artifact-free microdissection, and smooth hot-knife cutting to reduce the brain to dimensions suited to FIB-SEM. In addition to PLT-LTS, we designed a jig to microdissect and pre-fix the fly’s delicate brain and central nervous system. Collectively these methods optimize morphological preservation, allow us to image the brain usually at 8 nm per voxel, and simultaneously speed the formerly slow rate of FIB-SEM imaging

Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence

Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches. We have pursued different approaches within this pipeline structure. First, as a demonstration of data-driven discovery, the team has developed a technique for discovery of repeated subcircuits, or motifs. These were incorporated into a neural architecture search approach to evolve network architectures. Second, we have conducted analysis of the heading direction circuit in the fruit fly, which performs fusion of visual and angular velocity features, to explore augmenting existing computational models with new insight. Our team discovered a novel pattern of connectivity, implemented a new model, and demonstrated sensor fusion on a robotic platform. Third, the team analyzed circuitry for memory formation in the fruit fly connectome, enabling the design of a novel generative replay approach. Finally, the team has begun analysis of connectivity in mammalian cortex to explore potential improvements to transformer networks. These constraints increased network robustness on the most challenging examples in the CIFAR-10-C computer vision robustness benchmark task, while reducing learnable attention parameters by over an order of magnitude. Taken together, these results demonstrate multiple potential approaches to utilize insight from neural systems for developing robust and efficient machine learning techniques.Comment: 11 pages, 4 figure

A connectome and analysis of the adult Drosophila central brain.

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain