Reconstitution of Listeria motility: implications for the mechanism of force transduction

AbstractListeria monocytogenes and some other infectious bacteria polymerize their host cell’s actin into tails that propel the bacteria through the cytoplasm. Here we show that reconstitution of this behavior in simpler media resolves two aspects of the mechanism of force transduction. First, since dilute reconstitution media have no cytoskeleton, we consider what keeps the tail from being pushed backward rather than the bacterium being propelled forward. The dependence of the partitioning of motion on the friction coefficient of the tail is derived. Consistent with experiments, we find that the resistance of the tail to motion is sensitive to its length. That even small tails are stationary in intact cells is attributed to anchoring to the cytoskeleton. Second, the comparatively low viscosity of some reconstitution media magnifies the effects of diffusion, such that a large gap will develop between the bacterium and its tail if they are unattached. At the viscosities of diluted platelet extracts, steady-state gaps of several bacterium lengths are predicted. Since such gaps are not observed, we conclude that Listeria must be attached to their tails. We consider what purposes such attachments might serve under physiological conditions. The implications for related pathogens and amoeboid locomotion are also discussed

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

Structural decomposition of the chemical shielding tensor: Contributions to the asymmetry, anisotropy, and orientation

The nine elements of chemical shielding tensors contain important information about local structure, but the extraction of that information is difficult. Here we explore a semiempirical method that has the potential for providing relatively accessible structural correlations. The approach entails approximating the field-induced electron current density as entirely perpendicular to the applied field. This has two interesting consequences. ͑1͒ The resulting shielding tensor is perfectly symmetric. Thus, asymmetry in a shielding tensor is an indication of current density that is not orthogonal to the applied field. ͑2͒ The orientation dependence of the chemical shielding at a point of interest is related explicitly to the isotropic average of the chemical shielding at every point in the surrounding region. This suggests a relatively simple relationship between the orientation dependence of the chemical shielding and the molecular structure. Good correlation with experimental tensors is obtained with just one or two adjustable parameters in several series of compounds, including silicates, phosphates, hydrogen bonds, carboxyls, and amides. As expected, the results indicate that for a given center, the contribution to the shielding anisotropy that is associated with each bonded neighbor increases as the number of electrons at either the center or the neighbors increases

NeuTu: Software for Collaborative, Large-Scale, Segmentation-Based Connectome Reconstruction

Reconstructing a connectome from an EM dataset often requires a large effort of proofreading automatically generated segmentations. While many tools exist to enable tracing or proofreading, recent advances in EM imaging and segmentation quality suggest new strategies and pose unique challenges for tool design to accelerate proofreading. Namely, we now have access to very large multi-TB EM datasets where (1) many segments are largely correct, (2) segments can be very large (several GigaVoxels), and where (3) several proofreaders and scientists are expected to collaborate simultaneously. In this paper, we introduce NeuTu as a solution to efficiently proofread large, high-quality segmentation in a collaborative setting. NeuTu is a client program of our high-performance, scalable image database called DVID so that it can easily be scaled up. Besides common features of typical proofreading software, NeuTu tames unprecedentedly large data with its distinguishing functions, including: (1) low-latency 3D visualization of large mutable segmentations; (2) interactive splitting of very large false merges with highly optimized semi-automatic segmentation; (3) intuitive user operations for investigating or marking interesting points in 3D visualization; (4) visualizing proofreading history of a segmentation; and (5) real-time collaborative proofreading with lock-based concurrency control. These unique features have allowed us to manage the workflow of proofreading a large dataset smoothly without dividing them into subsets as in other segmentation-based tools. Most importantly, NeuTu has enabled some of the largest connectome reconstructions as well as interesting discoveries in the fly brain

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

Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain

Published in final edited form as: Cell. 2019 September 19; 179(1): 268–281.e13. doi:10.1016/j.cell.2019.07.042.Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons constitute more than 85 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.U01 MH114824 - NIMH NIH HHS; Z99 MH999999 - Intramural NIH HHS; Howard Hughes Medical Institute.Accepted manuscrip

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

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