Delivering genes across the blood-brain barrier LY6A, a novel cellular receptor for AAV-PHP.B capsids, bioRxiv, 2019-02-02
The engineered AAV-PHP.B family of adeno-associated virus efficiently delivers genes throughout the mouse central nervous system. To guide their application across disease models, and to inspire the development of translational gene therapy vectors useful for targeting neurological diseases in humans, we sought to elucidate the host factors responsible for the CNS tropism of AAV-PHP.B vectors. Leveraging CNS tropism differences across mouse strains, we conducted a genome-wide association study, and rapidly identified and verified LY6A as an essential receptor for the AAV-PHP.B vectors in brain endothelial cells. Importantly, this newly discovered mode of AAV binding and transduction is independent of other known AAV receptors and can be imported into different cell types to confer enhanced transduction by the AAV-PHP.B vectors.
biorxiv neuroscience 0-100-users 2019Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain, bioRxiv, 2019-02-02
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 comprise more than 75 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.
biorxiv neuroscience 200-500-users 2019SynQuant An Automatic Tool to Quantify Synapses from Microscopy Images, bioRxiv, 2019-02-02
AbstractMotivationSynapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness for different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.ResultsWe present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real data sets with ground truth annotation or manual labeling, SynQuant was demonstrated to outperform peer specialized synapse detection tools as well as generic spot detection methods, including 4 unsupervised and 11 variants of 3 supervised methods.AvailabilityJava source code, Fiji plug-in, and test data available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comyu-lab-vtSynQuant>httpsgithub.comyu-lab-vtSynQuant<jatsext-link>.Contactyug@vt.edu
biorxiv neuroscience 0-100-users 2019Freeze-frame imaging of synaptic activity using SynTagMA, bioRxiv, 2019-02-01
AbstractInformation within the brain travels from neuron to neuron across billions of synapses. At any given moment, only a small subset of neurons and synapses are active, but finding the active synapses in brain tissue has been a technical challenge. To tag active synapses in a user-defined time window, we developed SynTagMA. Upon 405 nm illumination, this genetically encoded marker of activity converts from green to red fluorescence if, and only if, it is bound to calcium. Targeted to presynaptic terminals, preSynTagMA allows discrimination between active and silent axons. Targeted to excitatory postsynapses, postSynTagMA creates a snapshot of synapses active just before photoconversion. To analyze large datasets, we developed software to identify and track the fluorescence of thousands of individual synapses in tissue. Together, these tools provide a high throughput method for repeatedly mapping active neurons and synapses in cell culture, slice preparations, and in vivo during behavior.
biorxiv neuroscience 100-200-users 2019Constant sub-second cycling between representations of possible futures in the hippocampus, bioRxiv, 2019-01-29
Cognitive faculties such as imagination, planning, and decision-making entail the ability to project into the future. Crucially, animal behavior in natural settings implies that the brain can generate representations of future scenarios not only quickly but also constantly over time, as external events continually unfold. Despite this insight, how the brain accomplishes this remains unknown. Here we report neural activity in the hippocampus encoding two future scenarios (two upcoming maze paths) in constant alternation at 8 Hz one scenario per 8 Hz cycle (125 ms). We further found that the underlying cycling dynamic generalized across multiple hippocampal representations (location and direction) relevant to future behavior. These findings identify an extremely fast and regular dynamical process capable of representing future possibilities.
biorxiv neuroscience 0-100-users 2019Facilitating open-science with realistic fMRI simulation validation and application, bioRxiv, 2019-01-29
Background With advances in methods for collecting and analyzing fMRI data, there is a concurrent need to understand how to reliably evaluate and optimally use these methods. Simulations of fMRI data can aid in both the evaluation of complex designs and the analysis of data. New Method We present fmrisim, a new Python package for standardized, realistic simulation of fMRI data. This package is part of BrainIAK a recently released open-source Python toolbox for advanced neuroimaging analyses. We describe how to use fmrisim to extract noise properties from real fMRI data and then create a synthetic dataset with matched noise properties and a user-specified signal. Results We validate the noise generated by fmrisim to show that it can approximate the noise properties of real data. We further show how fmrisim can help researchers find the optimal design in terms of power. Comparison with other methods fmrisim ports the functionality of other packages to the Python platform while extending what is available in order to make it seamless to simulate realistic fMRI data. Conclusions The fmrisim package holds promise for improving the design of fMRI experiments, which may facilitate both the pre-registration
biorxiv neuroscience 0-100-users 2019