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 2019Millefy visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets, bioRxiv, 2019-02-02
Background Read coverage of RNA sequencing data reflects gene expression and RNA processing events. Single-cell RNA sequencing (scRNA-seq) methods, particularly full-length ones, provide read coverage of many individual cells and have the potential to reveal cellular heterogeneity in RNA transcription and processing. However, visualization tools suited to highlighting cell-to-cell heterogeneity in read coverage are still lacking.Results Here, we have developed Millefy, a tool for visualizing read coverage of scRNA-seq data in genomic contexts. Millefy is designed to show read coverage of all individual cells at once in genomic contexts and to highlight cell-to-cell heterogeneity in read coverage. By visualizing read coverage of all cells as a heat map and dynamically reordering cells based on diffusion maps, Millefy facilitates discovery of local region-specific, cell-to-cell heterogeneity in read coverage, including variability of transcribed regions. Conclusions Millefy simplifies the examination of cellular heterogeneity in RNA transcription and processing events using scRNA-seq data. Millefy is available as an R package (httpsgithub.comyuifumillefy) and a Docker image to help use Millefy on the Jupyter notebook (httpshub.docker.comryuifudatascience-notebook-millefy).
biorxiv bioinformatics 0-100-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 2019Distinct characteristics of genes associated with phenome-wide variation in maize (Zea mays), bioRxiv, 2019-01-30
ABSTRACTNaturally occurring functional genetic variation is often employed to identify genetic loci that regulate specific traits. Existing approaches to link functional genetic variation to quantitative phenotypic outcomes typically evaluate one or several traits at a time. Advances in high throughput phenotyping now enable datasets which include information on dozens or hundreds of traits scored across multiple environments. Here, we develop an approach to use data from many phenotypic traits simultaneously to identify causal genetic loci. Using data for 260 traits scored across a maize diversity panel, we demonstrate that a distinct set of genes are identified relative to conventional genome wide association. The genes identified using this many-trait approach are more likely to be independently validated than the genes identified by conventional analysis of the same dataset. Genes identified by the new many-trait approach share a number of molecular, population genetic, and evolutionary features with a gold standard set of genes characterized through forward genetics. These features, as well as substantially stronger functional enrichment and purification, separate them from both genes identified by conventional genome wide association and from the overall population of annotated gene models. These results are consistent with a large subset of annotated gene models in maize playing little or no role in determining organismal phenotypes.
biorxiv bioinformatics 0-100-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