Scale-free Vertical Tracking Microscopy Towards Bridging Scales in Biological Oceanography, bioRxiv, 2019-04-16
AbstractUnderstanding key biophysical phenomena in the ocean often requires one to simultaneously focus on microscale entities, such as motile plankton and sedimenting particles, while maintaining the macroscale context of vertical transport in a highly stratified environment. This poses a conundrum How to measure single organisms, at microscale resolution, in the lab, while allowing them to freely move hundreds of meters in the vertical direction? We present a solution in the form of a scale-free, vertical tracking microscope based on a circular “hydrodynamic-treadmill”. Our technology allows us to transcend physiological and ecological scales, tracking organisms from marine zooplankton to single-cells over vertical scales of meters while resolving microflows and behavioral processes. We demonstrate measurements of sinking particles, including marine snow as they sediment tens of meters while capturing sub-particle-scale phenomena. We also demonstrate depth-patterned virtual-reality environments for novel behavioral analyses of microscale plankton. This technique offers a new experimental paradigm in microscale ocean biophysics by combining physiological-scale imaging with free movement in an ecological-scale patterned environment.One sentence summaryScale-free vertical tracking microscopy captures, for the first time, untethered behavioral dynamics at cellular resolution for marine plankton.
biorxiv biophysics 100-200-users 2019Rare variants contribute disproportionately to quantitative trait variation in yeast, bioRxiv, 2019-04-15
AbstractA detailed understanding of the sources of heritable variation is a central goal of modern genetics. Genome-wide association studies (GWAS) in humans1 have implicated tens of thousands of DNA sequence variants in disease risk and quantitative trait variation, but these variants fail to account for the entire heritability of diseases and traits. GWAS have by design focused on common DNA sequence variants; however, recent studies underscore the likely importance of the contribution of rare variants to heritable variation2. Further, finding the genes that underlie the GWAS signals remains a major challenge. Here, we use a unique model system to disentangle the contributions of common and rare variants to a large number of quantitative traits. We generated large crosses among 16 diverse yeast strains and identified thousands of quantitative trait loci (QTLs) that explain most of the heritable variation in 38 traits. We combined our results with sequencing data for 1,011 yeast isolates3 to decouple variant effect size estimation from allele frequency and showed that rare variants make a disproportionate contribution to trait variation as a consequence of their larger effect sizes. Evolutionary analyses revealed that this contribution is driven by rare variants that arose recently, that such variants are more likely to decrease fitness, and that negative selection has shaped the relationship between variant frequency and effect size. Finally, we leveraged the structure of the crosses to resolve hundreds of QTLs to single genes. These results refine our understanding of trait variation at the population level and suggest that studies of rare variants are a fertile ground for discovery of genetic effects.
biorxiv genetics 100-200-users 2019Generation of viral vectors specific to neuronal subtypes of targeted brain regions by Enhancer-Driven Gene Expression (EDGE), bioRxiv, 2019-04-14
SummaryUnderstanding brain function requires understanding neural circuits at the level of specificity at which they operate. While recent years have seen the development of a variety of remarkable molecular tools for the study of neural circuits, their utility is currently limited by the inability to deploy them in specific elements of native neural circuits, i.e. particular neuronal subtypes. One can obtain a degree of specificity with neuron-specific promoters, but native promoters are almost never sufficiently specific restricting this approach to transgenic animals. We recently showed that one can obtain transgenic mice with augmented anatomical specificity in targeted brain regions by identifying cis-regulatory elements (i.e. enhancers) uniquely active in those brain regions and combining them with a heterologous promoter, an approach we call EDGE (Enhancer-Driven Gene Expression). Here we extend this strategy to the generation of viral (rAAV) vectors, showing that when combined with the right minimal promoter they largely recapitulate the specificity seen in the corresponding transgenic lines in wildtype animals, even of another species. Because active enhancers can be identified in any tissue sample, this approach promises to enable the kind of circuit-specific manipulations in any species. This should not only greatly enhance our understanding of brain function, but may one day even provide novel therapeutic avenues to correct the imbalances in neural circuits underlying many disorders of the brain.
biorxiv neuroscience 0-100-users 2019Loss-of-function tolerance of enhancers in the human genome, bioRxiv, 2019-04-14
AbstractPrevious studies have surveyed the potential impact of loss-of-function (LoF) variants and identified LoF-tolerant protein-coding genes. However, the tolerance of human genomes to losing enhancers has not yet been evaluated. Here we present the catalog of LoF-tolerant enhancers using structural variants from whole-genome sequences. Using a conservative approach, we estimate that each individual human genome possesses at least 28 LoF-tolerant enhancers on average. We assessed the properties of LoF-tolerant enhancers in a unified regulatory network constructed by integrating tissue-specific enhancers and gene-gene interactions. We find that LoF-tolerant enhancers are more tissue-specific and regulate fewer and more dispensable genes. They are enriched in immune-related cells while LoF-intolerant enhancers are enriched in kidney and brainneuronal stem cells. We developed a supervised learning approach to predict the LoF-tolerance of enhancers, which achieved an AUROC of 96%. We predict 5,677 more enhancers would be likely tolerant to LoF and 75 enhancers that would be highly LoF-intolerant. Our predictions are supported by known set of disease enhancers and novel deletions from PacBio sequencing. The LoF-tolerance scores provided here will serve as an important reference for disease studies.
biorxiv genomics 0-100-users 2019A resource-efficient tool for mixed model association analysis of large-scale data, bioRxiv, 2019-04-12
ABSTRACTThe genome-wide association study (GWAS) has been widely used as an experimental design to detect associations between genetic variants and a phenotype. Two major confounding factors, population stratification and relatedness, could potentially lead to inflated GWAS test-statistics and thereby spurious associations. Mixed linear model (MLM)-based approaches can be used to account for sample structure. However, genome-wide association (GWA) analyses in biobank samples such as the UK Biobank (UKB) often exceed the capability of most existing MLM-based tools especially if the number of traits is large. Here, we developed an MLM-based tool (called fastGWA) that controls for population stratification by principal components and relatedness by a sparse genetic relationship matrix for GWA analyses of biobank-scale data. We demonstrated by extensive simulations that fastGWA is reliable, robust and highly resource-efficient. We then applied fastGWA to 2,173 traits on 456,422 array-genotyped and imputed individuals and 2,048 traits on 46,191 whole-exome-sequenced individuals in the UKB.
biorxiv genetics 0-100-users 2019Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment, bioRxiv, 2019-04-12
AbstractReconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections and dynamically adjust image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.
biorxiv neuroscience 100-200-users 2019