Biology subjects

Au, B.

Publications and source records attributed to Au, B..

6 recordsLinked to original sources

bioRxiv2025

Quantitative profiling of whole-brain connectomes at single-axon resolution using deep learning and high-resolution light sheet microscopy

Revealing how individual axons create a brain-wide connectome would be indispensable for understanding brain function and behavior, yet remains technically challenging. We introduce MAPL3, an end-to-end pipeline that integrates self-supervised learning with an innovative deep architecture to capture local and global brain-wide axonal projections. MAPL3 enables subject- and population-level quantitative laminar analysis, generalizes across experiments, and outperforms state-of-the-art methods. We showcase its ability to map the circuitry of the orbitofrontal cortex from single axons to whole-brain projectome.

neuroscience↗

bioRxiv2024

Visual circuitry for distance estimation in Drosophila

Animals must infer the three-dimensional structure of their environment from two-dimensional images on their retinas. In particular, visual cues like motion parallax and binocular disparity can be used to judge distances to objects. Studies across several animal models have found neural signals that correlate with visual distance, but the causal role of these neurons in distance estimation as well as the range of possible neural properties that can inform distance estimation have remained poorly understood. Here, we developed a novel high-throughput behavioral assay to identify neurons in the Drosophila visual system that are involved in distance estimation during free locomotion. We found that silencing the primary motion detectors in the fly visual system eliminated their ability to perceive distance, consistent with a reliance on motion parallax to judge distance. Through a targeted silencing screen of visual neurons during behavior and through in vivo two-photon microscopy, we identified a visual projection neuron that encodes the parallax signal in the relative motion of foreground and background. Interestingly, it differs from previously identified parallax-tuned neurons in its lack of direction selectivity both to moving bars and to moving backgrounds. This non-canonical tuning is interpretable in the context of parallax signals that the fly would likely encounter during naturalistic walking behavior. Our results demonstrate how both direction selective and non-direction selective feature-detecting neurons can contribute to distance estimation using parallax cues, providing a framework for considering broader classes of parallax-encoding neurons in distance estimation across visual systems.

neuroscience↗

bioRxiv2023

Drosophila integrates visual evidence and counterevidence in self motion estimation

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can confuse the movement of external objects with genuine self motion. Here, we show that stationary patterns on the retina, which constitute negative evidence against self rotation, are used by the fruit fly Drosophila to suppress inappropriate stabilizing rotational behavior. In silico experiments show that artificial neural networks optimized to distinguish self and world motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the flys motion- and optic flow-detectors. Our results exemplify how the compact brain of the fly incorporates negative evidence to improve heading stability, exploiting geometrical constraints of the visual world.

neuroscience↗

bioRxiv2020

Statistical model integrating interactions into genotype-phenotype association mapping: an application to reveal 3D-genetic basis underlying Autism

Biological interactions are prevalent in the functioning organisms. Correspondingly, statistical geneticists developed various models to identify genetic interactions through genotype-phenotype association mapping. The current standard protocols in practice test single variants or single regions (that contain multiple local variants) sequentially along the genome, followed by functional annotations that involve various aspects including interactions. The testing of genetic interactions upfront is rare in practice due to the burden of testing a huge number of combinations, which lead to the multiple-test problem and the risk of overfitting. In this work, we developed interaction-integrated linear mixed model (ILMM), a novel model that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems associated with combination searching. Three dimensional (3D) genomic interactions assessed by Hi-C experiments have led to unprecedented biological discoveries. However, the contribution of 3D genomic interactions to the genetic basis of complex diseases has yet to be quantified. Using 3D interacting regions as a priori information, we conducted both simulations and real data analysis to test ILMM. By applying ILMM to whole genome sequencing data for Autism Spectrum Disorders, or ASD (MSSNG) and transcriptome sequencing data (GTEx), we revealed the 3D-genetic basis of ASD and 3D-eQTLs for a substantial proportion of gene expression in brain tissues. Moreover, we have revealed a potential mechanism involving distal regulation between FOXP2 and DNMT3A conferring the risk of ASD. Software is freely available in our GitHub: https://github.com/theLongLab/Jawamix5

genetics↗

bioRxiv2020

Directed manipulation of membrane proteins by fluorescent magnetic nanoparticles

The plasma membrane is the interface through which cells interact with their environment. Membrane proteins are embedded in the lipid bilayer of the plasma membrane and their function in this context is often linked to their specific location and dynamics within the membrane. However, few methods are available for nanoscale manipulation of membrane protein location at the single molecule level. Here, we report the use of fluorescent magnetic nanoparticles (FMNPs) to track membrane molecules and to manipulate their movement. FMNPs allow single-particle tracking (SPT) at 10 nm spatial and 5 ms temporal resolution, and using a magnetic needle, we pull membrane components laterally through the membrane with femtonewton-range forces. In this way, we successfully dragged lipid-anchored and transmembrane proteins over the surface of living cells. Doing so, we detected submembrane barriers and in combination with super-resolution microscopy could localize these barriers to the actin cytoskeleton. We present here a versatile approach to probe membrane processes in live cells via the magnetic control of membrane protein motion.

biophysics↗

bioRxiv2020

A novel strategy for single-cell metabolic analysis highlights dynamic changes in immune subpopulations

A complex interaction of anabolic and catabolic metabolism underpins the ability of leukocytes to mount an immune response. Their capacity to respond and adapt to changing environments by metabolic reprogramming is crucial to their effector function. However, current methods lack the ability to interrogate this network of metabolic pathways at the single cell level within a heterogeneous population. Here we present Met-Flow, a novel flow cytometry-based method that captures the metabolic state of immune cells by targeting key proteins and rate-limiting enzymes across multiple pathways. We demonstrate the ability to simultaneously measure divergent metabolic profiles and dynamic remodeling in human peripheral blood mononuclear cells. Using Met-Flow, we discovered that glucose restriction and metabolic remodeling drive the expansion of an inflammatory central memory T cell subset. This method captures the complex metabolic state of any cell as it relates to its phenotype and function, leading to a greater understanding of the role of metabolic heterogeneity in immune responses.

immunology↗