Biology subjects

Tyson, A. L.

Publications and source records attributed to Tyson, A. L..

10 recordsLinked to original sources

bioRxiv2025

Mapping the magnetoreceptive brain: A 3D digital atlas of the migratory bird Eurasian blackcap (Sylvia atricapilla)

Birds undisputedly range amongst natures foremost navigators. To successfully navigate between breeding and wintering quarters, they, in addition to other natural orientation cues, rely on their ability to sense the Earths magnetic field. For this reason, migratory birds have become key model species for studying the sensory mechanisms underlying magnetic field-guided navigation, as evidenced by the identification of several brain regions believed to be involved in processing magnetic field information. However, there is as yet no readily accessible, high-resolution three-dimensional (3D) brain atlas to serve as a common reference within and across studies. Here we provide the neuroscience research community with the first freely available, digital, high-resolution (25 {micro}m), 3D bird brain atlas. It is based on light microscopy images from ten Eurasian blackcaps (Sylvia atricapilla), a night-migratory songbird widely used model species in magnetoreception and navigation research. We outline the individual steps for the creation of a brain atlas, from whole-brain imaging using serial-section, two-photon tomography, to the creation of an average template at an isotropic 25-{micro}m voxel size, and finally to brain area segmentation and annotation. In this first version of the atlas, we have mapped a total of 24 brain areas, including 6 principal compartments, 13 conspicuous anatomical subdivisions common to all bird species and 5 functionally defined areas of the visual and trigeminal sensory systems involved in processing magnetic field information. This atlas is accessible via the standardised BrainGlobe Atlas API, making it compatible with a growing suite of computational neuroanatomy tools provided by the BrainGlobe Initiative. This integration enables precise alignment of future experimental data to a common coordinate space, facilitating collaboration, data visualization and sharing. Furthermore, this resource enables the accurate localization and comparison of implanted devices, injection sites, and/or cell populations across individual brains, both within and across studies.

neuroscience↗

bioRxiv2024

NeuralPlayground: A Standardised Environment for Evaluating Models of Hippocampus and Entorhinal Cortex

Neural processes in the hippocampus and entorhinal cortex are thought to be crucial for spatial cognition. A growing variety of theoretical models have been proposed to capture the rich neural and behavioral phenomena associated with these circuits. However, systematic comparison of these theories, both against each other and against empirical data, remains challenging. To address this gap, we present NeuralPlayground, an open-source standardised software framework for comparisons between theory and experiment in the domain of spatial cognition. This Python software package offers a reproducible way to compare models against a centralised library of published experimental results, including neural recordings and animal behavior. The framework implements three Agents embodying different computational models; three Experiments comprising publicly available neural and behavioral datasets; a customisable 2-dimensional Arena (continuous and discrete) able to generate common and novel spatial layouts; and a Comparison tool that facilitates systematic comparisons between models and data. Each module can also be used separately, allowing standardised and flexible access to influential models and data sets. We hope NeuralPlayground, available on GitHub3, provides a starting point for a shared, standardized, open, and reproducible computational understanding of the role of the hippocampus and entorhinal cortex in spatial cognition.

neuroscience↗

bioRxiv2022

3D single-cell shape analysis of cancer cells using geometric deep learning

AO_SCPLOWBSTRACTC_SCPLOWAberrations in 3D cell morphogenesis are linked to diseases such as cancer. Yet there is little systems-level understanding of cell shape determination in 3D, largely because there is a paucity of data-driven methods to quantify and describe 3D cell shapes. We have addressed this need using unsupervised geometric deep learning to learn shape representations of over 95,000 melanoma cells imaged by 3D high-throughput light-sheet microscopy. We used a dynamic graph convolutional foldingnet autoencoder with improved deep embedded clustering to simultaneously learn lower-dimensional representations and classes of 3D cell shapes. We describe a landscape of 3D cell morphology using deep learning-derived 3D quantitative morphological signatures (3DQMS) across different substrate geometries, following treatment by different clinically relevant small molecules and systematic gene depletion in high-throughput. By data integration, we predict modes of action for different small molecules providing mechanistic insights and blueprints for biological re-engineering. Finally, we provide explainability and interpretability for deep learning models.

bioinformatics↗

bioRxiv2021

Tools for accurate post hoc determination of marker location within whole-brain microscopy images

To interpret in vivo experiments designed to understand brain function, high-resolution whole-brain microscopy provides a means for post hoc determination of the location of implanted devices and recorded cells in three dimensional brain space that is a critical step for data interrogation. Here we have developed Python-based tools (brainreg and brainreg-segment) to accurately map, in a common coordinate space, the position of dye-labelled probe tracks and two-photon imaged cell populations expressing fluorescent protein. The precise location of probes and cells were validated using physiological recordings and human raters that indicate accuracy levels to less than 70{micro}m. These flexible, open-source methodologies are expected to further evolve with need and to deliver the anatomical precision that is necessary for understanding the functional architecture of the brain.

neuroscience↗

bioRxiv2021

The retrosplenial cortex combines internal and external cues to encode head velocity during navigation

The extent to which we successfully navigate the environment depends on our ability to continuously track our heading direction and speed. Angular head velocity (AHV) cells, which encode the speed and direction of head turns during navigation, are fundamental to this process, yet the mechanisms that determine their function remain unknown. By performing chronic single-unit recordings in the retrosplenial cortex (RSP) of the mouse and tracking the activity of individual AHV neurons between freely moving and head-restrained conditions, we find that vestibular inputs dominate AHV signalling. In addition, we discover that self-generated optic flow input onto these neurons increases the gain and signal-to-noise ratio of angular velocity coding during free exploration. Psychophysical experiments and neural decoding further reveal that vestibular-visual integration increases the perceptual accuracy of egocentric angular velocity and the fidelity of its representation by RSP ensembles. We propose that while AHV coding is dependent on vestibular input, it also uses vision to maximise navigation accuracy in nocturnal and diurnal environments.

neuroscience↗

bioRxiv2020

A deep learning algorithm for 3D cell detection in whole mouse brain image datasets

Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. How-ever, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an opensource algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.

neuroscience↗

bioRxiv2020

Brainrender. A python based software for visualisation of neuroanatomical and morphological data.

The recent development of high-resolution three-dimensional (3D) digital brain atlases and high-throughput brain wide imaging techniques has fueled the generation of large datasets that can be registered to a common reference frame. This registration facilitates integrating data from different sources and resolutions to assemble rich multidimensional datasets. Generating insights from these new types of datasets depends critically on the ability to easily visualize and explore the data in an interactive manner. This is, however, a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. To address this challenge, we have developed brainrender: a generic, open-source Python package for simultaneous and interactive visualization of multidimensional datasets registered to brain atlases. Brainrender has been designed to facilitate the creation of complex custom renderings and can be used programmatically or through a graphical user interface. It can easily render different data types in the same visualization, including user-generated data, and enables seamless use of different brain atlases using the same code base. In addition, brainrender generates high-quality visualizations that can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

neuroscience↗

bioRxiv2019

Cyclin B1-Cdk1 binding to MAD1 links nuclear pore disassembly to chromosomal stability

How the cell completely reorganises its architecture when it divides is a problem that has fascinated researchers for almost 150 years. We now know that the core regulatory machinery is highly conserved in eukaryotes but how these multiple protein kinases, protein phosphatases, and ubiquitin ligases are coordinated to remodel the cell in a matter of minutes remains a major question. Cyclin B-CDK is the primary kinase that drives mitotic remodelling and here we show that it is targeted to the nuclear pore complex (NPC) by binding an acidic face of the spindle assembly checkpoint protein, MAD1. This localised Cyclin B1-CDK1 activity coordinates NPC disassembly with kinetochore assembly: it is needed for the proper release of MAD1 from the embrace of TPR at the nuclear pore, which enables MAD1 to be recruited to kinetochores before nuclear envelope breakdown, thereby strengthening the spindle assembly checkpoint to maintain genomic stability.

cell biology↗

bioRxiv2018

Optimisation and validation of hydrogel-based brain tissue clearing shows uniform expansion across anatomical regions and spatial scales

Imaging of fixed tissue is routine in experimental neuroscience, but is limited by the depth of tissue that can be imaged using conventional methods. Optical clearing of brain tissue using hydrogel-based methods (e.g. CLARITY) allows imaging of large volumes of tissue and is rapidly becoming commonplace in the field. However, these methods suffer from a lack of standardised protocols and validation of the effect they have upon tissue morphology. We present a simple and reliable protocol for tissue clearing along with a quantitative assessment of the effect of tissue clearing upon morphology. Tissue clearing caused tissue swelling (compared to conventional methods), but this swelling was shown to be similar across spatial scales and the variation was within limits acceptable to the field. The results of many studies rely upon an assumption of uniformity in tissue swelling, and by demonstrating this quantitatively, research using these methods can be interpreted more reliably.

neuroscience↗

bioRxiv2018

Within-subject combination of diffusion tensor MRI and CLARITY reveals alterations in the social brain network resulting from α-neurexin II deletion

BackgroundOf the many genetic mutations known to increase the risk of autism spectrum disorder, a large proportion cluster upon synaptic proteins. One such family of presynaptic proteins are the neurexins (NRXN), and recent genetic and mouse evidence has suggested a causative role for NRXN2 in generating altered social behaviours. Autism has been conceptualised as a disorder of atypical connectivity, yet how single-gene mutations affect such connectivity remains under-explored. To attempt to address this, we have developed a quantitative analysis of microstructure and structural connectivity leveraging diffusion tensor MRI (DTI) with high-resolution 3D imaging in optically cleared (CLARITY) brain tissue in the same mouse, applied here to the Nrxn2 knockout (KO) model. MethodsFixed brains of Nrxn2 KO mice underwent DTI using 9.4T MRI, and diffusion properties of socially-relevant brain regions were quantified. The same tissue was then subjected to CLARITY to immunolabel axons and cell bodies, which were also quantified. ResultsDTI revealed decreases in fractional anisotropy and increases in apparent diffusion coefficient in the amygdala (including the basolateral nuclei), the anterior cingulate cortex, the orbitofrontal cortex and the hippocampus. Radial diffusivity of the anterior cingulate cortex and orbitofrontal cortex was significantly increased in Nrxn2 KO mice, as were tracts between the amygdala and the orbitofrontal cortex. Using CLARITY, we find significantly altered axonal orientation in the amygdala, orbitofrontal cortex and the anterior cingulate cortex, which was unrelated to cell density. ConclusionsOur findings demonstrate that deleting a single neurexin gene (Nrxn2) induces atypical structural connectivity within socially-relevant brain regions. More generally, our combined within-subject DTI and CLARITY approach presents a new, more sensitive method of revealing hitherto undetectable differences in the autistic brain.

neuroscience↗