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Garrett, T. R.

Publications and source records attributed to Garrett, T. R..

3 recordsLinked to original sources

Molecular and spatial analysis of ganglion cells on retinal flatmounts: diversity, topography, and perivascularity

Diverse retinal ganglion cells (RGCs) transmit distinct visual features from the eye to the brain. Recent studies have categorized RGCs into 45 types in mice based on transcriptomic profiles, showing strong alignment with morphological and electrophysiological properties. However, little is known about how these types are spatially arranged on the two-dimensional retinal surface--an organization that influences visual encoding--and how their local microenvironments impact development and neurodegenerative responses. To address this gap, we optimized a workflow combining imaging-based spatial transcriptomics (MERFISH) and immunohistochemical co-staining on thin flatmount retinal sections. We used computational methods to register en face somata distributions of all molecularly defined RGC types. More than 75% (34/45) of types exhibited non-uniform distributions, likely reflecting adaptations of the retinas anatomy to the animals visual environment. By analyzing the local neighborhoods of each cell, we identified perivascular RGCs located near blood vessels. Seven RGC types are enriched in the perivascular niche, including members of intrinsically photosensitive RGC (ipRGC) and direction-selective RGC (DSGC) subclasses. Orthologous human RGC counterparts of perivascular types - Melanopsin-enriched ipRGCs and ON DSGCs - were also proximal to blood vessels, suggesting their perivascularity may be evolutionarily conserved. Following optic nerve crush in mice, the perivascular M1-ipRGCs and ON DSGCs showed preferential survival, suggesting that proximity to blood vessels may render cell-extrinsic neuroprotection to RGCs through an mTOR-independent mechanism. Overall, our work offers a resource characterizing the spatial profiles of RGC types, enabling future studies of retinal development, physiology, and neurodegeneration at individual neuron type resolution across the two-dimensional space.

neuroscience↗

An Open-source Tool for Analysis and Automatic Identification of Dendritic Spines Using Machine Learning

Synaptic plasticity, the cellular basis for learning and memory, is mediated by a complex biochemical network of signaling proteins. These proteins are compartmentalized in dendritic spines, the tiny, bulbous, post-synaptic structures found on neuronal dendrites. The ability to screen a high number of molecular targets for their effect on dendritic spine structural plasticity will require a high-throughput imaging system capable of stimulating and monitoring hundreds of dendritic spines in various conditions. For this purpose, we present a program capable of automatically identifying dendritic spines in live, fluorescent tissue. Our software relies on a machine learning approach to minimize any need for parameter tuning from the user. Custom thresholding and binarization functions serve to \"clean\" fluorescent images, and a neural network is trained using features based on the relative shape of the spine perimeter and its corresponding dendritic backbone. Our algorithm is rapid, flexible, has over 90% accuracy in spine detection, and bundled with our user-friendly, open-source, MATLAB-based software package for spine analysis.

neuroscience↗

Automated remote focusing, drift correction, and photostimulation to evaluate structural plasticity in dendritic spines

Long-term structural plasticity of dendritic spines plays a key role in synaptic plasticity, the cellular basis for learning and memory. The biochemical step is mediated by a complex network of signaling proteins in spines. Two-photon imaging techniques combined with two-photon glutamate uncaging allows researchers to induce and quantify structural plasticity in single dendritic spines. However, this method is laborious and slow, making it unsuitable for high throughput screening of factors necessary for structural plasticity. Here we introduce a MATLAB-based module built for Scanimage to automatically track, image, and stimulate multiple dendritic spines. We implemented an electrically tunable lens in combination with a drift correction algorithm to rapidly and continuously track targeted spines and correct sample movements. With a straightforward user interface to design custom multi-position experiments, we were able to adequately image and produce targeted plasticity in multiple dendritic spines using glutamate uncaging. Our methods are inexpensive, open source, and provides up to a five-fold increase in throughput for quantifying structural plasticity of dendritic spines.

neuroscience↗