Split Retina as an Improved Flatmount Preparation for Studying Inner Nuclear Layer Neurons in Vertebrate Retina.

Bipolar cells and horizontal cells of the vertebrate retina are the first neurons to process visual information after photons are detected by photoreceptors. They perform fundamental operations such as light adaptation, contrast sensitivity, and spatial and color opponency. A complete understanding of the precise circuitry and biochemical mechanisms that govern their behavior will advance visual neuroscience research and ophthalmological medicine. However, current preparations for examining bipolar and horizontal cells (retinal whole mounts and vertical slices) are limited in their capacity to capture the anatomy and physiology of these cells. In this work, we present a method for removing photoreceptor cell bodies from live, flatmount mouse retinas, providing enhanced access to bipolar and horizontal cells for efficient patch clamping and rapid immunolabeling. Split retinas are prepared by sandwiching an isolated mouse retina between two pieces of nitrocellulose, then gently peeling them apart. The separation splits the retina just above the outer plexiform layer to yield two pieces of nitrocellulose, one containing the photoreceptor cell bodies and another containing the remaining inner retina. Unlike vertical retina slices, the split retina preparation does not sever the dendritic processes of inner retinal neurons, allowing for recordings from bipolar and horizontal cells that integrate the contributions of gap junction-coupled networks and wide-field amacrine cells. This work demonstrates the versatility of this preparation for the study of horizontal and bipolar cells in electrophysiology, immunohistochemistry, and in situ hybridization experiments.

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.

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.