Abstract
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.
Highlights
Structural changes in dendritic spines, tiny postsynaptic protrusions on the dendritic surface of neurons, are considered to be the basis of synaptic plasticity [1] and are known to be important for learning and memory [2]
Dysfunctions in synaptic plasticity are a feature of affective disorders, neurodegenerative diseases, and aging-associated cognitive decline [1]
To isolate individual segments of the cell perimeter to be used as features for spine detection, the surface of the binary object needed to be smooth, lacking any spurious pixels or diagonally connected regions
Summary
Structural changes in dendritic spines, tiny postsynaptic protrusions on the dendritic surface of neurons, are considered to be the basis of synaptic plasticity [1] and are known to be important for learning and memory [2]. The backbone often retained some small kinks leftover from the initial skeletonization process As these kinks could introduce artifacts in the later perimeter distance calculation, they were removed by a custom smoothing algorithm adapted from Cheng at al. To isolate individual segments of the cell perimeter to be used as features for spine detection, the surface of the binary object needed to be smooth, lacking any spurious pixels or diagonally connected regions. A geodesic distance transforms was calculated using the spine backbone as a seed (Fig 2G and 2H), and PS features are represented as a 5 μm segment of pixel values along the resulting perimeter (Fig 4B and 4C). To make our tools accessible to users who may lack any significant coding expertise, we built a straightforward front-end user interface for viewing, analyzing, labeling, and segmenting images of dendritic spines in MATLAB (Fig 6). Once users are satisfied with their plugin configuration, the configuration can be saved, shared, commented on, and even rated for success at a certain task by multiple users (Fig 7F)
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