Biology subjects

Vecchi, J. T.

Publications and source records attributed to Vecchi, J. T..

3 recordsLinked to original sources

bioRxiv2021

Caldendrin represses neurite regeneration via a sex-dependent mechanism in sensory neurons

Caldendrin is a calmodulin-like Ca2+ binding protein that is expressed primarily in neurons and regulates multiple effectors including Cav1 L-type Ca2+ channels. Here, we tested the hypothesis that caldendrin regulates Cav1-dependent pathways that repress neurite growth in dorsal root ganglion neurons (DRGNs). By immunofluorescence, caldendrin was localized in medium- and large-diameter DRGNs. Consistent with an inhibitory effect of caldendrin on neurite growth, neurite initiation and growth was enhanced in dissociated DRGNs from caldendrin knockout (KO) mice compared to those from wild type (WT) mice. In an in vitro axotomy assay, caldendrin KO DRGNs grew longer neurites via a mechanism that was more sensitive to inhibitors of transcription as compared to WT DRGNs. Strong depolarization, which normally represses neurite growth through activation of Cav1 channels, had no effect on neurite growth in DRGN cultures from female caldendrin KO mice. Remarkably, DRGNs from caldendrin KO males were no different from those of WT males in terms of depolarization-dependent neurite growth repression. We conclude that caldendrin opposes neurite regeneration and growth, and this involves coupling of Cav1 channels to growth-inhibitory pathways in DRGNs of females but not males. Our findings suggest that caldendrin KO mice represent an ideal model in which to interrogate the transcriptional pathways controlling neurite regeneration and how these pathways may differ in males and females.

neuroscience↗

bioRxiv2021

NeuriteNet: A Convolutional Neural Network for determining morphological differences in neurite growth

BackgroundDuring development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due to the complex arborization and heterogeneous patterns of neurite growth in vitro. New MethodNeuriteNet is a Convolutional Neural Network (CNN) sorting model that uses a novel adaptation of the XRAI saliency map overlay, which is a region-based attribution method. NeuriteNet compares neuronal populations based on differences in neurite growth patterns, sorts them into respective groups, and overlays a saliency map indicating which areas differentiated the image for the sorting procedure. ResultsIn this study, we demonstrate that NeuriteNet effectively sorts images corresponding to dissociated neurons into control and treatment groups according to known morphological differences. Furthermore, the saliency map overlay highlights the distinguishing features of the neuron when sorting the images into treatment groups. NeuriteNet also identifies novel morphological differences in neurites of neurons cultured from control and genetically modified mouse strains. Comparison with Existing MethodsUnlike other neurite analysis platforms, NeuriteNet does not require manual manipulations, such as segmentation of neurites prior to analysis, and is more accurate than experienced researchers for categorizing neurons according to their pattern of neurite growth. ConclusionsNeuriteNet can be used to effectively screen for morphological differences in a heterogeneous group of neurons and to provide feedback on the key features distinguishing those groups via the saliency map overlay. HighlightsO_LINeuriteNet is a machine learning model developed to identify differences in control and experimental groups of cultured neurons based on morphological criteria. C_LIO_LINeuriteNets saliency map highlights the features of the image that associate the neuron with a particular group. C_LIO_LINeuriteNet outperforms trained researchers in assigning neurons to control or experimental groups with a known morphological difference as well as those with no previously described difference. C_LI

neuroscience↗

bioRxiv2021

Interaction of micropatterned topographical and biochemical cues to direct neurite growth from spiral ganglion neurons

Functional outcomes with neural prosthetic devices, such as cochlear implants, are limited in part due to physical separation between the stimulating elements and the neurons they stimulate. One strategy to close this gap aims to precisely guide neurite regeneration to position the neurites in closer proximity to electrode arrays. Here, we explore the ability of micropatterned biochemical and topographic guidance cues, singly and in combination, to direct the growth of spiral ganglion neuron (SGN) neurites, the neurons targeted by cochlear implants. Photopolymerization of methacrylate monomers was used to form unidirectional topographical features of ridges and grooves in addition to multidirectional patterns with 90{degrees} angle turns. Microcontact printing was also used to create similar uni- and multi-directional patterns of peptides on polymer surfaces. Biochemical cues included peptides that facilitate (laminin, LN) or repel (EphA4-Fc) neurite growth. On flat surfaces, SGN neurites preferentially grew on LN-coated stripes and avoided EphA4-Fc-coated stripes. LN or EphA4-Fc was selectively adsorbed onto the ridges or grooves to test the neurite response to a combination of topographical and biochemical cues. Coating the ridges with EphA4-Fc and grooves with LN lead to enhanced SGN alignment to topographical patterns. Conversely, EphA4-Fc coating on the grooves or LN coating on the ridges tended to disrupt alignment to topographical patterns. SGN neurites respond to combinations of topographical and biochemical cues and surface patterning that leverages both cues enhance guided neurite growth.

bioengineering↗