Robust cone-mediated signaling persists late into rod photoreceptor degeneration
Abstract
Rod photoreceptor degeneration causes deterioration in the morphology and physiology of cone photoreceptors along with changes in retinal circuits. These changes could diminish visual signaling at cone-mediated light levels, thereby limiting the efficacy of treatments such as gene therapy for rescuing normal, cone-mediated vision. However, the impact of progressive rod death on cone-mediated signaling remains unclear. To investigate the fidelity of retinal ganglion cell (RGC) signaling throughout disease progression, we used a mouse model of rod degeneration (Cngb1neo/neo). Despite clear deterioration of cone morphology with rod death, cone-mediated signaling among RGCs remained surprisingly robust: spatiotemporal receptive fields changed little and the mutual information between stimuli and spiking responses was relatively constant. This relative stability held until nearly all rods had died and cones had completely lost well-formed outer segments. Interestingly, RGC information rates were higher and more stable for natural movies than checkerboard noise as degeneration progressed. The main change in RGC responses with photoreceptor degeneration was a decrease in response gain. These results suggest that gene therapies for rod degenerative diseases are likely to prolong cone-mediated vision even if there are changes to cone morphology and density.
Data availability
Data to generate all summary plots in Figures 1-11 are included in the following GitHub repository: https://github.com/mishek-thapa/cng-data; they are also available as source data files with the manuscript. For physiology data, we have not provided the raw data files (voltage as a function of time on all electrodes) because these files are enormous (in excess of 5 TB). Raw data will be provided upon request by contacting the corresponding author. Requests will be met provided the data will not be used for commercial purposes. MATLAB code for information calculations are available in the above GitHub repository. The Cngbneo/neo mouse model is available to be shared upon request. Raw image files from Figure 1 can be found at doi:10.5061/dryad.x95x69pmq.
Article and author information
Author details
Funding
National Eye Institute (EY024280)
- Alapakkam P Sampath
- Jeannie Chen
- Greg D Field
National Eye Institute (EY5722)
- Greg D Field
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Marla B Feller, University of California, Berkeley, United States
Ethics
Animal experimentation: Mice were used according to Duke University Institutional Animal Care and Use Committee guidelines (protocol A084-21-04) and the Association for Research in Vision and Ophthalmology guidelines for the use of animals in vision research.
Version history
- Preprint posted: April 28, 2022 (view preprint)
- Received: May 13, 2022
- Accepted: August 25, 2022
- Accepted Manuscript published: August 30, 2022 (version 1)
- Version of Record published: October 13, 2022 (version 2)
- Version of Record updated: January 12, 2023 (version 3)
Copyright
© 2022, Scalabrino et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,076
- views
-
- 268
- downloads
-
- 9
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.
-
- Cell Biology
- Neuroscience
The cytosolic proteins synucleins and synapsins are thought to play cooperative roles in regulating synaptic vesicle (SV) recycling, but mechanistic insight is lacking. Here, we identify the synapsin E-domain as an essential functional binding-partner of α-synuclein (α-syn). Synapsin E-domain allows α-syn functionality, binds to α-syn, and is necessary and sufficient for enabling effects of α-syn at synapses of cultured mouse hippocampal neurons. Together with previous studies implicating the E-domain in clustering SVs, our experiments advocate a cooperative role for these two proteins in maintaining physiologic SV clusters.