Biology subjects

Posluszny, A.

Publications and source records attributed to Posluszny, A..

3 recordsLinked to original sources

bioRxiv2025

Sildenafil-driven cone PDE6 inhibition alters receptive-field properties of retinal ganglion cells ex vivo

Suppressing the phototransduction cascade has a profound impact on visual information processing in the retina. Here, we examined how acute silencing of photoreceptors alters stimulus preference of Retinal Ganglion Cells (RGCs) without affecting retinal anatomy. Using ex vivo recordings from C57BL/6 mouse retinas, we applied the phosphodiesterase 6 (PDE6) inhibitor Sildenafil and compared responses to flash and drifting-grating stimuli before and after treatment. Flash responses revealed six physiological RGC populations under control conditions. After Sildenafil, Off-responses were completely abolished, and most cells adopted On-like profiles with markedly prolonged response latencies. At the population level, orientation selectivity was preserved; however, RGCs lost the ability to detect spatial frequencies above 0.05 cycle/{degrees} and temporal frequencies above 2 cycle/s. A subset of cells increased their firing rates and transformed from orientation-selective into direction-selective cells, indicating reorganization of tuning properties. To assess photoreceptor specificity of Sildenafil, we tested Gnat1 (rod-deficient, cone-only) and Gnat2 (cone-deficient, rod-only) mouse models. Sildenafil completely removed visually evoked responses in Gnat1 retinas, but not in Gnat2, demonstrating preferential inhibition of cone PDE6. These results show that cone-selective suppression of phototransduction is sufficient to abolish Off-pathway signaling, reduce high-frequency visual sensitivity, and alter RGC receptive-field tuning. Beyond providing a mechanistic explanation for the visual side effects of PDE5/6 inhibitors, this establishes a pharmacological model to mimic early cone dysfunction and study inner retinal adaptation. Significance StatementPhotoreceptor degeneration alters visual processing long before anatomical changes are evident, but the early functional consequences for retinal circuits remain poorly defined. Here, we show that pharmacological suppression of phototransduction with the PDE6 inhibitor Sildenafil selectively abolishes cone-driven responses and profoundly alters retinal ganglion cell tuning. Off-pathway signaling is lost, spatiotemporal frequency sensitivity is reduced, and some cells acquire direction selectivity. These findings provide functional evidence that cone pathways are preferentially targeted by Sildenafil, offering a mechanistic explanation for its transient visual side effects in humans. More broadly, this approach establishes a pharmacological model for studying inner retinal adaptation to early cone dysfunction in degenerative disease.

neuroscience↗

bioRxiv2023

Deep Learning models for retinal cell classification

Data analysis is equally important as an experimental part of the scientists work. Therefore any reliable automatization would accelerate research. Histology is a good example, where scientists work with different cell types. The difficulty level can be severe while trying to distinguish cell types from one another. In this paper, we focus on the retina. The retina consists of eight basic cell types, creating a layered structure. Some types of cells overlap within the layer, and some differ significantly in size. Fast and thorough manual analysis of the cross-section is impossible. Even though Deep Learning models are applied in multiple domains, we observe little effort to automatize retinal analysis. Therefore, this research aims to create a model for classifying retinal cell types based on morphology in a cross-section of retinal cell images. In this study, we propose a classification Deep Learning model for retinal cell classification. We implemented two models, each tested in three different approaches: Small dataset, Extended dataset, and One cell type vs. All cell types. Although the problem presented to the trained model was simplified, a significant data imbalance was created from multiclass to binary classification, influencing the models performance. Both, Sequential and Transfer Learning models performed best with the Extended dataset. The Sequential model generated the best overall results. The obtained results allow us to place prepared models within the benchmark of published models. This paper proposes the first Deep Learning tool classifying retinal cell types based on a dataset prepared from publicly available images collated from multiple sources and images obtained in our laboratory. The multiclass approach with an extended dataset showed the best results. With more effort, the model could become an excellent analytical tool.

neuroscience↗

bioRxiv2020

Learning-induced plasticity in the barrel cortex is disrupted by inhibition of layer 4 somatostatin-containing interneurons

Learning-related plasticity in the cerebral cortex is linked to the action of disinhibitory circuits of interneurons. Pavlovian conditioning, in which stimulation of the vibrissae is used as conditioned stimulus, induces plastic enlargement of the cortical functional representation of vibrissae activated during conditioning, visualized with [14C]-2-deoxyglucose (2DG). Using layer-specific, cell-selective DREADD transductions, we examined the involvement of somatostatin- (SOM-INs) and vasoactive intestinal peptide (VIP-INs)-containing interneurons in the development of learning-related plastic changes. We injected DREADD-expressing vectors into layer IV (L4) barrels or layer II/III (L2/3) areas corresponding to activated vibrissae. The activity of interneurons was modulated during training, and 2DG maps were obtained 24 h later. In mice with L4 but not L2/3 SOM-INs suppressed during conditioning, the plastic change of whisker representation and the conditioned reaction were absent. No effect of inhibiting VIP-INs was found. We report that the activity of L4 SOM-INs is indispensable for learning-induced plastic change.

neuroscience↗