Biology subjects

Reagor, C. C.

Publications and source records attributed to Reagor, C. C..

3 recordsLinked to original sources

bioRxiv2025

Optogenetic interrogation of the lateral-line sensory system reveals mechanisms of pattern separation in the zebrafish brain

The ability of animals to interact with their environment hinges on the brains capacity to distinguish between patterns of sensory information and accurately attribute them to specific sensory organs. The mechanisms by which neuronal circuits discriminate and encode the source of sensory signals remain elusive. To address this, we utilized as a model the posterior lateral line system of larval zebrafish, which is used to detect water currents. This system comprises a series of mechanosensory organs called neuromasts, which are innervated by neurons from the posterior lateral line ganglion. By combining single-neuromast optogenetic stimulation with whole-brain calcium imaging, we developed a novel approach to investigate how inputs from neuromasts are processed. Upon stimulating individual neuromasts, we observed that neurons in the brain of the zebrafish show diverse selectivity properties despite a lack of topographic organization in second-order circuits. We further demonstrated that complex combinations of neuromast stimulation are represented by sparse ensembles of neurons within the medial octavolateralis nucleus (MON) and found that neuromast input can be integrated nonlinearly. Our approach offers an innovative method for spatiotemporally interrogating the zebrafish lateral line system and presents a valuable model for studying whole-brain sensory encoding.

neuroscience↗

bioRxiv2024

ybx1 acts upstream of atoh1a to promote the rapid regeneration of hair cells in zebrafish lateral-line neuromasts

Like the sensory organs of the human inner ear, the lateral-line neuromasts (NMs) of fish such as the zebrafish (Danio rerio) contain mechanosensory hair cells (HCs) that are surrounded by progenitors called supporting cells. Damaged NMs can quickly regenerate new HCs by expressing in the progenitors HC-specific genes such as atoh1a, the master regulator of HC fate. We used the supervised learning algorithm DELAY to infer regenerating NMs early gene-regulatory network (GRN) and identify adaptations that promote the rapid regeneration of lateral-line HCs in larval zebrafish. The central hub in the network, Y-box binding protein 1 (ybx1), is highly expressed in HC progenitors and young HCs and can recognize DNA-binding motifs in cyprinids candidate regeneration-responsive promoter elements for atoh1a. We showed that NMs from ybx1 mutant zebrafish larvae display consistent, regeneration-specific deficits in HC number and initiate both HC regeneration and atoh1a expression 20 % slower than in siblings. By demonstrating that ybx1 promotes rapid HC regeneration through early atoh1a upregulation, the results support DELAYs ability to identify key temporal regulators of gene expression.

genomics↗

bioRxiv2022

Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference

Identifying the causal interactions in gene-regulatory networks requires an accurate understanding of the time-lagged relationships between transcription factors and their target genes. Here we describe DELAY, a convolutional neural network for the inference of gene-regulatory relationships across pseudotime-ordered single-cell trajectories. We show that combining supervised deep learning with joint-probability matrices of pseudotime-lagged trajectories allows the network to overcome important limitations of ordinary Granger causality-based methods, such as the inability to infer cyclic relationships such as feedback loops. Our network outperforms several common methods for inferring gene regulation and predicts novel regulatory networks from scRNA-seq and scATAC-seq datasets given partial ground-truth labels. To validate this approach, we used DELAY to identify important genes and modules in the regulatory network of auditory hair cells, as well as likely DNA-binding partners for two hair cell cofactors (Hist1h1c and Ccnd1) and a novel binding sequence for the hair cell-specific transcription factor Fiz1. We provide an open-source implementation of DELAY at https://github.com/calebclayreagor/DELAY.

bioinformatics↗