bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.06.26.546569

Chemically-induced epileptic seizures in zebrafish: a systematic review

Chitolina, R.·Gallas-Lopes, M.·Reis, C. G.·Benvenutti, R.·Stahlhofer-Buss, T.·Calcagnotto, M. E.·Herrmann, A. P.·Piato, A.
View original source ↗Export metadata ↓

Abstract

The use of zebrafish as a model organism is gaining evidence in the field of epilepsy as it may help to understand the mechanisms underlying epileptic seizures. As zebrafish assays became popular, the heterogeneity between protocols increased, making it hard to choose a standard protocol to conduct research while also impairing the comparison of results between studies. We conducted a systematic review to comprehensively profile the chemically-induced seizure models in zebrafish. Literature searches were performed in PubMed, Scopus and Web of Science, followed by a two-step screening process based on inclusion/exclusion criteria. Qualitative data were extracted and a sample of 100 studies was randomly selected for risk of bias assessment. Out of the 1058 studies identified after removing duplicates, 201 met the inclusion criteria. We found that the most common chemoconvulsants used in the reviewed studies were pentylenetetrazole (n = 180), kainic acid (n = 11), and pilocarpine (n = 10), which increase seizure severity in a dose-dependent manner. The main outcomes assessed were seizure scores and locomotion. Significant variability between the protocols was observed for administration route, duration of exposure, and dose/concentration. Of the studies subjected to risk of bias assessment, most were rated as low risk of bias for selective reporting (94%), baseline characteristics of the animals (67%), and blinded outcome assessment (54%). Randomization procedures and incomplete data were rated as unclear in 81% and 68% of the studies, respectively. None of the studies reported the sample size calculation. Overall, these findings underscore the need for improved methodological and reporting practices to enhance reproducibility and reliability of zebrafish models for studying epilepsy. Our study offers a comprehensive overview of the current state of chemically-induced seizure models in zebrafish, highlighting the common chemoconvulsants used and the variability in protocol parameters. This may be particularly valuable to researchers interested in understanding the underlying mechanisms of epileptic seizures and screening potential drug candidates in zebrafish models. HIGHLIGHTS- We systematically reviewed the effects of chemically-induced seizures in zebrafish; - PTZ is the most used epileptic seizure inducer in zebrafish; - More than 50% of the studies fail to report data such as outlier exclusion criteria and sample size estimation; - The results showed a need for better standardization of protocols.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chitolina, R., Gallas-Lopes, M., Reis, C. G., Benvenutti, R., Stahlhofer-Buss, T., Calcagnotto, M. E., Herrmann, A. P., Piato, A.. 2023-06-28. Chemically-induced epileptic seizures in zebrafish: a systematic review. https://doi.org/10.1101/2023.06.26.546569

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗
bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗
bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗