# Proteomic Studies in Absence Epilepsy: A Systematic Review of Methodological Diversity and Implications for Data Interpretation

## TL;DR

This review examines proteomic methods used in absence epilepsy research to understand molecular mechanisms and improve study design.

## Contribution

The paper provides a systematic evaluation of proteomic methodologies in absence epilepsy, emphasizing methodological choices and their impact on data interpretation.

## Key findings

- Proteomic studies in absence epilepsy highlight pathways like synaptic dysfunction and neuroinflammation.
- Methodological diversity in proteomic approaches complicates data integration and reproducibility.
- New technologies like TIMS-PASEF and AI integration may enhance proteomic analysis in absence epilepsy.

## Abstract

Absence epilepsy (AE) is a common pediatric epilepsy syndrome marked by brief lapses in consciousness and characteristic 2.5–4 Hz spike-and-wave discharges on EEG. Although its clinical and electrophysiological features are well established, the molecular mechanisms underlying AE remain incompletely understood. Proteomic approaches offer a powerful means to explore these mechanisms; however, their application in AE remains limited and methodologically heterogeneous, which complicates data integration. In this review, proteomic methodologies applied in rodent models of absence epilepsy are critically examined, including genetic rat models such as Genetic Absence Epilepsy Rats from Strasbourg (GAERS) and Wistar Albino Glaxo rats from Rijswijk (WAG/Rij), monogenic mutant mouse models, and pharmacologically induced models. The technical workflow is described particularly, from tissue sampling and protein preparation (including gel-based and gel-free methods) to mass spectrometric analysis using data-dependent and data-independent acquisition strategies. Emerging technologies such as spatial proteomics, Trapped Ion Mobility Spectrometry coupled with Parallel Accumulation–Serial Fragmentation (TIMS-PASEF), and the integration of artificial intelligence are also evaluated in relation to their potential to address current technical limitations. Beyond synthesizing convergent molecular pathways including synaptic dysfunction, altered energy metabolism, and neuroinflammation, the review examines how methodological choices—such as model selection, brain region dissection, sample preparation protocols, and analytical platforms—contribute to experimental outcomes and data interpretation. By integrating current evidence with a focus on methodological aspects, this review provides a framework for designing more robust, reproducible, and clinically relevant proteomic studies in absence epilepsy.