Hyperdimensional Feature Extraction & Adaptive Gain Control in Capillary Electrophoresis-Based Single-Cell Analysis for Enhanced Resolution and Throughput

**Abstract:** This research presents a novel approach to improve resolution and throughput in capillary electrophoresis (CE) based single-cell analysis by leveraging hyperdimensional processing for feature extraction and adaptive gain control for signal amplification. By transforming complex single-cell electrophoretic patterns into high-dimensional vector representations, nuanced and subtle differences between cells can be amplified, surpassing the limitations of traditional peak area and migration time analyses. A closed-loop adaptive gain control algorithm, implemented within the CE system, dynamically optimizes the detection sensitivity based on real-time signal intensity, minimizing noise and maximizing signal-to-noise ratio (SNR) for accurate identification and quantification of cellular components. This technology offers a significant advancement in single-cell research, enabling deeper insights into cellular heterogeneity and facilitating large-scale analysis.