Speaker
Description
Microfluidic impedance cytometry (MIC) is a high throughput, label free technology for single cell analysis, with applications in cell classification and non invasive monitoring. However, extracting reliable biological information from MIC data remains challenging because the signals strongly depend on the experimental setup. This study investigates the integration of MIC with deep learning (DL) to enable effective processing of raw impedance data streams (i.e., electric current signals). Specifically, the research focuses on signal segmentation (i.e., event detection), which represents the first step of the processing pipeline and therefore influences the entire workflow.
For the first time, impedance data from multiple experimental setups were collected. They are characterized by raw traces with diverse attributes and event signals exhibiting distinct temporal shapes, thus forming a rich and comprehensive database. Several DL models were implemented and compared, including recurrent, convolutional, and encoder–decoder neural networks.
While all models demonstrated good segmentation performance, the encoder–decoder network outperformed the others, achieving a sensitivity and positive predictive value of 91.6% and 91.8%, respectively. Moreover, the network exhibited remarkable robustness when, after training, validation, and testing, it was further evaluated on additional previously unseen data.
We developed a universal framework for signal segmentation in MIC, addressing the challenge of cross-setup generalizability. By enabling efficient, high speed processing, the integration of MIC and DL lays the foundation for next generation single cell workflows with applications in diagnostics, drug discovery, and environmental monitoring.