# Preliminary Study on Sensor-Based Detection of an Adherent Cell’s Pre-Detachment Moment in a MPWM Microfluidic Extraction System

## TL;DR

This study introduces a non-invasive method using sensors and microfluidics to detect and optimize the moment when adherent cells detach, preserving their viability for biomedical applications.

## Contribution

A novel sensor-based MPWM system with real-time imaging and AI to detect pre-detachment moments in adherent cell extraction.

## Key findings

- Sensor-based detection reduces mechanical stress and preserves cell integrity during detachment.
- Real-time imaging and deep learning enable dynamic adjustment of suction parameters for efficient extraction.
- The method supports high-throughput biotechnology and regenerative medicine with minimal cell perturbation.

## Abstract

The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion proteins. To address these challenges, this study investigated sensor-based detection of the pre-detachment phase in a MPWM (Microfluidic Pulse Width Modulation) extraction system. Our approach integrates a micromechatronic system with a microfluidic suction circuit, real-time CCD imaging, and computational analysis to detect and characterize the pre-detachment moment before full extraction. A precisely controlled hydrodynamic force field progressively disrupts adhesion in multiple stages, reducing mechanical stress and preserving cell integrity. Real-time video analysis enables continuous monitoring of positional dynamics and oscillatory responses. Image processing and deep learning algorithms determine object center coordinates, allowing the MPWM system to dynamically adjust suction parameters. This optimizes detachment while minimizing liquid absorption and reflux volume, ensuring efficient extraction. By combining microfluidics, sensor detection, and AI-driven image processing, this study established a non-invasive method for optimizing adherent cell detachment. These findings have significant implications for single-cell research, regenerative medicine, and high-throughput biotechnology, ensuring maximal viability and minimal perturbation.