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Updated: May 22, 2025

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments
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ODEP-Based Robotic System for Micromanipulation and In-Flow Analysis of Primary Cells.

Joanna Filippi1,2, Paola Casti1,2, Valentina Lacconi3

  • 1Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy.

Cyborg and Bionic Systems (Washington, D.C.)
|May 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a robotic system using optically-induced dielectrophoresis (ODEP) and machine learning for automated single-cell phenotyping. The platform accurately distinguishes between different cell types, paving the way for advanced diagnostics.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Robotics

Background:

  • Characterizing multifactorial cellular defects is challenging due to complex genetic, environmental, and lifestyle interactions.
  • Early and accurate identification of cellular abnormalities is crucial for effective diagnosis and treatment.

Purpose of the Study:

  • To develop a robotic micromanipulation and analysis system for single-cell phenotyping.
  • To leverage optically-induced dielectrophoresis (ODEP), microfluidics, live-cell imaging, and machine learning for label-free cell analysis.
  • To demonstrate the system's ability to discriminate between distinct cell populations.

Main Methods:

  • Utilized optically-induced dielectrophoresis (ODEP) to generate nonuniform electric fields for cell manipulation.
  • Integrated microfluidics and live-cell imaging for real-time cell observation and measurement.
  • Employed machine learning algorithms to analyze cell responses, including centroid motion, electro-deformation, and orientation dynamics.
  • Developed a robotic platform for automated single-cell micro-operation and analysis.

Main Results:

  • The ODEP-based system successfully characterized cellular properties in an automated, flexible, and label-free manner.
  • Subtle differences at the single-cell level were elucidated by analyzing electrokinetic fingerprints and deformation dynamics.
  • The platform demonstrated the ability to discriminate between primary endometrial stromal cells from fertile patients and those with disrupted receptivity/selectivity equilibrium.
  • Achieved an average accuracy of 98% when analyzing multiple cells at the patient level.

Conclusions:

  • The combined ODEP-based robotic and automatic analysis platform offers a novel approach for single-cell phenotyping.
  • This technology has the potential for broad applications in clinical diagnosis and management of cellular pathologies.
  • The system provides a powerful tool for understanding and identifying cellular defects with high accuracy.