Cell Electrokinetic Fingerprint: A Novel Approach Based on Optically Induced Dielectrophoresis (ODEP) for In-Flow...

Area of Science:

• Biophysics
• Cell Biology
• Machine Learning

Background:

• Optically induced dielectrophoresis (ODEP) is a technique for manipulating cells using light.
• Existing ODEP systems face limitations in flow conditions and automated cell trajectory analysis.
• Distinguishing subtle cellular differences is crucial for cancer research and precision medicine.

Purpose of the Study:

• To develop a novel ODEP system capable of operating under flow conditions for automated cell trapping and 2D multi-frequency trajectory analysis.
• To characterize cell electrokinetic fingerprints using machine learning algorithms.
• To demonstrate the system's capability in discriminating cancer cell phenotypes relevant to drug response and gene expression.

Main Methods:

• Design and implementation of a novel ODEP system with virtual electrode barriers operating in an alternate mode.
• Utilizing time-lapse microscopy and cell tracking for motion analysis.
• Application of machine learning algorithms, specifically wavelet scattering transform (WST), for quantifying cell displacements and creating electrokinetic fingerprints.

Main Results:

• The ODEP system successfully trapped cells and induced 2D multi-frequency trajectories under flow conditions.
• Cellular dielectric phenotypes were discriminated in early drug-induced apoptosis of prostate cancer (PC3) cells.
• Differential expression of LOX-1 transcript levels in human colorectal adenocarcinoma (DLD-1) cells was successfully identified.

Conclusions:

• The proposed ODEP system offers enhanced discrimination capabilities for cellular phenotypes.
• This technology provides a new basis for ODEP-based assays to address cancer heterogeneity.
• The findings support the application of ODEP in precision medicine and pharmacological research for cancer studies.