Frequency discretization in dielectrophoretic assisted cell sorting arrays to isolate neural cells

Area of Science:

• Biotechnology
• Neuroscience
• Microfluidics

Background:

• Dielectrophoresis (DEP) is a biophysical technique used for manipulating cells.
• Existing DEP methods for cell sorting often lack statistical significance for neuronal populations.

Purpose of the Study:

• To develop and validate an automated dielectrophoretic assisted cell sorting (DACS) device for dielectric characterization and isolation of neural cells.
• To achieve statistically significant enrichment of neurons from heterogeneous neural stem/progenitor cells (NSPCs) and neuron mixtures.

Main Methods:

• Utilized dielectrophoretic principles in an automated microfluidic device with a multiplexed electrode array.
• Employed a novel microfluidic manifold for cell trapping and collection across discrete frequency bands.
• Integrated Monte-Carlo (MC) simulations to model dielectric dispersions and predict sorting efficiencies.

Main Results:

• Demonstrated the first statistically significant neuronal sorting using DACS.
• Achieved a 1.4-fold neuronal enrichment, consistent with MC simulation predictions.
• Characterized dielectric dispersions within heterogeneous cell populations to optimize sorting parameters.

Conclusions:

• The DACS device provides an effective platform for dielectric characterization and isolation of neural cells.
• The developed MC simulation model accurately predicts cell trapping behavior and sorting outcomes.
• This technology advances the ability to enrich specific neural cell types for research and therapeutic applications.