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Neural Network-Enabled Multiparametric Impedance Signal Templating for High throughput Single-Cell Deformability

Javad Jarmoshti1, Abdullah-Bin Siddique1, Aditya Rane2

  • 1Electrical & Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA.

Small (Weinheim an Der Bergstrasse, Germany)
|October 23, 2024
PubMed
Summary

This study introduces a new method using impedance cytometry and neural networks to measure cell deformability and electrical properties in real-time. This allows for rapid, accurate sorting of live cell subpopulations for drug screening and cancer research.

Keywords:
artificial intelligencedeformability cytometryimpedance cytometrymicrofluidicspancreatic cancersingle cell analysis

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Area of Science:

  • Biophysics
  • Cell Biology
  • Machine Learning

Background:

  • Cellular biophysical metrics change during metastasis and immune activation, offering potential for live cell subpopulation identification.
  • Image-based cytometry quantifies cell deformability but requires complex image reconstruction, limiting real-time sorting applications.
  • Impedance cytometry offers functional insights into cell viability and internal structure, complementing imaging techniques.

Purpose of the Study:

  • To develop a rapid, inline method for quantifying single-cell biophysical metrics for cell sorting.
  • To utilize impedance cytometry signals with a neural network for accurate cell deformability measurements.
  • To enable multiparametric classification of live cancer cells and associated fibroblasts.

Main Methods:

  • A multilayer perceptron neural network was employed for signal templating using raw impedance data from cells under extensional flow.
  • The neural network was trained with image-derived metrics to establish net electrical anisotropy, quantifying cell deformability.
  • Support vector machine models were used for multiparametric classification based on deformability and electrical physiology metrics.

Main Results:

  • The neural network approach accurately quantifies cell deformability across a wide range of anisotropies, minimizing errors from cell size variations.
  • The combined deformability and electrical physiology metrics enabled effective classification of live pancreatic cancer cells versus cancer-associated fibroblasts.
  • The method demonstrates potential for real-time, high-throughput analysis and sorting of live cell subpopulations.

Conclusions:

  • A novel impedance cytometry and neural network approach enables accurate, real-time measurement of cell deformability and electrical properties.
  • This technique facilitates multiparametric cell classification, crucial for applications like targeted drug screening and cancer diagnostics.
  • The developed method overcomes limitations of traditional imaging cytometry for inline cell sorting and analysis.