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Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine

Caroline A Ferguson1, James C M Hwang2, Yu Zhang1

  • 1Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA.

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This study presents a microfluidic device using electrical sensing and machine learning (ML) to detect cancer cell nuclei abnormalities. The technology offers a label-free approach for improved cancer diagnosis.

Keywords:
broadband impedancemachine learningmicrofluidicssingle-cell analysissupport vector machine

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Area of Science:

  • Biomedical Engineering
  • Cancer Diagnostics
  • Microfluidics

Background:

  • Traditional cancer diagnosis relies on visual histological staining, which can be time-consuming and subjective.
  • Machine learning (ML) is increasingly used for image analysis to enhance diagnostic accuracy.
  • Recent advancements include lab-on-a-chip platforms utilizing impedance spectroscopy for detecting nuclear abnormalities.

Purpose of the Study:

  • To develop and demonstrate a single-cell microfluidic device for detecting nuclear size alterations.
  • To establish a wideband electrical sensing and data analysis paradigm for identifying cancerous nuclei.
  • To improve cancer cell classification using ML with electrical signature data.

Main Methods:

  • Jurkat cells were treated to induce nucleus enlargement or shrinkage.
  • Broadband sensing was employed to acquire S-parameters of single cells.
  • ML models were trained and evaluated for binary and multiclass cell classification based on deduced frequency features.

Main Results:

  • The device successfully detected nuclei of altered sizes by analyzing broadband electrical sensing data.
  • Key frequencies correlated with nucleus size were identified, improving ML classification accuracy.
  • The electrical sensing platform demonstrated impressive accuracy in cell classification, even with heterogeneous cell populations.

Conclusions:

  • A novel analytical technique for electrical data analysis was demonstrated, aligning with theoretical models.
  • The developed electrical sensing platform, enhanced by ML, offers a promising label-free and flexible approach for cancer diagnosis.
  • This technology has the potential to significantly advance accessible and accurate cancer detection methods.