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Deep Learning-Based Prediction of Individual Cell α-Dispersion Capacitance from Morphological Features
Tae Young Kang1, Soojung Kim2, Yoon-Hwae Hwang3
1Institute for Future Earth, Pusan National University (PNU), Busan 46241, Republic of Korea.
Biosensors
|November 26, 2025
Summary
This study developed a deep learning method to correct for cell shape changes affecting electrical measurements. This improves the accuracy of cancer diagnostics by isolating true biological signals from measurement artifacts.
Area of Science:
- Biophysics
- Cell Biology
- Computational Biology
Background:
- Cellular membrane electrical properties offer insights into cell states and cancer diagnostics via epidermal growth factor receptor (EGFR) expression.
- Morphological changes during observation confound electrical measurements, masking true biological responses to epidermal growth factor (EGF).
Purpose of the Study:
- To develop a deep learning method to computationally link cellular morphology and electrical properties.
- To correct for morphology-induced measurement errors in electrical analysis of cells.
Main Methods:
- Combined optical trapping and capacitance measurements on HeLa cells under DPBS and EGF stimulation.
- Developed a convolutional neural network (CNN) to predict capacitance spectra from morphological images.
Main Results:
- CNN accurately predicted capacitance spectra (0.1-2 kHz) from morphological images (<10% error at 0.1-0.8 kHz).
- Method effectively isolated true biological responses by subtracting morphology-dependent capacitance components.
- Demonstrated robust prediction across diverse cell morphologies and experimental conditions.
Conclusions:
- The developed deep learning approach provides a computational framework for correcting morphology-induced errors in electrical measurements.
- This significantly enhances the precision and reliability of EGFR-based cancer diagnostics.
