Live Jurkat cells were trapped by dielectrophoresis on a coplanar waveguide and the resulted changes in its reflection and transmission coefficients were measured from 900 Hz to 40 GHz. The measurement confirms that the decrease of nucleus size in a cell increases its impacts on both the reflection and transmission coefficients. Being fast, compact and label free, broadband electrical sensing may be used to detect other changes of the nucleus morphology and DNA content, which could be useful for cancer diagnosis.
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Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning
Many recent efforts in the diagnostic field address the accessibility of cancer diagnosis. Typical histological staining methods identify cancer cells visually by a larger nucleus with more condensed chromatin. Machine learning (ML) has been incorporated into image analysis for improving this process. Recently, impedance spectrometers have been shown to generate all-inclusive lab-on-a-chip platforms to detect nucleus abnormities. In this paper, a wideband electrical sensor and data analysis paradigm that can identify nuclear changes shows the realization of a single-cell microfluidic device to detect nuclei of altered sizes. To model cells of altered nucleus, Jurkat cells were treated to enlarge or shrink their nucleus followed by broadband sensing to obtain the S-parameters of single cells. The ability to deduce important frequencies associated with nucleus size is demonstrated and used to improve classification models in both binary and multiclass scenarios, despite a heterogeneous and overlapping cell population. The important frequency features match those predicted in a double-shell circuit model published in prior work, demonstrating a coherent new analytical technique for electrical data analysis. The electrical sensing platform assisted by ML with impressive accuracy of cell classification looks forward to a label-free and flexible approach to cancer diagnosis.
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- Award ID(s):
- 1809623
- NSF-PAR ID:
- 10409840
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 23
- Issue:
- 2
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 1001
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s23021001
