Ultra-Wideband Impedance Spectroscopy of the Nucleus in a Live Cell

Du, Xiaotian; Ferguson, Caroline; Ma, Xiao; Cheng, Xuanhong; Hwang, J.C.M.

doi:10.1109/jerm.2021.3121258

Cited by 4 publications

(7 citation statements)

References 25 publications

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“…The model and equivalent circuit were further expanded to a double-shell model to identify the nucleus in the cytoplasm. The model includes shunt resistance-capacitance pairs in the cell membrane, cytoplasm, nuclear membrane, and nucleoplasm [301]. Although the bifurcation of β and γ relaxations had been previously observed [312], [301] established their correlation with the nucleus size.…”

Section: B Biological Cell Probingmentioning

confidence: 88%

“…The model includes shunt resistance-capacitance pairs in the cell membrane, cytoplasm, nuclear membrane, and nucleoplasm [301]. Although the bifurcation of β and γ relaxations had been previously observed [312], [301] established their correlation with the nucleus size. The ability to characterize the nucleus may lead to a better differentiation of normal and cancerous cells because that is where the major difference resides.…”

Section: B Biological Cell Probingmentioning

confidence: 88%

“…The frequency range was further extended to 900 Hz-40 GHz [300], bridging the gap between GHz impedance spectroscopy and traditional impedance spectroscopy in kHz and MHz. The extended frequency range allowed better characterization of not only cytoplasm, but also the cell nucleus [301].…”

Section: B Biological Cell Probingmentioning

confidence: 99%

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Applications of Microwaves in Medicine

Chiao

Lin

et al. 2023

IEEE J. Microw.

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Advances and updates in medical applications utilizing microwave techniques and technologies are reviewed in this paper. The article aims to provide an overview of enablers for microwave medical applications and their recent progress. The emphasis focuses on the applications of microwaves, in the following order, for 1) signal and data communication for implants and wearables through the human body, 2) electromagnetic energy transfer through tissues, 3) noninvasive, remote or in situ physical and biochemical sensing, and 4) therapeutic purposes by changing tissue properties with controlled thermal effects. For signal and data communication and wireless power transfer, implant and wearable applications are discussed in the categories of pacemakers, endoscopic capsules, brain interfaces, intraocular, cardiac and intracranial pressure sensors, neurostimulators, endoluminal implants, artificial retina, smart lenses, and cochlear implants. For noninvasive sensing, remote vital sign radar, biological cell probing, magnetic resonance and microwave imaging, biochemical, blood glucose, hydration and biomarker sensing applications are introduced. For therapeutic uses, the developments of microwave ablation, balloon angioplasty, and hyperthermia applications are reviewed. The scopes of this article mainly concentrate on the research and development efforts in the past 20 years. Recent review articles on specific topics are cited with accomplishment highlights and trends deliberated. At the end of this article, a brief history of the IEEE Microwave Theory and Techniques Society (MTT-S) Biological Effects and Medical Applications committee and the contributions by its members to the promotion and advancement of microwave technologies in medical fields are chronicled.

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Section: B Biological Cell Probingmentioning

confidence: 88%

Section: B Biological Cell Probingmentioning

confidence: 88%

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Applications of Microwaves in Medicine

Chiao

Lin

et al. 2023

IEEE J. Microw.

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“…Broadband spectroscopy by contrast relies on cell trapping and positional consistency for the duration of the frequency sweep for accuracy up to GHz frequencies. With broadband electrical sensor designs and more complex data analysis methods including our previous work, subcellular features such as the nucleus or ionic property changes can be interrogated by GHz or microwave range signal measurements [ 15 , 16 , 17 , 18 ]. These approaches hold promises in single-cell cancer diagnosis with low resource requirements.…”

Section: Introductionmentioning

confidence: 99%

“…In the reported method, single cells are non-invasively measured using an ultra-broadband electrical sensor to successfully interrogate chemically induced changes to simulate both nucleus shrinkage associated with cell apoptosis and enlargement associated with cancer development. While previous work using broadband spectroscopy has already established the intracellular properties of apoptotic nuclei using a double-shell model [ 18 ], chemically enlarged nuclei simulating cancer characteristics have not been. Using this on-chip, broadband sensing approach, a variety of unique spectra obtained serve as the foundation for the ML approach.…”

Section: Introductionmentioning

confidence: 99%

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

Ferguson

Hwang

Cheng

2023

Sensors

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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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