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CMOS based capacitive sensor matrix for characterizing and tracking of biological cells
The characterization and tracking of biological cells using biosensors are necessary for many scientific fields, specifically cell culture monitoring. Capacitive sensors offer a great solution due to their ability to extract many features such as the biological cells' position, shape, and capac...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381585/ https://www.ncbi.nlm.nih.gov/pubmed/35974084 http://dx.doi.org/10.1038/s41598-022-18005-1 |
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author | Abdelbaset, Reda El-Sehrawy, Yehia Morsy, Omar E. Ghallab, Yehya H. Ismail, Yehea |
author_facet | Abdelbaset, Reda El-Sehrawy, Yehia Morsy, Omar E. Ghallab, Yehya H. Ismail, Yehea |
author_sort | Abdelbaset, Reda |
collection | PubMed |
description | The characterization and tracking of biological cells using biosensors are necessary for many scientific fields, specifically cell culture monitoring. Capacitive sensors offer a great solution due to their ability to extract many features such as the biological cells' position, shape, and capacitance. Through this study, a CMOS-based biochip that consists of a matrix of capacitive sensors (CSM), utilizing a ring oscillator-based pixel readout circuit (PRC), is designed and simulated to track and characterize a single biological cell based on its aforementioned different features. The proposed biochip is simulated to characterize a single Hepatocellular carcinoma cell (HCC) and a single normal liver cell (NLC). COMSOL Multiphysics was used to extract the capacitance values of the HCC and NLC and test the CSM's performance at different distances from the analyte. The PRC's ability to detect the extracted capacitance values of the HCC and NLC is evaluated using Virtuoso Analog Design Environment. A novel algorithm is developed to animate and predict the location and shape of the tested biological cell depending on CSM's capacitance readings simultaneously using MATLAB R2022a script. The results of both models, the measured capacitance from CSM and the correlated frequency from the readout circuit, show the biochip's ability to characterize and distinguish between HCC and NLC. |
format | Online Article Text |
id | pubmed-9381585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93815852022-08-18 CMOS based capacitive sensor matrix for characterizing and tracking of biological cells Abdelbaset, Reda El-Sehrawy, Yehia Morsy, Omar E. Ghallab, Yehya H. Ismail, Yehea Sci Rep Article The characterization and tracking of biological cells using biosensors are necessary for many scientific fields, specifically cell culture monitoring. Capacitive sensors offer a great solution due to their ability to extract many features such as the biological cells' position, shape, and capacitance. Through this study, a CMOS-based biochip that consists of a matrix of capacitive sensors (CSM), utilizing a ring oscillator-based pixel readout circuit (PRC), is designed and simulated to track and characterize a single biological cell based on its aforementioned different features. The proposed biochip is simulated to characterize a single Hepatocellular carcinoma cell (HCC) and a single normal liver cell (NLC). COMSOL Multiphysics was used to extract the capacitance values of the HCC and NLC and test the CSM's performance at different distances from the analyte. The PRC's ability to detect the extracted capacitance values of the HCC and NLC is evaluated using Virtuoso Analog Design Environment. A novel algorithm is developed to animate and predict the location and shape of the tested biological cell depending on CSM's capacitance readings simultaneously using MATLAB R2022a script. The results of both models, the measured capacitance from CSM and the correlated frequency from the readout circuit, show the biochip's ability to characterize and distinguish between HCC and NLC. Nature Publishing Group UK 2022-08-16 /pmc/articles/PMC9381585/ /pubmed/35974084 http://dx.doi.org/10.1038/s41598-022-18005-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Abdelbaset, Reda El-Sehrawy, Yehia Morsy, Omar E. Ghallab, Yehya H. Ismail, Yehea CMOS based capacitive sensor matrix for characterizing and tracking of biological cells |
title | CMOS based capacitive sensor matrix for characterizing and tracking of biological cells |
title_full | CMOS based capacitive sensor matrix for characterizing and tracking of biological cells |
title_fullStr | CMOS based capacitive sensor matrix for characterizing and tracking of biological cells |
title_full_unstemmed | CMOS based capacitive sensor matrix for characterizing and tracking of biological cells |
title_short | CMOS based capacitive sensor matrix for characterizing and tracking of biological cells |
title_sort | cmos based capacitive sensor matrix for characterizing and tracking of biological cells |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381585/ https://www.ncbi.nlm.nih.gov/pubmed/35974084 http://dx.doi.org/10.1038/s41598-022-18005-1 |
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