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Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring
High-throughput data analysis challenges in laboratory automation and lab-on-a-chip devices’ applications are continuously increasing. In cell culture monitoring, specifically, the electrical cell-substrate impedance sensing technique (ECIS), has been extensively used for a wide variety of applicati...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864697/ https://www.ncbi.nlm.nih.gov/pubmed/31731413 http://dx.doi.org/10.3390/s19214639 |
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author | García, Elvira Pérez, Pablo Olmo, Alberto Díaz, Roberto Huertas, Gloria Yúfera, Alberto |
author_facet | García, Elvira Pérez, Pablo Olmo, Alberto Díaz, Roberto Huertas, Gloria Yúfera, Alberto |
author_sort | García, Elvira |
collection | PubMed |
description | High-throughput data analysis challenges in laboratory automation and lab-on-a-chip devices’ applications are continuously increasing. In cell culture monitoring, specifically, the electrical cell-substrate impedance sensing technique (ECIS), has been extensively used for a wide variety of applications. One of the main drawbacks of ECIS is the need for implementing complex electrical models to decode the electrical performance of the full system composed by the electrodes, medium, and cells. In this work we present a new approach for the analysis of data and the prediction of a specific biological parameter, the fill-factor of a cell culture, based on a polynomial regression, data-analytic model. The method was successfully applied to a specific ECIS circuit and two different cell cultures, N2A (a mouse neuroblastoma cell line) and myoblasts. The data-analytic modeling approach can be used in the decoding of electrical impedance measurements of different cell lines, provided a representative volume of data from the cell culture growth is available, sorting out the difficulties traditionally found in the implementation of electrical models. This can be of particular importance for the design of control algorithms for cell cultures in tissue engineering protocols, and labs-on-a-chip and wearable devices applications. |
format | Online Article Text |
id | pubmed-6864697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68646972019-12-23 Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring García, Elvira Pérez, Pablo Olmo, Alberto Díaz, Roberto Huertas, Gloria Yúfera, Alberto Sensors (Basel) Article High-throughput data analysis challenges in laboratory automation and lab-on-a-chip devices’ applications are continuously increasing. In cell culture monitoring, specifically, the electrical cell-substrate impedance sensing technique (ECIS), has been extensively used for a wide variety of applications. One of the main drawbacks of ECIS is the need for implementing complex electrical models to decode the electrical performance of the full system composed by the electrodes, medium, and cells. In this work we present a new approach for the analysis of data and the prediction of a specific biological parameter, the fill-factor of a cell culture, based on a polynomial regression, data-analytic model. The method was successfully applied to a specific ECIS circuit and two different cell cultures, N2A (a mouse neuroblastoma cell line) and myoblasts. The data-analytic modeling approach can be used in the decoding of electrical impedance measurements of different cell lines, provided a representative volume of data from the cell culture growth is available, sorting out the difficulties traditionally found in the implementation of electrical models. This can be of particular importance for the design of control algorithms for cell cultures in tissue engineering protocols, and labs-on-a-chip and wearable devices applications. MDPI 2019-10-25 /pmc/articles/PMC6864697/ /pubmed/31731413 http://dx.doi.org/10.3390/s19214639 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article García, Elvira Pérez, Pablo Olmo, Alberto Díaz, Roberto Huertas, Gloria Yúfera, Alberto Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring |
title | Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring |
title_full | Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring |
title_fullStr | Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring |
title_full_unstemmed | Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring |
title_short | Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring |
title_sort | data-analytics modeling of electrical impedance measurements for cell culture monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864697/ https://www.ncbi.nlm.nih.gov/pubmed/31731413 http://dx.doi.org/10.3390/s19214639 |
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