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Statistical Modeling of Single Target Cell Encapsulation

High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However...

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Detalles Bibliográficos
Autores principales: Moon, SangJun, Ceyhan, Elvan, Gurkan, Umut Atakan, Demirci, Utkan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3140975/
https://www.ncbi.nlm.nih.gov/pubmed/21814548
http://dx.doi.org/10.1371/journal.pone.0021580
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author Moon, SangJun
Ceyhan, Elvan
Gurkan, Umut Atakan
Demirci, Utkan
author_facet Moon, SangJun
Ceyhan, Elvan
Gurkan, Umut Atakan
Demirci, Utkan
author_sort Moon, SangJun
collection PubMed
description High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and enable reliable and repeatable control over the encapsulation of cells in droplets during the isolation process with high confidence level. We have modeled and experimentally verified a microdroplet-based cell encapsulation process for various combinations of cell loading and target cell concentrations. Here, we explain theoretically and validate experimentally a model to isolate and pattern single target cells from heterogeneous mixtures without using complex peripheral systems.
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spelling pubmed-31409752011-08-03 Statistical Modeling of Single Target Cell Encapsulation Moon, SangJun Ceyhan, Elvan Gurkan, Umut Atakan Demirci, Utkan PLoS One Research Article High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and enable reliable and repeatable control over the encapsulation of cells in droplets during the isolation process with high confidence level. We have modeled and experimentally verified a microdroplet-based cell encapsulation process for various combinations of cell loading and target cell concentrations. Here, we explain theoretically and validate experimentally a model to isolate and pattern single target cells from heterogeneous mixtures without using complex peripheral systems. Public Library of Science 2011-07-21 /pmc/articles/PMC3140975/ /pubmed/21814548 http://dx.doi.org/10.1371/journal.pone.0021580 Text en Moon et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Moon, SangJun
Ceyhan, Elvan
Gurkan, Umut Atakan
Demirci, Utkan
Statistical Modeling of Single Target Cell Encapsulation
title Statistical Modeling of Single Target Cell Encapsulation
title_full Statistical Modeling of Single Target Cell Encapsulation
title_fullStr Statistical Modeling of Single Target Cell Encapsulation
title_full_unstemmed Statistical Modeling of Single Target Cell Encapsulation
title_short Statistical Modeling of Single Target Cell Encapsulation
title_sort statistical modeling of single target cell encapsulation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3140975/
https://www.ncbi.nlm.nih.gov/pubmed/21814548
http://dx.doi.org/10.1371/journal.pone.0021580
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