An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.

Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts t...

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Autores principales: Dai Fei Elmer Ker, Lee E Weiss, Silvina N Junkers, Mei Chen, Zhaozheng Yin, Michael F Sandbothe, Seung-il Huh, Sungeun Eom, Ryoma Bise, Elvira Osuna-Highley, Takeo Kanade, Phil G Campbell
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Publicado: Public Library of Science (PLoS) 2011
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spelling oai:doaj.org-article:05b9968acb6d403faebdf13d1404340d2021-11-18T07:34:08ZAn engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.1932-620310.1371/journal.pone.0027672https://doaj.org/article/05b9968acb6d403faebdf13d1404340d2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22110715/?tool=EBIhttps://doaj.org/toc/1932-6203Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts to automate cell culture with robotic systems are underway, the majority of such systems still require human intervention to determine when to subculture. Thus, it is necessary to accurately and objectively determine the appropriate time for cell passaging. Optimal stem cell culturing that maintains cell pluripotency while maximizing cell yields will be especially important for efficient, cost-effective stem cell-based therapies. Toward this goal we developed a real-time computer vision-based system that monitors the degree of cell confluency with a precision of 0.791±0.031 and recall of 0.559±0.043. The system consists of an automated phase-contrast time-lapse microscope and a server. Multiple dishes are sequentially imaged and the data is uploaded to the server that performs computer vision processing, predicts when cells will exceed a pre-defined threshold for optimal cell confluency, and provides a Web-based interface for remote cell culture monitoring. Human operators are also notified via text messaging and e-mail 4 hours prior to reaching this threshold and immediately upon reaching this threshold. This system was successfully used to direct the expansion of a paradigm stem cell population, C2C12 cells. Computer-directed and human-directed control subcultures required 3 serial cultures to achieve the theoretical target cell yield of 50 million C2C12 cells and showed no difference for myogenic and osteogenic differentiation. This automated vision-based system has potential as a tool toward adaptive real-time control of subculturing, cell culture optimization and quality assurance/quality control, and it could be integrated with current and developing robotic cell cultures systems to achieve complete automation.Dai Fei Elmer KerLee E WeissSilvina N JunkersMei ChenZhaozheng YinMichael F SandbotheSeung-il HuhSungeun EomRyoma BiseElvira Osuna-HighleyTakeo KanadePhil G CampbellPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 11, p e27672 (2011)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Dai Fei Elmer Ker
Lee E Weiss
Silvina N Junkers
Mei Chen
Zhaozheng Yin
Michael F Sandbothe
Seung-il Huh
Sungeun Eom
Ryoma Bise
Elvira Osuna-Highley
Takeo Kanade
Phil G Campbell
An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.
description Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts to automate cell culture with robotic systems are underway, the majority of such systems still require human intervention to determine when to subculture. Thus, it is necessary to accurately and objectively determine the appropriate time for cell passaging. Optimal stem cell culturing that maintains cell pluripotency while maximizing cell yields will be especially important for efficient, cost-effective stem cell-based therapies. Toward this goal we developed a real-time computer vision-based system that monitors the degree of cell confluency with a precision of 0.791±0.031 and recall of 0.559±0.043. The system consists of an automated phase-contrast time-lapse microscope and a server. Multiple dishes are sequentially imaged and the data is uploaded to the server that performs computer vision processing, predicts when cells will exceed a pre-defined threshold for optimal cell confluency, and provides a Web-based interface for remote cell culture monitoring. Human operators are also notified via text messaging and e-mail 4 hours prior to reaching this threshold and immediately upon reaching this threshold. This system was successfully used to direct the expansion of a paradigm stem cell population, C2C12 cells. Computer-directed and human-directed control subcultures required 3 serial cultures to achieve the theoretical target cell yield of 50 million C2C12 cells and showed no difference for myogenic and osteogenic differentiation. This automated vision-based system has potential as a tool toward adaptive real-time control of subculturing, cell culture optimization and quality assurance/quality control, and it could be integrated with current and developing robotic cell cultures systems to achieve complete automation.
format article
author Dai Fei Elmer Ker
Lee E Weiss
Silvina N Junkers
Mei Chen
Zhaozheng Yin
Michael F Sandbothe
Seung-il Huh
Sungeun Eom
Ryoma Bise
Elvira Osuna-Highley
Takeo Kanade
Phil G Campbell
author_facet Dai Fei Elmer Ker
Lee E Weiss
Silvina N Junkers
Mei Chen
Zhaozheng Yin
Michael F Sandbothe
Seung-il Huh
Sungeun Eom
Ryoma Bise
Elvira Osuna-Highley
Takeo Kanade
Phil G Campbell
author_sort Dai Fei Elmer Ker
title An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.
title_short An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.
title_full An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.
title_fullStr An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.
title_full_unstemmed An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.
title_sort engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.
publisher Public Library of Science (PLoS)
publishDate 2011
url https://doaj.org/article/05b9968acb6d403faebdf13d1404340d
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