Abstract
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.
Recommended Citation
D. F. Ker and L. E. Weiss and S. N. Junkers and M. Chen and Z. Yin and M. F. Sandbothe and S. Huh and S. Eom and R. Bise and E. Osuna-Highley and T. Kanade and P. G. Campbell, "An Engineered Approach to Stem Cell Culture: Automating the Decision Process for Real-Time Adaptive Subculture of Stem Cells," PLoS ONE, vol. 6, no. 11, pp. 1 - 12, PLOS, Nov 2011.
The definitive version is available at https://doi.org/10.1371/journal.pone.0027672
Department(s)
Computer Science
Keywords and Phrases
Accuracy; Animal Cell; Automation; Bioengineering; Cell Culture Monitoring; Cell Line; Cell Population; Cell Strain C2C12; Computer Interface; Computer System; Controlled Study; Culture Technique; Decision Making; E-mail; Internet; Mouse; Nonhuman; Phase Contrast Microscopy; Stem Cell Culture; Time Lapse Imaging; Animal; Biological Model; Cell Engineering; Cell Proliferation; Cytology; Human; Image Processing; Methodology; Microscopy; Stem Cell; Time; Animals; Automation; Cell Culture Techniques; Cell Engineering; Cell Line; Cell Proliferation; Humans; Image Processing; Computer-assisted; Mice; Microscopy; Models; Biological; Stem Cells; Time Factors; User-Computer Interface
International Standard Serial Number (ISSN)
1932-6203
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2011 The Authors, All rights reserved.
Publication Date
01 Nov 2011
