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Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation
The translation of laboratory processes into scaled production systems suitable for manufacture is a significant challenge for cell based therapies; in particular there is a lack of analytical methods that are informative and efficient for process control. Here the potential of image analysis as one...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4991304/ https://www.ncbi.nlm.nih.gov/pubmed/26560993 http://dx.doi.org/10.1002/btpr.2199 |
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author | Smith, David Glen, Katie Thomas, Robert |
author_facet | Smith, David Glen, Katie Thomas, Robert |
author_sort | Smith, David |
collection | PubMed |
description | The translation of laboratory processes into scaled production systems suitable for manufacture is a significant challenge for cell based therapies; in particular there is a lack of analytical methods that are informative and efficient for process control. Here the potential of image analysis as one part of the solution to this issue is explored, using pluripotent stem cell colonies as a valuable and challenging exemplar. The Cell‐IQ live cell imaging platform was used to build image libraries of morphological culture attributes such as colony “edge,” “core periphery” or “core” cells. Conventional biomarkers, such as Oct3/4, Nanog, and Sox‐2, were shown to correspond to specific morphologies using immunostaining and flow cytometry techniques. Quantitative monitoring of these morphological attributes in‐process using the reference image libraries showed rapid sensitivity to changes induced by different media exchange regimes or the addition of mesoderm lineage inducing cytokine BMP4. The imaging sample size to precision relationship was defined for each morphological attribute to show that this sensitivity could be achieved with a relatively low imaging sample. Further, the morphological state of single colonies could be correlated to individual colony outcomes; smaller colonies were identified as optimum for homogenous early mesoderm differentiation, while larger colonies maintained a morphologically pluripotent core. Finally, we show the potential of the same image libraries to assess cell number in culture with accuracy comparable to sacrificial digestion and counting. The data supports a potentially powerful role for quantitative image analysis in the setting of in‐process specifications, and also for screening the effects of process actions during development, which is highly complementary to current analysis in optimization and manufacture. © 2015 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers, 32:215–223, 2016 |
format | Online Article Text |
id | pubmed-4991304 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49913042016-09-06 Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation Smith, David Glen, Katie Thomas, Robert Biotechnol Prog Process Sensing and Control The translation of laboratory processes into scaled production systems suitable for manufacture is a significant challenge for cell based therapies; in particular there is a lack of analytical methods that are informative and efficient for process control. Here the potential of image analysis as one part of the solution to this issue is explored, using pluripotent stem cell colonies as a valuable and challenging exemplar. The Cell‐IQ live cell imaging platform was used to build image libraries of morphological culture attributes such as colony “edge,” “core periphery” or “core” cells. Conventional biomarkers, such as Oct3/4, Nanog, and Sox‐2, were shown to correspond to specific morphologies using immunostaining and flow cytometry techniques. Quantitative monitoring of these morphological attributes in‐process using the reference image libraries showed rapid sensitivity to changes induced by different media exchange regimes or the addition of mesoderm lineage inducing cytokine BMP4. The imaging sample size to precision relationship was defined for each morphological attribute to show that this sensitivity could be achieved with a relatively low imaging sample. Further, the morphological state of single colonies could be correlated to individual colony outcomes; smaller colonies were identified as optimum for homogenous early mesoderm differentiation, while larger colonies maintained a morphologically pluripotent core. Finally, we show the potential of the same image libraries to assess cell number in culture with accuracy comparable to sacrificial digestion and counting. The data supports a potentially powerful role for quantitative image analysis in the setting of in‐process specifications, and also for screening the effects of process actions during development, which is highly complementary to current analysis in optimization and manufacture. © 2015 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers, 32:215–223, 2016 John Wiley and Sons Inc. 2015-11-28 2016 /pmc/articles/PMC4991304/ /pubmed/26560993 http://dx.doi.org/10.1002/btpr.2199 Text en © 2015 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Process Sensing and Control Smith, David Glen, Katie Thomas, Robert Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation |
title | Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation |
title_full | Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation |
title_fullStr | Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation |
title_full_unstemmed | Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation |
title_short | Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation |
title_sort | automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation |
topic | Process Sensing and Control |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4991304/ https://www.ncbi.nlm.nih.gov/pubmed/26560993 http://dx.doi.org/10.1002/btpr.2199 |
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