Limits...
Automated and online characterization of adherent cell culture growth in a microfabricated bioreactor.

Jaccard N, Macown RJ, Super A, Griffin LD, Veraitch FS, Szita N - J Lab Autom (2014)

Bottom Line: While suspension culture processes benefit from decades of development of instrumented bioreactors, adherent cultures are typically performed in static, noninstrumented flasks and well-plates.A machine learning-based algorithm enabled the specific detection of one cell type within a co-culture setting, such as human embryonic stem cells against the background of fibroblast cells.In addition, the algorithm did not confuse image artifacts resulting from microfabrication, such as scratches on surfaces, or dust particles, with cellular features.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemical Engineering, University College London, London, UK Centre for Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London, UK.

Show MeSH

Related in: MedlinePlus

Example of application of image processing to monitor stem cell growth. (A) Example of human embryonic stem cells (hESCs) growing in an in vitro fertilization dish. The size of the culture area makes it difficult to quickly image the whole reactor, but instead it is necessary to image only a few fields of view. Green shows colonies on day 1, red on day 3. (B) hESCs growing in the microfabricated bioreactor where it is possible to image the whole culture area. Green shows colonies on day 1, red on day 3. (C) Application of the same approach to mouse embryonic stem cells cultured in the microfabricated bioreactor. The use of machine learning in combination with basic image features enabled the detection of the cell despite the presence of background artifacts. (D) Online monitoring of mESC growth in the whole culture chamber. The bold line is the mean and light gray line the standard deviation across three independent trials.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2 - License 3
getmorefigures.php?uid=PMC4230958&req=5

fig3-2211068214529288: Example of application of image processing to monitor stem cell growth. (A) Example of human embryonic stem cells (hESCs) growing in an in vitro fertilization dish. The size of the culture area makes it difficult to quickly image the whole reactor, but instead it is necessary to image only a few fields of view. Green shows colonies on day 1, red on day 3. (B) hESCs growing in the microfabricated bioreactor where it is possible to image the whole culture area. Green shows colonies on day 1, red on day 3. (C) Application of the same approach to mouse embryonic stem cells cultured in the microfabricated bioreactor. The use of machine learning in combination with basic image features enabled the detection of the cell despite the presence of background artifacts. (D) Online monitoring of mESC growth in the whole culture chamber. The bold line is the mean and light gray line the standard deviation across three independent trials.

Mentions: The ability to detect hESC colonies was first demonstrated using in vitro fertilization (IVF) plates. Due to the relatively large growth area (2.9 cm2), only the central area where most of the colonies were seeded could be considered (Fig. 3A). By comparing images from day 3 of cultures with those acquired 24 h after seeding, it was possible to assess the growth of the colonies and create striking visual representations of this very dynamic system. The same principle was applied to hESCs growing in the microfabricated bioreactor (Fig. 3B). In contrast to the IVF case, the small dimensions of the culture chamber made it possible to monitor growth based on images of the whole culture area. This enabled determining the response of cells to perfusion: colonies were found to migrate, merge, or even wash out on rare occasions. These results were obtained using intermittent imaging. This approach was next applied to fully automated imaging of mESCs cultured in the reactor for long periods (5 days). The results showed that the image-processing method was able to detect mESC colonies accurately despite the prevalence of artifacts (Fig. 3C). Based on this detection, the confluency of the culture (i.e., the fraction of the culture area occupied by cells) was determined for the duration of the culture. Interestingly, the mean and standard deviation across three trials were relatively low (26%), demonstrating good reproducibility (Fig. 3D).


Automated and online characterization of adherent cell culture growth in a microfabricated bioreactor.

Jaccard N, Macown RJ, Super A, Griffin LD, Veraitch FS, Szita N - J Lab Autom (2014)

Example of application of image processing to monitor stem cell growth. (A) Example of human embryonic stem cells (hESCs) growing in an in vitro fertilization dish. The size of the culture area makes it difficult to quickly image the whole reactor, but instead it is necessary to image only a few fields of view. Green shows colonies on day 1, red on day 3. (B) hESCs growing in the microfabricated bioreactor where it is possible to image the whole culture area. Green shows colonies on day 1, red on day 3. (C) Application of the same approach to mouse embryonic stem cells cultured in the microfabricated bioreactor. The use of machine learning in combination with basic image features enabled the detection of the cell despite the presence of background artifacts. (D) Online monitoring of mESC growth in the whole culture chamber. The bold line is the mean and light gray line the standard deviation across three independent trials.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2 - License 3
Show All Figures
getmorefigures.php?uid=PMC4230958&req=5

fig3-2211068214529288: Example of application of image processing to monitor stem cell growth. (A) Example of human embryonic stem cells (hESCs) growing in an in vitro fertilization dish. The size of the culture area makes it difficult to quickly image the whole reactor, but instead it is necessary to image only a few fields of view. Green shows colonies on day 1, red on day 3. (B) hESCs growing in the microfabricated bioreactor where it is possible to image the whole culture area. Green shows colonies on day 1, red on day 3. (C) Application of the same approach to mouse embryonic stem cells cultured in the microfabricated bioreactor. The use of machine learning in combination with basic image features enabled the detection of the cell despite the presence of background artifacts. (D) Online monitoring of mESC growth in the whole culture chamber. The bold line is the mean and light gray line the standard deviation across three independent trials.
Mentions: The ability to detect hESC colonies was first demonstrated using in vitro fertilization (IVF) plates. Due to the relatively large growth area (2.9 cm2), only the central area where most of the colonies were seeded could be considered (Fig. 3A). By comparing images from day 3 of cultures with those acquired 24 h after seeding, it was possible to assess the growth of the colonies and create striking visual representations of this very dynamic system. The same principle was applied to hESCs growing in the microfabricated bioreactor (Fig. 3B). In contrast to the IVF case, the small dimensions of the culture chamber made it possible to monitor growth based on images of the whole culture area. This enabled determining the response of cells to perfusion: colonies were found to migrate, merge, or even wash out on rare occasions. These results were obtained using intermittent imaging. This approach was next applied to fully automated imaging of mESCs cultured in the reactor for long periods (5 days). The results showed that the image-processing method was able to detect mESC colonies accurately despite the prevalence of artifacts (Fig. 3C). Based on this detection, the confluency of the culture (i.e., the fraction of the culture area occupied by cells) was determined for the duration of the culture. Interestingly, the mean and standard deviation across three trials were relatively low (26%), demonstrating good reproducibility (Fig. 3D).

Bottom Line: While suspension culture processes benefit from decades of development of instrumented bioreactors, adherent cultures are typically performed in static, noninstrumented flasks and well-plates.A machine learning-based algorithm enabled the specific detection of one cell type within a co-culture setting, such as human embryonic stem cells against the background of fibroblast cells.In addition, the algorithm did not confuse image artifacts resulting from microfabrication, such as scratches on surfaces, or dust particles, with cellular features.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemical Engineering, University College London, London, UK Centre for Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London, UK.

Show MeSH
Related in: MedlinePlus