Automated and online characterization of adherent cell culture growth in a microfabricated bioreactor.
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.
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
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Mentions: We previously developed an image-processing approach to alleviate these issues to rapidly produce accurate and reliable data, suitable for the monitoring of adherent cell culture in our microfabricated bioreactor.8 First, instead of detecting cells based on pixel intensity, we employed BIFs that can be used to classify pixels according to local symmetries.17 For example, one of the features was sensitive to dark circular objects on brighter backgrounds and thus often indicated cells’ nuclei. Similarly, the “flat” feature often indicated background regions of the image with a less marked texture. Local histograms of BIFs were constructed for each pixel of the raw PCM image (Fig. 2A). A machine learning classifier was then used to classify each pixel of an image based on its associated histogram. To do so, it was first trained by manually annotating regions of the image as either of interest or not (hESC colonies and image background/fibroblast cells, respectively, in Fig. 2B). This process is very quick as it is not required to annotate the whole image; instead, ambiguous regions can be left unannotated. This is a key advantage of the method, as image-processing methods often require extensive and tedious parameter tweaking. In this case, the algorithm can be taught how to recognize new cell types in a matter of minutes.
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.