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Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation

journal contribution
posted on 12.05.2016, 13:50 by David Smith, Katie GlenKatie Glen, Rob ThomasRob Thomas
The translation of laboratory processes into scaled production systems suitable for manu-facture is a significant challenge for cell based therapies; in particular there is a lack ofanalytical methods that are informative and efficient for process control. Here the potentialof image analysis as one part of the solution to this issue is explored, using pluripotent stemcell colonies as a valuable and challenging exemplar. The Cell-IQ live cell imaging platformwas 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, andSox-2, were shown to correspond to specific morphologies using immunostaining and flowcytometry techniques. Quantitative monitoring of these morphological attributes in-processusing the reference image libraries showed rapid sensitivity to changes induced by differentmedia exchange regimes or the addit ion of mesoderm lineage inducing cytokine BMP4. Theimaging sample size to precision relationship was defined for each morphological attributeto 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 out-comes; smaller colonies were identified as optimum for homogenous early mesoderm differ-entiation, while larger colonies maintained a morphologically pluripotent core. Finally, weshow the potential of the same image libraries to assess cell number in culture with accu-racy comparable to sacrificial digestion and counting. The data supports a potentiallypowerful role for quantitative image analysis in the setting of in-process specifications, andalso for screening the effects of process actions during development, which is highly comple-mentary to current analysis in optimization and manufacture.


This work was supported by EPSRC Centre for Innovative Manufacturing grant EP/H028277/1. David Smith was funded by EPSRC Doctoral Training Centre in Regenerative Medicine grant EP/F500491/1 alongside CM-Technologies industry sponsor.



  • Mechanical, Electrical and Manufacturing Engineering

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215 - 223 (9)


SMITH, D., GLEN, K.E. and THOMAS, R.J., 2016. Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation. Biotechnology Progress, 32 (1), pp. 215 - 223.


American Institute of Chemical Engineers (Wiley)


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