Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation
2016-05-12T13:50:49Z (GMT) by
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.