title: Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation.
abstract: 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.
authors.affiliation: Centre for Biological Engineering, Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, U.K.
authors.forename: David
authors.initials: D
authors.lastname: Smith
authors.affiliation: Centre for Biological Engineering, Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, U.K.
authors.forename: Katie
authors.initials: K
authors.lastname: Glen
authors.affiliation: Centre for Biological Engineering, Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, U.K.
authors.forename: Robert
authors.initials: R
authors.lastname: Thomas
mesh_terms: D015415:Biomarkers; D002452:Cell Count; D018929:Cell Culture Techniques / Q000379:methods*; D019070:Cell Lineage / Q000235:genetics*; D053595:Embryonic Stem Cells / Q000166:cytology*; D005434:Flow Cytometry; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D057054:Molecular Imaging; D011786:Quality Control
chemical_list: D015415:Biomarkers
keywords: automation; cell-IQ; image analysis; pluripotent stem cell; process control