A high-content platform to characterise human induced pluripotent stem cell lines.
A high-content platform to characterise human induced pluripotent stem cell lines.
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DOI:
10.1016/j.ymeth.2015.11.012
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发表时间:
2016-03-01
期刊:
影响因子:
--
通讯作者:
Danovi D
中科院分区:
文献类型:
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作者:
Leha A;Moens N;Meleckyte R;Culley OJ;Gervasio MK;Kerz M;Reimer A;Cain SA;Streeter I;Folarin A;Stegle O;Kielty CM;HipSci Consortium;Durbin R;Watt FM;Danovi D
iPSCs show inter/intra-line/donor-variability hampering characterisation. HipSci generates, banks and provides iPSCs from hundreds of individual donors. iPSCs respond to different human plasma fibronectin concentrations on 96-well assays. Phenotypic features: cell number, proliferation, morphology and intercellular adhesion. The methodologies described can be tailored for disease-modelling and other cell types. Induced pluripotent stem cells (iPSCs) provide invaluable opportunities for future cell therapies as well as for studying human development, modelling diseases and discovering therapeutics. In order to realise the potential of iPSCs, it is crucial to comprehensively characterise cells generated from large cohorts of healthy and diseased individuals. The human iPSC initiative (HipSci) is assessing a large panel of cell lines to define cell phenotypes, dissect inter- and intra-line and donor variability and identify its key determinant components. Here we report the establishment of a high-content platform for phenotypic analysis of human iPSC lines. In the described assay, cells are dissociated and seeded as single cells onto 96-well plates coated with fibronectin at three different concentrations. This method allows assessment of cell number, proliferation, morphology and intercellular adhesion. Altogether, our strategy delivers robust quantification of phenotypic diversity within complex cell populations facilitating future identification of the genetic, biological and technical determinants of variance. Approaches such as the one described can be used to benchmark iPSCs from multiple donors and create novel platforms that can readily be tailored for disease modelling and drug discovery.