Induced pluripotent stem cells (iPSCs) are an essential tool for modeling how causal genetic variants impact cellular function in disease, as well as an emerging source of tissue for regenerative medicine. The preparation of somatic cells, their reprogramming and the subsequent verification of iPSC pluripotency are laborious, manual processes limiting the scale and reproducibility of this technology. Here we describe a modular, robotic platform for iPSC reprogramming enabling automated, high-throughput conversion of skin biopsies into iPSCs and differentiated cells with minimal manual intervention. We demonstrate that automated reprogramming and the pooled selection of polyclonal pluripotent cells results in high-quality, stable iPSCs. These lines display less line-to-line variation than either manually produced lines or lines produced through automation followed by single-colony subcloning. The robotic platform we describe will enable the application of iPSCs to population-scale biomedical problems including the study of complex genetic diseases and the development of personalized medicines.

The transfer of somatic cell nuclei into oocytes can give rise to pluripotent stem cells that are consistently equivalent to embryonic stem cells, holding promise for autologous cell replacement therapy. Although methods to induce pluripotent stem cells from somatic cells by transcription factors are widely used in basic research, numerous differences between induced pluripotent stem cells and embryonic stem cells have been reported, potentially affecting their clinical use. Because of the therapeutic potential of diploid embryonic stem-cell lines derived from adult cells of diseased human subjects, we have systematically investigated the parameters affecting efficiency of blastocyst development and stem-cell derivation. Here we show that improvements to the oocyte activation protocol, including the use of both kinase and translation inhibitors, and cell culture in the presence of histone deacetylase inhibitors, promote development to the blastocyst stage. Developmental efficiency varied between oocyte donors, and was inversely related to the number of days of hormonal stimulation required for oocyte maturation, whereas the daily dose of gonadotropin or the total number of metaphase II oocytes retrieved did not affect developmental outcome. Because the use of concentrated Sendai virus for cell fusion induced an increase in intracellular calcium concentration, causing premature oocyte activation, we used diluted Sendai virus in calcium-free medium. Using this modified nuclear transfer protocol, we derived diploid pluripotent stem-cell lines from somatic cells of a newborn and, for the first time, an adult, a female with type 1 diabetes.

The discovery of induced pluripotent stem cells (iPSCs) and concurrent development of protocols for their cell-type specific differentiation have revolutionized studies of diseases and raised the possibility that personalized medicine may be achievable. Realizing the full potential of iPSC will require addressing the challenges inherent in obtaining appropriate cells for millions of individuals while meeting the regulatory requirements of delivering therapy and keeping costs affordable. Critical to making PSC based cell therapy widely accessible is determining which mode of cell collection, storage and distribution, will work. In this manuscript we suggest that moderate sized bank where a diverse set of lines carrying different combinations of commonly present HLA alleles are banked and differentiated cells are made available to matched recipients as need dictates may be a solution. We discuss the issues related to developing such a bank and how it could be constructed and propose a bank of selected HLA phenotypes from carefully screened healthy individuals as a solution to delivering personalized medicine.

Drug discovery for diseases such as Parkinson’s disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinson’s disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platform’s robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinson’s disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

Yaffe and colleagues discuss the issues surrounding the authentication and quality of induced pluripotent stem cells.