Induced pluripotent stem cell (iPSC) technology has enormous potential to provide improved cellular models of human disease. However, variable genetic and phenotypic characterisation of many existing iPSC lines limits their potential use for research and therapy. Here, we describe the systematic generation, genotyping and phenotyping of 711 iPSC lines derived from 301 healthy individuals by the Human Induced Pluripotent Stem Cells Initiative (HipSci: http://www.hipsci.org). Our study outlines the major sources of genetic and phenotypic variation in iPSCs and establishes their suitability as models of complex human traits and cancer. Through genome-wide profiling we find that 5-46% of the variation in different iPSC phenotypes, including differentiation capacity and cellular morphology, arises from differences between individuals. Additionally, we assess the phenotypic consequences of rare, genomic copy number mutations that are repeatedly observed in iPSC reprogramming and present a comprehensive map of common regulatory variants affecting the transcriptome of human pluripotent cells.

Induced pluripotent stem cell (iPSC) technology has enormous potential to provide improved cellular models of human disease. However, variable genetic and phenotypic characterisation of many existing iPSC lines limits their potential use for research and therapy. Here, we describe the systematic generation, genotyping and phenotyping of 522 open access human iPSCs derived from 189 healthy male and female individuals as part of the Human Induced Pluripotent Stem Cells Initiative (HipSci:http://www.hipsci.org). Our study provides a comprehensive picture of the major sources of genetic and phenotypic variation in iPSCs and establishes their suitability for use in genetic studies of complex human traits and cancer. Using a combination of genomewide analyses we find that 5-25% of the variation in different iPSC phenotypes, including differentiation capacity and cellular morphology, arises from differences between individuals. We also assess the phenotypic effects of rare, genomic copy number mutations that are recurrently seen following iPSC reprogramming and present an initial map of common regulatory variants affecting the transcriptome of pluripotent cells in humans.not peer-reviewed)

In this Article, the authors Fiona M. Watt and Richard Durbin should also have been included as 'jointly supervising' authors, and authors Oliver Stegle and Daniel J. Gaffney should also have been noted as 'equally contributing' authors. In addition, the Author Contributions section should have included the sentence: 'H.K. and A.G. contributed equally to this work; O.S. and D.J.G. contributed equally to this work' , as further clarification. The original Article has been corrected online.

Driven by improvements in speed and resolution of mass spectrometers (MS), the field of proteomics, which involves the large-scale detection and analysis of proteins in cells, tissues and organisms, continues to expand in scale and complexity. There is a resulting growth in datasets of both raw MS files and processed peptide and protein identifications. MS-based proteomics technology is also used increasingly to measure additional protein properties affecting cellular function and disease mechanisms, including post-translational modifications, protein–protein interactions, subcellular and tissue distributions. Consequently, biologists and clinicians need innovative tools to conveniently analyse, visualize and explore such large, complex proteomics data and to integrate it with genomics and other related large-scale datasets. We have created the Encyclopedia of Proteome Dynamics (EPD) to meet this need (https://peptracker.com/epd/). The EPD combines a polyglot persistent database and web-application that provides open access to integrated proteomics data for >30 000 proteins from published studies on human cells and model organisms. It is designed to provide a user-friendly interface, featuring graphical navigation with interactive visualizations that facilitate powerful data exploration in an intuitive manner. The EPD offers a flexible and scalable ecosystem to integrate proteomics data with genomics information, RNA expression and other related, large-scale datasets.

Background: Viral oncogenes and mutated proto-oncogenes are potent drivers of cancer malignancy. Downstream of the oncogenic trigger are alterations in protein properties that give rise to cellular transformation and the acquisition of malignant cellular phenotypes. Developments in mass spectrometry enable large-scale, multidimensional characterisation of proteomes. Such techniques could provide an unprecedented, unbiased view of how oncogene activation remodels a human cell proteome. Methods: Using quantitative MS-based proteomics and cellular assays, we analysed how transformation induced by activating v-Src kinase remodels the proteome and cellular phenotypes of breast epithelial (MCF10A) cells. SILAC MS was used to comprehensively characterise the MCF10A proteome and to measure v-Src-induced changes in protein abundance across seven time-points (1-72 hrs). We used pulse-SILAC MS ( Boisvert et al., 2012), to compare protein synthesis and turnover in control and transformed cells. Follow-on experiments employed a combination of cellular and functional assays to characterise the roles of selected Src-responsive proteins. Results: Src-induced transformation changed the expression and/or turnover levels of ~3% of proteins, affecting ~1.5% of the total protein molecules in the cell. Transformation increased the average rate of proteome turnover and disrupted protein homeostasis. We identify distinct classes of protein kinetics in response to Src activation. We demonstrate that members of the polycomb repressive complex 1 (PRC1) are important regulators of invasion and migration in MCF10A cells. Many Src-regulated proteins are present in low abundance and some are regulated post-transcriptionally. The signature of Src-responsive proteins is highly predictive of poor patient survival across multiple cancer types. Open access to search and interactively explore all these proteomic data is provided via the EPD database ( www.peptracker.com/epd). Conclusions: We present the first comprehensive analysis measuring how protein expression and protein turnover is affected by cell transformation, providing a detailed picture at the protein level of the consequences of activation of an oncogene.