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Deep Proteomes, iPS cells & Data Mountains

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Deep mining of proteomes, using mass spectrometry (MS) based proteomics technology, can provide invaluable insights, at a systems level, into both physiological responses in healthy cells and mechanisms causing disease phenotypes. This allows unbiased, global, quantitative measurements linking cellular phenotypes with changes in protein dynamics in both healthy and diseased cells. Furthermore, beyond identifying protein abundance, proteomics technology can now also provide a flexible suite of quantitative assays that can be used to characterize a multi-dimensional array of ‘Protein Properties’ that affect cell phenotypes. This includes, for example, subcellular protein localization, turnover rates, posttranslational modifications, cell cycle variation and specific protein complexes and proteinprotein interactions etc. A major challenge that now emerges from this ability to generate very large sets of proteomics data is how to manage, analyse and integrate the huge resulting volumes of complex information. I will describe a recent project where we have used quantitative proteomics for large-scale studies of human induced pluripotent stem cells (iPSCs), involving the analysis of over 200 iPSC lines derived from both healthy donors and patient cohorts with specific inherited disorders. This project highlights some of the technical and analytical challenges inherent in performing proteomic analyses at this scale. I will also describe user-friendly, computational tools we have built for the effective management and sharing of these large, multidimensional data sets (see;

This talk is part of the Biophysical Seminars series.

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