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SUMMARY:Deep Proteomes\, iPS cells &amp\; Data Mountains - Prof. Angus Lam
 ond\, Centre for Gene Regulation and Expression\, University of Dundee
DTSTART:20190501T093000Z
DTEND:20190501T103000Z
UID:TALK123673@talks.cam.ac.uk
CONTACT:Will Arter
DESCRIPTION:Deep mining of proteomes\, using mass spectrometry (MS) based 
 proteomics technology\, can\nprovide invaluable insights\, at a systems le
 vel\, into both physiological responses in healthy\ncells and mechanisms c
 ausing disease phenotypes. This allows unbiased\, global\,\nquantitative m
 easurements linking cellular phenotypes with changes in protein dynamics i
 n\nboth healthy and diseased cells. Furthermore\, beyond identifying prote
 in abundance\,\nproteomics technology can now also provide a flexible suit
 e of quantitative assays that can\nbe used to characterize a multi-dimensi
 onal array of ‘Protein Properties’ that affect cell\nphenotypes. This 
 includes\, for example\, subcellular protein localization\, turnover rates
 \, posttranslational\nmodifications\, cell cycle variation and specific pr
 otein complexes and proteinprotein\ninteractions etc. A major challenge th
 at now emerges from this ability to generate very\nlarge sets of proteomic
 s data is how to manage\, analyse and integrate the huge resulting\nvolume
 s of complex information. I will describe a recent project where we have u
 sed\nquantitative proteomics for large-scale studies of human induced plur
 ipotent stem cells\n(iPSCs)\, involving the analysis of over 200 iPSC line
 s derived from both healthy donors and\npatient cohorts with specific inhe
 rited disorders. This project highlights some of the technical\nand analyt
 ical challenges inherent in performing proteomic analyses at this scale. I
  will also\ndescribe user-friendly\, computational tools we have built for
  the effective management and\nsharing of these large\, multidimensional d
 ata sets (see\; www.peptracker.com/epd).
LOCATION:Department of Chemistry\, Cambridge\, Unilever lecture theatre
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