Home » События » Stem cells and bioinformatics challenges

Stem cells and bioinformatics challenges

Invited talk: Prof. Kiselev S.L.
Moscow, The Vavilov Institute of General Genetics (VIGG) (http://vigg.ru/)
When: October, 12, 2012; 2pm. Room number will be updated
Location: St Petersburg Academic University (http://spbau.ru)
8/3 Khlopina Str, St Petersburg, 194021, Russia

Abstract

Pluripotent stem cells are the cells that can differentiate into any cell type of the body. There are two sources for human pluripotent stem cells: embryonic stem (ES) cells derived from surplus early preimplantation embryos created for in vitro fertilization and induced pluripotent stem (iPS) cells generated by genetic reprogramming of somatic cells. ES cells have been an area of intense research during the last decade and two clinical trials have been recently approved. iPS cells were created only recently and most of the research has been focused on the iPS generation protocols and investigation of mechanisms of direct reprogramming. iPS technology makes possible to derive pluripotent stem cells from any patient. However there is a number of hurdles to be overcome before iPS cells will find a niche in practice. The important question is how close iPS cells are to their natural analogue ES cells in their genetic and epigenetic properties. To address this issue we recently established a system allowing human ES cells differentiation to any particular cell type followed by reverse procedure of reprogramming to the pluripotent state. Isogenic “forth and back” system makes possible to perform genome-wide detailed comparison of human pluripotent cells and to distinguish traces of the reprogramming process from the genetic and epigenetic differences between various allogenic cell lines.
Despite the equivalency of induced pluripotent and embryonic stem cells it is still an unresolved issue, we have generated a number of iPS cell lines from the patients with various disorders for disease modeling and new therapies development.
Growing numbers of genome-wide sequencing approaches, versions of microarrys and batch-to-batch variations as well as the amount of already collected data complicate output data analysis.