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that are unbiased and sensitive, such as a sequence-based approach,
would allow deeper analysis that reveals not only rare transcripts but
completely novel transcripts [20,21]. One such method, the Massively
Parallel Signature Sequencing (MPSS) technique (Lynx Technologies),
has generated sequenced tagged data into millions of transcripts,
allowing detailed sequencing of the transcriptome from many different
cell types and stages of embryonic development. The sensitivity of the
methodology allows for many new non-highly expressed transcripts to
be detected and quantified. Cross-comparisons across data sets that
include ES cells, various time points of postimplantation embryos, and
differentiated cells led to the identification of genes specifically regu-
lated during ES differentiation. The function of these novel genes
remains to be established.
Currently, a number of websites are available that allow access to
MPSS data for murine and human ES cells. In addition, an extensive
EST database has been generated from mouse early development and
ES cells. Gene expression profiles by microarray at multiple time points
throughout mouse preimplantation development [22,23] provide addi-
tional data sets for comparison. Finally, new methods, such as that
developed at the Genome Institute of Singapore based on a di-Tag tech-
nology (see below), provide the ability to map precisely the origins of
novel transcripts and reveal novel and naturally occurring fusion tran-
scripts that are a result of trans-splicing of RNA originating from
different chromosomes [21,24].
Altogether, these databases represent a repository of the most com-
prehensive information about the mammalian transcriptome that
challenges our imagination of the interrelatedness of all these genetic
elements, each with a unique function, that give life to and determine
the fate of ES cells.
Genome-Wide Screens for Gene Function Using ES Cells
While microarray technologies have been very efficient in generating
robust lists of genes that may be involved in the common process of
pluripotent maintenance or differentiation, the derivation of networks
from the analyses described above takes into account only a fraction of
the genes identified as stemness genes. The function of most of these
genes, particularly in the context of ES cell differentiation and how
they work together to maintain ES cells, remains largely unknown. The
paucity of such knowledge limits the functional networks that can be
extracted to enhance the capabilities of computational programs for
increased accuracy of predictive models of cellular responses.
Therefore, one of the major efforts in systems biology has been to seek
ways to perform high-throughput screens for gene function [25]. The
availability of the ES cell as a line of almost pure stem cells that can be
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