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Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny
Stem cells are unique in that they possess both the capacity to self-renew and thereby maintain their original pool as well as the capacity to differentiate into mature cells. In the past number of years, transcriptional profiling of enriched stem cell populations has been extensively performed in a...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Springer-Verlag
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2493868/ https://www.ncbi.nlm.nih.gov/pubmed/18560825 http://dx.doi.org/10.1007/s00251-008-0305-3 |
Sumario: | Stem cells are unique in that they possess both the capacity to self-renew and thereby maintain their original pool as well as the capacity to differentiate into mature cells. In the past number of years, transcriptional profiling of enriched stem cell populations has been extensively performed in an attempt to identify a universal stem cell gene expression signature. While stem-cell-specific transcripts were identified in each case, this approach has thus far been insufficient to identify a universal group of core “stemness” genes ultimately responsible for self-renewal and multipotency. Similarly, in the hematopoietic system, comparisons of transcriptional profiles between different hematopoietic cell stages have had limited success in revealing core genes ultimately responsible for the initiation of differentiation and lineage specification. Here, we propose that the combined use of transcriptional profiling and genetic linkage analysis, an approach called “genetical genomics”, can be a valuable tool to assist in the identification of genes and gene networks that specify “stemness” and cell fate decisions. We review past studies of hematopoietic cells that utilized transcriptional profiling and/or genetic linkage analysis, and discuss several potential future applications of genetical genomics. |
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