Gene Regulatory Networks Governing Hematopoietic Stem Cell Development and Identity

Abstract SCI-30

Several studies have addressed questions about transcriptional regulation within particular hematopoietic cell compartments. Few, however, have attempted to capture the transcriptional changes that occur during the dynamic transition from one compartment to another. We have profiled gene expression as multipotential progenitors underwent commitment and differentiation to two alternative lineages, focusing on the first 3 days of differentiation when the majority of decisions about cell fate are made. We have combined this with genome-wide identification of the targets of three key transcription factors before and after differentiation; GATA-2, usually associated with the stem/progenitor compartment; GATA-1 (erythroid); and PU.1 (myeloid). These data have been compiled into a custom-made queryable database, designed to be intuitive to use and to provide tools to interrogate the data on many levels. We used correlation analyses to associate transcription factor binding with particular modules of co-expressed genes, alongside detailed sequence analysis of bound regions. These approaches have informed our understanding of GATA factor switching, and highlighted novel roles for both GATA-2 and Pu.1 in erythroid cells. Overall, the data reveal greater degree of complexity in the interplay between these three factors in regulating hematopoiesis than has hitherto been described, and highlights the importance of a genome-wide approach to understanding complex regulatory systems. A significant challenge in the field is how to relate these types of population-based data to the action of transcriptional regulators within single cells where cell fate decisions ultimately are affected. As a step toward this, we have generated single cell profiles of gene expression for a limited set of transcriptional regulators in self-renewing and committed blood cells and used these data to build a stochastic computational model, which affords exploration of commitment scenarios in silico.