Meeting Abstract
P2.101 Sunday, Jan. 5 15:30 Defining a Beta Cell: An Objective Gene Signature Framework GREENFEST-ALLEN, E*; STOECKERT, C; MAGNUSON, M; University of Pennsylvania allenem@pcbi.upenn.edu
A growing body of biological work is dedicated to producing key cellular populations via directed culturing of embryonic stems cells (ESCs) or reprogramming of existing mature cell populations. Here, we present the system of pancreatic beta cell development as an exemplar for establishing a standard gene signature framework for defining target cell types and evaluating progress toward the goal of directed differentiation. Because loss of beta cell function and mass is a contributing factor of diabetes, many attempts have been made to generate beta cells from pre-existing cell populations; most have produced at best “beta-like” cells that share characteristics of both immature beta cells and the source populations or steps along the way. Here, we use knowledge of the genetic signatures of specific pancreatic cellular populations to better define both the end point and the pathway to mature beta cells. We performed comprehensive transcriptome profiling of twelve murine pancreatic progenitor and adult cell populations covering pancreatic differentiation from embryonic day 8.0 through post-natal day 60. Using these data, we estimated a gene-interaction network that was iteratively partitioned to identify a set of robust, connected sub-networks comprised of genes with similar expression profiles. Our network analysis further identified temporal alterations in gene-interactions regulating stepwise production of beta cells. In particular, we observed a strong demarcation between pre-and post-endocrine cell specification, suggesting that the genetic toolbox underlying early morphogenesis is distinct from that utilized in the differentiation of individual endocrine cells, once the endocrine cell-fate has been specified. Coupled with functional annotations derived from curated biological ontologies we used these data to delimitate a set of genetic and process-based signatures that define pancreatic cell populations.
