A Spatial and Temporal Map of C. elegans Gene Expression
- William Clayton Spencer1,
- Georg Zeller2,
- Joseph D. Watson3,
- Stefan R. Henz4,
- Kathie L. Watkins1,
- Rebecca D. McWhirter1,
- Sarah C. Petersen1,
- Vipin T. Sreedharan5,
- Christian Widmer5,
- Jeanyoung Jo6,
- Valerie Reinke6,
- Lisa Petrella7,
- Susan Strome7,
- Stephen Von Stetina8,
- Menachem Katz9,
- Shai Shaham9,
- Gunnar Raetsch5 and
- David M Miller1,10
- 1 Vanderbilt University;
- 2 European Molecular Biology Laboratory;
- 3 University of North Carolina, Chapel Hill;
- 4 Max Planck Institute for Developmental Biology;
- 5 Friedrich Miescher Laboratory of the Max Planck Society;
- 6 Yale University;
- 7 University of California, Santa Cruz;
- 8 Harvard University;
- 9 The Rockefeller University
- * Corresponding author; email: david.miller{at}vanderbilt.edu
Abstract
The C. elegans genome has been completely sequenced and the developmental anatomy of this model organism is described at single-cell resolution. Here we utilize strategies that exploit this precisely defined architecture to link gene expression to cell type. We obtained RNAs from specific cells and from each developmental stage using tissue-specific promoters to mark cells for isolation by FACS or for mRNA extraction by the mRNA-tagging method. We then generated gene expression profiles of >30 different cells and developmental stages using tiling arrays. Machine-learning-based analysis detected transcripts corresponding to established gene models and revealed novel transcriptionally active regions (TARs) in non-coding domains that comprise at least 10% of the total C. elegans genome. Our results show that ~75% of transcripts with detectable expression are differentially expressed among developmental stages and across cell types. Examination of known tissue- and cell-specific transcripts validates these data sets and suggests that newly identified TARs may exercise cell-specific functions. Additionally, we used self-organizing maps to define groups of co-regulated transcripts and applied regulatory element analysis to identify known transcription factor- and miRNA-binding sites, as well as novel motifs that likely function to control subsets of these genes. By using cell-specific, whole-genome profiling strategies, we have detected a large number of novel transcripts and produced high-resolution gene expression maps that provide a basis for establishing the roles of individual genes in cellular differentiation.
- Received September 3, 2010.
- Accepted December 8, 2010.
- Copyright © 2010, Cold Spring Harbor Laboratory Press
