SummaryDrosophila neuroblasts (NBs) have emerged as a model for stem cell biology that is ideal for genetic analysis but is limited by the lack of cell-type-specific gene expression data. Here, we describe a method for isolating large numbers of pure NBs and differentiating neurons that retain both cell-cycle and lineage characteristics. We determine transcriptional profiles by mRNA sequencing and identify 28 predicted NB-specific transcription factors that can be arranged in a network containing hubs for Notch signaling, growth control, and chromatin regulation. Overexpression and RNA interference for these factors identify Klumpfuss as a regulator of self-renewal. We show that loss of Klumpfuss function causes premature differentiation and that overexpression results in the formation of transplantable brain tumors. Our data represent a valuable resource for investigating Drosophila developmental neurobiology, and the described method can be applied to other invertebrate stem cell lineages as well.

Elegant tools are available for the genetic analysis of neural stem cell lineages in Drosophila, but a methodology for purifying stem cells and their differentiated progeny for transcriptome analysis is currently missing. Previous attempts to overcome this problem either involved using RNA isolated from whole larval brain tissue or co-transcriptional in vivo mRNA tagging. As both methods have limited cell type specificity, we developed a protocol for the isolation of Drosophila neural stem cells (neuroblasts, NBs) and their differentiated sibling cells by FACS. We dissected larval brains from fly strains expressing GFP under the control of a NB lineage-specific GAL4 line. Upon dissociation, we made use of differences in GFP intensity and cell size to separate NBs and neurons. The resulting cell populations are over 98% pure and can readily be used for live imaging or gene expression analysis. Our method is optimized for neural stem cells, but it can also be applied to other Drosophila cell types. Primary cell suspensions and sorted cell populations can be obtained within 1 d; material for deep-sequencing library preparation can be obtained within 4 d.

BackgroundGenome‐wide transcriptome analyses have given systems‐level insights into gene regulatory networks. Due to the limited depth of quantitative proteomics, however, our understanding of post‐transcriptional gene regulation and its effects on protein‐complex stoichiometry are lagging behind.ResultsHere, we employ deep sequencing and the isobaric tag for relative and absolute quantification (iTRAQ) technology to determine transcript and protein expression changes of a Drosophila brain tumor model at near genome‐wide resolution. In total, we quantify more than 6,200 tissue‐specific proteins, corresponding to about 70% of all transcribed protein‐coding genes. Using our integrated data set, we demonstrate that post‐transcriptional gene regulation varies considerably with biological function and is surprisingly high for genes regulating transcription. We combine our quantitative data with protein‐protein interaction data and show that post‐transcriptional mechanisms significantly enhance co‐regulation of protein‐complex subunits beyond transcriptional co‐regulation. Interestingly, our results suggest that only about 11% of the annotated Drosophila protein complexes are co‐regulated in the brain. Finally, we refine the composition of some of these core protein complexes by analyzing the co‐regulation of potential subunits.ConclusionsOur comprehensive transcriptome and proteome data provide a valuable resource for quantitative biology and offer novel insights into understanding post‐transcriptional gene regulation in a tumor model.