Genome Biology 2006, 7:219commentreviewsreports deposited researchinteractionsinformationrefereed researchMinireviewRNA interference pinpoints regulators of cell size and the cellcycleMegan J Cully* and Sally J Leevers†Addresses: *Signal Transduction Laboratory and †Growth Regulation Laboratory, Cancer Research UK London Research Institute, 44Lincoln’s Inn Fields, London WC2A 3PX, UK.Correspondence: Sally J Leevers. Email: [email protected] genome-wide RNA interference screens are being used to address an increasinglybroad spectrum of biological questions. In one recent screen, Drosophila cell cultures treated withdouble-stranded RNA were analyzed by flow cytometry, providing a wealth of new informationand identifying 488 regulators of the cell cycle, cell size, and cell death.Published: 30 May 2006Genome Biology 2006, 7:219 (doi:10.1186/gb-2006-7-5-219)The electronic version of this article is the complete one and can befound online at http://genomebiology.com/2006/7/5/219© 2006 BioMed Central Ltd The growth of an organism is the net result of a variety ofprocesses, including changes in cell size, cell division andapoptosis. These processes are regulated by intricate, inter-related molecular networks, and their disruption can havemajor biological consequences. In particular, the relation-ship between changes in cell size and the cell cycle has longfascinated researchers. It is complex, poorly understood, andvaries according to the organism, tissue type and develop-mental context. In yeast, large-scale genetic screens haveuncovered many genes involved in cell growth and the initia-tion of DNA synthesis (S phase) [1,2]. It is now clear thatyeast cells must grow to a certain minimal size before start-ing DNA synthesis, providing a ‘cell size checkpoint’ at thetransition from the preceding G1 phase to S phase (the G1/Stransition). Yeast is a unicellular organism, however, andthere is increasing evidence that the relationship betweencell growth and cell division may be different in metazoans.Excitingly, recent technical advances in high-throughput RNAinterference (RNAi) mean that large-scale screeningapproaches, somewhat analogous to the genetic screens inyeast, can now be applied to cultured metazoan cells.Drosophila hemocyte cell lines have emerged as popular cellsystems for this experimental approach for a number ofreasons. First, they are very amenable to RNAi mediated bydouble-stranded RNA (dsRNA): dsRNA molecules of morethan 500 bp can be easily introduced into these cells and arerapidly processed into short interfering RNAs (siRNAs).Second, there are significantly fewer genes in Drosophila thanin mammals, making the mammoth undertaking of a genome-wide screen a little less daunting. Finally, there is less geneticredundancy in Drosophila than in mammals, so depletion ofjust one gene is more likely to reveal a phenotype.Genomic screens for the total complement of protein kinases(the kinome) and general genome-wide screens have beenperformed in Drosophila cell cultures using diverse readoutssuch as cell shape, resistance to bacterial infection and tran-scriptional activity [3-8]. Bjorklund et al. [9] have recentlypublished one of the most comprehensive screens to date, inwhich they searched on a genome-wide scale for dsRNAsthat alter cell size, cell-cycle distribution and cell death. Thedataset they generated provides an excellent starting pointfor many new avenues of research. At the same time, thismassive undertaking highlights some of the bioinformaticchallenges associated with screens on this scale. Forexample, the data generated can be analyzed and presentedin various ways to highlight the different phenotypic effects(see the supplementary data accompanying [9]).The Taipale lab [9] used dsRNAs corresponding to 11,971individual cDNAs to target the silencing of approximately70% of known Drosophila genes. After 4 days culture, flow-cytometry profiles were generated for each dsRNA treatmentin triplicate to provide information on the distribution ofcells in different phases of the cell cycle as well as cell size.The simultaneous effect of each dsRNA on six different cel-lular phenotypes was recorded: the percentage of cells with aDNA content of 2N (percentage of cells in G1; 2N denotescells in G1); 4N cells (percentage of cells in G2, the phaseafter the DNA has been replicated); less than 2N (percentageof dying cells); and greater than 4N (percentage of cells withdefective cytokinesis); as well as the average cell size of theG1 population (G1 cell size), and the G2 population (G2 cellsize). A dsRNA was considered a ‘hit’ if it changed one ofthese percentages relative to control cells by more than 5standard deviations. The phenotypes of all the hits were thenclustered using an unbiased approach, allowing the authorsto identify groups of genes whose downregulation results insimilar phenotypes. In many cases, genes with similarknown functions clustered tightly together, but a number ofnew or unexpected groups of genes were also identified.Identifying genes involved in cell-cycleprogression One major aim of the screen by Bjorklund et al. [9] was toidentify genes involved in cell-cycle progression by screeningfor dsRNAs that alter the proportion of cells in differentphases of the cell cycle. Although these data are informativein themselves, more can be learnt when they are combinedwith data on any simultaneous changes in cell number. Thisis best illustrated by an example. dsRNAs can increase thepercentage of cells in G1 either by delaying progression fromG1 to S phase or by accelerating progression through Mphase (mitosis), and cell-number data can distinguishbetween these two possibilities. Cyclin E is known topromote the transition from G1 into S phase, and its deple-tion increased the proportion of cells in G1, presumably bydelaying their progression into S phase. Such an effect wouldhave been accompanied by a reduction in cell number. Theprotein kinase Wee1 inhibits progression through G2 and Mphase, and its depletion also increased the proportion ofcells in G1. In this case, however, the increased percentage ofthe population in G1 is likely to reflect accelerated progres-sion through M phase, and would therefore be accompaniedby an increase in cell number. Unfortunately, high-through-put flow cytometry does not allow the simultaneous collec-tion of reliable cell-number data. The current dataset mightbe fruitfully exploited, however,