The mechanism of phagocytic elimination of dying cells in Drosophila is poorly understood. This study was undertaken to examine the recognition and engulfment of apoptotic cells by Drosophila hemocytes/macrophages in vitro and in vivo. In the in vitro analysis, l(2)mbn cells (a cell line established from larval hemocytes of a tumorous Drosophila mutant) were used as phagocytes. When l(2)mbn cells were treated with the molting hormone 20-hydroxyecdysone, the cells acquired the ability to phagocytose apoptotic S2 cells, another Drosophila cell line. S2 cells undergoing cycloheximide-induced apoptosis exposed phosphatidylserine on their surface, but their engulfment by l(2)mbn cells did not seem to be mediated by phosphatidylserine. The level of Croquemort, a candidate phagocytosis receptor of Drosophila hemocytes/macrophages, increased in l(2)mbn cells after treatment with 20-hydroxyecdysone, whereas that of Draper, another candidate phagocytosis receptor, remained unchanged. However, apoptotic cell phagocytosis was reduced when the expression of Draper, but not of Croquemort, was inhibited by RNA interference in hormone-treated l(2)mbn cells. We next examined whether Draper is responsible for the phagocytosis of apoptotic cells in vivo using an assay for engulfment based on assessing DNA degradation of apoptotic cells in dICAD mutant embryos (which only occurred after ingestion by the phagocytes). RNA interference-mediated decrease in the level of Draper in embryos of mutant flies was accompanied by a decrease in the number of cells containing fragmented DNA. Furthermore, histochemical analyses of dispersed embryonic cells revealed that the level of phagocytosis of apoptotic cells by hemocytes/macrophages was reduced when Draper expression was inhibited. These results indicate that Drosophila hemocytes/macrophages execute Draper-mediated phagocytosis to eliminate apoptotic cells.

Background: Identification of differentially expressed genes (DEGs) under different experimental conditions is an important task in many microarray studies. However, choosing which method to use for a particular application is problematic because its performance depends on the evaluation metric, the dataset, and so on. In addition, when using the Affymetrix GeneChip ® system, researchers must select a preprocessing algorithm from a number of competing algorithms such as MAS, RMA, and DFW, for obtaining expression-level measurements. To achieve optimal performance for detecting DEGs, a suitable combination of gene selection method and preprocessing algorithm needs to be selected for a given probe-level dataset.

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BackgroundTo identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility.ResultsWe compared eight conventional methods for ranking genes: weighted average difference (WAD), average difference (AD), fold change (FC), rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT) with six preprocessing algorithms (PLIER, VSN, FARMS, multi-mgMOS (mmgMOS), MBEI, and GCRMA). A total of 36 real experimental datasets was evaluated on the basis of the area under the receiver operating characteristic curve (AUC) as a measure for both sensitivity and specificity. We found that the RP method performed well for VSN-, FARMS-, MBEI-, and GCRMA-preprocessed data, and the WAD method performed well for mmgMOS-preprocessed data. Our analysis of the MicroArray Quality Control (MAQC) project's datasets showed that the FC-based gene ranking methods (WAD, AD, FC, and RP) had a higher level of reproducibility: The percentages of overlapping genes (POGs) across different sites for the FC-based methods were higher overall than those for the t-statistic-based methods (modT, samT, shrinkT, and ibmT). In particular, POG values for WAD were the highest overall among the FC-based methods irrespective of the choice of preprocessing algorithm.ConclusionOur results demonstrate that to increase sensitivity, specificity, and reproducibility in microarray analyses, we need to select suitable combinations of preprocessing algorithms and gene ranking methods. We recommend the use of FC-based methods, in particular RP or WAD.