The Drosophila genome contains >13000 protein-coding genes, the majority of which remain poorly investigated. Important reasons include the lack of antibodies or reporter constructs to visualise these proteins. Here, we present a genome-wide fosmid library of 10000 GFP-tagged clones, comprising tagged genes and most of their regulatory information. For 880 tagged proteins, we created transgenic lines, and for a total of 207 lines, we assessed protein expression and localisation in ovaries, embryos, pupae or adults by stainings and live imaging approaches. Importantly, we visualised many proteins at endogenous expression levels and found a large fraction of them localising to subcellular compartments. By applying genetic complementation tests, we estimate that about two-thirds of the tagged proteins are functional. Moreover, these tagged proteins enable interaction proteomics from developing pupae and adult flies. Taken together, this resource will boost systematic analysis of protein expression and localisation in various cellular and developmental contexts.DOI: http://dx.doi.org/10.7554/eLife.12068.001

SummaryThe piRNA (PIWI-interacting RNA) pathway is a small RNA silencing system that acts in animal gonads and protects the genome against the deleterious influence of transposons. A major bottleneck in the field is the lack of comprehensive knowledge of the factors and molecular processes that constitute this pathway. We conducted an RNAi screen in Drosophila and identified ∼50 genes that strongly impact the ovarian somatic piRNA pathway. Many identified genes fall into functional categories that indicate essential roles for mitochondrial metabolism, RNA export, the nuclear pore, transcription elongation, and chromatin regulation in the pathway. Follow-up studies on two factors demonstrate that components acting at distinct hierarchical levels of the pathway were identified. Finally, we define CG2183/Gasz as an essential primary piRNA biogenesis factor in somatic and germline cells. Based on the similarities between insect and vertebrate piRNA pathways, our results have far-reaching implications for the understanding of this conserved genome defense system.

We present LABKIT, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. LABKIT is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as well as a memory efficient and fast implementation of the random forest based pixel classification algorithm as the foundation of our software. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. LABKIT is easy to install on virtually all laptops and workstations. Additionally, LABKIT is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in LABKIT via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Finally, LABKIT comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use LABKIT in a number of practical real-world use-cases.

To study the development and interactions of cells and tissues, multiple fluorescent markers need to be imaged efficiently in a single living organism. Instead of acquiring individual colours sequentially with filters, we created a platform based on line-scanning light sheet microscopy to record the entire spectrum for each pixel in a three-dimensional volume. We evaluated data sets with varying spectral sampling and determined the optimal channel width to be around 5 nm. With the help of these data sets, we show that our setup outperforms filter-based approaches with regard to image quality and discrimination of fluorophores. By spectral unmixing we resolved overlapping fluorophores with up to nanometre resolution and removed autofluorescence in zebrafish and fruit fly embryos.