SOD1 activity threshold and TOR signalling modulate VAP(P58S) aggregation via reactive oxygen species-induced proteasomal degradation in a Drosophila model of amyotrophic lateral sclerosis.

Familial amyotrophic lateral sclerosis (ALS) is an incurable, late-onset motor neuron disease, linked strongly to various causative genetic loci. ALS8 codes for a missense mutation, P56S, in VAMP-associated protein B (VAPB) that causes the protein to misfold and form cellular aggregates. Uncovering genes and mechanisms that affect aggregation dynamics would greatly help increase our understanding of the disease and lead to potential therapeutics. We developed a quantitative high-throughput Drosophila S2R+ cell-based kinetic assay coupled with fluorescent microscopy to score for genes involved in the modulation of aggregates of the fly orthologue, VAP(P58S), fused with GFP. A targeted RNA interference screen against 900 genes identified 150 hits that modify aggregation, including the ALS loci Sod1 and TDP43 (also known as TBPH), as well as genes belonging to the mTOR pathway. Further, a system to measure the extent of VAP(P58S) aggregation in the Drosophila larval brain was developed in order to validate the hits from the cell-based screen. In the larval brain, we find that reduction of SOD1 levels or decreased mTOR signalling reduces aggregation, presumably by increasing the levels of cellular reactive oxygen species (ROS). The mechanism of aggregate clearance is, primarily, proteasomal degradation, which appears to be triggered by an increase in ROS. We have thus uncovered an interesting interplay between SOD1, ROS and mTOR signalling that regulates the dynamics of VAP aggregation. Mechanistic processes underlying such cellular regulatory networks will lead to better understanding of the initiation and progression of ALS.This article has an associated First Person interview with the first author of the paper.

Correction: Population Distribution Analyses Reveal a Hierarchy of Molecular Players Underlying Parallel Endocytic Pathways.

[This corrects the article DOI: 10.1371/journal.pone.0100554.].

Metadata management for high content screening in OMERO.

High content screening (HCS) experiments create a classic data management challenge-multiple, large sets of heterogeneous structured and unstructured data, that must be integrated and linked to produce a set of "final" results. These different data include images, reagents, protocols, analytic output, and phenotypes, all of which must be stored, linked and made accessible for users, scientists, collaborators and where appropriate the wider community. The OME Consortium has built several open source tools for managing, linking and sharing these different types of data. The OME Data Model is a metadata specification that supports the image data and metadata recorded in HCS experiments. Bio-Formats is a Java library that reads recorded image data and metadata and includes support for several HCS screening systems. OMERO is an enterprise data management application that integrates image data, experimental and analytic metadata and makes them accessible for visualization, mining, sharing and downstream analysis. We discuss how Bio-Formats and OMERO handle these different data types, and how they can be used to integrate, link and share HCS experiments in facilities and public data repositories. OME specifications and software are open source and are available at https://www.openmicroscopy.org.

Publishing and sharing multi-dimensional image data with OMERO.

Imaging data are used in the life and biomedical sciences to measure the molecular and structural composition and dynamics of cells, tissues, and organisms. Datasets range in size from megabytes to terabytes and usually contain a combination of binary pixel data and metadata that describe the acquisition process and any derived results. The OMERO image data management platform allows users to securely share image datasets according to specific permissions levels: data can be held privately, shared with a set of colleagues, or made available via a public URL. Users control access by assigning data to specific Groups with defined membership and access rights. OMERO's Permission system supports simple data sharing in a lab, collaborative data analysis, and even teaching environments. OMERO software is open source and released by the OME Consortium at www.openmicroscopy.org.

Population distribution analyses reveal a hierarchy of molecular players underlying parallel endocytic pathways.

Single-cell-resolved measurements reveal heterogeneous distributions of clathrin-dependent (CD) and -independent (CLIC/GEEC: CG) endocytic activity in Drosophila cell populations. dsRNA-mediated knockdown of core versus peripheral endocytic machinery induces strong changes in the mean, or subtle changes in the shapes of these distributions, respectively. By quantifying these subtle shape changes for 27 single-cell features which report on endocytic activity and cell morphology, we organize 1072 Drosophila genes into a tree-like hierarchy. We find that tree nodes contain gene sets enriched in functional classes and protein complexes, providing a portrait of core and peripheral control of CD and CG endocytosis. For 470 genes we obtain additional features from separate assays and classify them into early- or late-acting genes of the endocytic pathways. Detailed analyses of specific genes at intermediate levels of the tree suggest that Vacuolar ATPase and lysosomal genes involved in vacuolar biogenesis play an evolutionarily conserved role in CG endocytosis.

Exploiting cell-to-cell variability to detect cellular perturbations.

Any single-cell-resolved measurement generates a population distribution of phenotypes, characterized by a mean, a variance, and a shape. Here we show that changes in the shape of a phenotypic distribution can signal perturbations to cellular processes, providing a way to screen for underlying molecular machinery. We analyzed images of a Drosophila S2R+ cell line perturbed by RNA interference, and tracked 27 single-cell features which report on endocytic activity, and cell and nuclear morphology. In replicate measurements feature distributions had erratic means and variances, but reproducible shapes; RNAi down-regulation reliably induced shape deviations in at least one feature for 1072 out of 7131 genes surveyed, as revealed by a Kolmogorov-Smirnov-like statistic. We were able to use these shape deviations to identify a spectrum of genes that influenced cell morphology, nuclear morphology, and multiple pathways of endocytosis. By preserving single-cell data, our method was even able to detect effects invisible to a population-averaged analysis. These results demonstrate that cell-to-cell variability contains accessible and useful biological information, which can be exploited in existing cell-based assays.