William J. Moore

Metadata matters: access to image data in the real world

Data sharing is important in the biological sciences to prevent duplication of effort, to promote scientific integrity, and to facilitate and disseminate scientific discovery. Sharing requires centralized repositories, and submission to and utility of these resources require common data formats. This is particularly challenging for multidimensional microscopy image data, which are acquired from a variety of platforms with a myriad of proprietary file formats (PFFs). In this paper, we describe an open standard format that we have developed for microscopy image data. We call on the community to use open image data standards and to insist that all imaging platforms support these file formats. This will build the foundation for an open image data repository.

OMERO: flexible, model-driven data management for experimental biology

Data-intensive research depends on tools that manage multidimensional, heterogeneous datasets. We built OME Remote Objects (OMERO), a software platform that enables access to and use of a wide range of biological data. OMERO uses a server-based middleware application to provide a unified interface for images, matrices and tables. OMEROs design and flexibility have enabled its use for light-microscopy, high-content-screening, electron-microscopy and even non-image-genotype data. OMERO is open-source software, available at http://openmicroscopy.org/.

Targeting of RCC1 to Chromosomes Is Required for Proper Mitotic Spindle Assembly in Human Cells

Ran GTPase is involved in several aspects of nuclear structure and function, including nucleocytoplasmic transport and nuclear envelope formation. Experiments using Xenopus egg extracts have shown that generation of Ran-GTP by the guanine nucleotide exchange factor RCC1 also plays roles in mitotic spindle assembly. Here, we have examined the localization and function of RCC1 in mitotic human cells. We show that RCC1, either the endogenous protein or that expressed as a fusion with green fluorescent protein (GFP), is localized predominantly to chromosomes in mitotic cells. This localization requires an N-terminal lysine-rich region that also contains a nuclear localization signal and is enhanced by interaction with Ran. Either mislocalization of GFP-RCC1 by removal of the N-terminal region or the expression of dominant Ran mutants that perturb the GTP/GDP cycle causes defects in mitotic spindle morphology, including misalignment of chromosomes and abnormal numbers of spindle poles. These results indicate that the generation of Ran-GTP in the vicinity of chromosomes by RCC1 is important for the fidelity of mitotic spindle assembly in human cells. Defects in this system may result in abnormal chromosome segregation and genomic instability, which are characteristic of many cancer cells.

Phosphorylation Regulates the Dynamic Interaction of RCC1 with Chromosomes during Mitosis

The small GTPase Ran has multiple roles during the cell division cycle, including nuclear transport, mitotic spindle assembly, and nuclear envelope formation. However, regulation of Ran during cell division is poorly understood. Ran-GTP is generated by the guanine nucleotide exchange factor RCC1, the localization of which to chromosomes is necessary for the fidelity of mitosis in human cells. Using photobleaching techniques, we show that the chromosomal interaction of human RCC1 fused to green fluorescent protein (GFP) changes during progression through mitosis by being highly dynamic during metaphase and more stable toward the end of mitosis. The interaction of RCC1 with chromosomes involves the interface of RCC1 with Ran and requires an N-terminal region containing a nuclear localization signal. We show that this region contains sites phosphorylated by mitotic protein kinases. One site, serine 11, is targeted by CDK1/cyclin B and is phosphorylated in mitotic human cells. Phosphorylation of the N-terminal region of RCC1 inhibits its binding to importin alpha/beta and maintains the mobility of RCC1 during metaphase. This mechanism may be important for the localized generation of Ran-GTP on chromatin after nuclear envelope breakdown and may play a role in the coordination of progression through mitosis.

Concentration of Ran on chromatin induces decondensation, nuclear envelope formation and nuclear pore complex assembly.

Nuclear envelope (NE) formation can be studied in a cell-free system made from Xenopus eggs. In this system, NE formation involves the small GTPase Ran. Ran associates with chromatin early in nuclear assembly and concentration of Ran on inert beads is sufficient to induce NE formation. Here, we show that Ran binds to chromatin prior to NE formation and recruits RCC1, the nucleotide exchange factor that generates Ran-GTP. In extracts prepared by high-speed centrifugation, increased concentrations of Ran are sufficient to induce chromatin decondensation and NE assembly. Using field emission in-lens scanning electron microscopy (FEISEM), we show that Ran promotes the formation of smoothed membranes and the assembly of nuclear pore complexes (NPCs). In contrast, RanT24N, a mutant that fails to bind GTP and inhibits RCC1, does not support efficient NE assembly, whereas RanQ69L, a mutant locked in a GTP-bound state, permits some membrane vesicle recruitment to chromatin, but inhibits vesicle fusion and NPC assembly. Thus, binding of Ran to chromatin, followed by local generation of Ran-GTP and GTP hydrolysis by Ran, induces chromatin decondensation, membrane vesicle recruitment, membrane formation and NPC assembly. We propose that the biological activity of Ran is determined by its targeting to structures such as chromatin as well as its guanine nucleotide bound state.

Data management challenges in three-dimensional EM

This report describes the outcomes of the Data Management Challenges in 3D Electron Microscopy workshop. Key topics discussed include data models, validation and raw-data archiving. The meeting participants agreed that the EMDataBank should take the lead in addressing these issues, and concrete action points were agreed upon that will have a substantial impact on the accessibility of three-dimensional EM data in biology and medicine.

Dynamic localisation of Ran GTPase during the cell cycle

BackgroundRan GTPase has multiple functions during the cell division cycle, including nucleocytoplasmic transport, mitotic spindle assembly and nuclear envelope formation. The activity of Ran is determined by both its guanine nucleotide-bound state and its subcellular localization.ResultsHere, we have characterised the localisation and mobility of Ran coupled to green fluorescent protein (GFP) during the cell cycle in live human cells. Ran-GFP is nuclear during interphase and is dispersed throughout the cell during mitosis. GFP-RanQ69L, a mutant locked in the GTP-bound state, is less highly concentrated in the nucleus and associates with nuclear pore complexes within the nuclear envelope. During mitosis, GFP-RanQ69L is excluded from chromosomes and localizes to the spindle. By contrast, GFP-RanT24N, a mutant with low affinity for nucleotides, interacts relatively stably with chromatin throughout the cell cycle and is highly concentrated on mitotic chromosomes.ConclusionThese results show that Ran interacts dynamically with chromatin, nuclear pore complexes and the mitotic spindle during the cell cycle. These interactions are dependent on the nucleotide-bound state of the protein. Our data indicate that Ran-GTP generated at chromatin is highly mobile and interacts dynamically with distal structures that are involved in nuclear transport and mitotic spindle assembly.

Metadata management for high content screening in OMERO.

Highlights • HCS data management is challenging due to its scale, complexity and heterogeneity.• OMERO and Bio-Formats are open-source tools for data access and management at scale.• OMERO and Bio-Formats can handle images, experimental metadata and analytic outputs.• Repositories integrating multiple image-based studies provide tests of the value of data integration.

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.

OMERO and Bio-Formats 5: flexible access to large bioimaging datasets at scale

The Open Microscopy Environment (OME) has built and released Bio-Formats, a Java-based proprietary file format conversion tool and OMERO, an enterprise data management platform under open source licenses. In this report, we describe new versions of Bio-Formats and OMERO that are specifically designed to support large, multi-gigabyte or terabyte scale datasets that are routinely collected across most domains of biological and biomedical research. Bio- Formats reads image data directly from native proprietary formats, bypassing the need for conversion into a standard format. It implements the concept of a file set, a container that defines the contents of multi-dimensional data comprised of many files. OMERO uses Bio-Formats to read files natively, and provides a flexible access mechanism that supports several different storage and access strategies. These new capabilities of OMERO and Bio-Formats make them especially useful for use in imaging applications like digital pathology, high content screening and light sheet microscopy that create routinely large datasets that must be managed and analyzed.