Modelling data across labs, genomes, space and time

A practical guide to bioimaging research data management in core facilities

Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data-driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well-established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging - Society for Microscopy and Image Analysis (GerBI-GMB), cross-institutional working groups and third-party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging-core-facility-centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer.

Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction

Inflammation and extracellular matrix (ECM) remodeling are important components regulating the response of the left ventricle to myocardial infarction (MI). Significant cellular- and molecular-level contributors can be identified by analyzing data acquired through high-throughput genomic and proteomic technologies that provide expression levels for thousands of genes and proteins. Large-scale data provide both temporal and spatial information that need to be analyzed and interpreted using systems biology approaches in order to integrate this information into dynamic models that predict and explain mechanisms of cardiac healing post-MI. In this review, we summarize the systems biology approaches needed to computationally simulate post-MI remodeling, including data acquisition, data analysis for biomarker classification and identification, data integration to build dynamic models, and data interpretation for biological functions. An example for applying a systems biology approach to ECM remodeling is presented as a reference illustration.

Laser scanning confocal microscopy: history, applications, and related optical sectioning techniques

Confocal microscopy is an established light microscopical technique for imaging fluorescently labeled specimens with significant three-dimensional structure. Applications of confocal microscopy in the biomedical sciences include the imaging of the spatial distribution of macromolecules in either fixed or living cells, the automated collection of 3D data, the imaging of multiple labeled specimens and the measurement of physiological events in living cells. The laser scanning confocal microscope continues to be chosen for most routine work although a number of instruments have been developed for more specific applications. Significant improvements have been made to all areas of the confocal approach, not only to the instruments themselves, but also to the protocols of specimen preparation, to the analysis, the display, the reproduction, sharing and management of confocal images using bioinformatics techniques.

How many molecular subtypes? Implications of the unique tumor principle in personalized medicine

Cancers are complex multifactorial diseases. For centuries, conventional organ-based classification system (i.e., breast cancer, lung cancer, colon cancer, colorectal cancer, prostate cancer, lymphoma, leukemia, and so on) has been utilized. Recently, molecular diagnostics has become an essential component in clinical decision-making. However, tumor evolution and behavior cannot accurately be predicted, despite numerous research studies reporting promising tumor biomarkers. To advance molecular diagnostics, a better understanding of intratumor and intertumor heterogeneity is essential. Tumor cells interact with the extracellular matrix and host non-neoplastic cells in the tumor microenvironment, which is influenced by genomic variation, hormones, and dietary, lifestyle and environmental exposures, implicated by molecular pathological epidemiology. Essentially, each tumor possesses its own unique characteristics in terms of molecular make-up, tumor microenvironment and interactomes within and between neoplastic and host cells. Starting from the unique tumor concept and paradigm, we can better classify tumors by molecular methods, and move closer toward personalized cancer medicine and prevention.

Establishing a knowledge trail from molecular experiments to clinical trials

During the development cycle of a new antibody therapy, the therapeutic agent will be tested on subsequently more biologically complex models. New experiments' designs are based upon data gathered from prior models. New researchers who inherit the data and researchers from groups with different cultures or expertise are often called upon to interpret these data. Experiments which are not recorded consistently or employ ambiguous terminology can make interpreting these results difficult. The researcher who had originally collected the data may not be at hand to correct any misunderstanding or offer clarification and data can be unknowingly misused. This introduces an element of risk into the therapy development process. We have developed a reporting guideline for recording therapy experiments. This guideline consists of a checklist of data to be recorded from antibody therapy experiments performed in molecular, cellular, animal and clinical model.

Multiscale modeling for biologists

Biomedical research frequently involves performing experiments and developing hypotheses that link different scales of biological systems such as, for instance, the scales of intracellular molecular interactions to the scale of cellular behavior and beyond to the behavior of cell populations. Computational modeling efforts that aim at exploring such multiscale systems quantitatively with the help of simulations have to incorporate several different simulation techniques because of the different time and space scales involved. Here, we provide a nontechnical overview of how different scales of experimental research can be combined with the appropriate computational modeling techniques. We also show that current modeling software permits building and simulating multiscale models without having to become involved with the underlying technical details of computational modeling.

Bisque: a platform for bioimage analysis and management

MOTIVATION

Advances in the field of microscopy have brought about the need for better image management and analysis solutions. Novel imaging techniques have created vast stores of images and metadata that are difficult to organize, search, process and analyze. These tasks are further complicated by conflicting and proprietary image and metadata formats, that impede analyzing and sharing of images and any associated data. These obstacles have resulted in research resources being locked away in digital media and file cabinets. Current image management systems do not address the pressing needs of researchers who must quantify image data on a regular basis.

RESULTS

We present Bisque, a web-based platform specifically designed to provide researchers with organizational and quantitative analysis tools for 5D image data. Users can extend Bisque with both data model and analysis extensions in order to adapt the system to local needs. Bisque's extensibility stems from two core concepts: flexible metadata facility and an open web-based architecture. Together these empower researchers to create, develop and share novel bioimage analyses. Several case studies using Bisque with specific applications are presented as an indication of how users can expect to extend Bisque for their own purposes.

Tracking in cell and developmental biology

The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.

Biological knowledge management: the emerging role of the Semantic Web technologies

New knowledge is produced at a continuously increasing speed, and the list of papers, databases and other knowledge sources that a researcher in the life sciences needs to cope with is actually turning into a problem rather than an asset. The adequate management of knowledge is therefore becoming fundamentally important for life scientists, especially if they work with approaches that thoroughly depend on knowledge integration, such as systems biology. Several initiatives to organize biological knowledge sources into a readily exploitable resourceome are presently being carried out. Ontologies and Semantic Web technologies revolutionize these efforts. Here, we review the benefits, trends, current possibilities, and the potential this holds for the biosciences.

Using semantic web technologies to annotate and align microarray designs

In this paper, we annotate and align two different gene expression microarray designs using the Genomic ELement Ontology (GELO). GELO is a new ontology that leverages an existing community resource, Sequence Ontology (SO), to create views of genomically-aligned data in a semantic web environment. We start the process by mapping array probes to genomic coordinates. The coordinates represent an implicit link between the probes and multiple genomic elements, such as genes, transcripts, miRNA, and repetitive elements, which are represented using concepts in SO. We then use the RDF Query Language (SPARQL) to create explicit links between the probes and the elements. We show how the approach allows us to easily determine the element coverage and genomic overlap of the two array designs. We believe that the method will ultimately be useful for integration of cancer data across multiple omic studies. The ontology and other materials described in this paper are available at http://krauthammerlab.med.yale.edu/wiki/Gelo.

Network integration and graph analysis in mammalian molecular systems biology

Abstraction of intracellular biomolecular interactions into networks is useful for data integration and graph analysis. Network analysis tools facilitate predictions of novel functions for proteins, prediction of functional interactions and identification of intracellular modules. These efforts are linked with drug and phenotype data to accelerate drug-target and biomarker discovery. This review highlights the currently available varieties of mammalian biomolecular networks, and surveys methods and tools to construct, compare, integrate, visualise and analyse such networks.

Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses

BACKGROUND

The explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation.

RESULTS

Over 40,000 annotated Influenza A protein sequences were collected by combining information from more than 90,000 documents from NCBI public databases. Metadata values were automatically extracted, aggregated and reconciled from several document fields by applying user-defined structural rules. For each property, values were recovered from >/=88.8% of records, with accuracy exceeding 96% in most cases. Because of semantic heterogeneity, each property required up to six different structural rules to be combined. Significant quality differences between databases were found: GenBank documents yield values more reliably than documents extracted from GenPept. Using a simple set of semantic rules and a reasoner, we reconstructed relationships between sequences from the same isolate, thus identifying 7640 isolates. Validation of isolate metadata against a simple ontology highlighted more than 400 inconsistencies, leading to over 3,000 property value corrections.

CONCLUSION

To overcome the quality issues inherent in public databases, automated knowledge aggregation with embedded intelligence is needed for large-scale analyses. Our results show that user-controlled intuitive approaches, based on combination of simple rules, can reliably automate various curation tasks, reducing the need for manual corrections to approximately 5% of the records. Emerging semantic technologies possess desirable features to support today's knowledge aggregation tasks, with a potential to bring immediate benefits to this field.

Open tools for storage and management of quantitative image data

The explosion in quantitative imaging has driven the need to develop tools for storing, managing, analyzing, and viewing large sets of data. In this chapter, we discuss tools we have built for storing large data sets for the lifetime of a typical research project. As part of the Open Microscopy Environment (OME) Consortium, we have built a series of open-source tools that support the manipulation and visualization of large sets of complex image data. Images from a number of proprietary file formats can be imported and then accessed from a single server running in a laboratory or imaging facility. We discuss the capabilities of the OME Server, a Perl-based data management system that is designed for large-scale analysis of image data using a web browser-based user interface. In addition, we have recently released a lighter weight Java-based OME Remote Objects Server that supports remote applications for managing and viewing image data. Together these systems provide a suite of tools for large-scale quantitative imaging that is now commonly used throughout cell and developmental biology.

Increasing the robustness and validity of RNAi screens

RNAi screening in mammalian cells has become a valuable method to identify and describe genetic relationships in both basic biology and disease mechanisms. Multiple efforts are underway to standardize how RNAi screening data are reported, including establishing experimental criteria for defining a validated hit from a screen, and the extent to which the primary screening data themselves are reported. These discussions have identified several key areas that require consistency, or at least understanding, before RNAi screening data can be used generally. Successfully addressing these targeted areas would broaden the use of RNAi screening data beyond advancing one or a few hits into validation experiments, to enable verification of primary screening data, and to facilitate comparisons between sample groups based on screening profiles. Areas for improving RNAi screening include general guidelines for validating hits from screens, the creation of standardized reporting structures for RNAi screening data, such as Minimum Information About an RNAi Experiment (MIARE), statistical methods for analyzing screening data that explicitly account for differences between screening RNAi reagents versus small molecules, and technical improvements to RNAi screening that improve the analysis of gene knockdowns, including multiparametric approaches, such as high-content screening. This review will discuss how these approaches can improve RNAi screening data at the community level and for an individual researcher trying to manage an RNAi screen.