Curtis T. Rueden

Fiji: an open-source platform for biological-image analysis

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

Collagen density promotes mammary tumor initiation and progression

BackgroundMammographically dense breast tissue is one of the greatest risk factors for developing breast carcinoma. Despite the strong clinical correlation, breast density has not been causally linked to tumorigenesis, largely because no animal model has existed for studying breast tissue density. Importantly, regions of high breast density are associated with increased stromal collagen. Thus, the influence of the extracellular matrix on breast carcinoma development and the underlying molecular mechanisms are not understood.MethodsTo study the effects of collagen density on mammary tumor formation and progression, we utilized a bi-transgenic tumor model with increased stromal collagen in mouse mammary tissue. Imaging of the tumors and tumor-stromal interface in live tumor tissue was performed with multiphoton laser-scanning microscopy to generate multiphoton excitation and spectrally resolved fluorescent lifetimes of endogenous fluorophores. Second harmonic generation was utilized to image stromal collagen.ResultsHerein we demonstrate that increased stromal collagen in mouse mammary tissue significantly increases tumor formation approximately three-fold (p < 0.00001) and results in a significantly more invasive phenotype with approximately three times more lung metastasis (p < 0.05). Furthermore, the increased invasive phenotype of tumor cells that arose within collagen-dense mammary tissues remains after tumor explants are cultured within reconstituted three-dimensional collagen gels. To better understand this behavior we imaged live tumors using nonlinear optical imaging approaches to demonstrate that local invasion is facilitated by stromal collagen re-organization and that this behavior is significantly increased in collagen-dense tissues. In addition, using multiphoton fluorescence and spectral lifetime imaging we identify a metabolic signature for flavin adenine dinucleotide, with increased fluorescent intensity and lifetime, in invading metastatic cells.ConclusionThis study provides the first data causally linking increased stromal collagen to mammary tumor formation and metastasis, and demonstrates that fundamental differences arise and persist in epithelial tumor cells that progressed within collagen-dense microenvironments. Furthermore, the imaging techniques and signature identified in this work may provide useful diagnostic tools to rapidly assess fresh tissue biopsies.

The ImageJ ecosystem: An open platform for biomedical image analysis

Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more‐advanced image processing and analysis techniques. A wide range of software is available—from commercial to academic, special‐purpose to Swiss army knife, small to large—but a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open‐source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the open‐software platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The softwares extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image‐processing algorithms. In this review, we use the ImageJ project as a case study of how open‐source software fosters its suites of software tools, making multitudes of image‐analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is self‐influenced by coevolving projects within the ImageJ ecosystem. Mol. Reprod. Dev. 82: 518–529, 2015.

Improved structure, function and compatibility for CellProfiler

UNLABELLED There is a strong and growing need in the biology research community for accurate, automated image analysis. Here, we describe CellProfiler 2.0, which has been engineered to meet the needs of its growing user base. It is more robust and user friendly, with new algorithms and features to facilitate high-throughput work. ImageJ plugins can now be run within a CellProfiler pipeline. AVAILABILITY AND IMPLEMENTATION CellProfiler 2.0 is free and open source, available at http://www.cellprofiler.org under the GPL v. 2 license. It is available as a packaged application for Macintosh OS X and Microsoft Windows and can be compiled for Linux. CONTACT anne@broadinstitute.org SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

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.

ImageJ2: ImageJ for the next generation of scientific image data

BackgroundImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science.ResultsWe rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace.ConclusionsScientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification

Summary: State‐of‐the‐art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time‐consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user‐designed image features or classifiers. Availability and Implementation: TWS is distributed as open‐source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation. Contact: ignacio.arganda@ehu.eus Supplementary information: Supplementary data are available at Bioinformatics online.

Nonlinear optical imaging and spectral-lifetime computational analysis of endogenous and exogenous fluorophores in breast cancer

Multiphoton laser scanning microscopy (MPLSM) utilizing techniques such as multiphoton excitation (MPE), second harmonic generation (SHG), and multiphoton fluorescence lifetime imaging and spectral lifetime imaging (FLIM and SLIM, respectively) are greatly expanding the degree of information obtainable with optical imaging in biomedical research. The application of these nonlinear optical approaches to the study of breast cancer holds particular promise. These noninvasive, multidimensional techniques are well suited to image exogenous fluorophores that allow relevant questions regarding protein localization and signaling to be addressed both in vivo and in vitro. Furthermore, MPLSM imaging of endogenous signals from collagen and fluorophores such as nicotinamide adenine dinucleotide (NADH) or flavin adenine dinucleotide (FAD), address important questions regarding the tumor-stromal interaction and the physiologic state of the cell. We demonstrate the utility of multimodal MPE/SHG/FLIM for imaging both exogenous and/or endogenous fluorophores in mammary tumors or relevant 3-D systems. Using SLIM, we present a method for imaging and differentiating signals from multiple fluorophores that can have overlapping spectra via SLIM Plotter-a computational tool for visualizing and analyzing large spectral-lifetime data sets.

Visualization approaches for multidimensional biological image data

Effective data analysis of the modern biological microscopy data set often necessitates a variety of different analysis strategies, and this often means the biologist may need to use a combination of software tools both commercial and often-times open source. To facilitate this process, there needs to be knowledge of what the approaches are and also practical ways of sharing this data in a nonproprietary way. Thus, for users of open source and commercial software, it is important to have common approaches for multidimensional data analysis that can be run in different software packages and still be effectively compared. Projects like the Open Microscopy Environment, which aim to allow data sharing between open source client tools like ImageJ and VisBio, and commercial packages like Volocity and Imaris via the XML data model are a needed first step in providing a framework or infrastructure for microscopy analysis. As the field has gotten more quantitative in its approaches, this need has only increased with the necessity of having a way to represent key attributes of the data in an open manner.

Tools for Visualizing Multidimensional Images from Living Specimens

Abstract Over the last 50 years modern cell biology has been driven by the development of powerful imaging techniques. In particular, new developments in light microscopy that provide the potential to image the dynamics of biological events have had significant impact. Optical sectioning techniques allow three-dimensional information to be obtained from living specimens noninvasively. When used with multimodal fluorescence microscopy, advanced optical sectioning techniques provide multidimensional image data that can reveal information not only about the changing cytoarchitecture of a cell but also about its physiology. These additional dimensions of information, although providing powerful tools, also pose significant visualization challenges to the investigator. Particularly in the current postgenomic era there is a greater need than ever for the development of effective tools for image visualization and management. In this review we discuss the visualization challenges presented by multidimensional imaging and describe three open-source software programs being developed to help address these challenges: ImageJ, the Open Microscopy Environment, and VisBio.